could you give the code for extract ae and maml featrue, we want to extract feature on own dataset about miccai21_mmq HOT 8 OPEN

from PIL import Image
img_size = [128, 128]
transform = transforms.Compose([lambda x: Image.open(x).convert('L'),
                                         transforms.Resize(img_size),
                                         transforms.ToTensor(),
                                lambda x: x/255.])

import os

from PIL import Image

path_to_images = 'pathVQA/images'

all_images = []

# Resize images
for r, d, f in os.walk(path_to_images):
    for file in f:
        if '.jpg' in file:
            all_images.append(os.path.join(r, file))

for i, image_file in enumerate(all_images):
    im = Image.open(image_file)
    im = im.resize((84, 84), resample=Image.LANCZOS)
    im.save(image_file)
    if i % 500 == 0:
        print(i)

img_size = [84, 84]
transform = transforms.Compose([lambda x: Image.open(x).convert('RGB'),
                                         transforms.Resize(img_size),
                                         transforms.ToTensor(),
                                         transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
                                         ])

import os

from PIL import Image

path_to_images = 'pathVQA/images'

all_images = []

# Resize images
for r, d, f in os.walk(path_to_images):
    for file in f:
        if '.jpg' in file:
            all_images.append(os.path.join(r, file))

for i, image_file in enumerate(all_images):
    im = Image.open(image_file)
    im = im.resize((128, 128), resample=Image.LANCZOS)
    im.save(image_file)
    if i % 500 == 0:
        print(i)

img_size = [128, 128]
transform = transforms.Compose([lambda x: Image.open(x).convert('L'),
                                         transforms.Resize(img_size),
                                         transforms.ToTensor(),
                                         transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
                                         ])