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import os
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from typing import Any
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import pytorch_lightning as L
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import torch
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from hydra.utils import instantiate
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from models.huggingface import Geolocalizer
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class EvalModule(L.LightningModule):
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def __init__(self, cfg):
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super().__init__()
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self.cfg = cfg
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os.chdir(cfg.network.root_dir)
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self.model = Geolocalizer.from_pretrained('osv5m/baseline')
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self.test_metrics = instantiate(cfg.test_metrics)
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def training_step(self, batch, batch_idx):
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pred = self.model(batch)
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pass
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@torch.no_grad()
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def validation_step(self, batch, batch_idx):
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pred = self.model(batch)
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pass
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def on_validation_epoch_end(self):
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pass
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@torch.no_grad()
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def test_step(self, batch, batch_idx):
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pred = self.model.forward_tensor(batch)
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self.test_metrics.update({"gps": pred}, batch)
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def on_test_epoch_end(self):
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metrics = self.test_metrics.compute()
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for metric_name, metric_value in metrics.items():
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self.log(
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f"test/{metric_name}",
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metric_value,
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sync_dist=True,
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on_step=False,
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on_epoch=True,
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)
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def lr_scheduler_step(self, scheduler, metric):
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scheduler.step(self.global_step)
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def get_parameter_names(model, forbidden_layer_types):
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"""
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Returns the names of the model parameters that are not inside a forbidden layer.
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Taken from HuggingFace transformers.
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"""
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result = []
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for name, child in model.named_children():
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result += [
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f"{name}.{n}"
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for n in get_parameter_names(child, forbidden_layer_types)
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if not isinstance(child, tuple(forbidden_layer_types))
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]
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result += list(model._parameters.keys())
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return result
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