70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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from mo.front.caffe.extractors.utils import get_canonical_axis_index
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node
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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 Squeeze(Op):
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op = 'Squeeze'
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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': __class__.op,
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'type': __class__.op,
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'version': 'opset1',
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'squeeze_dims': None,
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'reinterp_shape': True,
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'keep_at_least_1d': 0,
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'in_ports_count': 2,
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'out_ports_count': 1,
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'infer': __class__.infer,
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}, attrs)
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@staticmethod
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def infer(node: Node):
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real_squeeze_dims = int64_array([])
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input_shape = node.in_node().shape
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if input_shape is None:
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return
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output_shape = input_shape.copy()
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assert len(node.in_nodes()) == 2, 'The Squeeze node {} must have 2 inputs'.format(node.soft_get('name'))
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# TODO remove the following 'if' statement when IE start support 0D tensors
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squeeze_dims = node.in_port(1).data.get_value()
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if squeeze_dims.ndim == 0:
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squeeze_dims = squeeze_dims.reshape([1])
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for dim in squeeze_dims:
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if output_shape[dim] == 1:
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real_squeeze_dims = np.append(real_squeeze_dims, get_canonical_axis_index(output_shape, dim))
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else:
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raise Error('Trying to squeeze dimension not equal to 1 for node "{}"'.format(node.soft_get('name')))
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# if squeeze_dims empty then all 1s should be removed (tf specification of Squeeze op)
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if squeeze_dims.size == 0:
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for i in range(output_shape.size):
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if output_shape[i] == 1:
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real_squeeze_dims = np.append(real_squeeze_dims, get_canonical_axis_index(output_shape, i))
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output_shape = np.delete(output_shape, real_squeeze_dims)
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node.out_node().shape = output_shape
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# make dimensions positive to correctly translate from NHWC to NCHW layout
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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(real_squeeze_dims)
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if node.in_port(0).data.get_value() is not None:
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node.out_port(0).data.set_value(node.in_port(0).data.get_value().reshape(output_shape))
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# the squeeze_dim attribute will be converted to the second input in the end of the Middle phase
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PermuteInputs().set_input_permutation(node.in_node(1), node, 'input:0', 'axis')
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