635 lines
23 KiB
Python
635 lines
23 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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from functools import partial
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from unittest import mock
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import pytest
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import torch
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from torch.utils.data import DataLoader
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from torchmetrics import Accuracy, AveragePrecision
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from pytorch_lightning import LightningModule
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from pytorch_lightning.callbacks.base import Callback
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from pytorch_lightning.trainer import Trainer
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from pytorch_lightning.trainer.connectors.logger_connector.fx_validator import _FxValidator
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from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.helpers.boring_model import BoringModel, RandomDataset
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from tests.helpers.runif import RunIf
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from tests.models.test_hooks import get_members
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def test_fx_validator(tmpdir):
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funcs_name = sorted(get_members(Callback))
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callbacks_func = [
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"on_before_backward",
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"on_after_backward",
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"on_before_optimizer_step",
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"on_batch_end",
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"on_batch_start",
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"on_before_accelerator_backend_setup",
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"on_before_zero_grad",
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"on_epoch_end",
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"on_epoch_start",
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"on_fit_end",
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"on_configure_sharded_model",
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"on_fit_start",
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"on_init_end",
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"on_init_start",
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"on_keyboard_interrupt",
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"on_exception",
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"on_load_checkpoint",
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"on_pretrain_routine_end",
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"on_pretrain_routine_start",
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"on_sanity_check_end",
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"on_sanity_check_start",
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"on_save_checkpoint",
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"on_test_batch_end",
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"on_test_batch_start",
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"on_test_end",
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"on_test_epoch_end",
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"on_test_epoch_start",
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"on_test_start",
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"on_train_batch_end",
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"on_train_batch_start",
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"on_train_end",
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"on_train_epoch_end",
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"on_train_epoch_start",
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"on_train_start",
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"on_validation_batch_end",
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"on_validation_batch_start",
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"on_validation_end",
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"on_validation_epoch_end",
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"on_validation_epoch_start",
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"on_validation_start",
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"on_predict_batch_end",
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"on_predict_batch_start",
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"on_predict_end",
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"on_predict_epoch_end",
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"on_predict_epoch_start",
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"on_predict_start",
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"setup",
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"teardown",
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]
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not_supported = [
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"on_before_accelerator_backend_setup",
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"on_fit_end",
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"on_fit_start",
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"on_configure_sharded_model",
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"on_init_end",
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"on_init_start",
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"on_keyboard_interrupt",
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"on_exception",
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"on_load_checkpoint",
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"on_pretrain_routine_end",
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"on_pretrain_routine_start",
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"on_sanity_check_end",
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"on_sanity_check_start",
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"on_predict_batch_end",
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"on_predict_batch_start",
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"on_predict_end",
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"on_predict_epoch_end",
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"on_predict_epoch_start",
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"on_predict_start",
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"on_save_checkpoint",
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"on_test_end",
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"on_train_end",
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"on_validation_end",
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"setup",
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"teardown",
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]
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assert funcs_name == sorted(
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callbacks_func
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), "Detected new callback function. Need to add its logging permission to FxValidator and update this test"
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validator = _FxValidator()
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for func_name in funcs_name:
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# This summarizes where and what is currently possible to log using `self.log`
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is_stage = "train" in func_name or "test" in func_name or "validation" in func_name
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is_start = "start" in func_name or "batch" in func_name
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is_epoch = "epoch" in func_name
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on_step = is_stage and not is_start and not is_epoch
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on_epoch = True
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# creating allowed condition
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allowed = (
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is_stage
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or "batch" in func_name
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or "epoch" in func_name
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or "grad" in func_name
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or "backward" in func_name
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or "optimizer_step" in func_name
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)
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allowed = (
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allowed
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and "pretrain" not in func_name
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and "predict" not in func_name
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and func_name not in ["on_train_end", "on_test_end", "on_validation_end"]
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)
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if allowed:
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validator.check_logging(fx_name=func_name, on_step=on_step, on_epoch=on_epoch)
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if not is_start and is_stage:
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with pytest.raises(MisconfigurationException, match="must be one of"):
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validator.check_logging(fx_name=func_name, on_step=True, on_epoch=on_epoch)
