[TOPI] fix weight layout in conv2d_transpose (#616)

topi/python/topi/nn/conv2d_transpose.py

@@ -18,7 +18,7 @@ def conv2d_transpose_nchw(Input, Filter, strides, padding):
         4-D with shape [batch, in_channel, in_height, in_width]
 
     Filter : tvm.Tensor
-        4-D with shape [num_filter, in_channel, filter_height, filter_width]
+        4-D with shape [in_channel, num_filter, filter_height, filter_width]
 
     strides : tuple of two ints
         The spatial stride along height and width
@@ -32,7 +32,7 @@ def conv2d_transpose_nchw(Input, Filter, strides, padding):
         4-D with shape [batch, out_channel, out_height, out_width]
     """
     batch, in_c, in_h, in_w = Input.shape
-    out_c, _, filter_h, filter_w = Filter.shape
+    _, out_c, filter_h, filter_w = Filter.shape
     stride_h, stride_w = strides
     # dilate stage
     DilatedInput = dilate(Input, [1, 1, stride_h, stride_w], name='DilatedInput')
@@ -57,7 +57,7 @@ def conv2d_transpose_nchw(Input, Filter, strides, padding):
     Output = tvm.compute(
         (batch, out_c, out_h, out_w),
         lambda b, c, h, w: tvm.sum(
-            PaddedInput[b, dc, h+dh, w+dw] * Filter[c, dc, filter_h-1-dh, filter_w-1-dw],
+            PaddedInput[b, dc, h+dh, w+dw] * Filter[dc, c, filter_h-1-dh, filter_w-1-dw],
             axis=[dc, dh, dw]), tag="conv2d_transpose_nchw")
 
     return Output

topi/python/topi/testing/conv2d_transpose_nchw_python.py

 # pylint: disable=unused-variable
 """Transposed convolution in python"""
 import numpy as np
+import scipy
 import topi
 from topi.nn.util import get_pad_tuple
 
@@ -14,7 +15,7 @@ def conv2d_transpose_nchw_python(a_np, w_np, stride, padding):
         4-D with shape [batch, in_channel, in_height, in_width]
 
     w_np : numpy.ndarray
-        4-D with shape [num_filter, in_channel, filter_height, filter_width]
+        4-D with shape [in_channel, num_filter, filter_height, filter_width]
 
     stride : int or a list/tuple of two ints
         Stride size, or [stride_height, stride_width]
@@ -28,7 +29,7 @@ def conv2d_transpose_nchw_python(a_np, w_np, stride, padding):
         4-D with shape [batch, out_channel, out_height, out_width]
     """
     batch, in_c, in_h, in_w = a_np.shape
-    out_c, _, filter_h, filter_w = w_np.shape
+    _, out_c, filter_h, filter_w = w_np.shape
     if isinstance(stride, int):
         stride_h = stride_w = stride
     else:
@@ -46,6 +47,13 @@ def conv2d_transpose_nchw_python(a_np, w_np, stride, padding):
     padded_a_np[:, :, bpad_top:dilated_a_np.shape[2]+bpad_top, \
         bpad_left:dilated_a_np.shape[3]+bpad_left] = dilated_a_np
     # convolution stage
-    rotated_w_np = np.rot90(w_np, k=2, axes=(2, 3))
-    b_np = topi.testing.conv2d_nchw_python(padded_a_np, rotated_w_np, stride=1, padding='VALID')
+    out_h = (in_h - 1) * stride_h - fpad_top - fpad_bottom + filter_h
+    out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w
+    b_np = np.zeros((batch, out_c, out_h, out_w))
+    for n in range(batch):
+        for f in range(out_c):
+            for c in range(in_c):
+                out = scipy.signal.convolve2d(
+                    padded_a_np[n, c], w_np[c, f], mode='valid')
+                b_np[n, f] += out
     return b_np

topi/tests/python/test_topi_conv2d_transpose_nchw.py

@@ -10,7 +10,7 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel,
     in_height = in_width = in_size
 
     A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
-    W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
+    W = tvm.placeholder((in_channel, num_filter, kernel, kernel), name='W')
     B = topi.nn.conv2d_transpose_nchw(A, W, [stride, stride], padding)
     C = topi.nn.relu(B)