def test_multiple_input_tensor_list(self):
input_data = torch.randn(1, 300)
other_input_data = torch.randn(1, 300).long()
metrics = summary(MultipleInputNetDifferentDtypes(), [input_data, other_input_data])
assert metrics.input_size == [torch.Size([1, 300]), torch.Size([1, 300])]
def test_multiple_input_tensor_args() -> None:
input_data = torch.randn(1, 300)
other_input_data = torch.randn(1, 300).long()
metrics = summary(MultipleInputNetDifferentDtypes(), input_data,
other_input_data)
assert metrics.input_size == [torch.Size([1, 300])]
def test_dict_out(capsys: pytest.CaptureFixture[str]) -> None:
# TODO: expand this test to handle intermediate dict layers.
model = MultipleInputNetDifferentDtypes()
input_data = torch.randn(1, 300)
other_input_data = torch.randn(1, 300).long()
summary(model, input_data={"x1": input_data, "x2": other_input_data})
verify_output(capsys, "tests/test_output/dict_input.out")
def test_multiple_input_types(self):
model = MultipleInputNetDifferentDtypes()
input1 = (1, 300)
input2 = (1, 300)
if torch.cuda.is_available():
dtypes = [torch.cuda.FloatTensor, torch.cuda.LongTensor]
else:
dtypes = [torch.FloatTensor, torch.LongTensor]
results = summary(model, [input1, input2], dtypes=dtypes)
assert results.total_params == 31120
assert results.trainable_params == 31120
def test_multiple_input_types() -> None:
model = MultipleInputNetDifferentDtypes()
input_size = (1, 300)
if torch.cuda.is_available():
dtypes = [
torch.cuda.FloatTensor, # type: ignore[attr-defined]
torch.cuda.LongTensor, # type: ignore[attr-defined]
]
else:
dtypes = [torch.FloatTensor, torch.LongTensor]
results = summary(model, input_size=[input_size, input_size], dtypes=dtypes)
assert results.total_params == 31120
assert results.trainable_params == 31120