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else:
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assert func_name in not_supported
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with pytest.raises(MisconfigurationException, match="You can't"):
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validator.check_logging(fx_name=func_name, on_step=on_step, on_epoch=on_epoch)
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with pytest.raises(RuntimeError, match="Logging inside `foo` is not implemented"):
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validator.check_logging("foo", False, False)
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class HookedCallback(Callback):
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def __init__(self, not_supported):
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def call(hook, trainer, model=None, *_, **__):
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lightning_module = trainer.lightning_module or model
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if lightning_module is None:
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# `on_init_{start,end}` do not have the `LightningModule` available
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assert hook in ("on_init_start", "on_init_end")
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return
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if hook in not_supported:
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with pytest.raises(MisconfigurationException, match=not_supported[hook]):
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lightning_module.log("anything", 1)
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else:
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lightning_module.log(hook, 1)
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for h in get_members(Callback):
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setattr(self, h, partial(call, h))
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class HookedModel(BoringModel):
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def __init__(self, not_supported):
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super().__init__()
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pl_module_hooks = get_members(LightningModule)
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pl_module_hooks.difference_update(
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{
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"log",
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"log_dict",
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# the following are problematic as they do have `self._current_fx_name` defined some times but
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# not others depending on where they were called. So we cannot reliably `self.log` in them
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"on_before_batch_transfer",
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"transfer_batch_to_device",
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"on_after_batch_transfer",
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"get_progress_bar_dict",
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}
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)
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# remove `nn.Module` hooks
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module_hooks = get_members(torch.nn.Module)
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pl_module_hooks.difference_update(module_hooks)
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def call(hook, fn, *args, **kwargs):
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out = fn(*args, **kwargs)
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if hook in not_supported:
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with pytest.raises(MisconfigurationException, match=not_supported[hook]):
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self.log("anything", 1)
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else:
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self.log(hook, 1)
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return out
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for h in pl_module_hooks:
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attr = getattr(self, h)
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setattr(self, h, partial(call, h, attr))
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def test_fx_validator_integration(tmpdir):
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"""Tries to log inside all `LightningModule` and `Callback` hooks to check any expected errors."""
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not_supported = {
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None: "`self.trainer` reference is not registered",
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"on_before_accelerator_backend_setup": "You can't",
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"setup": "You can't",
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"configure_sharded_model": "You can't",
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"on_configure_sharded_model": "You can't",
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"configure_optimizers": "You can't",
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"on_fit_start": "You can't",
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"on_pretrain_routine_start": "You can't",
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"on_pretrain_routine_end": "You can't",
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"on_train_dataloader": "You can't",
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"train_dataloader": "You can't",
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"on_val_dataloader": "You can't",
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"val_dataloader": "You can't",
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"on_validation_end": "You can't",
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"on_train_end": "You can't",
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"on_fit_end": "You can't",
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"teardown": "You can't",
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"on_sanity_check_start": "You can't",
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"on_sanity_check_end": "You can't",
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"prepare_data": "You can't",
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"configure_callbacks": "You can't",
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"on_validation_model_eval": "You can't",
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"summarize": "not managed by the `Trainer",
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}
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model = HookedModel(not_supported)
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with pytest.raises(MisconfigurationException, match=not_supported[None]):
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model.log("foo", 1)
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callback = HookedCallback(not_supported)
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trainer = Trainer(
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default_root_dir=tmpdir,
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max_epochs=2,
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limit_train_batches=1,
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limit_val_batches=1,
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limit_test_batches=1,
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limit_predict_batches=1,
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callbacks=callback,
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)
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trainer.fit(model)
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not_supported.update(
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{
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# `lightning_module` ref is now present from the `fit` call
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"on_before_accelerator_backend_setup": "You can't",
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"on_test_dataloader": "You can't",
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"test_dataloader": "You can't",
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"on_test_model_eval": "You can't",
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"on_test_end": "You can't",
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}
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)
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trainer.test(model, verbose=False)
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not_supported.update({k: "ResultCollection` is not registered yet" for k in not_supported})
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not_supported.update(
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{
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"on_predict_dataloader": "ResultCollection` is not registered yet",
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"predict_dataloader": "ResultCollection` is not registered yet",
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"on_predict_model_eval": "ResultCollection` is not registered yet",
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"on_predict_start": "ResultCollection` is not registered yet",
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"on_predict_epoch_start": "ResultCollection` is not registered yet",
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"on_predict_batch_start": "ResultCollection` is not registered yet",
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"predict_step": "ResultCollection` is not registered yet",
