def test_half_padding_args(self):
x = torch.ones(4, 1, 28, 28)
conv_half = Conv().eval()
conv_half(x)
conv = Conv(2, p=1, s=2).eval()
conv(x)
assert conv._args == conv_half._args
def test_mul_list(self):
conv_layer = Conv().eval()
cs = (5, 3, 2)
convs = conv_layer * cs
assert convs[0] is conv_layer
assert all(conv._args[k] == conv_layer._args[k]
for k in conv_layer._args.keys() if k != 'c'
for conv in convs)
assert all(conv._args['c'] == c for conv, c in zip(convs, cs))
def test_conv_3d(self):
conv = Conv().eval()
conv(torch.randn(1, 2, 3, 4, 5))
assert isinstance(conv.layer, nn.Conv3d)
def test_double_padding(self):
conv = Conv(p='double').eval()
assert conv(torch.ones(4, 2, 28, 28)).shape == (4, 1, 56, 56)
def test_same_padding(self):
conv = Conv(p='same').eval()
assert conv(torch.ones(4, 1, 28, 28)).shape == (4, 1, 28, 28)
def test_half_padding(self):
conv = Conv().eval()
assert conv(torch.ones(4, 1, 28, 28)).shape == (4, 2, 14, 14)