FM / layers

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers/

/usr/local/lib64/python3.6/site-packages/caffe2/python/layers

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adaptive_weight.py	5687	0644	edit dl rm
add_bias.py	1396	0644	edit dl rm
arc_cosine_feature_map.py	7345	0644	edit dl rm
batch_huber_loss.py	3523	0644	edit dl rm
batch_lr_loss.py	11577	0644	edit dl rm
batch_mse_loss.py	2333	0644	edit dl rm
batch_normalization.py	3823	0644	edit dl rm
batch_sigmoid_cross_entropy_loss.py	1483	0644	edit dl rm
batch_softmax_loss.py	4580	0644	edit dl rm
blob_weighted_sum.py	2219	0644	edit dl rm
bpr_loss.py	1499	0644	edit dl rm
bucket_weighted.py	2355	0644	edit dl rm
build_index.py	1937	0644	edit dl rm
concat.py	4849	0644	edit dl rm
constant_weight.py	1208	0644	edit dl rm
conv.py	5050	0644	edit dl rm
dropout.py	1410	0644	edit dl rm
fc.py	9296	0644	edit dl rm
fc_without_bias.py	1954	0644	edit dl rm
fc_with_bootstrap.py	12788	0644	edit dl rm
feature_sparse_to_dense.py	14361	0644	edit dl rm
functional.py	4875	0644	edit dl rm
gather_record.py	3260	0644	edit dl rm
homotopy_weight.py	4306	0644	edit dl rm
label_smooth.py	3507	0644	edit dl rm
last_n_window_collector.py	2392	0644	edit dl rm
layers.py	17412	0644	edit dl rm
layer_normalization.py	4291	0644	edit dl rm
margin_rank_loss.py	1951	0644	edit dl rm
merge_id_lists.py	1500	0644	edit dl rm
pairwise_similarity.py	3549	0644	edit dl rm
position_weighted.py	2066	0644	edit dl rm
random_fourier_features.py	3187	0644	edit dl rm
reservoir_sampling.py	3013	0644	edit dl rm
sampling_train.py	2210	0644	edit dl rm
sampling_trainable_mixin.py	1366	0644	edit dl rm
select_record_by_context.py	2381	0644	edit dl rm
semi_random_features.py	5809	0644	edit dl rm
sparse_dropout_with_replacement.py	3943	0644	edit dl rm
sparse_feature_hash.py	4618	0644	edit dl rm
sparse_itemwise_dropout_with_replacement.py	3944	0644	edit dl rm
sparse_lookup.py	22170	0644	edit dl rm
split.py	2257	0644	edit dl rm
tags.py	4114	0644	edit dl rm
uniform_sampling.py	2779	0644	edit dl rm
__init__.py	943	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/layers/fc.py (9296B)

## @package fc
# Module caffe2.python.layers.fc

from caffe2.python.helpers.arg_scope import get_current_scope
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

def get_fc_predictor_version(fc_version):
    assert fc_version in ["fp32", "fp16"], (
        "Only support fp32 and fp16 for the fully connected layer "
        "in the predictor net, the provided FC precision is {}".format(fc_version)
    )
    return fc_version

class FC(SamplingTrainableMixin, ModelLayer):

def __init__(self, model, input_record, output_dims, weight_init=None,
                 bias_init=None, weight_optim=None, bias_optim=None, name='fc',
                 weight_reg=None, bias_reg=None, clip_param=None,
                 max_fc_size=None, axis=1, transposed=False,
                 uniform_weight_init_scale_numerator=1.0,
                 **kwargs):
        super(FC, self).__init__(model, name, input_record, **kwargs)
        assert isinstance(input_record, schema.Scalar), (
            "Incorrect input type {}".format(input_record))
        assert len(input_record.field_types()[0].shape) > 0, (
            "FC expects limited dimensions of the input tensor")
        assert axis >= 1, "axis {} should >= 1.".format(axis)
        self.axis = axis
        input_dims = np.prod(input_record.field_types()[0].shape[axis - 1:])

assert input_dims > 0, (
            "FC expects input dimensions > 0, got {}".format(input_dims))

self.clip_args = None
        if (clip_param is not None):
            assert len(clip_param) == 2, (
                'clip_param must be a tuple / list '
                'of length 2 and in the form of (clip_min, clip max)'
            )
            clip_min, clip_max = clip_param
            assert clip_min is not None or clip_max is not None, (
                'clip_min, and clip_max in clip_param cannot both be None'
            )
            assert (
                (clip_min is None or clip_max is None) or clip_min < clip_max
            ), (
                'clip_param = [clip_min, clip_max] must have clip_min < clip_max'
            )
            self.clip_args = {}
            if clip_min is not None:
                self.clip_args['min'] = clip_min
            if clip_max is not None:
                self.clip_args['max'] = clip_max

if uniform_weight_init_scale_numerator is None:
            uniform_weight_init_scale_numerator = 1.0

scale = math.sqrt(uniform_weight_init_scale_numerator / input_dims)
        weight_init = weight_init if weight_init else (
            'UniformFill', {'min': -scale, 'max': scale})
        bias_init = bias_init if bias_init else (
            'UniformFill', {'min': -scale, 'max': scale})

