269 lines
9.5 KiB
Python
269 lines
9.5 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from torch import nn
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from torch.utils.data import DataLoader, Dataset
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.utilities import DistributedType
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class DeterministicModel(LightningModule):
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def __init__(self, weights=None):
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super().__init__()
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self.training_step_called = False
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self.training_step_end_called = False
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self.training_epoch_end_called = False
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self.validation_step_called = False
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self.validation_step_end_called = False
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self.validation_epoch_end_called = False
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self.assert_backward = True
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self.l1 = nn.Linear(2, 3, bias=False)
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if weights is None:
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weights = torch.tensor([[4, 3, 5], [10, 11, 13]]).float()
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p = torch.nn.Parameter(weights, requires_grad=True)
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self.l1.weight = p
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def forward(self, x):
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return self.l1(x)
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def step(self, batch, batch_idx):
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x = batch
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bs = x.size(0)
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y_hat = self.l1(x)
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test_hat = y_hat.cpu().detach()
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assert torch.all(test_hat[:, 0] == 15.0)
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assert torch.all(test_hat[:, 1] == 42.0)
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out = y_hat.sum()
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assert out == (42.0 * bs) + (15.0 * bs)
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return out
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def count_num_graphs(self, result, num_graphs=0):
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for k, v in result.items():
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if isinstance(v, torch.Tensor) and v.grad_fn is not None:
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num_graphs += 1
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if isinstance(v, dict):
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num_graphs += self.count_num_graphs(v)
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return num_graphs
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# ---------------------------
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# scalar return
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# ---------------------------
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def training_step__scalar_return(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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self.training_step_called = True
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return acc
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def training_step_end__scalar(self, output):
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self.training_step_end_called = True
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# make sure loss has the grad
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assert isinstance(output, torch.Tensor)
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assert output.grad_fn is not None
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# make sure nothing else has grads
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assert self.count_num_graphs({'loss': output}) == 1
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assert output == 171
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return output
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def training_epoch_end__scalar(self, outputs):
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"""
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There should be an array of scalars without graphs that are all 171 (4 of them)
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"""
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self.training_epoch_end_called = True
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if self._distrib_type in (DistributedType.DP, DistributedType.DDP2):
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pass
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else:
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# only saw 4 batches
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assert len(outputs) == 4
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for batch_out in outputs:
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batch_out = batch_out['loss']
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assert batch_out == 171
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assert batch_out.grad_fn is None
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assert isinstance(batch_out, torch.Tensor)
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# --------------------------
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# dictionary returns
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# --------------------------
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def training_step__dict_return(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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logs = {'log_acc1': torch.tensor(12).type_as(acc), 'log_acc2': torch.tensor(7).type_as(acc)}
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pbar = {'pbar_acc1': torch.tensor(17).type_as(acc), 'pbar_acc2': torch.tensor(19).type_as(acc)}
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self.training_step_called = True
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return {'loss': acc, 'log': logs, 'progress_bar': pbar, 'train_step_test': torch.tensor(549).type_as(acc)}
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def training_step__for_step_end_dict(self, batch, batch_idx):
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"""sends outputs to training_batch_end"""
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acc = self.step(batch, batch_idx)
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logs = {'log_acc1': torch.tensor(12).type_as(acc), 'log_acc2': torch.tensor(7).type_as(acc)}
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pbar = {'pbar_acc1': torch.tensor(17).type_as(acc), 'pbar_acc2': torch.tensor(19).type_as(acc)}
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self.training_step_called = True
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result = {'loss': acc}
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result.update(logs)
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result.update(pbar)
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return result
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def training_step_end__dict(self, output):
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self.training_step_end_called = True
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# make sure loss has the grad
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assert 'loss' in output
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assert output['loss'].grad_fn is not None
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# make sure nothing else has grads
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assert self.count_num_graphs(output) == 1
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# make sure the other keys are there
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assert 'log_acc1' in output
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assert 'log_acc2' in output
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assert 'pbar_acc1' in output
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assert 'pbar_acc2' in output
