lightning/tests/base/model_train_steps.py

53 lines
1.7 KiB
Python

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC
class TrainingStepVariations(ABC):
"""
Houses all variations of training steps
"""
def training_step(self, batch, batch_idx, optimizer_idx=None):
"""Lightning calls this inside the training loop"""
self.training_step_called = True
# forward pass
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self(x)
# calculate loss
loss_train = self.loss(y, y_hat)
return {"loss": loss_train}
def training_step__multiple_dataloaders(self, batch, batch_idx, optimizer_idx=None):
"""Training step for multiple train loaders"""
assert isinstance(batch, dict)
assert len(batch) == 2
assert "a_b" in batch and "c_d_e" in batch, batch.keys()
assert isinstance(batch["a_b"], list) and len(batch["a_b"]) == 2
assert isinstance(batch["c_d_e"], list) and len(batch["c_d_e"]) == 3
# forward pass
x, y = batch["a_b"][0]
x = x.view(x.size(0), -1)
y_hat = self(x)
# calculate loss
loss_val = self.loss(y, y_hat)
return {"loss": loss_val}