forked from lfz/DSB2017
/
utils.py
308 lines (240 loc) · 10.3 KB
/
utils.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
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
import sys
import os
import numpy as np
import torch
def getFreeId():
import pynvml
pynvml.nvmlInit()
def getFreeRatio(id):
handle = pynvml.nvmlDeviceGetHandleByIndex(id)
use = pynvml.nvmlDeviceGetUtilizationRates(handle)
ratio = 0.5*(float(use.gpu+float(use.memory)))
return ratio
deviceCount = pynvml.nvmlDeviceGetCount()
available = []
for i in range(deviceCount):
if getFreeRatio(i)<70:
available.append(i)
gpus = ''
for g in available:
gpus = gpus+str(g)+','
gpus = gpus[:-1]
return gpus
def setgpu(gpuinput):
freeids = getFreeId()
if gpuinput=='all':
gpus = freeids
else:
gpus = gpuinput
if any([g not in freeids for g in gpus.split(',')]):
raise ValueError('gpu'+g+'is being used')
print('using gpu '+gpus)
os.environ['CUDA_VISIBLE_DEVICES']=gpus
return len(gpus.split(','))
class Logger(object):
def __init__(self,logfile):
self.terminal = sys.stdout
self.log = open(logfile, "a")
def write(self, message):
self.terminal.write(message)
self.log.write(message)
def flush(self):
#this flush method is needed for python 3 compatibility.
#this handles the flush command by doing nothing.
#you might want to specify some extra behavior here.
pass
def split4(data, max_stride, margin):
splits = []
data = torch.Tensor.numpy(data)
_,c, z, h, w = data.shape
w_width = np.ceil(float(w / 2 + margin)/max_stride).astype('int')*max_stride
h_width = np.ceil(float(h / 2 + margin)/max_stride).astype('int')*max_stride
pad = int(np.ceil(float(z)/max_stride)*max_stride)-z
leftpad = pad/2
pad = [[0,0],[0,0],[leftpad,pad-leftpad],[0,0],[0,0]]
data = np.pad(data,pad,'constant',constant_values=-1)
data = torch.from_numpy(data)
splits.append(data[:, :, :, :h_width, :w_width])
splits.append(data[:, :, :, :h_width, -w_width:])
splits.append(data[:, :, :, -h_width:, :w_width])
splits.append(data[:, :, :, -h_width:, -w_width:])
return torch.cat(splits, 0)
def combine4(output, h, w):
splits = []
for i in range(len(output)):
splits.append(output[i])
output = np.zeros((
splits[0].shape[0],
h,
w,
splits[0].shape[3],
splits[0].shape[4]), np.float32)
h0 = output.shape[1] / 2
h1 = output.shape[1] - h0
w0 = output.shape[2] / 2
w1 = output.shape[2] - w0
splits[0] = splits[0][:, :h0, :w0, :, :]
output[:, :h0, :w0, :, :] = splits[0]
splits[1] = splits[1][:, :h0, -w1:, :, :]
output[:, :h0, -w1:, :, :] = splits[1]
splits[2] = splits[2][:, -h1:, :w0, :, :]
output[:, -h1:, :w0, :, :] = splits[2]
splits[3] = splits[3][:, -h1:, -w1:, :, :]
output[:, -h1:, -w1:, :, :] = splits[3]
return output
def split8(data, max_stride, margin):
splits = []
if isinstance(data, np.ndarray):
c, z, h, w = data.shape
else:
_,c, z, h, w = data.size()
z_width = np.ceil(float(z / 2 + margin)/max_stride).astype('int')*max_stride
w_width = np.ceil(float(w / 2 + margin)/max_stride).astype('int')*max_stride
h_width = np.ceil(float(h / 2 + margin)/max_stride).astype('int')*max_stride
for zz in [[0,z_width],[-z_width,None]]:
for hh in [[0,h_width],[-h_width,None]]:
for ww in [[0,w_width],[-w_width,None]]:
if isinstance(data, np.ndarray):
splits.append(data[np.newaxis, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
else:
splits.append(data[:, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
if isinstance(data, np.ndarray):
return np.concatenate(splits, 0)
else:
return torch.cat(splits, 0)
def combine8(output, z, h, w):
splits = []
for i in range(len(output)):
splits.append(output[i])
output = np.zeros((
z,
h,
w,
splits[0].shape[3],
splits[0].shape[4]), np.float32)
z_width = z / 2
h_width = h / 2
w_width = w / 2
i = 0
for zz in [[0,z_width],[z_width-z,None]]:
for hh in [[0,h_width],[h_width-h,None]]:
for ww in [[0,w_width],[w_width-w,None]]:
output[zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1], :, :] = splits[i][zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1], :, :]
i = i+1
return output
def split16(data, max_stride, margin):
splits = []
_,c, z, h, w = data.size()
z_width = np.ceil(float(z / 4 + margin)/max_stride).astype('int')*max_stride
z_pos = [z*3/8-z_width/2,
z*5/8-z_width/2]
h_width = np.ceil(float(h / 2 + margin)/max_stride).astype('int')*max_stride
w_width = np.ceil(float(w / 2 + margin)/max_stride).astype('int')*max_stride
for zz in [[0,z_width],[z_pos[0],z_pos[0]+z_width],[z_pos[1],z_pos[1]+z_width],[-z_width,None]]:
for hh in [[0,h_width],[-h_width,None]]:
for ww in [[0,w_width],[-w_width,None]]:
