def test_caffe_conv2d_dynamic_input_infer(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': shape_array([1, 3, dynamic_dimension_value, 227])},
'conv_weights': {'shape': np.array([64, 3, 3, 3]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']},
'conv_node': {'pad_spatial_shape': np.array([[0, 0], [0, 0]]),
'conv_pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]]),
'dilation': np.array([1, 1, 1, 1]), 'bias_addable': True, 'bias_term': False,
'output_spatial_shape': None, 'output_shape': None,
'stride': np.array([1, 1, 1, 1]), 'group': 1,
'kernel_spatial_idx': np.array([2, 3]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'output': 64, 'kernel_spatial': np.array([3, 3]),
'spatial_dims': np.array([2, 3]), 'channel_dims': np.array([1]),
'batch_dims': np.array([0])}
})
conv_node = Node(graph, 'conv_node')
Convolution.infer(conv_node)
exp_shape = shape_array([1, 64, dynamic_dimension_value, 225])
res_shape = graph.node['conv_output']['shape']
self.assertTrue(strict_compare_tensors(exp_shape, res_shape))
def backend_attrs(self):
# the same attributes as in a regular convolution and additional attributes 'deformable_group', 'group'
# and 'bilinear_interpolation_pad'
attrs = Convolution(self.graph, {}).backend_attrs() + ['deformable_group', 'group']
if self.get_opset() == 'opset8':
attrs.append('bilinear_interpolation_pad')
return attrs
def extract(cls, node):
final_pads = get_pads(node.module)
# Extract strides attribute
strides = node.module.stride
final_strides = np.array([1, 1, *strides], dtype=np.int64)
# Extract dilations attribute
dilations = node.module.dilation
if isinstance(dilations, int):
dilations = [dilations, dilations]
final_dilations = np.array([1, 1, *dilations], dtype=np.int64)
attrs = {
'type': 'Deconvolution',
'pad': final_pads,
'stride': final_strides,
'dilation': final_dilations,
'group': node.module.groups,
'kernel_spatial': np.array(node.module.kernel_size,
dtype=np.int64),
'input_feature_channel': 1,
'output_feature_channel': 0,
'channel_dims': np.array([1], dtype=np.int64),
'batch_dims': np.array([0], dtype=np.int64),
'layout': 'NCHW',
'in_ports_count': 2,
'output': node.module.out_channels,
}
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def test_caffe_conv2d_infer_wrong_input_shape(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': np.array([1, 3, 1, 1])},
'conv_weights': {'shape': np.array([64, 3, 3, 3]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']},
'conv_node': {'pad_spatial_shape': np.array([[0, 0], [0, 0]]),
'conv_pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]]),
'dilation': np.array([1, 1, 1, 1]), 'bias_addable': True, 'bias_term': False,
'output_spatial_shape': None, 'output_shape': None,
'stride': np.array([1, 1, 1, 1]), 'group': 1,
'kernel_spatial_idx': np.array([2, 3]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'output': 64, 'kernel_spatial': np.array([3, 3]),
'spatial_dims': np.array([2, 3]), 'channel_dims': np.array([1]),
'batch_dims': np.array([0])}
})
conv_node = Node(graph, 'conv_node')
with self.assertRaises(Error):
Convolution.infer(conv_node)
def test_deconv_infer_no_shape(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': None},
'conv_weights': {'shape': np.array([1, 21, 16, 16]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']},
'conv_node': {'spatial_dims': np.array([2, 3]), 'batch_dims': np.array([0]),
'channel_dims': np.array([1]),
'pad_spatial_shape': np.array([[0, 0], [0, 0]]),
'kernel_spatial': np.array([4, 4]), 'output_spatial_shape': None,
'kernel_spatial_idx': np.array([2, 3]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'type': 'Deconvolution', 'output': 21, 'dilation': np.array([1, 1, 1, 1]),
