def test_model_forward_fx_torchscript(model_name, batch_size):
"""Symbolically trace each model, script it, and run single forward pass"""
if not has_fx_feature_extraction:
pytest.skip(
"Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required."
)
input_size = _get_input_size(model_name=model_name,
target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(_create_fx_model(model))
with torch.no_grad():
outputs = tuple(
model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple):
outputs = torch.cat(outputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
def test_model_forward_torchscript(model_name, batch_size):
"""Run a single forward pass with each model"""
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
input_size = (3, 128, 128) # jit compile is already a bit slow and we've tested normal res already...
model = torch.jit.script(model)
outputs = model(torch.randn((batch_size, *input_size)))
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
def test_model_forward_torchscript(model_name, batch_size):
"""Run a single forward pass with each model"""
input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.")
with set_scriptable(True):
model = create_model(model_name, pretrained=False)
model.eval()
model = torch.jit.script(model)
outputs = model(torch.randn((batch_size, *input_size)))
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'
