def test_conv_transpose(self):
# Fix test in the future.
return
device = "CUDA"
if not supports_device(device):
raise unittest.SkipTest(
"Backend doesn't support device {}".format(device))
node_def = helper.make_node("ConvTranspose", ["X", "weights"], ["Y"],
pads=[1, 1])
x_shape = [1, 5, 4]
x = self._get_rnd(x_shape)
weight_shape = [5, 3, 2]
weights = self._get_rnd(weight_shape)
output = run_node(node_def, [x, weights], device=device)
out_shape = [x_shape[0], weight_shape[1], x_shape[2]]
test_output = np.zeros(out_shape)
for b in range(0, x_shape[0]):
for m in range(0, weight_shape[1]):
for h in range(0, x_shape[2]):
v = 0
for c in range(0, x_shape[1]):
for k in range(h, min(h + weight_shape[2],
x_shape[2])):
v += x[b][c][k] * weights[c][m][k - h]
test_output[b][m][h] = v
np.testing.assert_almost_equal(output["Y"], test_output, decimal=5)
def test_average_pool(self):
# TODO: fix this test
return
device = "CUDA"
if not supports_device(device):
raise unittest.SkipTest(
"Backend doesn't support device {}".format(device))
shape = [1, 1, 40, 40]
node_def = helper.make_node("AveragePool", ["X"], ["Y"],
kernel_shape=[1, 2],
pads=[1, 1],
strides=[1, 1])
x = self._get_rnd(shape)
output = run_node(node_def, [x], device=device)
test_output = np.zeros(shape)
for i1 in range(0, shape[0]):
for i2 in range(0, shape[1]):
for j1 in range(0, shape[2]):
for j2 in range(0, shape[3]):
test_output[i1][i2][j1][j2] = 0
count = 0
for k in range(j2, min(j2 + 2, shape[3])):
test_output[i1][i2][j1][j2] += x[i1][i2][j1][k]
count += 1
test_output[i1][i2][j1][j2] /= count
np.testing.assert_almost_equal(output["Y"], test_output)
def test_conv(self):
device = "CUDA"
if not supports_device(device):
raise unittest.SkipTest(
"Backend doesn't support device {}".format(device))
N, C, H, W = 4, 3, 5, 5
x_shape = [N, C, H, W]
K, kH, kW = 6, 3, 3
weight_shape = [K, C, kH, kW]
node_def = helper.make_node("Conv", ["X", "weights"], ["Y"],
pads=[1, 1, 1, 1],
kernel_shape=[kH, kW])
x = self._get_rnd(x_shape)
weights = self._get_rnd(weight_shape)
output = run_node(node_def, [x, weights], device=device)
out_shape = [N, K, H, W]
test_output = np.zeros(out_shape)
for n in range(N):
for c in range(C):
for h in range(H):
for w in range(W):
for k in range(K):
for kh in range(kH):
for kw in range(kW):
h_in_range = (h - kH // 2 + kh) < H and (
h - kH // 2 + kh) >= 0
w_in_range = (w - kW // 2 + kw) < W and (
w - kW // 2 + kw) >= 0
if h_in_range and w_in_range:
test_output[n][k][h][w] += (
x[n][c][h - kH // 2 +
kh][w - kW // 2 + kw] *
weights[k][c][kh][kw])
np.testing.assert_almost_equal(output["Y"], test_output, decimal=5)