splits.append(data[:, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
return torch.cat(splits, 0)
def combine16(output, z, h, w):
splits = []
for i in range(len(output)):
splits.append(output[i])
output = np.zeros((
z,
h,
w,
splits[0].shape[3],
splits[0].shape[4]), np.float32)
z_width = z / 4
h_width = h / 2
w_width = w / 2
splitzstart = splits[0].shape[0]/2-z_width/2
z_pos = [z*3/8-z_width/2,
z*5/8-z_width/2]
i = 0
for zz,zz2 in zip([[0,z_width],[z_width,z_width*2],[z_width*2,z_width*3],[z_width*3-z,None]],
[[0,z_width],[splitzstart,z_width+splitzstart],[splitzstart,z_width+splitzstart],[z_width*3-z,None]]):
for hh in [[0,h_width],[h_width-h,None]]:
for ww in [[0,w_width],[w_width-w,None]]:
output[zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1], :, :] = splits[i][zz2[0]:zz2[1], hh[0]:hh[1], ww[0]:ww[1], :, :]
i = i+1
return output
def split32(data, max_stride, margin):
splits = []
_,c, z, h, w = data.size()
z_width = np.ceil(float(z / 2 + margin)/max_stride).astype('int')*max_stride
w_width = np.ceil(float(w / 4 + margin)/max_stride).astype('int')*max_stride
h_width = np.ceil(float(h / 4 + margin)/max_stride).astype('int')*max_stride
w_pos = [w*3/8-w_width/2,
w*5/8-w_width/2]
h_pos = [h*3/8-h_width/2,
h*5/8-h_width/2]
for zz in [[0,z_width],[-z_width,None]]:
for hh in [[0,h_width],[h_pos[0],h_pos[0]+h_width],[h_pos[1],h_pos[1]+h_width],[-h_width,None]]:
for ww in [[0,w_width],[w_pos[0],w_pos[0]+w_width],[w_pos[1],w_pos[1]+w_width],[-w_width,None]]:
splits.append(data[:, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
return torch.cat(splits, 0)
def combine32(splits, z, h, w):
output = np.zeros((
z,
h,
w,
splits[0].shape[3],
splits[0].shape[4]), np.float32)
z_width = int(np.ceil(float(z) / 2))
h_width = int(np.ceil(float(h) / 4))
w_width = int(np.ceil(float(w) / 4))
splithstart = splits[0].shape[1]/2-h_width/2
splitwstart = splits[0].shape[2]/2-w_width/2
i = 0
for zz in [[0,z_width],[z_width-z,None]]:
for hh,hh2 in zip([[0,h_width],[h_width,h_width*2],[h_width*2,h_width*3],[h_width*3-h,None]],
[[0,h_width],[splithstart,h_width+splithstart],[splithstart,h_width+splithstart],[h_width*3-h,None]]):
for ww,ww2 in zip([[0,w_width],[w_width,w_width*2],[w_width*2,w_width*3],[w_width*3-w,None]],
[[0,w_width],[splitwstart,w_width+splitwstart],[splitwstart,w_width+splitwstart],[w_width*3-w,None]]):
output[zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1], :, :] = splits[i][zz[0]:zz[1], hh2[0]:hh2[1], ww2[0]:ww2[1], :, :]
i = i+1
return output
def split64(data, max_stride, margin):
splits = []
_,c, z, h, w = data.size()
z_width = np.ceil(float(z / 4 + margin)/max_stride).astype('int')*max_stride
w_width = np.ceil(float(w / 4 + margin)/max_stride).astype('int')*max_stride
h_width = np.ceil(float(h / 4 + margin)/max_stride).astype('int')*max_stride
z_pos = [z*3/8-z_width/2,
z*5/8-z_width/2]
w_pos = [w*3/8-w_width/2,
w*5/8-w_width/2]
h_pos = [h*3/8-h_width/2,
h*5/8-h_width/2]
for zz in [[0,z_width],[z_pos[0],z_pos[0]+z_width],[z_pos[1],z_pos[1]+z_width],[-z_width,None]]:
for hh in [[0,h_width],[h_pos[0],h_pos[0]+h_width],[h_pos[1],h_pos[1]+h_width],[-h_width,None]]:
for ww in [[0,w_width],[w_pos[0],w_pos[0]+w_width],[w_pos[1],w_pos[1]+w_width],[-w_width,None]]:
splits.append(data[:, :, zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1]])
return torch.cat(splits, 0)
def combine64(output, z, h, w):
splits = []
for i in range(len(output)):
splits.append(output[i])
output = np.zeros((
z,
h,
w,
splits[0].shape[3],
splits[0].shape[4]), np.float32)
z_width = int(np.ceil(float(z) / 4))
h_width = int(np.ceil(float(h) / 4))
w_width = int(np.ceil(float(w) / 4))
splitzstart = splits[0].shape[0]/2-z_width/2
splithstart = splits[0].shape[1]/2-h_width/2
splitwstart = splits[0].shape[2]/2-w_width/2
i = 0
for zz,zz2 in zip([[0,z_width],[z_width,z_width*2],[z_width*2,z_width*3],[z_width*3-z,None]],
[[0,z_width],[splitzstart,z_width+splitzstart],[splitzstart,z_width+splitzstart],[z_width*3-z,None]]):
for hh,hh2 in zip([[0,h_width],[h_width,h_width*2],[h_width*2,h_width*3],[h_width*3-h,None]],
[[0,h_width],[splithstart,h_width+splithstart],[splithstart,h_width+splithstart],[h_width*3-h,None]]):
for ww,ww2 in zip([[0,w_width],[w_width,w_width*2],[w_width*2,w_width*3],[w_width*3-w,None]],
[[0,w_width],[splitwstart,w_width+splitwstart],[splitwstart,w_width+splitwstart],[w_width*3-w,None]]):
output[zz[0]:zz[1], hh[0]:hh[1], ww[0]:ww[1], :, :] = splits[i][zz2[0]:zz2[1], hh2[0]:hh2[1], ww2[0]:ww2[1], :, :]
i = i+1
return output