'group': 1, 'stride': np.array([1, 1, 2, 2]), 'output_shape': None}
})
deconv_node = Node(graph, 'conv_node')
with self.assertRaisesRegex(Error, "Input data shape is None for node.*"):
Convolution.infer(deconv_node)
def test_caffe_conv2d_infer(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': np.array([1, 3, 227, 227])},
'conv_weights': {'shape': np.array([64, 3, 3, 3]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']},
'conv_node': {'pad_spatial_shape': np.array([[0, 0], [0, 0]]),
'conv_pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]]),
'dilation': np.array([1, 1, 1, 1]), 'bias_addable': True, 'bias_term': False,
'output_spatial_shape': None, 'output_shape': None,
'stride': np.array([1, 1, 1, 1]), 'group': 1,
'kernel_spatial_idx': np.array([2, 3]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'output': 64, 'kernel_spatial': np.array([3, 3]),
'spatial_dims': np.array([2, 3]), 'channel_dims': np.array([1]),
'batch_dims': np.array([0])}
})
conv_node = Node(graph, 'conv_node')
Convolution.infer(conv_node)
exp_shape = np.array([1, 64, 225, 225])
res_shape = graph.node['conv_output']['shape']
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def extract(cls, node):
proto_layer, model_layer = node.pb, node.model_pb
if not proto_layer:
raise Error('Protobuf layer can not be empty')
deconv_param = proto_layer.convolution_param
params = conv_set_params(deconv_param, 'Deconv2D')
attrs = conv_create_attrs(params)
attrs.update({
'type': 'Deconvolution',
'op': 'Deconv2D',
'get_group': lambda node: node.group,
'get_output_feature_dim': lambda node: node.output,
'input_feature_channel': 0,
'output_feature_channel': 1,
})
# Embed weights and biases as attributes
# It will be moved to a separate nodes in special pass
attrs.update(weights_biases(deconv_param.bias_term, model_layer))
attrs.update(layout_attrs())
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 2, 3)
def get_num_groups(node):
if 'group' in node:
return node.group
elif node.in_node(0).shape is not None and node.kernel_shape is not None \
and node.in_node(0).shape[node.channel_dims[0]] is not dynamic_dimension \
and node.kernel_shape[node.input_feature_channel] is not dynamic_dimension:
# if group attribute is not defined, number of groups is calculated
# from number of input channels and filter channel size
return node.in_node(0).shape[
node.channel_dims] // node.kernel_shape[
node.input_feature_channel]
else:
return 1
attrs.update({
'op':
__class__.op,
'get_group':
get_num_groups,
'get_output_feature_dim':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),
inv=int64_array([2, 3, 1, 0]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
proto_layer, model_layer = node.pb, node.model_pb
if not proto_layer:
raise Error('Protobuf layer can not be empty')
conv_param = proto_layer.convolution_param
conv_type = 'ConvND' if len(proto_layer.bottom) > 1 else 'Conv2D'
params = conv_set_params(conv_param, conv_type)
attrs = conv_create_attrs(params)
attrs.update({
'op': __class__.op,
'get_group': lambda node: node.group,
'get_output_feature_dim': lambda node: node.output,
'weights_index': 1 if conv_type == 'Conv2D' else 2
})
# Embed weights and biases as attributes
# It will be moved to a separate nodes in special pass
attrs.update(
weights_biases(conv_param.bias_term,
model_layer,
start_index=len(proto_layer.bottom),
proto=conv_param))
attrs.update(layout_attrs())
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node: Node) -> bool:
"""
Extract conv parameters from node.parameters.
node.parameters like file descriptor object.
