use tf.layers for Deconv implementation

examples/GAN/ConditionalGAN-mnist.py

@@ -42,10 +42,10 @@ class Model(GANModelDesc):
 
         y = tf.reshape(y, [-1, 1, 1, 10])
         l = tf.concat([l, tf.tile(y, [1, 7, 7, 1])], 3)
-        l = Deconv2D('deconv1', l, [14, 14, 64 * 2], 5, 2, nl=BNReLU)
+        l = Deconv2D('deconv1', l, 64 * 2, 5, 2, nl=BNReLU)
 
         l = tf.concat([l, tf.tile(y, [1, 14, 14, 1])], 3)
-        l = Deconv2D('deconv2', l, [28, 28, 1], 5, 2, nl=tf.identity)
+        l = Deconv2D('deconv2', l, 1, 5, 2, nl=tf.identity)
         l = tf.nn.tanh(l, name='gen')
         return l
 

examples/GAN/DCGAN.py

@@ -51,10 +51,10 @@ class Model(GANModelDesc):
         l = tf.reshape(l, [-1, 4, 4, nf * 8])
         l = BNReLU(l)
         with argscope(Deconv2D, nl=BNReLU, kernel_shape=4, stride=2):
-            l = Deconv2D('deconv1', l, [8, 8, nf * 4])
-            l = Deconv2D('deconv2', l, [16, 16, nf * 2])
-            l = Deconv2D('deconv3', l, [32, 32, nf])
-            l = Deconv2D('deconv4', l, [64, 64, 3], nl=tf.identity)
+            l = Deconv2D('deconv1', l, nf * 4)
+            l = Deconv2D('deconv2', l, nf * 2)
+            l = Deconv2D('deconv3', l, nf)
+            l = Deconv2D('deconv4', l, 3, nl=tf.identity)
             l = tf.tanh(l, name='gen')
         return l
 

examples/GAN/InfoGAN-mnist.py

@@ -52,8 +52,8 @@ class Model(GANModelDesc):
         l = FullyConnected('fc0', z, 1024, nl=BNReLU)
         l = FullyConnected('fc1', l, 128 * 7 * 7, nl=BNReLU)
         l = tf.reshape(l, [-1, 7, 7, 128])
-        l = Deconv2D('deconv1', l, [14, 14, 64], 4, 2, nl=BNReLU)
-        l = Deconv2D('deconv2', l, [28, 28, 1], 4, 2, nl=tf.identity)
+        l = Deconv2D('deconv1', l, 64, 4, 2, nl=BNReLU)
+        l = Deconv2D('deconv2', l, 1, 4, 2, nl=tf.identity)
         l = tf.sigmoid(l, name='gen')
         return l
 

tensorpack/models/conv2d.py

@@ -4,9 +4,9 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import tensorflow as tf
-from .common import layer_register, VariableHolder
+from .common import layer_register, VariableHolder, rename_get_variable
 from ..utils.argtools import shape2d, shape4d
-from .shape_utils import StaticDynamicAxis
+from ..utils.develop import log_deprecated
 
 __all__ = ['Conv2D', 'Deconv2D']
 
@@ -80,7 +80,7 @@ def Conv2D(x, out_channel, kernel_shape,
 
 
 @layer_register(log_shape=True)
-def Deconv2D(x, out_shape, kernel_shape,
+def Deconv2D(x, out_channel, kernel_shape,
              stride, padding='SAME',
              W_init=None, b_init=None,
              nl=tf.identity, use_bias=True,
@@ -91,8 +91,7 @@ def Deconv2D(x, out_shape, kernel_shape,
     Args:
         x (tf.Tensor): a tensor of shape NHWC.
             Must have known number of channels, but can have other unknown dimensions.
-        out_shape: (h, w, channel) tuple, or just a integer channel,
-            then (h, w) will be calculated by input_shape * stride
+        out_channel: the output number of channel.
         kernel_shape: (h, w) tuple or a int.
         stride: (h, w) tuple or a int.
         padding (str): 'valid' or 'same'. Case insensitive.
@@ -113,47 +112,41 @@ def Deconv2D(x, out_shape, kernel_shape,
     channel_axis = 3 if data_format == 'NHWC' else 1
     in_channel = in_shape[channel_axis]
     assert in_channel is not None, "[Deconv2D] Input cannot have unknown channel!"
-    kernel_shape = shape2d(kernel_shape)
-    stride2d = shape2d(stride)
-    stride4d = shape4d(stride, data_format=data_format)
-    padding = padding.upper()
-    in_shape_dyn = tf.shape(x)
 
