c8dd831fc3
* Added transformation config to support automl efficientdet-4 model * Added configuration file to convert Automl EfficientDet model * Updated unit test for Pack * Added instruction on how to convert EfficientDet Tensorflow model * Updated documentation on how to convert EfficientDet model * Updated a documentation with instruction on how to convert Automl EfficientDet.
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import numpy as np
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.perm_inputs import PermuteInputs
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from mo.ops.op import Op
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from mo.utils.error import Error
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class Unsqueeze(Op):
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"""
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The operation that inserts dimensions of size one into specific positions of the input layer. The dimensions are
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specified in the second input.
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"""
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op = 'Unsqueeze'
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enabled = False
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def __init__(self, graph, attrs: dict):
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super().__init__(graph, {
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'op': self.op,
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'type': self.op,
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'version': 'opset1',
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'unsqueeze_dims': None,
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'reinterp_shape': True,
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'in_ports_count': 2,
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'out_ports_count': 1,
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'infer': self.infer
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}, attrs)
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@staticmethod
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def infer(node):
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if len(node.in_nodes()) <= 1:
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raise Error('There is no input with unsqueeze dims for the node {}'.format(node.soft_get('name')))
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unsqueeze_dims = node.in_port(1).data.get_value()
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if unsqueeze_dims is None:
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raise Error('The dimensions to unsqueeze are not defined for the node {}'.format(node.soft_get('name')))
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unsqueeze_dims = int64_array(unsqueeze_dims)
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input_value = node.in_port(0).data.get_value()
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input_shape = node.in_port(0).data.get_shape()
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# TODO remove the following line when the Inference Engine plugins support 0D tensors
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if unsqueeze_dims.ndim == 0:
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unsqueeze_dims = int64_array([unsqueeze_dims.item()])
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# make dimensions positive to correctly translate from NHWC to NCHW layout
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unsqueeze_dims = int64_array([dim + len(node.in_port(0).data.get_shape()) + 1 if dim < 0 else dim
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for dim in unsqueeze_dims])
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if node.in_port(1).get_source().node.op == 'Const':
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node.in_port(1).data.set_value(unsqueeze_dims)
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output_shape = int64_array(input_shape.copy())
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for dim in unsqueeze_dims:
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output_shape = np.insert(output_shape, dim, 1)
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if input_value is not None:
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node.out_port(0).data.set_value(input_value.reshape(output_shape))
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else:
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node.out_port(0).data.set_shape(int64_array(output_shape))
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PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:0', 'axis')
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