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utils.py
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utils.py
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import numpy as np
def accuracy(output, target, topk=(1,)):
pred = output.topk(max(topk), 1, True, True)[1].t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
return [float(correct[:k].reshape(-1).float().sum(0, keepdim=True).cpu().numpy()) for k in topk]
def unpickle(file):
import pickle
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='latin1')
return dict
def get_cifar10_classes(file):
"""
Get the Cifar10 classes as a list for AddText transform
"""
data = unpickle(file)
classes = data['label_names']
return classes
def get_cifar100_classes(file):
"""
Get the Cifar100 classes as a list for AddText transform
"""
data = unpickle(file)
classes = data['fine_label_names']
return classes
def prepare_caltech101(path, transforms=None):
image_paths = list(paths.list_images(path + '/101_ObjectCategories'))
data = []
labels = []
for img_path in tqdm(image_paths):
label = img_path.split(os.path.sep)[-2]
if label == "BACKGROUND_Google":
continue
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
data.append(img)
labels.append(label)
data = np.array(data)
labels = np.array(labels)
return data, labels