def accuracy(inputs, input_lengths, labels, batch_size):
n_correct = 0
_, inputs = inputs.max(2)
inputs = inputs.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(inputs.data, input_lengths.data, raw=False)
for pred, target in zip(sim_preds, labels):
if pred == target:
n_correct += 1
return n_correct / float(batch_size)
lines = []
print('evaluating...')
num_files = len(files)
for i in tqdm(range(num_files)):
file = 'word_{}.png'.format(i + 1)
im_fn = os.path.join(image_folder, file)
img = cv.imread(im_fn)
img = cv.resize(img, (imgW, imgH), cv.INTER_CUBIC)
# img = image_resize(img, width=imgW, height=imgH, inter=cv.INTER_CUBIC)
img = img[..., ::-1] # RGB
img = transforms.ToPILImage()(img)
img = transformer(img)
img = img.to(device)
img = img.unsqueeze(0)
preds = model(img)
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
preds_size = Variable(torch.IntTensor([preds.size(0)]))
sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
lines.append('{}, \"{}\"\n'.format(file, sim_pred))
with open('submit.txt', 'w') as file:
file.writelines(lines)