def load_pretrained_model(prefix):
checkpoint = torch.load(add_prefix(prefix, 'model_best.pth.tar'))
model = resnet18(is_ptrtrained=False)
print('load pretrained resnet18 successfully.')
model.load_state_dict(remove_prefix(checkpoint['state_dict']))
# print('best acc=%.4f' % checkpoint['best_accuracy'])
return model
def make_dataset(dir, max_dataset_size=float("inf")):
cache = dir.rstrip('/') + '.txt'
if os.path.isfile(cache):
print("Using filelist cached at %s" % cache)
with open(cache) as f:
images = [line.strip() for line in f]
# patch up image list with new loading method
if images[0].startswith(dir):
print("Using image list from older version")
image_list = []
for image in images:
image_list.append(image)
else:
print("Adding prefix to saved image list")
image_list = []
prefix = os.path.dirname(dir.rstrip('/'))
for image in images:
image_list.append(os.path.join(prefix, image))
return image_list
print("Walking directory ...")
images = []
assert os.path.isdir(dir), '%s is not a valid directory' % dir
for root, _, fnames in sorted(os.walk(dir, followlinks=True)):
for fname in fnames:
if is_image_file(fname):
path = os.path.join(root, fname)
images.append(path)
image_list = images[:min(max_dataset_size, len(images))]
with open(cache, 'w') as f:
prefix = os.path.dirname(dir.rstrip('/')) + '/'
for i in image_list:
f.write('%s\n' % util.remove_prefix(i, prefix))
return image_list
def resnet18(is_ptrtrained, path=None):
classifier = ResNet(BasicBlock, [2, 2, 2, 2])
print('use custom-defined resnet18 as classifier and the last convolutional layer feature size is (4,4).')
print(classifier)
if is_ptrtrained:
checkpoint = torch.load(path)
classifier.load_state_dict(remove_prefix(checkpoint['state_dict']))
print('load pretrained classifier!')
return classifier
def load_pretrained_model(pretrained_path, model_type):
checkpoint = torch.load(add_prefix(pretrained_path, 'model_best.pth.tar'))
if model_type == 'vgg':
model = vgg19(pretrained=False, num_classes=2)
print('load vgg successfully.')
elif model_type == 'resnet':
model = resnet18(is_ptrtrained=False)
print('load resnet18 successfully.')
else:
raise ValueError('')
model.load_state_dict(remove_prefix(checkpoint['state_dict']))
return model
def load_pretrained_model(prefix, model_type):
if model_type == 'resnet':
model = resnet18(is_ptrtrained=False)
elif model_type == 'vgg':
model = vgg19(num_classes=2, pretrained=False)
else:
raise ValueError('')
checkpoint = torch.load(add_prefix(prefix, 'model_best.pth.tar'))
print('load pretrained model successfully.')
model.load_state_dict(remove_prefix(checkpoint['state_dict']))
print('best acc=%.4f' % checkpoint['best_accuracy'])
return model
def __init__(self,
root,
loader=folder.default_loader,
extensions=folder.IMG_EXTENSIONS,
transform=None,
target_transform=None,
is_valid_file=None,
return_path=False):
super(ImageDataset, self).__init__(root,
transform=transform,
target_transform=target_transform)
# self.root = root
classes, class_to_idx = self._find_classes(self.root)
cache = self.root.rstrip('/') + '.txt'
if os.path.isfile(cache):
print("Using directory list at: %s" % cache)
with open(cache) as f:
samples = []
for line in f:
(path, idx) = line.strip().split(';')
samples.append((os.path.join(self.root, path), int(idx)))
else:
print("Walking directory: %s" % self.root)
samples = folder.make_dataset(self.root, class_to_idx, extensions,
is_valid_file)
with open(cache, 'w') as f:
for line in samples:
path, label = line
f.write('%s;%d\n' % (util.remove_prefix(
path, self.root).lstrip('/'), label))
if len(samples) == 0:
raise (RuntimeError("Found 0 files in subfolders of: " +
self.root + "\n"
"Supported extensions are: " +
",".join(extensions)))
self.loader = loader
self.extensions = extensions
self.classes = classes
self.class_to_idx = class_to_idx
self.samples = samples
self.targets = [s[1] for s in samples]
self.imgs = samples
self.return_path = return_path
def load_pretrained_model(prefix):
checkpoint = torch.load(add_prefix(prefix, 'model_best.pth.tar'))
model = vgg19(num_classes=2, pretrained=False)
print('load pretrained vgg19 successfully.')
model.load_state_dict(remove_prefix(checkpoint['state_dict']))
return model