Transpose conv bug (#1326)

* Fixed a bug when calculating output shape of valid/full padded Conv2DTranspose

* Added valid padding test and nchw test to TestConv2DTranspose

* Removed NCHW test from TestConv2DTranspose (Conv2DCustomBackpropInputOp only supports NHWC)

* removed black line

* Added valid padding test back to TestConv2DTranspose

* tf.Dimension(None) is None never evaluates to True

tensorpack/models/conv2d.py

@@ -206,6 +206,7 @@ def Conv2DTranspose(
             "Unsupported arguments due to Keras bug in TensorFlow 1.13"
         data_format = get_data_format(data_format, keras_mode=False)
         shape_dyn = tf.shape(inputs)
+        shape_sta = inputs.shape.as_list()
         strides2d = shape2d(strides)
         kernel_shape = shape2d(kernel_size)
 
@@ -218,23 +219,23 @@ def Conv2DTranspose(
             shape_res2d = shape2d(0)
 
         if data_format == 'NCHW':
-            channels_in = inputs.shape[1]
+            channels_in = shape_sta[1]
             out_shape_dyn = tf.stack(
                 [shape_dyn[0], filters,
                  shape_dyn[2] * strides2d[0] + shape_res2d[0],
                  shape_dyn[3] * strides2d[1] + shape_res2d[1]])
             out_shape3_sta = [filters,
-                              None if inputs.shape[2] is None else int(inputs.shape[2] * strides2d[0]) + shape_res2d[0],
-                              None if inputs.shape[3] is None else int(inputs.shape[3] * strides2d[1]) + shape_res2d[1]]
+                              None if shape_sta[2] is None else shape_sta[2] * strides2d[0] + shape_res2d[0],
+                              None if shape_sta[3] is None else shape_sta[3] * strides2d[1] + shape_res2d[1]]
         else:
-            channels_in = inputs.shape[-1]
+            channels_in = shape_sta[-1]
             out_shape_dyn = tf.stack(
                 [shape_dyn[0],
                  shape_dyn[1] * strides2d[0] + shape_res2d[0],
                  shape_dyn[2] * strides2d[1] + shape_res2d[1],
                  filters])
-            out_shape3_sta = [None if inputs.shape[1] is None else int(inputs.shape[1] * strides2d[0]) + shape_res2d[0],
-                              None if inputs.shape[2] is None else int(inputs.shape[2] * strides2d[1]) + shape_res2d[1],
+            out_shape3_sta = [None if shape_sta[1] is None else shape_sta[1] * strides2d[0] + shape_res2d[0],
+                              None if shape_sta[2] is None else shape_sta[2] * strides2d[1] + shape_res2d[1],
                               filters]
 
         W = tf.get_variable('W', kernel_shape + [filters, channels_in], initializer=kernel_initializer)