-
Notifications
You must be signed in to change notification settings - Fork 8
/
data_loader_dali.py
163 lines (125 loc) · 6.84 KB
/
data_loader_dali.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
import torch
import numpy as np
from torch.utils.data import DataLoader, Dataset, DistributedSampler
from torch import Tensor
#concurrent futures
import concurrent.futures as cf
#dali stuff
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.fn as fn
import nvidia.dali.types as types
from nvidia.dali.plugin.pytorch import DALIGenericIterator, LastBatchPolicy
# O(8) transformations
from .symmetry import get_isomorphism_axes_angle
def get_data_loader_distributed(params, world_rank, device_id = 0):
train_loader = DaliDataLoader(params, params.train_path_npy_data, params.train_path_npy_label, params.Nsamples,
num_workers=params.num_data_workers, device_id=device_id, validation=False)
if params.enable_benchy:
from benchy.torch import BenchmarkGenericIteratorWrapper
train_loader = BenchmarkGenericIteratorWrapper(train_loader, params.local_batch_size)
validation_loader = DaliDataLoader(params, params.val_path_npy_data, params.val_path_npy_label, params.Nsamples_val,
num_workers=params.num_data_workers, device_id=device_id, validation=True)
return train_loader, validation_loader
class DaliDataLoader(object):
"""Random crops"""
def get_pipeline(self, params, data_file, label_file, num_samples, num_workers, device_id, validation):
# since we aren't using DistributedSampler with DALI to reduce the number of samples per rank,
# we manually adjust the length of the DALI pipeline when running distributed training
self.num_samples = num_samples//(params.global_batch_size//params.local_batch_size)
self.num_batches = self.num_samples//params.local_batch_size
# construct master object
pipeline = Pipeline(batch_size = params.local_batch_size,
num_threads = num_workers,
device_id = device_id)
# helper function for retrieving the crop window
def get_crop_coords(rng, length, size, batch_size):
rstart = rng.randint(low=0, high=length-size, size=(batch_size, 3), dtype=np.int32)
rend = rstart + size
return rstart, rend
length = params.box_size[0] if not validation else params.box_size[1]
with pipeline:
rstart, rend = fn.external_source(source = lambda x: get_crop_coords(self.rng, length, params.data_size, params.local_batch_size),
num_outputs = 2,
no_copy = False)
data = fn.readers.numpy(device = 'cpu',
name = "data_input",
files = [data_file] * self.num_samples,
cache_header_information = True,
roi_start = rstart,
roi_end = rend,
roi_axes = [0, 1, 2])
label = fn.readers.numpy(device = 'cpu',
name = "label_input",
files = [label_file] * self.num_samples,
cache_header_information = True,
roi_start = rstart,
roi_end = rend,
roi_axes = [0, 1, 2])
# upload to gpu
data, label = data.gpu(), label.gpu()
# get random numbers
axes, angles = fn.external_source(source = lambda x: get_isomorphism_axes_angle(self.rng, params.local_batch_size),
device = "cpu",
num_outputs = 2,
no_copy = False,
parallel = False)
flip = fn.random.coin_flip(device = 'cpu',
shape=(3))
# copy to gpu: not necessary
data_rot = fn.rotate(data,
device = "gpu",
angle = angles,
axis = axes,
keep_size = True)
label_rot = fn.rotate(label,
device = "gpu",
angle = angles,
axis = axes,
keep_size = True)
# flip
data_rot = fn.flip(data,
device = 'gpu',
depthwise = flip[0],
horizontal = flip[1],
vertical = flip[2])
label_rot = fn.flip(label,
device = 'gpu',
depthwise = flip[0],
horizontal = flip[1],
vertical = flip[2])
# a final transposition to ncdhw layout
data_out = fn.transpose(data_rot,
device = "gpu",
perm = [3, 0, 1, 2])
label_out = fn.transpose(label_rot,
device = "gpu",
perm = [3, 0, 1, 2])
pipeline.set_outputs(data_out, label_out)
return pipeline
def __init__(self, params, data_file, label_file, num_samples, num_workers=1, device_id=0, validation=False):
# extract relevant parameters
self.batch_size = params.local_batch_size
self.size = params.data_size
# RNG
self.rng = np.random.RandomState(seed=12345)
# shape gymnastics
N, D, H, W = self.batch_size, self.size, self.size, self.size
self.inp_shape = [N, 4, D, H, W]
self.tar_shape = [N, 5, D, H, W]
self.inp_strides = [ D*H*W*4, 1, H*W*4, W*4, 4]
self.tar_strides = [ D*H*W*5, 1, H*W*5, W*5, 5]
# construct pipeline
self.pipe = self.get_pipeline(params, data_file, label_file, num_samples, num_workers, device_id, validation)
self.pipe.build()
self.iterator = DALIGenericIterator([self.pipe], ['inp', 'tar'],
reader_name = "data_input",
last_batch_policy = LastBatchPolicy.PARTIAL,
auto_reset = True,
prepare_first_batch = True)
def __len__(self):
return self.num_batches
def __iter__(self):
for token in self.iterator:
inp = token[0]['inp']
tar = token[0]['tar']
yield inp, tar