attr['data_format'] = attr['data_format'].decode("utf-8")
flip_layout = False
+ if opname == 'conv_transpose' and attr['data_format'] == 'NHWC':
+ # transform to NCHW for TVM backend compatible and set 'flip_layout'
+ # to have output flip back to NHWC
+ tmp_shape = attr['_input_shapes'][inputs[2]]
+ tmp_shape = [tmp_shape[ii] for ii in (0, 3, 1, 2)]
+ inputs[2] = _op.transpose(inputs[2], axes=(0, 3, 1, 2))
+ attr['_input_shapes'][inputs[2]] = tmp_shape
+ attr['strides'][1], attr['strides'][2], attr['strides'][3] = \
+ attr['strides'][3], attr['strides'][1], attr['strides'][2]
+ attr['data_format'] = 'NCHW'
+ flip_layout = True
+
+ inputs_data = inputs[0] if opname != 'conv_transpose' else inputs[2]
+
# NCHW Layout require weights transpose
if attr['data_format'] == 'NCHW':
tmp_shape = attr['_input_shapes'][inputs[1]]
- if opname == 'conv':
+ if opname in ['conv', 'conv_transpose']:
tmp_shape = [tmp_shape[ii] for ii in (3, 2, 0, 1)]
inputs[1] = _op.transpose(inputs[1], axes=(3, 2, 0, 1))
else:
inputs[1] = _op.transpose(inputs[1], axes=(2, 3, 0, 1))
attr['_input_shapes'][inputs[1]] = tmp_shape
- input_shape = attr['_input_shapes'][inputs[0]]
+ input_shape = attr['_input_shapes'][inputs_data]
weights_shape = attr['_input_shapes'][inputs[1]]
if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
input_shape = [input_shape[ii] for ii in (0, 3, 1, 2)]
- inputs[0] = _op.transpose(inputs[0], axes=(0, 3, 1, 2))
- if opname == 'conv':
+ inputs_data = _op.transpose(inputs_data, axes=(0, 3, 1, 2))
+ if opname in ['conv', 'conv_transpose']:
weights_shape = [weights_shape[ii] for ii in (3, 2, 0, 1)]
inputs[1] = _op.transpose(inputs[1], axes=(3, 2, 0, 1))
else:
attr['kernel_shape'] = (weights_shape[0], weights_shape[1])
if opname == 'conv':
attr['channels'] = weights_shape[3]
+ elif opname == 'conv_transpose':
+ attr['channels'] = weights_shape[2]
else:
attr['channels'] = input_shape[3] * depth_mult
attr['kernel_shape'] = (weights_shape[2], weights_shape[3])
if opname == 'conv':
attr['channels'] = weights_shape[0]
+ elif opname == 'conv_transpose':
+ attr['channels'] = weights_shape[1]
else:
attr['channels'] = input_shape[1] * depth_mult
if attr['channels'] < 0:
if attr['data_format'] == 'NHWC':
- inputs[0] = _op.nn.pad(data=inputs[0],
- pad_width=((0, 0),
- (pad_v[0], pad_v[1]),
- (pad_h[0], pad_h[1]),
- (0, 0)))
+ 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[0] = _op.nn.pad(data=inputs[0],
- pad_width=((0, 0),
- (0, 0),
- (pad_v[0], pad_v[1]),
- (pad_h[0], pad_h[1])))
+ 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]
raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))
if 'kernel_layout' not in attr:
- if opname == 'conv':
+ if opname in ['conv', 'conv_transpose']:
attr['kernel_layout'] = 'HWIO' if attr['data_format'] == 'NHWC' else 'OIHW'
else:
attr['kernel_layout'] = 'HWOI' if attr['data_format'] == 'NHWC' else 'OIHW'
- use_bias = len(inputs) == 3
+ use_bias = len(inputs) == (3 if opname != 'conv_transpose' else 4)
channel_axis = 1 if attr['data_format'] == "NCHW" else 3
# Ignore the new attributes from TF2.0, for now.
out = AttrCvt(
- op_name=_dimension_picker('conv'),
+ op_name=_dimension_picker('conv', \
+ surfix="_transpose" if opname == 'conv_transpose' else ""),
ignores=['explicit_paddings'],
transforms={
'kernel_shape': 'kernel_size',
'data_format': 'data_layout',
'dilations': ('dilation', (0, 0)),
'group': ('groups', 1)},
- custom_check=_dimension_constraint())([inputs[0], inputs[1]], attr)
+ custom_check=_dimension_constraint())([inputs_data, inputs[1]], attr)
if use_bias:
- out = _op.nn.bias_add(out, inputs[2], axis=channel_axis)
+ out = _op.nn.bias_add(out,
+ inputs[2] if opname != 'conv_transpose' else inputs[3],
+ axis=channel_axis)
if flip_layout:
out = _op.transpose(out, axes=(0, 2, 3, 1))
'Concat' : _concat(),
'ConcatV2' : _concatV2(),
'Conv2D' : _conv('conv'),
+ 'Conv2DBackpropInput' : _conv('conv_transpose'),
'CropAndResize' : _crop_and_resize(),
'DecodeJpeg' : _decode_image(),
'DepthwiseConv2dNative' : _conv('depthwise'),
def _test_convolution(opname, tensor_in_sizes, filter_in_sizes,
- dilations, strides, padding, data_format):
+ dilations, strides, padding, data_format,
+ deconv_output_shape=[]):
""" One iteration of convolution with given shapes and attributes """
total_size_1 = np.prod(tensor_in_sizes)
compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'Conv2D:0')
+ elif opname == 'conv_transpose':
+ nn_ops.conv2d_transpose(in_data,
+ in_filter,
+ output_shape=deconv_output_shape,
+ strides=strides,
+ padding=padding,
+ data_format=data_format)
+
+ compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
+ 'Placeholder:0', 'conv2d_transpose:0')
else:
nn_ops.depthwise_conv2d_native(in_data,
in_filter,
_test_convolution('depthwise', [4, 124, 17, 17], [1, 1, 124, 1], [1, 1], [1, 1], 'SAME', 'NCHW')
_test_convolution('depthwise', [4, 12, 17, 17], [3, 3, 12, 1], [1, 1], [2, 2], 'VALID', 'NCHW')
_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, 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, 32, 8, 8], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID',
+ 'NCHW', [4, 12, 17, 17])
_test_convolution('conv', [4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
_test_convolution('conv', [4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC')
_test_convolution('depthwise', [4, 17, 17, 124], [1, 1, 124, 1], [1, 1], [1, 1], 'SAME', 'NHWC')
_test_convolution('depthwise', [4, 17, 17, 12], [3, 3, 12, 1], [1, 1], [2, 2], 'VALID', 'NHWC')
_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, 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, 8, 8, 32], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID',
+ 'NHWC', [4, 17, 17, 12])
+
#######################################################################
# BiasAdd