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"on_predict_batch_end": "ResultCollection` is not registered yet",
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"on_predict_epoch_end": "ResultCollection` is not registered yet",
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"on_predict_end": "ResultCollection` is not registered yet",
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}
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)
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trainer.predict(model)
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@RunIf(min_gpus=2)
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def test_epoch_results_cache_dp(tmpdir):
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root_device = torch.device("cuda", 0)
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class TestModel(BoringModel):
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def training_step(self, *args, **kwargs):
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result = super().training_step(*args, **kwargs)
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self.log("train_loss_epoch", result["loss"], on_step=False, on_epoch=True)
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return result
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def training_step_end(self, training_step_outputs): # required for dp
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loss = training_step_outputs["loss"].mean()
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return loss
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def training_epoch_end(self, outputs):
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assert all(out["loss"].device == root_device for out in outputs)
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assert self.trainer.callback_metrics["train_loss_epoch"].device == root_device
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def validation_step(self, *args, **kwargs):
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val_loss = torch.rand(1, device=torch.device("cuda", 1))
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self.log("val_loss_epoch", val_loss, on_step=False, on_epoch=True)
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return val_loss
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def validation_epoch_end(self, outputs):
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assert all(loss.device == root_device for loss in outputs)
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assert self.trainer.callback_metrics["val_loss_epoch"].device == root_device
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def test_step(self, *args, **kwargs):
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test_loss = torch.rand(1, device=torch.device("cuda", 1))
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self.log("test_loss_epoch", test_loss, on_step=False, on_epoch=True)
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return test_loss
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def test_epoch_end(self, outputs):
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assert all(loss.device == root_device for loss in outputs)
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assert self.trainer.callback_metrics["test_loss_epoch"].device == root_device
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def train_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=4)
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def val_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=4)
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def test_dataloader(self):
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return DataLoader(RandomDataset(32, 64), batch_size=4)
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model = TestModel()
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trainer = Trainer(
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default_root_dir=tmpdir, accelerator="dp", gpus=2, limit_train_batches=2, limit_val_batches=2, max_epochs=1
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)
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trainer.fit(model)
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trainer.test(model)
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def test_can_return_tensor_with_more_than_one_element(tmpdir):
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"""Ensure {validation,test}_step return values are not included as callback metrics.
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#6623
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"""
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class TestModel(BoringModel):
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def validation_step(self, batch, *args, **kwargs):
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return {"val": torch.tensor([0, 1])}
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def validation_epoch_end(self, outputs):
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# ensure validation step returns still appear here
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assert len(outputs) == 2
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assert all(list(d) == ["val"] for d in outputs) # check keys
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assert all(torch.equal(d["val"], torch.tensor([0, 1])) for d in outputs) # check values
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def test_step(self, batch, *args, **kwargs):
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return {"test": torch.tensor([0, 1])}
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def test_epoch_end(self, outputs):
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assert len(outputs) == 2
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assert all(list(d) == ["test"] for d in outputs) # check keys
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assert all(torch.equal(d["test"], torch.tensor([0, 1])) for d in outputs) # check values
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2, progress_bar_refresh_rate=0)
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trainer.fit(model)
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trainer.validate(model)
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trainer.test(model)
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def test_logging_to_progress_bar_with_reserved_key(tmpdir):
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"""Test that logging a metric with a reserved name to the progress bar raises a warning."""
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class TestModel(BoringModel):
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def training_step(self, *args, **kwargs):
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output = super().training_step(*args, **kwargs)
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self.log("loss", output["loss"], prog_bar=True)
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return output
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
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with pytest.warns(UserWarning, match="The progress bar already tracks a metric with the .* 'loss'"):
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trainer.fit(model)
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@pytest.mark.parametrize("add_dataloader_idx", [False, True])
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def test_auto_add_dataloader_idx(tmpdir, add_dataloader_idx):
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"""test that auto_add_dataloader_idx argument works."""
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class TestModel(BoringModel):
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def val_dataloader(self):
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dl = super().val_dataloader()
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return [dl, dl]
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def validation_step(self, *args, **kwargs):
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output = super().validation_step(*args[:-1], **kwargs)
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if add_dataloader_idx:
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name = "val_loss"
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else:
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name = f"val_loss_custom_naming_{args[-1]}"
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self.log(name, output["x"], add_dataloader_idx=add_dataloader_idx)
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return output
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model = TestModel()
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model.validation_epoch_end = None
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trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2)
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trainer.fit(model)
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logged = trainer.logged_metrics
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# Check that the correct keys exist
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if add_dataloader_idx:
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assert "val_loss/dataloader_idx_0" in logged
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assert "val_loss/dataloader_idx_1" in logged
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else:
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assert "val_loss_custom_naming_0" in logged
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assert "val_loss_custom_naming_1" in logged
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def test_metrics_reset(tmpdir):
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"""Tests that metrics are reset correctly after the end of the train/val/test epoch."""
