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Tests: fix deprecated TM mape #8830

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Merged
merged 2 commits into from
Aug 10, 2021
Merged

Tests: fix deprecated TM mape #8830

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6ee3c97
fix mape
Borda Aug 10, 2021
372ca01
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Borda Aug 10, 2021
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8 changes: 4 additions & 4 deletions tests/callbacks/test_quantization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@

import pytest
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.callbacks import QuantizationAwareTraining
Expand All @@ -42,7 +42,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
trainer = Trainer(**trainer_args)
trainer.fit(model, datamodule=dm)
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
qcb = QuantizationAwareTraining(observer_type=observe, modules_to_fuse=fusing_layers, quantize_on_fit_end=convert)
Expand All @@ -51,7 +51,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):

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
assert torch.allclose(org_score, quant_score, atol=0.45)
model_path = trainer.checkpoint_callback.best_model_path
Expand All @@ -70,7 +70,7 @@ def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):

# todo: make it work also with strict loading
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)


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