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8d45f555d7
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.:f152a79be9/caffe2/python/net_printer.py (L184-L190)f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)Pull Request resolved: https://github.com/pytorch/pytorch/pull/94587 Approved by: https://github.com/ezyang
45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
## @package add_bias
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# Module caffe2.python.layers.add_bias
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from caffe2.python import schema
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from caffe2.python.layers.layers import ModelLayer
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import math
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class AddBias(ModelLayer):
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def __init__(self, model, input_record, bias_init=None,
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bias_optim=None, name='add_bias'):
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super().__init__(model, name, input_record)
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assert isinstance(input_record, schema.Scalar), "Incorrect input type"
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assert len(input_record.field_type().shape) > 0, (
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"AddBias expects limited dimensions of the input tensor")
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input_dims = input_record.field_type().shape[0]
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assert input_dims > 0, (
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"AddBias expects input dimensions > 0, got {}".format(input_dims))
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scale = math.sqrt(1.0 / input_dims)
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bias_init = bias_init if bias_init else (
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'UniformFill', {'min': -scale, 'max': scale})
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self.b = self.create_param(
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param_name='b',
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shape=[input_dims, ],
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initializer=bias_init,
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optimizer=bias_optim,
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)
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self.output_schema = schema.Scalar(
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(input_record.field_type().base, (input_dims, )),
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self.get_next_blob_reference('output')
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)
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def add_ops(self, net):
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net.Add(self.input_record.field_blobs() + [self.b],
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self.output_schema.field_blobs(), broadcast=1)
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