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class TestModel(LightningModule):
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def __init__(self):
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super().__init__()
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self.layer = torch.nn.Linear(32, 1)
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def _create_metrics(self):
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acc = Accuracy()
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acc.reset = mock.Mock(side_effect=acc.reset)
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ap = AveragePrecision(num_classes=1, pos_label=1)
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ap.reset = mock.Mock(side_effect=ap.reset)
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return acc, ap
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def setup(self, stage):
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fn = stage
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if fn == "fit":
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for stage in ("train", "validate"):
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acc, ap = self._create_metrics()
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self.add_module(f"acc_{fn}_{stage}", acc)
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self.add_module(f"ap_{fn}_{stage}", ap)
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else:
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acc, ap = self._create_metrics()
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stage = self.trainer.state.stage
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self.add_module(f"acc_{fn}_{stage}", acc)
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self.add_module(f"ap_{fn}_{stage}", ap)
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def forward(self, x):
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return self.layer(x)
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def _step(self, batch):
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fn, stage = self.trainer.state.fn, self.trainer.state.stage
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logits = self(batch)
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loss = logits.sum()
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self.log(f"loss/{fn}_{stage}", loss)
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acc = self._modules[f"acc_{fn}_{stage}"]
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ap = self._modules[f"ap_{fn}_{stage}"]
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preds = torch.rand(len(batch)) # Fake preds
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labels = torch.randint(0, 1, [len(batch)]) # Fake targets
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acc(preds, labels)
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ap(preds, labels)
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# Metric.forward calls reset so reset the mocks here
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acc.reset.reset_mock()
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ap.reset.reset_mock()
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self.log(f"acc/{fn}_{stage}", acc)
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self.log(f"ap/{fn}_{stage}", ap)
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return loss
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def training_step(self, batch, batch_idx, *args, **kwargs):
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return self._step(batch)
|
|
|
|
def validation_step(self, batch, batch_idx, *args, **kwargs):
|
|
if self.trainer.sanity_checking:
|
|
return
|
|
return self._step(batch)
|
|
|
|
def test_step(self, batch, batch_idx, *args, **kwargs):
|
|
return self._step(batch)
|
|
|
|
def configure_optimizers(self):
|
|
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
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|
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
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|
return [optimizer], [lr_scheduler]
|
|
|
|
def train_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 64))
|
|
|
|
def val_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 64))
|
|
|
|
def test_dataloader(self):
|
|
return DataLoader(RandomDataset(32, 64))
|
|
|
|
def _assert_called(model, fn, stage):
|
|