:param node: Convolution node
:return:
"""
pb = node.parameters
kernel = read_token_value(pb, b'<PatchDim>')
stride = read_token_value(pb, b'<PatchStep>')
patch_stride = read_token_value(pb, b'<PatchStride>')
read_learning_info(pb)
collect_until_whitespace(pb)
weights, weights_shape = read_binary_matrix(pb)
collect_until_whitespace(pb)
biases = read_binary_vector(pb)
if (patch_stride - kernel) % stride != 0:
raise Error(
'Kernel size and stride does not correspond to `patch_stride` attribute of Convolution layer. ' +
refer_to_faq_msg(93))
output = biases.shape[0]
if weights_shape[0] != output:
raise Error('Weights shape does not correspond to the `output` attribute of Convolution layer. ' +
refer_to_faq_msg(93))
mapping_rule = {
'output': output,
'patch_stride': patch_stride,
'bias_term': None,
'pad': np.array([[0, 0], [0, 0], [0, 0], [0, 0]], dtype=np.int64),
'pad_spatial_shape': np.array([[0, 0], [0, 0]], dtype=np.int64),
'dilation': np.array([1, 1, 1, 1], dtype=np.int64),
'kernel': np.array([1, 1, 1, kernel], dtype=np.int64),
'stride': np.array([1, 1, 1, stride], dtype=np.int64),
'kernel_spatial': np.array([1, kernel], dtype=np.int64),
'input_feature_channel': 1,
'output_feature_channel': 0,
'kernel_spatial_idx': [2, 3],
'group': 1,
'reshape_kernel': True,
}
mapping_rule.update(layout_attrs())
embed_input(mapping_rule, 1, 'weights', weights)
embed_input(mapping_rule, 2, 'biases', biases)
mapping_rule['bias_addable'] = len(biases) > 0
Convolution.update_node_stat(node, mapping_rule)
return cls.enabled
def add_convolution_to_swap_xy_coordinates(graph: Graph, input_node: Node,
coordinates_size: int):
"""
The function add convolution node after the node 'input_node' to swap xy coordinates of the boxes produced
by the node 'input_node'. It is expected that box coordinates are located in the fastest changing dimension of the
'input_node' output, i.e. the input tensor could be reshaped to [num_boxes, 4] or [num_boxes, 5]. If the size is 5,
then the 0-th element for each of num_boxes blocks is not changed and element 1 is swapped with element 2, element 3
is swapped with element 4. This is the case when boxes coordinates are produced by the layer "Proposal". The exact
amount of elements in each block is equal to the 'coordinates_size' parameter.
:param graph: graph to operate on.
:param input_node: node producing boxes coordinates.
:param coordinates_size: integer value equal to 4 or 5.
:return convolution node that swaps coordinates.
"""
# swap of input tensor with 4 or 5 numbers describing boxes are supported
assert (coordinates_size in [4, 5])
input_reshape_4d_node = create_op_node_with_second_input(
graph, Reshape, int64_array([-1, 1, 1, coordinates_size]),
dict(name=input_node.name + '/reshape_4d'), input_node)
mark_input_as_in_correct_layout(input_reshape_4d_node, 0)
# do not mark second input because the reshape works in initial model layout and needs to be transformed to NCHW
mark_output_as_in_correct_layout(input_reshape_4d_node, 0)
if coordinates_size == 5:
# zero indexed element is not box coordinate ("batch id" in case of Proposal)
conv_filter_data = mo_array(
mo_array([[[[1, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 0, 0, 0],
[0, 0, 0, 0, 1], [0, 0, 0, 1, 0]]]],
dtype=np.float32))
else:
conv_filter_data = mo_array(
mo_array(
[[[[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]]]],
dtype=np.float32))
conv_filter_data = np.transpose(conv_filter_data, [2, 3, 0, 1])
conv_filter_const_op = Const(graph, dict(value=conv_filter_data))
conv_filter_const_node = conv_filter_const_op.create_node(
[], dict(name=input_node.name + '/weights'))
conv_op = Convolution(
graph, {
'bias_addable': True,
'channel_dims': mo_array([3]),
'batch_dims': mo_array([0]),
'input_feature_channel': 0,
'output_feature_channel': 1,
'group': 1,
'layout': 'NHWC',
})
return conv_op.create_node([input_reshape_4d_node, conv_filter_const_node],
dict(name=input_node.name + "/conv"))
def extract(cls, node):
