def load_model(opt):
global model
global device
global idx2label
print('Loading checkpoint from %s' % opt.model)
checkpoint = torch.load(opt.model,
map_location=lambda storage, loc: storage)
print('Loading vocab from checkpoint at %s' % opt.model)
vocab = checkpoint['vocab']
idx2label = {v: k for k, v in vocab.items()}
print('Building model...')
if opt.gpu == -1:
device = torch.device("cpu")
else:
device = torch.device("cuda", opt.gpu)
model = CNN(len(vocab))
# end of patch for backward compatibility
print("Loading model parameters from checkpoint...")
model.load_state_dict(checkpoint['model'], strict=False)
model.to(device)
model.eval()
self.errors: List[Error] = []
def parse(self, html: bytes) -> List[Error]:
page = self._parser(html)
items = page.find_all(**ERROR_FEEDBACK)
for it in items:
self.errors.append(Error(it.text))
return self.errors
if __name__ == "__main__":
model = CNN()
model.load("checkpoints/0228_ori/model.pth")
model.cuda()
model.eval()
target_path = "dataset/crawled_data"
if not os.path.exists(target_path):
os.makedirs(target_path)
os.makedirs(os.path.join(target_path, "ori"))
crawled_data = []
image_cnt = 1
error_cnt = 1
num_runs = 10000
for _ in tqdm(range(num_runs)):
client = HTTPRequest()
error_feedback = ErrorFeedback()