diff --git a/DATASET.md b/DATASET.md new file mode 100644 index 0000000000000000000000000000000000000000..fc90bcd0f86a85fef67ca443ac35d58abd42c05f --- /dev/null +++ b/DATASET.md @@ -0,0 +1,34 @@ +### Dataset +To download the datataset, run: +```python +# download the full dataset +from huggingface_hub import snapshot_download +snapshot_download(repo_id="osv5m/osv5m", local_dir="datasets/osv5m", repo_type='dataset') +``` + +and finally extract: +```python +import os +import zipfile +for root, dirs, files in os.walk("datasets/osv5m"): + for file in files: + if file.endswith(".zip"): + with zipfile.ZipFile(os.path.join(root, file), 'r') as zip_ref: + zip_ref.extractall(root) + os.remove(os.path.join(root, file)) +``` + +You can also directly load the dataset using `load_dataset`: +```python +from datasets import load_dataset +dataset = load_dataset('osv5m/osv5m', full=False) +``` +where with `full` you can specify whether you want to load the complete metadata (default: `False`). + +If you only want to download the test set, you can run the script below: +```python +from huggingface_hub import hf_hub_download +for i in range(5): + hf_hub_download(repo_id="osv5m/osv5m", filename=str(i).zfill(2)+'.zip', subfolder="images/test", repo_type='dataset', local_dir="datasets/osv5m") + hf_hub_download(repo_id="osv5m/osv5m", filename="README.md", repo_type='dataset', local_dir="datasets/osv5m") +``` \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..15c1aac6889d85f2ce67b1f8e25d134781099ada --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Nicolas Dufour + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/callbacks/__init__.py b/callbacks/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..7e2064a43f692ee9010e8f92f9b647bdb61488b9 --- /dev/null +++ b/callbacks/__init__.py @@ -0,0 +1,3 @@ +from .ema import EMACallback +from .fix_nans import FixNANinGrad +from .data import IncreaseDataEpoch diff --git a/callbacks/__pycache__/__init__.cpython-310.pyc b/callbacks/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5949a25c462d7dfc0c3667a5b61a55e1480a36ee Binary files /dev/null and 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100644 index 0000000000000000000000000000000000000000..4706e5f21fcd415f69407e401326ba472291e167 --- /dev/null +++ b/callbacks/data.py @@ -0,0 +1,11 @@ +from pytorch_lightning.callbacks import Callback + + +class IncreaseDataEpoch(Callback): + def __init__(self): + super().__init__() + + def on_train_epoch_start(self, trainer, pl_module): + epoch = pl_module.current_epoch + if hasattr(trainer.datamodule.train_dataset, "shared_epoch"): + trainer.datamodule.train_dataset.shared_epoch.set_value(epoch) diff --git a/callbacks/ema.py b/callbacks/ema.py new file mode 100755 index 0000000000000000000000000000000000000000..bf65a7bfc358234712206de408761e2b2880d102 --- /dev/null +++ b/callbacks/ema.py @@ -0,0 +1,102 @@ +from pytorch_lightning import Callback +import copy +import itertools +import torch +import contextlib +from torch.distributed.fsdp import FullyShardedDataParallel + + +class EMACallback(Callback): + def __init__( + self, + module_attr_name, + ema_module_attr_name, + decay=0.999, + start_ema_step=0, + init_ema_random=True, + ): + super().__init__() + self.decay = decay + self.module_attr_name = module_attr_name + self.ema_module_attr_name = ema_module_attr_name + self.start_ema_step = start_ema_step + self.init_ema_random = init_ema_random + + def on_train_start(self, trainer, pl_module): + if pl_module.global_step == 0: + if not hasattr(pl_module, self.module_attr_name): + raise ValueError( + f"Module {pl_module} does not have attribute {self.module_attr_name}" + ) + if not hasattr(pl_module, self.ema_module_attr_name): + pl_module.add_module( + self.ema_module_attr_name, + copy.deepcopy(getattr(pl_module, self.module_attr_name)) + .eval() + .requires_grad_(False), + ) + self.reset_ema(pl_module) + + def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx): + if pl_module.global_step == self.start_ema_step: + self.reset_ema(pl_module) + elif ( + pl_module.global_step < self.start_ema_step + and pl_module.global_step % 100 == 0 + ): + ## slow ema updates for visualisation + self.update_ema(pl_module, decay=0.9) + elif pl_module.global_step > self.start_ema_step: + self.update_ema(pl_module, decay=self.decay) + + def update_ema(self, pl_module, decay=0.999): + ema_module = getattr(pl_module, self.ema_module_attr_name) + module = getattr(pl_module, self.module_attr_name) + context_manager = self.get_model_context_manager(module) + with context_manager: + with torch.no_grad(): + ema_params = ema_module.state_dict() + for name, param in itertools.chain( + module.named_parameters(), module.named_buffers() + ): + if name in ema_params: + if param.requires_grad: + ema_params[name].copy_( + ema_params[name].detach().lerp(param.detach(), decay) + ) + + def get_model_context_manager(self, module): + fsdp_enabled = is_model_fsdp(module) + model_context_manager = contextlib.nullcontext() + if fsdp_enabled: + model_context_manager = module.summon_full_params(module) + return model_context_manager + + def reset_ema(self, pl_module): + ema_module = getattr(pl_module, self.ema_module_attr_name) + if self.init_ema_random: + ema_module.init_weights() + else: + module = getattr(pl_module, self.module_attr_name) + context_manager = self.get_model_context_manager(module) + with context_manager: + ema_params = ema_module.state_dict() + for name, param in itertools.chain( + module.named_parameters(), module.named_buffers() + ): + if name in ema_params: + ema_params[name].copy_(param.detach()) + + +def is_model_fsdp(model: torch.nn.Module) -> bool: + try: + if isinstance(model, FullyShardedDataParallel): + return True + + # Check if model is wrapped with FSDP + for _, obj in model.named_children(): + if isinstance(obj, FullyShardedDataParallel): + return True + return False + except ImportError: + return False diff --git a/callbacks/fix_nans.py b/callbacks/fix_nans.py new file mode 100755 index 0000000000000000000000000000000000000000..51c1d829a4eaa2b14b2c30e54ead3d153d77ac1a --- /dev/null +++ b/callbacks/fix_nans.py @@ -0,0 +1,55 @@ +import logging +from pytorch_lightning.callbacks import Callback +import torch + +log = logging.getLogger(__name__) + + +class FixNANinGrad(Callback): + def __init__(self, monitor): + super().__init__() + self.monitor = monitor + self.continuous_nan_batchs = 0 + + def on_before_optimizer_step(self, trainer, pl_module, optimizer) -> None: + has_nan = [] + is_inf = [] + for name, param in pl_module.named_parameters(): + if param.grad is not None: + if torch.isnan(param.grad).any(): + has_nan.append(name) + if torch.isinf(param.grad).any(): + is_inf.append(name) + torch.nan_to_num(param.grad, nan=0, posinf=0, neginf=0, out=param.grad) + if len(has_nan) > 0: + print(f"Found NaN in {has_nan}") + if len(is_inf) > 0: + print(f"Found Inf in {is_inf}") + + def on_train_batch_end( + self, + trainer, + pl_module, + outputs, + batch, + batch_idx, + ) -> None: + logs = trainer.callback_metrics + i = 0 + found_metric = False + while i < len(self.monitor) and not found_metric: + if self.monitor[i] in logs.keys(): + current = logs[self.monitor[i]].squeeze() + found_metric = True + else: + i += 1 + if not found_metric: + raise ValueError("Asked metric not in logs") + + if not torch.isfinite(current): + self.continuous_nan_batchs += 1 + if self.continuous_nan_batchs >= 5: + trainer.should_stop = True + log.info("Training interrupted because of NaN in {self.monitor}") + else: + self.continuous_nan_batchs = 0 diff --git a/configs/computer/a100.yaml b/configs/computer/a100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..60ac8bd5263b64cad5b659b1f71a0752f6edfe96 --- /dev/null +++ b/configs/computer/a100.yaml @@ -0,0 +1,8 @@ +devices: 1 +progress_bar_refresh_rate: 2 +num_workers: 8 +sync_batchnorm: False +accelerator: gpu +precision: 32 +strategy: auto +num_nodes: 1 diff --git a/configs/computer/cluster-node-a100.yaml b/configs/computer/cluster-node-a100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d60903dca91d09422eefb572a41060bde0aac7b1 --- /dev/null +++ b/configs/computer/cluster-node-a100.yaml @@ -0,0 +1,8 @@ +devices: 8 +num_workers: 8 +progress_bar_refresh_rate: 2 +sync_batchnorm: True +accelerator: gpu +precision: 32 +strategy: ddp +num_nodes: 1 diff --git a/configs/computer/cluster-node-v100.yaml b/configs/computer/cluster-node-v100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..48da9ac269cedd97f8619e92e54986a8124f6bd7 --- /dev/null +++ b/configs/computer/cluster-node-v100.yaml @@ -0,0 +1,8 @@ +devices: 4 +num_workers: 10 +progress_bar_refresh_rate: 2 +sync_batchnorm: True +accelerator: gpu +precision: 32 +strategy: ddp +num_nodes: 1 diff --git a/configs/computer/cpu.yaml b/configs/computer/cpu.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6e4e49bbe84d4bfbf0ed4849db41a20aa27d9dc2 --- /dev/null +++ b/configs/computer/cpu.yaml @@ -0,0 +1,8 @@ +devices: null +num_workers: 0 +progress_bar_refresh_rate: 2 +sync_batchnorm: False +accelerator: cpu +precision: 32 +strategy: auto +num_nodes: null diff --git a/configs/computer/h100.yaml b/configs/computer/h100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8509aa21fc99c38e44b05d250658b45d5300cfb7 --- /dev/null +++ b/configs/computer/h100.yaml @@ -0,0 +1,8 @@ +devices: 1 +progress_bar_refresh_rate: 2 +num_workers: 24 +sync_batchnorm: False +accelerator: gpu +precision: 32 +strategy: auto +num_nodes: 1 diff --git a/configs/computer/v100.yaml b/configs/computer/v100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d0ac2cc4c2aef6ee3a941f8508e20f5585487f8b --- /dev/null +++ b/configs/computer/v100.yaml @@ -0,0 +1,8 @@ +devices: 1 +num_workers: 10 +progress_bar_refresh_rate: 2 +sync_batchnorm: False +accelerator: gpu +precision: 32 +strategy: auto +num_nodes: 1 diff --git a/configs/config.yaml b/configs/config.yaml new file mode 100755 index 0000000000000000000000000000000000000000..2e8bb7bfa19bf4e77042bd9fa26c9cceab8207fd --- /dev/null +++ b/configs/config.yaml @@ -0,0 +1,90 @@ +defaults: + - model: default + - computer: v100 + - dataset: osv5m_emb + - stage: null + - _self_ + - exp: ??? + +model: + val_metrics: + _target_: metrics.distance_based.HaversineMetrics + acc_radiuses: + - 1 + - 25 + - 200 + - 750 + - 2500 + acc_area: [] + test_metrics: + _target_: metrics.distance_based.HaversineMetrics + acc_radiuses: + - 1 + - 25 + - 200 + - 750 + - 2500 + acc_area: ${areas} + +datamodule: + _target_: data.datamodule.ImageDataModule + train_dataset: ${dataset.train_dataset} + val_dataset: ${dataset.val_dataset} + test_dataset: ${dataset.test_dataset} + full_batch_size: ${dataset.full_batch_size} + eval_batch_size: ${dataset.eval_batch_size} + num_workers: ${computer.num_workers} + num_nodes: ${computer.num_nodes} + num_devices: ${computer.devices} + val_proportion: 0.02 + +trainer: + _target_: pytorch_lightning.Trainer + devices: ${computer.devices} + accelerator: ${computer.accelerator} + strategy: ${computer.strategy} + num_nodes: ${computer.num_nodes} + precision: ${computer.precision} + max_steps: 1000000 + val_check_interval: 25000 + check_val_every_n_epoch: null + +logger: + _target_: pytorch_lightning.loggers.WandbLogger + save_dir: ${root_dir} + name: ${experiment_name}${logger_suffix} + project: diff_plonk + log_model: False + offline: False + +checkpoints: + _target_: pytorch_lightning.callbacks.ModelCheckpoint + dirpath: ${root_dir}/checkpoints/${experiment_name} + filename: 'epoch_{epoch}' + monitor: val/loss + save_last: True + save_top_k: 0 + every_n_epochs: 1 + enable_version_counter: False + +progress_bar: + _target_: pytorch_lightning.callbacks.TQDMProgressBar + refresh_rate: ${computer.progress_bar_refresh_rate} + +data_dir: ${root_dir}/datasets +root_dir: ${hydra:runtime.cwd} +experiment_name: ${dataset.name}_${model.name}_${experiment_name_suffix} +experiment_name_suffix: base +logger_suffix: "" +mode: train # change that to eval to do the testing +areas: ['country', 'region', 'sub-region', 'city'] +class_name: null +streetclip: False +blur: False +text_tuning: False + +hydra: + run: + dir: outputs/${hydra.job.name}/${now:%Y-%m-%d_%H-%M-%S}/${experiment_name} + job: + chdir: true diff --git a/configs/dataset/baselines/im2gps.yaml b/configs/dataset/baselines/im2gps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..92b82f56a040038421a0bbfe94861b53178538c2 --- /dev/null +++ b/configs/dataset/baselines/im2gps.yaml @@ -0,0 +1,16 @@ +dataset: + name: im2gps + full_batch_size: 512 + test_dataset: + _partial_: true + _target_: data.data.Baseline + path: ${data_dir}/baselines/im2gps + which: 'im2gps' + transforms: ${dataset.test_transform} +datamodule: + _target_: data.datamodule.BaselineDataModule + test_dataset: ${dataset.test_dataset} + full_batch_size: ${dataset.full_batch_size} + num_workers: ${computer.num_workers} + num_nodes: ${computer.num_nodes} + num_devices: ${computer.devices} \ No newline at end of file diff --git a/configs/dataset/baselines/im2gps3k.yaml b/configs/dataset/baselines/im2gps3k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..41175f42584df9183f910d1820b8647c0f0e9d5c --- /dev/null +++ b/configs/dataset/baselines/im2gps3k.yaml @@ -0,0 +1,16 @@ +dataset: + name: im2gps3k + full_batch_size: 512 + test_dataset: + _partial_: true + _target_: data.data.Baseline + path: ${data_dir}/baselines/im2gps3k + which: 'im2gps3k' + transforms: ${dataset.test_transform} +datamodule: + _target_: data.datamodule.BaselineDataModule + test_dataset: ${dataset.test_dataset} + full_batch_size: ${dataset.full_batch_size} + num_workers: ${computer.num_workers} + num_nodes: ${computer.num_nodes} + num_devices: ${computer.devices} \ No newline at end of file diff --git a/configs/dataset/baselines/yfcc4k.yaml b/configs/dataset/baselines/yfcc4k.yaml new file mode 100644 index 0000000000000000000000000000000000000000..65537b67f3a51da9eab936c5482dfca783190a19 --- /dev/null +++ b/configs/dataset/baselines/yfcc4k.yaml @@ -0,0 +1,16 @@ +dataset: + name: yfcc4k + full_batch_size: 512 + test_dataset: + _partial_: true + _target_: data.data.Baseline + path: ${data_dir}/baselines/yfcc4k + which: 'yfcc4k' + transforms: ${dataset.test_transform} +datamodule: + _target_: data.datamodule.BaselineDataModule + test_dataset: ${dataset.test_dataset} + full_batch_size: ${dataset.full_batch_size} + num_workers: ${computer.num_workers} + num_nodes: ${computer.num_nodes} + num_devices: ${computer.devices} \ No newline at end of file diff --git a/configs/dataset/combined_emb.yaml b/configs/dataset/combined_emb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..10024808d2d63536ae2634d14c98a6bc7cdb3c90 --- /dev/null +++ b/configs/dataset/combined_emb.yaml @@ -0,0 +1,38 @@ +defaults: + - train_transform: empty + - test_transform: empty + - _self_ + +name: iNaturalist_OSV5M_YFCC100M_${dataset.embedding_name} +full_batch_size: 2048 +cond_dim: 1024 +eval_batch_size: 4096 +output_type: emb +embedding_name: dinov2_vitl14_registers + +train_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/YFCC100M/train/ ${data_dir}/osv5m/train/ ${data_dir}/inaturalist/train/ ${data_dir}/osv5m/train/ ${data_dir}/inaturalist/train/ + train: true + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] + +val_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/YFCC100M/yfcc4k/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] + +test_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/YFCC100M/yfcc4k/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] diff --git a/configs/dataset/inaturalist_emb.yaml b/configs/dataset/inaturalist_emb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a3fe6084032bd4ce3c143bc430159d654e8b3604 --- /dev/null +++ b/configs/dataset/inaturalist_emb.yaml @@ -0,0 +1,38 @@ +defaults: + - train_transform: empty + - test_transform: empty + - _self_ + +name: iNaturalist_${dataset.embedding_name} +full_batch_size: 512 +cond_dim: 1024 +eval_batch_size: 4096 +output_type: emb +embedding_name: dinov2_vitl14_registers + +train_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/inaturalist/train/ + train: true + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] + +val_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/inaturalist/val/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] + +test_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/inaturalist/test/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] diff --git a/configs/dataset/osv5m.yaml b/configs/dataset/osv5m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..91d8c5a3f515fb7b2ef2599c145e0520f9187b1b --- /dev/null +++ b/configs/dataset/osv5m.yaml @@ -0,0 +1,43 @@ +defaults: + - train_transform: fast_clip + - test_transform: fast_clip + - _self_ + +name: osv5m +full_batch_size: 2048 +eval_batch_size: 4096 +train_dataset: + _partial_: true + _target_: data.data.OSV5M + path: ${data_dir}/osv5m/ + split: train + class_name: ${class_name} + transforms: ${dataset.train_transform} + is_baseline: ${is_baseline} + areas: ${areas} + streetclip: ${streetclip} + blur: ${blur} + +val_dataset: + _partial_: true + _target_: data.data.OSV5M + path: ${data_dir}/osv5m/ + split: val + class_name: ${class_name} + transforms: ${dataset.test_transform} + is_baseline: ${is_baseline} + areas: ${areas} + streetclip: ${streetclip} + blur: ${blur} + +test_dataset: + _partial_: true + _target_: data.data.OSV5M + path: ${data_dir}/osv5m/ + split: test + class_name: ${class_name} + transforms: ${dataset.test_transform} + is_baseline: ${is_baseline} + areas: ${areas} + streetclip: ${streetclip} + blur: ${blur} diff --git a/configs/dataset/osv5m_emb.yaml b/configs/dataset/osv5m_emb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b3d594ea23f200374a0486d76ea4fb77521b49e4 --- /dev/null +++ b/configs/dataset/osv5m_emb.yaml @@ -0,0 +1,38 @@ +defaults: + - train_transform: empty + - test_transform: empty + - _self_ + +name: osv5m_${dataset.embedding_name} +full_batch_size: 1024 +eval_batch_size: 4096 +cond_dim: 1024 +output_type: emb +embedding_name: street_clip + +train_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/osv5m/train/ + train: true + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] + +val_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/osv5m/val/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: ["unique_country", "unique_region", "unique_sub-region", "unique_city"] + +test_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/osv5m/test/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: ["unique_country", "unique_region", "unique_sub-region", "unique_city"] diff --git a/configs/dataset/test_transform/center_crop.yaml b/configs/dataset/test_transform/center_crop.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a96f2e574f56a28142be4a8298917e0cc205ceeb --- /dev/null +++ b/configs/dataset/test_transform/center_crop.yaml @@ -0,0 +1,12 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.ToTensor + - _target_: utils.image_processing.CenterCrop + ratio: "1:1" + - _target_: torchvision.transforms.Resize + size: ${dataset.img_resolution} + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.Normalize + mean: 0.5 + std: 0.5 diff --git a/configs/dataset/test_transform/clip.yaml b/configs/dataset/test_transform/clip.yaml new file mode 100755 index 0000000000000000000000000000000000000000..3d4ff8b0466161f26be883ee4a0dbe2bb1b9be47 --- /dev/null +++ b/configs/dataset/test_transform/clip.yaml @@ -0,0 +1,2 @@ +_target_: data.transforms.ClipTransform +split: val diff --git a/configs/dataset/test_transform/empty.yaml b/configs/dataset/test_transform/empty.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bbd8dd7bde63f7e764f5ad36d680d1d7d14b6de9 --- /dev/null +++ b/configs/dataset/test_transform/empty.yaml @@ -0,0 +1,2 @@ +_target_: data.data.null_transform +_partial_: true \ No newline at end of file diff --git a/configs/dataset/test_transform/fast_clip.yaml b/configs/dataset/test_transform/fast_clip.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45b6a08732e0466ba225038b8e1a27fffb3f66c7 --- /dev/null +++ b/configs/dataset/test_transform/fast_clip.yaml @@ -0,0 +1,12 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.Resize + size: 224 + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.CenterCrop + size: 224 + - _target_: torchvision.transforms.ToTensor + - _target_: torchvision.transforms.Normalize + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] diff --git a/configs/dataset/test_transform/fast_resnet.yaml b/configs/dataset/test_transform/fast_resnet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fdbabe78156489a27370fa60e69e539170fbe150 --- /dev/null +++ b/configs/dataset/test_transform/fast_resnet.yaml @@ -0,0 +1,12 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.Resize + size: 224 + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.CenterCrop + size: 224 + - _target_: torchvision.transforms.ToTensor + - _target_: torchvision.transforms.Normalize + mean: [0.485 ,0.456 ,0.406] + std: [0.229, 0.224, 0.225] \ No newline at end of file diff --git a/configs/dataset/test_transform/none.yaml b/configs/dataset/test_transform/none.yaml new file mode 100755 index 0000000000000000000000000000000000000000..711c1f0b1d1101281d28c9a95c19d7c0da2ae838 --- /dev/null +++ b/configs/dataset/test_transform/none.yaml @@ -0,0 +1,6 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.ToTensor + - _target_: torchvision.transforms.Normalize + mean: 0.5 + std: 0.5 diff --git a/configs/dataset/train_transform/augmentation.yaml b/configs/dataset/train_transform/augmentation.yaml new file mode 100644 index 0000000000000000000000000000000000000000..393367070b772728740332907ec2f66c5025f591 --- /dev/null +++ b/configs/dataset/train_transform/augmentation.yaml @@ -0,0 +1,85 @@ +_target_: data.augmentation.ImageAugmentation +names: "standard_augmentation,geometric_augmentation,clip_transform" + +# always apply clip_transform at the end +clip_transform: + _target_: torchvision.transforms.Compose + transforms: + - _target_: torchvision.transforms.Resize + size: 224 + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.CenterCrop + size: 224 + - _target_: torchvision.transforms.ToTensor + - _target_: torchvision.transforms.Normalize + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] + +standard_augmentation: + _target_: data.augmentation.StandardAugmentation + # by default, we all augmentation methods + names: "brightness,contrast,sharpness,color,blur,gaussian_noise" + + # random PIL brigtness + brightness: + _target_: data.augmentation.PillowBrightness + p: 0.2 + factor_interval: [0.5, 1.5] + + # random PIL contrast + contrast: + _target_: data.augmentation.PillowContrast + p: 0.2 + factor_interval: [0.3, 3] + + # random PIL sharpness + sharpness: + _target_: data.augmentation.PillowSharpness + p: 0.2 + factor_interval: [0.5, 30.0] + + # random PIL color + color: + _target_: data.augmentation.PillowColor + p: 0.2 + factor_interval: [0.0, 2.0] + + # random PIL blur + blur: + _target_: data.augmentation.PillowBlur + p: 0.2 + factor_interval: [1, 2] + + # random numpy gaussian noise + gaussian_noise: + _target_: data.augmentation.NumpyGaussianNoise + p: 0.2 + factor_interval: [0.1, 0.04] + +geometric_augmentation: + _target_: data.augmentation.GeometricAugmentation + # by default, we all augmentation methods + names: "random_rotation,random_resized_crop,random_horizontal_flip" + + # random rotation + random_rotation: + _target_: torchvision.transforms.RandomRotation + degrees: [-15, 15] + + # random crop + random_resized_crop: + _target_: torchvision.transforms.RandomResizedCrop + scale: [0.5, 1.0] + ratio: [0.9, 1.1] + size: 224 + + # random horizontal flip + random_horizontal_flip: + _target_: torchvision.transforms.RandomHorizontalFlip + p: 0.5 + + # random vertical flip + random_vertical_flip: + _target_: torchvision.transforms.RandomVerticalFlip + p: 0.5 diff --git a/configs/dataset/train_transform/center_crop.yaml b/configs/dataset/train_transform/center_crop.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa4fb03d5db39c49fafde5c6550b95b3f61a0205 --- /dev/null +++ b/configs/dataset/train_transform/center_crop.yaml @@ -0,0 +1,14 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.ToTensor + - _target_: utils.image_processing.CenterCrop + ratio: "1:1" + - _target_: torchvision.transforms.Resize + size: ${dataset.img_resolution} + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.RandomHorizontalFlip + p: 0.5 + - _target_: torchvision.transforms.Normalize + mean: 0.5 + std: 0.5 diff --git a/configs/dataset/train_transform/clip.yaml b/configs/dataset/train_transform/clip.yaml new file mode 100755 index 0000000000000000000000000000000000000000..3d4ff8b0466161f26be883ee4a0dbe2bb1b9be47 --- /dev/null +++ b/configs/dataset/train_transform/clip.yaml @@ -0,0 +1,2 @@ +_target_: data.transforms.ClipTransform +split: val diff --git a/configs/dataset/train_transform/empty.yaml b/configs/dataset/train_transform/empty.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bbd8dd7bde63f7e764f5ad36d680d1d7d14b6de9 --- /dev/null +++ b/configs/dataset/train_transform/empty.yaml @@ -0,0 +1,2 @@ +_target_: data.data.null_transform +_partial_: true \ No newline at end of file diff --git a/configs/dataset/train_transform/fast_clip.yaml b/configs/dataset/train_transform/fast_clip.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45b6a08732e0466ba225038b8e1a27fffb3f66c7 --- /dev/null +++ b/configs/dataset/train_transform/fast_clip.yaml @@ -0,0 +1,12 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.Resize + size: 224 + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.CenterCrop + size: 224 + - _target_: torchvision.transforms.ToTensor + - _target_: torchvision.transforms.Normalize + mean: [0.48145466, 0.4578275, 0.40821073] + std: [0.26862954, 0.26130258, 0.27577711] diff --git a/configs/dataset/train_transform/fast_resnet.yaml b/configs/dataset/train_transform/fast_resnet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fdbabe78156489a27370fa60e69e539170fbe150 --- /dev/null +++ b/configs/dataset/train_transform/fast_resnet.yaml @@ -0,0 +1,12 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.Resize + size: 224 + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.CenterCrop + size: 224 + - _target_: torchvision.transforms.ToTensor + - _target_: torchvision.transforms.Normalize + mean: [0.485 ,0.456 ,0.406] + std: [0.229, 0.224, 0.225] \ No newline at end of file diff --git a/configs/dataset/train_transform/none.yaml b/configs/dataset/train_transform/none.yaml new file mode 100755 index 0000000000000000000000000000000000000000..0d54fe0045915b325145491307e283face27b3c2 --- /dev/null +++ b/configs/dataset/train_transform/none.yaml @@ -0,0 +1,7 @@ +_target_: torchvision.transforms.Compose +transforms: + - _target_: torchvision.transforms.Resize + size: 224 + interpolation: 3 + antialias: true + - _target_: torchvision.transforms.ToTensor diff --git a/configs/dataset/yfcc_emb.yaml b/configs/dataset/yfcc_emb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..30e42f8c30b3b68cafce66baa9241c2957987bb0 --- /dev/null +++ b/configs/dataset/yfcc_emb.yaml @@ -0,0 +1,38 @@ +defaults: + - train_transform: empty + - test_transform: empty + - _self_ + +name: iNaturalist_${dataset.embedding_name} +full_batch_size: 2048 +cond_dim: 1024 +eval_batch_size: 4096 +output_type: emb +embedding_name: dinov2_vitl14_registers + +train_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/YFCC100M/train/ + train: true + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] + +val_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/YFCC100M/yfcc4k/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] + +test_dataset: + _partial_: true + _target_: data.webdataset.GPSWebdataset + root: ${data_dir}/YFCC100M/yfcc4k/ + train: false + embedding_name: ${dataset.embedding_name} + return_image: false + metadata_attributes: [] diff --git a/configs/exp/YFCC100M_geoadalnmlp_r2_small_sigmoid_diffusion.yaml b/configs/exp/YFCC100M_geoadalnmlp_r2_small_sigmoid_diffusion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4b3410bff2182d3f5d1b044850974900a6326ab8 --- /dev/null +++ b/configs/exp/YFCC100M_geoadalnmlp_r2_small_sigmoid_diffusion.yaml @@ -0,0 +1,35 @@ +# @package _global_ + +defaults: + - override /dataset: yfcc_emb + - override /model: emb_cond + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: ddpm + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: diffusion +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid +areas: [] \ No newline at end of file diff --git a/configs/exp/YFCC100M_geoadalnmlp_r3_small_linear_flow_rieman.yaml b/configs/exp/YFCC100M_geoadalnmlp_r3_small_linear_flow_rieman.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0fee68fbf405a91b29ec434bb78476b848c30f3d --- /dev/null +++ b/configs/exp/YFCC100M_geoadalnmlp_r3_small_linear_flow_rieman.yaml @@ -0,0 +1,32 @@ +# @package _global_ + +defaults: + - override /dataset: yfcc_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: linear + - override /model/inference_noise_scheduler: linear + - override /model/loss: riemannian_flow_matching + - override /model/manifold: sphere + - override /model/val_sampler: riemannian_flow_matching + - override /model/test_sampler: riemannian_flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + interpolant: flow_matching + +dataset: + full_batch_size: 1024 + +areas: [] + +experiment_name_suffix: small_sigmoid diff --git a/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_diffusion.yaml b/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_diffusion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1672bd4cde5c447efae5f390785244c090f77b18 --- /dev/null +++ b/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_diffusion.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +defaults: + - override /dataset: yfcc_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: ddpm + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: diffusion + +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid +areas: [] diff --git a/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow.yaml b/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fb204d93b7f0d4ce333fbdd61e1dff12ce4ba87e --- /dev/null +++ b/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow.yaml @@ -0,0 +1,38 @@ +# @package _global_ + +defaults: + - override /dataset: yfcc_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: flow_matching + - override /model/val_sampler: flow_matching + - override /model/test_sampler: flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: flow_matching + +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid +areas: [] \ No newline at end of file diff --git a/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml b/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d62acd07ffa09c8c618fda364da2910da20202dc --- /dev/null +++ b/configs/exp/YFCC100M_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml @@ -0,0 +1,40 @@ +# @package _global_ + +defaults: + - override /dataset: yfcc_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: riemannian_flow_matching + - override /model/manifold: sphere + - override /model/val_sampler: riemannian_flow_matching + - override /model/test_sampler: riemannian_flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: flow_matching + +dataset: + full_batch_size: 1024 + +areas: [] + +experiment_name_suffix: small_sigmoid diff --git a/configs/exp/YFCC100M_geoadalnmlp_von_fisher.yaml b/configs/exp/YFCC100M_geoadalnmlp_von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aba9726efc25aac006d3c6c50c273ef0b2b9d4bb --- /dev/null +++ b/configs/exp/YFCC100M_geoadalnmlp_von_fisher.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +defaults: + - override /dataset: yfcc_emb + - override /model: von_fisher + - override /model/network: geo_adaln_mlp_von_fisher + - override /model/loss: von_fisher + - override /model/val_sampler: von_fisher + - override /model/test_sampler: von_fisher + - _self_ + +model: + network: + depth: 11 # To compensate the increase in params + dim: 512 + optimizer: + optim: + lr: 1e-4 + weight_decay: 0.05 +dataset: + full_batch_size: 1024 +trainer: + gradient_clip_val: 0.05 + gradient_clip_algorithm: norm +areas: [] +experiment_name_suffix: von_fisher \ No newline at end of file diff --git a/configs/exp/YFCC100M_geoadalnmlp_von_fisher_mixture.yaml b/configs/exp/YFCC100M_geoadalnmlp_von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3ec04a70472c2417e47750f078e9ccea2b5d12d8 --- /dev/null +++ b/configs/exp/YFCC100M_geoadalnmlp_von_fisher_mixture.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +defaults: + - override /dataset: yfcc_emb + - override /model: von_fisher_mixture + - override /model/network: geo_adaln_mlp_von_fisher_mixture + - override /model/loss: von_fisher_mixture + - override /model/val_sampler: von_fisher_mixture + - override /model/test_sampler: von_fisher_mixture + - _self_ + +model: + network: + depth: 11 # To compensate the increase in params + dim: 512 + optimizer: + optim: + lr: 1e-5 + weight_decay: 0.05 +dataset: + full_batch_size: 1024 +trainer: + gradient_clip_val: 0.01 + gradient_clip_algorithm: norm +experiment_name_suffix: von_fisher_mixture +areas: [] \ No newline at end of file diff --git a/configs/exp/combined_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml b/configs/exp/combined_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b047cd07a5e3cb138be093a2a30729296b067bdf --- /dev/null +++ b/configs/exp/combined_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml @@ -0,0 +1,40 @@ +# @package _global_ + +defaults: + - override /dataset: combined_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: riemannian_flow_matching + - override /model/manifold: sphere + - override /model/val_sampler: riemannian_flow_matching + - override /model/test_sampler: riemannian_flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: flow_matching + +dataset: + full_batch_size: 1024 + +areas: [] + +experiment_name_suffix: small_sigmoid diff --git a/configs/exp/iNaturalist_geoadalnmlp_r2_small_sigmoid_diffusion.yaml b/configs/exp/iNaturalist_geoadalnmlp_r2_small_sigmoid_diffusion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b9e44b2af3045a6f59891cd205606bbf0e8a2e10 --- /dev/null +++ b/configs/exp/iNaturalist_geoadalnmlp_r2_small_sigmoid_diffusion.yaml @@ -0,0 +1,36 @@ +# @package _global_ + +defaults: + - override /dataset: inaturalist_emb + - override /model: emb_cond + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: ddpm + - _self_ + +model: + network: + depth: 12 + dim: 256 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.1 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: diffusion +dataset: + full_batch_size: 512 + +areas: [] + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_diffusion.yaml b/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_diffusion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e87f9bbacf609fc627e85bd183d4adae9def3a10 --- /dev/null +++ b/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_diffusion.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +defaults: + - override /dataset: inaturalist_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: ddpm + - _self_ + +model: + network: + depth: 12 + dim: 256 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.1 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: diffusion + +dataset: + full_batch_size: 512 + +areas: [] + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow.yaml b/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6252b122ff2ea716be8ccec15cc583c075e420b3 --- /dev/null +++ b/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow.yaml @@ -0,0 +1,39 @@ +# @package _global_ + +defaults: + - override /dataset: inaturalist_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: flow_matching + - override /model/val_sampler: flow_matching + - override /model/test_sampler: flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 256 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.1 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: flow_matching + +dataset: + full_batch_size: 512 + +areas: [] + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml b/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml new file mode 100644 index 0000000000000000000000000000000000000000..904eeac8ecf2d1980c3261db2fcf4eb1450fe4ab --- /dev/null +++ b/configs/exp/iNaturalist_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml @@ -0,0 +1,40 @@ +# @package _global_ + +defaults: + - override /dataset: inaturalist_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: riemannian_flow_matching + - override /model/manifold: sphere + - override /model/val_sampler: riemannian_flow_matching + - override /model/test_sampler: riemannian_flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 256 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.1 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: flow_matching + +dataset: + full_batch_size: 512 + +areas: [] + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/iNaturalist_geoadalnmlp_von_fisher.yaml b/configs/exp/iNaturalist_geoadalnmlp_von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..86c7400c44efaa9f306329d738d18c8b5c9af946 --- /dev/null +++ b/configs/exp/iNaturalist_geoadalnmlp_von_fisher.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +defaults: + - override /dataset: inaturalist_emb + - override /model: von_fisher + - override /model/network: geo_adaln_mlp_von_fisher + - override /model/loss: von_fisher + - override /model/val_sampler: von_fisher + - override /model/test_sampler: von_fisher + - _self_ + +model: + network: + depth: 11 # To compensate the increase in params + dim: 256 + optimizer: + optim: + lr: 1e-4 + weight_decay: 0.1 +dataset: + full_batch_size: 512 +trainer: + gradient_clip_val: 0.01 + gradient_clip_algorithm: norm +areas: [] +experiment_name_suffix: von_fisher \ No newline at end of file diff --git a/configs/exp/iNaturalist_geoadalnmlp_von_fisher_mixture.yaml b/configs/exp/iNaturalist_geoadalnmlp_von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dfbc6019225b699de292cefd27e9d31da3515240 --- /dev/null +++ b/configs/exp/iNaturalist_geoadalnmlp_von_fisher_mixture.yaml @@ -0,0 +1,26 @@ +# @package _global_ + +defaults: + - override /dataset: inaturalist_emb + - override /model: von_fisher_mixture + - override /model/network: geo_adaln_mlp_von_fisher_mixture + - override /model/loss: von_fisher_mixture + - override /model/val_sampler: von_fisher_mixture + - override /model/test_sampler: von_fisher_mixture + - _self_ + +model: + network: + depth: 11 # To compensate the increase in params + dim: 256 + optimizer: + optim: + lr: 1e-5 + weight_decay: 0.1 +dataset: + full_batch_size: 512 +trainer: + gradient_clip_val: 0.01 + gradient_clip_algorithm: norm +areas: [] +experiment_name_suffix: von_fisher_mixture diff --git a/configs/exp/osv_5m_geoadalnmlp_r2_small_sigmoid_diffusion.yaml b/configs/exp/osv_5m_geoadalnmlp_r2_small_sigmoid_diffusion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5c931fc74996f63e194b09d94876203421f908cd --- /dev/null +++ b/configs/exp/osv_5m_geoadalnmlp_r2_small_sigmoid_diffusion.yaml @@ -0,0 +1,34 @@ +# @package _global_ + +defaults: + - override /dataset: osv5m_emb + - override /model: emb_cond + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: ddpm + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: diffusion +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/osv_5m_geoadalnmlp_r3_small_linear_flow_riemann.yaml b/configs/exp/osv_5m_geoadalnmlp_r3_small_linear_flow_riemann.