Spaces:
Build error
Build error
File size: 5,754 Bytes
6d314be |
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 |
import os
import glob
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from PIL import Image
from torchvision import transforms, utils
class MyDataSet(data.Dataset):
def __init__(self, image_dir=None, label_dir=None, output_size=(256, 256), noise_in=None, training_set=True, video_data=False, train_split=0.9):
self.image_dir = image_dir
self.normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
self.resize = transforms.Compose([
transforms.Resize(output_size),
transforms.ToTensor()
])
self.noise_in = noise_in
self.video_data = video_data
self.random_rotation = transforms.Compose([
transforms.Resize(output_size),
transforms.RandomPerspective(distortion_scale=0.05, p=1.0),
transforms.ToTensor()
])
# load image file
train_len = None
self.length = 0
self.image_dir = image_dir
if image_dir is not None:
img_list = [glob.glob1(self.image_dir, ext) for ext in ['*jpg','*png']]
image_list = [item for sublist in img_list for item in sublist]
image_list.sort()
train_len = int(train_split*len(image_list))
if training_set:
self.image_list = image_list[:train_len]
else:
self.image_list = image_list[train_len:]
self.length = len(self.image_list)
# load label file
self.label_dir = label_dir
if label_dir is not None:
self.seeds = np.load(label_dir)
if train_len is None:
train_len = int(train_split*len(self.seeds))
if training_set:
self.seeds = self.seeds[:train_len]
else:
self.seeds = self.seeds[train_len:]
if self.length == 0:
self.length = len(self.seeds)
def __len__(self):
return self.length
def __getitem__(self, idx):
img = None
if self.image_dir is not None:
img_name = os.path.join(self.image_dir, self.image_list[idx])
image = Image.open(img_name)
img = self.resize(image)
if img.size(0) == 1:
img = torch.cat((img, img, img), dim=0)
img = self.normalize(img)
# generate image
if self.label_dir is not None:
torch.manual_seed(self.seeds[idx])
z = torch.randn(1, 512)[0]
if self.noise_in is None:
n = [torch.randn(1, 1)]
else:
n = [torch.randn(noise.size())[0] for noise in self.noise_in]
if img is None:
return z, n
else:
return z, img, n
else:
return img
class Car_DataSet(data.Dataset):
def __init__(self, image_dir=None, label_dir=None, output_size=(512, 512), noise_in=None, training_set=True, video_data=False, train_split=0.9):
self.image_dir = image_dir
self.normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
self.resize = transforms.Compose([
transforms.Resize((384, 512)),
transforms.Pad(padding=(0, 64, 0, 64)),
transforms.ToTensor()
])
self.noise_in = noise_in
self.video_data = video_data
self.random_rotation = transforms.Compose([
transforms.Resize(output_size),
transforms.RandomPerspective(distortion_scale=0.05, p=1.0),
transforms.ToTensor()
])
# load image file
train_len = None
self.length = 0
self.image_dir = image_dir
if image_dir is not None:
img_list = [glob.glob1(self.image_dir, ext) for ext in ['*jpg','*png']]
image_list = [item for sublist in img_list for item in sublist]
image_list.sort()
train_len = int(train_split*len(image_list))
if training_set:
self.image_list = image_list[:train_len]
else:
self.image_list = image_list[train_len:]
self.length = len(self.image_list)
# load label file
self.label_dir = label_dir
if label_dir is not None:
self.seeds = np.load(label_dir)
if train_len is None:
train_len = int(train_split*len(self.seeds))
if training_set:
self.seeds = self.seeds[:train_len]
else:
self.seeds = self.seeds[train_len:]
if self.length == 0:
self.length = len(self.seeds)
def __len__(self):
return self.length
def __getitem__(self, idx):
img = None
if self.image_dir is not None:
img_name = os.path.join(self.image_dir, self.image_list[idx])
image = Image.open(img_name)
img = self.resize(image)
if img.size(0) == 1:
img = torch.cat((img, img, img), dim=0)
img = self.normalize(img)
if self.video_data:
img_2 = self.random_rotation(image)
img_2 = self.normalize(img_2)
img_2 = torch.where(img_2 > -1, img_2, img)
img = torch.cat([img, img_2], dim=0)
# generate image
if self.label_dir is not None:
torch.manual_seed(self.seeds[idx])
z = torch.randn(1, 512)[0]
n = [torch.randn_like(noise[0]) for noise in self.noise_in]
if img is None:
return z, n
else:
return z, img, n
else:
return img
|