attr = get_mxnet_layer_attrs(node.symbol_dict)
kernel = attr.tuple("kernel", int, None)
stride = attr.tuple("stride", int,
tuple(np.ones(len(kernel), dtype=np.int64)))
padding = attr.tuple("pad", int,
tuple(np.zeros(len(kernel), dtype=np.int64)))
dilate = attr.tuple("dilate", int,
tuple(np.ones(len(kernel), dtype=np.int64)))
group = attr.int("num_group", 1)
output = attr.int("num_filter", None)
bias_term = not attr.bool("no_bias", True)
target_shape = attr.tuple("target_shape", int, None)
if target_shape:
target_shape = int64_array(target_shape)
final_dilations = int64_array([1, 1, *[d for d in dilate]
]) if dilate is not None else None
node_attrs = {
'op': __class__.op,
'type': 'Deconvolution',
'bias_addable': True,
'bias_term': bias_term,
'pad': int64_array([[0, 0], [0, 0],
*[[pad, pad] for pad in padding]]),
'pad_spatial_shape': int64_array([[pad, pad] for pad in padding]),
'dilation': final_dilations,
'output_spatial_shape': target_shape,
'original_output_spatial_shape': target_shape,
'output_shape': None,
'stride': int64_array([1, 1, *[s for s in stride]]),
'group': group,
'output': output,
'kernel_spatial': int64_array([k for k in kernel]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'kernel_spatial_idx': None,
'reshape_kernel': True,
'spatial_dims': None,
'channel_dims': int64_array([1]),
'batch_dims': int64_array([0]),
'layout': 'NCHW',
'get_pad': DeconvFrontExtractor.get_pad,
}
output_padding = attr.tuple("adj", int, None)
if target_shape is None and output_padding:
node_attrs["output_padding"] = int64_array(
[0, 0, *[s for s in output_padding]])
# update the attributes of the node
Convolution.update_node_stat(node, node_attrs)
return cls.enabled
def extract(cls, node):
attrs = get_mxnet_layer_attrs(node.symbol_dict)
scale = attrs.int("scale", 1)
num_filter = attrs.int("num_filter", 0)
mode = attrs.str("sample_type", None)
if mode == 'nearest':
node_attrs = {
'factor': attrs.int("scale", 1),
'mode': mode,
'antialias': 0,
'axes': int64_array([2, 3]),
}
Interpolate.update_node_stat(node, node_attrs)
elif mode == 'bilinear':
"""
Bilinear UpSampling uses deconvolution algorithm under the hood.
For MXNet Bilinear UpSampling op just wrapper over Deconvolution op.
Inputs data:
input1 - input data
input2 - deconvolution weight
"""
kernel = 2 * scale - scale % 2
stride = scale
pad = math.ceil((scale - 1) / 2)
num_group = num_filter
node_attrs = {
'op': __class__.op,
'type': 'Deconvolution',
'bias_addable': True,
'bias_term': False,
'pad': int64_array([[0, 0], [0, 0], [pad, pad], [pad, pad]]),
'pad_spatial_shape': int64_array([[pad, pad], [pad, pad]]),
'dilation': None,
'output_spatial_shape': None,
'output_shape': None,
'stride': int64_array([1, 1, stride, stride]),
'group': num_group,
'output': num_filter,
'kernel_spatial': int64_array([kernel, kernel]),
'input_feature_channel': 0,
'output_feature_channel': 1,
'kernel_spatial_idx': None,
'reshape_kernel': True,
'spatial_dims': None,
'channel_dims': int64_array([1]),
'batch_dims': int64_array([0]),
'layout': 'NCHW',
'get_pad': DeconvFrontExtractor.get_pad,
}
Convolution.update_node_stat(node, node_attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 3, 4)
attrs.update({
'op':
__class__.op,
'get_group':
lambda node: 1,
'get_output_feature_dim':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([4, 3, 0, 1, 2]),
inv=int64_array([2, 3, 4, 1, 0]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 2, 2)
attrs.update({
'op':
__class__.op,
'kernel_spatial_idx':
np.array([0, 1], dtype=np.int64),
'get_group':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_output_feature_dim':
lambda node: node.kernel_shape[-1] * node.kernel_shape[-2],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([2, 3, 0, 1]),
inv=int64_array([2, 3, 0, 1]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attrs = tf_create_attrs(node, 2, 3)
attrs.update({
'op':
__class__.op,
'get_group':
lambda node: node.group
if 'group' in node and node.group is not None else node.in_node(0).
