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Commit c855882a by zhengdi Committed by Yao Wang
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[FRONTEND][TENSORFLOW] Some bug fixes for tensorflow NCHW data_format (#3514)

parent b6dc7826
Showing with 90 additions and 16 deletions
nnvm/python/nnvm/frontend/tensorflow.py
......@@ -205,8 +205,12 @@ def _conv(opname):
# NCHW Layout require weights transpose
if attr['data_format'] == 'NCHW':
tmp_shape = attr['_input_shapes'][inputs[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]]
......@@ -238,12 +242,12 @@ def _conv(opname):
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
......@@ -256,6 +260,9 @@ def _conv(opname):
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
......@@ -459,7 +466,11 @@ def _reshape():
def _bias_add():
def _impl(inputs, attr, params):
return _sym.broadcast_add(inputs[0], inputs[1])
if attr['data_format'].decode("utf-8") == 'NCHW':
bias = _sym.reshape(inputs[1], newshape=(1, -1, 1, 1))
else:
bias = inputs[1]
return _sym.broadcast_add(inputs[0], bias)
return _impl
def _squeeze():
......
python/tvm/relay/frontend/tensorflow.py
......@@ -361,8 +361,12 @@ def _conv(opname):
# NCHW Layout require weights transpose
if attr['data_format'] == 'NCHW':
tmp_shape = attr['_input_shapes'][inputs[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]]
......@@ -394,12 +398,12 @@ def _conv(opname):
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
......@@ -411,8 +415,10 @@ def _conv(opname):
'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
......
tests/python/frontend/tensorflow/test_forward.py
......@@ -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 """
......@@ -244,6 +244,7 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
strides = [1, 1] + strides
dilations = [1, 1] + dilations
if opname == 'conv':
nn_ops.conv2d(in_data,
in_filter,
strides=strides,
......@@ -253,18 +254,74 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
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
......
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