-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathloaddata4.py
More file actions
197 lines (160 loc) · 8.1 KB
/
loaddata4.py
File metadata and controls
197 lines (160 loc) · 8.1 KB
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
import pandas as pd
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
import random
from nyu_transform import *
from torch.utils.data.sampler import SubsetRandomSampler
class depthDataset(Dataset):
"""Face Landmarks dataset."""
def __init__(self, csv_file, transform=None,nrows=None):
self.frame = pd.read_csv(csv_file, header=None, nrows=nrows)
self.transform = transform
def __getitem__(self, idx):
image_name = self.frame.iloc[idx, 0]
depth_name = self.frame.iloc[idx, 1]
masks_name = self.frame.iloc[idx, 2]
# # obtain random mask
# masks = np.load(masks_name)
# Nmasks = np.size(masks,2) # size along 3rd dimension
# np.random.seed(0) # change this during actual training
# randmask = np.random.randint(0,Nmasks)
# mask = masks[:,:,randmask]
# mask = Image.fromarray(np.uint8(255*mask))
# obtain random mask
masks = np.load(masks_name)
mask = Image.fromarray(np.uint8(255*masks))
image = Image.open(image_name)
depth = Image.open(depth_name)
sample = {'image': image, 'depth': depth, 'mask': mask}
if self.transform:
sample = self.transform(sample)
return sample
def __len__(self):
return len(self.frame)
def getTrainingData(batch_size=64):
__imagenet_pca = {
'eigval': torch.Tensor([0.2175, 0.0188, 0.0045]),
'eigvec': torch.Tensor([
[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203],
])
}
__imagenet_stats = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
# these are calculated in MATLAB, shouldn't make a huge difference?
__nyu_stats = {'mean': [0.481,0.411,0.392],
'std': [0.289,0.296,0.309]}
transformed_training = depthDataset(csv_file='./data/nyu2_train4_1percent.csv',
transform=transforms.Compose([ Scale(240),
RandomHorizontalFlip(),
RandomRotate(5),
CenterCrop([304, 228], [152, 114]),
ToTensor(is_test=True),
Lighting(0.1, __imagenet_pca[
'eigval'], __imagenet_pca['eigvec']),
ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
),
Normalize(__imagenet_stats['mean'],
__imagenet_stats['std']),
Binarize(1),
#ImageAlphaChannel()
])
)
dataloader_training = DataLoader(transformed_training, batch_size,
shuffle=False, num_workers=4, pin_memory=False)
return dataloader_training
def getTestingData(batch_size=64,nrows=None):
__imagenet_stats = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
# these are calculated in MATLAB, shouldn't make a huge difference?
__nyu_stats = {'mean': [0.481,0.411,0.392],
'std': [0.289,0.296,0.309]}
# scale = random.uniform(1, 1.5)
transformed_testing = depthDataset(csv_file='./data/nyu2_test4_1percent.csv',
transform=transforms.Compose([
Scale(240),
CenterCrop([304, 228], [152, 114]),
ToTensor(is_test=True),
Normalize(__imagenet_stats['mean'],
__imagenet_stats['std']),
Binarize(0.5),
# ImageAlphaChannel()
]),nrows=nrows)
dataloader_testing = DataLoader(transformed_testing, batch_size,
shuffle=False, num_workers=4, pin_memory=False)
dataloader = {}
dataloader['val'] = dataloader_testing
return dataloader
def getTrainValData(batch_size=64,nrows=None):
__imagenet_pca = {
'eigval': torch.Tensor([0.2175, 0.0188, 0.0045]),
'eigvec': torch.Tensor([
[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203],
])
}
__imagenet_stats = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
__nyu_stats = {'mean': [0.481,0.411,0.392],
'std': [0.289,0.296,0.309]}
dataset = depthDataset(csv_file='./data/nyu2_train4_1percent.csv',
transform=transforms.Compose([
Scale(240),
CenterCrop([304, 228], [152, 114]),
ToTensor(is_test=True),
Normalize(__imagenet_stats['mean'],
__imagenet_stats['std']),
Binarize(0.5),
# ImageAlphaChannel()
]),nrows=nrows)
validation_split = .2
shuffle_dataset = True
# random_seed= 42 ###### change during testing
# Creating data indices for training and validation splits:
dataset_size = len(dataset)
indices = list(range(dataset_size))
split = int(np.floor(validation_split * dataset_size))
if shuffle_dataset :
# np.random.seed(random_seed)
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
# Creating PT data samplers and loaders:
train_sampler = SubsetRandomSampler(train_indices)
valid_sampler = SubsetRandomSampler(val_indices)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
sampler=train_sampler)
validation_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
sampler=valid_sampler)
dataloader = {}
dataloader['train'] = train_loader
dataloader['val'] = validation_loader
return dataloader
def getTestingSingle(batch_size=1):
__imagenet_stats = {'mean': [0.485, 0.456, 0.406],
'std': [0.229, 0.224, 0.225]}
# these are calculated in MATLAB, shouldn't make a huge difference?
__nyu_stats = {'mean': [0.481,0.411,0.392],
'std': [0.289,0.296,0.309]}
# scale = random.uniform(1, 1.5)
transformed_testing = depthDataset(csv_file='./data/nyu2_test4_1percent.csv',
transform=transforms.Compose([
Scale(240),
CenterCrop([304, 228], [152, 114]),
ToTensor(is_test=True),
Normalize(__imagenet_stats['mean'],
__imagenet_stats['std']),
Binarize(0.8),
#ImageAlphaChannel()
]),nrows=1)
dataloader_testing = DataLoader(transformed_testing, batch_size,
shuffle=False, num_workers=4, pin_memory=False)
dataloader = {}
dataloader['val'] = dataloader_testing
return dataloader