133 lines
4.4 KiB
Python
133 lines
4.4 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.ops.elementwise import Add
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from mo.front.common.replacement import FrontReplacementSubgraph
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from mo.front.tf.graph_utils import create_op_node_with_second_input
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from mo.graph.graph import Node, Graph
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from mo.utils.error import Error
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from mo.utils.find_inputs import find_outputs
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from mo.utils.utils import refer_to_faq_msg
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def apply_biases_to_last_layer(graph, counts):
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"""
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When user provides counts file, it is a file that contains log-apriory probabilities,
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technically it should be subtracted from the bias of the last layer unless it is a SoftMax.
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Case 1:
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weights ---\
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biases ---\
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some layer ---> AffineTransform ---> SoftMax
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Then, counts are applied to biases of Affine Transform:
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weights ---\
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(biases - counts) ---\
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some layer ---> AffineTransform ---> SoftMax
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Case 2:
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weights ---\
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biases ---\
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some layer ---> AffineTransform
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Just takes the last layer and updates biases:
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weights ---\
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(biases - counts) ---\
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some layer ---> AffineTransform
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Parameters
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----------
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graph
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counts
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Returns
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-------
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"""""
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outputs_ids = find_outputs(graph)
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for output in outputs_ids.copy():
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node = Node(graph, output)
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if node.op != 'Assign':
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continue
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outputs_ids.remove(output)
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if len(outputs_ids) > 1:
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raise Error('Ambiguity in applying counts to several outputs.')
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elif len(outputs_ids) == 0:
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raise Error('No outputs were found')
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target_node = Node(graph, outputs_ids[0])
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if target_node.op == 'SoftMax':
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target_node = target_node.in_port(0).get_source().node
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sub_node = create_op_node_with_second_input(graph, Add, -counts, {'name': 'sub_counts'})
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target_node.out_port(0).get_connection().set_source(sub_node.out_port(0))
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sub_node.in_port(0).connect(target_node.out_port(0))
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def read_counts_file(file_path):
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with open(file_path, 'r') as f:
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file_content = f.readlines()
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if len(file_content) > 1:
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raise Error('Expect counts file to be one-line file. ' +
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refer_to_faq_msg(90))
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counts_line = file_content[0].strip().replace('[', '').replace(']', '')
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try:
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counts = np.fromstring(counts_line, dtype=float, sep=' ')
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except TypeError:
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raise Error('Expect counts file to contain list of floats.' +
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refer_to_faq_msg(90))
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cutoff = 1.00000001e-10
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cutoff_idxs = np.where(counts < cutoff)
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counts[cutoff_idxs] = cutoff
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scale = 1.0 / np.sum(counts)
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counts = np.log(counts * scale) # pylint: disable=assignment-from-no-return
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counts[cutoff_idxs] += np.finfo(np.float32).max / 2
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return counts
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class ApplyCountsFilePattern(FrontReplacementSubgraph):
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"""
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Pass applies counts file as biases to last layer
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"""
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enabled = True
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graph_condition = [lambda graph: graph.graph['cmd_params'].counts is not None]
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def run_after(self):
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from extensions.front.output_cut import OutputCut
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from extensions.front.MoveEmbeddedInputsToInputs import MoveEmbeddedInputsToInputs
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return [MoveEmbeddedInputsToInputs,
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OutputCut,
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]
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def run_before(self):
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from extensions.front.MatMul_normalizer import FullyConnectedDecomposer
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return [FullyConnectedDecomposer,
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]
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def find_and_replace_pattern(self, graph: Graph):
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try:
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counts = read_counts_file(graph.graph['cmd_params'].counts)
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except Exception as e:
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raise Error('Model Optimizer is not able to read counts file {}'.format(graph.graph['cmd_params'].counts) +
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refer_to_faq_msg(92)) from e
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apply_biases_to_last_layer(graph, counts)
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