Delete vietocr/translate.py

vietocr/translate.py

@@ -1,62 +0,0 @@
-import torch
-import numpy as np
-import math
-from PIL import Image
-from torch.nn.functional import softmax
-
-def translate(img, model, max_seq_length=128, sos_token=1, eos_token=2):
-    "data: BxCXHxW"
-    model.eval()
-
-    with torch.no_grad():
-        src = model.cnn(img)
-        memory = model.transformer.forward_encoder(src)
-
-        translated_sentence = [[sos_token]*len(img)]
-
-        max_length = 0
-
-        while max_length <= max_seq_length and not all(np.any(np.asarray(translated_sentence).T==eos_token, axis=1)):
-            tgt_inp = torch.LongTensor(translated_sentence)
-            
-            output, memory = model.transformer.forward_decoder(tgt_inp, memory)
-            output = softmax(output, dim=-1)
-
-            _, indices  = torch.topk(output, 5)
-            
-            indices = indices[:, -1, 0]
-            indices = indices.tolist()
-
-            translated_sentence.append(indices)   
-            max_length += 1
-
-        translated_sentence = np.asarray(translated_sentence).T
-    
-    return translated_sentence
-
-def resize(w, h, expected_height, image_min_width, image_max_width):
-    new_w = int(expected_height * float(w) / float(h))
-    round_to = 10
-    new_w = math.ceil(new_w/round_to)*round_to
-    new_w = max(new_w, image_min_width)
-    new_w = min(new_w, image_max_width)
-
-    return new_w, expected_height
-
-def process_image(image, image_height, image_min_width, image_max_width):
-    img = image.convert('RGB')
-
-    w, h = img.size
-    new_w, image_height = resize(w, h, image_height, image_min_width, image_max_width)
-
-    img = img.resize((new_w, image_height), Image.Resampling.LANCZOS)
-
-    img = np.asarray(img).transpose(2,0, 1)
-    img = img/255
-    return img
-
-def process_input(image, image_height, image_min_width, image_max_width):
-    img = process_image(image, image_height, image_min_width, image_max_width)
-    img = img[np.newaxis, ...]
-    img = torch.FloatTensor(img)
-    return img