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_huber_loss.py (3523B)

# @package batch_huber_loss
# Module caffe2.python.layers.batch_huber_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 BatchHuberLoss(ModelLayer):

def __init__(self, model, input_record, name='batch_huber_loss', delta=1.0, **kwargs):
        super(BatchHuberLoss, self).__init__(model, name, input_record, **kwargs)

assert delta > 0

self._delta = delta

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 = net.Squeeze(
            self.input_record.prediction(),
            net.NextScopedBlob('squeezed_prediction'),
            dims=[1]
        )

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()
                )
            )

const_delta = net.ConstantFill(
            label,
            net.NextScopedBlob("delta"),
            value=self._delta,
            dtype=core.DataType.FLOAT,
        )

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

const_delta = net.StopGradient(
            const_delta,
            net.NextScopedBlob('stopped_delta')
        )

# abs_error = np.abs(true - pred)
        abs_error = net.L1Distance(
            [label, prediction], net.NextScopedBlob("abs_error")
        )

# quadratic = 0.5*min(abs_error, delta)^2, linear = delta*max(abs_error-delta, 0)
        min_error = net.Min(
            [abs_error, const_delta], net.NextScopedBlob("min_error_delta")
        )

quadratic_term = net.Scale(
            net.Sqr(min_error), scale=float(0.5)
        )

linear_term = net.Mul(
            [
                net.Sub([abs_error, min_error]),
                const_delta,
            ],
            net.NextScopedBlob("huber_linear_term")
        )

# huber = 0.5 * min(abs_error, delta)^2 + delta * max(abs_error-delta, 0)
        huber_dist = net.Add(
            [quadratic_term, linear_term], net.NextScopedBlob("huber_dist")
        )

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')],
            )
            huber_dist = net.Mul(
                [huber_dist, weight_blob],
                net.NextScopedBlob("weighted_huber_distance"),
            )

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

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