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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
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
## @package BlobWeightedSum
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# Module caffe2.python.layers.blob_weighted_sum
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from caffe2.python import schema
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from caffe2.python.layers.layers import ModelLayer
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class BlobWeightedSum(ModelLayer):
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"""
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This layer implements the weighted sum:
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weighted element-wise sum of input blobs.
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"""
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def __init__(
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self,
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model,
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input_record,
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init_weights=None,
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weight_optim=None,
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name='blob_weighted_sum',
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**kwargs
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):
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super(BlobWeightedSum, self).__init__(model, name, input_record, **kwargs)
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self.blobs = self.input_record.field_blobs()
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self.num_weights = len(self.blobs)
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assert self.num_weights > 1, (
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"BlobWeightedSum expects more than one input blobs"
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)
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assert len(input_record.field_types()[0].shape) > 0, (
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"BlobWeightedSum expects limited dimensions of the input tensor"
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)
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assert all(
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input_record.field_types()[0].shape == input_record.field_types()[i].shape
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for i in range(1, self.num_weights)
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), "Shape of input blobs should be the same shape {}".format(
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input_record.field_types()[0].shape
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)
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if init_weights:
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assert self.num_weights == len(init_weights), (
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"the size of init_weights should be the same as input blobs, "
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"expects {}, got {}".format(self.num_weights, len(init_weights))
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)
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else:
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init_weights = [1.0] * self.num_weights
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self.weights = [
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self.create_param(
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param_name="w_{}".format(idx),
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shape=[1],
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initializer=('ConstantFill', {'value': float(init_weights[idx])}),
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optimizer=weight_optim
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) for idx in range(self.num_weights)
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]
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self.output_schema = schema.Scalar(
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input_record.field_types()[0],
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self.get_next_blob_reference('blob_weighted_sum_out')
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)
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def add_ops(self, net):
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net.WeightedSum(
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[x for pair in zip(self.blobs, self.weights) for x in pair],
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self.output_schema(),
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grad_on_w=True,
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)
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