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5a31ffd41250fe0abe628f0de14f2a9da2d33127 --- /dev/null +++ b/configs/exp/osv_5m_geoadalnmlp_r3_small_linear_flow_riemann.yaml @@ -0,0 +1,30 @@ +# @package _global_ + +defaults: + - override /dataset: osv5m_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: linear + - override /model/inference_noise_scheduler: linear + - override /model/loss: riemannian_flow_matching + - override /model/manifold: sphere + - override /model/val_sampler: riemannian_flow_matching + - override /model/test_sampler: riemannian_flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + interpolant: flow_matching + +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_diffusion.yaml b/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_diffusion.yaml new file mode 100644 index 0000000000000000000000000000000000000000..df953892119cd50b386e950ebfd7e4e14a874761 --- /dev/null +++ b/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_diffusion.yaml @@ -0,0 +1,35 @@ +# @package _global_ + +defaults: + - override /dataset: osv5m_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: ddpm + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: diffusion + +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_flow.yaml b/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_flow.yaml new file mode 100644 index 0000000000000000000000000000000000000000..05459ee799d32a8ed3e87c841ac59959df7239c0 --- /dev/null +++ b/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_flow.yaml @@ -0,0 +1,37 @@ +# @package _global_ + +defaults: + - override /dataset: osv5m_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: flow_matching + - override /model/val_sampler: flow_matching + - override /model/test_sampler: flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: flow_matching + +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml b/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5bfc89b84e0397c6aa6b363e59c4dad076414eea --- /dev/null +++ b/configs/exp/osv_5m_geoadalnmlp_r3_small_sigmoid_flow_riemann.yaml @@ -0,0 +1,38 @@ +# @package _global_ + +defaults: + - override /dataset: osv5m_emb + - override /model: emb_cond_cartesian + - override /model/network: geo_adaln_mlp + - override /model/train_noise_scheduler: sigmoid + - override /model/inference_noise_scheduler: sigmoid + - override /model/loss: riemannian_flow_matching + - override /model/manifold: sphere + - override /model/val_sampler: riemannian_flow_matching + - override /model/test_sampler: riemannian_flow_matching + - _self_ + +model: + network: + depth: 12 + dim: 512 + optimizer: + optim: + lr: 8e-4 + weight_decay: 0.05 + loss: + cond_drop_rate: 0.1 + train_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + inference_noise_scheduler: + start: -7 + end: 3 + tau: 1.0 + interpolant: flow_matching + +dataset: + full_batch_size: 1024 + +experiment_name_suffix: small_sigmoid \ No newline at end of file diff --git a/configs/exp/osv_5m_geoadalnmlp_von_fisher.yaml b/configs/exp/osv_5m_geoadalnmlp_von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d48f03164a22adbeceb57c3039acd0ed81f7d02 --- /dev/null +++ b/configs/exp/osv_5m_geoadalnmlp_von_fisher.yaml @@ -0,0 +1,25 @@ +# @package _global_ + +defaults: + - override /dataset: osv5m_emb + - override /model: von_fisher + - override /model/network: geo_adaln_mlp_von_fisher + - override /model/loss: von_fisher + - override /model/val_sampler: von_fisher + - override /model/test_sampler: von_fisher + - _self_ + +model: + network: + depth: 11 # To compensate the increase in params + dim: 512 + optimizer: + optim: + lr: 1e-4 + weight_decay: 0.05 +dataset: + full_batch_size: 1024 +trainer: + gradient_clip_val: 0.05 + gradient_clip_algorithm: norm +experiment_name_suffix: von_fisher \ No newline at end of file diff --git a/configs/exp/osv_5m_geoadalnmlp_von_fisher_mixture.yaml b/configs/exp/osv_5m_geoadalnmlp_von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..96c0191c064b8f7a673512c365656816c41da1c4 --- /dev/null +++ b/configs/exp/osv_5m_geoadalnmlp_von_fisher_mixture.yaml @@ -0,0 +1,25 @@ +# @package _global_ + +defaults: + - override /dataset: osv5m_emb + - override /model: von_fisher_mixture + - override /model/network: geo_adaln_mlp_von_fisher_mixture + - override /model/loss: von_fisher_mixture + - override /model/val_sampler: von_fisher_mixture + - override /model/test_sampler: von_fisher_mixture + - _self_ + +model: + network: + depth: 11 # To compensate the increase in params + dim: 512 + optimizer: + optim: + lr: 1e-4 + weight_decay: 0.05 +dataset: + full_batch_size: 1024 +trainer: + gradient_clip_val: 0.05 + gradient_clip_algorithm: norm +experiment_name_suffix: von_fisher_mixture diff --git a/configs/model/cond_preprocessing/embedding.yaml b/configs/model/cond_preprocessing/embedding.yaml new file mode 100644 index 0000000000000000000000000000000000000000..050e6bb944d1be3b0f99479606bb1e28646a7e4e --- /dev/null +++ b/configs/model/cond_preprocessing/embedding.yaml @@ -0,0 +1,3 @@ +_target_: models.preprocessing.PrecomputedPreconditioning +input_key: emb +output_key: emb \ No newline at end of file diff --git a/configs/model/data_preprocessing/gps.yaml b/configs/model/data_preprocessing/gps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0b6fcf8b60f7a404b33dd13b2d558e2f4e49d0f2 --- /dev/null +++ b/configs/model/data_preprocessing/gps.yaml @@ -0,0 +1,4 @@ +_target_: models.preprocessing.NormGPS +input_key: gps +output_key: x_0 +normalize: False \ No newline at end of file diff --git a/configs/model/data_preprocessing/gps_to_cartesian.yaml b/configs/model/data_preprocessing/gps_to_cartesian.yaml new file mode 100644 index 0000000000000000000000000000000000000000..efb04d5c18c34b397f0133d823c65d602b1284ee --- /dev/null +++ b/configs/model/data_preprocessing/gps_to_cartesian.yaml @@ -0,0 +1,3 @@ +_target_: models.preprocessing.GPStoCartesian +input_key: gps +output_key: x_0 \ No newline at end of file diff --git a/configs/model/data_preprocessing/normalized_gps.yaml b/configs/model/data_preprocessing/normalized_gps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..769a4ba35855891260a8302fa528a0d4a8474ebc --- /dev/null +++ b/configs/model/data_preprocessing/normalized_gps.yaml @@ -0,0 +1,4 @@ +_target_: models.preprocessing.NormGPS +input_key: gps +output_key: x_0 +normalize: True \ No newline at end of file diff --git a/configs/model/emb_cond.yaml b/configs/model/emb_cond.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7f00df12fd3ad41aece86d88fd8ba509ae6f4d8c --- /dev/null +++ b/configs/model/emb_cond.yaml @@ -0,0 +1,24 @@ +defaults: + - optimizer: lamb + - lr_scheduler: warmup_cosine_decay + - network: geo_adaln_mlp + - train_noise_scheduler: sigmoid + - inference_noise_scheduler: cosine_simple + - preconditioning: ddpm + - data_preprocessing: normalized_gps + - cond_preprocessing: embedding + - postprocessing: renorm_gps + - loss: ddpm + - val_sampler: ddim + - test_sampler: ddpm + - manifold: null + - _self_ + +network: + input_dim: 2 +name: GeoMLP_R2 +ema_decay: 0.999 +start_ema_step: 0 +cfg_rate: 2.0 +interpolant: flow_matching +compute_nll: true \ No newline at end of file diff --git a/configs/model/emb_cond_cartesian.yaml b/configs/model/emb_cond_cartesian.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f8cc9be47b5f89fcd3dea1b00e6185ff0aade5d4 --- /dev/null +++ b/configs/model/emb_cond_cartesian.yaml @@ -0,0 +1,25 @@ +defaults: + - optimizer: lamb + - lr_scheduler: warmup_cosine_decay + - network: geo_adaln_mlp + - train_noise_scheduler: sigmoid + - inference_noise_scheduler: cosine_simple + - preconditioning: ddpm + - data_preprocessing: gps_to_cartesian + - cond_preprocessing: embedding + - postprocessing: cartesian_to_gps + - loss: ddpm + - val_sampler: ddim + - test_sampler: ddpm + - manifold: null + - _self_ + +network: + input_dim: 3 +name: GeoMLP_R3 +ema_decay: 0.999 +start_ema_step: 0 +cfg_rate: 2.0 +interpolant: flow_matching +compute_nll: true +compute_swarms: False \ No newline at end of file diff --git a/configs/model/inference_noise_scheduler/cosine.yaml b/configs/model/inference_noise_scheduler/cosine.yaml new file mode 100644 index 0000000000000000000000000000000000000000..40f48f84d118c3af534e0c9031a05b117a75ce6f --- /dev/null +++ b/configs/model/inference_noise_scheduler/cosine.yaml @@ -0,0 +1,5 @@ +_target_: models.schedulers.CosineScheduler +start: 1 +end: 0 +tau: 1 +clip_min: 1e-9 \ No newline at end of file diff --git a/configs/model/inference_noise_scheduler/cosine_simple.yaml b/configs/model/inference_noise_scheduler/cosine_simple.yaml new file mode 100644 index 0000000000000000000000000000000000000000..03cc697ce7cb3c49009e8875ca1e964a12cce76a --- /dev/null +++ b/configs/model/inference_noise_scheduler/cosine_simple.yaml @@ -0,0 +1,3 @@ +_target_: models.schedulers.CosineSchedulerSimple +ns: 2e-4 +ds: 2.5e-4 \ No newline at end of file diff --git a/configs/model/inference_noise_scheduler/linear.yaml b/configs/model/inference_noise_scheduler/linear.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bc3438e62d22e6dcda127cd40b4f95975110a1be --- /dev/null +++ b/configs/model/inference_noise_scheduler/linear.yaml @@ -0,0 +1,4 @@ +_target_: models.schedulers.LinearScheduler +start: 1 +end: 0 +clip_min: 1e-9 \ No newline at end of file diff --git a/configs/model/inference_noise_scheduler/sigmoid.yaml b/configs/model/inference_noise_scheduler/sigmoid.yaml new file mode 100644 index 0000000000000000000000000000000000000000..30e86fb03187baa8e52ce148eb7a03cc6ac60751 --- /dev/null +++ b/configs/model/inference_noise_scheduler/sigmoid.yaml @@ -0,0 +1,5 @@ +_target_: models.schedulers.SigmoidScheduler +start: -3 +end: 3 +tau: 0.9 +clip_min: 1e-9 \ No newline at end of file diff --git a/configs/model/loss/ddpm.yaml b/configs/model/loss/ddpm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..46cbf01edccfbfd23876171874c4aa94dce2ba12 --- /dev/null +++ b/configs/model/loss/ddpm.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.losses.DDPMLoss +cond_drop_rate: 0.0 +conditioning_key: ${model.cond_preprocessing.output_key} \ No newline at end of file diff --git a/configs/model/loss/flow_matching.yaml b/configs/model/loss/flow_matching.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3a852addf69537611882f4be34eac2232376deb8 --- /dev/null +++ b/configs/model/loss/flow_matching.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.losses.FlowMatchingLoss +cond_drop_rate: 0.0 +conditioning_key: ${model.cond_preprocessing.output_key} \ No newline at end of file diff --git a/configs/model/loss/riemannian_flow_matching.yaml b/configs/model/loss/riemannian_flow_matching.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fc98b0f5dbbae06d27001929079f84cf8d016e47 --- /dev/null +++ b/configs/model/loss/riemannian_flow_matching.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.losses.RiemannianFlowMatchingLoss +cond_drop_rate: 0.0 +conditioning_key: ${model.cond_preprocessing.output_key} \ No newline at end of file diff --git a/configs/model/loss/von_fisher.yaml b/configs/model/loss/von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..43a10449886f7cbdb1a0b2ed5f96855508df9842 --- /dev/null +++ b/configs/model/loss/von_fisher.yaml @@ -0,0 +1,2 @@ +_partial_: true +_target_: models.losses.VonFisherLoss diff --git a/configs/model/loss/von_fisher_mixture.yaml b/configs/model/loss/von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..77f6a340b73d903167e735d35d05f9baf5f31306 --- /dev/null +++ b/configs/model/loss/von_fisher_mixture.yaml @@ -0,0 +1,2 @@ +_partial_: true +_target_: models.losses.VonFisherMixtureLoss diff --git a/configs/model/lr_scheduler/warmup.yaml b/configs/model/lr_scheduler/warmup.yaml new file mode 100644 index 0000000000000000000000000000000000000000..18970870f95e94d92f2e97820fe3537f004510b4 --- /dev/null +++ b/configs/model/lr_scheduler/warmup.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: utils.lr_scheduler.WarmupLR +warmup_steps: 500 + diff --git a/configs/model/lr_scheduler/warmup_cosine_decay.yaml b/configs/model/lr_scheduler/warmup_cosine_decay.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2d45d5a8d1f3e0a23b8cabecffb0c0b3487f9d32 --- /dev/null +++ b/configs/model/lr_scheduler/warmup_cosine_decay.yaml @@ -0,0 +1,5 @@ +_partial_: true +_target_: utils.lr_scheduler.WarmupCosineDecayLR +warmup_steps: 500 +total_steps: ${trainer.max_steps} + diff --git a/configs/model/manifold/sphere.yaml b/configs/model/manifold/sphere.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b5c3d19adda604d27c8987d487cd8e96463a796e --- /dev/null +++ b/configs/model/manifold/sphere.yaml @@ -0,0 +1 @@ +_target_: utils.manifolds.Sphere \ No newline at end of file diff --git a/configs/model/network/geo_adaln_mlp.yaml b/configs/model/network/geo_adaln_mlp.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0c43b224a37bb86f988c67b7bbe51848684d8d1c --- /dev/null +++ b/configs/model/network/geo_adaln_mlp.yaml @@ -0,0 +1,6 @@ +_target_: models.networks.mlp.GeoAdaLNMLP +input_dim: 2 +dim: 256 +depth: 8 +expansion: 4 +cond_dim: ${dataset.cond_dim} \ No newline at end of file diff --git a/configs/model/network/geo_adaln_mlp_von_fisher.yaml b/configs/model/network/geo_adaln_mlp_von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7486447cea43cbe381b8db535cd7ebfb029a2f8d --- /dev/null +++ b/configs/model/network/geo_adaln_mlp_von_fisher.yaml @@ -0,0 +1,6 @@ +_target_: models.networks.mlp.GeoAdaLNMLPVonFisher +input_dim: 2 +dim: 256 +depth: 8 +expansion: 4 +cond_dim: ${dataset.cond_dim} \ No newline at end of file diff --git a/configs/model/network/geo_adaln_mlp_von_fisher_mixture.yaml b/configs/model/network/geo_adaln_mlp_von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9e58831469d78688de23ce656a348a0567a02fe0 --- /dev/null +++ b/configs/model/network/geo_adaln_mlp_von_fisher_mixture.yaml @@ -0,0 +1,7 @@ +_target_: models.networks.mlp.GeoAdaLNMLPVonFisherMixture +input_dim: 2 +dim: 256 +depth: 8 +expansion: 4 +cond_dim: ${dataset.cond_dim} +num_mixtures: 3 \ No newline at end of file diff --git a/configs/model/network/geo_mlp.yaml b/configs/model/network/geo_mlp.yaml new file mode 100644 index 0000000000000000000000000000000000000000..af35cf5f33b1f2d6f7d7b3ef3eb70ad78e10c2d7 --- /dev/null +++ b/configs/model/network/geo_mlp.yaml @@ -0,0 +1,5 @@ +_target_: models.networks.mlp.GeoConcatNMLP +input_dim: 2 +hidden_dim: 512 +depth: 5 +cond_dim: ${dataset.cond_dim} \ No newline at end of file diff --git a/configs/model/optimizer/adam.yaml b/configs/model/optimizer/adam.yaml new file mode 100755 index 0000000000000000000000000000000000000000..55490d3492168181115ef90949a1232fece3f7b5 --- /dev/null +++ b/configs/model/optimizer/adam.yaml @@ -0,0 +1,7 @@ +optim: + _target_: torch.optim.Adam + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 0.01 + +exclude_ln_and_biases_from_weight_decay: False \ No newline at end of file diff --git a/configs/model/optimizer/adamw.yaml b/configs/model/optimizer/adamw.yaml new file mode 100755 index 0000000000000000000000000000000000000000..7b6217c6a98035ffa390a6ea0c8930754698d8f6 --- /dev/null +++ b/configs/model/optimizer/adamw.yaml @@ -0,0 +1,7 @@ +optim: + _target_: torch.optim.AdamW + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 0.01 + +exclude_ln_and_biases_from_weight_decay: False \ No newline at end of file diff --git a/configs/model/optimizer/lamb.yaml b/configs/model/optimizer/lamb.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bb78f090fb8805a9886e000c963a92f8ee31fea9 --- /dev/null +++ b/configs/model/optimizer/lamb.yaml @@ -0,0 +1,7 @@ +optim: + _target_: utils.optimizers.Lamb + lr: 1e-3 + betas: [0.9, 0.999] + weight_decay: 0.01 + +exclude_ln_and_biases_from_weight_decay: False \ No newline at end of file diff --git a/configs/model/optimizer/sgd.yaml b/configs/model/optimizer/sgd.yaml new file mode 100644 index 0000000000000000000000000000000000000000..15f1c6c52521dc794c5bbc7a2740c5d0659fa6eb --- /dev/null +++ b/configs/model/optimizer/sgd.yaml @@ -0,0 +1,6 @@ +optim: + _target_: torch.optim.SGD + lr: 1e-3 + weight_decay: 0.01 + +exclude_ln_and_biases_from_weight_decay: False \ No newline at end of file diff --git a/configs/model/postprocessing/cartesian_to_gps.yaml b/configs/model/postprocessing/cartesian_to_gps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2202006d9ecb1bf660e5a0fc2f3e926474e60fcf --- /dev/null +++ b/configs/model/postprocessing/cartesian_to_gps.yaml @@ -0,0 +1 @@ +_target_: models.postprocessing.CartesiantoGPS \ No newline at end of file diff --git a/configs/model/postprocessing/renorm_gps.yaml b/configs/model/postprocessing/renorm_gps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..52eeec67054fd33c2c68ac98a19a47e2c3ace3be --- /dev/null +++ b/configs/model/postprocessing/renorm_gps.yaml @@ -0,0 +1 @@ +_target_: models.postprocessing.UnormGPS \ No newline at end of file diff --git a/configs/model/preconditioning/ddpm.yaml b/configs/model/preconditioning/ddpm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a3c58541a52b3cdab50b5c6f4107cd70eff0b0bf --- /dev/null +++ b/configs/model/preconditioning/ddpm.yaml @@ -0,0 +1 @@ +_target_: models.preconditioning.DDPMPrecond \ No newline at end of file diff --git a/configs/model/preconditioning/edm.yaml b/configs/model/preconditioning/edm.yaml new file mode 100755 index 0000000000000000000000000000000000000000..874e3409007eccd37ed21b94a06dc6e674ad9f7d --- /dev/null +++ b/configs/model/preconditioning/edm.yaml @@ -0,0 +1,6 @@ +_partial_: true +_target_: models.preconditioning.EDMPrecond +label_dim: ${data.label_dim} +sigma_min: 0 +sigma_max: !!float .inf +sigma_data: 0.5 \ No newline at end of file diff --git a/configs/model/test_sampler/ddim.yaml b/configs/model/test_sampler/ddim.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9082c83795f8045fbb5a65d1cfeb4cb59633ecc2 --- /dev/null +++ b/configs/model/test_sampler/ddim.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.ddim.ddim_sampler +num_steps: 250 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/test_sampler/ddpm.yaml b/configs/model/test_sampler/ddpm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bffa1d3ec5beae8011206aa29cf61b70722aa7b7 --- /dev/null +++ b/configs/model/test_sampler/ddpm.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.ddpm.ddpm_sampler +num_steps: 1000 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/test_sampler/edm.yaml b/configs/model/test_sampler/edm.yaml new file mode 100755 index 0000000000000000000000000000000000000000..510025144aa050d415c83d115cf70d30429b3721 --- /dev/null +++ b/configs/model/test_sampler/edm.yaml @@ -0,0 +1,10 @@ +_partial_: true +_target_: models.samplers.edm.edm_sampler +num_steps: 18 +sigma_min: 0.002 +sigma_max: 80 +rho: 7 +S_churn: 0 +S_min: 0 +S_max: !!float .inf +S_noise: 1 \ No newline at end of file diff --git a/configs/model/test_sampler/flow_matching.yaml b/configs/model/test_sampler/flow_matching.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0501d356ba495ea7fa94ff9d88fda6f282cc6740 --- /dev/null +++ b/configs/model/test_sampler/flow_matching.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.flow_sampler.flow_sampler +num_steps: 250 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/test_sampler/riemannian_flow_matching.yaml b/configs/model/test_sampler/riemannian_flow_matching.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c3274cb8fc20973c374cb01137e1ed68fa5f7d2 --- /dev/null +++ b/configs/model/test_sampler/riemannian_flow_matching.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.riemannian_flow_sampler.riemannian_flow_sampler +num_steps: 250 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/test_sampler/von_fisher.yaml b/configs/model/test_sampler/von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4d1fc32e1dcc3d4744ef47708f0dad714cf52aed --- /dev/null +++ b/configs/model/test_sampler/von_fisher.yaml @@ -0,0 +1,2 @@ +_partial_: true +_target_: models.samplers.von_fisher_sampling.vMF_sampler diff --git a/configs/model/test_sampler/von_fisher_mixture.yaml b/configs/model/test_sampler/von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cc4ae2d2f42ae8eda2396d62eed91f7c99e9e5f6 --- /dev/null +++ b/configs/model/test_sampler/von_fisher_mixture.yaml @@ -0,0 +1,2 @@ +_partial_: true +_target_: models.samplers.von_fisher_sampling.vMF_mixture_sampler diff --git a/configs/model/train_noise_scheduler/cosine.yaml b/configs/model/train_noise_scheduler/cosine.yaml new file mode 100644 index 0000000000000000000000000000000000000000..40f48f84d118c3af534e0c9031a05b117a75ce6f --- /dev/null +++ b/configs/model/train_noise_scheduler/cosine.yaml @@ -0,0 +1,5 @@ +_target_: models.schedulers.CosineScheduler +start: 1 +end: 0 +tau: 1 +clip_min: 1e-9 \ No newline at end of file diff --git a/configs/model/train_noise_scheduler/cosine_simple.yaml b/configs/model/train_noise_scheduler/cosine_simple.yaml new file mode 100644 index 0000000000000000000000000000000000000000..03cc697ce7cb3c49009e8875ca1e964a12cce76a --- /dev/null +++ b/configs/model/train_noise_scheduler/cosine_simple.yaml @@ -0,0 +1,3 @@ +_target_: models.schedulers.CosineSchedulerSimple +ns: 2e-4 +ds: 2.5e-4 \ No newline at end of file diff --git a/configs/model/train_noise_scheduler/linear.yaml b/configs/model/train_noise_scheduler/linear.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bc3438e62d22e6dcda127cd40b4f95975110a1be --- /dev/null +++ b/configs/model/train_noise_scheduler/linear.yaml @@ -0,0 +1,4 @@ +_target_: models.schedulers.LinearScheduler +start: 1 +end: 0 +clip_min: 1e-9 \ No newline at end of file diff --git a/configs/model/train_noise_scheduler/sigmoid.yaml b/configs/model/train_noise_scheduler/sigmoid.yaml new file mode 100644 index 0000000000000000000000000000000000000000..30e86fb03187baa8e52ce148eb7a03cc6ac60751 --- /dev/null +++ b/configs/model/train_noise_scheduler/sigmoid.yaml @@ -0,0 +1,5 @@ +_target_: models.schedulers.SigmoidScheduler +start: -3 +end: 3 +tau: 0.9 +clip_min: 1e-9 \ No newline at end of file diff --git a/configs/model/val_sampler/ddim.yaml b/configs/model/val_sampler/ddim.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9082c83795f8045fbb5a65d1cfeb4cb59633ecc2 --- /dev/null +++ b/configs/model/val_sampler/ddim.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.ddim.ddim_sampler +num_steps: 250 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/val_sampler/ddpm.yaml b/configs/model/val_sampler/ddpm.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bffa1d3ec5beae8011206aa29cf61b70722aa7b7 --- /dev/null +++ b/configs/model/val_sampler/ddpm.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.ddpm.ddpm_sampler +num_steps: 1000 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/val_sampler/edm.yaml b/configs/model/val_sampler/edm.yaml new file mode 100755 index 0000000000000000000000000000000000000000..510025144aa050d415c83d115cf70d30429b3721 --- /dev/null +++ b/configs/model/val_sampler/edm.yaml @@ -0,0 +1,10 @@ +_partial_: true +_target_: models.samplers.edm.edm_sampler +num_steps: 18 +sigma_min: 0.002 +sigma_max: 80 +rho: 7 +S_churn: 0 +S_min: 0 +S_max: !!float .inf +S_noise: 1 \ No newline at end of file diff --git a/configs/model/val_sampler/flow_matching.yaml b/configs/model/val_sampler/flow_matching.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0501d356ba495ea7fa94ff9d88fda6f282cc6740 --- /dev/null +++ b/configs/model/val_sampler/flow_matching.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.flow_sampler.flow_sampler +num_steps: 250 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/val_sampler/riemannian_flow_matching.yaml b/configs/model/val_sampler/riemannian_flow_matching.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9c3274cb8fc20973c374cb01137e1ed68fa5f7d2 --- /dev/null +++ b/configs/model/val_sampler/riemannian_flow_matching.yaml @@ -0,0 +1,4 @@ +_partial_: true +_target_: models.samplers.riemannian_flow_sampler.riemannian_flow_sampler +num_steps: 250 +cfg_rate: ${model.cfg_rate} \ No newline at end of file diff --git a/configs/model/val_sampler/von_fisher.yaml b/configs/model/val_sampler/von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4d1fc32e1dcc3d4744ef47708f0dad714cf52aed --- /dev/null +++ b/configs/model/val_sampler/von_fisher.yaml @@ -0,0 +1,2 @@ +_partial_: true +_target_: models.samplers.von_fisher_sampling.vMF_sampler diff --git a/configs/model/val_sampler/von_fisher_mixture.yaml b/configs/model/val_sampler/von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cc4ae2d2f42ae8eda2396d62eed91f7c99e9e5f6 --- /dev/null +++ b/configs/model/val_sampler/von_fisher_mixture.yaml @@ -0,0 +1,2 @@ +_partial_: true +_target_: models.samplers.von_fisher_sampling.vMF_mixture_sampler diff --git a/configs/model/von_fisher.yaml b/configs/model/von_fisher.yaml new file mode 100644 index 0000000000000000000000000000000000000000..80f5429564432fa096e65b4910234b38423930f5 --- /dev/null +++ b/configs/model/von_fisher.yaml @@ -0,0 +1,19 @@ +defaults: + - optimizer: lamb + - lr_scheduler: warmup_cosine_decay + - network: geo_adaln_mlp_von_fisher + - preconditioning: ddpm + - data_preprocessing: gps_to_cartesian + - cond_preprocessing: embedding + - postprocessing: cartesian_to_gps + - loss: von_fisher + - val_sampler: von_fisher + - test_sampler: von_fisher + - _self_ + +network: + input_dim: 3 +name: GeoMLP_R3_VonFisher +ema_decay: 0.999 +start_ema_step: 0 +interpolant: von_fisher \ No newline at end of file diff --git a/configs/model/von_fisher_mixture.yaml b/configs/model/von_fisher_mixture.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ae27b7edda558d08d2746d28d98dfe80b78ef573 --- /dev/null +++ b/configs/model/von_fisher_mixture.yaml @@ -0,0 +1,19 @@ +defaults: + - optimizer: lamb + - lr_scheduler: warmup_cosine_decay + - network: geo_adaln_mlp_von_fisher_mixture + - preconditioning: ddpm + - data_preprocessing: gps_to_cartesian + - cond_preprocessing: embedding + - postprocessing: cartesian_to_gps + - loss: von_fisher_mixture + - val_sampler: von_fisher_mixture + - test_sampler: von_fisher_mixture + - _self_ + +network: + input_dim: 3 +name: GeoMLP_R3_VonFisher_Mixture +ema_decay: 0.999 +start_ema_step: 0 +interpolant: von_fisher \ No newline at end of file diff --git a/configs/stage/debug.yaml b/configs/stage/debug.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1e4e1f2a87a3d8f36cde2ea5de293f59a7bd1cdc --- /dev/null +++ b/configs/stage/debug.yaml @@ -0,0 +1,4 @@ +# @package _global_ + + +stage: debug \ No newline at end of file diff --git a/configs/stage/profile.yaml b/configs/stage/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..88e6403662ceab281728b3de29b0701aedb26887 --- /dev/null +++ b/configs/stage/profile.yaml @@ -0,0 +1,20 @@ +# @package _global_ + +trainer: + max_steps: 15 + profiler: + _target_: pytorch_lightning.profilers.PyTorchProfiler + dirpath: ${root_dir}/profiler_log/${experiment_name} + schedule: + _target_: torch.profiler.schedule + skip_first: 5 + wait: 2 + warmup: 1 + active: 3 + repeat: 0 + on_trace_ready: + _target_: torch.profiler.tensorboard_trace_handler + dir_name: ${root_dir}/profiler_log/${experiment_name} + with_stack: True + record_shapes: True + with_modules: True \ No newline at end of file diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/__pycache__/__init__.cpython-310.pyc b/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..157c9f16e680e71bc2f453357e5d909cecb5f82b Binary files /dev/null and b/data/__pycache__/__init__.cpython-310.pyc differ diff --git a/data/__pycache__/data.cpython-310.pyc b/data/__pycache__/data.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..390395da20b6668ac351f1b076eb64b48b3d0ff4 Binary files /dev/null and b/data/__pycache__/data.cpython-310.pyc differ diff --git a/data/__pycache__/datamodule.cpython-310.pyc b/data/__pycache__/datamodule.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..69cf78dd176620e42e6585ffa0e80ecc6e43ea1c Binary files /dev/null and b/data/__pycache__/datamodule.cpython-310.pyc differ diff --git a/data/__pycache__/webdataset.cpython-310.pyc b/data/__pycache__/webdataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..92658145e6c8c63008b5e1ced04fc6d5657e7ad6 Binary files /dev/null and b/data/__pycache__/webdataset.cpython-310.pyc differ diff --git a/data/augmentation.py b/data/augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..bfd49bcd5cf985398146d99c13b1cd7d7928ea6e --- /dev/null +++ b/data/augmentation.py @@ -0,0 +1,223 @@ +""" +Adapted from https://github.com/nv-nguyen/template-pose/blob/main/src/utils/augmentation.py +""" + +from torchvision import transforms +from PIL import ImageEnhance, ImageFilter, Image +import numpy as np +import random +import logging +from torchvision.transforms import RandomResizedCrop, ToTensor + + +class PillowRGBAugmentation: + def __init__(self, pillow_fn, p, factor_interval): + self._pillow_fn = pillow_fn + self.p = p + self.factor_interval = factor_interval + + def __call__(self, PIL_image): + if random.random() <= self.p: + factor = random.uniform(*self.factor_interval) + if PIL_image.mode != "RGB": + logging.warning( + f"Error when apply data aug, image mode: {PIL_image.mode}" + ) + imgs = imgs.convert("RGB") + logging.warning(f"Success to change to {PIL_image.mode}") + PIL_image = (self._pillow_fn(PIL_image).enhance(factor=factor)).convert( + "RGB" + ) + return PIL_image + + +class PillowSharpness(PillowRGBAugmentation): + def __init__( + self, + p=0.3, + factor_interval=(0, 40.0), + ): + super().__init__( + pillow_fn=ImageEnhance.Sharpness, + p=p, + factor_interval=factor_interval, + ) + + +class PillowContrast(PillowRGBAugmentation): + def __init__( + self, + p=0.3, + factor_interval=(0.5, 1.6), + ): + super().__init__( + pillow_fn=ImageEnhance.Contrast, + p=p, + factor_interval=factor_interval, + ) + + +class PillowBrightness(PillowRGBAugmentation): + def __init__( + self, + p=0.5, + factor_interval=(0.5, 2.0), + ): + super().__init__( + pillow_fn=ImageEnhance.Brightness, + p=p, + factor_interval=factor_interval, + ) + + +class PillowColor(PillowRGBAugmentation): + def __init__( + self, + p=1, + factor_interval=(0.0, 20.0), + ): + super().__init__( + pillow_fn=ImageEnhance.Color, + p=p, + factor_interval=factor_interval, + ) + + +class PillowBlur: + def __init__(self, p=0.4, factor_interval=(1, 3)): + self.p = p + self.k = random.randint(*factor_interval) + + def __call__(self, PIL_image): + if random.random() <= self.p: + PIL_image = PIL_image.filter(ImageFilter.GaussianBlur(self.k)) + return PIL_image + + +class NumpyGaussianNoise: + def __init__(self, p, factor_interval=(0.01, 0.3)): + self.noise_ratio = random.uniform(*factor_interval) + self.p = p + + def __call__(self, img): + if random.random() <= self.p: + img = np.copy(img) + noisesigma = random.uniform(0, self.noise_ratio) + gauss = np.random.normal(0, noisesigma, img.shape) * 255 + img = img + gauss + + img[img > 255] = 255 + img[img < 0] = 0 + return Image.fromarray(np.uint8(img)) + + +class StandardAugmentation: + def __init__( + self, names, brightness, contrast, sharpness, color, blur, gaussian_noise + ): + self.brightness = brightness + self.contrast = contrast + self.sharpness = sharpness + self.color = color + self.blur = blur + self.gaussian_noise = gaussian_noise + + # define a dictionary of augmentation functions to be applied + self.names = names.split(",") + self.augmentations = { + "brightness": self.brightness, + "contrast": self.contrast, + "sharpness": self.sharpness, + "color": self.color, + "blur": self.blur, + "gaussian_noise": self.gaussian_noise, + } + + def __call__(self, img): + for name in self.names: + img = self.augmentations[name](img) + return img + + +class GeometricAugmentation: + def __init__( + self, + names, + random_resized_crop, + random_horizontal_flip, + random_vertical_flip, + random_rotation, + ): + self.random_resized_crop = random_resized_crop + self.random_horizontal_flip = random_horizontal_flip + self.random_vertical_flip = random_vertical_flip + self.random_rotation = random_rotation + self.names = names.split(",") + + self.augmentations = { + "random_resized_crop": self.random_resized_crop, + "random_horizontal_flip": self.random_horizontal_flip, + "random_vertical_flip": self.random_vertical_flip, + "random_rotation": self.random_rotation, + } + + def __call__(self, img): + for name in self.names: + img = self.augmentations[name](img) + return img + + +class ImageAugmentation: + def __init__( + self, names, clip_transform, standard_augmentation, geometric_augmentation + ): + self.clip_transform = clip_transform + self.standard_augmentation = standard_augmentation + self.geometric_augmentation = geometric_augmentation + self.names = names.split(",") + self.transforms = { + "clip_transform": self.clip_transform, + "standard_augmentation": self.standard_augmentation, + "geometric_augmentation": self.geometric_augmentation, + } + print(f"Image augmentation: {self.names}") + + def __call__(self, img): + for name in self.names: + img = self.transforms[name](img) + return img + + +if __name__ == "__main__": + # sanity check + import glob + import torchvision.transforms as transforms + from torchvision.utils import save_image + from omegaconf import DictConfig, OmegaConf + from hydra.utils import instantiate + import torch + from PIL import Image + + augmentation_config = OmegaConf.load( + "./configs/dataset/train_transform/augmentation.yaml" + ) + augmentation_config.names = "standard_augmentation,geometric_augmentation" + augmentation_transform = instantiate(augmentation_config) + img_paths = glob.glob("./datasets/osv5m/test/images/*.jpg") + + num_try = 20 + num_try_per_image = 8 + num_imgs = 8 + + for idx in range(num_try): + imgs = [] + for idx_img in range(num_imgs): + img = Image.open(img_paths[idx_img]) + for idx_try in range(num_try_per_image): + if idx_try == 0: + imgs.append(ToTensor()(img.resize((224, 224)))) + img_aug = augmentation_transform(img.copy()) + img_aug = ToTensor()(img_aug) + imgs.append(img_aug) + imgs = torch.stack(imgs) + save_image(imgs, f"augmentation_{idx:03d}.png", nrow=9) diff --git a/data/data.py b/data/data.py new file mode 100644 index 0000000000000000000000000000000000000000..5764650391e9eed57cebb370836574e70ec4a1db --- /dev/null +++ b/data/data.py @@ -0,0 +1,789 @@ +import numpy as np +import pandas as pd +import torch +import random +import pickle +from os.path import join +from os.path import isfile +from PIL import Image +from sklearn.model_selection import train_test_split +from torch.utils.data import Dataset +from torchvision.transforms import ( + Compose, + RandomCrop, + CenterCrop, + RandomHorizontalFlip, + ToTensor, +) +import time +from torchvision.transforms import GaussianBlur +from torchvision import transforms +from pathlib import Path +import json +from tqdm import tqdm +import multiprocessing as mp +import ctypes + + +def normalize(lat, lon): + """Used to put all lat lon inside ±90 and ±180.""" + lat = (lat + 90) % 360 - 90 + if lat > 90: + lat = 180 - lat + lon += 180 + lon = (lon + 180) % 360 - 180 + return lat, lon + + +def collate_fn(batch): + """Collate function for the dataloader. + Args: + batch (list): list of dictionaries with keys "img", "gps", "idx" and optionally "label" + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + keys = list(batch[0].keys()) + if "weight" in batch[0].keys(): + keys.remove("weight") + output = {} + for key in [ + "idx", + "unique_country", + "unique_region", + "unique_sub-region", + "unique_city", + "img_idx", + "text", + ]: + if key in keys: + idx = [x[key] for x in batch] + output[key] = idx + keys.remove(key) + if "img" in keys and isinstance(batch[0]["img"], Image.Image): + output["img"] = [x["img"] for x in batch] + keys.remove("img") + for key in keys: + if not ("text" in key): + output[key] = torch.stack([x[key] for x in batch]) + return output + + +def collate_fn_streetclip(batch): + """Collate function for the dataloader. + Args: + batch (list): list of dictionaries with keys "img", "gps", "idx" and optionally "label" + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + keys = list(batch[0].keys()) + if "weight" in batch[0].keys(): + keys.remove("weight") + output = {} + for key in [ + "idx", + "unique_country", + "unique_region", + "unique_sub-region", + "unique_city", + "img_idx", + "img", + "text", + ]: + if key in keys: + idx = [x[key] for x in batch] + output[key] = idx + keys.remove(key) + for key in keys: + if not ("text" in key): + output[key] = torch.stack([x[key] for x in batch]) + return output + + +def collate_fn_denstity(batch): + """Collate function for the dataloader. + Args: + batch (list): list of dictionaries with keys "img", "gps", "idx" and optionally "label" + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + keys = list(batch[0].keys()) + if "weight" in batch[0].keys(): + keys.remove("weight") + # Sample indices based on the weights + weights = np.array([x["weight"] for x in batch]) + normalized_weights = weights / np.sum(weights) + sampled_indices = np.random.choice( + len(batch), size=len(batch), p=normalized_weights, replace=True + ) + output = {} + for key in [ + "idx", + "unique_country", + "unique_region", + "unique_sub-region", + "unique_city", + "img_idx", + "text", + ]: + if key in keys: + idx = [batch[i][key] for i in sampled_indices] + output[key] = idx + keys.remove(key) + for key in keys: + if not ("text" in key): + output[key] = torch.stack([batch[i][key] for i in sampled_indices]) + return output + + +def collate_fn_streetclip_denstity(batch): + """Collate function for the dataloader. + Args: + batch (list): list of dictionaries with keys "img", "gps", "idx" and optionally "label" + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + keys = list(batch[0].keys()) + if "weight" in batch[0].keys(): + keys.remove("weight") + # Sample indices based on the weights + weights = np.array([x["weight"] for x in batch]) + normalized_weights = weights / np.sum(weights) + sampled_indices = np.random.choice( + len(batch), size=len(batch), p=normalized_weights, replace=True + ) + output = {} + for key in [ + "idx", + "unique_country", + "unique_region", + "unique_sub-region", + "unique_city", + "img_idx", + "img", + "text", + ]: + if key in keys: + idx = [batch[i][key] for i in sampled_indices] + output[key] = idx + keys.remove(key) + for key in keys: + if not ("text" in key): + output[key] = torch.stack([batch[i][key] for i in sampled_indices]) + return output + + +def collate_fn_contrastive(batch): + """Collate function for the dataloader. + Args: + batch (list): list of dictionaries with keys "img", "gps", "idx" and optionally "label" + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + output = collate_fn(batch) + pos_img = torch.stack([x["pos_img"] for x in batch]) + output["pos_img"] = pos_img + return output + + +def collate_fn_contrastive_density(batch): + """Collate function for the dataloader. + Args: + batch (list): list of dictionaries with keys "img", "gps", "idx" and optionally "label" + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + keys = list(batch[0].keys()) + if "weight" in batch[0].keys(): + keys.remove("weight") + # Sample indices based on the weights + weights = np.array([x["weight"] for x in batch]) + normalized_weights = weights / np.sum(weights) + sampled_indices = np.random.choice( + len(batch), size=len(batch), p=normalized_weights, replace=True + ) + output = {} + for key in [ + "idx", + "unique_country", + "unique_region", + "unique_sub-region", + "unique_city", + "img_idx", + ]: + if key in keys: + idx = [batch[i][key] for i in sampled_indices] + output[key] = idx + keys.remove(key) + for key in keys: + if not ("text" in key): + output[key] = torch.stack([batch[i][key] for i in sampled_indices]) + return output + + +class iNaturalist(Dataset): + def __init__( + self, + path, + transforms, + split="train", + output_type="image", + embedding_name="dinov2", + ): + super().