def deconv2d_layer_concat(x,
name,
W_s,
concat_x,
output_shape=None,
stride=2,
stddev=0.02,
if_relu=False):
'''
Deconv2d operator for U-Net concat.
Args:
x: inputs
W_s: shape of weight
output_shape: shape after deconv2d
'''
if output_shape == None:
x_shape = tf.shape(x)
output_shape = tf.stack(
[x_shape[0], x_shape[1] * 2, x_shape[2] * 2, x_shape[3] // 2])
W_t = utils.weight_variable(W_s, stddev=stddev, name='W_' + name)
b_t = utils.bias_variable([W_s[2]], name='b_' + name)
#conv_t = utils.conv2d_transpose_strided_valid(x, W_t, b_t, output_shape, stride)
conv_t = utils.conv2d_transpose_strided(x, W_t, b_t, output_shape, stride)
if if_relu:
conv_t = tf.nn.relu(conv_t, name=name + '_relu')
conv_concat = utils.crop_and_concat(concat_x, conv_t)
return conv_concat
def deconv2d_layer(x, name, W_s, output_shape=None, stride=2):
'''Deconv2d operator
Args:
x: inputs
W_s: shape of weight
output_shape: shape after deconv2d
'''
W_t = utils.weight_variable(W_s, name='W_' + name)
b_t = utils.bias_variable([W_s[2]], name='b_' + name)
conv_t = utils.conv2d_transpose_strided(x, W_t, b_t, output_shape, stride)
print('conv_%s: ' % name, conv_t.get_shape())
return conv_t