488 lines
19 KiB
Python
488 lines
19 KiB
Python
import torch
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from torch import nn
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from torch.utils.data import Dataset, DataLoader
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from pytorch_lightning import TrainResult, EvalResult
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from pytorch_lightning.core.lightning import LightningModule
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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([
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[4, 3, 5],
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[10, 11, 13]
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]).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 assert_graph_count(self, result, count=1):
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counts = self.count_num_graphs(result)
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assert counts == count
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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.use_dp or self.use_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 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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prototype_loss = outputs[0]
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return prototype_loss
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def training_step_no_default_callbacks_for_train_loop(self, batch, batch_idx):
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"""
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Early stop and checkpoint only on these values
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"""
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acc = self.step(batch, batch_idx)
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result = TrainResult(minimize=acc)
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assert 'early_step_on' not in result
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assert 'checkpoint_on' in result
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return result
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def training_step_no_callbacks_result_obj(self, batch, batch_idx):
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"""
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Early stop and checkpoint only on these values
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"""
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acc = self.step(batch, batch_idx)
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result = TrainResult(minimize=acc, checkpoint_on=False)
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assert 'early_step_on' not in result
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assert 'checkpoint_on' not in result
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return result
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def training_step_result_log_epoch_and_step_for_callbacks(self, batch, batch_idx):
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"""
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Early stop and checkpoint only on these values
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"""
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acc = self.step(batch, batch_idx)
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self.assert_backward = False
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losses = [20, 19, 18, 10, 15, 14, 9, 11, 11, 20]
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idx = self.current_epoch
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loss = acc + losses[idx]
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result = TrainResult(minimize=loss, early_stop_on=loss, checkpoint_on=loss)
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return result
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def training_step_result_log_step_only(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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result = TrainResult(minimize=acc)
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# step only metrics
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result.log(f'step_log_and_pbar_acc1_b{batch_idx}', torch.tensor(11).type_as(acc), prog_bar=True)
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result.log(f'step_log_acc2_b{batch_idx}', torch.tensor(12).type_as(acc))
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result.log(f'step_pbar_acc3_b{batch_idx}', torch.tensor(13).type_as(acc), logger=False, prog_bar=True)
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self.training_step_called = True
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return result
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def training_step_result_log_epoch_only(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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result = TrainResult(minimize=acc)
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result.log(f'epoch_log_and_pbar_acc1_e{self.current_epoch}', torch.tensor(14).type_as(acc),
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on_epoch=True, prog_bar=True, on_step=False)
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result.log(f'epoch_log_acc2_e{self.current_epoch}', torch.tensor(15).type_as(acc),
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on_epoch=True, on_step=False)
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result.log(f'epoch_pbar_acc3_e{self.current_epoch}', torch.tensor(16).type_as(acc),
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on_epoch=True, logger=False, prog_bar=True, on_step=False)
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self.training_step_called = True
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return result
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def training_step_result_log_epoch_and_step(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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result = TrainResult(minimize=acc)
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val_1 = (5 + batch_idx) * (self.current_epoch + 1)
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val_2 = (6 + batch_idx) * (self.current_epoch + 1)
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val_3 = (7 + batch_idx) * (self.current_epoch + 1)
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result.log('step_epoch_log_and_pbar_acc1', torch.tensor(val_1).type_as(acc),
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on_epoch=True, prog_bar=True)
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result.log('step_epoch_log_acc2', torch.tensor(val_2).type_as(acc),
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on_epoch=True)