__init__() + self.split = split + with open(Path(path) / f"{split}.json", "r") as f: + self.metadata = json.load(f) + self.metadata = [ + datapoint + for datapoint in self.metadata["images"] + if "latitude" in datapoint and datapoint["latitude"] is not None + ] + self.path = path + self.transforms = transforms + self.output_type = output_type + self.embedding_name = embedding_name + + self.collate_fn = collate_fn + + def __getitem__(self, i): + output = {} + if "image" in self.output_type: + image_path = Path(self.path) / "images" / self.metadata[i]["file_name"] + img = self.transforms(Image.open(image_path)) + output["img"] = img + if "emb" in self.output_type: + emb_path = ( + Path(self.path) + / "embeddings" + / self.embedding_name + / self.metadata[i]["file_name"].replace(".jpg", ".npy") + ) + output["emb"] = torch.tensor(np.load(emb_path)) + lat, lon = normalize( + self.metadata[i]["latitude"], self.metadata[i]["longitude"] + ) + output["gps"] = torch.tensor( + [np.radians(lat), np.radians(lon)], dtype=torch.float + ) + output["idx"] = i + output["img_idx"] = self.metadata[i]["id"] + return output + + def __len__(self): + return len(self.metadata) + + +class OSV5M(Dataset): + csv_dtype = {"category": str, "country": str, "city": str} # Don't remove. + + def __init__( + self, + path, + transforms, + split="train", + class_name=None, + aux_data=[], + is_baseline=False, + areas=["country", "region", "sub-region", "city"], + streetclip=False, + suff="", + blur=False, + output_type="image", + embedding_name="dinov2", + ): + """Initializes the dataset. + Args: + path (str): path to the dataset + transforms (torchvision.transforms): transforms to apply to the images + split (str): split to use (train, val, test) + class_name (str): category to use (e.g. "city") + aux_data (list of str): auxilliary datas to use + areas (list of str): regions to perform accuracy + streetclip (bool): if the model is streetclip, do not use transform + suff (str): suffix of test csv + blur (bool): blur bottom of images or not + output_type (str): type of output (image or emb) + """ + self.suff = suff + self.path = path + self.aux = len(aux_data) > 0 + self.aux_list = aux_data + self.split = split + if split == "select": + self.df = self.load_split(split) + split = "test" + else: + self.df = self.load_split(split) + self.split = split + if "image" in output_type: + self.image_data_folder = join( + path, + "images", + ("train" if split == "val" else split), + ) + self.image_dict_names = {} + for root, _, files in os.walk(self.image_data_folder): + for file in files: + self.image_dict_names[file] = os.path.join(root, file) + if "emb" in output_type: + self.emb_data_folder = join( + path, + "embeddings", + embedding_name, + ("train" if split == "val" else split), + ) + self.emb_dict_names = {} + for root, _, files in os.walk(self.emb_data_folder): + for file in files: + self.emb_dict_names[file] = os.path.join(root, file) + + self.output_type = output_type + + self.is_baseline = is_baseline + if self.aux: + self.aux_data = {} + for col in self.aux_list: + if col in ["land_cover", "climate", "soil"]: + self.aux_data[col] = pd.get_dummies(self.df[col], dtype=float) + if col == "climate": + for i in range(31): + if not (i in list(self.aux_data[col].columns)): + self.aux_data[col][i] = 0 + desired_order = [i for i in range(31)] + desired_order.remove(20) + self.aux_data[col] = self.aux_data[col][desired_order] + else: + self.aux_data[col] = self.df[col].apply(lambda x: [x]) + + self.areas = ["_".join(["unique", area]) for area in areas] + if class_name is None: + self.class_name = class_name + elif "quadtree" in class_name: + self.class_name = class_name + else: + self.class_name = "_".join(["unique", class_name]) + ex = self.extract_classes(self.class_name) + self.df = self.df[ + ["id", "latitude", "longitude", "weight"] + self.areas + ex + ].fillna("NaN") + if self.class_name in self.areas: + self.df.columns = list(self.df.columns)[:-1] + [self.class_name + "_2"] + self.transforms = transforms + self.collate_fn = collate_fn + self.collate_fn_density = collate_fn_denstity + self.blur = blur + self.streetclip = streetclip + if self.streetclip: + self.collate_fn = collate_fn_streetclip + self.collate_fn_density = collate_fn_streetclip_denstity + + def load_split(self, split): + """Returns a new dataset with the given split.""" + start_time = time.time() + if split == "test": + df = pd.read_csv(join(self.path, "test.csv"), dtype=self.csv_dtype) + # extract coord + longitude = df["longitude"].values + latitude = df["latitude"].values + # Create bins + num_bins = 100 + lon_bins = np.linspace(longitude.min(), longitude.max(), num_bins) + lat_bins = np.linspace(latitude.min(), latitude.max(), num_bins) + # compute density and weights + hist, _, _ = np.histogram2d(longitude, latitude, bins=[lon_bins, lat_bins]) + weights = 1.0 / np.power(hist[df["lon_bin"], df["lat_bin"]], 0.75) + normalized_weights = weights / np.sum(weights) + df["weight"] = normalized_weights + return df + elif split == "select": + df = pd.read_csv(join(self.path, "select.csv"), dtype=self.csv_dtype) + # extract coord + longitude = df["longitude"].values + latitude = df["latitude"].values + # Create bins + num_bins = 100 + lon_bins = np.linspace(longitude.min(), longitude.max(), num_bins) + lat_bins = np.linspace(latitude.min(), latitude.max(), num_bins) + # compute density and weights + hist, _, _ = np.histogram2d(longitude, latitude, bins=[lon_bins, lat_bins]) + weights = 1.0 / np.power(hist[df["lon_bin"], df["lat_bin"]], 0.75) + normalized_weights = weights / np.sum(weights) + df["weight"] = normalized_weights + return df + else: + if len(self.suff) == 0: + df = pd.read_csv(join(self.path, "train.csv"), dtype=self.csv_dtype) + else: + df = pd.read_csv( + join(self.path, "train" + "_" + self.suff + ".csv"), + dtype=self.csv_dtype, + ) + + # extract coord + longitude = df["longitude"].values + latitude = df["latitude"].values + # Create bins + num_bins = 100 + lon_bins = np.linspace(longitude.min(), longitude.max(), num_bins) + lat_bins = np.linspace(latitude.min(), latitude.max(), num_bins) + # compute density and weights + hist, _, _ = np.histogram2d(longitude, latitude, bins=[lon_bins, lat_bins]) + weights = 1.0 / np.power(hist[df["lon_bin"], df["lat_bin"]], 0.75) + normalized_weights = weights / np.sum(weights) + df["weight"] = normalized_weights + + test_df = df.sample( + n=int(0.1 * len(df)), + weights=normalized_weights, + replace=False, + random_state=42, + ) + + end_time = time.time() + print(f"Loading {split} dataset took {(end_time - start_time):.2f} seconds") + + if split == "val": + return test_df + else: + return df.drop(test_df.index) + + def extract_classes(self, tag=None): + """Extracts the categories from the dataset.""" + if tag is None: + self.has_labels = False + return [] + splits = ["train", "test"] if self.is_baseline else ["train"] + # splits = ["train", "test"] + print(f"Loading categories from {splits}") + + # concatenate all categories from relevant splits to find the unique ones. + self.categories = sorted( + pd.concat( + [pd.read_csv(join(self.path, f"{split}.csv"))[tag] for split in splits] + ) + .fillna("NaN") + .unique() + .tolist() + ) + + if "NaN" in self.categories: + self.categories.remove("NaN") + if self.split != "test": + self.df = self.df.dropna(subset=[tag]) + # compute the total number of categories - this name is fixed and will be used as a lookup during init + self.num_classes = len(self.categories) + + # create a mapping from category to index + self.category_to_index = { + category: i for i, category in enumerate(self.categories) + } + self.has_labels = True + return [tag] + + def __getitem__(self, i): + """Returns an item from the dataset. + Args: + i (int): index of the item + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + x = list(self.df.iloc[i]) # id, latitude, longitude, {category} + output = {} + if "image" in self.output_type: + if self.streetclip: + img = Image.open(self.image_dict_names[f"{int(x[0])}.jpg"]) + elif self.blur: + img = transforms.ToTensor()( + Image.open(self.image_dict_names[f"{int(x[0])}.jpg"]) + ) + u = GaussianBlur(kernel_size=13, sigma=2.0) + bottom_part = img[:, -14:, :].unsqueeze(0) + blurred_bottom = u(bottom_part) + img[:, -14:, :] = blurred_bottom.squeeze() + img = self.transforms(transforms.ToPILImage()(img)) + else: + img = self.transforms( + Image.open(self.image_dict_names[f"{int(x[0])}.jpg"]) + ) + output["img"] = img + if "emb" in self.output_type: + output["emb"] = torch.FloatTensor( + np.load(self.emb_dict_names[f"{int(x[0])}.npy"]) + ) + + lat, lon = normalize(x[1], x[2]) + gps = torch.FloatTensor([np.radians(lat), np.radians(lon)]).squeeze(0) + + output.update( + { + "gps": gps, + "idx": i, + "img_idx": int(x[0]), + "weight": x[3], + } + ) + + for count, area in enumerate(self.areas): + output[area] = x[ + count + 4 + ] #'country': x[3], 'region': x[4], 'sub-region': x[5], 'city': x[6]} + + if self.has_labels: + if x[-1] in self.categories: + output["label"] = torch.LongTensor( + [self.category_to_index[x[-1]]] + ).squeeze(-1) + else: + output["label"] = torch.LongTensor([-1]).squeeze(-1) + if self.aux: + for col in self.aux_list: + output[col] = torch.FloatTensor(self.aux_data[col].iloc[i]) + return output + + def __len__(self): + return len(self.df) + + +class ContrastiveOSV5M(OSV5M): + def __init__( + self, + path, + transforms, + split="train", + class_name=None, + aux_data=[], + class_name2=None, + blur=False, + ): + """ + class_name2 (str): if not None, we do contrastive an other class than the one specified for classif + """ + super().__init__( + path, + transforms, + split=split, + class_name=class_name, + aux_data=aux_data, + blur=blur, + ) + self.add_label = False + if not (class_name2 is None) and split != "test" and split != "select": + self.add_label = True + self.class_name = class_name2 + self.extract_classes_contrastive(tag=class_name2) + self.df = self.df.reset_index(drop=True) + self.dict_classes = { + value: indices.tolist() + for value, indices in self.df.groupby(self.class_name).groups.items() + } + self.collate_fn = collate_fn_contrastive + self.random_crop = RandomCrop(224) # use when no positive image is available + + def sample_positive(self, i): + """ + sample positive image from the same city, country if it is available + otherwise, apply different crop to the image + """ + x = self.df.iloc[i] # id, latitude, longitude, {category} + class_name = x[self.class_name] + idxs = self.dict_classes[class_name] + idxs.remove(i) + + if len(idxs) > 0: + idx = random.choice(idxs) + x = self.df.iloc[idx] + pos_img = self.transforms( + Image.open(self.dict_names[f"{int(x['id'])}.jpg"]) + ) + else: + pos_img = self.random_crop( + self.transforms(Image.open(self.dict_names[f"{int(x['id'])}.jpg"])) + ) + return pos_img + + def extract_classes_contrastive(self, tag=None): + """Extracts the categories from the dataset.""" + if tag is None: + self.has_labels = False + return [] + splits = ["train", "test"] if self.is_baseline else ["train"] + # splits = ["train", "test"] + print(f"Loading categories from {splits}") + + # concatenate all categories from relevant splits to find the unique ones. + categories = sorted( + pd.concat( + [pd.read_csv(join(self.path, f"{split}.csv"))[tag] for split in splits] + ) + .fillna("NaN") + .unique() + .tolist() + ) + # create a mapping from category to index + self.contrastive_category_to_index = { + category: i for i, category in enumerate(categories) + } + + def __getitem__(self, i): + output = super().__getitem__(i) + pos_img = self.sample_positive(i) + output["pos_img"] = pos_img + if self.add_label: + output["label_contrastive"] = torch.LongTensor( + [self.contrastive_category_to_index[self.df[self.class_name].iloc[i]]] + ).squeeze(-1) + return output + + +class TextContrastiveOSV5M(OSV5M): + def __init__( + self, + path, + transforms, + split="train", + class_name=None, + aux_data=[], + blur=False, + ): + super().__init__( + path, + transforms, + split=split, + class_name=class_name, + aux_data=aux_data, + blur=blur, + ) + self.df = self.df.reset_index(drop=True) + + def get_text(self, i): + """ + sample positive image from the same city, country if it is available + otherwise, apply different crop to the image + """ + x = self.df.iloc[i] # id, latitude, longitude, {category} + l = [ + name.split("_")[-1] + for name in [ + x["unique_city"], + x["unique_sub-region"], + x["unique_region"], + x["unique_country"], + ] + ] + + pre = False + sentence = "An image of " + if l[0] != "NaN": + sentence += "the city of " + sentence += l[0] + pre = True + + if l[1] != "NaN": + if pre: + sentence += ", in " + sentence += "the area of " + sentence += l[1] + pre = True + + if l[2] != "NaN": + if pre: + sentence += ", in " + sentence += "the region of " + sentence += l[2] + pre = True + + if l[3] != "NaN": + if pre: + sentence += ", in " + sentence += l[3] + + return sentence + + def __getitem__(self, i): + output = super().__getitem__(i) + output["text"] = self.get_text(i) + return output + + +import os +import json + + +class Baseline(Dataset): + def __init__( + self, + path, + which, + transforms, + ): + """Initializes the dataset. + Args: + path (str): path to the dataset + which (str): which baseline to use (im2gps, im2gps3k) + transforms (torchvision.transforms): transforms to apply to the images + """ + baselines = { + "im2gps": self.load_im2gps, + "im2gps3k": self.load_im2gps, + "yfcc4k": self.load_yfcc4k, + } + self.path = path + self.samples = baselines[which]() + self.transforms = transforms + self.collate_fn = collate_fn + self.class_name = which + + def load_im2gps( + self, + ): + json_path = join(self.path, "info.json") + with open(json_path) as f: + data = json.load(f) + + samples = [] + for f in os.listdir(join(self.path, "images")): + if len(data[f]): + lat = float(data[f][-4].replace("latitude: ", "")) + lon = float(data[f][-3].replace("longitude: ", "")) + samples.append((f, lat, lon)) + + return samples + + def load_yfcc4k( + self, + ): + samples = [] + with open(join(self.path, "info.txt")) as f: + lines = f.readlines() + for line in lines: + x = line.split("\t") + f, lon, lat = x[1], x[12], x[13] + samples.append((f + ".jpg", float(lat), float(lon))) + + return samples + + def __getitem__(self, i): + """Returns an item from the dataset. + Args: + i (int): index of the item + Returns: + dict: dictionary with keys "img", "gps", "idx" and optionally "label" + """ + img_path, lat, lon = self.samples[i] + img = self.transforms( + Image.open(join(self.path, "images", img_path)).convert("RGB") + ) + lat, lon = normalize(lat, lon) + gps = torch.FloatTensor([np.radians(lat), np.radians(lon)]).squeeze(0) + + return { + "img": img, + "gps": gps, + "idx": i, + } + + def __len__(self): + return len(self.samples) + + +null_transform = lambda x: x diff --git a/data/datamodule.py b/data/datamodule.py new file mode 100755 index 0000000000000000000000000000000000000000..f0cb1748944309e6176c20017ed128ee88ebc370 --- /dev/null +++ b/data/datamodule.py @@ -0,0 +1,162 @@ +import pytorch_lightning as L +from torch.utils.data import DataLoader, random_split +import torch +import time +import webdataset as wds +from torch.utils.data import default_collate +import math +from PIL import Image + + +class ImageDataModule(L.LightningDataModule): + def __init__( + self, + train_dataset, + val_dataset, + test_dataset, + full_batch_size, + num_workers, + eval_batch_size=None, + num_nodes=1, + num_devices=1, + val_proportion=0.1, + ): + super().__init__() + self._builders = { + "train": train_dataset, + "val": val_dataset, + "test": test_dataset, + } + self.num_workers = num_workers + self.collate_fn = dict_collate_fn() + self.full_batch_size = full_batch_size + self.train_batch_size = full_batch_size // (num_nodes * num_devices) + if eval_batch_size is None: + self.eval_batch_size = self.train_batch_size + self.full_eval_batch_size = self.full_batch_size + else: + self.eval_batch_size = eval_batch_size // (num_nodes * num_devices) + self.full_eval_batch_size = eval_batch_size + print(f"Each GPU will receive {self.train_batch_size} images for training") + print(f"Each GPU will receive {self.eval_batch_size} images for evaluation") + self.val_proportion = val_proportion + self.world_size = num_nodes * num_devices + + def setup(self, stage=None): + """Setup the datamodule. + Args: + stage (str): stage of the datamodule + Is be one of "fit" or "test" or None + """ + print("Stage", stage) + start_time = time.time() + if stage == "fit" or stage is None: + self.train_dataset = self._builders["train"]() + self.train_dataset, self.num_train_batches = self.get_webdataset_length( + self.train_dataset, + dict_collate_fn(), + self.full_batch_size, + self.train_batch_size, + ) + self.val_dataset = self._builders["val"]() + self.val_dataset, self.num_val_batches = self.get_webdataset_length( + self.val_dataset, + dict_collate_fn(), + self.full_eval_batch_size, + self.eval_batch_size, + 0, + ) + print(f"Train dataset size: {len(self.train_dataset)}") + print(f"Val dataset size: {len(self.val_dataset)}") + else: + self.test_dataset = self._builders["test"]() + self.test_dataset, self.num_test_batches = self.get_webdataset_length( + self.test_dataset, + dict_collate_fn(), + self.full_eval_batch_size, + self.eval_batch_size, + self.num_workers, + ) + print(f"Test dataset size: {len(self.test_dataset)}") + end_time = time.time() + print(f"Setup took {(end_time - start_time):.2f} seconds") + + def train_dataloader(self): + return wds.WebLoader( + self.train_dataset, + batch_size=None, + shuffle=False, + num_workers=self.num_workers, + # persistent_workers=self.num_workers > 1, + ).with_length(self.num_train_batches) + # return DataLoader( + # self.train_dataset, + # batch_size=self.batch_size, + # shuffle=True, + # pin_memory=False, + # drop_last=True, + # num_workers=self.num_workers, + # collate_fn=self.train_dataset.collate_fn, + # ) + + def val_dataloader(self): + return wds.WebLoader( + self.val_dataset, + batch_size=None, + shuffle=False, + num_workers=0, + ).with_length(self.num_val_batches) + + def test_dataloader(self): + return wds.WebLoader( + self.test_dataset, + batch_size=None, + shuffle=False, + num_workers=0, + ).with_length(self.num_test_batches) + + def get_webdataset_length( + self, dataset, collate_fn, full_batch_size, batch_size, num_workers=0 + ): + dataset = dataset.compose( + wds.batched( + batch_size, + partial=self.world_size > 1, + collation_fn=collate_fn, + # dict_collate_and_pad(["flan_t5_xl"], max_length=256), + ) + ) + num_samples = dataset.num_samples + if self.world_size > 1: + num_batches = math.ceil(num_samples / full_batch_size) + num_workers = max(1, num_workers) + + num_worker_batches = math.ceil(num_batches / num_workers) + num_batches = num_worker_batches * num_workers + num_samples = num_batches * full_batch_size + + dataset = dataset.with_epoch(num_worker_batches).with_length( + num_worker_batches + ) + else: + num_batches = math.ceil(num_samples / batch_size) + + dataset = dataset.with_epoch(num_batches).with_length(num_batches) + return dataset, num_batches + + +def dict_collate_fn(): + def dict_collate(batch): + output_dict = {} + if isinstance(batch[0], dict): + for key in batch[0].keys(): + output_dict[key] = dict_collate([item[key] for item in batch]) + else: + # Check if the batch contains PIL images + if isinstance(batch[0], Image.Image): + output_dict = batch # Return list of PIL images directly + else: + output_dict = default_collate(batch) + return output_dict + + return dict_collate diff --git a/data/extract_embeddings/__init__.py b/data/extract_embeddings/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/extract_embeddings/__pycache__/__init__.cpython-310.pyc b/data/extract_embeddings/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d0de6686959cbbe7bd0be9c8bf0075b1ad940aa4 Binary files /dev/null and b/data/extract_embeddings/__pycache__/__init__.cpython-310.pyc differ diff --git a/data/extract_embeddings/__pycache__/dataset_with_path.cpython-310.pyc b/data/extract_embeddings/__pycache__/dataset_with_path.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..540d082efc1664c2273af19b75d2b514cf4d5454 Binary files /dev/null and b/data/extract_embeddings/__pycache__/dataset_with_path.cpython-310.pyc differ diff --git a/data/extract_embeddings/__pycache__/utils.cpython-310.pyc b/data/extract_embeddings/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e4e5ad10d0a53fa9bd1bddd79fec24a8d3044d7d Binary files /dev/null and b/data/extract_embeddings/__pycache__/utils.cpython-310.pyc differ diff --git a/data/extract_embeddings/dataset_with_path.py b/data/extract_embeddings/dataset_with_path.py new file mode 100644 index 0000000000000000000000000000000000000000..832886bc84dc5e1c4784390b71ae354f5128b60a --- /dev/null +++ b/data/extract_embeddings/dataset_with_path.py @@ -0,0 +1,28 @@ +from PIL import Image +from pathlib import Path +import torch +import numpy as np +from tqdm import tqdm + + +class ImageWithPathDataset(torch.utils.data.Dataset): + def __init__(self, root_image_path, output_path, transform=None): + self.root_image_path = root_image_path + self.image_paths = list(root_image_path.glob("**/*.jpg")) + self.transform = transform + self.output_path = output_path + + def __len__(self): + return len(self.image_paths) + + def __getitem__(self, idx): + image_path = self.image_paths[idx] + image = Image.open(image_path).convert("RGB") + if self.transform: + image = self.transform(image) + output_emb_path = self.output_path / image_path.parent.relative_to( + self.root_image_path + ) + output_emb_path.mkdir(exist_ok=True, parents=True) + output_emb_path = output_emb_path / image_path.stem + return image, output_emb_path diff --git a/data/extract_embeddings/dino_v2.py b/data/extract_embeddings/dino_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..b271180d2c98ab4b74121befb0323921d5b90660 --- /dev/null +++ b/data/extract_embeddings/dino_v2.py @@ -0,0 +1,88 @@ +import os, sys + +# Ajouter le répertoire racine au chemin +root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(root_dir) + +import torch +from utils.image_processing import CenterCrop +from data.extract_embeddings.dataset_with_path import ImageWithPathDataset +import torch +from torchvision import transforms +from pathlib import Path + + +from tqdm import tqdm +import numpy as np +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument( + "--number_of_splits", + type=int, + help="Number of splits to process", + default=1, +) +parser.add_argument( + "--split_index", + type=int, + help="Index of the split to process", + default=0, +) +parser.add_argument( + "--input_path", + type=str, + help="Path to the input dataset", +) +parser.add_argument( + "--output_path", + type=str, + help="Path to the output dataset", +) + +args = parser.parse_args() + +device = "cuda" if torch.cuda.is_available() else "cpu" + +model = torch.hub.load("facebookresearch/dinov2", "dinov2_vitl14_reg") +model = torch.compile(model, mode="max-autotune") +model.eval() +model.to(device) + +input_path = Path(args.input_path) +output_path = Path(args.output_path) + +output_path.mkdir(exist_ok=True, parents=True) +augmentation = transforms.Compose( + [ + CenterCrop(ratio="1:1"), + transforms.Resize(336, interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] +) +dataset = ImageWithPathDataset(input_path, output_path, transform=augmentation) +dataset = torch.utils.data.Subset( + dataset, + range( + args.split_index * len(dataset) // args.number_of_splits, + ( + (args.split_index + 1) * len(dataset) // args.number_of_splits + if args.split_index != args.number_of_splits - 1 + else len(dataset) + ), + ), +) + +batch_size = 128 +dataloader = torch.utils.data.DataLoader( + dataset, batch_size=batch_size, num_workers=16, collate_fn=lambda x: zip(*x) +) + +for images, output_emb_paths in tqdm(dataloader): + images = torch.stack(images, dim=0).to(device) + with torch.no_grad(): + embeddings = model(images) + numpy_embeddings = embeddings.cpu().numpy() + for emb, output_emb_path in zip(numpy_embeddings, output_emb_paths): + np.save(f"{output_emb_path}.npy", emb) diff --git a/data/extract_embeddings/launch_embedding_extraction.py b/data/extract_embeddings/launch_embedding_extraction.py new file mode 100644 index 0000000000000000000000000000000000000000..e1a9c39d95b0b0aed3ed4c28682c6b6f23e24e29 --- /dev/null +++ b/data/extract_embeddings/launch_embedding_extraction.py @@ -0,0 +1,79 @@ +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) +import argparse +import os + +from jean_zay.launch import JeanZayExperiment + + +def parse_mode(): + parser = argparse.ArgumentParser( + description="Extract embeddings from a dataset using DINOv2" + ) + parser.add_argument( + "--launch", + action="store_true", + help="Launch the experiment", + ) + parser.add_argument( + "--number_of_splits", + type=int, + help="Number of splits to process", + default=1, + ) + parser.add_argument( + "--input_path", + type=str, + help="Path to the input dataset", + ) + parser.add_argument( + "--output_path", + type=str, + help="Path to the output dataset", + ) + args = parser.parse_args() + + return args + + +args = parse_mode() + +cmd_modifiers = [] +exps = [] + +exp_name = f"preprocess_data" +job_name = f"preprocess_data" +jz_exp = JeanZayExperiment( + exp_name, + job_name, + slurm_array_nb_jobs=args.number_of_splits, + cmd_path="data/extract_embeddings/dino_v2.py", + num_nodes=1, + num_gpus_per_node=1, + qos="t3", + account="mya", + gpu_type="h100", + time="02:00:00", +) + +exps.append(jz_exp) + +trainer_modifiers = {} + +exp_modifier = { + "--input_path": args.input_path, + "--output_path": args.output_path, + "--number_of_splits": args.number_of_splits, + "--split_index": "${SLURM_ARRAY_TASK_ID}", +} + +cmd_modifiers.append(dict(trainer_modifiers, **exp_modifier)) + + +if __name__ == "__main__": + for exp, cmd_modifier in zip(exps, cmd_modifiers): + exp.build_cmd(cmd_modifier) + if args.launch == True: + exp.launch() diff --git a/data/extract_embeddings/so_siglip.py b/data/extract_embeddings/so_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..ff95dba2dd446403814dd6b6639b09ee33779b17 --- /dev/null +++ b/data/extract_embeddings/so_siglip.py @@ -0,0 +1,44 @@ +import os, sys + +import torch.amp + +# Ajouter le répertoire racine au chemin +root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(root_dir) + +from PIL import Image +from pathlib import Path +import torch +from transformers import AutoProcessor, AutoModelForZeroShotImageClassification +import numpy as np +from tqdm import tqdm +from data.extract_embeddings.dataset_with_path import ImageWithPathDataset + + +device = "cuda" if torch.cuda.is_available() else "cpu" +model = AutoModelForZeroShotImageClassification.from_pretrained( + "google/siglip-so400m-patch14-384" +).vision_model.to(device) +processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-384") + +input_path = Path("datasets/osv5m/images") +output_path = Path("datasets/osv5m/embeddings/so_siglip") + +output_path.mkdir(exist_ok=True, parents=True) + +dataset = ImageWithPathDataset(input_path, output_path) +model = torch.compile(model, fullgraph=True) + +batch_size = 64 +dataloader = torch.utils.data.DataLoader( + dataset, batch_size=batch_size, num_workers=16, collate_fn=lambda x: zip(*x) +) +with torch.amp.autocast("cuda", enabled=True, dtype=torch.bfloat16), torch.no_grad(): + for images, output_emb_paths in tqdm(dataloader): + inputs = processor(images=images, return_tensors="pt") + inputs = {k: v.to(device) for k, v in inputs.items()} + outputs = model(**inputs) + embeddings = outputs.last_hidden_state[:, 0] + numpy_embeddings = embeddings.cpu().numpy() + for emb, output_emb_path in zip(numpy_embeddings, output_emb_paths): + np.save(f"{output_emb_path}.npy", emb) diff --git a/data/extract_embeddings/street_clip.py b/data/extract_embeddings/street_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..11e3a8eccc92551e7ddaaa8cbbd18252ceaa81c1 --- /dev/null +++ b/data/extract_embeddings/street_clip.py @@ -0,0 +1,40 @@ +import os, sys + +# Ajouter le répertoire racine au chemin +root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")) +sys.path.append(root_dir) + +from PIL import Image +from pathlib import Path +import torch +from transformers import CLIPProcessor, CLIPVisionModel +import numpy as np +from tqdm import tqdm +from data.extract_embeddings.dataset_with_path import ImageWithPathDataset + + +device = "cuda" if torch.cuda.is_available() else "cpu" +model = CLIPVisionModel.from_pretrained("geolocal/StreetCLIP").to(device) +processor = CLIPProcessor.from_pretrained("geolocal/StreetCLIP") + +input_path = Path("datasets/osv5m/images") +output_path = Path("datasets/osv5m/embeddings/street_clip") + +output_path.mkdir(exist_ok=True, parents=True) + +dataset = ImageWithPathDataset(input_path) + +batch_size = 128 +dataloader = torch.utils.data.DataLoader( + dataset, batch_size=batch_size, num_workers=16, collate_fn=lambda x: zip(*x) +) + +for images, output_emb_paths in tqdm(dataloader): + inputs = processor(images=images, return_tensors="pt") + inputs = {k: v.to(device) for k, v in inputs.items()} + with torch.no_grad(): + outputs = model(**inputs) + embeddings = outputs.last_hidden_state[:, 0] + numpy_embeddings = embeddings.cpu().numpy() + for emb, output_emb_path in zip(numpy_embeddings, output_emb_paths): + np.save(f"{output_emb_path}.npy", emb) diff --git a/data/to_webdataset/inaturalist_to_wds.py b/data/to_webdataset/inaturalist_to_wds.py new file mode 100644 index 0000000000000000000000000000000000000000..d3d1912d52b196c099d5258c50739a0f5954959c --- /dev/null +++ b/data/to_webdataset/inaturalist_to_wds.py @@ -0,0 +1,132 @@ +import webdataset as wds +from pathlib import Path +import json +import numpy as np +from PIL import Image + + +def main( + src_json, + dest_folder, + num_samples_per_tar=10000, + number_of_jobs=10, + job_offset=0, +): + with open(src_json, "r") as f: + data = json.load(f) + import pandas as pd + + root_path = Path(src_json).parent + + # Convert images list to pandas dataframe + data_df = pd.DataFrame(data["images"]) + if "annotations" in data: + has_annotations = True + annotations_df = pd.DataFrame(data["annotations"]) + # Join the dataframes on id to get category_id from annotations + data_df = data_df.merge( + annotations_df[["id", "category_id"]], + left_on="id", + right_on="id", + how="left", + ) + categories_df = pd.DataFrame(data["categories"]) + data_df = data_df.merge( + categories_df[ + [ + "id", + "name", + "common_name", + "supercategory", + "kingdom", + "phylum", + "class", + "order", + "family", + "genus", + "specific_epithet", + ] + ], + left_on="category_id", + right_on="id", + how="left", + ) + data_df.rename( + columns={ + "id_x": "id", + }, + inplace=True, + ) + del data_df["id_y"] + else: + has_annotations = False + data_df = data_df[data_df["latitude"].notna() & data_df["longitude"].notna()] + num_samples = len(data_df) + num_total_tar = num_samples // num_samples_per_tar + ( + 1 if num_samples % num_samples_per_tar > 0 else 0 + ) + number_of_tar_per_job = num_total_tar // number_of_jobs + if job_offset == number_of_jobs - 1: + data_df = data_df.iloc[ + number_of_tar_per_job * job_offset * num_samples_per_tar : + ] + else: + data_df = data_df.iloc[ + number_of_tar_per_job + * job_offset + * num_samples_per_tar : number_of_tar_per_job + * (job_offset + 1) + * num_samples_per_tar + ] + print(f"Processing job {job_offset} with {len(data_df)} / {num_samples} samples") + print(f"Number of tar: {number_of_tar_per_job} / {num_total_tar}") + print(f"Start shard: {number_of_tar_per_job * job_offset}") + with wds.ShardWriter( + str(Path(dest_folder) / "%04d.tar"), + maxcount=num_samples_per_tar, + start_shard=number_of_tar_per_job * job_offset, + ) as sink: + for i in range(len(data_df)): + row = data_df.iloc[i] + image_path = Path(root_path) / Path("images") / row["file_name"] + dinov2_embedding_path = ( + Path(root_path) + / Path("embeddings") + / Path("dinov2") + / f"{row['file_name'].replace('.jpg', '.npy')}" + ) + sample = { + "__key__": str(row["id"]), + "jpg": Image.open(image_path).convert("RGB"), + "dinov2_vitl14_registers.npy": np.load(dinov2_embedding_path), + "json": row.to_dict(), + } + sink.write(sample) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--src_json", help="pixel_input_folder") + parser.add_argument("--dest", help="path to destination web") + parser.add_argument( + "--num_samples_per_tar", + help="number of samples per tar", + type=int, + default=10000, + ) + parser.add_argument("--number_of_jobs", help="number of jobs", type=int, default=10) + parser.add_argument("--job_offset", help="job offset", type=int, default=0) + args = parser.parse_args() + + dest = Path(args.dest) + dest.mkdir(exist_ok=True, parents=True) + + main( + args.src_json, + args.dest, + args.num_samples_per_tar, + args.number_of_jobs, + args.job_offset, + ) diff --git a/data/to_webdataset/launch_inaturalist_preprocessing.py b/data/to_webdataset/launch_inaturalist_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..b0c72512fde2af8e50c43c8bb9b8cae0b12f484a --- /dev/null +++ b/data/to_webdataset/launch_inaturalist_preprocessing.py @@ -0,0 +1,73 @@ +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) +import argparse +import os + +from jean_zay.launch import JeanZayExperiment + + +def parse_mode(): + parser = argparse.ArgumentParser( + description="Extract embeddings from a dataset using DINOv2" + ) + parser.add_argument( + "--launch", + action="store_true", + help="Launch the experiment", + ) + parser.add_argument("--src_json", help="path to src json") + parser.add_argument("--dest", help="path to dest") + parser.add_argument( + "--num_samples_per_tar", + help="number of samples per tar", + type=int, + default=10000, + ) + parser.add_argument("--number_of_jobs", help="number of jobs", type=int, default=10) + args = parser.parse_args() + + return args + + +args = parse_mode() + +cmd_modifiers = [] +exps = [] + +exp_name = f"inaturalist_preprocessing" +job_name = f"inaturalist_preprocessing" +jz_exp = JeanZayExperiment( + exp_name, + job_name, + slurm_array_nb_jobs=args.number_of_jobs, + cmd_path="data/to_webdataset/inaturalist_to_wds.py", + num_nodes=1, + num_gpus_per_node=1, + qos="t3", + account="syq", + gpu_type="v100", + time="1:00:00", +) + +exps.append(jz_exp) + +trainer_modifiers = {} + +exp_modifier = { + "--src_json": args.src_json, + "--dest": args.dest, + "--num_samples_per_tar": args.num_samples_per_tar, + "--number_of_jobs": args.number_of_jobs, + "--job_offset": "${SLURM_ARRAY_TASK_ID}", +} + +cmd_modifiers.append(dict(trainer_modifiers, **exp_modifier)) + + +if __name__ == "__main__": + for exp, cmd_modifier in zip(exps, cmd_modifiers): + exp.build_cmd(cmd_modifier) + if args.launch == True: + exp.launch() diff --git a/data/to_webdataset/launch_osv_5m_embeddings.py b/data/to_webdataset/launch_osv_5m_embeddings.py new file mode 100644 index 0000000000000000000000000000000000000000..1d50e456dac7c931da7f34a6c8d51323cd5ccbe0 --- /dev/null +++ b/data/to_webdataset/launch_osv_5m_embeddings.py @@ -0,0 +1,63 @@ +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) +import argparse +import os + +from jean_zay.launch import JeanZayExperiment + + +def parse_mode(): + parser = argparse.ArgumentParser(description="Process some integers.") + parser.add_argument("--launch", action="store_true") + parser.add_argument("--src", help="path to source files") + parser.add_argument("--dest", help="path to destination files") + args = parser.parse_args() + + return args + + +args = parse_mode() + +dataset_path = Path(args.src) + +list_of_shards = list(dataset_path.glob("*.tar")) +list_of_shards.sort() + + +cmd_modifiers = [] +exps = [] + +exp_name = f"preprocess_data" +job_name = f"preprocess_data" +jz_exp = JeanZayExperiment( + exp_name, + job_name, + slurm_array_nb_jobs=len(list_of_shards), + cmd_path="data/to_webdataset/osv_to_wds.py", + num_nodes=1, + qos="t3", + account="syq", + gpu_type="a100", + time="01:00:00", +) + +exps.append(jz_exp) + +trainer_modifiers = {} + +exp_modifier = { + "--src": dataset_path, + "--dest": Path(args.dest), + "--shard_id": "${SLURM_ARRAY_TASK_ID}", +} + +cmd_modifiers.append(dict(trainer_modifiers, **exp_modifier)) + + +if __name__ == "__main__": + for exp, cmd_modifier in zip(exps, cmd_modifiers): + exp.build_cmd(cmd_modifier) + if args.launch == True: + exp.launch() diff --git a/data/to_webdataset/launch_yfcc_preprocessing.py b/data/to_webdataset/launch_yfcc_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..2af2294d673e6ff698e1e565e8df7181b4131efa --- /dev/null +++ b/data/to_webdataset/launch_yfcc_preprocessing.py @@ -0,0 +1,76 @@ +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) +import argparse +import os + +from jean_zay.launch import JeanZayExperiment + + +def parse_mode(): + parser = argparse.ArgumentParser( + description="Extract embeddings from YFCC dataset using DINOv2" + ) + parser.add_argument( + "--launch", + action="store_true", + help="Launch the experiment", + ) + parser.add_argument("--src_csv_dir", help="path to source csv directory") + parser.add_argument("--src_images_dir", help="path to source images directory") + parser.add_argument("--dest", help="path to destination") + parser.add_argument( + "--num_samples_per_tar", + help="number of samples per tar", + type=int, + default=10000, + ) + parser.add_argument("--batch_size", help="batch size", type=int, default=256) + args = parser.parse_args() + + return args + + +args = parse_mode() + +number_of_jobs = len(list(Path(args.src_csv_dir).glob("*.csv"))) +cmd_modifiers = [] +exps = [] + +exp_name = f"yfcc_preprocessing" +job_name = f"yfcc_preprocessing" +jz_exp = JeanZayExperiment( + exp_name, + job_name, + slurm_array_nb_jobs=number_of_jobs, + cmd_path="data/to_webdataset/yfcc_to_wds.py", + num_nodes=1, + num_gpus_per_node=1, + qos="t3", + account="syq", + gpu_type="a100", + time="1:30:00", +) + +exps.append(jz_exp) + +trainer_modifiers = {} + +exp_modifier = { + "--src_csv_dir": args.src_csv_dir, + "--src_images_dir": args.src_images_dir, + "--dest": args.dest, + "--num_samples_per_tar": args.num_samples_per_tar, + "--job_offset": "${SLURM_ARRAY_TASK_ID}", + "--batch_size": args.batch_size, +} + +cmd_modifiers.append(dict(trainer_modifiers, **exp_modifier)) + + +if __name__ == "__main__": + for exp, cmd_modifier in zip(exps, cmd_modifiers): + exp.build_cmd(cmd_modifier) + if args.launch == True: + exp.launch() diff --git a/data/to_webdataset/osv_to_wds.py b/data/to_webdataset/osv_to_wds.py new file mode 100644 index 0000000000000000000000000000000000000000..85b40591413c0b5e0da0c9526c6b9668c911183f --- /dev/null +++ b/data/to_webdataset/osv_to_wds.py @@ -0,0 +1,138 @@ +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) +import argparse +import json +from collections import UserDict +from pathlib import Path + +import numpy as np +import torch +import webdataset as wds +from PIL import Image +from torchvision import transforms +from tqdm import tqdm +from webdataset.autodecode import ImageHandler +from utils.image_processing import CenterCrop + +print("Loading dinov2") +augmentation_dinov2 = transforms.Compose( + [ + CenterCrop(ratio="1:1"), + transforms.Resize(336, interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] +) + +device = "cuda" if torch.cuda.is_available() else "cpu" + +dinov2_model = torch.hub.load("facebookresearch/dinov2", "dinov2_vitl14_reg") +dinov2_model.eval() +dinov2_model.to(device) +print(f"Model loaded on {device}") + + +def dict_collate(batch): + output_dict = {} + if isinstance(batch[0], dict): + for key in batch[0].keys(): + list_key = [d[key] for d in batch] + if key != "json": + output_dict[key] = dict_collate(list_key) + else: + output_dict[key] = list_key + return output_dict + elif isinstance(batch[0], Image.Image): + return [img for img in batch] + else: + return torch.utils.data.dataloader.default_collate(batch) + + +def log_and_continue(exn): + """Call in an exception handler to ignore any exception, issue a warning, and continue.""" + # logging.warning(f"Handling webdataset error ({repr(exn)}). Ignoring.") + return True + + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def add_clip_scores_and_embeddings(src, dest, batch_size=512): + dataset = wds.DataPipeline( + wds.SimpleShardList(str(src)), + wds.split_by_worker, + wds.tarfile_to_samples(), + wds.rename( + __key__="__key__", + dino_image="jpg", + image="jpg", + street_clip="street_clip.npy", + json="json", + ), + wds.decode( + ImageHandler("pilrgb", ["dino_image"]) + ), # avoid encoding decoding jpeg for true + wds.map_dict( + dino_image=augmentation_dinov2, + image=lambda x: x, + street_clip=lambda x: x, + json=lambda x: x, + ), + wds.to_tuple( + "__key__", + "dino_image", + "street_clip", + "image", + "json", + ), + wds.batched(batch_size), + ) + loader = wds.WebLoader(dataset, num_workers=8, batch_size=None) + with wds.TarWriter(str(dest)) as sink: + for batch in tqdm(loader, total=10000 // batch_size): + ( + keys, + dino_image, + street_clip, + image, + json, + ) = batch + dino_image = dino_image.to(device) + with torch.no_grad(): + dino_embedding = dinov2_model(dino_image).cpu().numpy() + for i in range(len(keys)): + sample = { + "__key__": keys[i], + "jpg": image[i], + "street_clip.npy": street_clip[i], + "json": json[i], + "dinov2_vitl14_registers.npy": dino_embedding[i], + } + sink.write(sample) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--src", help="path to source files") + parser.add_argument("--dest", help="path to destination files") + parser.add_argument("--shard_id", help="shard id") + args = parser.parse_args() + + src = Path(args.src) + list_of_shards = list(src.glob("*.tar")) + list_of_shards.sort() + shard = str(list_of_shards[int(args.shard_id)]).split("/")[-1] + dest = Path(args.dest) + dest.mkdir(exist_ok=True, parents=True) + batch_size = 256 + + print(f"Loading {shard}") + + tar_name = shard.split(".")[0] + + src_shard = src / shard # f"{{{tar_name}...