def test_model_backward(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False, num_classes=42)
num_params = sum([x.numel() for x in model.parameters()])
model.eval()
input_size = model.default_cfg['input_size']
if not is_model_default_key(model_name, 'fixed_input_size'):
min_input_size = get_model_default_value(model_name, 'min_input_size')
if min_input_size is not None:
input_size = min_input_size
else:
if any([x > MAX_BWD_SIZE for x in input_size]):
# cap backward test at 128 * 128 to keep resource usage down
input_size = tuple([min(x, MAX_BWD_SIZE) for x in input_size])
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
outputs.mean().backward()
for n, x in model.named_parameters():
assert x.grad is not None, f'No gradient for {n}'
num_grad = sum(
[x.grad.numel() for x in model.parameters() if x.grad is not None])
assert outputs.shape[-1] == 42
assert num_params == num_grad, 'Some parameters are missing gradients'
assert not torch.isnan(outputs).any(), 'Output included NaNs'
def test_model_forward(model_name, batch_size):
"""Run a single forward pass with each model"""
model = create_model(model_name, pretrained=False)
model.eval()
input_size = model.default_cfg['input_size']
if any([x > MAX_FWD_SIZE for x in input_size]):
if is_model_default_key(model_name, 'fixed_input_size'):
pytest.skip("Fixed input size model > limit.")
# cap forward test at max res 384 * 384 to keep resource down
input_size = tuple([min(x, MAX_FWD_SIZE) for x in input_size])
inputs = torch.randn((batch_size, *input_size))
outputs = model(inputs)
assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs'