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003f97cefa
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13822 as title Reviewed By: xianjiec Differential Revision: D12996338 fbshipit-source-id: 1aa61e71e2d79535325ea7034c82e1cb6bf3a9f6
160 lines
6.5 KiB
Python
160 lines
6.5 KiB
Python
## @package fc
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# Module caffe2.python.layers.fc
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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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 FC(SamplingTrainableMixin, ModelLayer):
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def __init__(self, model, input_record, output_dims, weight_init=None,
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bias_init=None, weight_optim=None, bias_optim=None, name='fc',
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weight_reg=None, bias_reg=None, clip_param=None,
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max_fc_size=None, axis=1,
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**kwargs):
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super(FC, self).__init__(model, name, input_record, **kwargs)
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assert isinstance(input_record, schema.Scalar), (
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"Incorrect input type {}".format(input_record))
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assert len(input_record.field_types()[0].shape) > 0, (
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"FC expects limited dimensions of the input tensor")
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assert axis >= 1, "axis {} should >= 1.".format(axis)
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self.axis = axis
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input_dims = np.prod(input_record.field_types()[0].shape[axis - 1:])
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assert input_dims > 0, (
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"FC expects input dimensions > 0, got {}".format(input_dims))
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self.clip_args = None
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if (clip_param is not None):
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assert len(clip_param) == 2, (
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'clip_param must be a tuple / list '
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'of length 2 and in the form of (clip_min, clip max)'
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)
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clip_min, clip_max = clip_param
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assert clip_min is not None or clip_max is not None, (
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'clip_min, and clip_max in clip_param cannot both be None'
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)
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assert (
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(clip_min is None or clip_max is None) or clip_min < clip_max
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), (
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'clip_param = [clip_min, clip_max] must have clip_min < clip_max'
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)
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self.clip_args = {}
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if clip_min is not None:
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self.clip_args['min'] = clip_min
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if clip_max is not None:
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self.clip_args['max'] = clip_max
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scale = math.sqrt(1.0 / input_dims)
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weight_init = weight_init if weight_init else (
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'UniformFill', {'min': -scale, 'max': scale})
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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.output_dim_vec = FC.calculate_fc_output_dims(
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max_fc_size, input_dims, output_dims)
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if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
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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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regularizer=weight_reg)
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self.b = self.create_param(param_name='b',
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shape=[output_dims, ],
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initializer=bias_init,
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optimizer=bias_optim,
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regularizer=bias_reg)
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else:
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self.w_vec = []
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self.b_vec = []
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for idx, output_dim in enumerate(self.output_dim_vec):
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self.w_vec.append(self.create_param(param_name='w_sub_{}'.format(idx),
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shape=[output_dim, input_dims],
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initializer=weight_init,
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optimizer=weight_optim,
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regularizer=weight_reg))
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self.b_vec.append(self.create_param(param_name='b_sub_{}'.format(idx),
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shape=[output_dim, ],
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initializer=weight_init,
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optimizer=weight_optim,
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regularizer=weight_reg))
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if axis == 1:
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output_shape = (output_dims, )
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else:
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output_shape = list(input_record.field_types()[0].shape)[0: axis - 1]
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output_shape = tuple(output_shape + [output_dims])
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self.output_schema = schema.Scalar(
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(np.float32, output_shape),
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self.get_next_blob_reference('output')
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)
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@staticmethod
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def calculate_fc_output_dims(max_fc_size, input_dim, output_dim):
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if not max_fc_size or max_fc_size < 0:
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return None
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assert max_fc_size >= input_dim, "Currently we split along the output " \
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"dimension. So we need max_fc_size >= input_dim. But, max_fc_size: " \
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"{}, input_dim: {}".format(max_fc_size, input_dim)
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output_dim_allowed = int(np.floor(max_fc_size / input_dim))
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num_fc = int(np.floor((output_dim - 1) / output_dim_allowed) + 1)
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output_dim_vec = [output_dim_allowed] * (num_fc - 1)
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output_dim_vec.append(output_dim - sum(output_dim_vec))
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return output_dim_vec
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def _add_ops(self, net, params):
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if self.clip_args is not None:
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clipped_params = [net.NextScopedBlob(
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'clipped_%s' % str(p)) for p in params]
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for p, cp in zip(params, clipped_params):
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net.Clip([p], [cp], **self.clip_args)
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params = clipped_params
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if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
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net.FC(self.input_record.field_blobs() + params,
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self.output_schema.field_blobs(), axis=self.axis, **self.kwargs)
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else:
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w_vec = params[:int(len(params) / 2)]
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b_vec = params[int(len(params) / 2):]
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assert len(w_vec) == len(b_vec)
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output_blob_vec = []
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for i in range(len(self.output_dim_vec)):
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output_blob = net.NextScopedBlob(
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'output_sub_{}'.format(i))
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output_blob_vec.append(
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net.FC(self.input_record.field_blobs() +
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[w_vec[i], b_vec[i]],
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[output_blob], axis=self.axis, **self.kwargs))
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net.Concat(output_blob_vec,
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self.output_schema.field_blobs() +
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[self.output_schema.field_blobs()[0] + "_concat_dims"])
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@property
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def param_blobs(self):
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if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
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return [self.w, self.b]
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else:
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return self.w_vec + self.b_vec
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