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6498fe6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | import torch
import numpy as np
import math
from PIL import Image
from torch.nn.functional import log_softmax, softmax
from Vocr.model.transformerocr import VietOCR
from Vocr.model.vocab import Vocab
from Vocr.model.beam import Beam
def batch_translate_beam_search(img, model, beam_size=4, candidates=1, max_seq_length=128, sos_token=1, eos_token=2):
# img: NxCxHxW
model.eval()
device = img.device
sents = []
with torch.no_grad():
src = model.cnn(img)
print(src.shap)
memories = model.transformer.forward_encoder(src)
for i in range(src.size(0)):
# memory = memories[:,i,:].repeat(1, beam_size, 1) # TxNxE
memory = model.transformer.get_memory(memories, i)
sent = beamsearch(memory, model, device, beam_size, candidates, max_seq_length, sos_token, eos_token)
sents.append(sent)
sents = np.asarray(sents)
return sents
def translate_beam_search(img, model, beam_size=4, candidates=1, max_seq_length=128, sos_token=1, eos_token=2):
# img: 1xCxHxW
model.eval()
device = img.device
with torch.no_grad():
src = model.cnn(img)
memory = model.transformer.forward_encoder(src) # TxNxE
sent = beamsearch(memory, model, device, beam_size, candidates, max_seq_length, sos_token, eos_token)
return sent
def beamsearch(memory, model, device, beam_size=4, candidates=1, max_seq_length=128, sos_token=1, eos_token=2):
# memory: Tx1xE
model.eval()
beam = Beam(beam_size=beam_size, min_length=0, n_top=candidates, ranker=None, start_token_id=sos_token,
end_token_id=eos_token)
with torch.no_grad():
# memory = memory.repeat(1, beam_size, 1) # TxNxE
memory = model.transformer.expand_memory(memory, beam_size)
for _ in range(max_seq_length):
tgt_inp = beam.get_current_state().transpose(0, 1).to(device) # TxN
decoder_outputs, memory = model.transformer.forward_decoder(tgt_inp, memory)
log_prob = log_softmax(decoder_outputs[:, -1, :].squeeze(0), dim=-1)
beam.advance(log_prob.cpu())
if beam.done():
break
scores, ks = beam.sort_finished(minimum=1)
hypothesises = []
for i, (times, k) in enumerate(ks[:candidates]):
hypothesis = beam.get_hypothesis(times, k)
hypothesises.append(hypothesis)
return [1] + [int(i) for i in hypothesises[0][:-1]]
def translate(img, model, max_seq_length=128, sos_token=1, eos_token=2):
"""data: BxCXHxW"""
model.eval()
device = img.device
with torch.no_grad():
src = model.cnn(img)
memory = model.transformer.forward_encoder(src)
translated_sentence = [[sos_token] * len(img)]
char_probs = [[1] * 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).to(device)
# output = model(img, tgt_inp, tgt_key_padding_mask=None)
# output = model.transformer(src, tgt_inp, tgt_key_padding_mask=None)
output, memory = model.transformer.forward_decoder(tgt_inp, memory)
output = softmax(output, dim=-1)
output = output.to('cpu')
values, indices = torch.topk(output, 5)
indices = indices[:, -1, 0]
indices = indices.tolist()
values = values[:, -1, 0]
values = values.tolist()
char_probs.append(values)
translated_sentence.append(indices)
max_length += 1
del output
translated_sentence = np.asarray(translated_sentence).T
char_probs = np.asarray(char_probs).T
char_probs = np.multiply(char_probs, translated_sentence > 3)
char_probs = np.sum(char_probs, axis=-1) / (char_probs > 0).sum(-1)
return translated_sentence, char_probs
def build_model(config):
vocab = Vocab(config['vocab'])
device = config['device']
model = VietOCR(len(vocab),
config['backbone'],
config['cnn'],
config['transformer'],
config['seq_modeling'])
model = model.to(device)
return model, vocab
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.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
def predict(filename, config):
img = Image.open(filename)
img = process_input(img)
img = img.to(config['device'])
model, vocab = build_model(config)
s = translate(img, model)[0].tolist()
s = vocab.decode(s)
return s
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