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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] |
|
|
