def val(test_loader): test_iter = iter(test_loader) max_iter = len(data_loader) n_correct = 0 n_total = 0 for i in range(max_iter): data = test_iter.next() cpu_images = data[0] cpu_labels = data[1] [num] = cpu_labels.shape utils.loadData(image, cpu_images) preds = MODEL(image) arg_max = preds.argmax(1).cpu.numpy() labels = cpu_labels.numpy() correct = np.sum(arg_max == labels) n_correct += correct n_total += num acc = n_correct / float(n_total) return acc
def trainBatch():
data1 = train_iter1.next()
data2 = train_iter2.next()
cpu_images = torch.cat((data1[0], data2[0]), 0)
cpu_texts1 = data1[1] + data2[1]
cpu_texts2 = data1[3] + data2[3]
utils.loadData(image, cpu_images)
t1, l1 = converter.encode(cpu_texts1, scanned=True)
utils.loadData(text1_ori, t1)
utils.loadData(length_ori, l1)
t2, l2 = converter.encode(cpu_texts2, scanned=True)
utils.loadData(text2_ori, t2)
N = len(cpu_texts1)
if opt.LR is True:
preds1, preds2 = MODEL(image,
length_ori,
text1_ori,
text2_ori,
cpu_texts=cpu_texts1)
text1_new = text1_ori
text2_new = text2_ori
cost_pred1 = criterion(preds1, text1_new) / 2.0
cost_pred2 = criterion(preds2, text2_new) / 2.0
loss_pred_avg1.add(cost_pred1)
loss_pred_avg2.add(cost_pred2)
cost = cost_pred1 + cost_pred2
else:
preds1 = MODEL(image,
length_ori,
text1_ori,
None,
cpu_texts=cpu_texts1)
text1_new = text1_ori
cost_pred1 = criterion(preds1, text1_new)
loss_pred_avg1.add(cost_pred1)
cost = cost_pred1
loss_avg.add(cost)
MODEL.zero_grad()
cost.backward()
optimizer.step()
return cost
def train_batch():
data = train_iter.next()
cpu_images = data[0]
cpu_labels = data[1]
utils.loadData(image, cpu_images)
utils.loadData(ori_label, cpu_labels)
# print('ori_label.shape',ori_label.shape)
preds = MODEL(image)
# print('pred---', preds.shape)
# print('label--', ori_label.shape)
cost = criterion(preds, ori_label)
# print('cost-------', cost)
loss.add(cost)
MODEL.zero_grad()
cost.backward()
optimizer.step()
def main():
input_size = 10000
embedding_size = 24
output_size = 5
learning_rate = 0.01
oov_token = '<OOV>'
loss = 'sparse_categorical_crossentropy'
optimizer = Adam(learning_rate=learning_rate)
epochs = 1
train_val_split = 0.2
sentences, sentiments = helper.get_data('data/train.tsv')
sentences = helper.remove_stopwords(sentences, 'data/stopwords')
max_length = len(max(sentences, key=len))
tokenizer = helper.get_tokenizer(input_size, oov_token, sentences)
padded_sentences = helper.convert_to_sequences(tokenizer, sentences,
max_length)
train_padded_sentences, validation_padded_sentences, train_sentiments, validation_sentiments = \
train_test_split(
padded_sentences, sentiments, test_size=train_val_split, random_state=42
)
train_padded_sentences = np.array(train_padded_sentences)
train_sentiments = np.array(train_sentiments)
validation_padded_sentences = np.array(validation_padded_sentences)
validation_sentiments = np.array(validation_sentiments)
layers = [
tf.keras.layers.Embedding(input_size,
embedding_size,
input_length=max_length),
# tf.keras.layers.LSTM(32),
# tf.keras.layers.Conv1D(filters=64, kernel_size=5, activation='relu'),
# tf.keras.layers.MaxPooling1D(pool_size=4),
# tf.keras.layers.Dropout(0.2),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(units=24, activation='relu'),
tf.keras.layers.Dense(units=output_size, activation='softmax')
]
model = MODEL(input_size, output_size, layers, loss, optimizer, epochs)
model.__train__(train_padded_sentences, train_sentiments,
validation_padded_sentences, validation_sentiments)
model.__plot_graph__('accuracy')
def val_beam(dataset, max_iter=9999):
rotate90 = dataset.ifRotate90
data_loader = torch.utils.data.DataLoader(dataset,
shuffle=False,
batch_size=opt.batchSize,
num_workers=1) # opt.batchSize
val_iter = iter(data_loader)
max_iter = min(max_iter, len(data_loader))
n_correct = 0
n_total = 0
for i in range(max_iter):
data = val_iter.next()
ori_cpu_images = data[0]
flag_rotate90 = data[2]
cpu_texts1 = data[1]
cpu_texts2 = data[3]
t1, l1 = converter.encode(cpu_texts1, scanned=True)
t2, l2 = converter.encode(cpu_texts2, scanned=True)
utils.loadData(text1_ori, t1)
utils.loadData(text2_ori, t2)
utils.loadData(length_ori, l1)
All_preds_add5EOS1 = []
All_scores1 = []
All_preds_add5EOS2 = []
All_scores2 = []
cpu_images = ori_cpu_images
utils.loadData(image, cpu_images)
if opt.LR:
local_preds1, local_scores1, local_preds2, local_scores2 = MODEL(
image,
length_ori,
text1_ori,
text2_ori,
test=True,
cpu_texts=cpu_texts1)
All_preds_add5EOS1.append(local_preds1)
All_preds_add5EOS2.append(local_preds2)
All_scores1.append(local_scores1)
All_scores2.append(local_scores2)
else:
local_preds1, local_scores1 = MODEL(image,
length_ori,
text1_ori,
None,
test=True,
cpu_texts=cpu_texts1)
All_preds_add5EOS1.append(local_preds1)
All_scores1.append(local_scores1)
length_label = (length_ori - 1).data.cpu().numpy()
# %%% Left/Right Rotate %%%
if rotate90 == True:
PIL_imgs = [
toPIL(ori_cpu_images[i].div(2).sub(-0.5))
for i in range(ori_cpu_images.shape[0])
]
PIL_imgs_left90 = [
PIL_imgs[i].transpose(Image.ROTATE_90).resize(
(opt.imgW, opt.imgH), Image.BILINEAR)
if flag_rotate90[i] else PIL_imgs[i]
for i in range(ori_cpu_images.shape[0])
]
PIL_imgs_right90 = [
PIL_imgs[i].transpose(Image.ROTATE_270).resize(
(opt.imgW, opt.imgH), Image.BILINEAR)
if flag_rotate90[i] else PIL_imgs[i]
for i in range(ori_cpu_images.shape[0])
]
imgs_Tensor_left90 = [
toTensor(PIL_imgs_left90[i])
for i in range(ori_cpu_images.shape[0])
]
imgs_Tensor_right90 = [
toTensor(PIL_imgs_right90[i])
for i in range(ori_cpu_images.shape[0])
]
# Left
cpu_images = torch.stack(imgs_Tensor_left90)
cpu_images.sub_(0.5).div_(0.5)
utils.loadData(image, cpu_images)
if opt.LR:
local_preds1, local_scores1, local_preds2, local_scores2, _ = MODEL(
image,
length_ori,
text1_ori,
text2_ori,
test=True,
cpu_texts=cpu_texts1)
All_preds_add5EOS1.append(local_preds1)
All_preds_add5EOS2.append(local_preds2)
All_scores1.append(local_scores1)
All_scores2.append(local_scores2)
else:
local_preds1, local_scores1, _ = MODEL(image,
length_ori,
text1_ori,
None,
test=True,
cpu_texts=cpu_texts1)
All_preds_add5EOS1.append(local_preds1)
All_scores1.append(local_scores1)
# Right
cpu_images = torch.stack(imgs_Tensor_right90)
cpu_images.sub_(0.5).div_(0.5)
utils.loadData(image, cpu_images)
if opt.LR:
local_preds1, local_scores1, local_preds2, local_scores2, _ = MODEL(
image,
length_ori,
text1_ori,
text2_ori,
test=True,
cpu_texts=cpu_texts1)
All_preds_add5EOS1.append(local_preds1)
All_preds_add5EOS2.append(local_preds2)
All_scores1.append(local_scores1)
All_scores2.append(local_scores2)
else:
local_preds1, local_scores1, _ = MODEL(image,
length_ori,
text1_ori,
None,
test=True,
cpu_texts=cpu_texts1)
All_preds_add5EOS1.append(local_preds1)
All_scores1.append(local_scores1)
# Start to decode
preds_add5EOS1 = []
preds_score1 = []
for j in range(cpu_images.size(0)):
text_begin = 0 if j == 0 else (length_ori.data[:j].sum() + j * 5)
max_score = -99999
max_index = 0
for index in range(len(All_scores1)):
local_score = All_scores1[index][j]
if local_score > max_score:
max_score = local_score
max_index = index
preds_add5EOS1.extend(
All_preds_add5EOS1[max_index][text_begin:text_begin +
int(length_ori[j].data) + 5])
preds_score1.append(max_score)
preds_add5EOS1 = torch.stack(preds_add5EOS1)
sim_preds_add5eos1 = converter.decode(preds_add5EOS1.data,
length_ori.data + 5)
if opt.LR:
preds_add5EOS2 = []
preds_score2 = []
for j in range(cpu_images.size(0)):
text_begin = 0 if j == 0 else (length_ori.data[:j].sum() +
j * 5)
max_score = -99999
max_index = 0
for index in range(len(All_scores2)):
local_score = All_scores2[index][j]
if local_score > max_score:
max_score = local_score
max_index = index
preds_add5EOS2.extend(
