1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
|
## @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())
|
