970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
118 lines
5.0 KiB
Python
118 lines
5.0 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 extensions.back.ForceStrictPrecision import ForceStrictPrecision
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from extensions.ops.elementwise import Add
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from mo.back.replacement import BackReplacementPattern
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Graph, Node
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from mo.ops.const import Const
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from mo.ops.shape import Shape
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from mo.ops.strided_slice import StridedSlice
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class CropToStridedSlice(BackReplacementPattern):
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enabled = True
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force_clean_up = True
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def run_before(self):
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return [ForceStrictPrecision]
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@staticmethod
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def pattern():
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return dict(
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nodes=[
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('crop', dict(type='Crop'))
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],
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edges=[]
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)
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@staticmethod
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def mask_normalizer(shape_rank: int, axes: np.ndarray, values: np.ndarray):
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mask = np.zeros(shape_rank, dtype=np.int64)
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for i, axis in enumerate(axes):
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mask[axis] = values[i]
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return mask
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@staticmethod
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def list_to_ndarray(val):
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return np.array(val) if np.array(val).ndim != 0 else np.array([val])
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def replace_pattern(self, graph: Graph, match: [str, Node]):
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node = match['crop']
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assert node.has_valid('axis')
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node.axis = self.list_to_ndarray(node.axis)
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in_shape = node.in_port(0).data.get_shape()
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shape_rank = in_shape.size
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axis_mask = int64_array([1 if i in node.axis else 0 for i in range(shape_rank)])
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begin_mask = axis_mask.copy()
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end_mask = axis_mask.copy()
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if len(node.in_nodes()) == 2 and node.has_valid('offset'):
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# Crop Type 1
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begin = Const(graph, {'value': self.mask_normalizer(shape_rank, node.axis, node.offset)}).create_node()
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shape = Shape(graph, {'name': node.name + '/shape_of_crop'}).create_node()
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end = Add(graph, {'name': node.name + '/end'}).create_node()
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node.in_port(1).get_connection().get_source().connect(shape.in_port(0))
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node.in_port(1).disconnect()
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shape.out_port(0).connect(end.in_port(0))
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begin.out_port(0).connect(end.in_port(1))
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elif node.has_valid('dim') and node.has_valid('offset'):
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# Crop Type 2
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node.dim = self.list_to_ndarray(node.dim)
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node.offset = self.list_to_ndarray(node.offset)
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assert node.dim.size == node.offset.size == node.axis.size
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begin = Const(graph, {'value': self.mask_normalizer(shape_rank, node.axis, node.offset)}).create_node()
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end_values = np.array([node.offset[i] + node.dim[i] for i in range(len(node.dim))])
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end = Const(graph, {'value': self.mask_normalizer(shape_rank, node.axis, end_values)}).create_node()
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elif node.has_valid('crop_begin') and node.has_valid('crop_end'):
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# Crop Type 3
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node.crop_begin = self.list_to_ndarray(node.crop_begin)
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node.crop_end = self.list_to_ndarray(node.crop_end)
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assert len(node.crop_begin) == len(node.crop_end) == len(node.axis)
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begin = Const(graph, {'value': self.mask_normalizer(shape_rank, node.axis, node.crop_begin)}).create_node()
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shape = Shape(graph, {'name': node.name + '/shape_of_crop'}).create_node()
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const = Const(graph,
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{'value': -1 * self.mask_normalizer(shape_rank, node.axis, node.crop_end)}).create_node()
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end = Add(graph, {'name': node.name + '/end'}).create_node()
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node.in_port(0).get_connection().get_source().connect(shape.in_port(0))
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shape.out_port(0).connect(end.in_port(0))
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const.out_port(0).connect(end.in_port(1))
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else:
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raise Exception("Unknown type of Crop")
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source = node.in_port(0).get_connection().get_source()
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stride = Const(graph, {'value': np.ones(shape_rank, dtype=np.int64)}).create_node()
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ss = StridedSlice(graph, {'name': 'Crop_', 'begin_mask': begin_mask, 'end_mask': end_mask, 'new_axis_mask': np.array([0]),
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'shrink_axis_mask': np.array([0]), 'ellipsis_mask': np.array([0])}).create_node()
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source.connect(ss.in_port(0))
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begin.out_port(0).connect(ss.in_port(1))
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end.out_port(0).connect(ss.in_port(2))
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stride.out_port(0).connect(ss.in_port(3))
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node.in_port(0).disconnect()
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node.out_port(0).get_connection().set_source(ss.out_port(0))
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ss['force_precision_in_ports'] = {1: 'int64', 2: 'int64', 3: 'int64'}
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