def test_forward_output_on_cpu_equals_output_on_gpu(self):
N, C, H, W = shape = 2, 4, 10, 10
kernel_size = 3
padding = 1
for groups in [1, 2]:
inputs = torch.arange(np.prod(shape),
dtype=torch.float32).reshape(*shape)
offset_channels = kernel_size * kernel_size * 2
offset = torch.full((N, offset_channels, H, W),
0.5,
dtype=torch.float32)
deform_gpu = DeformConv(C,
C,
kernel_size=kernel_size,
padding=padding,
groups=groups).to("cuda")
deform_gpu.weight = torch.nn.Parameter(
torch.ones_like(deform_gpu.weight))
output_gpu = deform_gpu(inputs.to("cuda"),
offset.to("cuda")).detach().cpu().numpy()
deform_cpu = DeformConv(C,
C,
kernel_size=kernel_size,
padding=padding,
groups=groups).to("cpu")
deform_cpu.weight = torch.nn.Parameter(
torch.ones_like(deform_cpu.weight))
output_cpu = deform_cpu(inputs.to("cpu"),
offset.to("cpu")).detach().numpy()
self.assertTrue(np.allclose(output_gpu.flatten(),
output_cpu.flatten()))
def test_forward_output_on_cpu(self):
device = torch.device("cpu")
N, C, H, W = shape = 1, 1, 5, 5
kernel_size = 3
padding = 1
inputs = torch.arange(np.prod(shape),
dtype=torch.float32).reshape(*shape).to(device)
offset_channels = kernel_size * kernel_size * 2
offset = torch.full((N, offset_channels, H, W),
0.5,
dtype=torch.float32).to(device)
# Test DCN v1 on cpu
deform = DeformConv(C, C, kernel_size=kernel_size,
padding=padding).to(device)
deform.weight = torch.nn.Parameter(torch.ones_like(deform.weight))
output = deform(inputs, offset)
output = output.detach().cpu().numpy()
deform_results = np.array([
[30, 41.25, 48.75, 45, 28.75],
[62.25, 81, 90, 80.25, 50.25],
[99.75, 126, 135, 117.75, 72.75],
[105, 131.25, 138.75, 120, 73.75],
[71.75, 89.25, 93.75, 80.75, 49.5],
])
self.assertTrue(np.allclose(output.flatten(),
deform_results.flatten()))
def test_forward_output(self):
device = torch.device("cuda")
N, C, H, W = shape = 1, 1, 5, 5
kernel_size = 3
padding = 1
inputs = torch.arange(np.prod(shape),
dtype=torch.float32).reshape(*shape).to(device)
"""
0 1 2 3 4
5 6 7 8 9
10 11 12 13 14
15 16 17 18 19
20 21 22 23 24
"""
offset_channels = kernel_size * kernel_size * 2
offset = torch.full((N, offset_channels, H, W),
0.5,
dtype=torch.float32).to(device)
# Test DCN v1
deform = DeformConv(C, C, kernel_size=kernel_size,
padding=padding).to(device)
deform.weight = torch.nn.Parameter(torch.ones_like(deform.weight))
output = deform(inputs, offset)
output = output.detach().cpu().numpy()
deform_results = np.array([
[30, 41.25, 48.75, 45, 28.75],
[62.25, 81, 90, 80.25, 50.25],
[99.75, 126, 135, 117.75, 72.75],
[105, 131.25, 138.75, 120, 73.75],
[71.75, 89.25, 93.75, 80.75, 49.5],
])
self.assertTrue(np.allclose(output.flatten(),
deform_results.flatten()))
# Test DCN v2
mask_channels = kernel_size * kernel_size
mask = torch.full((N, mask_channels, H, W), 0.5,
dtype=torch.float32).to(device)
modulate_deform = ModulatedDeformConv(C,
C,
kernel_size,
padding=padding,
bias=False).to(device)
modulate_deform.weight = deform.weight
output = modulate_deform(inputs, offset, mask)
output = output.detach().cpu().numpy()
self.assertTrue(
np.allclose(output.flatten(),
deform_results.flatten() * 0.5))