95 lines
2.8 KiB
Python
95 lines
2.8 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 math
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from abc import ABC
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from collections import OrderedDict
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import torch
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class TrainingStepVariations(ABC):
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"""
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Houses all variations of training steps
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"""
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test_step_inf_loss = float('inf')
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def training_step(self, batch, batch_idx, optimizer_idx=None):
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"""Lightning calls this inside the training loop"""
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self.training_step_called = True
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# forward pass
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x, y = batch
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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# calculate loss
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loss_train = self.loss(y, y_hat)
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log_train = loss_train
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# alternate between tensors and scalars for "log" and "progress_bar"
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if batch_idx % 2 == 0:
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log_train = log_train.item()
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output = OrderedDict({
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'loss': loss_train,
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'progress_bar': {
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'some_val': log_train * log_train
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},
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'log': {
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'train_some_val': log_train * log_train
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},
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})
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return output
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def training_step__inf_loss(self, batch, batch_idx, optimizer_idx=None):
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output = self.training_step(batch, batch_idx, optimizer_idx)
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if batch_idx == self.test_step_inf_loss:
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if isinstance(output, dict):
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output['loss'] *= torch.tensor(math.inf) # make loss infinite
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else:
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output /= 0
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return output
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def training_step__multiple_dataloaders(self, batch, batch_idx, optimizer_idx=None):
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"""Training step for multiple train loaders"""
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assert isinstance(batch, dict)
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assert len(batch) == 2
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assert 'a' in batch and 'b' in batch
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# forward pass
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x, y = batch['a']
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x = x.view(x.size(0), -1)
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y_hat = self(x)
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# calculate loss
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loss_val = self.loss(y, y_hat)
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log_val = loss_val
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# alternate between tensors and scalars for "log" and "progress_bar"
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if batch_idx % 2 == 0:
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log_val = log_val.item()
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output = OrderedDict({
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'loss': loss_val,
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'progress_bar': {
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'some_val': log_val * log_val
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},
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'log': {
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'train_some_val': log_val * log_val
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},
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})
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return output
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