FazBrowse GitHub Viewer
|
Trending
|
URL:
|
Home
Tools:
[Download Repo ZIP]
[View Raw Code]
[Original HTTPS Page]
CaffeOnACL/src/caffe/layers/memory_data_layer.cpp at master · 2php/CaffeOnACL · GitHub
2php
CaffeOnACL
Repository navigation
Code
Pull requests
Actions
Projects
Wiki
Security and quality
Insights
Expand file tree
Breadcrumbs
CaffeOnACL
/
src
/
caffe
/
layers
/
memory_data_layer.cpp
Copy path
More file actions
More file actions
Latest commit
History
History
History
123 lines (112 loc) · 4.31 KB
Breadcrumbs
CaffeOnACL
/
src
/
caffe
/
layers
/
memory_data_layer.cpp
Copy path
File metadata and controls
123 lines (112 loc) · 4.31 KB
Raw
Copy raw file
Download raw file
Open symbols panel
Edit and raw actions
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
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
#
ifdef
USE_OPENCV
#
include
<
opencv2/core/core.hpp
>
#
endif
//
USE_OPENCV
#
include
<
vector
>
#
include
"
caffe/layers/memory_data_layer.hpp
"
namespace
caffe
{
template
<
typename
Dtype>
void
MemoryDataLayer<Dtype>::DataLayerSetUp(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
batch_size_ =
this
->
layer_param_
.
memory_data_param
().
batch_size
();
channels_ =
this
->
layer_param_
.
memory_data_param
().
channels
();
height_ =
this
->
layer_param_
.
memory_data_param
().
height
();
width_ =
this
->
layer_param_
.
memory_data_param
().
width
();
size_ = channels_ * height_ * width_;
CHECK_GT
(batch_size_ * size_,
0
) <<
"
batch_size, channels, height, and width must be specified and
"
"
positive in memory_data_param
"
;
vector<
int
>
label_shape
(
1
, batch_size_);
top[
0
]->
Reshape
(batch_size_, channels_, height_, width_);
top[
1
]->
Reshape
(label_shape);
added_data_.
Reshape
(batch_size_, channels_, height_, width_);
added_label_.
Reshape
(label_shape);
data_ =
NULL
;
labels_ =
NULL
;
added_data_.
cpu_data
();
added_label_.
cpu_data
();
}
template
<
typename
Dtype>
void
MemoryDataLayer<Dtype>::AddDatumVector(
const
vector<Datum>& datum_vector) {
CHECK
(!has_new_data_) <<
"
Can't add data until current data has been consumed.
"
;
size_t
num = datum_vector.
size
();
CHECK_GT
(num,
0
) <<
"
There is no datum to add.
"
;
CHECK_EQ
(num % batch_size_,
0
) <<
"
The added data must be a multiple of the batch size.
"
;
added_data_.
Reshape
(num, channels_, height_, width_);
added_label_.
Reshape
(num,
1
,
1
,
1
);
//
Apply data transformations (mirror, scale, crop...)
this
->
data_transformer_
->
Transform
(datum_vector, &added_data_);
//
Copy Labels
Dtype* top_label = added_label_.
mutable_cpu_data
();
for
(
int
item_id =
0
; item_id < num; ++item_id) {
top_label[item_id] = datum_vector[item_id].
label
();
}
//
num_images == batch_size_
Dtype* top_data = added_data_.
mutable_cpu_data
();
Reset
(top_data, top_label, num);
has_new_data_ =
true
;
}
#
ifdef
USE_OPENCV
template
<
typename
Dtype>
void
MemoryDataLayer<Dtype>::AddMatVector(
const
vector<cv::Mat>& mat_vector,
const
vector<
int
>& labels) {
size_t
num = mat_vector.
size
();
CHECK
(!has_new_data_) <<
"
Can't add mat until current data has been consumed.
"
;
CHECK_GT
(num,
0
) <<
"
There is no mat to add
"
;
CHECK_EQ
(num % batch_size_,
0
) <<
"
The added data must be a multiple of the batch size.
"
;
added_data_.
Reshape
(num, channels_, height_, width_);
added_label_.
Reshape
(num,
1
,
1
,
1
);
//
Apply data transformations (mirror, scale, crop...)
this
->
data_transformer_
->
Transform
(mat_vector, &added_data_);
//
Copy Labels
Dtype* top_label = added_label_.
mutable_cpu_data
();
for
(
int
item_id =
0
; item_id < num; ++item_id) {
top_label[item_id] = labels[item_id];
}
//
num_images == batch_size_
Dtype* top_data = added_data_.
mutable_cpu_data
();
Reset
(top_data, top_label, num);
has_new_data_ =
true
;
}
#
endif
//
USE_OPENCV
template
<
typename
Dtype>
void
MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels,
int
n) {
CHECK
(data);
CHECK
(labels);
CHECK_EQ
(n % batch_size_,
0
) <<
"
n must be a multiple of batch size
"
;
//
Warn with transformation parameters since a memory array is meant to
//
be generic and no transformations are done with Reset().
if
(
this
->
layer_param_
.
has_transform_param
()) {
LOG
(
WARNING
) <<
this
->
type
() <<
"
does not transform array data on Reset()
"
;
}
data_ = data;
labels_ = labels;
n_ = n;
pos_ =
0
;
}
template
<
typename
Dtype>
void
MemoryDataLayer<Dtype>::set_batch_size(
int
new_size) {
CHECK
(!has_new_data_) <<
"
Can't change batch_size until current data has been consumed.
"
;
batch_size_ = new_size;
added_data_.
Reshape
(batch_size_, channels_, height_, width_);
added_label_.
Reshape
(batch_size_,
1
,
1
,
1
);
}
template
<
typename
Dtype>
void
MemoryDataLayer<Dtype>::Forward_cpu(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
CHECK
(data_) <<
"
MemoryDataLayer needs to be initialized by calling Reset
"
;
top[
0
]->
Reshape
(batch_size_, channels_, height_, width_);
top[
1
]->
Reshape
(batch_size_,
1
,
1
,
1
);
top[
0
]->
set_cpu_data
(data_ + pos_ * size_);
top[
1
]->
set_cpu_data
(labels_ + pos_);
pos_ = (pos_ + batch_size_) % n_;
if
(pos_ ==
0
)
has_new_data_ =
false
;
}
INSTANTIATE_CLASS
(MemoryDataLayer);
REGISTER_LAYER_CLASS
(MemoryData);
}
//
namespace caffe
Back
|
FazBrowse Home
|
New Git URL