mirror of
https://github.com/zebrajr/pytorch.git
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234591a809
Summary: changes on metrics and mtml. Differential Revision: D6457175 fbshipit-source-id: 1a162c519191f290e8e919cc7fe978f502ec2840
98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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## @package batch_mse_loss
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# Module caffe2.python.layers.batch_mse_loss
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core, schema
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from caffe2.python.layers.layers import (
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ModelLayer,
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)
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from caffe2.python.layers.tags import (
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Tags
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)
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import numpy as np
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class BatchMSELoss(ModelLayer):
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def __init__(self, model, input_record, name='batch_mse_loss', **kwargs):
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super(BatchMSELoss, self).__init__(model, name, input_record, **kwargs)
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assert schema.is_schema_subset(
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schema.Struct(
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('label', schema.Scalar()),
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('prediction', schema.Scalar())
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),
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input_record
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)
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self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
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self.output_schema = schema.Scalar(
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np.float32,
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self.get_next_blob_reference('output'))
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def add_ops(self, net):
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prediction = net.Squeeze(
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self.input_record.prediction(),
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net.NextScopedBlob('squeezed_prediction'),
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dims=[1]
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)
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label = self.input_record.label.field_blobs()
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if self.input_record.label.field_type().base != (
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self.input_record.prediction.field_type().base):
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label = net.Cast(
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label,
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net.NextScopedBlob('cast_label'),
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to=schema.data_type_for_dtype(
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self.input_record.prediction.field_type()
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)
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)
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label = net.StopGradient(
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label,
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net.NextScopedBlob('stopped_label')
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)
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l2dist = net.SquaredL2Distance(
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[label, prediction],
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net.NextScopedBlob('l2')
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)
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if 'weight' in self.input_record.fields:
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weight_blob = self.input_record.weight()
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if self.input_record.weight.field_type().base != np.float32:
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weight_blob = net.Cast(
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weight_blob,
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weight_blob + '_float32',
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to=core.DataType.FLOAT
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)
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weight_blob = net.StopGradient(
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[weight_blob],
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[net.NextScopedBlob('weight_stop_gradient')],
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)
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l2dist = net.Mul(
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[l2dist, weight_blob],
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net.NextScopedBlob('weighted_l2_distance'),
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)
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net.AveragedLoss(l2dist, self.output_schema.field_blobs())
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