mirrors/pytorch-image-models
1
0
Fork 0
mirror of https://github.com/huggingface/pytorch-image-models.git synced 2025-06-03 15:01:08 +08:00
Code Issues Projects Releases Wiki Activity

More FX test tweaks

Browse Source
This commit is contained in:
Ross Wightman 2021-11-22 09:46:43 -08:00
parent f0507f6da6
commit 1e51c2d02e
1 changed files with 14 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

25
tests/test_models.py
View File

@ -348,6 +348,7 @@ if 'GITHUB_ACTIONS' in os.environ:
'vgg*', 'vgg*',
'vit_large*', 'vit_large*',
'xcit_large*', 'xcit_large*',
'mixer_l*',
] ]
@ -368,15 +369,16 @@ def test_model_forward_fx(model_name, batch_size):
input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE) input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
if max(input_size) > MAX_FWD_FX_SIZE: if max(input_size) > MAX_FWD_FX_SIZE:
pytest.skip("Fixed input size model > limit.") pytest.skip("Fixed input size model > limit.")
inputs = torch.randn((batch_size, *input_size)) with torch.no_grad():
outputs = model(inputs) inputs = torch.randn((batch_size, *input_size))
if isinstance(outputs, tuple): outputs = model(inputs)
outputs = torch.cat(outputs) if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
model = _create_fx_model(model) model = _create_fx_model(model)
fx_outputs = tuple(model(inputs).values()) fx_outputs = tuple(model(inputs).values())
if isinstance(fx_outputs, tuple): if isinstance(fx_outputs, tuple):
fx_outputs = torch.cat(fx_outputs) fx_outputs = torch.cat(fx_outputs)
assert torch.all(fx_outputs == outputs) assert torch.all(fx_outputs == outputs)
assert outputs.shape[0] == batch_size assert outputs.shape[0] == batch_size
@ -440,9 +442,10 @@ def test_model_forward_fx_torchscript(model_name, batch_size):
model.eval() model.eval()
model = torch.jit.script(_create_fx_model(model)) model = torch.jit.script(_create_fx_model(model))
outputs = tuple(model(torch.randn((batch_size, *input_size))).values()) with torch.no_grad():
if isinstance(outputs, tuple): outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
outputs = torch.cat(outputs) if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
assert outputs.shape[0] == batch_size assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs' assert not torch.isnan(outputs).any(), 'Output included NaNs'