{tar_name}}}.tar" + + print(f"Processing {src_shard} to {dest / shard}") + add_clip_scores_and_embeddings(src_shard, dest / shard, batch_size) diff --git a/data/to_webdataset/process_yfcc_metadata.py b/data/to_webdataset/process_yfcc_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..9de3e127a17994ff097a3d553ce074252b3e6d16 --- /dev/null +++ b/data/to_webdataset/process_yfcc_metadata.py @@ -0,0 +1,99 @@ +import dask +import dask.dataframe as dd +from dask.diagnostics import ProgressBar + +with ProgressBar(): + ddf = dd.read_csv( + "../datasets/YFCC100M/yfcc100m_dataset", + names=[ + "photo_id", + "user_nsid", + "user_nickname", + "date_taken", + "date_uploaded", + "capture_device", + "title", + "description", + "user_tags", + "machine_tags", + "longitude", + "latitude", + "accuracy", + "page_url", + "download_url", + "license_name", + "license_url", + "server_id", + "farm_id", + "secret", + "secret_original", + "extension", + "media_type", + ], + dtype={ + "photo_id": str, + "user_nsid": str, + "user_nickname": str, + "user_tags": str, + "machine_tags": str, + "longitude": float, + "latitude": float, + "accuracy": float, + "server_id": str, + "farm_id": str, + "secret": str, + "secret_original": str, + "extension": str, + "media_type": float, + }, + sep="\t", + ) + ddf = ddf[ + [ + "photo_id", + "longitude", + "latitude", + "accuracy", + "extension", + "download_url", + "media_type", + ] + ] + filtered_ddf = ddf[ + ddf["longitude"].notnull() + & ddf["latitude"].notnull() + & (ddf["media_type"] == 0) + ] + del ddf["media_type"] + hash_ddf = dd.read_csv( + "../datasets/YFCC100M/yfcc100m_hash", + names=["photo_id", "hash"], + dtype={"photo_id": str, "hash": str}, + sep="\t", + ) + filtered_ddf = filtered_ddf.merge(hash_ddf, on="photo_id", how="left") + # Read the 4k photo IDs + with open("../datasets/YFCC100M/yfcc_4k_ids.txt", "r") as f: + test_photo_ids = set(f.read().splitlines()) + + # Split the dataframe based on whether photo_id is in test set + filter = filtered_ddf["photo_id"].isin(test_photo_ids) + test_ddf = filtered_ddf[filter] + train_ddf = filtered_ddf[~filter] + + train_ddf = train_ddf[train_ddf["accuracy"] >= 12] + + # Save the split dataframes + test_ddf.to_csv( + "../datasets/YFCC100M/yfcc_4k_dataset_with_gps.csv", + sep="\t", + index=False, + single_file=True, + ) + train_ddf = train_ddf.repartition(npartitions=len(train_ddf) // 100000 + 1) + train_ddf.to_csv( + "../datasets/YFCC100M/yfcc100m_dataset_with_gps_train/*.csv", + sep="\t", + index=False, + single_file=False, + ) diff --git a/data/to_webdataset/rebalance_csv.py b/data/to_webdataset/rebalance_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..200bf543ba246e20d7c20b165cbc3c4e896ce9bb --- /dev/null +++ b/data/to_webdataset/rebalance_csv.py @@ -0,0 +1,74 @@ +import csv +import os +import sys +import glob +import tqdm + + +def split_csv_files(input_files, output_dir, lines_per_file=100000): + # Ensure output directory exists + os.makedirs(output_dir, exist_ok=True) + + # Initialize counters + total_lines = 0 + file_count = 0 + current_line_count = 0 + + # Initialize the first output file + output_file = os.path.join(output_dir, f"{str(file_count).zfill(3)}.csv") + output_writer = open(output_file, "w", newline="") + csv_writer = None + + try: + for file_path in tqdm.tqdm(input_files, desc="Processing files"): + with open(file_path, "r") as csv_file: + csv_reader = csv.reader(csv_file) + + # Initialize writer once we have the header row + if csv_writer is None: + header = next(csv_reader) + csv_writer = csv.writer(output_writer) + csv_writer.writerow(header) + + # Process each line in the current file + for row in csv_reader: + if current_line_count >= lines_per_file: + # Close the current file and start a new one + output_writer.close() + file_count += 1 + current_line_count = 0 + output_file = os.path.join( + output_dir, f"{str(file_count).zfill(3)}.csv" + ) + output_writer = open(output_file, "w", newline="") + csv_writer = csv.writer(output_writer) + csv_writer.writerow(header) # Write header to new file + + # Write row to the current output file + csv_writer.writerow(row) + current_line_count += 1 + total_lines += 1 + + finally: + # Close the last output file + if output_writer: + output_writer.close() + + print(f"Total lines processed: {total_lines}") + print(f"Files created: {file_count + 1}") + + +if __name__ == "__main__": + input_dir = "../datasets/YFCC100M/yfcc100m_dataset_with_gps_train" + output_dir = "../datasets/YFCC100M/yfcc100m_dataset_with_gps_train_balanced" + lines_per_file = 100000 + + # Get all CSV files in input directory + input_files = glob.glob(os.path.join(input_dir, "*.csv")) + + if not input_files: + print(f"No CSV files found in {input_dir}") + sys.exit(1) + + print(f"Found {len(input_files)} CSV files") + split_csv_files(input_files, output_dir, lines_per_file) diff --git a/data/to_webdataset/yfcc_to_wds.py b/data/to_webdataset/yfcc_to_wds.py new file mode 100644 index 0000000000000000000000000000000000000000..62297ecaeac64113e89b3247f716e380ee4e675b --- /dev/null +++ b/data/to_webdataset/yfcc_to_wds.py @@ -0,0 +1,162 @@ +import webdataset as wds +from pathlib import Path +import pandas as pd +import numpy as np +from PIL import Image +import torch +import torchvision.transforms as transforms +from torch.utils.data import Dataset, DataLoader +from utils.image_processing import CenterCrop +from tqdm import tqdm +import os + +tqdm.pandas() + +print("Loading dinov2") +augmentation_dinov2 = transforms.Compose( + [ + CenterCrop(ratio="1:1"), + transforms.Resize(336, interpolation=transforms.InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] +) + +device = "cuda" if torch.cuda.is_available() else "cpu" + +model = torch.hub.load("facebookresearch/dinov2", "dinov2_vitl14_reg") +model.eval() +model.to(device) +print(f"Model loaded on {device}") + + +class YFCCDataset(Dataset): + def __init__(self, csv_path, images_root): + self.df = pd.read_csv(csv_path, sep="\t") + self.df = self.df[self.df["latitude"].notna() & self.df["longitude"].notna()] + self.images_root = Path(images_root) + + # Create image paths and check existence + print("Checking image existence...") + self.df["image_path"] = self.df["hash"].progress_apply( + lambda x: self.images_root / x[:3] / x[3:6] / f"{x}.jpg" + ) + + def __len__(self): + return len(self.df) + + def __getitem__(self, idx): + row = self.df.iloc[idx] + image_path = row["image_path"] + + if not image_path.exists(): + print(f"Image {image_path} does not exist") + return None + + # Read the JPEG file directly as bytes + with open(image_path, "rb") as f: + jpg_data = f.read() + + image = Image.open(image_path).convert("RGB") + image = augmentation_dinov2(image) + + # Convert metadata to dict and ensure all values are JSON serializable + metadata = row.to_dict() + del metadata["image_path"] + + return { + "image": image, + "jpg_data": jpg_data, + "photo_id": str(row["photo_id"]), + "metadata": metadata, + } + + +def custom_collate(batch): + """ + Custom collate function to handle dictionary items from the dataset + """ + return { + "image": torch.stack([item["image"] for item in batch if item is not None]), + "jpg_data": [item["jpg_data"] for item in batch if item is not None], + "photo_id": [item["photo_id"] for item in batch if item is not None], + "metadata": [item["metadata"] for item in batch if item is not None], + } + + +def process_batch(batch, model, device): + images = batch["image"].to(device) # No need to stack, already stacked in collate + with torch.no_grad(): + embeddings = model(images).cpu().numpy() + + samples = [] + for i in range(len(batch["photo_id"])): + sample = { + "__key__": batch["photo_id"][i], + "jpg": batch["jpg_data"][i], + "dinov2_vitl14_registers.npy": embeddings[i], + "json": batch["metadata"][i], + } + samples.append(sample) + return samples + + +def main( + src_csv, + src_images, + dest_folder, + num_samples_per_tar=10000, + job_offset=0, + batch_size=32, +): + print(f"Loading dataset") + dataset = YFCCDataset(src_csv, src_images) + dataloader = DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=8, + pin_memory=True, + collate_fn=custom_collate, # Add the custom collate function + ) + + print(f"Processing job {job_offset} with {len(dataset)} samples") + with wds.ShardWriter( + str(Path(dest_folder) / "%04d.tar"), + maxcount=num_samples_per_tar, + start_shard=10 * job_offset, + ) as sink: + for batch in tqdm(dataloader): + samples = process_batch(batch, model, device) + for sample in samples: + sink.write(sample) + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--src_csv_dir", help="pixel_input_folder") + parser.add_argument("--src_images_dir", help="path to source images") + parser.add_argument("--dest", help="path to destination web") + parser.add_argument( + "--num_samples_per_tar", + help="number of samples per tar", + type=int, + default=10000, + ) + parser.add_argument("--job_offset", help="job offset", type=int, default=0) + parser.add_argument("--batch_size", help="batch size", type=int, default=256) + args = parser.parse_args() + + dest = Path(args.dest) + dest.mkdir(exist_ok=True, parents=True) + + main( + Path(args.src_csv_dir) / f"{str(args.job_offset).zfill(3)}.csv", + args.src_images_dir, + args.dest, + args.num_samples_per_tar, + args.job_offset, + args.batch_size, + ) diff --git a/data/transforms.py b/data/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..3d3378c74b95fe8cce80b19fe6de00aa44bbbbdd --- /dev/null +++ b/data/transforms.py @@ -0,0 +1,44 @@ +from transformers import CLIPProcessor + + +class ClipTransform(object): + def __init__(self, split): + self.transform = CLIPProcessor.from_pretrained("geolocal/StreetCLIP") + + def __call__(self, x): + # return self.transform(images=x, return_tensors="pt")["pixel_values"].squeeze(0) + return self.transform(images=[x], return_tensors="pt") + + +if __name__ == "__main__": + # sanity check + import glob + import torchvision.transforms as transforms + from torchvision.utils import save_image + from omegaconf import DictConfig, OmegaConf + from hydra.utils import instantiate + import torch + from PIL import Image + + fast_clip_config = OmegaConf.load( + "./configs/dataset/train_transform/fast_clip.yaml" + ) + fast_clip_transform = instantiate(fast_clip_config) + clip_transform = ClipTransform(None) + + img_paths = glob.glob("./datasets/osv5m/test/images/*.jpg") + original_imgs, re_implemted_imgs, diff = [], [], [] + + for i in range(16): + img = Image.open(img_paths[i]) + clip_img = clip_transform(img) + fast_clip_img = fast_clip_transform(img) + original_imgs.append(clip_img) + re_implemted_imgs.append(fast_clip_img) + max_diff = (clip_img - fast_clip_img).abs() + diff.append(max_diff) + if max_diff.max() > 1e-5: + print(max_diff.max()) + original_imgs = torch.stack(original_imgs) + re_implemted_imgs = torch.stack(re_implemted_imgs) + diff = torch.stack(diff) diff --git a/data/webdataset.py b/data/webdataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d2c9f6fa802aa6ba0b3b6c078460970dab7749fa --- /dev/null +++ b/data/webdataset.py @@ -0,0 +1,408 @@ +import glob +import json +import logging +import os +import random +from collections import OrderedDict +from multiprocessing import Value +from pathlib import Path + +import braceexpand +import numpy as np +import pandas as pd +import torch +import webdataset as wds +from lightning_fabric.utilities.rank_zero import _get_rank +from PIL import Image +from torch.utils.data import Dataset, get_worker_info +from tqdm import tqdm +from webdataset.tariterators import ( + base_plus_ext, + tar_file_expander, + url_opener, + valid_sample, +) +from functools import partial +import math + + +class GPSWebdataset(wds.DataPipeline): + def __init__( + self, + root, + image_transforms=None, + distributed=True, + train=True, + epoch=0, + seed=3407, + embedding_name=None, + return_image=True, + shard_shuffle_size=2000, + shard_shuffle_initial=500, + sample_shuffle_size=5000, + sample_shuffle_initial=1000, + metadata_attributes=[], + ): + self.image_transforms = image_transforms + dataset_tar_files = [] + # Get a list of all tar files in the directory + if " " in root: + root = root.split(" ") + print(f"Using multiple dataset[s: {root}") + if isinstance(root, str): + tar_files = [f for f in os.listdir(root) if f.endswith(".tar")] + + # Sort the list of tar files + tar_files.sort() + + first_tar_file = tar_files[0].split(".")[0] + last_tar_file = tar_files[-1].split(".")[0] + + for tar_file in tar_files: + dataset_tar_files.append(f"{root}/{tar_file}") + + dataset_pattern = f"{root}/{{{first_tar_file}..{last_tar_file}}}.tar" + self.num_samples, _ = get_dataset_size(dataset_pattern) + elif isinstance(root, list): + num_samples = 0 + for r in root: + tar_files = [f for f in os.listdir(r) if f.endswith(".tar")] + tar_files.sort() + first_tar_file = tar_files[0].split(".")[0] + last_tar_file = tar_files[-1].split(".")[0] + + for tar_file in tar_files: + dataset_tar_files.append(f"{r}/{tar_file}") + + num_samples += get_dataset_size( + f"{r}/{{{first_tar_file}..{last_tar_file}}}.tar" + )[0] + self.num_samples = num_samples + else: + raise ValueError( + f"root must be a string or list of strings. Got {type(root)}" + ) + rank = _get_rank() + self.shared_epoch = SharedEpoch(epoch) + pipeline = [wds.SimpleShardList(dataset_tar_files)] + + if distributed: + if train: + pipeline.extend( + [ + detshuffle2( + bufsize=shard_shuffle_size, + initial=shard_shuffle_initial, + seed=seed, + epoch=self.shared_epoch, + ), + wds.split_by_node, + wds.split_by_worker, + tarfile_to_samples_nothrow, + wds.shuffle( + bufsize=sample_shuffle_size, + initial=sample_shuffle_initial, + ), + ] + ) + else: + pipeline.extend( + [wds.split_by_node, wds.split_by_worker, tarfile_to_samples_nothrow] + ) + else: + if train: + pipeline.extend( + [ + wds.shuffle( + bufsize=shard_shuffle_size, + initial=sample_shuffle_initial, + ), + wds.split_by_worker, + tarfile_to_samples_nothrow, + wds.shuffle( + bufsize=sample_shuffle_size, + initial=sample_shuffle_initial, + ), + ] + ) + else: + pipeline.extend([wds.split_by_worker, tarfile_to_samples_nothrow]) + outputs_transforms = OrderedDict() + outputs_rename = OrderedDict() + if return_image: + outputs_rename["img.jpg"] = "jpg;png;webp;jpeg" + outputs_transforms["img.jpg"] = ( + self.image_transforms + if self.image_transforms is not None + else lambda x: x + ) + if embedding_name is not None: + outputs_rename[f"emb.npy"] = f"{embedding_name}.npy" + outputs_transforms[f"emb.npy"] = lambda x: torch.from_numpy(x) + if metadata_attributes != []: + for attr in metadata_attributes: + outputs_rename[f"{attr}.json"] = f"json" + outputs_transforms[f"{attr}.json"] = partial(get_attr, attr=attr) + outputs_rename["gps"] = "json" + outputs_transforms["gps"] = get_gps + pipeline.extend( + [ + wds.rename(**outputs_rename), + filter_dict_keys(*outputs_rename.keys(), handler=log_and_continue), + ] + ) + if return_image: + pipeline.append(wds.decode("pilrgb", handler=log_and_continue)) + else: + pipeline.append(wds.decode(handler=log_and_continue)) + pipeline.extend( + [ + wds.map_dict(**outputs_transforms, handler=log_and_continue), + wds.rename( + **{k.split(".")[0]: k for k in outputs_transforms.keys()}, + ), + ] + ) + + super().__init__(*pipeline) + + def __len__(self): + return self.num_samples + + +def normalize_gps(lat, lon): + """Used to put all lat lon inside ±90 and ±180.""" + lat = (lat + 90) % 360 - 90 + if lat > 90: + lat = 180 - lat + lon += 180 + lon = (lon + 180) % 360 - 180 + return lat, lon + + +def get_attr(metadata, attr): + # datapoint = json.loads(metadata) + attr_value = metadata[attr] + if isinstance(attr_value, float) and math.isnan(attr_value): + return "NaN" + else: + return attr_value + + +def get_gps(metadata): + datapoint = json.loads(metadata) + lat, lon = normalize_gps( + float(datapoint["latitude"]), float(datapoint["longitude"]) + ) + gps = torch.tensor([np.radians(lat), np.radians(lon)], dtype=torch.float) + return gps + + +def get_dataset_size(shards): + shards_list, _ = expand_urls(shards) + dir_path = os.path.dirname(shards_list[0]) + sizes_filename = os.path.join(dir_path, "sizes.json") + if os.path.exists(sizes_filename): + sizes = json.load(open(sizes_filename, "r")) + total_size = sum([int(sizes[os.path.basename(shard)]) for shard in shards_list]) + else: + total_size = 0 # num samples undefined + sizes = {} + for shard in tqdm(shards_list): + dataset = wds.WebDataset(shard) + num_samples = sum(1 for _ in dataset) + total_size += num_samples + sizes[os.path.basename(shard)] = num_samples + print(f"Total number of samples: {total_size}") + with open(sizes_filename, "w") as f: + json.dump(sizes, f) + + num_shards = len(shards_list) + return total_size, num_shards + + +def expand_urls(urls, weights=None): + if weights is None: + expanded_urls = wds.shardlists.expand_urls(urls) + return expanded_urls, None + if isinstance(urls, str): + urllist = urls.split("::") + weights = weights.split("::") + assert len(weights) == len( + urllist + ), f"Expected the number of data components ({len(urllist)}) and weights({len(weights)}) to match." + weights = [float(weight) for weight in weights] + all_urls, all_weights = [], [] + for url, weight in zip(urllist, weights): + expanded_url = list(braceexpand.braceexpand(url)) + expanded_weights = [weight for _ in expanded_url] + all_urls.extend(expanded_url) + all_weights.extend(expanded_weights) + return all_urls, all_weights + else: + all_urls = list(urls) + return all_urls, weights + + +class SharedEpoch: + def __init__(self, epoch: int = 0): + self.shared_epoch = Value("i", epoch) + + def set_value(self, epoch): + self.shared_epoch.value = epoch + + def get_value(self): + return self.shared_epoch.value + + +# _SHARD_SHUFFLE_SIZE = 256 +# _SHARD_SHUFFLE_INITIAL = 128 +# _SAMPLE_SHUFFLE_SIZE = 5000 +# _SAMPLE_SHUFFLE_INITIAL = 1000 + + +class detshuffle2(wds.PipelineStage): + def __init__( + self, + bufsize=1000, + initial=100, + seed=0, + epoch=-1, + ): + self.bufsize = bufsize + self.initial = initial + self.seed = seed + self.epoch = epoch + + def run(self, src): + if isinstance(self.epoch, SharedEpoch): + epoch = self.epoch.get_value() + else: + # NOTE: this is epoch tracking is problematic in a multiprocess (dataloader workers or train) + # situation as different workers may wrap at different times (or not at all). + self.epoch += 1 + epoch = self.epoch + rng = random.Random() + if self.seed < 0: + # If seed is negative, we use the worker's seed, this will be different across all nodes/workers + seed = pytorch_worker_seed(epoch) + else: + # This seed to be deterministic AND the same across all nodes/workers in each epoch + seed = self.seed + epoch + rng.seed(seed) + return wds.filters._shuffle(src, self.bufsize, self.initial, rng) + + +def pytorch_worker_seed(increment=0): + """get dataloader worker seed from pytorch""" + worker_info = get_worker_info() + if worker_info is not None: + # favour using the seed already created for pytorch dataloader workers if it exists + seed = worker_info.seed + if increment: + # space out seed increments so they can't overlap across workers in different iterations + seed += increment * max(1, worker_info.num_workers) + return seed + # fallback to wds rank based seed + return wds.utils.pytorch_worker_seed() + + +def log_and_continue(exn): + """Call in an exception handler to ignore any exception, issue a warning, and continue.""" + logging.warning(f"Handling webdataset error ({repr(exn)}). Ignoring.") + return True + + +def group_by_keys_nothrow( + data, keys=base_plus_ext, lcase=True, suffixes=None, handler=None +): + """Return function over iterator that groups key, value pairs into samples. + + :param keys: function that splits the key into key and extension (base_plus_ext) + :param lcase: convert suffixes to lower case (Default value = True) + """ + current_sample = None + for filesample in data: + assert isinstance(filesample, dict) + fname, value = filesample["fname"], filesample["data"] + prefix, suffix = keys(fname) + if prefix is None: + continue + if lcase: + suffix = suffix.lower() + # FIXME webdataset version throws if suffix in current_sample, but we have a potential for + # this happening in the current LAION400m dataset if a tar ends with same prefix as the next + # begins, rare, but can happen since prefix aren't unique across tar files in that dataset + if ( + current_sample is None + or prefix != current_sample["__key__"] + or suffix in current_sample + ): + if valid_sample(current_sample): + yield current_sample + current_sample = dict(__key__=prefix, __url__=filesample["__url__"]) + if suffixes is None or suffix in suffixes: + current_sample[suffix] = value + if valid_sample(current_sample): + yield current_sample + + +def tarfile_to_samples_nothrow(src, handler=log_and_continue): + # NOTE this is a re-impl of the webdataset impl with group_by_keys that doesn't throw + streams = url_opener(src, handler=handler) + files = tar_file_expander(streams, handler=handler) + samples = group_by_keys_nothrow(files, handler=handler) + return samples + + +def filter_no_caption_or_no_image(sample): + has_caption = "txt" in sample + has_image = ( + "png" in sample or "jpg" in sample or "jpeg" in sample or "webp" in sample + ) + return has_caption and has_image + + +def filter_metadata(sample, min_image_size, min_clip_score): + metadata = json.loads(sample["json"]) + width = metadata["width"] + height = metadata["height"] + clip_score = metadata["clip_score"] / 100 + return ( + width >= min_image_size + and height >= min_image_size + and clip_score >= min_clip_score + ) + + +def _filter_dict_keys( + data, + *args, + handler=wds.reraise_exception, + missing_is_error=True, + none_is_error=None, +): + """Convert dict samples to tuples.""" + if none_is_error is None: + none_is_error = missing_is_error + if len(args) == 1 and isinstance(args[0], str) and " " in args[0]: + args = args[0].split() + + for sample in data: + try: + result = { + f: wds.getfirst(sample, f, missing_is_error=missing_is_error) + for f in args + } + print + if none_is_error and any(x is None for x in result): + raise ValueError(f"to_tuple {args} got {sample.keys()}") + yield result + except Exception as exn: + if handler(exn): + continue + else: + break + + +filter_dict_keys = wds.pipelinefilter(_filter_dict_keys) diff --git a/datasets/.empty b/datasets/.empty new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/datasets/YFCC100M b/datasets/YFCC100M new file mode 120000 index 0000000000000000000000000000000000000000..455a3225ce7415c21c34761f1e3391f6923623a7 --- /dev/null +++ b/datasets/YFCC100M @@ -0,0 +1 @@ +/home/dufour/Documents/datasets/YFCC100M \ No newline at end of file diff --git a/datasets/inaturalist b/datasets/inaturalist new file mode 120000 index 0000000000000000000000000000000000000000..01b19279908537d27ddda36b473c4be61eaae2d3 --- /dev/null +++ b/datasets/inaturalist @@ -0,0 +1 @@ +/home/dufour/Documents/datasets/inaturalist \ No newline at end of file diff --git a/datasets/osv5m b/datasets/osv5m new file mode 120000 index 0000000000000000000000000000000000000000..013afbf10606ba9d5ff0b39e597be5c0cce66dbb --- /dev/null +++ b/datasets/osv5m @@ -0,0 +1 @@ +/home/dufour/Documents/datasets/osv5m \ No newline at end of file diff --git a/demo/__init__.py b/demo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/demo/demo.py b/demo/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..92e1eb4e1f2e50ffde118c2995702d77f4dc6ea3 --- /dev/null +++ b/demo/demo.py @@ -0,0 +1,388 @@ +import streamlit as st +import pandas as pd +from PIL import Image +import torch +from pipe import PlonkPipeline +from pathlib import Path +from streamlit_extras.colored_header import colored_header +import plotly.express as px +import requests +from io import BytesIO + +# Set page config +st.set_page_config( + page_title="Around the World in 80 Timesteps", page_icon="🗺️", layout="wide" +) + +device = "cuda" if torch.cuda.is_available() else "cpu" +PROJECT_ROOT = Path(__file__).parent.parent.absolute() +# Define checkpoint path +CHECKPOINT_DIR = PROJECT_ROOT / "checkpoints" + +MODEL_NAMES = { + "PLONK_YFCC": "nicolas-dufour/PLONK_YFCC", + "PLONK_OSV_5M": "nicolas-dufour/PLONK_OSV_5M", + "PLONK_iNaturalist": "nicolas-dufour/PLONK_iNaturalist", +} + + +@st.cache_resource +def load_model(model_name): + """Load the model and cache it to prevent reloading""" + try: + pipe = PlonkPipeline(model_path=model_name) + return pipe + except Exception as e: + st.error(f"Error loading model: {str(e)}") + st.stop() + + +PIPES = {model_name: load_model(MODEL_NAMES[model_name]) for model_name in MODEL_NAMES} + + +def predict_location(image, model_name, cfg=0.0, num_samples=256): + with torch.no_grad(): + batch = {"img": [], "emb": []} + + # If image is already a PIL Image, use it directly + if isinstance(image, Image.Image): + img = image.convert("RGB") + else: + img = Image.open(image).convert("RGB") + + pipe = PIPES[model_name] + + # Get regular predictions + predicted_gps = pipe(img, batch_size=num_samples, cfg=cfg, num_steps=32) + + # Get single high-confidence prediction + high_conf_gps = pipe(img, batch_size=1, cfg=2.0, num_steps=32) + return { + "lat": predicted_gps[:, 0].astype(float).tolist(), + "lon": predicted_gps[:, 1].astype(float).tolist(), + "high_conf_lat": high_conf_gps[0, 0].astype(float), + "high_conf_lon": high_conf_gps[0, 1].astype(float), + } + + +def load_example_images(): + """Load example images from the examples directory""" + examples_dir = Path(__file__).parent / "examples" + if not examples_dir.exists(): + st.error( + """ + Examples directory not found. Please create the following structure: + demo/ + └── examples/ + ├── eiffel_tower.jpg + ├── colosseum.jpg + ├── taj_mahal.jpg + ├── statue_liberty.jpg + └── sydney_opera.jpg + """ + ) + return {} + + examples = {} + for img_path in examples_dir.glob("*.jpg"): + # Use filename without extension as the key + name = img_path.stem.replace("_", " ").title() + examples[name] = str(img_path) + + if not examples: + st.warning("No example images found in the examples directory.") + + return examples + + +def resize_image_for_display(image, max_size=400): + """Resize image while maintaining aspect ratio""" + # Get current size + width, height = image.size + + # Calculate ratio to maintain aspect ratio + if width > height: + if width > max_size: + ratio = max_size / width + new_size = (max_size, int(height * ratio)) + else: + if height > max_size: + ratio = max_size / height + new_size = (int(width * ratio), max_size) + + # Only resize if image is larger than max_size + if width > max_size or height > max_size: + return image.resize(new_size, Image.Resampling.LANCZOS) + return image + + +def load_image_from_url(url): + """Load an image from a URL""" + try: + response = requests.get(url) + response.raise_for_status() # Raise an exception for bad status codes + return Image.open(BytesIO(response.content)) + except Exception as e: + st.error(f"Error loading image from URL: {str(e)}") + return None + + +def main(): + # Custom CSS + st.markdown( + """ + + """, + unsafe_allow_html=True, + ) + + # Header with custom styling + colored_header( + label="🗺️ Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation", + description="Upload an image and our model, PLONK, will predict possible locations! In red we will sample one point with guidance scale 2.0 for the best guess.

Project page: https://nicolas-dufour.github.io/plonk", + color_name="red-70", + ) + + # Adjust column ratio to give 2/3 of the space to the map + col1, col2 = st.columns([1, 2], gap="large") + + with col1: + # Add model selection before the sliders + model_name = st.selectbox( + "🤖 Select Model", + options=MODEL_NAMES.keys(), + index=0, # Default to YFCC + help="Choose which PLONK model variant to use for prediction.", + ) + + # Modify the slider columns to accommodate both controls + col_slider1, col_slider2 = st.columns([0.5, 0.5]) + with col_slider1: + cfg_value = st.slider( + "🎯 Guidance scale", + min_value=0.0, + max_value=5.0, + value=0.0, + step=0.1, + help="Scale for classifier-free guidance during sampling. A small value makes the model predictions display the diversity of the model, while a large value makes the model predictions more conservative but potentially more accurate.", + ) + + with col_slider2: + num_samples = st.number_input( + "🎲 Number of samples", + min_value=1, + max_value=5000, + value=1000, + step=1, + help="Number of location predictions to generate. More samples give better coverage but take longer to compute.", + ) + + st.markdown("### 📸 Choose your image") + tab1, tab2, tab3 = st.tabs(["Upload", "URL", "Examples"]) + + with tab1: + uploaded_file = st.file_uploader( + "Choose an image...", + type=["png", "jpg", "jpeg"], + help="Supported formats: PNG, JPG, JPEG", + ) + + if uploaded_file is not None: + st.markdown('
', unsafe_allow_html=True) + original_image = Image.open(uploaded_file) + display_image = resize_image_for_display( + original_image.copy(), max_size=300 + ) + st.image( + display_image, caption="Uploaded Image", use_container_width=True + ) + st.markdown("
", unsafe_allow_html=True) + + if st.button("🔍 Predict Location", key="predict_upload"): + with st.spinner("🌍 Analyzing image and predicting locations..."): + predictions = predict_location( + original_image, + model_name=model_name, + cfg=cfg_value, + num_samples=num_samples, + ) + st.session_state["predictions"] = predictions + + with tab2: + url = st.text_input("Enter image URL:", key="image_url") + + if url: + image = load_image_from_url(url) + if image: + st.markdown( + '
', unsafe_allow_html=True + ) + display_image = resize_image_for_display(image.copy(), max_size=300) + st.image( + display_image, + caption="Image from URL", + use_container_width=True, + ) + st.markdown("
", unsafe_allow_html=True) + + if st.button("🔍 Predict Location", key="predict_url"): + with st.spinner( + "🌍 Analyzing image and predicting locations..." + ): + predictions = predict_location( + image, + model_name=model_name, + cfg=cfg_value, + num_samples=num_samples, + ) + st.session_state["predictions"] = predictions + + with tab3: + examples = load_example_images() + st.markdown('
', unsafe_allow_html=True) + example_cols = st.columns(len(examples)) + + for idx, (name, path) in enumerate(examples.items()): + with example_cols[idx]: + original_image = Image.open(path) + display_image = resize_image_for_display( + original_image.copy(), max_size=150 + ) + + if st.container().button( + "📸", + key=f"img_{name}", + help=f"Click to predict location for {name}", + use_container_width=True, + ): + with st.spinner( + "🌍 Analyzing image and predicting locations..." + ): + predictions = predict_location( + original_image, + model_name=model_name, + cfg=cfg_value, + num_samples=num_samples, + ) + st.session_state["predictions"] = predictions + st.rerun() + + st.image(display_image, caption=name, use_container_width=True) + st.markdown("
", unsafe_allow_html=True) + + with col2: + st.markdown("### 🌍 Predicted Locations") + + if "predictions" in st.session_state: + pred = st.session_state["predictions"] + + # Create DataFrame for all predictions + df = pd.DataFrame( + { + "lat": pred["lat"], + "lon": pred["lon"], + "type": ["Sample"] * len(pred["lat"]), + } + ) + + # Add high-confidence prediction + df = pd.concat( + [ + df, + pd.DataFrame( + { + "lat": [pred["high_conf_lat"]], + "lon": [pred["high_conf_lon"]], + "type": ["Best Guess"], + } + ), + ] + ) + + # Create a more interactive map using Plotly + fig = px.scatter_mapbox( + df, + lat="lat", + lon="lon", + zoom=2, + opacity=0.6, + color="type", + color_discrete_map={"Sample": "blue", "Best Guess": "red"}, + mapbox_style="carto-positron", + ) + + fig.update_traces(selector=dict(name="Best Guess"), marker_size=15) + + fig.update_layout( + margin={"r": 0, "t": 0, "l": 0, "b": 0}, + height=500, + showlegend=True, + legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01), + ) + + # Display map in a container + with st.container(): + st.plotly_chart(fig, use_container_width=True) + + # Display stats in a styled container + with st.container(): + st.markdown( + f""" +
+

📊 Prediction Statistics

+

Number of sampled locations: {len(pred["lat"])}

+

Best guess location: {pred["high_conf_lat"]:.2f}°, {pred["high_conf_lon"]:.2f}°

+
+ """, + unsafe_allow_html=True, + ) + else: + # Empty state with better styling + st.markdown( + """ +
+

👆 Upload an image and click 'Predict Location'

+

The predicted locations will appear here on an interactive map.

+
+ """, + unsafe_allow_html=True, + ) + + +if __name__ == "__main__": + main() diff --git a/demo/examples/Kilimanjaro.jpg b/demo/examples/Kilimanjaro.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e24de5aaed4856ef2138f865d35a86f7bc6d0e50 Binary files /dev/null and b/demo/examples/Kilimanjaro.jpg differ diff --git a/demo/examples/README.md b/demo/examples/README.md new file mode 100644 index 0000000000000000000000000000000000000000..deedf6b96d4565e56f12e48aabbc34fa1745a79e --- /dev/null +++ b/demo/examples/README.md @@ -0,0 +1,15 @@ +# Example Images + +This directory contains example images for the demo: + +- eiffel_tower.jpg - The Eiffel Tower in Paris +- colosseum.jpg - The Colosseum in Rome +- taj_mahal.jpg - The Taj Mahal in Agra +- statue_liberty.jpg - The Statue of Liberty in New York +- sydney_opera.jpg - The Sydney Opera House + +Please ensure all images are: +1. Free to use / properly licensed +2. Good quality (at least 800x600) +3. Clearly showing recognizable landmarks +4. Named descriptively with underscores between words \ No newline at end of file diff --git a/demo/examples/condor.jpg b/demo/examples/condor.jpg new file mode 100644 index 0000000000000000000000000000000000000000..885dc24797eb8f635cf87fe15fa41bce0375060a Binary files /dev/null and b/demo/examples/condor.jpg differ diff --git a/demo/examples/pigeon.png b/demo/examples/pigeon.png new file mode 100644 index 0000000000000000000000000000000000000000..ed7a1a184ff2733aa0ef77aec47d0659bc4cbe22 Binary files /dev/null and b/demo/examples/pigeon.png differ diff --git a/evaluation.py b/evaluation.py new file mode 100755 index 0000000000000000000000000000000000000000..a6a15177c2676d709943c794ed1ce2eeda7e3db4 --- /dev/null +++ b/evaluation.py @@ -0,0 +1,72 @@ +import os +from models.module import DiffGeolocalizer +import hydra +from os.path import join + +import torch + +from omegaconf import OmegaConf +from omegaconf import open_dict +from hydra.utils import instantiate + +from models.eval_best_model import EvalModule + +torch.set_float32_matmul_precision("high") + +# Registering the "eval" resolver allows for advanced config +# interpolation with arithmetic operations in hydra: +# https://omegaconf.readthedocs.io/en/2.3_branch/how_to_guides.html +OmegaConf.register_new_resolver("eval", eval) + + +def load_model(cfg, dict_config, wandb_id): + logger = instantiate(cfg.logger, id=wandb_id, resume="allow") + log_dict = {"model": dict_config["model"], "dataset": dict_config["dataset"]} + logger._wandb_init.update({"config": log_dict}) + model = EvalModule(cfg.model) + trainer = instantiate( + cfg.trainer, strategy=cfg.trainer.strategy + ) # , logger=logger) + return trainer, model + + +def hydra_boilerplate(cfg): + dict_config = OmegaConf.to_container(cfg, resolve=True) + trainer, model = load_model(cfg, dict_config, cfg.wandb_id) + return trainer, model + + +import copy + + +def init_datamodule(cfg): + datamodule = instantiate(cfg.datamodule) + return datamodule + + +if __name__ == "__main__": + import sys + + sys.argv = ( + [sys.argv[0]] + + ["+pt_model_path=${hydra:runtime.config_sources}"] + + sys.argv[1:] + ) + + @hydra.main(config_path="configs", config_name="config", version_base=None) + def main(cfg): + # print(hydra.runtime.config_sources) + with open_dict(cfg): + path = cfg.pt_model_path[1]["path"] + cfg.wandb_id = join(path, "wandb_id.txt") + cfg.checkpoint = join(path, "last.ckpt") + cfg.computer.devices = 1 + + ( + trainer, + model, + ) = hydra_boilerplate(cfg) + datamodule = init_datamodule(cfg) + trainer.test(model, datamodule=datamodule) + + main() diff --git a/metrics/__init__.py b/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/metrics/__pycache__/__init__.cpython-310.pyc b/metrics/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc04d054db2e09150bf8eeb6c36f3679574b7cc2 Binary files /dev/null and b/metrics/__pycache__/__init__.cpython-310.pyc differ diff --git a/metrics/__pycache__/distance_based.cpython-310.pyc b/metrics/__pycache__/distance_based.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5ab30fdf09d33e8447fad5705dcb4912ddee90bb Binary files /dev/null and b/metrics/__pycache__/distance_based.cpython-310.pyc differ diff --git a/metrics/__pycache__/utils.cpython-310.pyc b/metrics/__pycache__/utils.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..146d0260759b1a071fa1dfdd804899160e9f6a47 Binary files /dev/null and b/metrics/__pycache__/utils.cpython-310.pyc differ diff --git a/metrics/distance_based.py b/metrics/distance_based.py new file mode 100644 index 0000000000000000000000000000000000000000..204024ef1b6ebb0575619dcb459e1484e7c0fd34 --- /dev/null +++ b/metrics/distance_based.py @@ -0,0 +1,272 @@ +import torch + +from metrics.utils import haversine, reverse +from sklearn.metrics import pairwise_distances +from torchmetrics import Metric +import numpy as np +from utils.kde import BatchedKDE +from tqdm import tqdm + + +class HaversineMetrics(Metric): + """ + Computes the average haversine distance between the predicted and ground truth points. + Compute the accuracy given some radiuses. + Compute the Geoguessr score given some radiuses. + + Args: + acc_radiuses (list): list of radiuses to compute the accuracy from + acc_area (list): list of areas to compute the accuracy from. + """ + + def __init__( + self, + acc_radiuses=[], + acc_area=["country", "region", "sub-region", "city"], + use_kde=False, + manifold_k=3, + ): + super().__init__() + self.use_kde = use_kde + self.add_state("haversine_sum", default=torch.tensor(0.0), dist_reduce_fx="sum") + self.add_state("geoguessr_sum", default=torch.tensor(0.0), dist_reduce_fx="sum") + for acc in acc_radiuses: + self.add_state( + f"close_enough_points_{acc}", + default=torch.tensor(0.0), + dist_reduce_fx="sum", + ) + for acc in acc_area: + self.add_state( + f"close_enough_points_{acc}", + default=torch.tensor(0.0), + dist_reduce_fx="sum", + ) + self.add_state( + f"count_{acc}", default=torch.tensor(0), dist_reduce_fx="sum" + ) + self.acc_radius = acc_radiuses + self.acc_area = acc_area + self.add_state("count", default=torch.tensor(0), dist_reduce_fx="sum") + self.add_state( + "real_points", + [], + dist_reduce_fx=None, + ) + self.add_state( + "fake_points", + [], + dist_reduce_fx=None, + ) + self.manifold_k = manifold_k + + def update(self, pred, gt): + if self.use_kde: + (x_mode, y_mode), kde = estimate_kde_mode(pred["gps"]) + # self.nll_sum += -torch.log( + # kde.score(gt["gps"].unsqueeze(1).to(pred["gps"].device)) + # ).sum() + pred["gps"] = torch.stack([x_mode, y_mode], dim=1) + # Handle NaN values without modifying the original inputs + if pred["gps"].isnan().any(): + valid_mask = ~pred["gps"].isnan().any(dim=1) + pred_gps = pred["gps"][valid_mask] + gt_gps = gt["gps"][valid_mask] + if len(pred_gps) == 0: # Skip if no valid predictions remain + return + else: + pred_gps = pred["gps"] + gt_gps = gt["gps"] + haversine_distance = haversine(pred_gps, gt_gps) + for acc in self.acc_radius: + self.__dict__[f"close_enough_points_{acc}"] += ( + haversine_distance < acc + ).sum() + if len(self.acc_area) > 0: + area_pred, area_gt = reverse(pred_gps, gt, self.acc_area) + for acc in self.acc_area: + self.__dict__[f"close_enough_points_{acc}"] += ( + area_pred[acc] == area_gt["_".join(["unique", acc])] + ).sum() + self.__dict__[f"count_{acc}"] += len(area_gt["_".join(["unique", acc])]) + self.haversine_sum += haversine_distance.sum() + self.geoguessr_sum += 5000 * torch.exp(-haversine_distance / 1492.7).sum() + self.real_points.append(gt_gps) + self.fake_points.append(pred_gps) + self.count += pred_gps.shape[0] + + def compute(self): + output = { + "Haversine": self.haversine_sum / self.count, + "Geoguessr": self.geoguessr_sum / self.count, + } + for acc in self.acc_radius: + output[f"Accuracy_{acc}_km_radius"] = ( + self.__dict__[f"close_enough_points_{acc}"] / self.count + ) + for acc in self.acc_area: + output[f"Accuracy_{acc}"] = ( + self.__dict__[f"close_enough_points_{acc}"] + / self.