dec7df17ed
* Remove unnnecessary ir_version checks in the MO * Cleaned up 'backend_attrs_v2' function * Small clean up from the 'TFCustomSubgraphCall' * Clean up the MO extractor attributes mapping * Renamed PreluOp to PReLU
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
"""
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Copyright (C) 2017-2020 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.layout import shape_for_layout, get_height_dim, get_batch_dim, get_features_dim, get_width_dim
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node, Graph
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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 DepthToSpaceOp(Op):
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op = 'DepthToSpace'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'op': self.op,
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'type': self.op,
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'version': 'opset1',
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'mode': 'blocks_first',
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'infer': self.infer,
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'in_ports_count': 1,
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'out_ports_count': 1,
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}
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super().__init__(graph, mandatory_props, attrs)
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def supported_attrs(self):
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return ['mode', 'block_size']
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@staticmethod
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def infer(node: Node):
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in_shape = node.in_node().shape
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if in_shape.size != 4:
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raise Error('TensorFlow DepthToSpace operation is supported for 4D \'NHWC\' input layout only. '
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'Current input shape is \'{}\''.format(in_shape))
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layout = node.graph.graph['layout']
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N = in_shape[get_batch_dim(layout, 4)]
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H = in_shape[get_height_dim(layout, 4)]
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W = in_shape[get_width_dim(layout, 4)]
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C = in_shape[get_features_dim(layout, 4)]
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block_size = node['block_size']
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if C % (block_size ** 2):
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raise Error('Feature dimensions of input tensor of DepthToSpace operation have to be divisible by square '
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'of DepthToSpace \'block_size\' parameter. Input tensor shape = {}. Feature dimension = {}. '
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'block_size = {}'.format(in_shape, C, block_size))
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out_shape = shape_for_layout(layout,
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batch=N,
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features=int(C / (block_size ** 2)),
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height=int(H * block_size),
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width=int(W * block_size))
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assert np.prod(in_shape) == np.prod(out_shape)
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node.out_node().shape = int64_array(out_shape)
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