Evgeny Lazarev
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
108 lines
4.1 KiB
Python
108 lines
4.1 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.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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def broadcastable(broadcast_from, broadcast_to):
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"""Check if shape broadcast_from can be broadcasted to broadcast_to"""
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broadcast_to = np.array(broadcast_to, dtype=np.int64)
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broadcast_from = np.array(broadcast_from, dtype=np.int64)
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if broadcast_from.size > broadcast_to.size:
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return False
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broadcast_from = np.concatenate(
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(np.array([1] * (broadcast_to.size - broadcast_from.size), dtype=np.int64), broadcast_from))
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return np.all(np.logical_or(broadcast_from == 1, broadcast_from == broadcast_to))
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def round_half_up(n):
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return np.floor(n + 0.5)
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class FakeQuantize(Op):
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op = 'FakeQuantize'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': self.op,
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'op': self.op,
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'version': 'opset1',
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'levels': None,
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'is_eltwise': True,
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'infer': self.infer,
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'in_ports_count': 5,
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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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if self.attrs['levels'] is None:
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raise Error("FakeQuantize operation has no levels parameter")
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def supported_attrs(self):
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return [
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'levels',
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]
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@staticmethod
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def infer(node: Node):
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assert len(node.in_nodes()) == 5
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assert len(node.out_nodes()) == 1
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inputs = [node.in_node(i) for i in range(5)]
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x, input_low, input_high, output_low, output_high = inputs
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assert x.has_valid('shape')
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# TODO Check all input[1..4] shapes are broadcastable to intput[0] shape
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assert all([broadcastable(inputs[i].shape, inputs[0].shape) for i in range(1, 5)]), \
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"Not all shapes from FakeQuantize inputs can be broadcasted to input[0] for node {}".format(
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node.soft_get('name'))
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node.out_node().shape = x.shape.copy()
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if all([node.in_node(i).has_valid('value') for i in range(5)]):
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x, input_low, input_high, output_low, output_high = \
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[np.array(np.broadcast_to(node.value, x.value.shape), dtype=np.float32) for node in inputs]
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assert node.has_valid('levels')
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assert isinstance(node.levels, int)
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underflow_mask = x <= input_low
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overflow_mask = x > input_high
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# pylint: disable=assignment-from-no-return
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middle_mask = np.logical_not(np.logical_or(underflow_mask, overflow_mask))
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def middle_part(x, input_low, input_high, output_low, output_high):
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return round_half_up((x - input_low) / (input_high - input_low) * (node.levels - 1)) / \
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(node.levels - 1) * (output_high - output_low) + output_low
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output = np.zeros_like(x)
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# pylint: disable=unsupported-assignment-operation
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output[middle_mask] = middle_part(
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x[middle_mask],
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input_low[middle_mask],
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input_high[middle_mask],
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output_low[middle_mask],
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output_high[middle_mask],
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)
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# pylint: disable=unsupported-assignment-operation
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output[overflow_mask] = output_high[overflow_mask]
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# pylint: disable=unsupported-assignment-operation
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output[underflow_mask] = output_low[underflow_mask]
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if not node.has_and_set('stop_value_propagation'):
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node.out_node().value = output
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