Spaces:
Running
Running
File size: 18,942 Bytes
03f6091 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 |
# -*- coding: utf-8 -*-
r"""
Model Base
==============
Abstract base class used to build new modules inside Polos.
This class is just an extention of PyTorch Lightning main module:
https://pytorch-lightning.readthedocs.io/en/0.8.4/lightning-module.html
"""
from argparse import Namespace
from os import path
import os
from typing import Dict, Generator, List, Tuple, Union
import click
import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader, RandomSampler, Subset, Dataset
from PIL import Image
import pytorch_lightning as ptl
from polos.models.encoders import Encoder, str2encoder
from polos.schedulers import str2scheduler
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
class ModelBase(ptl.LightningModule):
"""
Extends PyTorch Lightning with a common structure and interface
that will be shared across all architectures.
:param hparams: Namespace with hyper-parameters
"""
class ModelConfig:
"""
The ModelConfig class is used to define model hyper-parameters that
are used to initialize our Lightning Modules. These parameters are
then overwritted with the values defined in the YAML file and coverted
to a Namespace to initialize the model.
:param model: Model class name (to be replaced with the model specified in the YAML)
-------------------- Training Parameters -------------------------
:param batch_size: Batch size used during training.
:param nr_frozen_epochs: Number of epochs we keep the encoder model frozen.
:param keep_embeddings_frozen: Keeping the embeddings frozen is a usefull way to save some GPU memory usage.
This is critical to fine-tune large models in GPUs with less than 32GB memory.
-------------------- Optimizer Parameters -------------------------
:param optimizer: Optimizer class to be used.
:param learning_rate: Overall learning rate.
-------------------- Scheduler Parameters -------------------------
:param scheduler: Scheduler class to be used.
:param warmup_steps: Warmup steps (only used for schedulers with warmup period).
-------------------- Architecture Parameters -------------------------
:param encoder_model: Encoder class to be used.
:param pretrained_model: Encoder checkpoint (e.g. xlmr.base vs xlmr.large)
:param pool: Pooling technique to extract the sentence embeddings.
Options: {max, avg, default, cls} where default uses the `default` sentence embedding
returned by the encoder (e.g. BERT pooler_output) and `cls` is the first token of the
sequence and depends on the selected layer.
:param load_weights: Loads weights from a checkpoint file that match the architecture.
-------------------- Data Parameters -------------------------
:param train_path: Path to the training data.
:param val_path: Path to the validation data.
:param test_path: Path to the test data.
:param loader_workers: Number of workers used to load and tokenize data during training.
:param monitor: Metric to be displayed in tqdm bar. Same as trainer monitor flag!
"""
model: str = None
# TODO: rankerγ’γγ«γ γ¨γγγεΏ
θ¦οΌθ¦θͺΏζ»
# γγγγγγ
encoder_learning_rate: float = 1e-06
layerwise_decay: float = 1.0
layer: str = "mix"
scalar_mix_dropout: float = 0.0
loss: str = "mse"
hidden_sizes: str = "1024"
activations: str = "Tanh"
dropout: float = 0.1
final_activation: str = "Sigmoid"
# γγγγΎγ§γ
# Training details
batch_size: int = 8
nr_frozen_epochs: int = 0
keep_embeddings_frozen: bool = False
# Optimizer
optimizer: str = "Adam"
learning_rate: float = 1e-05
# Scheduler
scheduler: str = "constant"
warmup_steps: int = None
# Architecture Definition
encoder_model: str = "XLMR"
pretrained_model: str = "xlmr.base"
pool: str = "avg"
load_weights: str = False
# Data
train_path: str = None
val_path: str = None
test_path: str = None
train_img_dir_path: str = None
val_img_dir_path: str = None
test_img_dir_path: str = None
loader_workers: int = 8
monitor: str = "kendall"
def __init__(self, initial_data: dict) -> None:
for key in initial_data:
if hasattr(self, key):
setattr(self, key, initial_data[key])
def namespace(self) -> Namespace:
return Namespace(
**{
name: getattr(self, name)
for name in dir(self)
if not callable(getattr(self, name)) and not name.startswith("__")
}
)
def __init__(self, hparams: Namespace) -> None:
super(ModelBase, self).__init__()
if isinstance(hparams, dict):
self.hparams = Namespace(**hparams)
else:
self.hparams = hparams
self.encoder = self._build_encoder()
# Model initialization
self._build_model()