acc = model._modules[f"acc_{fn}_{stage}"]
|
|
ap = model._modules[f"ap_{fn}_{stage}"]
|
|
acc.reset.assert_called_once()
|
|
ap.reset.assert_called_once()
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(
|
|
default_root_dir=tmpdir,
|
|
limit_train_batches=2,
|
|
limit_val_batches=2,
|
|
limit_test_batches=2,
|
|
max_epochs=1,
|
|
progress_bar_refresh_rate=0,
|
|
num_sanity_val_steps=2,
|
|
checkpoint_callback=False,
|
|
)
|
|
|
|
trainer.fit(model)
|
|
_assert_called(model, "fit", "train")
|
|
_assert_called(model, "fit", "validate")
|
|
|
|
trainer.validate(model)
|
|
_assert_called(model, "validate", "validate")
|
|
|
|
trainer.test(model)
|
|
_assert_called(model, "test", "test")
|
|
|
|
|
|
def test_result_collection_on_tensor_with_mean_reduction():
|
|
result_collection = ResultCollection(True, torch.device("cpu"))
|
|
product = [(True, True), (False, True), (True, False), (False, False)]
|
|
values = torch.arange(1, 10).float() # need to convert to float() due to precision issues using torch 1.4
|
|
batches = values * values
|
|
|
|
for i, v in enumerate(values):
|
|
for prog_bar in [False, True]:
|
|
for logger in [False, True]:
|
|
for on_step, on_epoch in product:
|
|
name = "loss"
|
|
if on_step:
|
|
name += "_on_step"
|
|
if on_epoch:
|
|
name += "_on_epoch"
|
|
if prog_bar:
|
|
name += "_prog_bar"
|
|
if logger:
|
|
name += "_logger"
|
|
result_collection.log(
|
|
"training_step",
|
|
name,
|
|
v,
|
|
on_step=on_step,
|
|
on_epoch=on_epoch,
|
|
batch_size=batches[i],
|
|
prog_bar=prog_bar,
|
|
logger=logger,
|
|
)
|
|
|
|
total_value = sum(values * batches)
|
|
total_batches = sum(batches)
|
|
assert result_collection["training_step.loss_on_step_on_epoch"].value == total_value
|
|
assert result_collection["training_step.loss_on_step_on_epoch"].cumulated_batch_size == total_batches
|
|
|
|
batch_metrics = result_collection.metrics(True)
|
|
max_ = max(values)
|
|
assert batch_metrics["pbar"] == {
|
|
"loss_on_step_on_epoch_prog_bar_step": max_,
|
|
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
|
|
"loss_on_step_prog_bar": max_,
|
|
"loss_on_step_prog_bar_logger": max_,
|
|
}
|
|
assert batch_metrics["log"] == {
|
|
"loss_on_step_on_epoch_logger_step": max_,
|
|
"loss_on_step_logger": max_,
|
|
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
|
|
"loss_on_step_prog_bar_logger": max_,
|
|
}
|
|
assert batch_metrics["callback"] == {
|
|
"loss_on_step": max_,
|
|
"loss_on_step_logger": max_,
|
|
"loss_on_step_on_epoch": max_,
|
|
"loss_on_step_on_epoch_logger": max_,
|
|
"loss_on_step_on_epoch_logger_step": max_,
|
|
"loss_on_step_on_epoch_prog_bar": max_,
|
|
"loss_on_step_on_epoch_prog_bar_logger": max_,
|
|
"loss_on_step_on_epoch_prog_bar_logger_step": max_,
|
|
"loss_on_step_on_epoch_prog_bar_step": max_,
|
|
"loss_on_step_on_epoch_step": max_,
|
|
"loss_on_step_prog_bar": max_,
|
|
"loss_on_step_prog_bar_logger": max_,
|
|
}
|
|
|
|
epoch_metrics = result_collection.metrics(False)
|
|
mean = total_value / total_batches
|
|
assert epoch_metrics["pbar"] == {
|
|
"loss_on_epoch_prog_bar": mean,
|
|
"loss_on_epoch_prog_bar_logger": mean,
|
|
"loss_on_step_on_epoch_prog_bar_epoch": mean,
|
|
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
|
|
}
|
|
assert epoch_metrics["log"] == {
|
|
"loss_on_epoch_logger": mean,
|
|
"loss_on_epoch_prog_bar_logger": mean,
|
|
"loss_on_step_on_epoch_logger_epoch": mean,
|
|
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
|
|
}
|
|
assert epoch_metrics["callback"] == {
|
|
"loss_on_epoch": mean,
|
|
"loss_on_epoch_logger": mean,
|
|
"loss_on_epoch_prog_bar": mean,
|
|
"loss_on_epoch_prog_bar_logger": mean,
|
|
"loss_on_step_on_epoch": mean,
|
|
"loss_on_step_on_epoch_epoch": mean,
|
|
"loss_on_step_on_epoch_logger": mean,
|
|
"loss_on_step_on_epoch_logger_epoch": mean,
|
|
"loss_on_step_on_epoch_prog_bar": mean,
|
|
"loss_on_step_on_epoch_prog_bar_epoch": mean,
|
|
"loss_on_step_on_epoch_prog_bar_logger": mean,
|
|
"loss_on_step_on_epoch_prog_bar_logger_epoch": mean,
|
|
}
|
|
|
|
|
|
def test_logged_metrics_has_logged_epoch_value(tmpdir):
|
|
class TestModel(BoringModel):
|
|
def training_step(self, batch, batch_idx):
|
|
self.log("epoch", -batch_idx, logger=True)
|
|
return super().training_step(batch, batch_idx)
|
|
|
|
model = TestModel()
|
|
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2)
|
|
trainer.fit(model)
|
|
|
|
# should not get overridden if logged manually
|
|
assert trainer.logged_metrics == {"epoch": -1}
|