self.output_dim_vec = FC.calculate_fc_output_dims(
            max_fc_size, input_dims, output_dims)

self.transposed = transposed
        if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
            weight_shape = [input_dims, output_dims] if transposed else [output_dims, input_dims]
            self.w = self.create_param(param_name='w',
                                       shape=weight_shape,
                                       initializer=weight_init,
                                       optimizer=weight_optim,
                                       regularizer=weight_reg)

self.b = self.create_param(param_name='b',
                                       shape=[output_dims, ],
                                       initializer=bias_init,
                                       optimizer=bias_optim,
                                       regularizer=bias_reg)
        else:
            self.w_vec = []
            self.b_vec = []

for idx, output_dim in enumerate(self.output_dim_vec):
                weight_shape = [input_dims, output_dim] if transposed else [output_dim, input_dims]
                self.w_vec.append(self.create_param(param_name='w_sub_{}'.format(idx),
                                             shape=weight_shape,
                                             initializer=weight_init,
                                             optimizer=weight_optim,
                                             regularizer=weight_reg))

self.b_vec.append(self.create_param(param_name='b_sub_{}'.format(idx),
                                             shape=[output_dim, ],
                                             initializer=weight_init,
                                             optimizer=weight_optim,
                                             regularizer=weight_reg))
        if axis == 1:
            output_shape = (output_dims, )
        else:
            output_shape = list(input_record.field_types()[0].shape)[0: axis - 1]
            output_shape = tuple(output_shape + [output_dims])

self.output_schema = schema.Scalar(
            (np.float32, output_shape),
            self.get_next_blob_reference('output')
        )

@staticmethod
    def calculate_fc_output_dims(max_fc_size, input_dim, output_dim):

if not max_fc_size or max_fc_size < 0:
            return None

assert max_fc_size >= input_dim, "Currently we split along the output " \
            "dimension. So we need max_fc_size >= input_dim. But, max_fc_size: " \
            "{}, input_dim: {}".format(max_fc_size, input_dim)

output_dim_allowed = int(np.floor(max_fc_size / input_dim))
        num_fc = int(np.floor((output_dim - 1) / output_dim_allowed) + 1)

output_dim_vec = [output_dim_allowed] * (num_fc - 1)

output_dim_vec.append(output_dim - sum(output_dim_vec))

return output_dim_vec

def _insert_fc_ops(self, net, params, outputs, version):
        """
        Args:
            net: the caffe2 net to insert operator
            params: weight and bias for FC
            outputs: the output blobs
            version: support fp32 and fp16 for now.
        """
        if version == "fp32":
            if self.transposed:
                return net.FCTransposed(
                    self.input_record.field_blobs() + params,
                    outputs,
                    axis=self.axis,
                    **self.kwargs
                )
            else:
                return net.FC(
                    self.input_record.field_blobs() + params,
                    outputs,
                    axis=self.axis,
                    **self.kwargs
                )
        elif version == "fp16":
            return net.FbFCPacked(
                self.input_record.field_blobs() + params,
                outputs,
                axis=self.axis,
                **self.kwargs
            )
        else:
            raise Exception("unsupported FC type version {}".format(version))

def _add_ops(self, net, params, version):
        """
        Args:
            params : the weight and bias,
                passed by either add_ops or add_train_ops function
            version : fp16 or fp32, might support in8 in the future.
        """
        if self.clip_args is not None:
            clipped_params = [net.NextScopedBlob(
                'clipped_%s' % str(p)) for p in params]
            for p, cp in zip(params, clipped_params):
                net.Clip([p], [cp], **self.clip_args)
            params = clipped_params

if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
            self._insert_fc_ops(net, params, self.output_schema.field_blobs(), version)
        else:
            w_vec = params[:int(len(params) / 2)]
            b_vec = params[int(len(params) / 2):]

assert len(w_vec) == len(b_vec)

output_blob_vec = []

for i in range(len(self.output_dim_vec)):
                output_blob = net.NextScopedBlob(
                    'output_sub_{}'.format(i))
                insert_ret = self._insert_fc_ops(
                    net, [w_vec[i], b_vec[i]], [output_blob], version
                )
                output_blob_vec.append(insert_ret)
            net.Concat(output_blob_vec,
                       self.output_schema.field_blobs() +
                       [self.output_schema.field_blobs()[0] + "_concat_dims"])

def add_ops(self, net):
        """Both the predict net and the eval net will call this function
        """
        version_info = get_current_scope().get(
            get_fc_predictor_version.__name__, {'fc_version': 'fp32'}
        )
        predictor_fc_fp_version = version_info['fc_version']
        self._add_ops(net, self.param_blobs, predictor_fc_fp_version)

def add_train_ops(self, net):
        # use the train_param_blobs to be consistent with the SamplingTrain unittest
        self._add_ops(net, self.train_param_blobs, "fp32")

def get_fp16_compatible_parameters(self):
        if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
            return [self.w]
        else:
            return self.w_vec

@property
    def param_blobs(self):
        if self.output_dim_vec is None or len(self.output_dim_vec) == 1:
            return [self.w, self.b]
        else:
            return self.w_vec + self.b_vec

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