def layer4_1(x):
pad4_1 = F.pad(x, (1, 1, 1, 1), 'reflect')
conv4_1 = PF.convolution(
pad4_1, 256, kernel=(
3, 3), stride=(
1, 1), name='layer4_1.1')
conv4_1 = F.instance_normalization(
conv4_1, gamma=None, beta=None, channel_axis=1)
conv4_1 = PF.prelu(conv4_1, name='layer4_1.3')
pad4_2 = F.pad(conv4_1, (1, 1, 1, 1), 'reflect')
conv4_2 = PF.convolution(
pad4_2, 64, kernel=(
3, 3), stride=(
1, 1), name='layer4_1.5')
conv4_2 = F.instance_normalization(
conv4_2, gamma=None, beta=None, channel_axis=1)
conv4_2 = PF.prelu(conv4_2, name='layer4_1.7')
up4_1 = F.interpolate(
conv4_2,
scale=(
2,
2),
mode='nearest',
align_corners=False)
return up4_1
def layer3_1(x):
pad3_1 = F.pad(x, (1, 1, 1, 1), 'reflect')
conv3_1 = PF.convolution(
pad3_1, 128, kernel=(
3, 3), stride=(
1, 1), name='layer3_1.1')
conv3_1 = F.instance_normalization(
conv3_1, gamma=None, beta=None, channel_axis=1)
conv3_1 = PF.prelu(conv3_1, name='layer3_1.3')
pad3_2 = F.pad(conv3_1, (1, 1, 1, 1), 'reflect')
conv3_2 = PF.convolution(
pad3_2, 64, kernel=(
3, 3), stride=(
1, 1), name='layer3_1.5')
conv3_2 = F.instance_normalization(
conv3_2, gamma=None, beta=None, channel_axis=1)
conv3_2 = PF.prelu(conv3_2, name='layer3_1.7')
return conv3_2
def res_block(x, out_ch, name):
with nn.parameter_scope(name):
residual = x
out = F.pad(x, (1, 1, 1, 1), 'reflect')
out = PF.convolution(
out, out_ch, kernel=(
3, 3), stride=(
1, 1), name='conv1')
out = F.instance_normalization(
out, gamma=None, beta=None, channel_axis=1)
out = PF.prelu(out)
out = F.pad(out, (1, 1, 1, 1), 'reflect')
out = PF.convolution(
out, out_ch, kernel=(
3, 3), stride=(
1, 1), name='conv2')
out = F.instance_normalization(
out, gamma=None, beta=None, channel_axis=1)
out += residual
out = PF.prelu(out)
return out
def _normalize(x, norm_type, channel_axis=1):
if norm_type.lower() == "in":
return F.instance_normalization(x,
gamma=None,
beta=None,
channel_axis=channel_axis)
elif norm_type.lower() == "bn":
return F.batch_normalization(x,
gamma=None,
beta=None,
mean=None,
variance=None,
axes=channel_axis)
else:
raise ValueError("unknown norm_type: {}".format(norm_type))
def test_instance_normalization_forward_backward(seed, x_shape, batch_axis,
channel_axis, output_stat):
rng = np.random.RandomState(seed)
input = np.array(rng.randn(*x_shape).astype(np.float32))
eps = 1e-05
stat_shape = tuple([
x_shape[i] if i in _force_list(batch_axis) + [
channel_axis,
] else 1 for i in range(len(x_shape))
])
beta = rng.randn(*stat_shape).astype(np.float32)
gamma = rng.randn(*stat_shape).astype(np.float32)
x = nn.Variable.from_numpy_array(input)
v_beta = nn.Variable.from_numpy_array(beta)
v_gamma = nn.Variable.from_numpy_array(gamma)
output = F.instance_normalization(x, v_beta, v_gamma, channel_axis,
batch_axis, eps, output_stat)
ref = ref_instance_normalization(input, beta, gamma, channel_axis,
batch_axis, eps, output_stat)
if output_stat:
tmp = F.sink(*output)
tmp.forward()
tmp.backward()
for o, r in zip(output, ref):
assert o.shape == r.shape
assert_allclose(o.d, r, atol=1e-2, rtol=1e-5)
else:
output.forward()
output.backward()
assert output.shape == ref.shape
assert_allclose(output.d, ref, atol=1e-2, rtol=1e-5)
def ins_norm(x):
conv_i_norm = F.instance_normalization(x,
gamma=None,
beta=None,
channel_axis=1)
return conv_i_norm
