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

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Dec 28, 2019
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[FRONTEND][TF] conv2d_transpose 'SAME' support kernel more than 1x1 #4484

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d1505f1
[FRONTEND][TF] conv3d_transpose 'SAME' support kernel more than 1x1
optima2005 Dec 9, 2019
e0a4af8
revised per as review comments
optima2005 Dec 10, 2019
8dcc50a
add more fallback wolkaround to make all tests pass
optima2005 Dec 26, 2019
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47 changes: 30 additions & 17 deletions python/tvm/relay/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,6 +205,12 @@ def _impl(inputs, attr, params):
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]
Expand Down Expand Up @@ -281,12 +287,17 @@ def _impl(inputs, attr, params):
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]
Expand All @@ -295,21 +306,23 @@ def _impl(inputs, attr, params):
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),
(pad_v[0], pad_v[1]),
(pad_h[0], pad_h[1]),
(0, 0)))
else:
inputs_data = _op.nn.pad(data=inputs_data,
pad_width=((0, 0),
(0, 0),
(pad_v[0], pad_v[1]),
(pad_h[0], pad_h[1])))

if attr['data_format'] == 'NHWC':
inputs_data = _op.nn.pad(data=inputs_data,
pad_width=((0, 0),
(pad_v[0], pad_v[1]),
(pad_h[0], pad_h[1]),
(0, 0)))
attr['padding'] = [0, 0]
else:
inputs_data = _op.nn.pad(data=inputs_data,
pad_width=((0, 0),
(0, 0),
(pad_v[0], pad_v[1]),
(pad_h[0], pad_h[1])))

attr['padding'] = [0, 0]
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 ' \
Expand Down
16 changes: 14 additions & 2 deletions src/relay/op/nn/convolution.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -248,10 +248,22 @@ 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) {
Expand Down
26 changes: 26 additions & 0 deletions tests/python/frontend/tensorflow/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -362,10 +362,23 @@ 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])
# cuda target not working
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What is the reason why it doen't work for cuda

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The case failed due to the "Cannot prove" error when compiling the module. see #4470. It seems there is no fix for it yet.

#_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',
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Can you also add test case for

kernel 2x2, strides 2x2, SAME
kernel 3x3, strides 2x2, SAME

E.g. if input is 5x5 then valid outputs are 9x9 or 10x10 (you can use one or another in the output_shape tensor) regardless of the kernel size.

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The strides in transpose convolution is used to dilate the input, see this. If the input is dilated, there would be no way to get 'SAME' size of output by only padding.

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ok. I can get your point. 'SAME' means the size of the enlarged input size(by dilation).

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TF code for kernel 2x2, strides 2x2 and padding SAME is

dshape=(1,5,5,4)
#hwoi
kshape=(2,2,2,4)
oshape=(1,9,9,2)
# or 
oshape=(1,10,10,2)
dtype='float32'
with tf.Session() as sess:
    x = tf.placeholder(shape=dshape, dtype=dtype)
    w = tf.placeholder(shape=kshape, dtype=dtype)
    dc = tf.nn.conv2d_transpose(x, w, output_shape=oshape, strides=(1,2,2,1), padding='SAME')

    res_dc = sess.run(dc, feed_dict={x: data, w:weight})

'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)
Expand All @@ -388,10 +401,23 @@ 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])
# cuda target not working
#_test_convolution('conv_transpose', [4, 8, 8, 32], [3, 3, 176, 32], [1, 1], [2, 2], 'SAME',
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How about this?

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The case failed due to the "Cannot prove" error when compiling the module. see #4470. It seems there is no fix for it yet.

# '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)
Expand Down
9 changes: 7 additions & 2 deletions topi/python/topi/nn/util.py
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Expand Up @@ -103,8 +103,13 @@ def get_pad_tuple(padding, kernel):
"""
# compute the padding size
if isinstance(padding, (tuple, list)):
pad_h = padding[0] * 2
pad_w = padding[1] * 2
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":
Expand Down