# Loss criterion initialization.
self._build_loss()
# The encoder always starts in a frozen state.
if self.hparams.nr_frozen_epochs > 0:
self._frozen = True
self.freeze_encoder()
else:
self._frozen = False
if (
hasattr(self.hparams, "keep_embeddings_frozen")
and self.hparams.keep_embeddings_frozen
):
self.encoder.freeze_embeddings()
self.nr_frozen_epochs = self.hparams.nr_frozen_epochs
def _build_loss(self):
""" Initializes the loss function/s. """
pass
def _build_model(self) -> ptl.LightningModule:
"""
Initializes the estimator architecture.
"""
# Compatibility with previous Polos versions
if (
hasattr(self.hparams, "load_weights")
and self.hparams.load_weights
and path.exists(self.hparams.load_weights)
):
click.secho(f"Loading weights from {self.hparams.load_weights}", fg="red")
self.load_weights(self.hparams.load_weights)
def _build_encoder(self) -> Encoder:
"""
Initializes the encoder.
"""
try:
return str2encoder[self.hparams.encoder_model].from_pretrained(self.hparams)
except KeyError:
raise Exception(f"{self.hparams.encoder_model} invalid encoder model!")
def _build_optimizer(self, parameters: Generator) -> torch.optim.Optimizer:
"""
Initializes the Optimizer.
:param parameters: Module.parameters.
"""
if hasattr(torch.optim, self.hparams.optimizer):
return getattr(torch.optim, self.hparams.optimizer)(
params=parameters, lr=self.hparams.learning_rate
)
else:
raise Exception(f"{self.hparams.optimizer} invalid optimizer!")
def _build_scheduler(
self, optimizer: torch.optim.Optimizer
) -> torch.optim.lr_scheduler.LambdaLR:
"""
Initializes the Scheduler.
:param optimizer: PyTorch optimizer
"""
self.epoch_total_steps = len(self.train_dataset) // (
self.hparams.batch_size * max(1, self.trainer.num_gpus)
)
self.total_steps = self.epoch_total_steps * float(self.trainer.max_epochs)
try:
return {
"scheduler": str2scheduler[self.hparams.scheduler].from_hparams(
optimizer, self.hparams, num_training_steps=self.total_steps
),
"interval": "step", # called after each training step
}
except KeyError:
raise Exception(f"{self.hparams.scheduler} invalid scheduler!")
def read_csv(self, path: str) -> List[dict]:
"""Reads a comma separated value file.
:param path: path to a csv file.
:return: List of records as dictionaries
"""
df = pd.read_csv(path)
return df.to_dict("records")
def freeze_encoder(self) -> None:
""" Freezes the encoder layer. """
self.encoder.freeze()
def unfreeze_encoder(self) -> None:
""" un-freezes the encoder layer. """
if self._frozen:
if self.trainer.is_global_zero:
click.secho("\nEncoder model fine-tuning", fg="red")
self.encoder.unfreeze()
self._frozen = False
if (
hasattr(self.hparams, "keep_embeddings_frozen")
and self.hparams.keep_embeddings_frozen
):
self.encoder.freeze_embeddings()
def on_epoch_end(self):
""" Hook used to unfreeze encoder during training. """
if self.current_epoch + 1 >= self.nr_frozen_epochs and self._frozen:
self.unfreeze_encoder()
self._frozen = False
def predict(
self, samples: Dict[str, str]
) -> (Dict[str, Union[str, float]], List[float]):
"""Function that runs a model prediction,
:param samples: dictionary with expected model sequences.
