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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
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
## @package fc_without_bias
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# Module caffe2.python.layers.fc_without_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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from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
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import math
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import numpy as np
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class FCWithoutBias(SamplingTrainableMixin, ModelLayer):
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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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output_dims,
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weight_init=None,
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weight_optim=None,
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name='fc_without_bias',
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uniform_weight_init_scale_numerator=1.0,
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**kwargs
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):
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super().__init__(model, name, input_record, **kwargs)
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assert isinstance(input_record, schema.Scalar), "Incorrect input type"
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assert len(input_record.field_types()[0].shape) > 0, (
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"FCWithoutBias expects limited dimensions of the input tensor"
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)
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input_dims = input_record.field_types()[0].shape[0]
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assert input_dims > 0, (
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"FCWithoutBias expects input dimensions > 0, got {}".format(input_dims)
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)
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self.output_schema = schema.Scalar(
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(np.float32, (output_dims, )),
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self.get_next_blob_reference('output')
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)
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scale = math.sqrt(uniform_weight_init_scale_numerator / input_dims)
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weight_init = weight_init if weight_init else (
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'UniformFill', {'min': -scale,
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'max': scale}
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)
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self.w = self.create_param(param_name='w',
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shape=[output_dims, input_dims],
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initializer=weight_init,
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optimizer=weight_optim)
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def _add_ops(self, net, params):
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net.MatMul(
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self.input_record.field_blobs() + params,
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self.output_schema.field_blobs(), trans_b=1, **self.kwargs
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)
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@property
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def param_blobs(self):
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return [self.w]
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