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[FRONTEND][TENSORFLOW] Some bug fixes for tensorflow NCHW data_format (…
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…apache#3514)
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@vv1133 @wweic
vv1133 authored and wweic committed Aug 9, 2019
1 parent e4cd280 commit 1f964e6
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Showing 3 changed files with 102 additions and 28 deletions.
21 changes: 16 additions & 5 deletions nnvm/python/nnvm/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -206,8 +206,12 @@ def _impl(inputs, attr, params):
# NCHW Layout require weights transpose
if attr['data_format'] == 'NCHW':
tmp_shape = attr['_input_shapes'][inputs[1]]
tmp_shape = [tmp_shape[ii] for ii in (3, 2, 0, 1)]
inputs[1] = _sym.transpose(inputs[1], axes=(3, 2, 0, 1))
if opname == 'conv':
tmp_shape = [tmp_shape[ii] for ii in (3, 2, 0, 1)]
inputs[1] = _sym.transpose(inputs[1], axes=(3, 2, 0, 1))
else:
tmp_shape = [tmp_shape[ii] for ii in (2, 3, 0, 1)]
inputs[1] = _sym.transpose(inputs[1], axes=(2, 3, 0, 1))
attr['_input_shapes'][inputs[1]] = tmp_shape

input_shape = attr['_input_shapes'][inputs[0]]
Expand Down Expand Up @@ -239,12 +243,12 @@ def _impl(inputs, attr, params):
attr['dilations'] = (attr['dilations'][1], attr['dilations'][2])
attr['strides'] = (attr['strides'][1], attr['strides'][2])
elif attr['data_format'] == 'NCHW':
depth_mult, _, kernel_h, kernel_w = weights_shape
_, depth_mult, kernel_h, kernel_w = weights_shape
attr['kernel_shape'] = (weights_shape[2], weights_shape[3])
if opname == 'conv':
attr['channels'] = weights_shape[0]
else:
attr['channels'] = input_shape[0] * depth_mult
attr['channels'] = input_shape[1] * depth_mult
if attr['channels'] < 0:
attr['channels'] *= -1

Expand All @@ -257,6 +261,9 @@ def _impl(inputs, attr, params):


if opname == 'depthwise':
if depth_mult > 1:
raise tvm.error.OpNotImplemented('depth_mult > 1 of operator DepthwiseConv2dNative'
' is not supported.')
attr['groups'] = attr['channels']

# Fix padding
Expand Down Expand Up @@ -460,8 +467,12 @@ def _impl(inputs, attr, params):

def _bias_add():
def _impl(inputs, attr, params):
if attr['data_format'].decode("utf-8") == 'NCHW':
bias = _sym.reshape(inputs[1], newshape=(1, -1, 1, 1))
else:
bias = inputs[1]
lhs = inputs[0]
rhs = inputs[1]
rhs = bias
if "_input_shapes" in attr:
lhs_shape = attr["_input_shapes"][lhs]
rhs_shape = attr["_input_shapes"][rhs]
Expand Down
16 changes: 11 additions & 5 deletions python/tvm/relay/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -361,8 +361,12 @@ def _impl(inputs, attr, params):
# NCHW Layout require weights transpose
if attr['data_format'] == 'NCHW':
tmp_shape = attr['_input_shapes'][inputs[1]]
tmp_shape = [tmp_shape[ii] for ii in (3, 2, 0, 1)]
inputs[1] = _op.transpose(inputs[1], axes=(3, 2, 0, 1))
if opname == 'conv':
tmp_shape = [tmp_shape[ii] for ii in (3, 2, 0, 1)]
inputs[1] = _op.transpose(inputs[1], axes=(3, 2, 0, 1))
else:
tmp_shape = [tmp_shape[ii] for ii in (2, 3, 0, 1)]
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]]
Expand Down Expand Up @@ -394,12 +398,12 @@ def _impl(inputs, attr, params):
attr['dilations'] = (attr['dilations'][1], attr['dilations'][2])
attr['strides'] = (attr['strides'][1], attr['strides'][2])
elif attr['data_format'] == 'NCHW':
depth_mult, _, kernel_h, kernel_w = weights_shape
_, depth_mult, kernel_h, kernel_w = weights_shape
attr['kernel_shape'] = (weights_shape[2], weights_shape[3])
if opname == 'conv':
attr['channels'] = weights_shape[0]
else:
attr['channels'] = input_shape[0] * depth_mult
attr['channels'] = input_shape[1] * depth_mult
if attr['channels'] < 0:
attr['channels'] *= -1