__dict__[f"count_{acc}"] + ) + real_points = torch.cat(self.real_points, dim=0) + fake_points = torch.cat(self.fake_points, dim=0) + ( + output["precision"], + output["recall"], + output["density"], + output["coverage"], + ) = self.manifold_metrics(real_points, fake_points, self.manifold_k) + return output + + def compute_pairwise_distance(self, data_x, data_y=None): + """ + Args: + data_x: numpy.ndarray([N, feature_dim], dtype=np.float32) + data_y: numpy.ndarray([N, feature_dim], dtype=np.float32) + Returns: + numpy.ndarray([N, N], dtype=np.float32) of pairwise distances. + """ + if data_y is None: + data_y = data_x + + dists = pairwise_distances(data_x, data_y, metric="haversine", n_jobs=8) + return dists + + def get_kth_value(self, unsorted, k, axis=-1): + """ + Args: + unsorted: numpy.ndarray of any dimensionality. + k: int + Returns: + kth values along the designated axis. + """ + indices = np.argpartition(unsorted, k, axis=axis)[..., :k] + k_smallests = np.take_along_axis(unsorted, indices, axis=axis) + kth_values = k_smallests.max(axis=axis) + return kth_values + + def compute_nearest_neighbour_distances(self, input_features, nearest_k): + """ + Args: + input_features: numpy.ndarray([N, feature_dim], dtype=np.float32) + nearest_k: int + Returns: + Distances to kth nearest neighbours. + """ + distances = self.compute_pairwise_distance(input_features) + radii = self.get_kth_value(distances, k=nearest_k + 1, axis=-1) + return radii + + def compute_prdc(self, real_features, fake_features, nearest_k): + """ + Computes precision, recall, density, and coverage given two manifolds. + Args: + real_features: numpy.ndarray([N, feature_dim], dtype=np.float32) + fake_features: numpy.ndarray([N, feature_dim], dtype=np.float32) + nearest_k: int. + Returns: + dict of precision, recall, density, and coverage. + """ + + real_nearest_neighbour_distances = self.compute_nearest_neighbour_distances( + real_features, nearest_k + ) + fake_nearest_neighbour_distances = self.compute_nearest_neighbour_distances( + fake_features, nearest_k + ) + distance_real_fake = self.compute_pairwise_distance( + real_features, fake_features + ) + + precision = ( + ( + distance_real_fake + < np.expand_dims(real_nearest_neighbour_distances, axis=1) + ) + .any(axis=0) + .mean() + ) + + recall = ( + ( + distance_real_fake + < np.expand_dims(fake_nearest_neighbour_distances, axis=0) + ) + .any(axis=1) + .mean() + ) + + density = (1.0 / float(nearest_k)) * ( + distance_real_fake + < np.expand_dims(real_nearest_neighbour_distances, axis=1) + ).sum(axis=0).mean() + + coverage = ( + distance_real_fake.min(axis=1) < real_nearest_neighbour_distances + ).mean() + + return precision, recall, density, coverage + + def manifold_metrics(self, real_features, fake_features, nearest_k, num_splits=20): + """ + Computes precision, recall, density, and coverage given two manifolds. + Args: + real_features: torch.Tensor([N, feature_dim], dtype=torch.float32) + fake_features: torch.Tensor([N, feature_dim], dtype=torch.float32) + nearest_k: int. + num_splits: int. Number of splits to use for computing metrics. + Returns: + dict of precision, recall, density, and coverage. + """ + real_features = real_features.chunk(num_splits, dim=0) + fake_features = fake_features.chunk(num_splits, dim=0) + precision, recall, density, coverage = [], [], [], [] + for real, fake in tqdm( + zip(real_features, fake_features), desc="Computing manifold" + ): + p, r, d, c = self.compute_prdc( + real.cpu().numpy(), fake.cpu().numpy(), nearest_k=nearest_k + ) + precision.append(torch.tensor(p, device=real.device)) + recall.append(torch.tensor(r, device=real.device)) + density.append(torch.tensor(d, device=real.device)) + coverage.append(torch.tensor(c, device=real.device)) + return ( + torch.stack(precision).mean().item(), + torch.stack(recall).mean().item(), + torch.stack(density).mean().item(), + torch.stack(coverage).mean().item(), + ) + + +def estimate_kde_mode(points): + kde = BatchedKDE() + kde.fit(points) + batch_size = points.shape[0] + X, Y, positions = batched_make_grid(points.cpu()) + X = X.to(points.device) + Y = Y.to(points.device) + positions = positions.to(points.device) + Z = kde.score(positions).reshape(X.shape) + + x_mode = X.reshape(batch_size, -1)[ + torch.arange(batch_size), Z.reshape(batch_size, -1).argmax(dim=1) + ] + y_mode = Y.reshape(batch_size, -1)[ + torch.arange(batch_size), Z.reshape(batch_size, -1).argmax(dim=1) + ] + return (x_mode, y_mode), kde + + +def make_grid(points): + (lat_min, long_min), _ = points.min(dim=-2) + (lat_max, long_max), _ = points.max(dim=-2) + x = torch.linspace(lat_min, lat_max, 100) + y = torch.linspace(long_min, long_max, 100) + X, Y = torch.meshgrid(x, y) + positions = torch.vstack([X.flatten(), Y.flatten()]).transpose(-1, -2) + return X, Y, positions + + +batched_make_grid = torch.vmap(make_grid) diff --git a/metrics/elo.py b/metrics/elo.py new file mode 100644 index 0000000000000000000000000000000000000000..d1dfe5a3686345f7a2748189c966e0577bf1dd9f --- /dev/null +++ b/metrics/elo.py @@ -0,0 +1,21 @@ +import os +import torch +from metrics.utils import haversine + +from torchmetrics import Metric + + +class HaversineELOMetric(Metric): + """ + Computes the ELO score of the current network given previous players + + Args: + previous_players_scores (str): path to the csv containing the scores of the previous players + previous_players_predictions (str): path to the folder containing the predictions of the previous players + tag (str): tag of the current experiment + + """ + + def __init__(self, cache_folder, tag): + ### TODO + pass diff --git a/metrics/utils.py b/metrics/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d365fc49f3c534a73a5a14cbc33d3c6f1d2fb599 --- /dev/null +++ b/metrics/utils.py @@ -0,0 +1,104 @@ +import torch +import reverse_geocoder +import numpy as np + + +def haversine(pred, gt): + # expects inputs to be np arrays in (lat, lon) format as radians + # N x 2 + + # calculate the difference in latitude and longitude between the predicted and ground truth points + lat_diff = pred[:, 0] - gt[:, 0] + lon_diff = pred[:, 1] - gt[:, 1] + + # calculate the haversine formula components + lhs = torch.sin(lat_diff / 2) ** 2 + rhs = torch.cos(pred[:, 0]) * torch.cos(gt[:, 0]) * torch.sin(lon_diff / 2) ** 2 + a = lhs + rhs + + # calculate the final distance using the haversine formula + c = 2 * torch.arctan2(torch.sqrt(a), torch.sqrt(1 - a)) + distance = 6371 * c + + return distance + +def haversine_np(pred, gt): + # expects inputs to be np arrays in (lat, lon) format as radians + # N x 2 + + # calculate the difference in latitude and longitude between the predicted and ground truth points + lat_diff = pred[0] - gt[0] + lon_diff = pred[1] - gt[1] + + # calculate the haversine formula components + lhs = np.sin(lat_diff / 2) ** 2 + rhs = np.cos(pred[0]) * np.cos(gt[0]) * np.sin(lon_diff / 2) ** 2 + a = lhs + rhs + + # calculate the final distance using the haversine formula + c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)) + distance = 6371 * c + + return distance + + +def reverse(pred, gt, area): + df = {} + gt_area = {} + nan_mask = {} + areas = ["_".join(["unique", ar]) for ar in area] + if "unique_continent" in areas: + areas.remove("unique_continent") + for ar in areas: + inter = np.array(gt[ar]) + nan_mask[ar] = inter != "nan" + gt_area[ar] = inter[nan_mask[ar]] + location = reverse_geocoder.search( + [ + (lat, lon) + for lat, lon in zip( + np.degrees(pred[:, 0].cpu()), np.degrees(pred[:, 1].cpu()) + ) + ] + ) + if "continent" in area: + continent = torch.load("continent.pt") + inter = np.array([l.get("cc", "") for l in location])[ + nan_mask["unique_country"] + ] + df["continent"] = np.array([continent[i] for i in inter]) + gt_area["unique_continent"] = np.array( + [continent[i] for i in gt_area["unique_country"]] + ) + + if "country" in area: + df["country"] = np.array([l.get("cc", "") for l in location])[ + nan_mask["unique_country"] + ] + if "region" in area: + df["region"] = np.array( + ["_".join([l.get("admin1", ""), l.get("cc", "")]) for l in location] + )[nan_mask["unique_region"]] + if "sub-region" in area: + df["sub-region"] = np.array( + [ + "_".join([l.get("admin2", ""), l.get("admin1", ""), l.get("cc", "")]) + for l in location + ] + )[nan_mask["unique_sub-region"]] + if "city" in area: + df["city"] = np.array( + [ + "_".join( + [ + l.get("name", ""), + l.get("admin2", ""), + l.get("admin1", ""), + l.get("cc", ""), + ] + ) + for l in location + ] + )[nan_mask["unique_city"]] + + return df, gt_area diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f7d0cf6b01d1b4a37d939c99564a626d2eaca162 --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,2 @@ +# Empty file to make the directory a Python package +from .pretrained_models import Plonk diff --git a/models/__pycache__/__init__.cpython-310.pyc b/models/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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scheduler, + cond_drop_rate=0.0, + conditioning_key="label", + ): + self.scheduler = scheduler + self.cond_drop_rate = cond_drop_rate + self.conditioning_key = conditioning_key + + def __call__(self, preconditioning, network, batch, generator=None): + x_0 = batch["x_0"] + batch_size = x_0.shape[0] + device = x_0.device + t = torch.rand(batch_size, device=device, dtype=x_0.dtype, generator=generator) + gamma = self.scheduler(t).unsqueeze(-1) + n = torch.randn(x_0.shape, dtype=x_0.dtype, device=device, generator=generator) + y = torch.sqrt(gamma) * x_0 + torch.sqrt(1 - gamma) * n + batch["y"] = y + conditioning = batch[self.conditioning_key] + if conditioning is not None and self.cond_drop_rate > 0: + drop_mask = ( + torch.rand(batch_size, device=device, generator=generator) + < self.cond_drop_rate + ) + conditioning[drop_mask] = torch.zeros_like(conditioning[drop_mask]) + batch[self.conditioning_key] = conditioning.detach() + batch["gamma"] = gamma.squeeze(-1).squeeze(-1).squeeze(-1) + D_n = preconditioning(network, batch) + loss = (D_n - n) ** 2 + return loss + + +class FlowMatchingLoss: + def __init__( + self, + scheduler, + cond_drop_rate=0.0, + conditioning_key="label", + ): + self.scheduler = scheduler + self.cond_drop_rate = cond_drop_rate + self.conditioning_key = conditioning_key + + def __call__(self, preconditioning, network, batch, generator=None): + x_0 = batch["x_0"] + batch_size = x_0.shape[0] + device = x_0.device + t = torch.rand(batch_size, device=device, dtype=x_0.dtype, generator=generator) + gamma = self.scheduler(t).unsqueeze(-1) + n = torch.randn(x_0.shape, dtype=x_0.dtype, device=device, generator=generator) + y = gamma * x_0 + (1 - gamma) * n + batch["y"] = y + conditioning = batch[self.conditioning_key] + if conditioning is not None and self.cond_drop_rate > 0: + drop_mask = ( + torch.rand(batch_size, device=device, generator=generator) + < self.cond_drop_rate + ) + conditioning[drop_mask] = torch.zeros_like(conditioning[drop_mask]) + batch[self.conditioning_key] = conditioning.detach() + batch["gamma"] = gamma.squeeze(-1).squeeze(-1).squeeze(-1) + D_n = preconditioning(network, batch) + loss = (D_n - (x_0 - n)) ** 2 + return loss + + +class RiemannianFlowMatchingLoss: + def __init__( + self, + scheduler, + cond_drop_rate=0.0, + conditioning_key="label", + ): + self.scheduler = scheduler + self.cond_drop_rate = cond_drop_rate + self.conditioning_key = conditioning_key + self.manifold = Sphere() + self.manifold_dim = 3 + + def __call__(self, preconditioning, network, batch, generator=None): + x_1 = batch["x_0"] + batch_size = x_1.shape[0] + device = x_1.device + t = torch.rand(batch_size, device=device, dtype=x_1.dtype, generator=generator) + gamma = self.scheduler(t).unsqueeze(-1) + x_0 = self.manifold.random_base(x_1.shape[0], self.manifold_dim).to(x_1) + + def cond_u(x0, x1, t): + path = geodesic(self.manifold, x0, x1) + x_t, u_t = jvp(path, (t,), (torch.ones_like(t).to(t),)) + return x_t, u_t + + y, u_t = vmap(cond_u)(x_0, x_1, gamma) + y = y.reshape(batch_size, self.manifold_dim) + u_t = u_t.reshape(batch_size, self.manifold_dim) + batch["y"] = y + conditioning = batch[self.conditioning_key] + if conditioning is not None and self.cond_drop_rate > 0: + drop_mask = ( + torch.rand(batch_size, device=device, generator=generator) + < self.cond_drop_rate + ) + conditioning[drop_mask] = torch.zeros_like(conditioning[drop_mask]) + batch[self.conditioning_key] = conditioning.detach() + batch["gamma"] = gamma.squeeze(-1).squeeze(-1).squeeze(-1) + D_n = preconditioning(network, batch) + diff = D_n - u_t + loss = self.manifold.inner(y, diff, diff).mean() / self.manifold_dim + return loss + + +class VonFisherLoss: + def __init__(self, dim=3): + self.dim = dim + + def __call__(self, preconditioning, network, batch, generator=None): + x = batch["x_0"] + mu, kappa = preconditioning(network, batch) + loss = ( + torch.log((kappa + 1e-8)) + - torch.log(torch.tensor(4 * torch.pi, dtype=kappa.dtype)) + - log_sinh(kappa) + + kappa * (mu * x).sum(dim=-1, keepdim=True) + ) + return -loss + + +class VonFisherMixtureLoss: + def __init__(self, dim=3): + self.dim = dim + + def __call__(self, preconditioning, network, batch, generator=None): + x = batch["x_0"] + mu_mixture, kappa_mixture, weights = preconditioning(network, batch) + loss = 0 + for i in range(mu_mixture.shape[1]): + mu = mu_mixture[:, i] + kappa = kappa_mixture[:, i].unsqueeze(1) + loss += weights[:, i].unsqueeze(1) * ( + kappa + * torch.exp(kappa * ((mu * x).sum(dim=-1, keepdim=True) - 1)) + / (1e-8 + 2 * torch.pi * (1 - torch.exp(-2 * kappa))) + ) + return -torch.log(loss) + + +def log_sinh(x): + return x + torch.log(1e-8 + (1 - torch.exp(-2 * x)) / 2) diff --git a/models/module.py b/models/module.py new file mode 100755 index 0000000000000000000000000000000000000000..4342d2d17c989561a31ce327bbcf4f660e8a7bc3 --- /dev/null +++ b/models/module.py @@ -0,0 +1,813 @@ +from typing import Any +import pytorch_lightning as L +import torch +import torch.nn as nn +from hydra.utils import instantiate +import copy +import pandas as pd +import numpy as np +from tqdm import tqdm +from utils.manifolds import Sphere +from torch.func import jacrev, vjp, vmap +from torchdiffeq import odeint +from geoopt import ProductManifold, Euclidean +from models.samplers.riemannian_flow_sampler import ode_riemannian_flow_sampler + + +class DiffGeolocalizer(L.LightningModule): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.network = instantiate(cfg.network) + # self.network = torch.compile(self.network, fullgraph=True) + self.input_dim = cfg.network.input_dim + self.train_noise_scheduler = instantiate(cfg.train_noise_scheduler) + self.inference_noise_scheduler = instantiate(cfg.inference_noise_scheduler) + self.data_preprocessing = instantiate(cfg.data_preprocessing) + self.cond_preprocessing = instantiate(cfg.cond_preprocessing) + self.preconditioning = instantiate(cfg.preconditioning) + + self.ema_network = copy.deepcopy(self.network).requires_grad_(False) + self.ema_network.eval() + self.postprocessing = instantiate(cfg.postprocessing) + self.val_sampler = instantiate(cfg.val_sampler) + self.test_sampler = instantiate(cfg.test_sampler) + self.loss = instantiate(cfg.loss)( + self.train_noise_scheduler, + ) + self.val_metrics = instantiate(cfg.val_metrics) + self.test_metrics = instantiate(cfg.test_metrics) + self.manifold = instantiate(cfg.manifold) if hasattr(cfg, "manifold") else None + + self.interpolant = cfg.interpolant + + def training_step(self, batch, batch_idx): + with torch.no_grad(): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + loss = self.loss(self.preconditioning, self.network, batch).mean() + self.log( + "train/loss", + loss, + sync_dist=True, + on_step=True, + on_epoch=True, + batch_size=batch_size, + ) + return loss + + def on_before_optimizer_step(self, optimizer): + if self.global_step == 0: + no_grad = [] + for name, param in self.network.named_parameters(): + if param.grad is None: + no_grad.append(name) + if len(no_grad) > 0: + print("Parameters without grad:") + print(no_grad) + + def on_validation_start(self): + self.validation_generator = torch.Generator(device=self.device).manual_seed( + 3407 + ) + self.validation_generator_ema = torch.Generator(device=self.device).manual_seed( + 3407 + ) + + def validation_step(self, batch, batch_idx): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + loss = self.loss( + self.preconditioning, + self.network, + batch, + generator=self.validation_generator, + ).mean() + self.log( + "val/loss", + loss, + sync_dist=True, + on_step=False, + on_epoch=True, + batch_size=batch_size, + ) + if hasattr(self, "ema_model"): + loss_ema = self.loss( + self.preconditioning, + self.ema_network, + batch, + generator=self.validation_generator_ema, + ).mean() + self.log( + "val/loss_ema", + loss_ema, + sync_dist=True, + on_step=False, + on_epoch=True, + batch_size=batch_size, + ) + # nll = -self.compute_exact_loglikelihood(batch).mean() + # self.log( + # "val/nll", + # nll, + # sync_dist=True, + # on_step=False, + # on_epoch=True, + # batch_size=batch_size, + # ) + + # def on_validation_epoch_end(self): + # metrics = self.val_metrics.compute() + # for metric_name, metric_value in metrics.items(): + # self.log( + # f"val/{metric_name}", + # metric_value, + # sync_dist=True, + # on_step=False, + # on_epoch=True, + # ) + + def on_test_start(self): + self.test_generator = torch.Generator(device=self.device).manual_seed(3407) + + def test_step_simple(self, batch, batch_idx): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + if isinstance(self.manifold, Sphere): + x_N = self.manifold.random_base( + batch_size, + self.input_dim, + device=self.device, + ) + x_N = x_N.reshape(batch_size, self.input_dim) + else: + x_N = torch.randn( + batch_size, + self.input_dim, + device=self.device, + generator=self.test_generator, + ) + cond = batch[self.cfg.cond_preprocessing.output_key] + + samples = self.sample( + x_N=x_N, + cond=cond, + stage="val", + generator=self.test_generator, + cfg=self.cfg.cfg_rate, + ) + self.test_metrics.update({"gps": samples}, batch) + if self.cfg.compute_nll: + nll = -self.compute_exact_loglikelihood(batch, cfg=0).mean() + self.log( + "test/NLL", + nll, + sync_dist=True, + on_step=False, + on_epoch=True, + batch_size=batch_size, + ) + + def test_best_nll(self, batch, batch_idx): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + num_sample_per_cond = 32 + if isinstance(self.manifold, Sphere): + x_N = self.manifold.random_base( + batch_size * num_sample_per_cond, + self.input_dim, + device=self.device, + ) + x_N = x_N.reshape(batch_size * num_sample_per_cond, self.input_dim) + else: + x_N = torch.randn( + batch_size * num_sample_per_cond, + self.input_dim, + device=self.device, + generator=self.test_generator, + ) + cond = ( + batch[self.cfg.cond_preprocessing.output_key] + .unsqueeze(1) + .repeat(1, num_sample_per_cond, 1) + .view(-1, batch[self.cfg.cond_preprocessing.output_key].shape[-1]) + ) + samples = self.sample_distribution( + x_N, + cond, + sampling_batch_size=32768, + stage="val", + generator=self.test_generator, + cfg=0, + ) + samples = samples.view(batch_size * num_sample_per_cond, -1) + batch_swarm = {"gps": samples, "emb": cond} + nll_batch = -self.compute_exact_loglikelihood(batch_swarm, cfg=0) + nll_batch = nll_batch.view(batch_size, num_sample_per_cond, -1) + nll_best = nll_batch[ + torch.arange(batch_size), nll_batch.argmin(dim=1).squeeze(1) + ] + self.log( + "test/best_nll", + nll_best.mean(), + sync_dist=True, + on_step=False, + on_epoch=True, + ) + samples = samples.view(batch_size, num_sample_per_cond, -1)[ + torch.arange(batch_size), nll_batch.argmin(dim=1).squeeze(1) + ] + self.test_metrics.update({"gps": samples}, batch) + + def test_step(self, batch, batch_idx): + if self.cfg.compute_swarms: + self.test_best_nll(batch, batch_idx) + else: + self.test_step_simple(batch, batch_idx) + + def on_test_epoch_end(self): + metrics = self.test_metrics.compute() + for metric_name, metric_value in metrics.items(): + self.log( + f"test/{metric_name}", + metric_value, + sync_dist=True, + on_step=False, + on_epoch=True, + ) + + def configure_optimizers(self): + if self.cfg.optimizer.exclude_ln_and_biases_from_weight_decay: + parameters_names_wd = get_parameter_names(self.network, [nn.LayerNorm]) + parameters_names_wd = [ + name for name in parameters_names_wd if "bias" not in name + ] + optimizer_grouped_parameters = [ + { + "params": [ + p + for n, p in self.network.named_parameters() + if n in parameters_names_wd + ], + "weight_decay": self.cfg.optimizer.optim.weight_decay, + "layer_adaptation": True, + }, + { + "params": [ + p + for n, p in self.network.named_parameters() + if n not in parameters_names_wd + ], + "weight_decay": 0.0, + "layer_adaptation": False, + }, + ] + optimizer = instantiate( + self.cfg.optimizer.optim, optimizer_grouped_parameters + ) + else: + optimizer = instantiate(self.cfg.optimizer.optim, self.network.parameters()) + if "lr_scheduler" in self.cfg: + scheduler = instantiate(self.cfg.lr_scheduler)(optimizer) + return [optimizer], [{"scheduler": scheduler, "interval": "step"}] + else: + return optimizer + + def lr_scheduler_step(self, scheduler, metric): + scheduler.step(self.global_step) + + def sample( + self, + batch_size=None, + cond=None, + x_N=None, + num_steps=None, + stage="test", + cfg=0, + generator=None, + return_trajectories=False, + postprocessing=True, + ): + if x_N is None: + assert batch_size is not None + if isinstance(self.manifold, Sphere): + x_N = self.manifold.random_base( + batch_size, self.input_dim, device=self.device + ) + x_N = x_N.reshape(batch_size, self.input_dim) + else: + x_N = torch.randn(batch_size, self.input_dim, device=self.device) + batch = {"y": x_N} + if stage == "val": + sampler = self.val_sampler + elif stage == "test": + sampler = self.test_sampler + else: + raise ValueError(f"Unknown stage {stage}") + batch[self.cfg.cond_preprocessing.input_key] = cond + batch = self.cond_preprocessing(batch, device=self.device) + if num_steps is None: + output = sampler( + self.ema_model, + batch, + conditioning_keys=self.cfg.cond_preprocessing.output_key, + scheduler=self.inference_noise_scheduler, + cfg_rate=cfg, + generator=generator, + return_trajectories=return_trajectories, + ) + else: + output = sampler( + self.ema_model, + batch, + conditioning_keys=self.cfg.cond_preprocessing.output_key, + scheduler=self.inference_noise_scheduler, + num_steps=num_steps, + cfg_rate=cfg, + generator=generator, + return_trajectories=return_trajectories, + ) + if return_trajectories: + return ( + self.postprocessing(output[0]) if postprocessing else output[0], + [ + self.postprocessing(frame) if postprocessing else frame + for frame in output[1] + ], + ) + else: + return self.postprocessing(output) if postprocessing else output + + def sample_distribution( + self, + x_N, + cond, + sampling_batch_size=2048, + num_steps=None, + stage="test", + cfg=0, + generator=None, + return_trajectories=False, + ): + if return_trajectories: + x_0 = [] + trajectories = [] + i = -1 + for i in range(x_N.shape[0] // sampling_batch_size): + x_N_batch = x_N[i * sampling_batch_size : (i + 1) * sampling_batch_size] + cond_batch = cond[ + i * sampling_batch_size : (i + 1) * sampling_batch_size + ] + out, trajectories = self.sample( + cond=cond_batch, + x_N=x_N_batch, + num_steps=num_steps, + stage=stage, + cfg=cfg, + generator=generator, + return_trajectories=return_trajectories, + ) + x_0.append(out) + trajectories.append(trajectories) + if x_N.shape[0] % sampling_batch_size != 0: + x_N_batch = x_N[(i + 1) * sampling_batch_size :] + cond_batch = cond[(i + 1) * sampling_batch_size :] + out, trajectories = self.sample( + cond=cond_batch, + x_N=x_N_batch, + num_steps=num_steps, + stage=stage, + cfg=cfg, + generator=generator, + return_trajectories=return_trajectories, + ) + x_0.append(out) + trajectories.append(trajectories) + x_0 = torch.cat(x_0, dim=1) + trajectories = [torch.cat(frame, dim=1) for frame in trajectories] + return x_0, trajectories + else: + x_0 = [] + i = -1 + for i in range(x_N.shape[0] // sampling_batch_size): + x_N_batch = x_N[i * sampling_batch_size : (i + 1) * sampling_batch_size] + cond_batch = cond[ + i * sampling_batch_size : (i + 1) * sampling_batch_size + ] + out = self.sample( + cond=cond_batch, + x_N=x_N_batch, + num_steps=num_steps, + stage=stage, + cfg=cfg, + generator=generator, + return_trajectories=return_trajectories, + ) + x_0.append(out) + if x_N.shape[0] % sampling_batch_size != 0: + x_N_batch = x_N[(i + 1) * sampling_batch_size :] + cond_batch = cond[(i + 1) * sampling_batch_size :] + out = self.sample( + cond=cond_batch, + x_N=x_N_batch, + num_steps=num_steps, + stage=stage, + cfg=cfg, + generator=generator, + return_trajectories=return_trajectories, + ) + x_0.append(out) + x_0 = torch.cat(x_0, dim=0) + return x_0 + + def model(self, *args, **kwargs): + return self.preconditioning(self.network, *args, **kwargs) + + def ema_model(self, *args, **kwargs): + return self.preconditioning(self.ema_network, *args, **kwargs) + + def compute_exact_loglikelihood( + self, + batch=None, + x_1=None, + cond=None, + t1=1.0, + num_steps=1000, + rademacher=False, + data_preprocessing=True, + cfg=0, + ): + nfe = [0] + if batch is None: + batch = {"x_0": x_1, "emb": cond} + if data_preprocessing: + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + timesteps = self.inference_noise_scheduler( + torch.linspace(0, t1, 2).to(batch["x_0"]) + ) + with torch.inference_mode(mode=False): + + def odefunc(t, tensor): + nfe[0] += 1 + t = t.to(tensor) + gamma = self.inference_noise_scheduler(t) + x = tensor[..., : self.input_dim] + y = batch["emb"] + + def vecfield(x, y): + if cfg > 0: + batch_vecfield = { + "y": x, + "emb": y, + "gamma": gamma.reshape(-1), + } + model_output_cond = self.ema_model(batch_vecfield) + batch_vecfield_uncond = { + "y": x, + "emb": torch.zeros_like(y), + "gamma": gamma.reshape(-1), + } + model_output_uncond = self.ema_model(batch_vecfield_uncond) + model_output = model_output_cond + cfg * ( + model_output_cond - model_output_uncond + ) + + else: + batch_vecfield = { + "y": x, + "emb": y, + "gamma": gamma.reshape(-1), + } + model_output = self.ema_model(batch_vecfield) + + if self.interpolant == "flow_matching": + d_gamma = self.inference_noise_scheduler.derivative(t).reshape( + -1, 1 + ) + return d_gamma * model_output + elif self.interpolant == "diffusion": + alpha_t = self.inference_noise_scheduler.alpha(t).reshape(-1, 1) + return ( + -1 / 2 * (alpha_t * x - torch.abs(alpha_t) * model_output) + ) + else: + raise ValueError(f"Unknown interpolant {self.interpolant}") + + if rademacher: + v = torch.randint_like(x, 2) * 2 - 1 + else: + v = None + dx, div = output_and_div(vecfield, x, y, v=v) + div = div.reshape(-1, 1) + del t, x + return torch.cat([dx, div], dim=-1) + + x_1 = batch["x_0"] + state1 = torch.cat([x_1, torch.zeros_like(x_1[..., :1])], dim=-1) + with torch.no_grad(): + if False and isinstance(self.manifold, Sphere): + print("Riemannian flow sampler") + product_man = ProductManifold( + (self.manifold, self.input_dim), (Euclidean(), 1) + ) + state0 = ode_riemannian_flow_sampler( + odefunc, + state1, + manifold=product_man, + scheduler=self.inference_noise_scheduler, + num_steps=num_steps, + ) + else: + print("ODE solver") + state0 = odeint( + odefunc, + state1, + t=torch.linspace(0, t1, 2).to(batch["x_0"]), + atol=1e-6, + rtol=1e-6, + method="dopri5", + options={"min_step": 1e-5}, + )[-1] + x_0, logdetjac = state0[..., : self.input_dim], state0[..., -1] + if self.manifold is not None: + x_0 = self.manifold.projx(x_0) + logp0 = self.manifold.base_logprob(x_0) + else: + logp0 = ( + -1 / 2 * (x_0**2).sum(dim=-1) + - self.input_dim + * torch.log(torch.tensor(2 * np.pi, device=x_0.device)) + / 2 + ) + print(f"nfe: {nfe[0]}") + logp1 = logp0 + logdetjac + logp1 = logp1 / (self.input_dim * np.log(2)) + return logp1 + + +def get_parameter_names(model, forbidden_layer_types): + """ + Returns the names of the model parameters that are not inside a forbidden layer. + Taken from HuggingFace transformers. + """ + result = [] + for name, child in model.named_children(): + result += [ + f"{name}.{n}" + for n in get_parameter_names(child, forbidden_layer_types) + if not isinstance(child, tuple(forbidden_layer_types)) + ] + # Add model specific parameters (defined with nn.Parameter) since they are not in any child. + result += list(model._parameters.keys()) + return result + + +# for likelihood computation +def div_fn(u): + """Accepts a function u:R^D -> R^D.""" + J = jacrev(u, argnums=0) + return lambda x, y: torch.trace(J(x, y).squeeze(0)) + + +def output_and_div(vecfield, x, y, v=None): + if v is None: + dx = vecfield(x, y) + div = vmap(div_fn(vecfield))(x, y) + else: + vecfield_x = lambda x: vecfield(x, y) + dx, vjpfunc = vjp(vecfield_x, x) + vJ = vjpfunc(v)[0] + div = torch.sum(vJ * v, dim=-1) + return dx, div + + +class VonFisherGeolocalizer(L.LightningModule): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.network = instantiate(cfg.network) + # self.network = torch.compile(self.network, fullgraph=True) + self.input_dim = cfg.network.input_dim + self.data_preprocessing = instantiate(cfg.data_preprocessing) + self.cond_preprocessing = instantiate(cfg.cond_preprocessing) + self.preconditioning = instantiate(cfg.preconditioning) + + self.ema_network = copy.deepcopy(self.network).requires_grad_(False) + self.ema_network.eval() + self.postprocessing = instantiate(cfg.postprocessing) + self.val_sampler = instantiate(cfg.val_sampler) + self.test_sampler = instantiate(cfg.test_sampler) + self.loss = instantiate(cfg.loss)() + self.val_metrics = instantiate(cfg.val_metrics) + self.test_metrics = instantiate(cfg.test_metrics) + + def training_step(self, batch, batch_idx): + with torch.no_grad(): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + loss = self.loss(self.preconditioning, self.network, batch).mean() + self.log( + "train/loss", + loss, + sync_dist=True, + on_step=True, + on_epoch=True, + batch_size=batch_size, + ) + return loss + + def on_before_optimizer_step(self, optimizer): + if self.global_step == 0: + no_grad = [] + for name, param in self.network.named_parameters(): + if param.grad is None: + no_grad.append(name) + if len(no_grad) > 0: + print("Parameters without grad:") + print(no_grad) + + def on_validation_start(self): + self.validation_generator = torch.Generator(device=self.device).manual_seed( + 3407 + ) + self.validation_generator_ema = torch.Generator(device=self.device).manual_seed( + 3407 + ) + + def validation_step(self, batch, batch_idx): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + loss = self.loss( + self.preconditioning, + self.network, + batch, + generator=self.validation_generator, + ).mean() + self.log( + "val/loss", + loss, + sync_dist=True, + on_step=False, + on_epoch=True, + batch_size=batch_size, + ) + if hasattr(self, "ema_model"): + loss_ema = self.loss( + self.preconditioning, + self.ema_network, + batch, + generator=self.validation_generator_ema, + ).mean() + self.log( + "val/loss_ema", + loss_ema, + sync_dist=True, + on_step=False, + on_epoch=True, + batch_size=batch_size, + ) + + def on_test_start(self): + self.test_generator = torch.Generator(device=self.device).manual_seed(3407) + + def test_step(self, batch, batch_idx): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + cond = batch[self.cfg.cond_preprocessing.output_key] + + samples = self.sample(cond=cond, stage="test") + self.test_metrics.update({"gps": samples}, batch) + nll = -self.compute_exact_loglikelihood(batch).mean() + self.log( + "test/NLL", + nll, + sync_dist=True, + on_step=False, + on_epoch=True, + batch_size=batch_size, + ) + + def on_test_epoch_end(self): + metrics = self.test_metrics.compute() + for metric_name, metric_value in metrics.items(): + self.log( + f"test/{metric_name}", + metric_value, + sync_dist=True, + on_step=False, + on_epoch=True, + ) + + def configure_optimizers(self): + if self.cfg.optimizer.exclude_ln_and_biases_from_weight_decay: + parameters_names_wd = get_parameter_names(self.network, [nn.LayerNorm]) + parameters_names_wd = [ + name for name in parameters_names_wd if "bias" not in name + ] + optimizer_grouped_parameters = [ + { + "params": [ + p + for n, p in self.network.named_parameters() + if n in parameters_names_wd + ], + "weight_decay": self.cfg.optimizer.optim.weight_decay, + "layer_adaptation": True, + }, + { + "params": [ + p + for n, p in self.network.named_parameters() + if n not in parameters_names_wd + ], + "weight_decay": 0.0, + "layer_adaptation": False, + }, + ] + optimizer = instantiate( + self.cfg.optimizer.optim, optimizer_grouped_parameters + ) + else: + optimizer = instantiate(self.cfg.optimizer.optim, self.network.parameters()) + if "lr_scheduler" in self.cfg: + scheduler = instantiate(self.cfg.lr_scheduler)(optimizer) + return [optimizer], [{"scheduler": scheduler, "interval": "step"}] + else: + return optimizer + + def lr_scheduler_step(self, scheduler, metric): + scheduler.step(self.global_step) + + def sample( + self, + batch_size=None, + cond=None, + postprocessing=True, + stage="val", + ): + batch = {} + if stage == "val": + sampler = self.val_sampler + elif stage == "test": + sampler = self.test_sampler + else: + raise ValueError(f"Unknown stage {stage}") + batch[self.cfg.cond_preprocessing.input_key] = cond + batch = self.cond_preprocessing(batch, device=self.device) + output = sampler( + self.ema_model, + batch, + ) + return self.postprocessing(output) if postprocessing else output + + def model(self, *args, **kwargs): + return self.preconditioning(self.network, *args, **kwargs) + + def ema_model(self, *args, **kwargs): + return self.preconditioning(self.ema_network, *args, **kwargs) + + def compute_exact_loglikelihood( + self, + batch=None, + ): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + return -self.loss(self.preconditioning, self.ema_network, batch) + + +class RandomGeolocalizer(L.LightningModule): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.test_metrics = instantiate(cfg.test_metrics) + self.data_preprocessing = instantiate(cfg.data_preprocessing) + self.cond_preprocessing = instantiate(cfg.cond_preprocessing) + self.postprocessing = instantiate(cfg.postprocessing) + + def test_step(self, batch, batch_idx): + batch = self.data_preprocessing(batch) + batch = self.cond_preprocessing(batch) + batch_size = batch["x_0"].shape[0] + samples = torch.randn(batch_size, 3, device=self.device) + samples = samples / samples.norm(dim=-1, keepdim=True) + samples = self.postprocessing(samples) + self.test_metrics.update({"gps": samples}, batch) + + def on_test_epoch_end(self): + metrics = self.test_metrics.compute() + for metric_name, metric_value in metrics.items(): + self.log( + f"test/{metric_name}", + metric_value, + sync_dist=True, + on_step=False, + on_epoch=True, + ) diff --git a/models/networks/__init__.py b/models/networks/__init__.py new file mode 100755 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/networks/__pycache__/__init__.cpython-310.pyc b/models/networks/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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b/models/networks/mlp.py @@ -0,0 +1,190 @@ +import torch.nn as nn +from models.positional_embeddings import FourierEmbedding, PositionalEmbedding +from models.networks.transformers import FusedMLP +import torch +import torch.nn.functional as F +import numpy as np +from einops import rearrange + + +class TimeEmbedder(nn.Module): + def __init__( + self, + noise_embedding_type: str, + dim: int, + time_scaling: float, + expansion: int = 4, + ): + super().__init__() + self.encode_time = ( + PositionalEmbedding(num_channels=dim, endpoint=True) + if noise_embedding_type == "positional" + else FourierEmbedding(num_channels=dim) + ) + self.time_scaling = time_scaling + self.map_time = nn.Sequential( + nn.Linear(dim, dim * expansion), + nn.SiLU(), + nn.Linear(dim * expansion, dim * expansion), + ) + + def forward(self, t): + time = self.encode_time(t * self.time_scaling) + time_mean = time.mean(dim=-1, keepdim=True) + time_std = time.std(dim=-1, keepdim=True) + time = (time - time_mean) / time_std + return self.map_time(time) + + +def get_timestep_embedding(timesteps, embedding_dim, dtype=torch.float32): + assert len(timesteps.shape) == 1 + timesteps = timesteps * 1000.0 + + half_dim = embedding_dim // 2 + emb = np.log(10000) / (half_dim - 1) + emb = (torch.arange(half_dim, dtype=dtype, device=timesteps.device) * -emb).exp() + emb = timesteps.to(dtype)[:, None] * emb[None, :] + emb = torch.cat([emb.sin(), emb.cos()], dim=-1) + if embedding_dim % 2 == 1: # zero pad + emb = F.pad(emb, (0, 1)) + assert emb.shape == (timesteps.shape[0], embedding_dim) + return emb + + +class AdaLNMLPBlock(nn.Module): + def __init__(self, dim, expansion): + super().__init__() + self.mlp = FusedMLP( + dim, dropout=0.0, hidden_layer_multiplier=expansion, activation=nn.GELU + ) + self.ada_map = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 3)) + self.ln = nn.LayerNorm(dim, elementwise_affine=False) + + nn.init.zeros_(self.mlp[-1].weight) + nn.init.zeros_(self.mlp[-1].bias) + + def forward(self, x, y): + gamma, mu, sigma = self.ada_map(y).chunk(3, dim=-1) + x_res = (1 + gamma) * self.ln(x) + mu + x = x + self.mlp(x_res) * sigma + return x + + +class GeoAdaLNMLP(nn.Module): + def __init__(self, input_dim, dim, depth, expansion, cond_dim): + super().__init__() + self.time_embedder = TimeEmbedder("positional", dim // 4, 1000, expansion=4) + self.cond_mapper = nn.Linear(cond_dim, dim) + self.initial_mapper = nn.Linear(input_dim, dim) + self.blocks = nn.ModuleList( + [AdaLNMLPBlock(dim, expansion) for _ in range(depth)] + ) + self.final_adaln = nn.Sequential( + nn.SiLU(), + nn.Linear(dim, dim * 2), + ) + self.final_ln = nn.LayerNorm(dim, elementwise_affine=False) + self.final_linear = nn.Linear(dim, input_dim) + + def forward(self, batch): + x = batch["y"] + x = self.initial_mapper(x) + gamma = batch["gamma"] + cond = batch["emb"] + t = self.time_embedder(gamma) + cond = self.cond_mapper(cond) + cond = cond + t + for block in self.blocks: + x = block(x, cond) + gamma_last, mu_last = self.final_adaln(cond).chunk(2, dim=-1) + x = (1 + gamma_last) * self.final_ln(x) + mu_last + x = self.final_linear(x) + return x + + +class GeoAdaLNMLPVonFisher(nn.Module): + def __init__(self, input_dim, dim, depth, expansion, cond_dim): + super().__init__() + self.cond_mapper = nn.Linear(cond_dim, dim) + self.blocks = nn.ModuleList( + [AdaLNMLPBlock(dim, expansion) for _ in range(depth)] + ) + self.final_adaln = nn.Sequential( + nn.SiLU(), + nn.Linear(dim, dim * 2), + ) + self.final_ln = nn.LayerNorm(dim, elementwise_affine=False) + self.mu_predictor = nn.Sequential( + FusedMLP(dim, dropout=0.0, hidden_layer_multiplier=2, activation=nn.GELU), + nn.Linear(dim, input_dim), + ) + self.kappa_predictor = nn.Sequential( + FusedMLP(dim, dropout=0.0, hidden_layer_multiplier=2, activation=nn.GELU), + nn.Linear(dim, 1), + torch.nn.Softplus(), + ) + self.init_registers = torch.nn.Parameter(torch.randn(dim), requires_grad=True) + torch.nn.init.trunc_normal_( + self.init_registers, std=0.02, a=-2 * 0.02, b=2 * 0.02 + ) + + def forward(self, batch): + cond = batch["emb"] + cond = self.cond_mapper(cond) + x = self.init_registers.unsqueeze(0).repeat(cond.shape[0], 1) + for block in self.blocks: + x = block(x, cond) + gamma_last, mu_last = self.final_adaln(cond).chunk(2, dim=-1) + x = (1 + gamma_last) * self.final_ln(x) + mu_last + mu = self.mu_predictor(x) + mu = mu / mu.norm(dim=-1, keepdim=True) + kappa = self.kappa_predictor(x) + return mu, kappa + + +class GeoAdaLNMLPVonFisherMixture(nn.Module): + def __init__(self, input_dim, dim, depth, expansion, cond_dim, num_mixtures=3): + super().__init__() + self.cond_mapper = nn.Linear(cond_dim, dim) + self.blocks = nn.ModuleList( + [AdaLNMLPBlock(dim, expansion) for _ in range(depth)] + ) + self.final_adaln = nn.Sequential( + nn.SiLU(), + nn.Linear(dim, dim * 2), + ) + self.final_ln = nn.LayerNorm(dim, elementwise_affine=False) + self.mu_predictor = nn.Sequential( + FusedMLP(dim, dropout=0.0, hidden_layer_multiplier=2, activation=nn.GELU), + nn.Linear(dim, input_dim * num_mixtures), + ) + self.kappa_predictor = nn.Sequential( + FusedMLP(dim, dropout=0.0, hidden_layer_multiplier=2, activation=nn.GELU), + nn.Linear(dim, num_mixtures), + torch.nn.Softplus(), + ) + self.mixture_weights = nn.Sequential( + FusedMLP(dim, dropout=0.0, hidden_layer_multiplier=2, activation=nn.GELU), + nn.Linear(dim, num_mixtures), + torch.nn.Softmax(dim=-1), + ) + self.num_mixtures = num_mixtures + self.init_registers = torch.nn.Parameter(torch.randn(dim), requires_grad=True) + torch.nn.init.trunc_normal_( + self.init_registers, std=0.02, a=-2 * 0.02, b=2 * 0.02 + ) + + def forward(self, batch): + cond = batch["emb"] + cond = self.cond_mapper(cond) + x = self.init_registers.unsqueeze(0).repeat(cond.shape[0], 1) + for block in self.blocks: + x = block(x, cond) + gamma_last, mu_last = self.final_adaln(cond).chunk(2, dim=-1) + x = (1 + gamma_last) * self.final_ln(x) + mu_last + mu = self.mu_predictor(x) + mu = rearrange(mu, "b (n d) -> b n d", n=self.num_mixtures) + mu = mu / mu.norm(dim=-1, keepdim=True) + kappa = self.kappa_predictor(x) + weights = self.mixture_weights(x) + return mu, kappa, weights diff --git a/models/networks/transformers.py b/models/networks/transformers.py new file mode 100644 index 0000000000000000000000000000000000000000..0b344090d066c0bd7c1b3e6eccaa90a01b244298 --- /dev/null +++ b/models/networks/transformers.py @@ -0,0 +1,329 @@ +import torch +import torch.nn as nn +from torch import Tensor +import math + +from models.positional_embeddings import PositionalEmbedding, FourierEmbedding +from einops import rearrange + +torch.fx.wrap("rearrange") +from typing import Tuple, Optional +from einops._torch_specific import allow_ops_in_compiled_graph # requires einops>=0.6.1 + +allow_ops_in_compiled_graph() + + +class FusedMLP(nn.Sequential): + def __init__( + self, + dim_model: int, + dropout: float, + activation: nn.Module, + hidden_layer_multiplier: int = 4, + bias: bool = True, + ): + super().__init__( + nn.Linear(dim_model, dim_model * hidden_layer_multiplier, bias=bias), + activation(), + nn.Dropout(dropout), + nn.Linear(dim_model * hidden_layer_multiplier, dim_model, bias=bias), + ) + + +def _cast_if_autocast_enabled(tensor): + if torch.is_autocast_enabled(): + if tensor.device.type == "cuda": + dtype = torch.get_autocast_gpu_dtype() + elif tensor.device.type == "cpu": + dtype = torch.get_autocast_cpu_dtype() + else: + raise NotImplementedError() + return tensor.to(dtype=dtype) + return tensor + + +class LayerNorm16Bits(torch.nn.LayerNorm): + """ + 16-bit friendly version of torch.nn.LayerNorm + """ + + def __init__( + self, + normalized_shape, + eps=1e-06, + elementwise_affine=True, + device=None, + dtype=None, + ): + super().__init__( + normalized_shape=normalized_shape, + eps=eps, + elementwise_affine=elementwise_affine, + device=device, + dtype=dtype, + ) + + def forward(self, x): + module_device = x.device + downcast_x = _cast_if_autocast_enabled(x) + downcast_weight = ( + _cast_if_autocast_enabled(self.weight) + if self.weight is not None + else self.weight + ) + downcast_bias = ( + _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias + ) + with torch.autocast(enabled=False, device_type=module_device.type): + return nn.functional.layer_norm( + downcast_x, + self.normalized_shape, + downcast_weight, + downcast_bias, + self.eps, + ) + + +class StochatichDepth(nn.Module): + def __init__(self, p: float): + super().__init__() + self.survival_prob = 1.0 - p + + def forward(self, x: Tensor) -> Tensor: + if self.training and self.survival_prob < 1: + mask = ( + torch.empty(x.shape[0], 1, 1, device=x.device).uniform_() + + self.survival_prob + ) + mask = mask.floor() + if self.survival_prob > 0: + mask = mask / self.survival_prob + return x * mask + else: + return x + + +class CrossAttentionOp(nn.Module): + def __init__( + self, attention_dim, num_heads, dim_q, dim_kv, use_biases=True, is_sa=False + ): + super().__init__() + self.dim_q = dim_q + self.dim_kv = dim_kv + self.attention_dim = attention_dim + self.num_heads = num_heads + self.use_biases = use_biases + self.is_sa = is_sa + if self.is_sa: + self.qkv = nn.Linear(dim_q, attention_dim * 3, bias=use_biases) + else: + self.q = nn.Linear(dim_q, attention_dim, bias=use_biases) + self.kv = nn.Linear(dim_kv, attention_dim * 2, bias=use_biases) + self.out = nn.Linear(attention_dim, dim_q, bias=use_biases) + + def forward(self, x_to, x_from=None, attention_mask=None, materialize_sdpa=False): + if x_from is None: + x_from = x_to + if self.is_sa: + q, k, v = self.qkv(x_to).chunk(3, dim=-1) + else: + q = self.q(x_to) + k, v = self.kv(x_from).chunk(2, dim=-1) + q = rearrange(q, "b n (h d) -> b h n d", h=self.num_heads) + k = rearrange(k, "b n (h d) -> b h n d", h=self.num_heads) + v = rearrange(v, "b n (h d) -> b h n d", h=self.num_heads) + if attention_mask is not None: + attention_mask = attention_mask.unsqueeze(1) + if materialize_sdpa: + x = self.materialize_sdpa(q, k, v, attention_mask) + else: + x = torch.nn.functional.scaled_dot_product_attention( + q, k, v, attn_mask=attention_mask + ) + x = rearrange(x, "b h n d -> b n (h d)") + x = self.out(x) + return x + + def materialize_sdpa(self, q, k, v, attn_mask=None): + scale = 1.0 / math.sqrt(q.shape[-1]) + + attn_matrix = torch.einsum("b h i d, b h j d -> b h i j", q, k) * scale + if attn_mask is not None: + attn_matrix = attn_matrix * attn_mask + attn_matrix = torch.nn.functional.softmax(attn_matrix, dim=-1) + return torch.einsum("b h i j, b h j d -> b h i d", attn_matrix, v) + + +class CrossAttentionBlock(nn.Module): + def __init__( + self, + dim_q: int, + dim_kv: int, + num_heads: int, + attention_dim: int = 0, + mlp_multiplier: int = 4, + dropout: float = 0.0, + stochastic_depth: float = 0.0, + use_biases: bool = True, + retrieve_attention_scores: bool = False, + use_16_bits_layer_norm: bool = False, + ): + super().__init__() + if use_16_bits_layer_norm and not retrieve_attention_scores: + LayerNorm = LayerNorm16Bits + else: + LayerNorm = nn.LayerNorm + self.retrieve_attention_scores = retrieve_attention_scores + self.initial_to_ln = LayerNorm(dim_q, eps=1e-6) + attention_dim = min(dim_q, dim_kv) if attention_dim == 0 else attention_dim + self.ca = CrossAttentionOp( + attention_dim, num_heads, dim_q, dim_kv, is_sa=False, use_biases=use_biases + ) + self.ca_stochastic_depth = StochatichDepth(stochastic_depth) + self.middle_ln = LayerNorm(dim_q, eps=1e-6) + self.ffn = FusedMLP( + dim_model=dim_q, + dropout=dropout, + activation=nn.GELU, + hidden_layer_multiplier=mlp_multiplier, + bias=use_biases, + ) + self.ffn_stochastic_depth = StochatichDepth(stochastic_depth) + + self.register_parameter( + "attention_mask_dummy", + nn.Parameter(torch.ones(1, 1, dtype=torch.bool), requires_grad=False), + ) + + def forward( + self, + to_tokens: Tensor, + from_tokens: Tensor, + to_token_mask: Optional[Tensor] = None, + from_token_mask: Optional[Tensor] = None, + ) -> Tensor: + if to_token_mask is None and from_token_mask is None: + attention_mask = None + else: + if to_token_mask is None: + to_token_mask = self.attention_mask_dummy.expand( + to_tokens.shape[0], + to_tokens.shape[1], + ) + if from_token_mask is None: + from_token_mask = self.attention_mask_dummy.expand( + from_tokens.shape[0], + from_tokens.shape[1], + ) + attention_mask = from_token_mask.unsqueeze(1) * to_token_mask.unsqueeze(2) + if self.retrieve_attention_scores: + attention_output = self.ca( + self.initial_to_ln(to_tokens), + from_tokens, + attention_mask=attention_mask, + materialize_sdpa=True, + ) + else: + attention_output = self.ca( + self.initial_to_ln(to_tokens), + from_tokens, + attention_mask=attention_mask, + ) + to_tokens = to_tokens + self.ca_stochastic_depth(attention_output) + to_tokens = to_tokens + self.ffn_stochastic_depth( + self.ffn(self.middle_ln(to_tokens)) + ) + return to_tokens + + +class SelfAttentionBlock(nn.Module): + def __init__( + self, + dim_qkv: int, + num_heads: int, + attention_dim: int = 0, + mlp_multiplier: int = 4, + dropout: float = 0.0, + stochastic_depth: float = 0.0, + use_biases: bool = True, + use_layer_scale: bool = False, + layer_scale_value: float = 0.1, + retrieve_attention_scores: bool = False, + use_16_bits_layer_norm: bool = False, + ): + super().