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