Expand All @@ -411,8 +415,10 @@ def _impl(inputs, attr, params):
'not valid.'
raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))


if opname == 'depthwise':
if depth_mult > 1:
raise tvm.error.OpNotImplemented('depth_mult > 1 of operator DepthwiseConv2dNative'
' is not supported.')
attr['groups'] = attr['channels']

# Fix padding
Expand Down
93 changes: 75 additions & 18 deletions tests/python/frontend/tensorflow/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -223,7 +223,7 @@ def test_forward_pooling():
# Convolution
# -----------

def _test_convolution(tensor_in_sizes, filter_in_sizes,
def _test_convolution(opname, tensor_in_sizes, filter_in_sizes,
dilations, strides, padding, data_format):
""" One iteration of convolution with given shapes and attributes """

Expand All @@ -244,27 +244,84 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
strides = [1, 1] + strides
dilations = [1, 1] + dilations

nn_ops.conv2d(in_data,
in_filter,
strides=strides,
dilations=dilations,
padding=padding,
data_format=data_format)
if opname == 'conv':
nn_ops.conv2d(in_data,
in_filter,
strides=strides,
dilations=dilations,
padding=padding,
data_format=data_format)

compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'Conv2D:0')
compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'Conv2D:0')
else:
nn_ops.depthwise_conv2d_native(in_data,
in_filter,
strides=strides,
dilations=dilations,
padding=padding,
data_format=data_format)

compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'DepthwiseConv2dNative:0')

def test_forward_convolution():
if is_gpu_available():
_test_convolution([4, 176, 8, 8], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NCHW')
_test_convolution([4, 19, 17, 17], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NCHW')
_test_convolution([4, 124, 17, 17], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NCHW')
_test_convolution([4, 12, 17, 17], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NCHW')

_test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
_test_convolution([4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC')
_test_convolution([4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NHWC')
_test_convolution([4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NHWC')
_test_convolution('conv', [4, 176, 8, 8], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NCHW')
_test_convolution('conv', [4, 19, 17, 17], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NCHW')
_test_convolution('conv', [4, 124, 17, 17], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NCHW')
_test_convolution('conv', [4, 12, 17, 17], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NCHW')
_test_convolution('depthwise', [4, 176, 8, 8], [1, 1, 176, 1], [1, 1], [1, 1], 'SAME', 'NCHW')
_test_convolution('depthwise', [4, 19, 17, 17], [3, 3, 19, 1], [1, 1], [2, 2], 'VALID', 'NCHW')
_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('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('conv', [4, 17, 17, 124], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NHWC')
_test_convolution('conv', [4, 17, 17, 12], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NHWC')
_test_convolution('depthwise', [4, 8, 8, 176], [1, 1, 176, 1], [1, 1], [1, 1], 'SAME', 'NHWC')
_test_convolution('depthwise', [4, 17, 17, 19], [3, 3, 19, 1], [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')

#######################################################################
# BiasAdd
# -----------
def _test_biasadd(tensor_in_sizes, data_format):
""" One iteration of biasadd with given shapes and attributes """

total_size_1 = 1
for s in tensor_in_sizes:
total_size_1 *= s
tensor_bias_sizes = [tensor_in_sizes[1]] if data_format == 'NCHW' else [tensor_in_sizes[3]]
total_size_2 = tensor_bias_sizes[0]
# Initializes the input tensor with array containing incrementing
# numbers from 1.
data_array = [f * 1.0 for f in range(1, total_size_1 + 1)]
bias_array = [f * 1.0 for f in range(1, total_size_2 + 1)]

with tf.Graph().as_default():
in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
in_bias = constant_op.constant(bias_array, shape=tensor_bias_sizes, dtype='float32')
nn_ops.bias_add(in_data,
in_bias,
data_format=data_format)

compare_tf_with_tvm(np.reshape(data_array, tensor_in_sizes).astype('float32'),
'Placeholder:0', 'BiasAdd:0')

def test_forward_biasadd():
if is_gpu_available():
_test_biasadd([4, 176, 8, 8], 'NCHW')
_test_biasadd([1, 100, 1, 1], 'NCHW')
_test_biasadd([4, 19, 17, 17], 'NCHW')
_test_biasadd([4, 124, 3, 3], 'NCHW')

_test_biasadd([4, 8, 8, 176], 'NHWC')
_test_biasadd([1, 1, 1, 100], 'NHWC')
_test_biasadd([4, 17, 17, 19], 'NHWC')
_test_biasadd([4, 3, 3, 124], 'NHWC')

#######################################################################
# SpaceToBatchND
Expand Down

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