[TOP] fix weight layout in conv2d_transpose (apache#220)

* update tvm

* [TOP] fix weight layout in conv2d_transpose

src/top/nn/convolution.cc

@@ -150,14 +150,11 @@ inline bool Conv2DTransposeInferShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(param.dilation.ndim(), 2U)
       << "incorrect dilate size: " << param.dilation;
 
-  TShape wshape({param.channels / param.groups,
-                 dshape_nchw[1] / param.groups,
+  TShape wshape({dshape_nchw[1],
+                 param.channels / param.groups,
                  param.kernel_size[0],
                  param.kernel_size[1]});
-
   wshape = ConvertLayout(wshape, kNCHW, param.layout);
-  wshape[0] *= param.groups;
-
   NNVM_ASSIGN_INPUT_SHAPE(attrs, *in_shape, Conv2DTransposeParam::kWeight, wshape);
 
   if (param.use_bias) {
@@ -192,7 +189,7 @@ said convolution.
 
 - **data**: This depends on the `layout` parameter. Input is 4D array of shape
             (batch_size, in_channels, height, width) if `layout` is `NCHW`.
-- **weight**: (channels, in_channels, kernel_size[0], kernel_size[1])
+- **weight**: (in_channels, channels, kernel_size[0], kernel_size[1])
 - **bias**: (channels,)
 - **out**:  This depends on the `layout` parameter. Output is 4D array of shape
             (batch_size, channels, out_height, out_width) if `layout` is `NCHW`.

tests/python/compiler/test_top_level2.py

@@ -60,20 +60,20 @@ def test_conv2d_transpose():
                              name="y", padding=(1,1), output_padding=(2,2))
     dtype = "float32"
     dshape = (1, 3, 18, 18)
-    kshape = (10, 3, 3, 3)
+    kshape = (3, 10, 3, 3)
     oshape = (1, 10, 37, 37)
     shape_dict = {"x": dshape}
     for target, ctx in ctx_list():
         graph, lib, _ = nnvm.compiler.build(y, target, shape_dict)
         m = graph_runtime.create(graph, lib, ctx)
         data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
         kernel = tvm.nd.array(np.random.uniform(size=kshape).astype(dtype))
-        bias = tvm.nd.array(np.random.uniform(size=kshape[0]).astype(dtype))
+        bias = tvm.nd.array(np.random.uniform(size=kshape[1]).astype(dtype))
         m.run(x=data, y_weight=kernel, y_bias=bias)
         out = m.get_output(0, tvm.nd.empty(oshape, dtype))
         c_np = topi.testing.conv2d_transpose_nchw_python(
             data.asnumpy(), kernel.asnumpy(), 2, 1)
-        c_np = c_np + bias.asnumpy().reshape(kshape[0], 1, 1)
+        c_np = c_np + bias.asnumpy().reshape(kshape[1], 1, 1)
         d_np = np.zeros(shape=oshape)
         d_np[:,:,0:c_np.shape[2],0:c_np.shape[3]] = c_np
         np.testing.assert_allclose(out.asnumpy(), d_np, rtol=1e-5)