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result.log('step_epoch_pbar_acc3', torch.tensor(val_3).type_as(acc),
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on_epoch=True, logger=False, prog_bar=True)
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self.training_step_called = True
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return result
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def training_epoch_end_return_for_log_epoch_and_step(self, result):
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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.use_dp or self.use_ddp2:
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pass
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else:
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# only saw 4 batches
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assert isinstance(result, TrainResult)
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result.step_step_epoch_log_and_pbar_acc1 = result.step_step_epoch_log_and_pbar_acc1.prod()
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result.epoch_step_epoch_log_and_pbar_acc1 = result.epoch_step_epoch_log_and_pbar_acc1.prod()
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result.step_step_epoch_log_acc2 = result.step_step_epoch_log_acc2.prod()
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result.epoch_step_epoch_log_acc2 = result.epoch_step_epoch_log_acc2.prod()
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result.step_step_epoch_pbar_acc3 = result.step_step_epoch_pbar_acc3.prod()
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result.epoch_step_epoch_pbar_acc3 = result.epoch_step_epoch_pbar_acc3.prod()
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result.log('epoch_end_log_acc', torch.tensor(1212).type_as(result.epoch_step_epoch_log_acc2),
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logger=True, on_epoch=True)
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result.log('epoch_end_pbar_acc', torch.tensor(1213).type_as(result.epoch_step_epoch_log_acc2),
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logger=False, prog_bar=True, on_epoch=True)
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result.log('epoch_end_log_pbar_acc', torch.tensor(1214).type_as(result.epoch_step_epoch_log_acc2),
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logger=True, prog_bar=True, on_epoch=True)
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return result
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# --------------------------
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# EvalResults
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# --------------------------
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def validation_step_result_callbacks(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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self.assert_backward = False
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losses = [20, 19, 20, 21, 22, 23]
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idx = self.current_epoch
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loss = acc + losses[idx]
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result = EvalResult(early_stop_on=loss, checkpoint_on=loss)
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self.validation_step_called = True
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return result
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def validation_step_result_no_callbacks(self, batch, batch_idx):
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acc = self.step(batch, batch_idx)
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self.assert_backward = False
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losses = [20, 19, 20, 21, 22, 23, 50, 50, 50, 50, 50, 50]
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idx = self.current_epoch
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loss = acc + losses[idx]
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result = EvalResult(checkpoint_on=loss)
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self.validation_step_called = True
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return result
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def validation_step_result_only_epoch_metrics(self, batch, batch_idx):
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"""
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Only track epoch level metrics
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"""
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acc = self.step(batch, batch_idx)
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result = EvalResult(checkpoint_on=acc, early_stop_on=acc)
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# step only metrics
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result.log('no_val_no_pbar', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=False)
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result.log('val_step_log_acc', torch.tensor(11 + batch_idx).type_as(acc), prog_bar=False, logger=True)
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result.log('val_step_log_pbar_acc', torch.tensor(12 + batch_idx).type_as(acc), prog_bar=True, logger=True)
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result.log('val_step_pbar_acc', torch.tensor(13 + batch_idx).type_as(acc), prog_bar=True, logger=False)
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self.validation_step_called = True
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return result
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def validation_step_result_only_step_metrics(self, batch, batch_idx):
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"""
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Only track epoch level metrics
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"""
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acc = self.step(batch, batch_idx)
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result = EvalResult(checkpoint_on=acc, early_stop_on=acc)
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# step only metrics
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result.log('no_val_no_pbar', torch.tensor(11 + batch_idx).type_as(acc),
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prog_bar=False, logger=False, on_epoch=False, on_step=True)
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result.log('val_step_log_acc', torch.tensor(11 + batch_idx).type_as(acc),
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prog_bar=False, logger=True, on_epoch=False, on_step=True)
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result.log('val_step_log_pbar_acc', torch.tensor(12 + batch_idx).type_as(acc),
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prog_bar=True, logger=True, on_epoch=False, on_step=True)