shape[node.channel_dims] // node.kernel_shape[
node.input_feature_channel],
'get_output_feature_dim':
lambda node: node.kernel_shape[node.output_feature_channel],
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),
inv=int64_array([2, 3, 1, 0]))
})
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def test_deconv_infer_ideal(self):
graph = build_graph(nodes_attributes,
[('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{'conv_output': {'shape': None},
'conv_input': {'shape': np.array([1, 21, 16, 16])},
'conv_weights': {'shape': np.array([1, 21, 4, 4]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']},
'conv_node': {#'spatial_dims': np.array([2, 3]), 'batch_dims': np.array([0]),
'channel_dims': np.array([1]), 'bias_addable': True, 'bias_term': False,
'batch_dims': np.array([0]),
'pad_spatial_shape': np.array([[0, 0], [0, 0]]),
'kernel_spatial': np.array([4, 4]), 'output_spatial_shape': None,
'kernel_spatial_idx': np.array([2, 3]),
'input_feature_channel': 1,
'output_feature_channel': 0,
'output_padding': np.array([0, 0, 1, 1]),
'type': 'Deconvolution', 'output': 21, 'dilation': np.array([1, 1, 1, 1]),
'group': 1, 'stride': np.array([1, 1, 2, 2]), 'output_shape': None}
})
deconv_node = Node(graph, 'conv_node')
Convolution.infer(deconv_node)
res_shape = deconv_node['output_shape']
exp_shape = np.array([1, 21, 35, 35])
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
# Check that after double infer shape and pad attrs do not changes
Convolution.infer(deconv_node)
for i in range(0, len(exp_shape)):
self.assertEqual(exp_shape[i], res_shape[i])
def replace_timeheightconv(self, graph: Graph, node: Node):
req_time_offsets = node.soft_get('time_offsets')
offsets = node.soft_get("offsets", [[]])
all_time_offsets = list(set(offsets[:, 0]))
all_time_offsets.sort()
in_name = node.soft_get('name', node.id)
rename_node(node, in_name + '/to_delete')
# create memoryoffsets for context gathering
# we need concat if time offsets more than 1
concat = Concat(graph,
attrs={
'name': in_name + '/Concat',
'in_ports_count': len(all_time_offsets)
}).create_node()
i = 0
for t in all_time_offsets:
# if time offset included in required_time_offsets we don't need default value
has_default = t not in req_time_offsets
memoff = MemoryOffset(graph,
attrs={
'name':
in_name + '/MemoryOffset_' + str(i),
't':
t,
'has_default':
has_default,
'splitted':
False,
'pair_name':
in_name + '/MemoryOffset_pair_' + str(i)
}).create_node()
concat.in_port(i).connect(memoff.out_port(0))
memoff.in_port(0).connect(node.in_port(0).get_source())
i = i + 1
stride = node.soft_get("height_subsample", 1)
kernel = int64_array([0, 0])
kernel[0] = len(set(offsets[:, 0]))
kernel[1] = len(set(offsets[:, 1]))
pad_h = int64_array([0, 0])
pad_h[0] = -min(offsets[:, 1]) if min(offsets[:, 1]) < 0 else 0
pad_h[1] = stride * node.height_out - (node.height_in -
max([max(offsets[:, 1]), 0]))
dilation_t = (max(offsets[:, 0]) - min(offsets[:, 0])) / (
kernel[0] - 1) if kernel[0] > 1 else 1
dilation_h = (max(offsets[:, 1]) - min(offsets[:, 1])) / (
kernel[1] - 1) if kernel[0] > 1 else 1
conv_attrs = {
'name':
in_name,
'output':
node['out_channels'],
'height_in':
node.height_in,
'bias_term':
None,
'pad':
int64_array([[0, 0], [0, 0], [0, 0], pad_h]),
'pad_spatial_shape':
int64_array([[0, 0], pad_h]),
'dilation':
int64_array([1, 1, dilation_t, dilation_h]),
'kernel':
int64_array(
[node.out_channels, node.in_channels, kernel[0], kernel[1]]),
'stride':
int64_array([1, 1, 1, stride]),
'kernel_spatial':
kernel,
'input_feature_channel':
1,
'output_feature_channel':
0,
'channel_dims':
int64_array([1]),
'spatial_dims':
int64_array([2, 3]),
'batch_dims':
int64_array([0]),
