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/functional.py (4875B)

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

from caffe2.python import core, schema, scope, workspace
from caffe2.python.layers.layers import (
    ModelLayer,
)
import caffe2.proto.caffe2_pb2 as caffe2_pb2
import numpy as np
import logging

logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

class Functional(ModelLayer):

def __init__(self, model, input_record, output_names_or_num, function,
                 name='functional', output_dtypes=None, tags=None, **kwargs):

# allow coercion
        input_record = schema.as_record(input_record)

super(Functional, self).__init__(model, name, input_record, tags=tags, **kwargs)
        self._function = function
        self._kwargs = kwargs
        return_struct = (
            isinstance(output_names_or_num, list) or
            (isinstance(output_names_or_num, int) and
             output_names_or_num != 1)
        )

with scope.NameScope(self.name, reset=True):
            if isinstance(output_names_or_num, int):
                struct_output_schema = schema.NewRecord(
                    model.net, schema.RawTuple(output_names_or_num))
            elif isinstance(output_names_or_num, schema.Field):
                self.output_schema = output_names_or_num.clone(keep_blobs=True)
                return
            else:
                if not isinstance(output_names_or_num, list):
                    output_names_or_num = [output_names_or_num]
                out_tuple = [(out, np.void) for out in output_names_or_num]
                struct_output_schema = schema.NewRecord(
                    model.net, schema.Struct(*out_tuple))

num_outputs = len(struct_output_schema.field_blobs())

# functional layer returns Struct if more than one outputs or output is
        # a list, otherwise Scalar
        if return_struct:
            self.output_schema = struct_output_schema
        else:
            self.output_schema = struct_output_schema[0]

# If output_dtypes is provided, use it for output schema. Otherwise
        # the shape and type will be inferred.
        if output_dtypes is not None:
            if not isinstance(output_dtypes, list):
                output_dtypes = [output_dtypes] * num_outputs
            assert len(output_dtypes) == num_outputs
            for dtype, scalar in zip(output_dtypes,
                                     self.output_schema.all_scalars()):
                scalar.set_type(dtype)
            return

# Fake execution of the function to infer shapes and types automatically
        had_issues = False
        try:
            type_net = core.Net('_temp_type_and_shape_inference_net')
            schema.InitEmptyRecord(type_net, input_record, enforce_types=True)

function(type_net, self.input_record, self.output_schema, **kwargs)
            (shapes, types) = workspace.InferShapesAndTypes([type_net], {})
            for i in range(num_outputs):
                scalar_schema = (self.output_schema[i] if return_struct
                                 else self.output_schema)
                blob = scalar_schema()
                if blob not in types or blob not in shapes:
                    had_issues = True
                    continue
                if shapes[blob] == []:
                    # Scalar type
                    shape = tuple()
                elif shapes[blob][0] == 0:
                    shape = tuple(shapes[blob][1:])
                else:
                    logger.warning("unexpected shape: {}".format(shapes[blob]))
                    # If batch dimension is not first - give up on shape
                    # inference for that blob
                    had_issues = True
                    continue

# TODO(amalevich): Move it to some shared library
                dtype = None
                if types[blob] == caffe2_pb2.TensorProto.DOUBLE:
                    dtype = (np.float64, shape)
                elif types[blob] == caffe2_pb2.TensorProto.FLOAT:
                    dtype = (np.float32, shape)
                elif types[blob] == caffe2_pb2.TensorProto.INT32:
                    dtype = (np.int32, shape)
                elif types[blob] == caffe2_pb2.TensorProto.INT64:
                    dtype = (np.int64, shape)
                elif types[blob] == caffe2_pb2.TensorProto.FLOAT16:
                    dtype = (np.float16, shape)

if dtype is not None:
                    scalar_schema.set_type(dtype)
        except TypeError as ex:
            had_issues = True
            logger.warning(str(ex))

if had_issues:
            logger.warning(
                "Type inference had problems for layer: {}".format(self.name))

def add_ops(self, net):
        self._function(
            net, self.input_record, self.output_schema, **(self._kwargs))

cmd: