[FRONTEND][TF] conv2d_transpose 'SAME' support kernel more than 1x1 (#4484)

* [FRONTEND][TF] conv3d_transpose 'SAME' support kernel more than 1x1

* revised per as review comments

* add more fallback wolkaround to make all tests pass

python/tvm/relay/frontend/tensorflow.py

@@ -269,6 +269,12 @@ def _conv(opname):
             attr['strides'][1], attr['strides'][2], attr['strides'][3] = \
                 attr['strides'][3], attr['strides'][1], attr['strides'][2]
             attr['data_format'] = 'NCHW'
+
+            if opname == 'conv_transpose' and len(attr['_output_shapes']) > 0:
+                tmp_shape = attr['_output_shapes'][0]
+                tmp_shape = [tmp_shape[ii] for ii in (0, 3, 1, 2)]
+                attr['_output_shapes'][0] = tmp_shape
+
             flip_layout = True
 
         inputs_data = inputs[0] if opname != 'conv_transpose' else inputs[2]
@@ -345,12 +351,17 @@ def _conv(opname):
         elif attr['padding'] == 'SAME':
             stride_h, stride_w = attr['strides']
             kernel_h, kernel_w = attr['kernel_shape']
+
+            pdata_shape = input_shape
+            if opname == 'conv_transpose' and len(attr['_output_shapes']) > 0:
+                pdata_shape = attr['_output_shapes'][0]
+
             if attr['data_format'] == 'NHWC':
-                in_h = input_shape[1]
-                in_w = input_shape[2]
+                in_h = pdata_shape[1]
+                in_w = pdata_shape[2]
             else:
-                in_h = input_shape[2]
-                in_w = input_shape[3]
+                in_h = pdata_shape[2]
+                in_w = pdata_shape[3]
 
             dilation_h = attr['dilations'][0]
             dilation_w = attr['dilations'][1]
@@ -359,7 +370,7 @@ def _conv(opname):
             pad_v = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
             pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)
 
-
+            if opname != 'conv_transpose':
                 if attr['data_format'] == 'NHWC':
                     inputs_data = _op.nn.pad(data=inputs_data,
                                              pad_width=((0, 0),
@@ -374,6 +385,8 @@ def _conv(opname):
                                                         (pad_h[0], pad_h[1])))
 
                 attr['padding'] = [0, 0]
+            else:
+                attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
 
         else:
             msg = 'Value {} in attribute "padding" of operator Conv is not ' \

src/relay/op/nn/convolution.cc

@@ -249,10 +249,22 @@ bool Conv2DTransposeRel(const Array<Type>& types,
   }
   // dilation
   Array<IndexExpr> oshape({dshape_nchw[0], channels, 0, 0});
+  auto pad_h = param->padding[0];
+  auto pad_w = param->padding[1];
+  if (param->padding.size() == 2) {
+    pad_h *= 2;
+    pad_w *= 2;
+  } else if (param->padding.size() == 4) {
+    pad_h += param->padding[2];
+    pad_w += param->padding[3];
+  } else {
+    CHECK_EQ(param->padding.size(), 4) << " Padding should be 2 or 4, but got "
+        << param->padding.size();
+  }
   oshape.Set(2, (param->strides[0] * (dshape_nchw[2] - 1) + dilated_ksize_y -
-                 2 * param->padding[0] + param->output_padding[0]));
+                 pad_h + param->output_padding[0]));
   oshape.Set(3, (param->strides[1] * (dshape_nchw[3] - 1) + dilated_ksize_x -
-                 2 * param->padding[1] + param->output_padding[1]));
+                 pad_w + param->output_padding[1]));
 