'kernel_spatial_idx':
int64_array([2, 3]),
'group':
1,
'reshape_kernel':
True,
'bias_addable':
True,
}
conv = Convolution(graph, attrs=conv_attrs).create_node()
conv.in_port(0).connect(concat.out_port(0))
conv.in_port(1).connect(node.in_port(1).get_source())
# change layout for weights from OHWI to OIHW
# in future should be replaced by common Permute mechanics
weights = conv.in_port(1).get_source().node.value
weights = weights.reshape(
int64_array([node.out_channels, -1, node.in_channels]))
weights = weights.transpose(int64_array([0, 2, 1]))
weights = weights.flatten()
conv.in_port(1).get_source().node.value = weights
conv.in_port(2).connect(node.in_port(2).get_source())
node.out_port(0).get_connection().set_source(conv.out_port(0))
graph.remove_node(node.id)
def test_conv_infer_3D_convolution(self):
graph = build_graph(nodes_attributes,
[
('conv_input', 'conv_node'),
('conv_weights', 'conv_node'),
('conv_node', 'conv_output'),
('conv_output', 'op_output')
],
{
'conv_output': {
'shape': None
},
'conv_input': {
'shape': int64_array([1, 3, 16, 224, 224])
},
'conv_weights': {
'shape': int64_array([3, 64, 1, 7, 7]),
'dim_attrs': ['spatial_dims', 'channel_dims', 'batch_dims', 'axis']
},
'conv_node': {
'type': 'Convolution',
'bias_term': None,
'stride': None,
'dilation': None,
'batch_dims': int64_array([0]),
'channel_dims': int64_array([1]),
'output_spatial_shape': None,
'input_feature_channel': 0,
'output_feature_channel': 1,
'group': 1,
'output_shape': None,
'layout': 'NCHW'
}
})
conv_node = Node(graph, 'conv_node')
conv_output = Node(graph, 'conv_output')
Convolution.infer(conv_node)
# Check bias_term attribute
self.assertTrue(conv_node.has_valid('bias_term'))
self.assertTrue(not conv_node.bias_term)
# Check kernel_spatial_idx attr detection
self.assertTrue(conv_node.has_valid('kernel_spatial_idx'))
self.assertTrue(np.array_equal(int64_array([2, 3, 4]), conv_node.kernel_spatial_idx))
# Check spatial_dims attr detection
self.assertTrue(conv_node.has_valid('spatial_dims'))
self.assertTrue(np.array_equal(int64_array([2, 3, 4]), conv_node.spatial_dims))
# Check kernel_spatial attr detection
self.assertTrue(conv_node.has_valid('kernel_spatial'))
self.assertTrue(np.array_equal(int64_array([1, 7, 7]), conv_node.kernel_spatial))
# Check output attribute
self.assertTrue(conv_node.has_valid('output'))
self.assertEqual(64, conv_node.output)
# Check dilation value. Should be set to default
self.assertTrue(conv_node.has_valid('dilation'))
self.assertTrue(np.array_equal(int64_array([1, 1, 1, 1, 1]), conv_node.dilation))
# Check stride value. Should be set to default
self.assertTrue(conv_node.has_valid('stride'))
self.assertTrue(np.array_equal(int64_array([1, 1, 1, 1, 1]), conv_node.stride))
# Check pad value. Should be set to default
self.assertTrue(conv_node.has_valid('pad'))
self.assertTrue(np.array_equal(int64_array([[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]), conv_node.pad))
# Check pad_spatial_shape
self.assertTrue(conv_node.has_valid('pad_spatial_shape'))
self.assertTrue(np.array_equal(int64_array([[0, 0], [0, 0], [0, 0]]), conv_node.pad_spatial_shape))
# Check resulting output shape
self.assertTrue(np.array_equal(int64_array([1, 64, 16, 218, 218]), conv_output.shape))
def extract(cls, node):
# Extract pads attribute
# In case if pads is not specified it will be set in default (1) in infer function
pads = onnx_attr(node,
'pads',
'ints',
default=None,
dst_type=lambda x: int64_array(x))
assert pads is None or len(pads) % 2 == 0
final_pad = None
if pads is not None:
pads = pads.reshape([2, -1])
pads = np.transpose(pads)
final_pad = int64_array([[0, 0], [0, 0], *pads])
