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/batch_mse_loss.py (2333B)

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

from caffe2.python import core, schema
from caffe2.python.layers.layers import (
    ModelLayer,
)
from caffe2.python.layers.tags import (
    Tags
)
import numpy as np

class BatchMSELoss(ModelLayer):

def __init__(self, model, input_record, name='batch_mse_loss', **kwargs):
        super(BatchMSELoss, self).__init__(model, name, input_record, **kwargs)

assert schema.is_schema_subset(
            schema.Struct(
                ('label', schema.Scalar()),
                ('prediction', schema.Scalar())
            ),
            input_record
        )
        self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])

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

def add_ops(self, net):
        prediction = self.input_record.prediction()
        label = self.input_record.label.field_blobs()
        if self.input_record.label.field_type().base != (
                self.input_record.prediction.field_type().base):

label = net.Cast(
                label,
                net.NextScopedBlob('cast_label'),
                to=schema.data_type_for_dtype(
                    self.input_record.prediction.field_type()
                )
            )

label = net.ExpandDims(label, 1, dims=[1])

label = net.StopGradient(
            label,
            net.NextScopedBlob('stopped_label')
        )

l2dist = net.SquaredL2Distance(
            [label, prediction],
            net.NextScopedBlob('l2')
        )

if 'weight' in self.input_record.fields:
            weight_blob = self.input_record.weight()
            if self.input_record.weight.field_type().base != np.float32:
                weight_blob = net.Cast(
                    weight_blob,
                    weight_blob + '_float32',
                    to=core.DataType.FLOAT
                )
            weight_blob = net.StopGradient(
                [weight_blob],
                [net.NextScopedBlob('weight_stop_gradient')],
            )
            l2dist = net.Mul(
                [l2dist, weight_blob],
                net.NextScopedBlob('weighted_l2_distance'),
            )

net.AveragedLoss(l2dist, self.output_schema.field_blobs())

## @package batch_mse_loss # Module caffe2.python.layers.batch_mse_loss from caffe2.python import core, schema from caffe2.python.layers.layers import ( ModelLayer, ) from caffe2.python.layers.tags import ( Tags ) import numpy as np class BatchMSELoss(ModelLayer): def __init__(self, model, input_record, name='batch_mse_loss', **kwargs): super(BatchMSELoss, self).__init__(model, name, input_record, **kwargs) assert schema.is_schema_subset( schema.Struct( ('label', schema.Scalar()), ('prediction', schema.Scalar()) ), input_record ) self.tags.update([Tags.EXCLUDE_FROM_PREDICTION]) self.output_schema = schema.Scalar( np.float32, self.get_next_blob_reference('output')) def add_ops(self, net): prediction = self.input_record.prediction() label = self.input_record.label.field_blobs() if self.input_record.label.field_type().base != ( self.input_record.prediction.field_type().base): label = net.Cast( label, net.NextScopedBlob('cast_label'), to=schema.data_type_for_dtype( self.input_record.prediction.field_type() ) ) label = net.ExpandDims(label, 1, dims=[1]) label = net.StopGradient( label, net.NextScopedBlob('stopped_label') ) l2dist = net.SquaredL2Distance( [label, prediction], net.NextScopedBlob('l2') ) if 'weight' in self.input_record.fields: weight_blob = self.input_record.weight() if self.input_record.weight.field_type().base != np.float32: weight_blob = net.Cast( weight_blob, weight_blob + '_float32', to=core.DataType.FLOAT ) weight_blob = net.StopGradient( [weight_blob], [net.NextScopedBlob('weight_stop_gradient')], ) l2dist = net.Mul( [l2dist, weight_blob], net.NextScopedBlob('weighted_l2_distance'), ) net.AveragedLoss(l2dist, self.output_schema.field_blobs())