   DataType out_dtype = param->out_dtype;
   if (out_dtype.bits() == 0) {

tests/python/frontend/tensorflow/test_forward.py

@@ -403,10 +403,22 @@ def test_forward_convolution():
         _test_convolution('depthwise', [4, 12, 17, 17], [3, 3, 12, 2], [1, 1], [2, 2], 'VALID', 'NCHW')
         _test_convolution('conv_transpose', [4, 32, 8, 8], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME',
                           'NCHW', [4, 176, 8, 8])
+        _test_convolution('conv_transpose', [4, 32, 8, 8], [2, 2, 176, 32], [1, 1], [1, 1], 'SAME',
+                          'NCHW', [4, 176, 8, 8])
+        _test_convolution('conv_transpose', [4, 32, 8, 8], [2, 2, 176, 32], [1, 1], [2, 2], 'SAME',
+                          'NCHW', [4, 176, 15, 15])
+        _test_convolution('conv_transpose', [4, 32, 8, 8], [3, 3, 176, 32], [1, 1], [1, 1], 'SAME',
+                          'NCHW', [4, 176, 8, 8])
+        _test_convolution('conv_transpose', [4, 32, 8, 8], [3, 3, 176, 32], [1, 1], [2, 2], 'SAME',
+                          'NCHW', [4, 176, 15, 15])
+        _test_convolution('conv_transpose', [4, 32, 8, 8], [3, 3, 176, 32], [1, 1], [2, 2], 'SAME',
+                          'NCHW', [4, 176, 16, 16])
         _test_convolution('conv_transpose', [4, 19, 8, 8], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID',
                           'NCHW', [4, 19, 17, 17])
         _test_convolution('conv_transpose', [4, 19, 17, 17], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME',
                           'NCHW', [4, 124, 17, 17])
+        _test_convolution('conv_transpose', [4, 19, 17, 17], [3, 3, 124, 19], [1, 1], [1, 1], 'SAME',
+                          'NCHW', [4, 124, 17, 17])
         _test_convolution('conv_transpose', [4, 32, 8, 8], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID',
                           'NCHW', [4, 12, 17, 17])
         # kernel 2x2, strides (2,2)
@@ -429,10 +441,22 @@ def test_forward_convolution():
     _test_convolution('depthwise', [4, 17, 17, 12], [3, 3, 12, 2], [1, 1], [2, 2], 'VALID', 'NHWC')
     _test_convolution('conv_transpose', [4, 8, 8, 32], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME',
                       'NHWC', [4, 8, 8, 176])
+    _test_convolution('conv_transpose', [4, 8, 8, 32], [2, 2, 176, 32], [1, 1], [1, 1], 'SAME',
+                      'NHWC', [4, 8, 8, 176])
+    _test_convolution('conv_transpose', [4, 8, 8, 32], [2, 2, 176, 32], [1, 1], [2, 2], 'SAME',
+                      'NHWC', [4, 15, 15, 176])
+    _test_convolution('conv_transpose', [4, 8, 8, 32], [3, 3, 176, 32], [1, 1], [1, 1], 'SAME',
+                      'NHWC', [4, 8, 8, 176])
+    _test_convolution('conv_transpose', [4, 8, 8, 32], [3, 3, 176, 32], [1, 1], [2, 2], 'SAME',
+                      'NHWC', [4, 15, 15, 176])
+    _test_convolution('conv_transpose', [4, 8, 8, 32], [3, 3, 176, 32], [1, 1], [2, 2], 'SAME',
+                      'NHWC', [4, 16, 16, 176])
     _test_convolution('conv_transpose', [4, 8, 8, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID',
                       'NHWC', [4, 17, 17, 19])
     _test_convolution('conv_transpose', [4, 17, 17, 19], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME',
                       'NHWC', [4, 17, 17, 124])
+    _test_convolution('conv_transpose', [4, 17, 17, 19], [3, 3, 124, 19], [1, 1], [1, 1], 'SAME',
+                      'NHWC', [4, 17, 17, 124])
     _test_convolution('conv_transpose', [4, 8, 8, 32], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID',
                       'NHWC', [4, 17, 17, 12])
     # kernel 2x2, strides (2,2)

topi/python/topi/cuda/conv2d_transpose_nchw.py

@@ -197,6 +197,8 @@ def schedule_conv2d_transpose_nchw_cuda(cfg, outs):
                     do_fallback = False
                 elif (kh, kw) == (1, 1):
                     do_fallback = True
+                elif (stride_h, stride_w) == (2, 2):
+                    do_fallback = False
                 elif (kh, kw) == (stride_h, stride_w):
                     do_fallback = False
 

topi/python/topi/nn/util.py

@@ -103,8 +103,13 @@ def get_pad_tuple(padding, kernel):
     """
     # compute the padding size
     if isinstance(padding, (tuple, list)):
+        if len(padding) == 2:
             pad_h = padding[0] * 2
             pad_w = padding[1] * 2
+        elif len(padding) == 4:
+            return  padding[0], padding[1], padding[2], padding[3]
+        else:
+            raise ValueError("Size of padding can only be 2 or 4")
     elif isinstance(padding, int):
         pad_h = pad_w = padding * 2
     elif padding == "VALID":