You can also pass a list of dictionaries to predict an entire batch.
:return: Dictionary with input samples + scores and list with just the scores.
"""
pass
def forward(self, *args, **kwargs) -> Dict[str, torch.Tensor]:
"""
PyTorch Forward.
:return: Dictionary with model outputs to be passed to the loss function.
"""
pass
def compute_loss(
self, model_out: Dict[str, torch.Tensor], targets: Dict[str, torch.Tensor]
) -> torch.Tensor:
"""
Computes Loss value according to a loss function.
:param model_out: model specific output.
:param targets: Target score values [batch_size]
"""
pass
def prepare_sample(
self, sample: List[Dict[str, Union[str, float]]], inference: bool = False
) -> Union[
Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]], Dict[str, torch.Tensor]
]:
"""
Function that prepares a sample to input the model.
:param sample: List of dictionaries.
:param inference: If set to true prepares only the model inputs.
:returns: Tuple with 2 dictionaries (model inputs and targets). If `inference=True`
returns only the model inputs.
"""
pass
def configure_optimizers(
self,
) -> Tuple[List[torch.optim.Optimizer], List[torch.optim.lr_scheduler.LambdaLR]]:
"""
Function for setting up the optimizers and the schedulers to be used during training.
:returns: List with as many optimizers as we need and a list with the respective schedulers.
"""
optimizer = self._build_optimizer(self.parameters())
scheduler = self._build_scheduler(optimizer)
return [optimizer], [scheduler]
def compute_metrics(
self, outputs: List[Dict[str, torch.Tensor]]
) -> Dict[str, torch.Tensor]:
"""
Function that computes metrics of interest based on the list of outputs
you defined in validation_step.
"""
pass
def training_step(
self,
batch: Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]],
batch_nb: int,
*args,
**kwargs,
) -> Dict[str, torch.Tensor]:
"""
Runs one training step.
This usually consists in the forward function followed by the loss function.
:param batch: The output of your prepare_sample function.
:param batch_nb: Integer displaying which batch this is.
:returns: dictionary containing the loss and the metrics to be added to the lightning logger.
"""
batch_input, batch_target = batch
batch_prediction = self.forward(**batch_input)
loss_value = self.compute_loss(batch_prediction, batch_target)
# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
if self.trainer.use_dp or self.trainer.use_ddp2:
loss_value = loss_value.unsqueeze(0)
if (
self.nr_frozen_epochs < 1.0
and self.nr_frozen_epochs > 0.0
and batch_nb > self.epoch_total_steps * self.nr_frozen_epochs
):
self.unfreeze_encoder()
self._frozen = False
self.log("train_loss", loss_value, on_step=True, on_epoch=True)
return loss_value
def validation_step(
self,
batch: Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]],
batch_nb: int,
dataloader_idx: int,
) -> Dict[str, torch.Tensor]:
"""
Similar to the training step but with the model in eval mode.
:param batch: The output of your prepare_sample function.
:param batch_nb: Integer displaying which batch this is.
:param dataloader_idx: Integer displaying which dataloader this is.
:returns: dictionary passed to the validation_end function.
"""
batch_input, batch_target = batch
batch_prediction = self.forward(**batch_input)
loss_value = self.compute_loss(batch_prediction, batch_target)
# in DP mode (default) make sure if result is scalar, there's another dim in the beginning
if self.trainer.use_dp or self.trainer.use_ddp2:
loss_value = loss_value.unsqueeze(0)
return {
"val_loss": loss_value,
"val_prediction": batch_prediction,
"val_target": batch_target,
}
def validation_epoch_end(
self, outputs: List[Dict[str, torch.Tensor]]
) -> Dict[str, Dict[str, torch.Tensor]]:
"""
Function that takes as input a list of dictionaries returned by the validation_step
and measures the model performance accross the entire validation set.
:param outputs:
:returns: Dictionary with metrics to be added to the lightning logger.
"""
train_outs, val_outs = outputs
train_loss = torch.stack([x["val_loss"] for x in train_outs]).mean()
val_loss = torch.stack([x["val_loss"] for x in val_outs]).mean()
# Store Metrics for Reporting...
val_metrics = self.compute_metrics(val_outs)
val_metrics["avg_loss"] = val_loss
self.log_dict(val_metrics, prog_bar=True)
train_metrics = self.compute_metrics(train_outs)
train_metrics["avg_loss"] = train_loss
self.log_dict({"train_" + k: v for k, v in train_metrics.items()})
def test_step(
self,
batch: Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]],
batch_nb: int,
*args,
**kwargs,
) -> Dict[str, torch.Tensor]:
""" Redirects to the validation_step function """
return self.validation_step(batch, batch_nb, 0)
def test_epoch_end(
self, outputs: List[Dict[str, torch.Tensor]]
) -> Dict[str, Dict[str, torch.Tensor]]:
""" Computes metrics. """
return self.compute_metrics(outputs)
def setup(self, stage) -> None:
"""Data preparation function called before training by Lightning.
Equivalent to the prepare_data in previous Lightning Versions"""
self.train_dataset = self.read_csv(self.hparams.train_path)
self.val_dataset = self.read_csv(self.hparams.val_path)
self.train_dataset = CVPRDataset(self.train_dataset, self.hparams.train_img_dir_path)
self.val_dataset = CVPRDataset(self.val_dataset, self.hparams.val_img_dir_path)
print("[SPLIT]: Train: {}, Val: {}".format(len(self.train_dataset), len(self.val_dataset)))
# Always validate the model with 2k examples from training to control overfit.
train_subset = np.random.choice(a=len(self.train_dataset), size=2000)
self.train_subset = Subset(self.train_dataset, train_subset)
if self.hparams.test_path:
self.test_dataset = self.read_csv(self.hparams.test_path)
self.test_dataset = CVPRDataset(self.test_dataset, self.hparams.test_img_dir_path)
def train_dataloader(self) -> DataLoader:
""" Function that loads the train set. """
return DataLoader(
dataset=self.train_dataset,
sampler=RandomSampler(self.train_dataset),
batch_size=self.hparams.batch_size,
collate_fn=self.prepare_sample,
num_workers=self.hparams.loader_workers,
)
def val_dataloader(self) -> DataLoader:
""" Function that loads the validation set. """
return [
DataLoader(
dataset=self.train_subset,
batch_size=self.hparams.batch_size,
collate_fn=self.prepare_sample,
num_workers=self.hparams.loader_workers,
),
DataLoader(
dataset=self.val_dataset,
batch_size=self.hparams.batch_size,
collate_fn=self.prepare_sample,
num_workers=self.hparams.loader_workers,
),
]
def test_dataloader(self) -> DataLoader:
""" Function that loads the validation set. """
return DataLoader(
dataset=self.test_dataset,
batch_size=self.hparams.batch_size,
collate_fn=self.prepare_sample,
num_workers=self.hparams.loader_workers,
)
class CVPRDataset(Dataset):
def __init__(self, dataset, img_dir_path):
self.dataset = dataset
self.img_dir_path = img_dir_path
# Filter out entries with broken image files
for data in self.dataset:
assert self.is_image_ok(path.join(img_dir_path, f"{data['imgid']}"))
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
from copy import deepcopy
# Get image from df
imgid = self.dataset[idx]["imgid"]
img_name = path.join(self.img_dir_path, f"{imgid}")
# Get label from df
labels = deepcopy(self.dataset[idx])
# print(labels)
# Open image file
# from detectron2.data.detection_utils import read_image
# img = read_image(img_name, format="RGB")
img = Image.open(img_name).convert("RGB")
labels["img"] = img
return labels
@staticmethod
def is_image_ok(img_path):
# Try to open the image file
try:
img = Image.open(img_path)
img.verify()
return True
except (IOError, SyntaxError) as e:
return False
|