+    out_shape = out_channel
     if isinstance(out_shape, int):
         out_channel = out_shape
-        if data_format == 'NHWC':
-            shp3_0 = StaticDynamicAxis(in_shape[1], in_shape_dyn[1]).apply(lambda x: stride2d[0] * x)
-            shp3_1 = StaticDynamicAxis(in_shape[2], in_shape_dyn[2]).apply(lambda x: stride2d[1] * x)
-            shp3_dyn = [shp3_0.dynamic, shp3_1.dynamic, out_channel]
-            shp3_static = [shp3_0.static, shp3_1.static, out_channel]
-        else:
-            shp3_0 = StaticDynamicAxis(in_shape[2], in_shape_dyn[2]).apply(lambda x: stride2d[0] * x)
-            shp3_1 = StaticDynamicAxis(in_shape[3], in_shape_dyn[3]).apply(lambda x: stride2d[1] * x)
-            shp3_dyn = [out_channel, shp3_0.dynamic, shp3_1.dynamic]
-            shp3_static = [out_channel, shp3_0.static, shp3_1.static]
     else:
+        log_deprecated("Deconv2D(out_shape=[...])",
+                       "Use an integer 'out_channel' instead!", "2017-11-18")
         for k in out_shape:
             if not isinstance(k, int):
                 raise ValueError("[Deconv2D] out_shape {} is invalid!".format(k))
         out_channel = out_shape[channel_axis - 1]   # out_shape doesn't have batch
-        shp3_static = shp3_dyn = out_shape
-    filter_shape = kernel_shape + [out_channel, in_channel]
 
     if W_init is None:
         W_init = tf.contrib.layers.xavier_initializer_conv2d()
     if b_init is None:
         b_init = tf.constant_initializer()
-    W = tf.get_variable('W', filter_shape, initializer=W_init)
-    if use_bias:
-        b = tf.get_variable('b', [out_channel], initializer=b_init)
 
-    out_shape_dyn = tf.stack([tf.shape(x)[0]] + shp3_dyn)
-    conv = tf.nn.conv2d_transpose(
-        x, W, out_shape_dyn, stride4d, padding=padding, data_format=data_format)
-    conv.set_shape(tf.TensorShape([None] + shp3_static))
-    ret = nl(tf.nn.bias_add(conv, b, data_format=data_format) if use_bias else conv, name='output')
-
-    ret.variables = VariableHolder(W=W)
+    with rename_get_variable({'kernel': 'W', 'bias': 'b'}):
+        layer = tf.layers.Deconv2D(
+            out_channel, kernel_shape,
+            strides=stride, padding=padding,
+            data_format='channels_last' if data_format == 'NHWC' else 'channels_first',
+            activation=lambda x: nl(x, name='output'),
+            use_bias=use_bias,
+            kernel_initializer=W_init,
+            bias_initializer=b_init,
+            trainable=True)
+        ret = layer.apply(x, scope=tf.get_variable_scope())
+
+    # Check that we only supports out_shape = in_shape * stride
+    out_shape3 = ret.get_shape().as_list()[1:]
+    if not isinstance(out_shape, int):
+        assert list(out_shape) == out_shape3, "{} != {}".format(out_shape, out_shape3)
+
+    ret.variables = VariableHolder(W=layer.kernel)
     if use_bias:
-        ret.variables.b = b
+        ret.variables.b = layer.bias
     return ret

tensorpack/models/utils.py

@@ -66,5 +66,8 @@ def monkeypatch_tf_layers():
             from tensorflow.python.layers.normalization import BatchNormalization
             tf.layers.BatchNormalization = BatchNormalization
 
+            from tensorflow.python.layers.convolutional import Deconv2D
+            tf.layers.Deconv2D = Deconv2D
+
 
 monkeypatch_tf_layers()