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Tests: fix deprecated TM mape (#8830)

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Jirka Borovec 2021-08-10 11:26:05 +02:00 committed by GitHub
parent 3ef8cd654d
commit 3096ab88eb
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1 changed files with 4 additions and 4 deletions
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tests/callbacks/test_quantization.py
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@ -16,7 +16,7 @@ from typing import Callable, Union
import pytest import pytest
import torch import torch
from torchmetrics.functional import mean_relative_error from torchmetrics.functional import mean_absolute_percentage_error as mape
from pytorch_lightning import seed_everything, Trainer from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import QuantizationAwareTraining from pytorch_lightning.callbacks import QuantizationAwareTraining
@ -42,7 +42,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
trainer = Trainer(**trainer_args) trainer = Trainer(**trainer_args)
trainer.fit(model, datamodule=dm) trainer.fit(model, datamodule=dm)
org_size = get_model_size_mb(model) org_size = get_model_size_mb(model)
org_score = torch.mean(torch.tensor([mean_relative_error(model(x), y) for x, y in dm.test_dataloader()])) org_score = torch.mean(torch.tensor([mape(model(x), y) for x, y in dm.test_dataloader()]))
fusing_layers = [(f"layer_{i}", f"layer_{i}a") for i in range(3)] if fuse else None fusing_layers = [(f"layer_{i}", f"layer_{i}a") for i in range(3)] if fuse else None
qcb = QuantizationAwareTraining(observer_type=observe, modules_to_fuse=fusing_layers, quantize_on_fit_end=convert) qcb = QuantizationAwareTraining(observer_type=observe, modules_to_fuse=fusing_layers, quantize_on_fit_end=convert)
@ -51,7 +51,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
quant_calls = qcb._forward_calls quant_calls = qcb._forward_calls
assert quant_calls == qcb._forward_calls assert quant_calls == qcb._forward_calls
quant_score = torch.mean(torch.tensor([mean_relative_error(qmodel(x), y) for x, y in dm.test_dataloader()])) quant_score = torch.mean(torch.tensor([mape(qmodel(x), y) for x, y in dm.test_dataloader()]))
# test that the test score is almost the same as with pure training # test that the test score is almost the same as with pure training
assert torch.allclose(org_score, quant_score, atol=0.45) assert torch.allclose(org_score, quant_score, atol=0.45)
model_path = trainer.checkpoint_callback.best_model_path model_path = trainer.checkpoint_callback.best_model_path
@ -70,7 +70,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
# todo: make it work also with strict loading # todo: make it work also with strict loading
qmodel2 = RegressionModel.load_from_checkpoint(model_path, strict=False) qmodel2 = RegressionModel.load_from_checkpoint(model_path, strict=False)
quant2_score = torch.mean(torch.tensor([mean_relative_error(qmodel2(x), y) for x, y in dm.test_dataloader()])) quant2_score = torch.mean(torch.tensor([mape(qmodel2(x), y) for x, y in dm.test_dataloader()]))
assert torch.allclose(org_score, quant2_score, atol=0.45) assert torch.allclose(org_score, quant2_score, atol=0.45)