__init__() + if use_16_bits_layer_norm and not retrieve_attention_scores: + LayerNorm = LayerNorm16Bits + else: + LayerNorm = nn.LayerNorm + self.retrieve_attention_scores = retrieve_attention_scores + self.initial_ln = LayerNorm(dim_qkv, eps=1e-6) + attention_dim = dim_qkv if attention_dim == 0 else attention_dim + self.sa = CrossAttentionOp( + attention_dim, + num_heads, + dim_qkv, + dim_qkv, + is_sa=True, + use_biases=use_biases, + ) + self.sa_stochastic_depth = StochatichDepth(stochastic_depth) + self.middle_ln = LayerNorm(dim_qkv, eps=1e-6) + self.ffn = FusedMLP( + dim_model=dim_qkv, + dropout=dropout, + activation=nn.GELU, + hidden_layer_multiplier=mlp_multiplier, + bias=use_biases, + ) + self.ffn_stochastic_depth = StochatichDepth(stochastic_depth) + self.use_layer_scale = use_layer_scale + if use_layer_scale: + self.layer_scale_1 = nn.Parameter( + torch.ones(dim_qkv) * layer_scale_value, requires_grad=True + ) + self.layer_scale_2 = nn.Parameter( + torch.ones(dim_qkv) * layer_scale_value, requires_grad=True + ) + + self.register_parameter( + "attention_mask_dummy", + nn.Parameter(torch.ones(1, 1, dtype=torch.bool), requires_grad=False), + ) + + def forward( + self, + tokens: torch.Tensor, + token_mask: Optional[torch.Tensor] = None, + ): + if token_mask is None: + attention_mask = None + else: + attention_mask = token_mask.unsqueeze(1) * self.attention_mask_dummy.expand( + tokens.shape[0], + tokens.shape[1], + ).unsqueeze(2) + if self.retrieve_attention_scores: + attention_output = self.sa( + self.initial_ln(tokens), + attention_mask=attention_mask, + materialize_sdpa=True, + ) + else: + attention_output = self.sa( + self.initial_ln(tokens), + attention_mask=attention_mask, + ) + if self.use_layer_scale: + tokens = tokens + self.sa_stochastic_depth( + self.layer_scale_1 * attention_output + ) + tokens = tokens + self.ffn_stochastic_depth( + self.layer_scale_2 * self.ffn(self.middle_ln(tokens)) + ) + else: + tokens = tokens + self.sa_stochastic_depth(attention_output) + tokens = tokens + self.ffn_stochastic_depth( + self.ffn(self.middle_ln(tokens)) + ) + return tokens diff --git a/models/positional_embeddings.py b/models/positional_embeddings.py new file mode 100755 index 0000000000000000000000000000000000000000..58f3355b4d02e4af5b572b05007dbdecbbc468f9 --- /dev/null +++ b/models/positional_embeddings.py @@ -0,0 +1,41 @@ +import torch +import torch.nn as nn +import numpy as np + + +class PositionalEmbedding(nn.Module): + """ + Taken from https://github.com/NVlabs/edm + """ + + def __init__(self, num_channels, max_positions=10000, endpoint=False): + super().__init__() + self.num_channels = num_channels + self.max_positions = max_positions + self.endpoint = endpoint + freqs = torch.arange(start=0, end=self.num_channels // 2, dtype=torch.float32) + freqs = 2 * freqs / self.num_channels + freqs = (1 / self.max_positions) ** freqs + self.register_buffer("freqs", freqs) + + def forward(self, x): + x = torch.outer(x, self.freqs) + out = torch.cat([x.cos(), x.sin()], dim=1) + return out.to(x.dtype) + + +# ---------------------------------------------------------------------------- +# Timestep embedding used in the NCSN++ architecture. +class FourierEmbedding(nn.Module): + """ + Taken from https://github.com/NVlabs/edm + """ + + def __init__(self, num_channels, scale=16): + super().__init__() + self.register_buffer("freqs", torch.randn(num_channels // 2) * scale) + + def forward(self, x): + x = x.ger((2 * np.pi * self.freqs).to(x.dtype)) + x = torch.cat([x.cos(), x.sin()], dim=1) + return x diff --git a/models/postprocessing.py b/models/postprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..49b78f3599fa7cb8c798e4c72a80620365e8e96c --- /dev/null +++ b/models/postprocessing.py @@ -0,0 +1,24 @@ +import torch.nn as nn +import torch +import numpy as np + +class UnormGPS(nn.Module): + def __init__(self): + super().__init__() + self.register_buffer("gps_normalize", torch.Tensor([np.pi * 0.5, np.pi]).unsqueeze(0)) + + def forward(self, x): + """Unormalize latitude longtitude radians to -1, 1.""" + x = torch.clamp(x, -1, 1) + return x * self.gps_normalize + +class CartesiantoGPS(nn.Module): + def __init__(self): + super().__init__() + def forward(self, cartesian): + x = cartesian[:, 0] + y = cartesian[:, 1] + z = cartesian[:, 2] + lat = z.arcsin() + lon = y.atan2(x) + return torch.stack([lat, lon], dim=-1) \ No newline at end of file diff --git a/models/preconditioning.py b/models/preconditioning.py new file mode 100755 index 0000000000000000000000000000000000000000..098f09ab31131b407d22c3637eb9f0c0ba53a59d --- /dev/null +++ b/models/preconditioning.py @@ -0,0 +1,60 @@ +import torch +from torch import nn + +# ---------------------------------------------------------------------------- +# Improved preconditioning proposed in the paper "Elucidating the Design +# Space of Diffusion-Based Generative networks" (EDM). + + +class EDMPrecond(torch.nn.Module): + def __init__( + self, + network, + label_dim=0, # Number of class labels, 0 = unconditional. + sigma_min=0, # Minimum supported noise level. + sigma_max=float("inf"), # Maximum supported noise level. + sigma_data=0.5, # Expected standard deviation of the training data. + ): + super().__init__() + self.label_dim = label_dim + self.sigma_min = sigma_min + self.sigma_max = sigma_max + self.sigma_data = sigma_data + self.network = network + + def forward(self, x, sigma, conditioning=None, **network_kwargs): + x = x.to(torch.float32) + sigma = sigma.to(torch.float32).reshape(-1, 1, 1, 1) + conditioning = ( + None + if self.label_dim == 0 + else torch.zeros([1, self.label_dim], device=x.device) + if conditioning is None + else conditioning.to(torch.float32) + ) + + c_skip = self.sigma_data**2 / (sigma**2 + self.sigma_data**2) + c_out = sigma * self.sigma_data / (sigma**2 + self.sigma_data**2).sqrt() + c_in = 1 / (self.sigma_data**2 + sigma**2).sqrt() + c_noise = sigma.log() / 4 + + F_x = self.network( + (c_in * x), + c_noise.flatten(), + conditioning=conditioning, + **network_kwargs, + ) + D_x = c_skip * x + c_out * F_x.to(torch.float32) + return D_x + + def round_sigma(self, sigma): + return torch.as_tensor(sigma) + + +class DDPMPrecond(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, network, batch): + F_x = network(batch) + return F_x diff --git a/models/preprocessing.py b/models/preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..ccc4030d781427a29fafc889e4916d47bd7ba584 --- /dev/null +++ b/models/preprocessing.py @@ -0,0 +1,50 @@ +import torch +from torch import nn +import numpy as np + + +class NormGPS(nn.Module): + def __init__(self, input_key="gps", output_key="x_0", normalize=True): + super().__init__() + self.input_key = input_key + self.output_key = output_key + self.normalize = normalize + if self.normalize: + self.register_buffer( + "gps_normalize", 1 / torch.Tensor([np.pi * 0.5, np.pi]).unsqueeze(0) + ) + + def forward(self, batch): + """Normalize latitude longtitude radians to -1, 1.""" # not used currently + x = batch[self.input_key] + if self.normalize: + x = x * self.gps_normalize + batch[self.output_key] = x + return batch + +class GPStoCartesian(nn.Module): + def __init__(self, input_key="gps", output_key="x_0"): + super().__init__() + self.input_key = input_key + self.output_key = output_key + + def forward(self, batch): + """Project latitude longtitude radians to 3D coordinates.""" + x = batch[self.input_key] + lat, lon = x[:, 0], x[:, 1] + x = torch.stack([lat.cos() * lon.cos(), lat.cos() * lon.sin(), lat.sin()], dim=-1) + batch[self.output_key] = x + return batch + +class PrecomputedPreconditioning: + def __init__( + self, + input_key="emb", + output_key="emb", + ): + self.input_key = input_key + self.output_key = output_key + + def __call__(self, batch, device=None): + batch[self.output_key] = batch[self.input_key] + return batch diff --git a/models/pretrained_models.py b/models/pretrained_models.py new file mode 100644 index 0000000000000000000000000000000000000000..a3230d35c142646aaa7f9a9ba3bac5a39030d7dd --- /dev/null +++ b/models/pretrained_models.py @@ -0,0 +1,58 @@ +import sys +import os + +from models.networks.mlp import GeoAdaLNMLP +from huggingface_hub import PyTorchModelHubMixin +import torch +import argparse + +models_overrides = { + "YFCC100M_geoadalnmlp_r3_small_sigmoid_flow_riemann_10M_10M": "YFCC100M_geoadalnmlp_r3_small_sigmoid_flow_riemann", + "iNaturalist_geoadalnmlp_r3_small_sigmoid_flow_riemann_-7_3": "iNaturalist_geoadalnmlp_r3_small_sigmoid_flow_riemann", + "osv_5m_geoadalnmlp_r3_small_sigmoid_flow_riemann_-7_3": "osv_5m_geoadalnmlp_r3_small_sigmoid_flow_riemann", +} + + +class Plonk( + GeoAdaLNMLP, + PyTorchModelHubMixin, + repo_url="https://github.com/nicolas-dufour/plonk", + tags=["plonk", "geolocalization", "diffusion"], + license="mit", +): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + +def upload_model(checkpoint_dir, repo_name): + import hydra + from omegaconf import OmegaConf + + hydra.initialize(version_base=None, config_path=f"../configs") + cfg = hydra.compose( + config_name="config", + overrides=[ + f"exp={models_overrides[checkpoint_dir]}", + ], + ) + network_config = cfg.model.network + serialized_network_config = OmegaConf.to_container(network_config, resolve=True) + print(serialized_network_config) + del serialized_network_config["_target_"] + model = Plonk(**serialized_network_config) + ckpt = torch.load(f"checkpoints/{checkpoint_dir}/last.ckpt") + ckpt_state_dict = ckpt["state_dict"] + ckpt_state_dict = {k: v for k, v in ckpt_state_dict.items() if "ema_network" in k} + ckpt_state_dict = { + k.replace("ema_network.", ""): v for k, v in ckpt_state_dict.items() + } + model.load_state_dict(ckpt_state_dict) + model.push_to_hub(repo_name, commit_message="Fixed ckpt keys") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_dir", type=str, required=True) + parser.add_argument("--repo_name", type=str, required=True) + args = parser.parse_args() + upload_model(args.checkpoint_dir, args.repo_name) diff --git a/models/samplers/__init__.py b/models/samplers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3016adf2f25726b3e56835d76486203060fae1c8 --- /dev/null +++ b/models/samplers/__init__.py @@ -0,0 +1 @@ +# Empty file to make the directory a Python package diff --git a/models/samplers/__pycache__/__init__.cpython-310.pyc b/models/samplers/__pycache__/__init__.cpython-310.pyc new file mode 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cfg_rate=0, + generator=None, + return_trajectories=False, +): + if scheduler is None: + raise ValueError("Scheduler must be provided") + + x_cur = batch["y"].to(torch.float32) + if return_trajectories: + traj = [x_cur.detach()] + step_indices = torch.arange(num_steps + 1, dtype=torch.float32, device=x_cur.device) + steps = 1 - step_indices / num_steps + gammas = scheduler(steps) + dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16 + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch = {} + stacked_batch[conditioning_keys] = torch.cat( + [batch[conditioning_keys], torch.zeros_like(batch[conditioning_keys])], + dim=0, + ) + for step, (gamma_now, gamma_next) in enumerate(zip(gammas[:-1], gammas[1:])): + with torch.cuda.amp.autocast(dtype=dtype): + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch["y"] = torch.cat([x_cur, x_cur], dim=0) + stacked_batch["gamma"] = gamma_now.expand(x_cur.shape[0] * 2) + denoised_all = net(stacked_batch) + denoised_cond, denoised_uncond = denoised_all.chunk(2, dim=0) + denoised = denoised_cond * (1 + cfg_rate) - denoised_uncond * cfg_rate + else: + batch["y"] = x_cur + batch["gamma"] = gamma_now.expand(x_cur.shape[0]) + denoised = net(batch) + + x_pred = (x_cur - torch.sqrt(1 - gamma_now) * denoised) / torch.sqrt(gamma_now) + x_pred = torch.clamp(x_pred, -1, 1) + noise_pred = (x_cur - torch.sqrt(gamma_now) * x_pred) / torch.sqrt( + 1 - gamma_now + ) + x_next = ( + torch.sqrt(gamma_next) * x_pred + torch.sqrt(1 - gamma_next) * noise_pred + ) + x_cur = x_next + if return_trajectories: + traj.append(x_cur.detach().to(torch.float32)) + + if return_trajectories: + return x_cur.to(torch.float32), traj + else: + return x_cur.to(torch.float32) + + +def circular_transformation(x, min_val=-1, max_val=1): + return (x - min_val) % (max_val - min_val) + min_val diff --git a/models/samplers/ddpm.py b/models/samplers/ddpm.py new file mode 100644 index 0000000000000000000000000000000000000000..cc8510ab527d68c8448794e796c16bbb46a457d2 --- /dev/null +++ b/models/samplers/ddpm.py @@ -0,0 +1,187 @@ +import torch + + +def ddpm_sampler( + net, + batch, + conditioning_keys=None, + scheduler=None, + uncond_tokens=None, + num_steps=1000, + cfg_rate=0, + generator=None, + use_confidence_sampling=False, + use_uncond_token=True, + confidence_value=1.0, + unconfidence_value=0.0, +): + if scheduler is None: + raise ValueError("Scheduler must be provided") + + x_cur = batch["y"].to(torch.float32) + latents = batch["previous_latents"] + if use_confidence_sampling: + batch["confidence"] = ( + torch.ones(x_cur.shape[0], device=x_cur.device) * confidence_value + ) + step_indices = torch.arange(num_steps + 1, dtype=torch.float32, device=x_cur.device) + steps = 1 - step_indices / num_steps + gammas = scheduler(steps) + latents_cond = latents_uncond = latents + # dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16 + dtype = torch.float32 + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch = {} + for key in conditioning_keys: + if f"{key}_mask" in batch: + if use_confidence_sampling and not use_uncond_token: + stacked_batch[f"{key}_mask"] = torch.cat( + [batch[f"{key}_mask"], batch[f"{key}_mask"]], dim=0 + ) + else: + if ( + batch[f"{key}_mask"].shape[1] + > uncond_tokens[f"{key}_mask"].shape[1] + ): + uncond_mask = ( + torch.zeros_like(batch[f"{key}_mask"]) + if batch[f"{key}_mask"].dtype == torch.bool + else torch.ones_like(batch[f"{key}_mask"]) * -torch.inf + ) + uncond_mask[:, : uncond_tokens[f"{key}_mask"].shape[1]] = ( + uncond_tokens[f"{key}_mask"] + ) + else: + uncond_mask = uncond_tokens[f"{key}_mask"] + batch[f"{key}_mask"] = torch.cat( + [ + batch[f"{key}_mask"], + torch.zeros( + batch[f"{key}_mask"].shape[0], + uncond_tokens[f"{key}_embeddings"].shape[1] + - batch[f"{key}_mask"].shape[1], + device=batch[f"{key}_mask"].device, + dtype=batch[f"{key}_mask"].dtype, + ), + ], + dim=1, + ) + stacked_batch[f"{key}_mask"] = torch.cat( + [batch[f"{key}_mask"], uncond_mask], dim=0 + ) + if f"{key}_embeddings" in batch: + if use_confidence_sampling and not use_uncond_token: + stacked_batch[f"{key}_embeddings"] = torch.cat( + [ + batch[f"{key}_embeddings"], + batch[f"{key}_embeddings"], + ], + dim=0, + ) + else: + if ( + batch[f"{key}_embeddings"].shape[1] + > uncond_tokens[f"{key}_embeddings"].shape[1] + ): + uncond_tokens[f"{key}_embeddings"] = torch.cat( + [ + uncond_tokens[f"{key}_embeddings"], + torch.zeros( + uncond_tokens[f"{key}_embeddings"].shape[0], + batch[f"{key}_embeddings"].shape[1] + - uncond_tokens[f"{key}_embeddings"].shape[1], + uncond_tokens[f"{key}_embeddings"].shape[2], + device=uncond_tokens[f"{key}_embeddings"].device, + ), + ], + dim=1, + ) + elif ( + batch[f"{key}_embeddings"].shape[1] + < uncond_tokens[f"{key}_embeddings"].shape[1] + ): + batch[f"{key}_embeddings"] = torch.cat( + [ + batch[f"{key}_embeddings"], + torch.zeros( + batch[f"{key}_embeddings"].shape[0], + uncond_tokens[f"{key}_embeddings"].shape[1] + - batch[f"{key}_embeddings"].shape[1], + batch[f"{key}_embeddings"].shape[2], + device=batch[f"{key}_embeddings"].device, + ), + ], + dim=1, + ) + stacked_batch[f"{key}_embeddings"] = torch.cat( + [ + batch[f"{key}_embeddings"], + uncond_tokens[f"{key}_embeddings"], + ], + dim=0, + ) + elif key not in batch: + raise ValueError(f"Key {key} not in batch") + else: + if isinstance(batch[key], torch.Tensor): + if use_confidence_sampling and not use_uncond_token: + stacked_batch[key] = torch.cat([batch[key], batch[key]], dim=0) + else: + stacked_batch[key] = torch.cat( + [batch[key], uncond_tokens], dim=0 + ) + elif isinstance(batch[key], list): + if use_confidence_sampling and not use_uncond_token: + stacked_batch[key] = [*batch[key], *batch[key]] + else: + stacked_batch[key] = [*batch[key], *uncond_tokens] + else: + raise ValueError( + "Conditioning must be a tensor or a list of tensors" + ) + if use_confidence_sampling: + stacked_batch["confidence"] = torch.cat( + [ + torch.ones(x_cur.shape[0], device=x_cur.device) * confidence_value, + torch.ones(x_cur.shape[0], device=x_cur.device) + * unconfidence_value, + ], + dim=0, + ) + for step, (gamma_now, gamma_next) in enumerate(zip(gammas[:-1], gammas[1:])): + with torch.cuda.amp.autocast(dtype=dtype): + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch["y"] = torch.cat([x_cur, x_cur], dim=0) + stacked_batch["gamma"] = gamma_now.expand(x_cur.shape[0] * 2) + stacked_batch["previous_latents"] = ( + torch.cat([latents_cond, latents_uncond], dim=0) + if latents is not None + else None + ) + denoised_all, latents_all = net(stacked_batch) + denoised_cond, denoised_uncond = denoised_all.chunk(2, dim=0) + latents_cond, latents_uncond = latents_all.chunk(2, dim=0) + denoised = denoised_cond * (1 + cfg_rate) - denoised_uncond * cfg_rate + else: + batch["y"] = x_cur + batch["gamma"] = gamma_now.expand(x_cur.shape[0]) + batch["previous_latents"] = latents + denoised, latents = net( + batch, + ) + x_pred = (x_cur - torch.sqrt(1 - gamma_now) * denoised) / torch.sqrt(gamma_now) + x_pred = torch.clamp(x_pred, -1, 1) + noise_pred = (x_cur - torch.sqrt(gamma_now) * x_pred) / torch.sqrt( + 1 - gamma_now + ) + + log_alpha_t = torch.log(gamma_now) - torch.log(gamma_next) + alpha_t = torch.clip(torch.exp(log_alpha_t), 0, 1) + x_mean = torch.rsqrt(alpha_t) * ( + x_cur - torch.rsqrt(1 - gamma_now) * (1 - alpha_t) * noise_pred + ) + var_t = 1 - alpha_t + eps = torch.randn(x_cur.shape, device=x_cur.device, generator=generator) + x_next = x_mean + torch.sqrt(var_t) * eps + x_cur = x_next + return x_cur.to(torch.float32) diff --git a/models/samplers/edm.py b/models/samplers/edm.py new file mode 100755 index 0000000000000000000000000000000000000000..eae4976f5ada37e2ebc72deabede9e244db9ffcb --- /dev/null +++ b/models/samplers/edm.py @@ -0,0 +1,68 @@ +import torch +import numpy as np + + +def edm_sampler( + net, + x_N, + conditioning=None, + latents=None, + randn_like=torch.randn_like, + num_steps=18, + sigma_min=0.002, + sigma_max=80, + rho=7, + S_churn=0, + S_min=0, + S_max=float("inf"), + S_noise=1, +): + # Adjust noise levels based on what's supported by the network. + sigma_min = max(sigma_min, net.sigma_min) + sigma_max = min(sigma_max, net.sigma_max) + + # Time step discretization. + step_indices = torch.arange(num_steps, dtype=torch.float64, device=x_N.device) + t_steps = ( + sigma_max ** (1 / rho) + + step_indices + / (num_steps - 1) + * (sigma_min ** (1 / rho) - sigma_max ** (1 / rho)) + ) ** rho + t_steps = torch.cat( + [net.round_sigma(t_steps), torch.zeros_like(t_steps[:1])] + ) # t_N = 0 + + # Main sampling loop. + x_next = x_N.to(torch.float64) * t_steps[0] + for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])): # 0, ..., N-1 + x_cur = x_next + + # Increase noise temporarily. + gamma = ( + min(S_churn / num_steps, np.sqrt(2) - 1) if S_min <= t_cur <= S_max else 0 + ) + t_hat = net.round_sigma(t_cur + gamma * t_cur) + x_hat = x_cur + (t_hat**2 - t_cur**2).sqrt() * S_noise * randn_like(x_cur) + + # Euler step. + denoised, latents = net( + x_hat, t_hat.expand(x_cur.shape[0]), conditioning, previous_latents=latents + ) + denoised = denoised.to(torch.float64) + d_cur = (x_hat - denoised) / t_hat + x_next = x_hat + (t_next - t_hat) * d_cur + + # Apply 2nd order correction. + if i < num_steps - 1: + denoised, latents = net( + x_next, + t_next.expand(x_cur.shape[0]), + conditioning, + previous_latents=latents, + ) + denoised = denoised.to(torch.float64) + d_prime = (x_next - denoised) / t_next + x_next = x_hat + (t_next - t_hat) * (0.5 * d_cur + 0.5 * d_prime) + + return x_next diff --git a/models/samplers/flow_sampler.py b/models/samplers/flow_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..cc4609d415acd4a147e539bac467d5fd8bc4ae0f --- /dev/null +++ b/models/samplers/flow_sampler.py @@ -0,0 +1,57 @@ +import torch + + +def flow_sampler( + net, + batch, + conditioning_keys=None, + scheduler=None, + num_steps=250, + cfg_rate=0, + generator=None, + return_trajectories=False, +): + if scheduler is None: + raise ValueError("Scheduler must be provided") + + x_cur = batch["y"].to(torch.float32) + if return_trajectories: + traj = [x_cur.detach()] + step_indices = torch.arange(num_steps + 1, dtype=torch.float32, device=x_cur.device) + steps = 1 - step_indices / num_steps + gammas = scheduler(steps) + dtype = ( + torch.float32 + ) # torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16 + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch = {} + stacked_batch[conditioning_keys] = torch.cat( + [batch[conditioning_keys], torch.zeros_like(batch[conditioning_keys])], + dim=0, + ) + for step, (gamma_now, gamma_next) in enumerate(zip(gammas[:-1], gammas[1:])): + with torch.cuda.amp.autocast(dtype=dtype): + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch["y"] = torch.cat([x_cur, x_cur], dim=0) + stacked_batch["gamma"] = gamma_now.expand(x_cur.shape[0] * 2) + denoised_all = net(stacked_batch) + denoised_cond, denoised_uncond = denoised_all.chunk(2, dim=0) + denoised = denoised_cond * (1 + cfg_rate) - denoised_uncond * cfg_rate + else: + batch["y"] = x_cur + batch["gamma"] = gamma_now.expand(x_cur.shape[0]) + denoised = net(batch) + dt = gamma_next - gamma_now + x_next = x_cur + dt * denoised + x_cur = x_next + if return_trajectories: + traj.append(x_cur.detach().to(torch.float32)) + + if return_trajectories: + return x_cur.to(torch.float32), traj + else: + return x_cur.to(torch.float32) + + +def circular_transformation(x, min_val=-1, max_val=1): + return (x - min_val) % (max_val - min_val) + min_val diff --git a/models/samplers/riemannian_flow_sampler.py b/models/samplers/riemannian_flow_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..a541f820572fed15518b4432202baac2353e2df7 --- /dev/null +++ b/models/samplers/riemannian_flow_sampler.py @@ -0,0 +1,84 @@ +import torch +from utils.manifolds import Sphere +from tqdm.auto import tqdm + + +def riemannian_flow_sampler( + net, + batch, + manifold=Sphere(), + conditioning_keys=None, + scheduler=None, + num_steps=250, + cfg_rate=0, + generator=None, + return_trajectories=False, +): + if scheduler is None: + raise ValueError("Scheduler must be provided") + + x_cur = batch["y"].to(torch.float32) + if return_trajectories: + traj = [x_cur.detach()] + step_indices = torch.arange(num_steps + 1, dtype=torch.float32, device=x_cur.device) + steps = 1 - step_indices / num_steps + gammas = scheduler(steps) + dtype = torch.float32 + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch = {} + stacked_batch[conditioning_keys] = torch.cat( + [batch[conditioning_keys], torch.zeros_like(batch[conditioning_keys])], + dim=0, + ) + for step, (gamma_now, gamma_next) in enumerate(zip(gammas[:-1], gammas[1:])): + with torch.cuda.amp.autocast(dtype=dtype): + if cfg_rate > 0 and conditioning_keys is not None: + stacked_batch["y"] = torch.cat([x_cur, x_cur], dim=0) + stacked_batch["gamma"] = gamma_now.expand(x_cur.shape[0] * 2) + denoised_all = net(stacked_batch) + denoised_cond, denoised_uncond = denoised_all.chunk(2, dim=0) + denoised = denoised_cond * (1 + cfg_rate) - denoised_uncond * cfg_rate + else: + batch["y"] = x_cur + batch["gamma"] = gamma_now.expand(x_cur.shape[0]) + denoised = net(batch) + + dt = gamma_next - gamma_now + x_next = x_cur + dt * denoised # manifold.expmap(x_cur, dt * denoised) + x_next = manifold.projx(x_next) + x_cur = x_next + if return_trajectories: + traj.append(x_cur.detach().to(torch.float32)) + + if return_trajectories: + return x_cur.to(torch.float32), traj + else: + return x_cur.to(torch.float32) + + +def ode_riemannian_flow_sampler( + odefunc, + x_1, + manifold=Sphere(), + scheduler=None, + num_steps=1000, +): + if scheduler is None: + raise ValueError("Scheduler must be provided") + + x_cur = x_1.to(torch.float32) + steps = ( + torch.arange(num_steps + 1, dtype=torch.float32, device=x_cur.device) + / num_steps + ) + dtype = torch.float32 + for step, (t_now, t_next) in enumerate(zip(steps[:-1], steps[1:]), total=num_steps): + with torch.cuda.amp.autocast(dtype=dtype): + denoised = odefunc(t_now, x_cur) + gamma_now = scheduler(t_now) + gamma_next = scheduler(t_next) + dt = gamma_next - gamma_now + x_next = x_cur + dt * denoised # manifold.expmap(x_cur, dt * denoised) + x_next = manifold.projx(x_next) + x_cur = x_next + return x_cur.to(torch.float32) diff --git a/models/samplers/von_fisher_sampling.py b/models/samplers/von_fisher_sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..c3afab2e65aab43455f82243dd908ac77f9486b1 --- /dev/null +++ b/models/samplers/von_fisher_sampling.py @@ -0,0 +1,105 @@ +""" +Generate multivariate von Mises Fisher samples. +PyTorch implementation of the original code from: +https://github.com/clara-labs/spherecluster +""" + +import torch + +__all__ = ["sample_vMF"] + + +def vMF_sampler( + net, + batch, +): + mu, kappa = net(batch) + return sample_vMF(mu.T, kappa.squeeze(1)) + + +def vMF_mixture_sampler( + net, + batch, +): + mu_mixture, kappa_mixture, weights = net(batch) + # Sample mixture component indices based on weights + indices = torch.multinomial(weights, num_samples=1).squeeze() + # Select corresponding mu and kappa + mu = mu_mixture[torch.arange(mu_mixture.shape[0]), indices] + kappa = kappa_mixture[torch.arange(kappa_mixture.shape[0]), indices] + return sample_vMF(mu.T, kappa) + + +def sample_vMF(mu, kappa, num_samples=1): + """Generate N-dimensional samples from von Mises Fisher + distribution around center mu ∈ R^N with concentration kappa. + mu and kappa may be vectors, + mu should have shape (N,) or (N, 1), kappa should be scalar or vector of length N. + """ + if len(mu.shape) == 1: + mu = mu.unsqueeze(1) + + if isinstance(kappa, torch.Tensor): + dim = mu.shape[0] + assert mu.shape[1] == kappa.size(0) + else: + dim = mu.shape[0] + mu = mu.repeat(1, num_samples) + kappa = torch.full((num_samples,), kappa, device=mu.device, dtype=mu.dtype) + + # sample offset from center (on sphere) with spread kappa + w = _sample_weight(kappa, dim) + + # sample a point v on the unit sphere that's orthogonal to mu + v = _sample_orthonormal_to(mu) + + # compute new point + result = v * torch.sqrt(1.0 - w**2).unsqueeze(0) + w.unsqueeze(0) * mu + return result.T + + +def _sample_weight(kappa, dim): + """Rejection sampling scheme for sampling distance from center on + surface of the sphere. + """ + dim = dim - 1 # since S^{n-1} + try: + size = kappa.size(0) + except AttributeError: + size = 1 + + b = dim / (torch.sqrt(4.0 * kappa**2 + dim**2) + 2 * kappa) + x = (1.0 - b) / (1.0 + b) + c = kappa * x + dim * torch.log(1 - x**2) + + w = torch.zeros_like(kappa) + idx = torch.zeros_like(kappa, dtype=torch.bool) + + while True: + where_zero = ~idx + if torch.all(idx): + return w + + z = ( + torch.distributions.Beta(dim / 2.0, dim / 2.0) + .sample((size,)) + .to(kappa.device) + ) + _w = (1.0 - (1.0 + b) * z) / (1.0 - (1.0 - b) * z) + u = torch.rand(size, device=kappa.device) + + _idx = kappa * _w + dim * torch.log(1.0 - x * _w) - c >= torch.log(u) + + if not torch.any(_idx): + continue + + w[where_zero] = _w[where_zero] + idx[_idx] = True + + +def _sample_orthonormal_to(mu): + """Sample point on sphere orthogonal to mu.""" + v = torch.randn(mu.shape[0], mu.shape[1], device=mu.device) + proj_mu_v = mu * ((v * mu).sum(dim=0)) / torch.norm(mu, dim=0) ** 2 + orthto = v - proj_mu_v + return orthto / torch.norm(orthto, dim=0) diff --git a/models/schedulers.py b/models/schedulers.py new file mode 100644 index 0000000000000000000000000000000000000000..c5d5c3370e76ff8ffdf613f319f4b7782c3de55c --- /dev/null +++ b/models/schedulers.py @@ -0,0 +1,106 @@ +import torch + + +class SigmoidScheduler: + def __init__(self, start=-3, end=3, tau=1, clip_min=1e-9): + self.start = start + self.end = end + self.tau = tau + self.clip_min = clip_min + + self.v_start = torch.sigmoid(torch.tensor(self.start / self.tau)) + self.v_end = torch.sigmoid(torch.tensor(self.end / self.tau)) + + def __call__(self, t): + output = ( + -torch.sigmoid((t * (self.end - self.start) + self.start) / self.tau) + + self.v_end + ) / (self.v_end - self.v_start) + return torch.clamp(output, min=self.clip_min, max=1.0) + + def derivative(self, t): + x = (t * (self.end - self.start) + self.start) / self.tau + sigmoid_x = torch.sigmoid(x) + # Chain rule: d/dt of original function + return ( + -(self.end - self.start) + * sigmoid_x + * (1 - sigmoid_x) + / (self.tau * (self.v_end - self.v_start)) + ) + + def alpha(self, t): + return -self.derivative(t) / (1e-6 + self.__call__(t)) + + +class LinearScheduler: + def __init__(self, start=1, end=0, clip_min=1e-9): + self.start = start + self.end = end + self.clip_min = clip_min + + def __call__(self, t): + output = (self.end - self.start) * t + self.start + return torch.clamp(output, min=self.clip_min, max=1.0) + + def derivative(self, t): + return torch.tensor(self.end - self.start).to(t.device) + + def alpha(self, t): + return -self.derivative(t) / (1e-6 + self.__call__(t)) + + +class CosineScheduler: + def __init__( + self, + start: float = 1, + end: float = 0, + tau: float = 1.0, + clip_min: float = 1e-9, + ): + self.start = start + self.end = end + self.tau = tau + self.clip_min = clip_min + + self.v_start = torch.cos(torch.tensor(self.start) * torch.pi / 2) ** ( + 2 * self.tau + ) + self.v_end = torch.cos(torch.tensor(self.end) * torch.pi / 2) ** (2 * self.tau) + + def __call__(self, t: float) -> float: + output = ( + torch.cos((t * (self.end - self.start) + self.start) * torch.pi / 2) + ** (2 * self.tau) + - self.v_end + ) / (self.v_start - self.v_end) + return torch.clamp(output, min=self.clip_min, max=1.0) + + def derivative(self, t: float) -> float: + x = (t * (self.end - self.start) + self.start) * torch.pi / 2 + cos_x = torch.cos(x) + # Chain rule: d/dt of original function + return ( + -2 + * self.tau + * (self.end - self.start) + * torch.pi + / 2 + * cos_x + * (cos_x ** (2 * self.tau - 1)) + * torch.sin(x) + / (self.v_start - self.v_end) + ) + + +class CosineSchedulerSimple: + def __init__(self, ns: float = 0.0002, ds: float = 0.00025): + self.ns = ns + self.ds = ds + + def __call__(self, t: float) -> float: + return torch.cos(((t + self.ns) / (1 + self.ds)) * torch.pi / 2) ** 2 + + def derivative(self, t: float) -> float: + x = ((t + self.ns) / (1 + self.ds)) * torch.pi / 2 + return -torch.pi * torch.cos(x) * torch.sin(x) / (1 + self.ds) diff --git a/pipe.py b/pipe.py new file mode 100644 index 0000000000000000000000000000000000000000..669b9786ca78d0d13948f33156c0080198597fcf --- /dev/null +++ b/pipe.py @@ -0,0 +1,298 @@ +import torch +import random +import string +from transformers import AutoTokenizer, T5EncoderModel +from models.pretrained_models import Plonk +from models.samplers.riemannian_flow_sampler import riemannian_flow_sampler + +from models.postprocessing import CartesiantoGPS + +from models.schedulers import ( + SigmoidScheduler, + LinearScheduler, + CosineScheduler, +) +from models.preconditioning import DDPMPrecond +from torchvision import transforms +from transformers import CLIPProcessor, CLIPVisionModel +from utils.image_processing import CenterCrop +import numpy as np + +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") +MODELS = { + "nicolas-dufour/PLONK_YFCC": {"emb_name": "dinov2"}, + "nicolas-dufour/PLONK_OSV_5M": { + "emb_name": "street_clip", + }, + "nicolas-dufour/PLONK_iNaturalist": { + "emb_name": "dinov2", + }, +} + + +def scheduler_fn( + scheduler_type: str, start: float, end: float, tau: float, clip_min: float = 1e-9 +): + if scheduler_type == "sigmoid": + return SigmoidScheduler(start, end, tau, clip_min) + elif scheduler_type == "cosine": + return CosineScheduler(start, end, tau, clip_min) + elif scheduler_type == "linear": + return LinearScheduler(clip_min=clip_min) + else: + raise ValueError(f"Scheduler type {scheduler_type} not supported") + + +class DinoV2FeatureExtractor: + def __init__(self, device=device): + super().__init__() + self.device = device + self.emb_model = torch.hub.load("facebookresearch/dinov2", "dinov2_vitl14_reg") + self.emb_model.eval() + self.emb_model.to(self.device) + self.augmentation = transforms.Compose( + [ + CenterCrop(ratio="1:1"), + transforms.Resize( + 336, interpolation=transforms.InterpolationMode.BICUBIC + ), + transforms.ToTensor(), + transforms.Normalize( + mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225) + ), + ] + ) + + def __call__(self, batch): + embs = [] + with torch.no_grad(): + for img in batch["img"]: + emb = self.emb_model( + self.augmentation(img).unsqueeze(0).to(self.device) + ).squeeze(0) + embs.append(emb) + batch["emb"] = torch.stack(embs) + return batch + + +class StreetClipFeatureExtractor: + def __init__(self, device=device): + self.device = device + self.emb_model = CLIPVisionModel.from_pretrained("geolocal/StreetCLIP").to( + device + ) + self.processor = CLIPProcessor.from_pretrained("geolocal/StreetCLIP") + + def __call__(self, batch): + inputs = self.processor(images=batch["img"], return_tensors="pt") + inputs = {k: v.to(self.device) for k, v in inputs.items()} + with torch.no_grad(): + outputs = self.emb_model(**inputs) + embeddings = outputs.last_hidden_state[:, 0] + batch["emb"] = embeddings + return batch + + +def load_prepocessing(model_name, dtype=torch.float32): + if MODELS[model_name]["emb_name"] == "dinov2": + return DinoV2FeatureExtractor() + elif MODELS[model_name]["emb_name"] == "street_clip": + return StreetClipFeatureExtractor() + else: + raise ValueError(f"Embedding model {MODELS[model_name]['emb_name']} not found") + + +class PlonkPipeline: + """ + The CADT2IPipeline class is designed to facilitate the generation of images from text prompts using a pre-trained CAD model. + It integrates various components such as samplers, schedulers, and post-processing techniques to produce high-quality images. + + Initialization: + CADT2IPipeline( + model_path, + sampler="ddim", + scheduler="sigmoid", + postprocessing="sd_1_5_vae", + scheduler_start=-3, + scheduler_end=3, + scheduler_tau=1.1, + device="cuda", + ) + + Parameters: + model_path (str): Path to the pre-trained CAD model. + sampler (str): The sampling method to use. Options are "ddim", "ddpm", "dpm", "dpm_2S", "dpm_2M". Default is "ddim". + scheduler (str): The scheduler type to use. Options are "sigmoid", "cosine", "linear". Default is "sigmoid". + postprocessing (str): The post-processing method to use. Options are "consistency-decoder", "sd_1_5_vae". Default is "sd_1_5_vae". + scheduler_start (float): Start value for the scheduler. Default is -3. + scheduler_end (float): End value for the scheduler. Default is 3. + scheduler_tau (float): Tau value for the scheduler. Default is 1.1. + device (str): Device to run the model on. Default is "cuda". + + Methods: + model(*args, **kwargs): + Runs the preconditioning on the network with the provided arguments. + + __call__(...): + Generates images based on the provided conditions and parameters. + + Parameters: + cond (str or list of str): The conditioning text or list of texts. + num_samples (int, optional): Number of samples to generate. If not provided, it is inferred from cond. + x_N (torch.Tensor, optional): Initial noise tensor. If not provided, it is generated. + latents (torch.Tensor, optional): Previous latents. + num_steps (int, optional): Number of steps for the sampler. If not provided, the default is used. + sampler (callable, optional): Custom sampler function. If not provided, the default sampler is used. + scheduler (callable, optional): Custom scheduler function. If not provided, the default scheduler is used. + cfg (float): Classifier-free guidance scale. Default is 15. + guidance_type (str): Type of guidance. Default is "constant". + guidance_start_step (int): Step to start guidance. Default is 0. + generator (torch.Generator, optional): Random number generator. + coherence_value (float): Doherence value for sampling. Default is 1.0. + uncoherence_value (float): Uncoherence value for sampling. Default is 0.0. + unconfident_prompt (str, optional): Unconfident prompt text. + thresholding_type (str): Type of thresholding. Default is "clamp". + clamp_value (float): Clamp value for thresholding. Default is 1.0. + thresholding_percentile (float): Percentile for thresholding. Default is 0.995. + + Returns: + torch.Tensor: The generated image tensor after post-processing. + + to(device): + Moves the model and its components to the specified device. + + Parameters: + device (str): The device to move the model to (e.g., "cuda", "cpu"). + + Returns: + CADT2IPipeline: The pipeline instance with updated device. + + Example Usage: + pipe = CADT2IPipeline( + "nicolas-dufour/", + ) + pipe.to("cuda") + image = pipe( + "a beautiful landscape with a river and mountains", + num_samples=4, + ) + """ + + def __init__( + self, + model_path, + scheduler="sigmoid", + scheduler_start=-7, + scheduler_end=3, + scheduler_tau=1.0, + device=device, + ): + self.network = Plonk.from_pretrained(model_path).to(device) + self.network.requires_grad_(False).eval() + assert scheduler in [ + "sigmoid", + "cosine", + "linear", + ], f"Scheduler {scheduler} not supported" + self.scheduler = scheduler_fn( + scheduler, scheduler_start, scheduler_end, scheduler_tau + ) + self.cond_preprocessing = load_prepocessing(model_name=model_path) + self.postprocessing = CartesiantoGPS() + self.sampler = riemannian_flow_sampler + self.model_path = model_path + self.preconditioning = DDPMPrecond() + self.device = device + + def model(self, *args, **kwargs): + return self.preconditioning(self.network, *args, **kwargs) + + def __call__( + self, + images, + batch_size=None, + x_N=None, + num_steps=None, + scheduler=None, + cfg=0, + generator=None, + ): + """Sample from the model given conditioning. + + Args: + cond: Conditioning input (image or list of images) + batch_size: Number of samples to generate (inferred from cond if not provided) + x_N: Initial noise tensor (generated if not provided) + num_steps: Number of sampling steps (uses default if not provided) + sampler: Custom sampler function (uses default if not provided) + scheduler: Custom scheduler function (uses default if not provided) + cfg: Classifier-free guidance scale (default 15) + generator: Random number generator + + Returns: + Sampled GPS coordinates after postprocessing + """ + # Set up batch size and initial noise + shape = [3] + if not isinstance(images, list): + images = [images] + if x_N is None: + if batch_size is None: + if isinstance(images, list): + batch_size = len(images) + else: + batch_size = 1 + x_N = torch.randn( + batch_size, *shape, device=self.device, generator=generator + ) + else: + x_N = x_N.to(self.device) + if x_N.ndim == 3: + x_N = x_N.unsqueeze(0) + batch_size = x_N.shape[0] + + # Set up batch with conditioning + batch = {"y": x_N} + batch["img"] = images + batch = self.cond_preprocessing(batch) + if len(images) > 1: + assert len(images) == batch_size + else: + batch["emb"] = batch["emb"].repeat(batch_size, 1) + + # Use default sampler/scheduler if not provided + sampler = self.sampler + if scheduler is None: + scheduler = self.scheduler + # Sample from model + if num_steps is None: + output = sampler( + self.model, + batch, + conditioning_keys="emb", + scheduler=scheduler, + cfg_rate=cfg, + generator=generator, + ) + else: + output = sampler( + self.model, + batch, + conditioning_keys="emb", + scheduler=scheduler, + num_steps=num_steps, + cfg_rate=cfg, + generator=generator, + ) + + # Apply postprocessing and return + output = self.postprocessing(output) + # To degrees + output = np.degrees(output.detach().cpu().numpy()) + return output + + def to(self, device): + self.network.to(device) + self.postprocessing.to(device) + self.device = torch.device(device) + return self diff --git a/requirements.txt b/requirements.txt index 9190ed3582adea6a6012859ace320fcb5ac6897a..3dd61d2c35749654fe8ee00066d0762f0e2cf47f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,18 @@ -git+https://github.com/nicolas-dufour/plonk.git@master +joblib +wandb +hydra-core +numpy +scipy==1.13.1 pandas -torch -torchvision -streamlit_extras -plotly \ No newline at end of file +scikit-learn +pytorch-lightning +transformers +accelerate +peft +geos +reverse_geocoder +matplotlib +geoopt +einops +torchdiffeq +webdataset==0.2.57 \ No newline at end of file diff --git a/scripts/download-dataset.py b/scripts/download-dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..56059aca7d802505e534cf580c0cd6c62b340470 --- /dev/null +++ b/scripts/download-dataset.py @@ -0,0 +1,27 @@ +import os, zipfile +from huggingface_hub import snapshot_download + +# Define the base directory +base_dir = os.path.join(os.getcwd(), 'datasets') + +# Ensure the base directory exists +if not os.path.exists(base_dir): + os.mkdir(base_dir) + +# Define the specific dataset directory +dataset_dir = os.path.join(base_dir, "osv5m") + +# Ensure the specific dataset directory exists +if not os.path.exists(dataset_dir): + os.mkdir(dataset_dir) + +# Download the dataset +snapshot_download(repo_id="osv5m/osv5m", local_dir=dataset_dir, repo_type='dataset') + +# Extract zip files and remove them after extraction +for root, dirs, files in os.walk(dataset_dir): + for file in files: + if file.endswith(".zip"): + with zipfile.ZipFile(os.path.join(root, file), 'r') as zip_ref: + zip_ref.extractall(root) + os.remove(os.path.join(root, file)) diff --git a/scripts/preprocessing/enrich-metadata-adaptive-quadtrees.py b/scripts/preprocessing/enrich-metadata-adaptive-quadtrees.py new file mode 100644 index 0000000000000000000000000000000000000000..0b491985dc9e3f2086b3a3003f83d2676accaea0 --- /dev/null +++ b/scripts/preprocessing/enrich-metadata-adaptive-quadtrees.py @@ -0,0 +1,225 @@ +import hydra +import torch +import numpy as np +import pandas as pd +import statistics +from os.path import join, dirname +import matplotlib.pyplot as plt + + +class QuadTree(object): + def __init__(self, data, id="", depth=3, do_split=5000): + self.id = id + self.data = data + + coord = data[["latitude", "longitude"]].to_numpy() + + # if mins is None: + mins = coord.min(0) + # if maxs is None: + maxs = coord.max(0) + + self.mins = np.asarray(mins) + self.maxs = np.asarray(maxs) + self.sizes = self.maxs - self.mins + + self.children = [] + + # sort by latitude + sorted_data_lat = sorted(coord, key=lambda point: point[0]) + + # get the median lat + median_lat = statistics.median(point[0] for point in sorted_data_lat) + + # Divide the cell into two half-cells based on the median lat + data_left = [point for point in sorted_data_lat if point[0] <= median_lat] + data_right = [point for point in sorted_data_lat if point[0] > median_lat] + + # Sort the data points by long in each half-cell + sorted_data_left_lon = sorted(data_left, key=lambda point: point[1]) + sorted_data_right_lon = sorted(data_right, key=lambda point: point[1]) + + # Calculate the median ylong coordinate in each half-cell + median_lon_left = statistics.median(point[1] for point in sorted_data_left_lon) + median_lon_right = statistics.median( + point[1] for point in sorted_data_right_lon + ) + + if (depth > 0) and (len(self.data) >= do_split): + # split the data into four quadrants + data_q1 = data[ + (data["latitude"] < median_lat) & (data["longitude"] < median_lon_left) + ] + data_q2 = data[ + (data["latitude"] < median_lat) & (data["longitude"] >= median_lon_left) + ] + data_q3 = data[ + (data["latitude"] >= median_lat) + & (data["longitude"] < median_lon_right) + ] + data_q4 = data[ + (data["latitude"] >= median_lat) + & (data["longitude"] >= median_lon_right) + ] + + # recursively build a quad tree on each quadrant which has data + if data_q1.shape[0] > 0: + self.children.append( + QuadTree( + data_q1, + id + "0", + depth - 1, + do_split=do_split, + ) + ) + if data_q2.shape[0] > 0: + self.children.append( + QuadTree( + data_q2, + id + "1", + depth - 1, + do_split=do_split, + ) + ) + if data_q3.shape[0] > 0: + self.children.append( + QuadTree( + data_q3, + id + "2", + depth - 1, + do_split=do_split, + ) + ) + if data_q4.shape[0] > 0: + self.children.append( + QuadTree( + data_q4, + id + "3", + depth - 1, + do_split=do_split, + ) + ) + + def unwrap(self): + if len(self.children) == 0: + return {self.id: [self.mins, self.maxs, self.data.copy()]} + else: + d = dict() + for child in self.children: + d.update(child.unwrap()) + return d + + +def extract(qt, name_new_column): + cluster = qt.unwrap() + boundaries, data = {}, [] + for i, (id, vs) in zip(np.arange(len(cluster)), cluster.items()): + (min_lat, min_lon), (max_lat, max_lon), points = vs + points[name_new_column] = int(i) + data.append(points) + boundaries[i] = ( + float(min_lat), + float(min_lon), + float(max_lat), + float(max_lon), + points["latitude"].mean(), + points["longitude"].mean(), + ) + + data = pd.concat(data) + return boundaries, data + + +def vizu(name_new_column, df_train, boundaries, do_split): + plt.hist(df_train[name_new_column], bins=len(boundaries)) + plt.xlabel("Cluster ID") + plt.ylabel("Number of images") + plt.title("Cluster distribution") + plt.yscale("log") + plt.ylim(10, do_split) + plt.savefig(f"{name_new_column}_distrib.png") + plt.clf() + + plt.scatter( + df_train["longitude"].to_numpy(), + df_train["latitude"].to_numpy(), + c=np.random.permutation(len(boundaries))[df_train[name_new_column].to_numpy()], + cmap="tab20", + s=0.1, + alpha=0.5, + ) + plt.xlabel("Longitude") + plt.ylabel("Latitude") + plt.title("Quadtree map") + plt.savefig(f"{name_new_column}_map.png") + + +@hydra.main( + config_path="../configs/scripts", + config_name="enrich-metadata-quadtree", + version_base=None, +) +def main(cfg): + + data_path = join(cfg.data_dir, "osv5m") + name_new_column = f"adaptive_quadtree_{cfg.depth}_{cfg.do_split}" + + # Create clusters from train images + train_fp = join(data_path, f"train.csv") + df_train = pd.read_csv(train_fp) + + qt = QuadTree(df_train, depth=cfg.depth, do_split=cfg.do_split) + boundaries, df_train = extract(qt, name_new_column) + + vizu(name_new_column, df_train, boundaries, cfg.do_split) + + # Save clusters + boundaries = pd.DataFrame.from_dict( + boundaries, + orient="index", + columns=["min_lat", "min_lon", "max_lat", "max_lon", "mean_lat", "mean_lon"], + ) + boundaries.to_csv(f"{name_new_column}.csv", index_label="cluster_id") + + # Assign test images to clusters + test_fp = join(data_path, f"test.csv") + df_test = pd.read_csv(test_fp) + + above_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) > np.expand_dims( + boundaries["min_lat"].to_numpy(), 0 + ) + below_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) < np.expand_dims( + boundaries["max_lat"].to_numpy(), 0 + ) + above_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) > np.expand_dims( + boundaries["min_lon"].to_numpy(), 0 + ) + below_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) < np.expand_dims( + boundaries["max_lon"].to_numpy(), 0 + ) + + mask = np.logical_and( + np.logical_and(above_lat, below_lat), np.logical_and(above_lon, below_lon) + ) + + df_test[name_new_column] = np.argmax(mask, axis=1) + + # save index_to_gps_quadtree file + lat = torch.tensor(boundaries["mean_lat"]) + lon = torch.tensor(boundaries["mean_lon"]) + coord = torch.stack([lat / 90, lon / 180], dim=-1) + torch.save( + coord, + join( + data_path, f"index_to_gps_adaptive_quadtree_{cfg.depth}_{cfg.do_split}.pt" + ), + ) + + # Overwrite test.csv and train.csv + if cfg.overwrite_csv: + df_train.to_csv(train_fp, index=False) + df_test.to_csv(test_fp, index=False) + + +if __name__ == "__main__": + main() diff --git a/scripts/preprocessing/enrich-metadata-quadtree.py b/scripts/preprocessing/enrich-metadata-quadtree.py new file mode 100644 index 0000000000000000000000000000000000000000..f8f9be38523d35c75159ea63637780bb19fd9cc8 --- /dev/null +++ b/scripts/preprocessing/enrich-metadata-quadtree.py @@ -0,0 +1,208 @@ +import hydra +import numpy as np +import pandas as pd +from os.path import join, dirname +import matplotlib.pyplot as plt +import torch + + +class QuadTree(object): + def __init__(self, data, mins=None, maxs=None, id="", depth=3, do_split=1000): + self.id = id + self.data = data + + if mins is None: + mins = data[["latitude", "longitude"]].to_numpy().min(0) + if maxs is None: + maxs = data[["latitude", "longitude"]].to_numpy().max(0) + + self.mins = np.asarray(mins) + self.maxs = np.asarray(maxs) + self.sizes = self.maxs - self.mins + + self.children = [] + + mids = 0.5 * (self.mins + self.maxs) + xmin, ymin = self.mins + xmax, ymax = self.maxs + xmid, ymid = mids + + if (depth > 0) and (len(self.data) >= do_split): + # split the data into four quadrants + data_q1 = data[(data["latitude"] < mids[0]) & (data["longitude"] < mids[1])] + data_q2 = data[ + (data["latitude"] < mids[0]) & (data["longitude"] >= mids[1]) + ] + data_q3 = data[ + (data["latitude"] >= mids[0]) & (data["longitude"] < mids[1]) + ] + data_q4 = data[ + (data["latitude"] >= mids[0]) & (data["longitude"] >= mids[1]) + ] + + # recursively build a quad tree on each quadrant which has data + if data_q1.shape[0] > 0: + self.children.append( + QuadTree( + data_q1, + [xmin, ymin], + [xmid, ymid], + id + "0", + depth - 1, + do_split=do_split, + ) + ) + if data_q2.shape[0] > 0: + self.children.append( + QuadTree( + data_q2, + [xmin, ymid], + [xmid, ymax], + id + "1", + depth - 1, + do_split=do_split, + ) + ) + if data_q3.shape[0] > 0: + self.children.append( + QuadTree( + data_q3, + [xmid, ymin], + [xmax, ymid], + id + "2", + depth - 1, + do_split=do_split, + ) + ) + if data_q4.shape[0] > 0: + self.children.append( + QuadTree( + data_q4, + [xmid, ymid], + [xmax, ymax], + id + "3", + depth - 1, + do_split=do_split, + ) + ) + + def unwrap(self): + if len(self.children) == 0: + return {self.id: [self.mins, self.maxs, self.data.copy()]} + else: + d = dict() + for child in self.children: + d.update(child.unwrap()) + return d + + +def extract(qt, name_new_column): + cluster = qt.unwrap() + boundaries, data = {}, [] + id_to_quad = np.array(list(cluster.keys())) + for i, (id, vs) in zip(np.arange(len(cluster)), cluster.items()): + (min_lat, min_lon), (max_lat, max_lon), points = vs + points[name_new_column] = int(i) + data.append(points) + boundaries[i] = ( + float(min_lat), + float(min_lon), + float(max_lat), + float(max_lon), + points["latitude"].mean(), + points["longitude"].mean(), + ) + + data = pd.concat(data) + return boundaries, data, id_to_quad + + +def vizu(name_new_column, df_train, boundaries): + plt.hist(df_train[name_new_column], bins=len(boundaries)) + plt.xlabel("Cluster ID") + plt.ylabel("Number of images") + plt.title("Cluster distribution") + plt.yscale("log") + plt.savefig(f"{name_new_column}_distrib.png") + plt.clf() + + plt.scatter( + df_train["longitude"].to_numpy(), + df_train["latitude"].to_numpy(), + c=np.random.permutation(len(boundaries))[df_train[name_new_column].to_numpy()], + cmap="tab20", + s=0.1, + alpha=0.5, + ) + plt.xlabel("Longitude") + plt.ylabel("Latitude") + plt.title("Quadtree map") + plt.savefig(f"{name_new_column}_map.png") + + +@hydra.main( + config_path="../configs/scripts", + config_name="enrich-metadata-quadtree", + version_base=None, +) +def main(cfg): + data_path = join(cfg.data_dir, "osv5m") + name_new_column = f"quadtree_{cfg.depth}_{cfg.do_split}" + + # Create clusters from train images + train_fp = join(data_path, f"train.csv") + df_train = pd.read_csv(train_fp) + + qt = QuadTree(df_train, depth=cfg.depth, do_split=cfg.do_split) + boundaries, df_train, id_to_quad = extract(qt, name_new_column) + + vizu(name_new_column, df_train, boundaries) + + # Save clusters + boundaries = pd.DataFrame.from_dict( + boundaries, + orient="index", + columns=["min_lat", "min_lon", "max_lat", "max_lon", "mean_lat", "mean_lon"], + ) + boundaries.to_csv(f"{name_new_column}.csv", index_label="cluster_id") + + # Assign test images to clusters + test_fp = join(data_path, f"test.csv") + df_test = pd.read_csv(test_fp) + + above_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) > np.expand_dims( + boundaries["min_lat"].to_numpy(), 0 + ) + below_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) < np.expand_dims( + boundaries["max_lat"].to_numpy(), 0 + ) + above_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) > np.expand_dims( + boundaries["min_lon"].to_numpy(), 0 + ) + below_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) < np.expand_dims( + boundaries["max_lon"].to_numpy(), 0 + ) + + mask = np.logical_and( + np.logical_and(above_lat, below_lat), np.logical_and(above_lon, below_lon) + ) + + df_test[name_new_column] = np.argmax(mask, axis=1) + + # save index_to_gps_quadtree file + lat = torch.tensor(boundaries["mean_lat"]) + lon = torch.tensor(boundaries["mean_lon"]) + coord = torch.stack([lat / 90, lon / 180], dim=-1) + torch.save( + coord, join(data_path, f"index_to_gps_quadtree_{cfg.depth}_{cfg.do_split}.pt") + ) + + torch.save(id_to_quad, join(data_path, f"id_to_quad_{cfg.depth}_{cfg.do_split}.pt")) + # Overwrite test.csv and train.csv + if cfg.overwrite_csv: + df_train.to_csv(train_fp, index=False) + df_test.to_csv(test_fp, index=False) + + +if __name__ == "__main__": + main() diff --git a/scripts/preprocessing/enrich-metadata.py b/scripts/preprocessing/enrich-metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..e7300fe0564fe9a63fbaeb50d25756372cadd37c --- /dev/null +++ b/scripts/preprocessing/enrich-metadata.py @@ -0,0 +1,123 @@ +import os +import json +import joblib +import pandas as pd +import numpy as np +import reverse_geocoder +from os.path import join, dirname + + +class QuadTree(object): + def __init__( + self, data, mins=None, maxs=None, id="", depth=3, min_split=0, do_split=1000 + ): + self.id = id + self.data = data + + if mins is None: + mins = data[["latitude", "longitude"]].to_numpy().min(0) + if maxs is None: + maxs = data[["latitude", "longitude"]].to_numpy().max(0) + + self.mins = np.asarray(mins) + self.maxs = np.asarray(maxs) + self.sizes = self.maxs - self.mins + + self.children = [] + + mids = 0.5 * (self.mins + self.maxs) + xmin, ymin = self.mins + xmax, ymax = self.maxs + xmid, ymid = mids + + if depth > 0 and len(self.data) >= do_split: + # split the data into four quadrants + data_q1 = data[(data["latitude"] < mids[0]) & (data["longitude"] < mids[1])] + data_q2 = data[ + (data["latitude"] < mids[0]) & (data["longitude"] >= mids[1]) + ] + data_q3 = data[ + (data["latitude"] >= mids[0]) & (data["longitude"] < mids[1]) + ] + data_q4 = data[ + (data["latitude"] >= mids[0]) & (data["longitude"] >= mids[1]) + ] + + # recursively build a quad tree on each quadrant which has data + if data_q1.shape[0] > min_split: + self.children.append( + QuadTree(data_q1, [xmin, ymin], [xmid, ymid], id + "0", depth - 1) + ) + if data_q2.shape[0] > min_split: + self.children.append( + QuadTree(data_q2, [xmin, ymid], [xmid, ymax], id + "1", depth - 1) + ) + if data_q3.shape[0] > min_split: + self.children.append( + QuadTree(data_q3, [xmid, ymin], [xmax, ymid], id + "2", depth - 1) + ) + if data_q4.shape[0] > min_split: + self.children.append( + QuadTree(data_q4, [xmid, ymid], [xmax, ymax], id + "3", depth - 1) + ) + + def unwrap(self): + if len(self.children) == 0: + return {self.id: [self.mins, self.maxs, self.data.copy()]} + else: + d = dict() + for child in self.children: + d.update(child.unwrap()) + return d + + +def extract(qt): + cluster = qt.unwrap() + boundaries, data = {}, [] + for id, vs in cluster.items(): + (min_lat, min_lon), (max_lat, max_lon), points = vs + points["category"] = id + data.append(points) + boundaries[id] = ( + float(min_lat), + float(min_lon), + float(max_lat), + float(max_lon), + ) + + data = pd.concat(data) + return boundaries, data + + +if __name__ == "__main__": + # merge into one DataFrame + data_path = join(dirname(dirname(__file__)), "datasets", "osv5m") + train_fp = join(data_path, f"train.csv") + test_fp = join(data_path, f"test.csv") + + df_train = pd.read_csv(train_fp) + df_train["split"] = "train" + + df_test = pd.read_csv(test_fp) + df_test["split"] = "test" + + df = pd.concat([df_train, df_test]) + size_before = df.shape[0] + qt = QuadTree(df, depth=15) + boundaries, df = extract(qt) + assert df.shape[0] == size_before + + location = reverse_geocoder.search( + [(lat, lon) for lat, lon in zip(df["latitude"], df["longitude"])] + ) + df["city"] = [l.get("name", "") for l in location] + df["country"] = [l.get("cc", "") for l in location] + del location + + df_train = df[df["split"] == "train"].drop(["split"], axis=1) + df_test = df[df["split"] == "test"].drop(["split"], axis=1) + assert (df_train.shape[0] + df_test.shape[0]) == size_before + + json.dump(boundaries, open(join(data_path, "borders.json"), "w")) + df_train.to_csv(train_fp, index=False) + df_test.to_csv(test_fp, index=False) diff --git a/scripts/preprocessing/fix_namimbia.py b/scripts/preprocessing/fix_namimbia.py new file mode 100644 index 0000000000000000000000000000000000000000..61fcdc0b8c46b43a4a42e190dec25b6a972dff3f --- /dev/null +++ b/scripts/preprocessing/fix_namimbia.py @@ -0,0 +1,64 @@ +from os.path import join, dirname +import numpy as np +import pandas as pd + +if __name__ == "__main__": + # Define the list of cities + cities = [ + "Walvis Bay", + "Keetmanshoop", + "Warmbad", + "Rundu", + "Outapi", + "Karibib", + "Otjimbingwe", + "Ondangwa", + "Oranjemund", + "Maltahohe", + "Otavi", + "Outjo", + "Swakopmund", + "Gobabis", + "Karasburg", + "Opuwo", + "Hentiesbaai", + "Katima Mulilo", + "Oshikango", + "Bethanie", + "Ongandjera", + "Mariental", + "Bagani", + "Nkurenkuru", + "Usakos", + "Rehoboth", + "Aranos", + "Omaruru", + "Arandis", + "Windhoek", + "Khorixas", + "Okahandja", + "Grootfontein", + "Tsumeb", + ] + + csv_dtype = {"category": str, "country": str, "city": str} + for split in ["train", "test"]: + fp = join( + dirname(dirname(__file__)), "datasets", "osv5m", f"{split}.csv" + ) + + # Read the CSV file into a pandas DataFrame + df = pd.read_csv(fp, dtype=csv_dtype) + + # Check if the "country" column contains any of the cities in the list + mask = df["city"].isin(cities) + + # If a city is found, set the corresponding rows in the "country" column to 'NMB' + df.loc[mask, "country"] = "NMB" + assert all(map(lambda x: isinstance(x, str), df["country"].unique().tolist())) + + # Drop the columns that are all NaN + df.dropna(subset=["id", "latitude", "longitude"], inplace=True) + + # Save the modified DataFrame back to the CSV file + df.to_csv(fp, index=False) diff --git a/scripts/preprocessing/nearest-neighbors.py b/scripts/preprocessing/nearest-neighbors.py new file mode 100644 index 0000000000000000000000000000000000000000..244c5fc5337734dcaede3f3a599b804afc3026a4 --- /dev/null +++ b/scripts/preprocessing/nearest-neighbors.py @@ -0,0 +1,140 @@ +import sys, os +import json +from PIL import Image +from tqdm import tqdm +from os.path import dirname, join + +sys.path.append(dirname(dirname(__file__))) + +import torch +from transformers import AutoImageProcessor, AutoModel +from transformers import CLIPProcessor, CLIPModel +from transformers import pipeline + +from data.data import osv5m +from json_stream import streamable_list + +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +def load_model_clip(): + model = CLIPModel.from_pretrained("laion/CLIP-ViT-L-14-laion2B-s32B-b82K") + processor = CLIPProcessor.from_pretrained("laion/CLIP-ViT-L-14-laion2B-s32B-b82K") + return processor, model.to(DEVICE) + + +def load_model_dino(): + model = AutoModel.from_pretrained("facebook/dinov2-base") + processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base") + return processor, model.to(DEVICE) + + +def compute_dino(processor, model, x): + inputs = processor(images=x[0], return_tensors="pt", device=DEVICE).to(DEVICE) + outputs = model(**inputs) + last_hidden_states = outputs.last_hidden_state.cpu().numpy() + for i in range(len(x[0])): + yield [last_hidden_states[i].tolist(), x[1][i], x[2][i], x[3][i]] + + +def compute_clip(processor, model, x): + inputs = processor(images=x[0], return_tensors="pt", device=DEVICE).to(DEVICE) + features = model.get_image_features(**inputs) + features /= features.norm(dim=-1, keepdim=True) + features = features.cpu().numpy() + for i in range(len(x[0])): + yield [features[i].tolist(), x[1][i], x[2][i], x[3][i]] + + +def get_batch(dataset, batch_size): + data, lats, lons, ids = [], [], [], [] + for i in range(len(dataset)): + id, lat, lon = dataset.df.iloc[i] + data.append(Image.open(join(dataset.image_folder, f"{int(id)}.jpg"))) + lats.append(lat) + lons.append(lon) + ids.append(id) + if len(data) == batch_size: + yield data, lats, lons, ids + data, lats, lons, ids = [], [], [], [] + + if len(data) > 0: + yield data, lats, lons, ids + data, lats, lons, ids = [], [], [], [] + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--batch_size", type=int, default=256) + parser.add_argument("--compute_features", action="store_true") + parser.add_argument("--compute_nearest", action="store_true") + parser.add_argument("--json_path", default="features") + parser.add_argument("--which", type=str, default="clip", choices=["clip", "dino"]) + args = parser.parse_args() + json_path = join(args.json_path, args.which) + + os.makedirs(json_path, exist_ok=True) + if args.compute_features: + processor, model = ( + load_model_clip() if args.which == "clip" else load_model_dino() + ) + compute_fn = compute_clip if args.which == "clip" else compute_dino + + for split in ["test"]: #'train', + # open existing json and read as dictionary + json_path_ = join(json_path, f"{split}.json") + + dataset = OSV5M( + "datasets/osv5m", transforms=None, split=split, dont_split=True + ) + + @torch.no_grad() + def compute(batch_size): + for data in tqdm( + get_batch(dataset, batch_size), + total=len(dataset) // batch_size, + desc=f"Computing {split} on {args.which}", + ): + features = compute_fn(processor, model, data) + for feature, lat, lon, id in features: + yield feature, lat, lon, id + + data = streamable_list(compute(args.batch_size)) + json.dump(data, open(json_path_, "w"), indent=4) + + if args.compute_nearest: + from sklearn.metrics.pairwise import cosine_similarity + import numpy as np + + train, test = [ + json.load(open(join(json_path, f"{split}.json"), "r")) + for split in ["train", "test"] + ] + + def get_neighbors(k=10): + for i, test_data in enumerate(tqdm(test)): + feature, lat, lon, id = test_data + features_train = np.stack( + [np.array(train_data[0]) for train_data in train] + ) + cs = np.squeeze( + cosine_similarity(np.expand_dims(feature, axis=0), features_train), + axis=0, + ) + i = np.argsort(cs)[-k:][::-1].tolist() + yield [ + {n: x} + for idx in i + for n, x in zip( + ["feature", "lat", "lon", "id", "distance"], + train[idx] + + [ + cs[idx], + ], + ) + ] + + data = streamable_list(get_neighbors()) + json.dump(data, open(join(json_path, "nearest.json"), "w"), indent=4) diff --git a/scripts/preprocessing/preprocess.py b/scripts/preprocessing/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..5f00186b6718c88caa6f7b33a6e021c2f7e92089 --- /dev/null +++ b/scripts/preprocessing/preprocess.py @@ -0,0 +1,400 @@ +import pandas as pd +import torch +import numpy as np +from os.path import join +import matplotlib.pyplot as plt +import hydra + + +class QuadTree(object): + def __init__(self, data, mins=None, maxs=None, id="", depth=3, do_split=1000): + self.id = id + self.data = data + + if mins is None: + mins = data[["latitude", "longitude"]].to_numpy().min(0) + if maxs is None: + maxs = data[["latitude", "longitude"]].to_numpy().max(0) + + self.mins = np.asarray(mins) + self.maxs = np.asarray(maxs) + self.sizes = self.maxs - self.mins + + self.children = [] + + mids = 0.5 * (self.mins + self.maxs) + xmin, ymin = self.mins + xmax, ymax = self.maxs + xmid, ymid = mids + + if (depth > 0) and (len(self.data) >= do_split): + # split the data into four quadrants + data_q1 = data[(data["latitude"] < mids[0]) & (data["longitude"] < mids[1])] + data_q2 = data[ + (data["latitude"] < mids[0]) & (data["longitude"] >= mids[1]) + ] + data_q3 = data[ + (data["latitude"] >= mids[0]) & (data["longitude"] < mids[1]) + ] + data_q4 = data[ + (data["latitude"] >= mids[0]) & (data["longitude"] >= mids[1]) + ] + + # recursively build a quad tree on each quadrant which has data + if data_q1.shape[0] > 0: + self.children.append( + QuadTree( + data_q1, + [xmin, ymin], + [xmid, ymid], + id + "0", + depth - 1, + do_split=do_split, + ) + ) + if data_q2.shape[0] > 0: + self.children.append( + QuadTree( + data_q2, + [xmin, ymid], + [xmid, ymax], + id + "1", + depth - 1, + do_split=do_split, + ) + ) + if data_q3.shape[0] > 0: + self.children.append( + QuadTree( + data_q3, + [xmid, ymin], + [xmax, ymid], + id + "2", + depth - 1, + do_split=do_split, + ) + ) + if data_q4.shape[0] > 0: + self.children.append( + QuadTree( + data_q4, + [xmid, ymid], + [xmax, ymax], + id + "3", + depth - 1, + do_split=do_split, + ) + ) + + def unwrap(self): + if len(self.children) == 0: + return {self.id: [self.mins, self.maxs, self.data.copy()]} + else: + d = dict() + for child in self.children: + d.update(child.unwrap()) + return d + + +def extract(qt, name_new_column): + cluster = qt.unwrap() + boundaries, data = {}, [] + id_to_quad = np.array(list(cluster.keys())) + for i, (id, vs) in zip(np.arange(len(cluster)), cluster.items()): + (min_lat, min_lon), (max_lat, max_lon), points = vs + points[name_new_column] = int(i) + data.append(points) + boundaries[i] = ( + float(min_lat), + float(min_lon), + float(max_lat), + float(max_lon), + points["latitude"].mean(), + points["longitude"].mean(), + ) + + data = pd.concat(data) + return boundaries, data, id_to_quad + + +def vizu(name_new_column, df_train, boundaries, save_path): + plt.hist(df_train[name_new_column], bins=len(boundaries)) + plt.xlabel("Cluster ID") + plt.ylabel("Number of images") + plt.title("Cluster distribution") + plt.yscale("log") + plt.savefig(join(save_path, f"{name_new_column}_distrib.png")) + plt.clf() + + plt.scatter( + df_train["longitude"].to_numpy(), + df_train["latitude"].to_numpy(), + c=np.random.permutation(len(boundaries))[df_train[name_new_column].to_numpy()], + cmap="tab20", + s=0.1, + alpha=0.5, + ) + plt.xlabel("Longitude") + plt.ylabel("Latitude") + plt.title("Quadtree map") + plt.savefig(join(save_path, f"{name_new_column}_map.png")) + + +@hydra.main( + config_path="../../configs/scripts", + config_name="preprocess", + version_base=None, +) +def main(cfg): + data_path = join(cfg.data_dir, "osv5m") + save_path = cfg.data_dir + name_new_column = f"quadtree_{cfg.depth}_{cfg.do_split}" + + # Create clusters from train images + train_fp = join(data_path, f"train.csv") + df_train = pd.read_csv(train_fp, low_memory=False) + + qt = QuadTree(df_train, depth=cfg.depth, do_split=cfg.do_split) + boundaries, df_train, id_to_quad = extract(qt, name_new_column) + + vizu(name_new_column, df_train, boundaries, save_path) + + # Save clusters + boundaries = pd.DataFrame.from_dict( + boundaries, + orient="index", + columns=["min_lat", "min_lon", "max_lat", "max_lon", "mean_lat", "mean_lon"], + ) + boundaries.to_csv( + join(save_path, f"{name_new_column}.csv"), index_label="cluster_id" + ) + + # Assign test images to clusters + test_fp = join(data_path, f"test.csv") + df_test = pd.read_csv(test_fp) + + above_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) > np.expand_dims( + boundaries["min_lat"].to_numpy(), 0 + ) + below_lat = np.expand_dims(df_test["latitude"].to_numpy(), -1) < np.expand_dims( + boundaries["max_lat"].to_numpy(), 0 + ) + above_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) > np.expand_dims( + boundaries["min_lon"].to_numpy(), 0 + ) + below_lon = np.expand_dims(df_test["longitude"].to_numpy(), -1) < np.expand_dims( + boundaries["max_lon"].to_numpy(), 0 + ) + + mask = np.logical_and( + np.logical_and(above_lat, below_lat), np.logical_and(above_lon, below_lon) + ) + + df_test[name_new_column] = np.argmax(mask, axis=1) + + # save index_to_gps_quadtree file + lat = torch.tensor(boundaries["mean_lat"]) + lon = torch.tensor(boundaries["mean_lon"]) + coord = torch.stack([lat, lon], dim=-1) + torch.save( + coord, join(save_path, f"index_to_gps_quadtree_{cfg.depth}_{cfg.do_split}.pt") + ) + + torch.save(id_to_quad, join(save_path, f"id_to_quad_{cfg.depth}_{cfg.do_split}.pt")) + # Overwrite test.csv and train.csv + if cfg.overwrite_csv: + df_train.to_csv(train_fp, index=False) + df_test.to_csv(test_fp, index=False) + + df = pd.read_csv(join(data_path, "train.csv"), low_memory=False).fillna("NaN") + # Compute the average location for each unique country + country_avg = ( + df.groupby("unique_country")[["latitude", "longitude"]].mean().reset_index() + ) + country_avg.to_csv( + join(save_path, "country_center.csv"), + columns=["unique_country", "latitude", "longitude"], + index=False, + ) + # Compute the average location for each unique admin1 (region) + region_avg = ( + df.groupby(["unique_region"])[["latitude", "longitude"]].mean().reset_index() + ) + region_avg.to_csv( + join(save_path, "region_center.csv"), + columns=["unique_region", "latitude", "longitude"], + index=False, + ) + # Compute the average location for each unique admin2 (area) + area_avg = ( + df.groupby(["unique_sub-region"])[["latitude", "longitude"]] + .mean() + .reset_index() + ) + area_avg.to_csv( + join(save_path, "sub-region_center.csv"), + columns=["unique_sub-region", "latitude", "longitude"], + index=False, + ) + # Compute the average location for each unique city + city_avg = ( + df.groupby(["unique_city"])[["latitude", "longitude"]].mean().reset_index() + ) + city_avg.to_csv( + join(save_path, "city_center.csv"), + columns=["unique_city", "latitude", "longitude"], + index=False, + ) + + for class_name in [ + "unique_country", + "unique_sub-region", + "unique_region", + "unique_city", + ]: + # Load CSV data into a Pandas DataFrame + csv_file = class_name.split("_")[-1] + "_center.csv" + df = pd.read_csv(join(save_path, csv_file), low_memory=False) + + splits = ["train"] + categories = sorted( + pd.concat( + [ + pd.read_csv( + join(data_path, f"{split}.csv"), low_memory=False + )[class_name] + for split in splits + ] + ) + .fillna("NaN") + .unique() + .tolist() + ) + + if "NaN" in categories: + categories.remove("NaN") + + # compute the total number of categories - this name is fixed and will be used as a lookup during init + num_classes = len(categories) + + # create a mapping from category to index + category_to_index = {category: i for i, category in enumerate(categories)} + + dictionary = torch.zeros((num_classes, 2)) + for index, row in df.iterrows(): + key = row.iloc[0] + value = [row.iloc[1], row.iloc[2]] + if key in categories: + ( + dictionary[category_to_index[key], 0], + dictionary[category_to_index[key], 1], + ) = np.radians(row.iloc[1]), np.radians(row.iloc[2]) + + # Save the PyTorch tensor to a .pt file + output_file = join(save_path, "index_to_gps_" + class_name + ".pt") + torch.save(dictionary, output_file) + + train = pd.read_csv(join(data_path, "train.csv"), low_memory=False).fillna( + "NaN" + ) + + u = train.groupby("unique_city").sample(n=1) + + country_df = ( + u.pivot(index="unique_city", columns="unique_country", values="unique_city") + .notna() + .astype(int) + .fillna(0) + ) + country_to_idx = { + category: i for i, category in enumerate(list(country_df.columns)) + } + city_country_matrix = torch.tensor(country_df.values) / 1.0 + + region_df = ( + u.pivot(index="unique_city", columns="unique_region", values="unique_city") + .notna() + .astype(int) + .fillna(0) + ) + region_to_idx = {category: i for i, category in enumerate(list(region_df.columns))} + city_region_matrix = torch.tensor(region_df.values) / 1.0 + + country_df = ( + u.pivot(index="unique_city", columns="unique_country", values="unique_city") + .notna() + .astype(int) + .fillna(0) + ) + country_to_idx = { + category: i for i, category in enumerate(list(country_df.columns)) + } + city_country_matrix = torch.tensor(country_df.values) / 1.0 + + output_file = join(save_path, "city_to_country.pt") + torch.save(city_country_matrix, output_file) + + output_file = join(save_path, "country_to_idx.pt") + torch.save(country_to_idx, output_file) + + region_df = ( + u.pivot(index="unique_city", columns="unique_region", values="unique_city") + .notna() + .astype(int) + .fillna(0) + ) + region_to_idx = {category: i for i, category in enumerate(list(region_df.columns))} + city_region_matrix = torch.tensor(region_df.values) / 1.0 + + output_file = join(save_path, "city_to_region.pt") + torch.save(city_region_matrix, output_file) + + output_file = join(save_path, "region_to_idx.pt") + torch.save(region_to_idx, output_file) + + area_df = ( + u.pivot(index="unique_city", columns="unique_sub-region", values="unique_city") + .notna() + .astype(int) + .fillna(0) + ) + area_to_idx = {category: i for i, category in enumerate(list(area_df.columns))} + city_area_matrix = torch.tensor(area_df.values) / 1.0 + + output_file = join(save_path, "city_to_area.pt") + torch.save(city_area_matrix, output_file) + + output_file = join(save_path, "area_to_idx.pt") + torch.save(area_to_idx, output_file) + gt = torch.load(join(save_path, f"id_to_quad_{cfg.depth}_{cfg.do_split}.pt")) + matrixes = [] + dicts = [] + for i in range(1, cfg.depth): + # Step 2: Truncate strings to size cfg.depth - 1 + l = [s[: cfg.depth - i] if len(s) >= cfg.depth + 1 - i else s for s in gt] + + # Step 3: Get unique values in the modified list l + h = list(set(l)) + + # Step 4: Create a dictionary to map unique values to their index + h_dict = {value: index for index, value in enumerate(h)} + dicts.append(h_dict) + + # Step 5: Initialize a torch matrix with zeros + matrix = torch.zeros((len(gt), len(h))) + + # Step 6: Fill in the matrix with 1s based on the mapping + for h in range(len(gt)): + j = h_dict[l[h]] + matrix[h, j] = 1 + matrixes.append(matrix) + + output_file = join(save_path, "quadtree_matrixes.pt") + torch.save(matrixes, output_file) + + output_file = join(save_path, "quadtree_dicts.pt") + torch.save(dicts, output_file) + + +if __name__ == "__main__": + main() diff --git a/scripts/preprocessing/train-val-split.py b/scripts/preprocessing/train-val-split.py new file mode 100644 index 0000000000000000000000000000000000000000..3d6b3df79e4fedb17c7c0c810033adaf839a2b59 --- /dev/null +++ b/scripts/preprocessing/train-val-split.py @@ -0,0 +1,15 @@ +import os +from os.path import dirname, join + +import pandas as pd +from sklearn.model_selection import train_test_split + +if __name__ == "__main__": + data_path = join(dirname(dirname(__file__)), "datasets", "osv5m") + train_fp = join(data_path, f"train.csv") + val_fp = join(data_path, f"val.csv") + os.makedirs(dirname(val_fp), exist_ok=True) + df = pd.read_csv(train_fp, dtype={"category": str, "country": str, "city": str}) + df_train, df_val = train_test_split(df, stratify=df["category"], test_size=0.1) + df_train.to_csv(train_fp, index=False) + df_val.to_csv(val_fp, index=False) diff --git a/scripts/retrieval/backbone.py b/scripts/retrieval/backbone.py new file mode 100644 index 0000000000000000000000000000000000000000..1178096c6ee3b784ab26fab25572454a99590a6a --- /dev/null +++ b/scripts/retrieval/backbone.py @@ -0,0 +1,152 @@ +from os.path import join +import PIL +import numpy as np +import pandas as pd +import reverse_geocoder +from torch.utils.data import Dataset + + +class GeoDataset(Dataset): + def __init__(self, image_folder, annotation_file, transformation, tag="image_id"): + self.image_folder = image_folder + gt = pd.read_csv(annotation_file, dtype={tag: str}) + files = set([f.replace(".jpg", "") for f in os.listdir(image_folder)]) + gt = gt[gt[tag].isin(files)] + self.processor = transformation + self.gt = [ + (g[1][tag], g[1]["latitude"], g[1]["longitude"]) for g in gt.iterrows() + ] + self.tag = tag + + def fid(self, i): + return self.gt[i][0] + + def latlon(self, i): + return self.gt[i][1] + + def __len__(self): + return len(self.gt) + + def __getitem__(self, idx): + fp = join(self.image_folder, self.gt[idx][0] + ".jpg") + return self.processor(self, idx, fp) + + +def load_plonk(path): + import hydra + from hydra import initialize, compose + from models.module import DiffGeolocalizer + from omegaconf import OmegaConf, open_dict + from os.path import join + from hydra.utils import instantiate + + # load config from path + # make path relative to current_dir + with initialize(version_base=None, config_path="osv5m__best_model"): + cfg = compose(config_name="config", overrides=[]) + + checkpoint = torch.load(join(path, "last.ckpt")) + del checkpoint["state_dict"][ + "model.backbone.clip.vision_model.embeddings.position_ids" + ] + torch.save(checkpoint, join(path, "last2.ckpt")) + + with open_dict(cfg): + cfg.checkpoint = join(path, "last2.ckpt") + + cfg.num_classes = 11399 + cfg.model.network.mid.instance.final_dim = cfg.num_classes * 3 + cfg.model.network.head.final_dim = cfg.num_classes * 3 + cfg.model.network.head.instance.quadtree_path = join(path, "quadtree_10_1000.csv") + + cfg.dataset.train_dataset.path = "" + cfg.dataset.val_dataset.path = "" + cfg.dataset.test_dataset.path = "" + cfg.logger.save_dir = "" + cfg.data_dir = "" + cfg.root_dir = "" + cfg.mode = "test" + cfg.model.network.backbone.instance.path = ( + "laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K" + ) + transform = instantiate(cfg.dataset.test_transform) + model = DiffGeolocalizer.load_from_checkpoint( + join(path, "last2.ckpt"), cfg=cfg.model + ) + os.remove(join(path, "last2.ckpt")) + + @torch.no_grad() + def inference(model, x): + return x[0], model.model.backbone({"img": x[1].to(model.device)})[:, 0, :].cpu() + + def collate_fn(batch): + return [b[0] for b in batch], torch.stack([b[1] for b in batch], dim=0) + + def operate(self, idx, fp): + proc = self.processor(PIL.Image.open(fp)) + return self.gt[idx][0], proc + + return model, operate, inference, collate_fn + + +def load_clip(which): + # We evaluate on: + # - "openai/clip-vit-base-patch32" + # - "openai/clip-vit-large-patch14-336" + # - "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" + # - "laion/CLIP-ViT-L-14-DataComp.XL-s13B-b90K" + # - "geolocal/StreetCLIP" + from transformers import CLIPProcessor, CLIPModel + + @torch.no_grad() + def inference(model, img): + image_ids = img.data.pop("image_id") + image_input = img.to(model.device) + image_input["pixel_values"] = image_input["pixel_values"].squeeze(1) + features = model.get_image_features(**image_input) + features /= features.norm(dim=-1, keepdim=True) + return image_ids, features.cpu() + + processor = CLIPProcessor.from_pretrained(which) + + def operate(self, idx, fp): + pil = PIL.Image.open(fp) + proc = processor(images=pil, return_tensors="pt") + proc["image_id"] = self.gt[idx][0] + return proc + + return CLIPModel.from_pretrained(which), operate, inference, None + + +def load_dino(which): + # We evaluate on: + # - 'facebook/dinov2-large' + from transformers import AutoImageProcessor, AutoModel + + @torch.no_grad() + def inference(model, img): + image_ids = img.data.pop("image_id") + image_input = img.to(model.device) + image_input["pixel_values"] = image_input["pixel_values"].squeeze(1) + features = model(**image_input).last_hidden_state[:, 0] + features /= features.norm(dim=-1, keepdim=True) + return image_ids, features.cpu() + + processor = AutoImageProcessor.from_pretrained("facebook/dinov2-large") + + def operate(self, idx, fp): + pil = PIL.Image.open(fp) + proc = processor(images=pil, return_tensors="pt") + proc["image_id"] = self.gt[idx][0] + return proc + + return AutoModel.from_pretrained("facebook/dinov2-large"), operate, inference, None + + +def get_backbone(name): + if os.path.isdir(name): + return load_plonk(name) + elif "clip" in name.lower(): + return load_clip(name) + elif "dino" in name.lower(): + return load_dino(name) diff --git a/scripts/retrieval/retrieval.py b/scripts/retrieval/retrieval.py new file mode 100644 index 0000000000000000000000000000000000000000..bfe4c46652955ca8b644ccd4d876d3b7f3720442 --- /dev/null +++ b/scripts/retrieval/retrieval.py @@ -0,0 +1,143 @@ +import os +import sys +import PIL +import json +import torch +import numpy as np +import pandas as pd +import operator + +from PIL import Image +from itertools import cycle +from tqdm.auto import tqdm, trange +from os.path import join +from PIL import Image + +from tqdm import tqdm +from torch.utils.data import Dataset, DataLoader +from torch.nn import functional as F + +from backbone import get_backbone +from utils import haversine, get_filenames, get_match_values, compute_print_accuracy + + +def compute_features(path, data_dir, csv_file, tag, args): + data = GeoDataset(data_dir, csv_file, tag=tag) + if not os.path.isdir(test_features_dir) or len( + os.listdir(test_features_dir) + ) != len(data): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model, transform, inference, collate_fn = get_backbone(args.name) + dataloader = DataLoader( + data, + batch_size=args.batch_size, + shuffle=False, + num_workers=8, + collate_fn=collate_fn, + ) + model = model.to(device) + os.makedirs(path, exist_ok=True) + + for i, x in enumerate(tqdm(dataloader)): + image_ids, features = inference(model, x) + # save features as numpy array + for j, image_id in zip(range(features.shape[0]), image_ids): + np.save(join(path, f"{image_id}.npy"), features[j].unsqueeze(0).numpy()) + + +def get_results(args, train_test): + import joblib + + if not os.path.isfile(join(args.features_parent, ".cache", "1-nn.pkl")): + import faiss, glob, bisect + + # import sys; sys.exit(0) + indexes = [ + get_filenames(idx) for idx in tqdm(range(1, 6), desc="Loading indexes...") + ] + + train_gt = pd.read_csv( + join(args.data_parent, args.annotation_file), dtype={"image_id": str} + )[["image_id", "latitude", "longitude"]] + test_gt = pd.read_csv(test_path_csv, dtype={"id": str})[ + ["id", "latitude", "longitude"] + ] + + # make a map between image_id and lat/lon + train_gt = { + g[1]["image_id"]: np.array([g[1]["latitude"], g[1]["longitude"]]) + for g in tqdm( + train_gt.iterrows(), total=len(train_gt), desc="Loading train_gt" + ) + } + test_gt = { + g[1]["id"]: np.array([g[1]["latitude"], g[1]["longitude"]]) + for g in tqdm( + test_gt.iterrows(), total=len(test_gt), desc="Loading test_gt" + ) + } + + train_test = [] + os.makedirs(join(args.features_parent, ".cache"), exist_ok=True) + for f in tqdm(os.listdir(test_features_dir)): + query_vector = np.load(join(test_features_dir, f)) + + neighbors = [] + for index, ids in indexes: + distances, indices = index.search(query_vector, 1) + distances, indices = np.squeeze(distances), np.squeeze(indices) + bisect.insort( + neighbors, (ids[indices], distances), key=operator.itemgetter(1) + ) + + neighbors = list(reversed(neighbors)) + train_gps = train_gt[neighbors[0][0].replace(".npy", "")][None, :] + test_gps = test_gt[f.replace(".npy", "")][None, :] + train_test.append((train_gps, test_gps)) + joblib.dump(train_test, join(args.features_parent, ".cache", "1-nn.pkl")) + else: + train_test = joblib.load(join(args.features_parent, ".cache", "1-nn.pkl")) + + return train_test + + +if __name__ == "__main__": + # make a train/eval argparser + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--id", type=int, default=1) # maybe need to remove/refactor + parser.add_argument("--batch_size", type=int, default=512) + parser.add_argument( + "--annotation_file", type=str, required=False, default="train.csv" + ) + parser.add_argument("--name", type=str, default="openai/clip-vit-base-patch32") + parser.add_argument("--features_parent", type=str, default="faiss/") + parser.add_argument("--data_parent", type=str, default="data/") + parser.add_argument("--test", action="store_true") + + args = parser.parse_args() + args.features_parent = join(args.features_parent, args.name) + if args.test: + csv_file = join(args.data_parent, "test.csv") + data_dir = join(args.data_parent, "test") + path = join(args.features_parent, "features-test") + model = get_backbone(args.name) + compute_features(path, data_dir, csv_file, tag="id", args=args) + train_test = get_results(args, train_test) + + from collections import Counter + + N, pos = Counter(), Counter() + for train_gps, test_gps in tqdm(train_test, desc="Computing accuracy..."): + get_match_values(train_gps, test_gps, N, pos) + + for train_gps, test_gps in tqdm(train_test, desc="Computing haversine..."): + haversine(train_gps, test_gps, N, pos) + + compute_print_accuracy(N, pos) + else: + csv_file = join(args.data_parent, args.annotation_file) + path = join(args.features_parent, f"features-{args.id}") + data_dir = join(args.data_parent, f"images-{args.id}", "train") + compute_features(path, data_dir, csv_file, tag="image_id", args=args) diff --git a/scripts/retrieval/street-clip-zero-shot.py b/scripts/retrieval/street-clip-zero-shot.py new file mode 100644 index 0000000000000000000000000000000000000000..72f35494048cedc085c79e1217d8609552a87307 --- /dev/null +++ b/scripts/retrieval/street-clip-zero-shot.py @@ -0,0 +1,299 @@ +import traceback +import os +import sys +import PIL +import json +import torch +import numpy as np +import pandas as pd +import operator +import joblib +import reverse_geocoder + +from PIL import Image +from itertools import cycle +from tqdm.auto import tqdm, trange +from os.path import join +from PIL import Image + +from tqdm import tqdm +from collections import Counter +from transformers import CLIPProcessor, CLIPModel +from torch.utils.data import Dataset, DataLoader +from torch.nn import functional as F +from utils import haversine + + +class GeoDataset(Dataset): + def __init__(self, image_folder, annotation_file, tag="image_id"): + self.image_folder = image_folder + gt = pd.read_csv(annotation_file, dtype={tag: str}) + files = set([f.replace(".jpg", "") for f in os.listdir(image_folder)]) + gt = gt[gt[tag].isin(files)] + self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") + self.gt = [ + (g[1][tag], g[1]["latitude"], g[1]["longitude"]) for g in gt.iterrows() + ] + self.tag = tag + + def fid(self, i): + return self.gt[i][0] + + def latlon(self, i): + return self.gt[i][1] + + def __len__(self): + return len(self.gt) + + def __getitem__(self, idx): + fp = join(self.image_folder, self.gt[idx][0] + ".jpg") + pil = PIL.Image.open(fp) + proc = self.processor(images=pil, return_tensors="pt") + proc["image_id"] = self.gt[idx][0] + return proc + + +@torch.no_grad() +def compute_features_clip(img, model): + image_ids = img.data.pop("image_id") + image_input = img.to(model.device) + image_input["pixel_values"] = image_input["pixel_values"].squeeze(1) + features = model.get_image_features(**image_input) + features /= features.norm(dim=-1, keepdim=True) + return image_ids, features.cpu() + + +def get_prompts(country, region, sub_region, city): + a = country if country != "" else None + b, c, d = None, None, None + if a is not None: + b = country + ", " + region if region != "" else None + if b is not None: + c = ( + country + ", " + region + ", " + sub_region + if sub_region != "" + else None + ) + d = ( + country + ", " + region + ", " + sub_region + ", " + city + if city != "" + else None + ) + return a, b, c, d + + +if __name__ == "__main__": + # make a train/eval argparser + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument( + "--annotation_file", type=str, required=False, default="train.csv" + ) + parser.add_argument( + "--features_parent", type=str, default="/home/isig/gaia-v2/faiss/street-clip" + ) + parser.add_argument( + "--data_parent", type=str, default="/home/isig/gaia-v2/loic-data/" + ) + + args = parser.parse_args() + test_path_csv = join(args.data_parent, "test.csv") + test_image_dir = join(args.data_parent, "test") + save_path = join(args.features_parent, "indexes/test.index") + test_features_dir = join(args.features_parent, "indexes/features-test") + + processor = CLIPProcessor.from_pretrained("geolocal/StreetCLIP") + device = "cuda" if torch.cuda.is_available() else "cpu" + model = CLIPModel.from_pretrained("geolocal/StreetCLIP").to(device) + + @torch.no_grad() + def compute_text_features_clip(text): + text_pt = processor(text=text, return_tensors="pt").to(device) + features = model.get_text_features(**text_pt) + features /= features.norm(dim=-1, keepdim=True) + return features.cpu().squeeze(0).numpy() + + import country_converter as coco + + if not os.path.isfile("text_street-clip-features.pkl"): + if not os.path.isfile("rg_cities1000.csv"): + os.system( + "wget https://raw.githubusercontent.com/thampiman/reverse-geocoder/master/reverse_geocoder/rg_cities1000.csv" + ) + + cities = pd.read_csv("rg_cities1000.csv") + cities = cities[["lat", "lon", "name", "admin1", "admin2", "cc"]] + reprs = {0: {}, 1: {}, 2: {}, 3: {}} + for line in tqdm( + cities.iterrows(), total=len(cities), desc="Creating hierarchy" + ): + lat, lon, city, region, sub_region, cc = line[1] + try: + city, region, sub_region, cc = [ + ("" if pd.isna(x) else x) + for x in [ + city, + region, + sub_region, + coco.convert(cc, to="name_short"), + ] + ] + a, b, c, d = get_prompts(cc, region, sub_region, city) + if a is not None: + if a not in reprs[0]: + reprs[0][a] = { + "gps": {(lat, lon)}, + "embedding": compute_text_features_clip(a), + } + else: + reprs[0][a]["gps"].add((lat, lon)) + + if b is not None: + if b not in reprs[1]: + reprs[1][b] = { + "gps": {(lat, lon)}, + "embedding": compute_text_features_clip(b), + } + else: + reprs[1][b]["gps"].add((lat, lon)) + + if c is not None: + if c not in reprs[2]: + reprs[2][c] = { + "gps": {(lat, lon)}, + "embedding": compute_text_features_clip(c), + } + else: + reprs[2][c]["gps"].add((lat, lon)) + + if d is not None: + if d not in reprs[3]: + reprs[3][d] = { + "gps": {(lat, lon)}, + "embedding": compute_text_features_clip( + d.replace(", , ", ", ") + ), + } + else: + reprs[3][d]["gps"].add((lat, lon)) + except Exception as e: + # print stack trace into file log.txt + with open("log.txt", "a") as f: + print(traceback.format_exc(), file=f) + + reprs[-1] = {"": {"gps": (0, 0), "embedding": compute_text_features_clip("")}} + + # compute mean for gps of all 'a' and 'b' and 'c' and 'd' + for i in range(4): + for k in reprs[i].keys(): + reprs[i][k]["gps"] = tuple( + np.array(list(reprs[i][k]["gps"])).mean(axis=0).tolist() + ) + + joblib.dump(reprs, "text_street-clip-features.pkl") + else: + reprs = joblib.load("text_street-clip-features.pkl") + + def get_loc(x): + location = reverse_geocoder.search(x[0].tolist())[0] + country = coco.convert(names=location["cc"], to="name_short") + region = location.get("admin1", "") + sub_region = location.get("admin2", "") + city = location.get("name", "") + a, b, c, d = get_prompts(country, region, sub_region, city) + return a, b, c, d + + def matches(embed, repr, control, gt, sw=None): + first_max = max( + ( + (k, embed.dot(v["embedding"])) + for k, v in repr.items() + if sw is None or k.startswith(sw) + ), + key=operator.itemgetter(1), + ) + if first_max[1] > embed.dot(control["embedding"]): + return repr[first_max[0]]["gps"], gt == first_max[0] + else: + return control["gps"], False + + def get_match_values(gt, embed, N, pos): + xa, xb, xc, xd = get_loc(gt) + + if xa is not None: + N["country"] += 1 + gps, flag = matches(embed, reprs[0], reprs[-1][""], xa) + if flag: + pos["country"] += 1 + if xb is not None: + N["region"] += 1 + gps, flag = matches(embed, reprs[1], reprs[0][xa], xb, sw=xa) + if flag: + pos["region"] += 1 + if xc is not None: + N["sub-region"] += 1 + gps, flag = matches( + embed, reprs[2], reprs[1][xb], xc, sw=xb + ) + if flag: + pos["sub-region"] += 1 + if xd is not None: + N["city"] += 1 + gps, flag = matches( + embed, reprs[3], reprs[2][xc], xd, sw=xc + ) + if flag: + pos["city"] += 1 + else: + if xd is not None: + N["city"] += 1 + gps, flag = matches( + embed, reprs[3], reprs[1][xb], xd, sw=xb + ", " + ) + if flag: + pos["city"] += 1 + + haversine(np.array(gps)[None, :], np.array(gt), N, pos) + + def compute_print_accuracy(N, pos): + for k in N.keys(): + pos[k] /= N[k] + + # pretty-print accuracy in percentage with 2 floating points + print( + f'Accuracy: {pos["country"]*100.0:.2f} (country), {pos["region"]*100.0:.2f} (region), {pos["sub-region"]*100.0:.2f} (sub-region), {pos["city"]*100.0:.2f} (city)' + ) + print( + f'Haversine: {pos["haversine"]:.2f} (haversine), {pos["geoguessr"]:.2f} (geoguessr)' + ) + + import joblib + + data = GeoDataset(test_image_dir, test_path_csv, tag="id") + test_gt = pd.read_csv(test_path_csv, dtype={"id": str})[ + ["id", "latitude", "longitude"] + ] + test_gt = { + g[1]["id"]: np.array([g[1]["latitude"], g[1]["longitude"]]) + for g in tqdm(test_gt.iterrows(), total=len(test_gt), desc="Loading test_gt") + } + + with open("/home/isig/gaia-v2/loic/plonk/test3_indices.txt", "r") as f: + # read lines + lines = f.readlines() + # remove whitespace characters like `\n` at the end of each line + lines = [l.strip() for l in lines] + # and convert to set + lines = set(lines) + + train_test = [] + N, pos = Counter(), Counter() + for f in tqdm(os.listdir(test_features_dir)): + if f.replace(".npy", "") not in lines: + continue + query_vector = np.squeeze(np.load(join(test_features_dir, f))) + test_gps = test_gt[f.replace(".npy", "")][None, :] + get_match_values(test_gps, query_vector, N, pos) + + compute_print_accuracy(N, pos) diff --git a/scripts/retrieval/utils.py b/scripts/retrieval/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c2b209954a07d15ecb1b6f7bb32732cb4da799f9 --- /dev/null +++ b/scripts/retrieval/utils.py @@ -0,0 +1,113 @@ +import os +import numpy as np +import reverse_geocoder + + +def get_loc(x): + location = reverse_geocoder.search(x[0].tolist())[0] + country = location.get("cc", "") + region = location.get("admin1", "") + sub_region = location.get("admin2", "") + city = location.get("name", "") + + a = country if country != "" else None + b, c, d = None, None, None + if a is not None: + b = country + "," + region if region != "" else None + if b is not None: + c = country + "," + region + "," + sub_region if sub_region != "" else None + d = ( + country + "," + region + "," + sub_region + "," + city + if city != "" + else None + ) + + return a, b, c, d + + +def get_match_values(pred, gt, N, pos): + xa, xb, xc, xd = get_loc(gt) + ya, yb, yc, yd = get_loc(pred) + + if xa is not None: + N["country"] += 1 + if xa == ya: + pos["country"] += 1 + if xb is not None: + N["region"] += 1 + if xb == yb: + pos["region"] += 1 + if xc is not None: + N["sub-region"] += 1 + if xc == yc: + pos["sub-region"] += 1 + if xd is not None: + N["city"] += 1 + if xd == yd: + pos["city"] += 1 + + +def compute_print_accuracy(N, pos): + for k in N.keys(): + pos[k] /= N[k] + + # pretty-print accuracy in percentage with 2 floating points + print( + f'Accuracy: {pos["country"]*100.0:.2f} (country), {pos["region"]*100.0:.2f} (region), {pos["sub-region"]*100.0:.2f} (sub-region), {pos["city"]*100.0:.2f} (city)' + ) + print( + f'Haversine: {pos["haversine"]:.2f} (haversine), {pos["geoguessr"]:.2f} (geoguessr)' + ) + + +def get_filenames(idx): + from autofaiss import build_index + + path = join(args.features_parent, f"features-{idx}/") + files = [f for f in os.listdir(path)] + full_files = [join(path, f) for f in os.listdir(path)] + index = build_index( + embeddings=np.concatenate([np.load(f) for f in tqdm(full_files)], axis=0), + nb_cores=12, + save_on_disk=False, + )[0] + return index, files + + +def normalize(x): + lat, lon = x[:, 0], x[:, 1] + """Used to put all lat lon inside ±90 and ±180.""" + lat = (lat + 90) % 360 - 90 + if lat > 90: + lat = 180 - lat + lon += 180 + lon = (lon + 180) % 360 - 180 + return np.stack([lat, lon], axis=1) + + +def haversine(pred, gt, N, p): + # expects inputs to be np arrays in (lat, lon) format as radians + # N x 2 + pred = np.radians(normalize(pred)) + gt = np.radians(normalize(gt)) + + # calculate the difference in latitude and longitude between the predicted and ground truth points + lat_diff = pred[:, 0] - gt[:, 0] + lon_diff = pred[:, 1] - gt[:, 1] + + # calculate the haversine formula components + lhs = np.sin(lat_diff / 2) ** 2 + rhs = np.cos(pred[:, 0]) * np.cos(gt[:, 0]) * np.sin(lon_diff / 2) ** 2 + a = lhs + rhs + + # calculate the final distance using the haversine formula + c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)) + + haversine_distance = 6371 * c[0] + geoguessr_sum = 5000 * np.exp(-haversine_distance / 1492.7) + + N["geoguessr"] += 1 + p["geoguessr"] += geoguessr_sum + + N["haversine"] += 1 + p["haversine"] += haversine_distance diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..e2ae4dd8095611d4078c5034b6b7082ae608523f --- /dev/null +++ b/setup.py @@ -0,0 +1,33 @@ +from setuptools import setup, find_packages + +setup( + name="diff_plonk", + version="0.1", + packages=find_packages(), + install_requires=[ + "torch", + "torchvision", + "joblib", + "wandb", + "hydra-core", + "numpy", + "scipy==1.13.1", + "pandas", + "scikit-learn", + "pytorch-lightning", + "transformers", + "accelerate", + "peft", + "geos", + "reverse_geocoder", + "matplotlib", + "geoopt", + "einops", + "torchdiffeq", + "webdataset==0.2.57", + "pytest", + "streamlit", + "streamlit-extras", + "plotly", + ], +) diff --git a/test.py b/test.py new file mode 100755 index 0000000000000000000000000000000000000000..77681ff94c2604dad787481deb00a7c9a4a7f42e --- /dev/null +++ b/test.py @@ -0,0 +1,85 @@ +import os +from models.module import DiffGeolocalizer +import hydra +import wandb +from os.path import isfile, join +from shutil import copyfile + +import torch + +from omegaconf import OmegaConf +from omegaconf import open_dict +from hydra.core.hydra_config import HydraConfig +from hydra.utils import instantiate +from pytorch_lightning.callbacks import LearningRateMonitor +from lightning_fabric.utilities.rank_zero import _get_rank + +from models.module import DiffGeolocalizer + +torch.set_float32_matmul_precision("high") # TODO do we need that? + +# Registering the "eval" resolver allows for advanced config +# interpolation with arithmetic operations in hydra: +# https://omegaconf.readthedocs.io/en/2.3_branch/how_to_guides.html +OmegaConf.register_new_resolver("eval", eval) + + +def load_model(cfg, dict_config, wandb_id): + logger = instantiate(cfg.logger, id=open(wandb_id, "r").read(), resume="allow") + model = DiffGeolocalizer.load_from_checkpoint(cfg.checkpoint, cfg=cfg.model) + trainer = instantiate(cfg.trainer, strategy=cfg.trainer.strategy, logger=logger) + return trainer, model + + +def hydra_boilerplate(cfg): + dict_config = OmegaConf.to_container(cfg, resolve=True) + trainer, model = load_model(cfg, dict_config, cfg.wandb_id) + return trainer, model + + +import copy + + +def generate_datamodules(cfg_): + for f in os.listdir(cfg_.test_dir): + cfg = copy.deepcopy(cfg_) + # open join(f, directory) with OmegaConf + with open_dict(cfg): + cfg_new = OmegaConf.load(join(cfg.test_dir, f)) + cfg.datamodule = cfg_new.datamodule + cfg.dataset = cfg_new.dataset + cfg.dataset.test_transform = cfg_.dataset.test_transform + + datamodule = instantiate(cfg.datamodule) + yield datamodule + + +if __name__ == "__main__": + import sys + + sys.argv = ( + [sys.argv[0]] + + ["+pt_model_path=${hydra:runtime.config_sources}"] + + sys.argv[1:] + ) + + @hydra.main(version_base=None) + def main(cfg): + # print(hydra.runtime.config_sources) + with open_dict(cfg): + path = cfg.pt_model_path[1]["path"] + cfg.wandb_id = join(path, "wandb_id.txt") + cfg.checkpoint = join(path, "last.ckpt") + cfg.computer.devices = 1 + + ( + trainer, + model, + ) = hydra_boilerplate(cfg) + for datamodule in generate_datamodules(cfg): + model.datamodule = datamodule + model.datamodule.setup() + print("Testing on", datamodule.test_dataset.class_name) + trainer.test(model, datamodule=datamodule) + + main() diff --git a/train.py b/train.py new file mode 100755 index 0000000000000000000000000000000000000000..3fd7f153786cd780ce4e1a501366b18fb851bdd4 --- /dev/null +++ b/train.py @@ -0,0 +1,146 @@ +import os +import hydra +import wandb +from os.path import isfile, join +from shutil import copyfile + +import torch + +from omegaconf import OmegaConf +from hydra.core.hydra_config import HydraConfig +from hydra.utils import instantiate +from pytorch_lightning.callbacks import LearningRateMonitor +from lightning_fabric.utilities.rank_zero import _get_rank +from callbacks import EMACallback, FixNANinGrad, IncreaseDataEpoch +from models.module import DiffGeolocalizer + +torch.set_float32_matmul_precision("high") # TODO do we need that? + +# Registering the "eval" resolver allows for advanced config +# interpolation with arithmetic operations in hydra: +# https://omegaconf.readthedocs.io/en/2.3_branch/how_to_guides.html +OmegaConf.register_new_resolver("eval", eval) + + +def wandb_init(cfg): + directory = cfg.checkpoints.dirpath + if isfile(join(directory, "wandb_id.txt")) and cfg.logger_suffix == "": + with open(join(directory, "wandb_id.txt"), "r") as f: + wandb_id = f.readline() + else: + rank = _get_rank() + wandb_id = wandb.util.generate_id() + print(f"Generated wandb id: {wandb_id}") + if rank == 0 or rank is None: + with open(join(directory, "wandb_id.txt"), "w") as f: + f.write(str(wandb_id)) + + return wandb_id + + +def load_model(cfg, dict_config, wandb_id, callbacks): + directory = cfg.checkpoints.dirpath + if isfile(join(directory, "last.ckpt")): + checkpoint_path = join(directory, "last.ckpt") + logger = instantiate(cfg.logger, id=wandb_id, resume="allow") + model = DiffGeolocalizer.load_from_checkpoint(checkpoint_path, cfg=cfg.model) + ckpt_path = join(directory, "last.ckpt") + print(f"Loading form checkpoint ... {ckpt_path}") + else: + ckpt_path = None + logger = instantiate(cfg.logger, id=wandb_id, resume="allow") + log_dict = {"model": dict_config["model"], "dataset": dict_config["dataset"]} + logger._wandb_init.update({"config": log_dict}) + model = DiffGeolocalizer(cfg.model) + + trainer, strategy = cfg.trainer, cfg.trainer.strategy + # from pytorch_lightning.profilers import PyTorchProfiler + + trainer = instantiate( + trainer, + strategy=strategy, + logger=logger, + callbacks=callbacks, + # profiler=PyTorchProfiler( + # dirpath="logs", + # schedule=torch.profiler.schedule(wait=1, warmup=3, active=3, repeat=1), + # on_trace_ready=torch.profiler.tensorboard_trace_handler("./logs"), + # record_shapes=True, + # with_stack=True, + # with_flops=True, + # with_modules=True, + # ), + ) + return trainer, model, ckpt_path + + +def project_init(cfg): + print("Working directory set to {}".format(os.getcwd())) + directory = cfg.checkpoints.dirpath + os.makedirs(directory, exist_ok=True) + copyfile(".hydra/config.yaml", join(directory, "config.yaml")) + + +def callback_init(cfg): + checkpoint_callback = instantiate(cfg.checkpoints) + progress_bar = instantiate(cfg.progress_bar) + lr_monitor = LearningRateMonitor() + ema_callback = EMACallback( + "network", + "ema_network", + decay=cfg.model.ema_decay, + start_ema_step=cfg.model.start_ema_step, + init_ema_random=False, + ) + fix_nan_callback = FixNANinGrad( + monitor=["train/loss"], + ) + increase_data_epoch_callback = IncreaseDataEpoch() + callbacks = [ + checkpoint_callback, + progress_bar, + lr_monitor, + ema_callback, + fix_nan_callback, + increase_data_epoch_callback, + ] + return callbacks + + +def init_datamodule(cfg): + datamodule = instantiate(cfg.datamodule) + return datamodule + + +def hydra_boilerplate(cfg): + dict_config = OmegaConf.to_container(cfg, resolve=True) + callbacks = callback_init(cfg) + datamodule = init_datamodule(cfg) + project_init(cfg) + wandb_id = wandb_init(cfg) + trainer, model, ckpt_path = load_model(cfg, dict_config, wandb_id, callbacks) + return trainer, model, datamodule, ckpt_path + + +@hydra.main(config_path="configs", config_name="config", version_base=None) +def main(cfg): + if "stage" in cfg and cfg.stage == "debug": + import lovely_tensors as lt + + lt.monkey_patch() + trainer, model, datamodule, ckpt_path = hydra_boilerplate(cfg) + model.datamodule = datamodule + # model = torch.compile(model) + if cfg.mode == "train": + trainer.fit(model, datamodule=datamodule, ckpt_path=ckpt_path) + elif cfg.mode == "eval": + trainer.test(model, datamodule=datamodule) + elif cfg.mode == "traineval": + cfg.mode = "train" + trainer.fit(model, datamodule=datamodule, ckpt_path=ckpt_path) + cfg.mode = "test" + trainer.test(model, datamodule=datamodule) + + +if __name__ == "__main__": + main() diff --git a/train_random.py b/train_random.py new file mode 100755 index 0000000000000000000000000000000000000000..01e9b526161aaffc54e6e94809bbab5dc93f1a73 --- /dev/null +++ b/train_random.py @@ -0,0 +1,146 @@ +import os +import hydra +import wandb +from os.path import isfile, join +from shutil import copyfile + +import torch + +from omegaconf import OmegaConf +from hydra.core.hydra_config import HydraConfig +from hydra.utils import instantiate +from pytorch_lightning.callbacks import LearningRateMonitor +from lightning_fabric.utilities.rank_zero import _get_rank +from callbacks import EMACallback, FixNANinGrad, IncreaseDataEpoch +from models.module import RandomGeolocalizer + +torch.set_float32_matmul_precision("high") # TODO do we need that? + +# Registering the "eval" resolver allows for advanced config +# interpolation with arithmetic operations in hydra: +# https://omegaconf.readthedocs.io/en/2.3_branch/how_to_guides.html +OmegaConf.register_new_resolver("eval", eval) + + +def wandb_init(cfg): + directory = cfg.checkpoints.dirpath + if isfile(join(directory, "wandb_id.txt")) and cfg.logger_suffix == "": + with open(join(directory, "wandb_id.txt"), "r") as f: + wandb_id = f.readline() + else: + rank = _get_rank() + wandb_id = wandb.util.generate_id() + print(f"Generated wandb id: {wandb_id}") + if rank == 0 or rank is None: + with open(join(directory, "wandb_id.txt"), "w") as f: + f.write(str(wandb_id)) + + return wandb_id + + +def load_model(cfg, dict_config, wandb_id, callbacks): + directory = cfg.checkpoints.dirpath + if isfile(join(directory, "last.ckpt")): + checkpoint_path = join(directory, "last.ckpt") + logger = instantiate(cfg.logger, id=wandb_id, resume="allow") + model = RandomGeolocalizer.load_from_checkpoint(checkpoint_path, cfg=cfg.model) + ckpt_path = join(directory, "last.ckpt") + print(f"Loading form checkpoint ... {ckpt_path}") + else: + ckpt_path = None + logger = instantiate(cfg.logger, id=wandb_id, resume="allow") + log_dict = {"model": dict_config["model"], "dataset": dict_config["dataset"]} + logger._wandb_init.update({"config": log_dict}) + model = RandomGeolocalizer(cfg.model) + + trainer, strategy = cfg.trainer, cfg.trainer.strategy + # from pytorch_lightning.profilers import PyTorchProfiler + + trainer = instantiate( + trainer, + strategy=strategy, + logger=logger, + callbacks=callbacks, + # profiler=PyTorchProfiler( + # dirpath="logs", + # schedule=torch.profiler.schedule(wait=1, warmup=3, active=3, repeat=1), + # on_trace_ready=torch.profiler.tensorboard_trace_handler("./logs"), + # record_shapes=True, + # with_stack=True, + # with_flops=True, + # with_modules=True, + # ), + ) + return trainer, model, ckpt_path + + +def project_init(cfg): + print("Working directory set to {}".format(os.getcwd())) + directory = cfg.checkpoints.dirpath + os.makedirs(directory, exist_ok=True) + copyfile(".hydra/config.yaml", join(directory, "config.yaml")) + + +def callback_init(cfg): + checkpoint_callback = instantiate(cfg.checkpoints) + progress_bar = instantiate(cfg.progress_bar) + lr_monitor = LearningRateMonitor() + ema_callback = EMACallback( + "network", + "ema_network", + decay=cfg.model.ema_decay, + start_ema_step=cfg.model.start_ema_step, + init_ema_random=False, + ) + fix_nan_callback = FixNANinGrad( + monitor=["train/loss"], + ) + increase_data_epoch_callback = IncreaseDataEpoch() + callbacks = [ + checkpoint_callback, + progress_bar, + lr_monitor, + ema_callback, + fix_nan_callback, + increase_data_epoch_callback, + ] + return callbacks + + +def init_datamodule(cfg): + datamodule = instantiate(cfg.datamodule) + return datamodule + + +def hydra_boilerplate(cfg): + dict_config = OmegaConf.to_container(cfg, resolve=True) + callbacks = callback_init(cfg) + datamodule = init_datamodule(cfg) + project_init(cfg) + wandb_id = wandb_init(cfg) + trainer, model, ckpt_path = load_model(cfg, dict_config, wandb_id, callbacks) + return trainer, model, datamodule, ckpt_path + + +@hydra.main(config_path="configs", config_name="config", version_base=None) +def main(cfg): + if "stage" in cfg and cfg.stage == "debug": + import lovely_tensors as lt + + lt.monkey_patch() + trainer, model, datamodule, ckpt_path = hydra_boilerplate(cfg) + model.datamodule = datamodule + # model = torch.compile(model) + if cfg.mode == "train": + trainer.fit(model, datamodule=datamodule, ckpt_path=ckpt_path) + elif cfg.mode == "eval": + trainer.test(model, datamodule=datamodule) + elif cfg.mode == "traineval": + cfg.mode = "train" + trainer.fit(model, datamodule=datamodule, ckpt_path=ckpt_path) + cfg.mode = "test" + trainer.test(model, datamodule=datamodule) + + +if __name__ == "__main__": + main() diff --git a/train_von_fisher.py b/train_von_fisher.py new file mode 100755 index 0000000000000000000000000000000000000000..fd684488f5d6c952c566d6df8d231d3d519b43db --- /dev/null +++ b/train_von_fisher.py @@ -0,0 +1,148 @@ +import os +import hydra +import wandb +from os.path import isfile, join +from shutil import copyfile + +import torch + +from omegaconf import OmegaConf +from hydra.core.hydra_config import HydraConfig +from hydra.utils import instantiate +from pytorch_lightning.callbacks import LearningRateMonitor +from lightning_fabric.utilities.rank_zero import _get_rank +from callbacks import EMACallback, FixNANinGrad, IncreaseDataEpoch +from models.module import VonFisherGeolocalizer + +torch.set_float32_matmul_precision("high") # TODO do we need that? + +# Registering the "eval" resolver allows for advanced config +# interpolation with arithmetic operations in hydra: +# https://omegaconf.readthedocs.io/en/2.3_branch/how_to_guides.html +OmegaConf.register_new_resolver("eval", eval) + + +def wandb_init(cfg): + directory = cfg.checkpoints.dirpath + if isfile(join(directory, "wandb_id.txt")): + with open(join(directory, "wandb_id.txt"), "r") as f: + wandb_id = f.readline() + else: + rank = _get_rank() + wandb_id = wandb.util.generate_id() + print(f"Generated wandb id: {wandb_id}") + if rank == 0 or rank is None: + with open(join(directory, "wandb_id.txt"), "w") as f: + f.write(str(wandb_id)) + + return wandb_id + + +def load_model(cfg, dict_config, wandb_id, callbacks): + directory = cfg.checkpoints.dirpath + if isfile(join(directory, "last.ckpt")): + checkpoint_path = join(directory, "last.ckpt") + logger = instantiate(cfg.logger, id=wandb_id, resume="allow") + model = VonFisherGeolocalizer.load_from_checkpoint( + checkpoint_path, cfg=cfg.model + ) + ckpt_path = join(directory, "last.ckpt") + print(f"Loading form checkpoint ... {ckpt_path}") + else: + ckpt_path = None + logger = instantiate(cfg.logger, id=wandb_id, resume="allow") + log_dict = {"model": dict_config["model"], "dataset": dict_config["dataset"]} + logger._wandb_init.update({"config": log_dict}) + model = VonFisherGeolocalizer(cfg.model) + + trainer, strategy = cfg.trainer, cfg.trainer.strategy + # from pytorch_lightning.profilers import PyTorchProfiler + + trainer = instantiate( + trainer, + strategy=strategy, + logger=logger, + callbacks=callbacks, + # profiler=PyTorchProfiler( + # dirpath="logs", + # schedule=torch.profiler.schedule(wait=1, warmup=3, active=3, repeat=1), + # on_trace_ready=torch.profiler.tensorboard_trace_handler("./logs"), + # record_shapes=True, + # with_stack=True, + # with_flops=True, + # with_modules=True, + # ), + ) + return trainer, model, ckpt_path + + +def project_init(cfg): + print("Working directory set to {}".format(os.getcwd())) + directory = cfg.checkpoints.dirpath + os.makedirs(directory, exist_ok=True) + copyfile(".hydra/config.yaml", join(directory, "config.yaml")) + + +def callback_init(cfg): + checkpoint_callback = instantiate(cfg.checkpoints) + progress_bar = instantiate(cfg.progress_bar) + lr_monitor = LearningRateMonitor() + ema_callback = EMACallback( + "network", + "ema_network", + decay=cfg.model.ema_decay, + start_ema_step=cfg.model.start_ema_step, + init_ema_random=False, + ) + fix_nan_callback = FixNANinGrad( + monitor=["train/loss"], + ) + increase_data_epoch_callback = IncreaseDataEpoch() + callbacks = [ + checkpoint_callback, + progress_bar, + lr_monitor, + ema_callback, + fix_nan_callback, + increase_data_epoch_callback, + ] + return callbacks + + +def init_datamodule(cfg): + datamodule = instantiate(cfg.datamodule) + return datamodule + + +def hydra_boilerplate(cfg): + dict_config = OmegaConf.to_container(cfg, resolve=True) + callbacks = callback_init(cfg) + datamodule = init_datamodule(cfg) + project_init(cfg) + wandb_id = wandb_init(cfg) + trainer, model, ckpt_path = load_model(cfg, dict_config, wandb_id, callbacks) + return trainer, model, datamodule, ckpt_path + + +@hydra.main(config_path="configs", config_name="config", version_base=None) +def main(cfg): + if "stage" in cfg and cfg.stage == "debug": + import lovely_tensors as lt + + lt.monkey_patch() + trainer, model, datamodule, ckpt_path = hydra_boilerplate(cfg) + model.datamodule = datamodule + # model = torch.compile(model) + if cfg.mode == "train": + trainer.fit(model, datamodule=datamodule, ckpt_path=ckpt_path) + elif cfg.mode == "eval": + trainer.test(model, datamodule=datamodule) + elif cfg.mode == "traineval": + cfg.mode = "train" + trainer.fit(model, datamodule=datamodule, ckpt_path=ckpt_path) + cfg.mode = "test" + trainer.test(model, datamodule=datamodule) + + +if __name__ == "__main__": + main() diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/utils/__pycache__/__init__.cpython-310.pyc b/utils/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cebd9149b0667285c4370c339055250c0cf8e9fe Binary files /dev/null and b/utils/__pycache__/__init__.cpython-310.pyc differ diff --git a/utils/__pycache__/image_processing.cpython-310.pyc b/utils/__pycache__/image_processing.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f0b861180242d2cfbbbcdd3b19a4764224dd6c3a Binary files /dev/null and b/utils/__pycache__/image_processing.cpython-310.pyc differ diff --git a/utils/__pycache__/kde.cpython-310.pyc b/utils/__pycache__/kde.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..986963634e61a31052e72a9052b1eaaab644d4d2 Binary files /dev/null and b/utils/__pycache__/kde.cpython-310.pyc differ diff --git a/utils/__pycache__/lr_scheduler.cpython-310.pyc b/utils/__pycache__/lr_scheduler.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..72403fa774c30de5390a894aa730388c3876e712 Binary files /dev/null and b/utils/__pycache__/lr_scheduler.cpython-310.pyc differ diff --git a/utils/__pycache__/manifolds.cpython-310.pyc b/utils/__pycache__/manifolds.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8c88ad0acdae9e1504ad1cc966a3a66a0320232f Binary files /dev/null and b/utils/__pycache__/manifolds.cpython-310.pyc differ diff --git a/utils/__pycache__/optimizers.cpython-310.pyc b/utils/__pycache__/optimizers.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a2b6a4cdcbe7edf9aa05c9aacf5aedec779fbd6b Binary files /dev/null and b/utils/__pycache__/optimizers.cpython-310.pyc differ diff --git a/utils/image_processing.py b/utils/image_processing.py new file mode 100755 index 0000000000000000000000000000000000000000..8f885eeefd3ff9f0152034b32ac441caa2b1a4cd --- /dev/null +++ b/utils/image_processing.py @@ -0,0 +1,58 @@ +import torch +import torch.nn.functional as F +import torchvision + + +def remap_image_torch(image): + image_torch = ((image + 1) / 2.0) * 255.0 + image_torch = torch.clip(image_torch, 0, 255).to(torch.uint8) + return image_torch + + +class CenterCrop(torch.nn.Module): + """Crops the given image at the center. Allows to crop to the maximum possible size. + Args: + size (sequence or int): Desired output size of the crop. If size is an + int instead of sequence like (h, w), a square crop (size, size) is + made. + ratio (str): Desired output ratio of the crop that will do the maximum possible crop with the given ratio. + """ + + def __init__(self, size=None, ratio="1:1"): + super().__init__() + self.size = size + self.ratio = ratio + + def forward(self, img): + """ + Args: + img (PIL Image or Tensor): Image to be cropped. + + Returns: + PIL Image or Tensor: Cropped image. + """ + if self.size is None: + if isinstance(img, torch.Tensor): + h, w = img.shape[-2:] + else: + w, h = img.size + ratio = self.ratio.split(":") + ratio = float(ratio[0]) / float(ratio[1]) + ratioed_w = int(h * ratio) + ratioed_h = int(w / ratio) + if w >= h: + if ratioed_h <= h: + size = (ratioed_h, w) + else: + size = (h, ratioed_w) + else: + if ratioed_w <= w: + size = (h, ratioed_w) + else: + size = (ratioed_h, w) + else: + size = self.size + return torchvision.transforms.functional.center_crop(img, size) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}(size={self.size})" diff --git a/utils/kde.py b/utils/kde.py new file mode 100644 index 0000000000000000000000000000000000000000..1afe32b79c03cb4ef266fb8def417f5b162d5a5c --- /dev/null +++ b/utils/kde.py @@ -0,0 +1,31 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import matplotlib.pyplot as plt + + +class BatchedKDE(nn.Module): + def __init__(self, bandwith=0.0): + super().__init__() + self.bandwidth = bandwith + self.X = None + + def fit(self, X: torch.Tensor): + self.mu = X + self.nmu2 = torch.sum(X * X, dim=-1, keepdim=True) + b, n, d = X.shape + if self.bandwidth == 0: + q = torch.quantile(X.view(b, -1), 0.75) - torch.quantile( + X.view(b, -1), 0.25 + ) + self.bandwidth = ( + 0.9 * torch.min(torch.std(X, dim=(1, 2)), q / 1.34) / pow(n, 0.2) + ) + + def score(self, X): + nx2 = torch.sum(X * X, dim=-1, keepdim=True) + dot = torch.einsum("bnd, bmd -> bnm", X, self.mu) + dist = nx2 + self.nmu2.transpose(1, 2) - 2 * dot + return torch.sum( + torch.exp(-dist / self.bandwidth.unsqueeze(-1).unsqueeze(-1)), dim=-1 + ) diff --git a/utils/lr_scheduler.py b/utils/lr_scheduler.py new file mode 100755 index 0000000000000000000000000000000000000000..f7136bef13d119dd3cff31b02b7226e96c88b4cd --- /dev/null +++ b/utils/lr_scheduler.py @@ -0,0 +1,96 @@ +import math + + +class WarmupLR: + """ + Linear Warmup learning rate scheduler. After warmup, learning rate is + constant. + + Args: + optimizer (torch.optim.Optimizer): optimizer + warmup_steps (int): number of warmup steps + + """ + + def __init__(self, optimizer, warmup_steps): + self.optimizer = optimizer + self.warmup_steps = warmup_steps + self.base_lr = None + + def get_lr(self, lr, step): + return lr * min(step / max(self.warmup_steps, 1), 1.0) + + def step(self, step): + if self.base_lr is None: + self.base_lr = [ + param_group["lr"] for param_group in self.optimizer.param_groups + ] + for param_group, base_lr_group in zip( + self.optimizer.param_groups, self.base_lr + ): + param_group["lr"] = self.get_lr(base_lr_group, step) + + def state_dict(self): + return { + key: value for key, value in self.__dict__.items() if key != "optimizer" + } + + def load_state_dict(self, state_dict): + self.__dict__.update(state_dict) + + +class WarmupCosineDecayLR: + """ + Linear Warmup learning rate scheduler. After warmup, learning rate is + constant. + After warmup, learning rate follows a cosine decay. + + Args: + optimizer (torch.optim.Optimizer): optimizer + warmup_steps (int): number of warmup steps + total_steps (int): total number of steps + rate (float): cosine decay rate + """ + + def __init__(self, optimizer, warmup_steps, total_steps, rate=1.0): + self.optimizer = optimizer + self.warmup_steps = warmup_steps + self.base_lr = None + self.total_steps = total_steps + self.rate = rate + + def get_lr(self, lr, step): + if step < self.warmup_steps: + return lr * min(step / max(self.warmup_steps, 1), 1.0) + else: + return ( + 0.5 + * lr + * ( + 1 + + math.cos( + self.rate + * math.pi + * (step - self.warmup_steps) + / (self.total_steps - self.warmup_steps) + ) + ) + ) + + def step(self, step): + if self.base_lr is None: + self.base_lr = [ + param_group["lr"] for param_group in self.optimizer.param_groups + ] + for param_group, base_lr_group in zip( + self.optimizer.param_groups, self.base_lr + ): + param_group["lr"] = self.get_lr(base_lr_group, step) + + def state_dict(self): + return { + key: value for key, value in self.__dict__.items() if key != "optimizer" + } + + def load_state_dict(self, state_dict): + self.__dict__.update(state_dict) diff --git a/utils/manifolds.py b/utils/manifolds.py new file mode 100644 index 0000000000000000000000000000000000000000..94be76b6377ea1969344338443282b99bed1b7a0 --- /dev/null +++ b/utils/manifolds.py @@ -0,0 +1,43 @@ +"""Copyright (c) Meta Platforms, Inc. and affiliates.""" + +import math +import torch +from geoopt.manifolds import Sphere as geoopt_Sphere + + +class Sphere(geoopt_Sphere): + def transp(self, x, y, v): + denom = 1 + self.inner(x, x, y, keepdim=True) + res = v - self.inner(x, y, v, keepdim=True) / denom * (x + y) + cond = denom.gt(1e-3) + return torch.where(cond, res, -v) + + def uniform_logprob(self, x): + dim = x.shape[-1] + return torch.full_like( + x[..., 0], + math.lgamma(dim / 2) - (math.log(2) + (dim / 2) * math.log(math.pi)), + ) + + def random_base(self, *args, **kwargs): + return self.random_uniform(*args, **kwargs) + + def base_logprob(self, *args, **kwargs): + return self.uniform_logprob(*args, **kwargs) + + +def geodesic(manifold, start_point, end_point): + shooting_tangent_vec = manifold.logmap(start_point, end_point) + + def path(t): + """Generate parameterized function for geodesic curve. + Parameters + ---------- + t : array-like, shape=[n_points,] + Times at which to compute points of the geodesics. + """ + tangent_vecs = torch.einsum("i,...k->...ik", t, shooting_tangent_vec) + points_at_time_t = manifold.expmap(start_point.unsqueeze(-2), tangent_vecs) + return points_at_time_t + + return path diff --git a/utils/model_utils.py b/utils/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a4ebf21894807ece574ae3f88664a635a2b431ab --- /dev/null +++ b/utils/model_utils.py @@ -0,0 +1,14 @@ +def print_trainable_parameters(model): + """ + Prints the number and percentage of trainable parameters in the model. + Useful for tracking % parameters trained for LoRA. + """ + trainable_params = 0 + all_param = 0 + for _, param in model.named_parameters(): + all_param += param.numel() + if param.requires_grad: + trainable_params += param.numel() + print( + f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}" + ) diff --git a/utils/optimizers.py b/utils/optimizers.py new file mode 100644 index 0000000000000000000000000000000000000000..28fbdea278efa62e899778951ecf157b773f640b --- /dev/null +++ b/utils/optimizers.py @@ -0,0 +1,111 @@ +"""Lamb optimizer.""" + +import torch +from torch.optim import Optimizer +import math + + +class Lamb(Optimizer): + r"""Implements Lamb algorithm. + It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_. + Arguments: + params (iterable): iterable of parameters to optimize or dicts defining + parameter groups + lr (float, optional): learning rate (default: 1e-3) + betas (Tuple[float, float], optional): coefficients used for computing + running averages of gradient and its square (default: (0.9, 0.999)) + eps (float, optional): term added to the denominator to improve + numerical stability (default: 1e-8) + weight_decay (float, optional): weight decay (L2 penalty) (default: 0) + adam (bool, optional): always use trust ratio = 1, which turns this into + Adam. Useful for comparison purposes. + .. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes: + https://arxiv.org/abs/1904.00962 + """ + + def __init__( + self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, adam=False + ): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + self.adam = adam + super(Lamb, self).__init__(params, defaults) + + def step(self, closure=None): + """Performs a single optimization step. + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad.data + if grad.is_sparse: + raise RuntimeError( + "Lamb does not support sparse gradients, consider SparseAdam instad." + ) + + state = self.state[p] + + # State initialization + if len(state) == 0: + state["step"] = 0 + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(p.data) + # Exponential moving average of squared gradient values + state["exp_avg_sq"] = torch.zeros_like(p.data) + + exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] + beta1, beta2 = group["betas"] + + state["step"] += 1 + + # Decay the first and second moment running average coefficient + # m_t + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + # v_t + exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) + + # Paper v3 does not use debiasing. + bias_correction1 = 1 - beta1 ** state["step"] + bias_correction2 = 1 - beta2 ** state["step"] + exp_avg_hat = exp_avg / bias_correction1 + exp_avg_sq_hat = exp_avg_sq / bias_correction2 + # Apply bias to lr to avoid broadcast. + step_size = group["lr"] + + do_layer_adaptation = ( + group["layer_adaptation"] + if "layer_adaptation" in group + else group["weight_decay"] > 0 + ) + + adam_step = exp_avg_hat / exp_avg_sq_hat.sqrt().add(group["eps"]) + if group["weight_decay"] != 0: + adam_step.add_(p.data, alpha=group["weight_decay"]) + if do_layer_adaptation: + weight_norm = p.data.norm(p=2) + adam_norm = adam_step.norm(p=2) + trust_ratio = torch.where( + weight_norm.ne(0), + torch.where(adam_norm.ne(0), weight_norm / adam_norm, 1), + 1, + ) + if self.adam or not do_layer_adaptation: + trust_ratio = 1 + + p.data.add_(adam_step, alpha=-step_size * trust_ratio) + return loss diff --git a/utils/quadtree_10_1000.csv b/utils/quadtree_10_1000.csv new file mode 100644 index 0000000000000000000000000000000000000000..43dc3fe224cd477a6d531e9eb9060041fc351050 --- /dev/null +++ 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