254 lines
8.1 KiB
Python
254 lines
8.1 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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"""Tests the evaluation loop."""
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.trainer.states import RunningStage
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from tests.helpers.deterministic_model import DeterministicModel
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def test__eval_step__flow(tmpdir):
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"""Tests that only training_step can be used."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def validation_step(self, batch, batch_idx):
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self.validation_step_called = True
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if batch_idx == 0:
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out = ["1", 2, torch.tensor(2)]
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if batch_idx > 0:
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out = {"something": "random"}
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return out
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.validation_step_end = None
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model.validation_epoch_end = 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=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.validation_step_called
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assert not model.validation_step_end_called
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assert not model.validation_epoch_end_called
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# simulate training manually
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trainer.state.stage = RunningStage.TRAINING
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batch_idx, batch = 0, next(iter(model.train_dataloader()))
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train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
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assert len(train_step_out) == 1
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train_step_out = train_step_out[0][0]
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assert isinstance(train_step_out["loss"], torch.Tensor)
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assert train_step_out["loss"].item() == 171
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# make sure the optimizer closure returns the correct things
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opt_closure = trainer.fit_loop.epoch_loop.batch_loop.optimizer_loop._make_closure(
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batch, batch_idx, 0, trainer.optimizers[0]
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)
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opt_closure_result = opt_closure()
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assert opt_closure_result.item() == 171
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def test__eval_step__eval_step_end__flow(tmpdir):
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"""Tests that only training_step can be used."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def validation_step(self, batch, batch_idx):
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self.validation_step_called = True
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if batch_idx == 0:
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out = ["1", 2, torch.tensor(2)]
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if batch_idx > 0:
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out = {"something": "random"}
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self.last_out = out
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return out
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def validation_step_end(self, out):
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self.validation_step_end_called = True
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assert self.last_out == out
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return out
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.validation_epoch_end = 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=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.validation_step_called
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assert model.validation_step_end_called
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assert not model.validation_epoch_end_called
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trainer.state.stage = RunningStage.TRAINING
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# make sure training outputs what is expected
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batch_idx, batch = 0, next(iter(model.train_dataloader()))
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train_step_out = trainer.fit_loop.epoch_loop.batch_loop.run(batch, batch_idx)
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assert len(train_step_out) == 1
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train_step_out = train_step_out[0][0]
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assert isinstance(train_step_out["loss"], torch.Tensor)
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assert train_step_out["loss"].item() == 171
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# make sure the optimizer closure returns the correct things
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opt_closure = trainer.fit_loop.epoch_loop.batch_loop.optimizer_loop._make_closure(
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batch, batch_idx, 0, trainer.optimizers[0]
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)
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opt_closure_result = opt_closure()
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assert opt_closure_result.item() == 171
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def test__eval_step__epoch_end__flow(tmpdir):
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"""Tests that only training_step can be used."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def validation_step(self, batch, batch_idx):
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self.validation_step_called = True
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if batch_idx == 0:
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out = ["1", 2, torch.tensor(2)]
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self.out_a = out
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if batch_idx > 0:
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out = {"something": "random"}
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self.out_b = out
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return out
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def validation_epoch_end(self, outputs):
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self.validation_epoch_end_called = True
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assert len(outputs) == 2
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out_a = outputs[0]
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out_b = outputs[1]
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assert out_a == self.out_a
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assert out_b == self.out_b
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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model.validation_step_end = 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=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.validation_step_called
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assert not model.validation_step_end_called
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assert model.validation_epoch_end_called
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def test__validation_step__step_end__epoch_end__flow(tmpdir):
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"""Tests that only training_step can be used."""
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class TestModel(DeterministicModel):
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def training_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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acc = acc + batch_idx
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self.training_step_called = True
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return acc
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def validation_step(self, batch, batch_idx):
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self.validation_step_called = True
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if batch_idx == 0:
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out = ["1", 2, torch.tensor(2)]
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self.out_a = out
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if batch_idx > 0:
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out = {"something": "random"}
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self.out_b = out
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self.last_out = out
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return out
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def validation_step_end(self, out):
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self.validation_step_end_called = True
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assert self.last_out == out
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return out
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def validation_epoch_end(self, outputs):
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self.validation_epoch_end_called = True
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assert len(outputs) == 2
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out_a = outputs[0]
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out_b = outputs[1]
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assert out_a == self.out_a
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assert out_b == self.out_b
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def backward(self, loss, optimizer, optimizer_idx):
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return LightningModule.backward(self, loss, optimizer, optimizer_idx)
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model = TestModel()
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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=2,
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log_every_n_steps=1,
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enable_model_summary=False,
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)
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trainer.fit(model)
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# make sure correct steps were called
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assert model.validation_step_called
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assert model.validation_step_end_called
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assert model.validation_epoch_end_called
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