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logs = {'log_acc1': output['log_acc1'] + 2, 'log_acc2': output['log_acc2'] + 2}
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pbar = {'pbar_acc1': output['pbar_acc1'] + 2, 'pbar_acc2': output['pbar_acc2'] + 2}
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acc = output['loss']
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return {'loss': acc, 'log': logs, 'progress_bar': pbar, 'train_step_end': acc}
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def training_epoch_end__dict(self, outputs):
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self.training_epoch_end_called = True
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if self._distrib_type in (DistributedType.DP, DistributedType.DDP2):
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pass
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else:
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# only saw 4 batches
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assert len(outputs) == 4
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for batch_out in outputs:
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assert len(batch_out.keys()) == 4
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assert self.count_num_graphs(batch_out) == 0
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last_key = 'train_step_end' if self.training_step_end_called else 'train_step_test'
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keys = ['loss', 'log', 'progress_bar', last_key]
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for key in keys:
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assert key in batch_out
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prototype_loss = outputs[0]['loss']
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logs = {'epoch_end_log_1': torch.tensor(178).type_as(prototype_loss)}
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pbar = {'epoch_end_pbar_1': torch.tensor(234).type_as(prototype_loss)}
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return {'log': logs, 'progress_bar': pbar}
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def validation_step__no_return(self, batch, batch_idx):
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self.validation_step_called = True
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self.step(batch, batch_idx)
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def validation_step__scalar_return(self, batch, batch_idx):
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self.validation_step_called = True
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acc = self.step(batch, batch_idx)
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return acc
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def validation_step__dummy_dict_return(self, batch, batch_idx):
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self.validation_step_called = True
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acc = self.step(batch, batch_idx)
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return {'some': acc, 'value': 'a'}
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def validation_step__dict_return(self, batch, batch_idx):
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self.validation_step_called = True
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acc = self.step(batch, batch_idx)
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logs = {'log_acc1': torch.tensor(12 + batch_idx).type_as(acc), 'log_acc2': torch.tensor(7).type_as(acc)}
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pbar = {'pbar_acc1': torch.tensor(17).type_as(acc), 'pbar_acc2': torch.tensor(19).type_as(acc)}
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return {'val_loss': acc, 'log': logs, 'progress_bar': pbar}
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def validation_step_end__no_return(self, val_step_output):
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assert len(val_step_output) == 3
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assert val_step_output['val_loss'] == 171
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assert val_step_output['log']['log_acc1'] >= 12
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assert val_step_output['progress_bar']['pbar_acc1'] == 17
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self.validation_step_end_called = True
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def validation_step_end(self, val_step_output):
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assert len(val_step_output) == 3
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assert val_step_output['val_loss'] == 171
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assert val_step_output['log']['log_acc1'] >= 12
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assert val_step_output['progress_bar']['pbar_acc1'] == 17
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self.validation_step_end_called = True
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val_step_output['val_step_end'] = torch.tensor(1802)
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return val_step_output
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def validation_epoch_end(self, outputs):
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assert len(outputs) == self.trainer.num_val_batches[0]
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for i, out in enumerate(outputs):
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assert out['log']['log_acc1'] >= 12 + i
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self.validation_epoch_end_called = True
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result = outputs[-1]
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result['val_epoch_end'] = torch.tensor(1233)
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return result
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# -----------------------------
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# DATA
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# -----------------------------
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def train_dataloader(self):
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return DataLoader(DummyDataset(), batch_size=3, shuffle=False)
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def val_dataloader(self):
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return DataLoader(DummyDataset(), batch_size=3, shuffle=False)
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def configure_optimizers(self):
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return torch.optim.Adam(self.parameters(), lr=0)
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def configure_optimizers__lr_on_plateau_epoch(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=0)
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lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
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scheduler = {'scheduler': lr_scheduler, 'interval': 'epoch', 'monitor': 'epoch_end_log_1'}
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return [optimizer], [scheduler]
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def configure_optimizers__lr_on_plateau_step(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=0)
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lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
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scheduler = {'scheduler': lr_scheduler, 'interval': 'step', 'monitor': 'pbar_acc1'}
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return [optimizer], [scheduler]
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def backward(self, loss, optimizer, optimizer_idx):
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if self.assert_backward:
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if self.trainer.precision == 16:
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assert loss > 171 * 1000
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else:
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assert loss == 171.0
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super().backward(loss, optimizer, optimizer_idx)
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class DummyDataset(Dataset):
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def __len__(self):
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return 12
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def __getitem__(self, idx):
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return torch.tensor([0.5, 1.0, 2.0])
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