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result.log('val_step_pbar_acc', torch.tensor(13 + batch_idx).type_as(acc),
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prog_bar=True, logger=False, on_epoch=False, on_step=True)
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result.log('val_step_batch_idx', torch.tensor(batch_idx).type_as(acc),
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prog_bar=True, logger=True, on_epoch=False, on_step=True)
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self.validation_step_called = True
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return result
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def validation_step_result_epoch_step_metrics(self, batch, batch_idx):
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"""
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Only track epoch level metrics
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"""
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acc = self.step(batch, batch_idx)
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result = EvalResult(checkpoint_on=acc, early_stop_on=acc)
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# step only metrics
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result.log('no_val_no_pbar', torch.tensor(11 + batch_idx).type_as(acc),
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prog_bar=False, logger=False, on_epoch=True, on_step=True)
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result.log('val_step_log_acc', torch.tensor(11 + batch_idx).type_as(acc),
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prog_bar=False, logger=True, on_epoch=True, on_step=True)
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result.log('val_step_log_pbar_acc', torch.tensor(12 + batch_idx).type_as(acc),
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prog_bar=True, logger=True, on_epoch=True, on_step=True)
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result.log('val_step_pbar_acc', torch.tensor(13 + batch_idx).type_as(acc),
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prog_bar=True, logger=False, on_epoch=True, on_step=True)
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result.log('val_step_batch_idx', torch.tensor(batch_idx).type_as(acc),
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prog_bar=True, logger=True, on_epoch=True, on_step=True)
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self.validation_step_called = True
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return result
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def validation_step_for_epoch_end_result(self, batch, batch_idx):
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"""
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EvalResult flows to epoch end (without step_end)
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"""
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acc = self.step(batch, batch_idx)
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result = EvalResult(checkpoint_on=acc, early_stop_on=acc)
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# step only metrics
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result.log('val_step_metric', torch.tensor(batch_idx).type_as(acc),
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prog_bar=True, logger=True, on_epoch=True, on_step=False)
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result.log('batch_idx', torch.tensor(batch_idx).type_as(acc),
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prog_bar=True, logger=True, on_epoch=True, on_step=False)
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self.validation_step_called = True
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return result
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def validation_epoch_end_result(self, result):
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self.validation_epoch_end_called = True
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if self.trainer.running_sanity_check:
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assert len(result.batch_idx) == 2
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else:
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assert len(result.batch_idx) == self.trainer.limit_val_batches
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expected_val = result.val_step_metric.sum() / len(result.batch_idx)
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result.val_step_metric = result.val_step_metric.mean()
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result.batch_idx = result.batch_idx.mean()
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assert result.val_step_metric == expected_val
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result.log('val_epoch_end_metric', torch.tensor(189).type_as(result.val_step_metric), prog_bar=True)
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return result
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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.use_dp or self.use_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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acc = 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_arbitary_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
|
|
|
|
self.validation_epoch_end_called = True
|
|
|
|
result = outputs[-1]
|
|
result['val_epoch_end'] = torch.tensor(1233)
|
|
return result
|
|
|
|
# -----------------------------
|
|
# DATA
|
|
# -----------------------------
|
|
def train_dataloader(self):
|
|
return DataLoader(DummyDataset(), batch_size=3, shuffle=False)
|
|
|
|
def val_dataloader(self):
|
|
return DataLoader(DummyDataset(), batch_size=3, shuffle=False)
|
|
|
|
def configure_optimizers(self):
|
|
return torch.optim.Adam(self.parameters(), lr=0)
|
|
|
|
def configure_optimizers__lr_on_plateau_epoch(self):
|
|
optimizer = torch.optim.Adam(self.parameters(), lr=0)
|
|
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
|
|
scheduler = {'scheduler': lr_scheduler, 'interval': 'epoch', 'monitor': 'epoch_end_log_1'}
|
|
return [optimizer], [scheduler]
|
|
|
|
def configure_optimizers__lr_on_plateau_step(self):
|
|
optimizer = torch.optim.Adam(self.parameters(), lr=0)
|
|
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
|
|
scheduler = {'scheduler': lr_scheduler, 'interval': 'step', 'monitor': 'pbar_acc1'}
|
|
return [optimizer], [scheduler]
|
|
|
|
def backward(self, trainer, loss, optimizer, optimizer_idx):
|
|
if self.assert_backward:
|
|
if self.trainer.precision == 16:
|
|
assert loss > 171 * 1000
|
|
else:
|
|
assert loss == 171.0
|
|
|
|
loss.backward()
|
|
|
|
|
|
class DummyDataset(Dataset):
|
|
|
|
def __len__(self):
|
|
return 12
|
|
|
|
def __getitem__(self, idx):
|
|
return torch.tensor([0.5, 1.0, 2.0])
|