@@ -35,11 +35,10 @@ class DeConv2d(Layer):
35
35
The stride step (height, width).
36
36
padding : str
37
37
The padding algorithm type: "SAME" or "VALID".
38
- batch_size : int or None
39
- Require if TF < 1.3, int or None.
40
- If None, try to find the `batch_size` from the first dim of net.outputs (you should define the `batch_size` in the input placeholder).
41
38
act : activation function
42
39
The activation function of this layer.
40
+ data_format : str
41
+ "channels_last" (NHWC, default) or "channels_first" (NCHW).
43
42
W_init : initializer
44
43
The initializer for the weight matrix.
45
44
b_init : initializer or None
@@ -61,11 +60,12 @@ def __init__(
61
60
prev_layer ,
62
61
n_filter = 32 ,
63
62
filter_size = (3 , 3 ),
64
- out_size = (30 , 30 ), # remove
63
+ # out_size=(30, 30), # remove
65
64
strides = (2 , 2 ),
66
65
padding = 'SAME' ,
67
- batch_size = None , # remove
66
+ # batch_size=None, # remove
68
67
act = None ,
68
+ data_format = 'channels_last' ,
69
69
W_init = tf .truncated_normal_initializer (stddev = 0.02 ),
70
70
b_init = tf .constant_initializer (value = 0.0 ),
71
71
W_init_args = None , # TODO: Remove when TF <1.3 not supported
@@ -86,8 +86,8 @@ def __init__(
86
86
raise ValueError ("len(strides) should be 2, DeConv2d and DeConv2dLayer are different." )
87
87
88
88
conv2d_transpose = tf .layers .Conv2DTranspose (
89
- filters = n_filter , kernel_size = filter_size , strides = strides , padding = padding , activation = self . act ,
90
- kernel_initializer = W_init , bias_initializer = b_init , name = name
89
+ filters = n_filter , kernel_size = filter_size , strides = strides , padding = padding , data_format = data_format ,
90
+ activation = self . act , kernel_initializer = W_init , bias_initializer = b_init , name = name
91
91
)
92
92
93
93
self .outputs = conv2d_transpose (self .inputs )
@@ -116,6 +116,8 @@ class DeConv3d(Layer):
116
116
The padding algorithm type: "SAME" or "VALID".
117
117
act : activation function
118
118
The activation function of this layer.
119
+ data_format : str
120
+ "channels_last" (NDHWC, default) or "channels_first" (NCDHW).
119
121
W_init : initializer
120
122
The initializer for the weight matrix.
121
123
b_init : initializer or None
@@ -138,6 +140,7 @@ def __init__(
138
140
strides = (2 , 2 , 2 ),
139
141
padding = 'SAME' ,
140
142
act = None ,
143
+ data_format = 'channels_last' ,
141
144
W_init = tf .truncated_normal_initializer (stddev = 0.02 ),
142
145
b_init = tf .constant_initializer (value = 0.0 ),
143
146
W_init_args = None , # TODO: Remove when TF <1.3 not supported
@@ -157,7 +160,7 @@ def __init__(
157
160
# with tf.variable_scope(name) as vs:
158
161
nn = tf .layers .Conv3DTranspose (
159
162
filters = n_filter , kernel_size = filter_size , strides = strides , padding = padding , activation = self .act ,
160
- kernel_initializer = W_init , bias_initializer = b_init , name = name
163
+ data_format = data_format , kernel_initializer = W_init , bias_initializer = b_init , name = name
161
164
)
162
165
163
166
self .outputs = nn (self .inputs )
0 commit comments