-
Notifications
You must be signed in to change notification settings - Fork 56
/
input_patch_data_layer.py
205 lines (153 loc) · 6.36 KB
/
input_patch_data_layer.py
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
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
#!/usr/bin/env python
"""
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
Author: Varun Jampani
"""
# Input data layer for training
# Adapted from
# https://github.com/LisaAnne/lisa-caffe-public/blob/lstm_video_deploy/examples/LRCN_activity_recognition/sequence_input_layer.py
import io
import numpy as np
import random
from multiprocessing import Pool
from threading import Thread
from init_caffe import *
from config import *
from fetch_and_transform_data import fetch_and_transform_patch_data
from random import Random
myrandom = Random(RAND_SEED)
class DataProcessor(object):
def __init__(self, patch_size, data_type, top_names, num_spixels):
self.top_names = top_names
self.patch_size = patch_size
self.data_type = data_type
self.num_spixels = num_spixels
def __call__(self, imgname):
data = fetch_and_transform_patch_data(imgname[0],
self.data_type,
self.top_names,
self.num_spixels,
self.patch_size)
return data
class sequenceGenerator(object):
def __init__(self, batch_size, data_type,
is_random, reset_count):
self.batch_size = batch_size
self.image_list = IMG_LIST[data_type]
self.data_type = data_type
self.is_random = is_random
self.idx = 0
self.rounds = 0
self.reset_count = reset_count
f = open(self.image_list, 'r')
self.img_names = f.readlines()
f.close()
self.num_images = len(self.img_names)
self.rand_generator = Random(RAND_SEED)
def __call__(self):
imgname_list = []
if self.is_random:
idx_list = self.rand_generator.sample(range(0, self.num_images),
self.batch_size)
else:
idx_list = range(self.idx, self.idx + self.batch_size)
idx_list = [f % self.num_images for f in idx_list]
for i in idx_list:
im_name = self.img_names[i][:-1]
imgname_list.append(im_name)
im_info = zip(imgname_list)
self.idx += self.batch_size
if self.idx >= self.num_images:
self.idx = self.idx - self.num_images
self.rounds += 1
if self.rounds >= self.reset_count:
self.rounds = 0
self.idx = 0
self.rand_generator = Random(RAND_SEED)
return im_info
def advance_batch(result, sequence_generator, data_processor, pool):
im_info = sequence_generator()
tmp = data_processor(im_info[0])
result['data'] = pool.map(data_processor, im_info)
class BatchAdvancer():
def __init__(self, result, sequence_generator, image_processor, pool):
self.result = result
self.image_processor = image_processor
self.sequence_generator = sequence_generator
self.pool = pool
def __call__(self):
return advance_batch(self.result,
self.sequence_generator,
self.image_processor,
self.pool)
class InputRead(caffe.Layer):
def initialize(self):
self.is_random_image_order = True
self.height = TRAIN_PATCH_HEIGHT
self.width = TRAIN_PATCH_WIDTH
self.batch_size = TRAIN_BATCH_SIZE
self.patch_size = [self.height, self.width]
self.num_tops = 5
self.top_names = ['img', 'spixel_init', 'feat_spixel_init', 'label', 'problabel']
self.top_channels = [3, 1, 1, 1, 50]
self.pool_size = 10
def setup(self, bottom, top):
random.seed(RAND_SEED)
params = self.param_str.split('_')
if len(params) < 2:
params = ['TRAIN', '1000000', '100']
print("Using standard initialization of params:", params)
data_type = str(params[0])
reset_count = int(params[1])
num_spixels = int(params[2])
self.initialize()
self.thread_result = {}
self.thread = None
pool_size = self.pool_size
spatial_size = [self.height, self.width]
self.data_processor = DataProcessor(self.patch_size, data_type,
self.top_names, num_spixels)
self.sequence_generator = sequenceGenerator(self.batch_size,
data_type,
self.is_random_image_order,
reset_count)
self.pool = Pool(processes=pool_size)
self.batch_advancer = BatchAdvancer(self.thread_result,
self.sequence_generator,
self.data_processor,
self.pool)
self.dispatch_worker()
print 'Outputs:', self.top_names
if len(top) != len(self.top_names):
raise Exception('Incorrect number of outputs (expected %d, got %d)' %
(len(self.top_names), len(top)))
self.join_worker()
def reshape(self, bottom, top):
for top_index, name in enumerate(self.top_names):
shape = (self.batch_size, self.top_channels[top_index],
self.height, self.width)
top[top_index].reshape(*shape)
pass
def forward(self, bottom, top):
if self.thread is not None:
self.join_worker()
new_result = {}
for t, name in enumerate(self.top_names):
new_result[self.top_names[t]] =\
[None]*len(self.thread_result['data'][0][0][self.top_names[t]])
for i in range(self.batch_size):
for t, name in enumerate(self.top_names):
top[t].data[i, ...] =\
self.thread_result['data'][i][0][self.top_names[t]]
self.dispatch_worker()
def dispatch_worker(self):
assert self.thread is None
self.thread = Thread(target=self.batch_advancer)
self.thread.start()
def join_worker(self):
assert self.thread is not None
self.thread.join()
self.thread = None
def backward(self, top, propagate_down, bottom):
pass