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materials.py
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materials.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from PIL import Image
import cv2
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchvision import transforms
class MMACDataSet(Dataset):
def __init__(self, root, train=True, transform=None):
if train:
anno_PATH = root+'train.csv'
img_PATH = root+'train/'
else:
anno_PATH = root+'valid.csv'
img_PATH = root+'valid/'
df = pd.read_csv(anno_PATH)
labels = df['spherical_equivalent'].values
image_names = df['image'].values
self.anno_PATH = anno_PATH
self.img_PATH = img_PATH
self.image_names = image_names
self.labels = labels
self.train = train
if transform is None:
self.transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize(size=(512, 512),
interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize([0.386, 0.186, 0.024],
[0.241, 0.125, 0.049])
])
else:
self.transform = transform
def __getitem__(self, index):
'''Take the index of item and returns the image and its labels'''
label = self.labels[index]
image_name = self.image_names[index]
image = cv2.imread(self.img_PATH+image_name)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = self.transform(image)
return image, torch.FloatTensor([label])
def __len__(self):
return len(self.image_names)
if __name__ == '__main__':
root = ''
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.RandomResizedCrop((512, 512), scale=(0.08, 1.0),
ratio=(0.75, 1.35), interpolation=transforms.InterpolationMode.BICUBIC),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomVerticalFlip(),
transforms.RandomRotation(degrees=[-180, 180],
fill=0,interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ColorJitter(brightness=0.04, contrast=0.04, saturation=0.04, hue=0.04),
transforms.RandomAdjustSharpness(sharpness_factor=2, p=0.5),
transforms.RandomEqualize(p=0.2),
transforms.ToTensor(),
transforms.Normalize([0.386, 0.186, 0.024],
[0.241, 0.125, 0.049]),
transforms.RandomErasing(p=0.25)
])
dataset = MMACDataSet(root, transform=transform)
train_loader = DataLoader(
dataset, batch_size=128, shuffle=False, drop_last=False, num_workers=0
)
mean = np.zeros(3)
std = np.zeros(3)
total_images = 0
images = []
for idx, data in enumerate(train_loader):
image, target = data
#images.append(image)
print(target.shape)
#images = torch.cat(images, dim=0)
#print("Mean:", np.mean(images.numpy(), axis=(0, 2, 3)))
#print("Std:", np.std(images.numpy(), axis=(0, 2, 3)))