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https://github.com/zebrajr/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
96 lines
3.4 KiB
Python
96 lines
3.4 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 label_smooth
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# Module caffe2.python.layers.label_smooth
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from caffe2.python import core, schema
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from caffe2.python.layers.layers import ModelLayer
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import numpy as np
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class LabelSmooth(ModelLayer):
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def __init__(
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self, model, label, smooth_matrix, name='label_smooth', **kwargs
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):
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super(LabelSmooth, self).__init__(model, name, label, **kwargs)
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self.label = label
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# shape as a list
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smooth_matrix = np.array(smooth_matrix).astype(np.float32).flatten()
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self.set_dim(smooth_matrix)
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self.set_smooth_matrix(smooth_matrix)
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self.output_schema = schema.Scalar(
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(np.float32, (self.dim, )),
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self.get_next_blob_reference('smoothed_label')
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)
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def set_dim(self, smooth_matrix):
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num_elements = smooth_matrix.size
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self.binary_prob_label = (num_elements == 2)
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if self.binary_prob_label:
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self.dim = 1
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else:
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assert np.sqrt(num_elements)**2 == num_elements
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self.dim = int(np.sqrt(num_elements))
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def set_smooth_matrix(self, smooth_matrix):
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if not self.binary_prob_label:
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self.smooth_matrix = self.model.add_global_constant(
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'%s_label_smooth_matrix' % self.name,
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array=smooth_matrix.reshape((self.dim, self.dim)),
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dtype=np.dtype(np.float32),
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)
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self.len = self.model.add_global_constant(
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'%s_label_dim' % self.name,
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array=self.dim,
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dtype=np.dtype(np.int64),
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)
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else:
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self.smooth_matrix = smooth_matrix
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def add_ops_for_binary_prob_label(self, net):
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if self.label.field_type().base != np.float32:
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float32_label = net.NextScopedBlob('float32_label')
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net.Cast([self.label()], [float32_label], to=core.DataType.FLOAT)
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else:
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float32_label = self.label()
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net.StumpFunc(
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float32_label,
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self.output_schema(),
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threshold=0.5,
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low_value=self.smooth_matrix[0],
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high_value=self.smooth_matrix[1],
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)
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def add_ops_for_categorical_label(self, net):
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if self.label.field_type().base != np.int64:
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int64_label = net.NextScopedBlob('int64_label')
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net.Cast([self.label()], [int64_label], to=core.DataType.INT64)
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else:
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int64_label = self.label()
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one_hot_label = net.NextScopedBlob('one_hot_label')
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net.OneHot([int64_label, self.len], [one_hot_label])
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net.MatMul([one_hot_label, self.smooth_matrix], self.output_schema())
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def add_ops(self, net):
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if self.binary_prob_label:
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self.add_ops_for_binary_prob_label(net)
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else:
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self.add_ops_for_categorical_label(net)
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