# Extract dilations attribute
# In case if dilations is not specified it will be set in default (1) in infer function
dilations = onnx_attr(node,
'dilations',
'ints',
default=None,
dst_type=lambda x: int64_array(x))
final_dilations = int64_array([1, 1, *dilations
]) if dilations is not None else None
# Extract dilations attribute
# In case if dilations is not specified it will be set in default (1) in infer function
strides = onnx_attr(node,
'strides',
'ints',
default=None,
dst_type=lambda x: int64_array(x))
final_strides = int64_array([1, 1, *strides
]) if strides is not None else None
kernel_shape = onnx_attr(node, 'kernel_shape', 'ints', default=None)
auto_pad = onnx_attr(node,
'auto_pad',
's',
default=None,
dst_type=get_onnx_autopad)
group = onnx_attr(node,
'group',
'i',
default=1,
dst_type=lambda x: int64_array(x))
attrs = {
'op':
__class__.op,
'auto_pad':
auto_pad,
'bias_addable':
True,
'bias_term':
None,
'pad':
final_pad,
'pad_spatial_shape':
int64_array(pads) if pads is not None else None,
'dilation':
final_dilations,
'output_spatial_shape':
None,
'output_shape':
None,
'stride':
final_strides,
'group':
group,
'output':
None,
'kernel_spatial':
int64_array(kernel_shape) if kernel_shape is not None else None,
'input_feature_channel':
1,
'output_feature_channel':
0,
'kernel_spatial_idx':
None, # Will be calculated in infer function (np.array([2, 3]))
'spatial_dims':
None, # Will be calculated in infer function
'channel_dims':
int64_array([1]),
'batch_dims':
int64_array([0]),
'layout':
'NCHW'
}
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
pads = onnx_attr(node, 'pads', 'ints', dst_type=int64_array)
auto_pad = onnx_attr(node,
'auto_pad',
's',
default=None,
dst_type=get_onnx_autopad)
if pads is not None:
if len(pads) % 2 != 0:
raise Error(
'ConvTranspose node {} specifies pads = {} which has odd number of elements. The model is not correct.',
node.soft_get('name'), pads)
pads = pads.reshape([2, -1])
pads = np.transpose(pads)
final_pads = int64_array([[0, 0], [0, 0], *pads
]) if pads is not None else None
dilations = onnx_attr(node, 'dilations', 'ints', default=None)
final_dilations = int64_array([1, 1, *dilations
]) if dilations is not None else None
strides = onnx_attr(node, 'strides', 'ints', default=None)
final_strides = int64_array([1, 1, *strides
]) if strides is not None else None
kernel_shape = onnx_attr(node,
'kernel_shape',
'ints',
dst_type=int64_array)
if kernel_shape is None:
raise Error(
'ConvTranspose node {} doesn\'t have explicitly defined kernel_shape. It is not supported.',
node.soft_get('name'))
output_padding = onnx_attr(node,
'output_padding',
'ints',
default=None)
final_output_padding = int64_array(
[0, 0, *output_padding]) if output_padding is not None else None
output_shape = onnx_attr(node,
'output_shape',
'ints',
default=None,
dst_type=int64_array)
attrs = {
'type':
'Deconvolution',
'op':
'Deconv2D',
'auto_pad':
auto_pad,
'bias_addable':
True,
'bias_term':
None, # will be deduced later; not really needed
'pad':
final_pads,
'dilation':
final_dilations,
'output_spatial_shape':
output_shape,
'original_output_spatial_shape':
output_shape,
'output_shape':
None,
'output_padding':
final_output_padding,
'stride':
final_strides,
'group':
onnx_attr(node, 'group', 'i', default=1),
'output':
None,
'spatial_dims':
None, # Will be calculated in infer function
'channel_dims':
int64_array([1]),
'batch_dims':
int64_array([0]),
'layout':
'NCHW',
'input_feature_channel':
0,
'output_feature_channel':
1,
'get_pad':
ConvTransposeFrontExtractor.get_pad,
'get_output_feature_dim':
lambda node: node.kernel_shape[node.output_feature_channel] * node.
group,
}
# update the attributes of the node
Convolution.update_node_stat(node, attrs)
return cls.enabled