118 lines
3.5 KiB
Python
118 lines
3.5 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 pytorch_lightning import Trainer
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from tests.base.boring_model import BoringModel
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def test_multiple_optimizers(tmpdir):
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"""
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Tests that only training_step can be used
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"""
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class TestModel(BoringModel):
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def on_train_epoch_start(self) -> None:
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self.opt_0_seen = False
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self.opt_1_seen = False
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def training_step(self, batch, batch_idx, optimizer_idx):
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if optimizer_idx == 0:
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self.opt_0_seen = True
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elif optimizer_idx == 1:
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self.opt_1_seen = True
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else:
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raise Exception('should only have two optimizers')
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self.training_step_called = True
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loss = self.step(batch[0])
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return loss
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def training_epoch_end(self, outputs) -> None:
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# outputs should be an array with an entry per optimizer
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assert len(outputs) == 2
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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return optimizer, optimizer_2
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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assert model.opt_0_seen
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assert model.opt_1_seen
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def test_multiple_optimizers_manual(tmpdir):
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"""
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Tests that only training_step can be used
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"""
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.automatic_optimization = False
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def on_train_epoch_start(self) -> None:
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self.opt_0_seen = False
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self.opt_1_seen = False
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def training_step(self, batch, batch_idx, optimizer_idx):
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# manual
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(opt_a, opt_b) = self.optimizers()
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loss_1 = self.step(batch[0])
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# fake generator
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self.manual_backward(loss_1, opt_a)
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opt_a.step()
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opt_a.zero_grad()
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# fake discriminator
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loss_2 = self.step(batch[0])
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self.manual_backward(loss_2, opt_b)
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opt_b.step()
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opt_b.zero_grad()
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def training_epoch_end(self, outputs) -> None:
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# outputs should be an array with an entry per optimizer
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assert len(outputs) == 2
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def configure_optimizers(self):
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optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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optimizer_2 = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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return optimizer, optimizer_2
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model = TestModel()
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model.val_dataloader = None
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trainer = Trainer(
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default_root_dir=tmpdir,
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limit_train_batches=2,
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limit_val_batches=2,
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max_epochs=1,
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log_every_n_steps=1,
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weights_summary=None,
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)
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trainer.fit(model)
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