All_preds_add5EOS2[max_index][text_begin:text_begin +
int(length_ori[j].data) + 5])
preds_score2.append(max_score)
preds_add5EOS2 = torch.stack(preds_add5EOS2)
sim_preds_add5eos2 = converter.decode(preds_add5EOS2.data,
length_ori.data + 5)
if opt.LR:
batch_index = 0
for pred1, target1, pred2, target2 in zip(sim_preds_add5eos1,
cpu_texts1,
sim_preds_add5eos2,
cpu_texts2):
if preds_score1[batch_index] > preds_score2[batch_index]:
pred = pred1
target = target1
else:
pred = pred2
target = target2
pred = pred.split(opt.sep)[0] + opt.sep
test_alphabet = dataset.test_alphabet.split(opt.sep)
pred = ''.join(
pred[i].lower() if pred[i].lower() in test_alphabet else ''
for i in range(len(pred)))
target = ''.join(target[i].lower() if target[i].lower() in
test_alphabet else ''
for i in range(len(target)))
if pred.lower() == target.lower():
n_correct += 1
n_total += 1
batch_index += 1
else:
for pred, target in zip(sim_preds_add5eos1, cpu_texts1):
pred = pred.split(opt.sep)[0] + opt.sep
test_alphabet = dataset.test_alphabet.split(opt.sep)
pred = ''.join(
pred[i].lower() if pred[i].lower() in test_alphabet else ''
for i in range(len(pred)))
target = ''.join(target[i].lower() if target[i].lower() in
test_alphabet else ''
for i in range(len(target)))
if pred.lower() == target.lower():
n_correct += 1
n_total += 1
accuracy = n_correct / float(n_total)
dataset_name = dataset.root.split('/')[-1]
print(dataset_name + ' ACCURACY -----> %.1f%%, ' % (accuracy * 100.0))
return accuracy
train_dataset_2,
batch_size=batchSize2,
shuffle=False,
sampler=dataset.randomSequentialSampler(train_dataset_2, batchSize2),
num_workers=int(opt.workers))
test_dataset1 = dataset.lmdbDataset(test=True,
root=opt.test_1,
transform=dataset.resizeNormalize(
(opt.imgW, opt.imgH)))
nclass = len(opt.alphabet.split(opt.sep))
converter = utils.strLabelConverterForAttention(opt.alphabet, opt.sep)
criterion = torch.nn.CrossEntropyLoss()
MODEL = MODEL(opt.n_bm, nclass, dec_layer=opt.dec_layer, LR=opt.LR)
# print("MODEL have {} paramerters in total".format(sum(x.numel() for x in MODEL.parameters())))
if opt.MODEL != '':
print('loading pretrained model from %s' % opt.MODEL)
state_dict = torch.load(opt.MODEL)
MODEL_state_dict_rename = OrderedDict()
for k, v in state_dict.items():
name = k.replace("module.", "") # remove `module.`
MODEL_state_dict_rename[name] = v
MODEL.load_state_dict(MODEL_state_dict_rename, strict=True)
image = torch.FloatTensor(opt.batchSize, 1, opt.imgH, opt.imgW)
text1_ori = torch.LongTensor(opt.batchSize * 5)
text2_ori = torch.LongTensor(opt.batchSize * 5)
cudnn.benchmark = True
if torch.cuda.is_available() and not opt.cuda:
print(
"WARNING: You have a CUDA device, so you should probably run with --cuda"
)
test_dataset1 = dataset.lmdbDataset(test=True,
root=opt.test_1,
transform=dataset.resizeNormalize(
(opt.imgW, opt.imgH)))
nclass = len(opt.alphabet.split(opt.sep))
converter = utils.strLabelConverterForAttention(opt.alphabet, opt.sep)
MODEL = MODEL(opt.n_bm, nclass, dec_layer=opt.dec_layer, LR=opt.LR)
# print("MODEL have {} paramerters in total".format(sum(x.numel() for x in MODEL.parameters())))
if opt.MODEL != '':
print('loading pretrained model from %s' % opt.MODEL)
state_dict = torch.load(opt.MODEL)
MODEL_state_dict_rename = OrderedDict()
for k, v in state_dict.items():
name = k.replace("module.", "") # remove `module.`
MODEL_state_dict_rename[name] = v
MODEL.load_state_dict(MODEL_state_dict_rename, strict=True)
image = torch.FloatTensor(opt.batchSize, 1, opt.imgH, opt.imgW)
text1_ori = torch.LongTensor(opt.batchSize * 5)
text2_ori = torch.LongTensor(opt.batchSize * 5)
def Load_train_data(args):
# Train data
train_dataset_1 = dataset.lmdbDataset( args.alphabet,root=args.train_1,
transform=dataset.resizeNormalize((args.imgW, args.imgH)))
assert train_dataset_1
train_dataset = train_dataset_1
if args.train_2!=None:
train_dataset_2 = dataset.lmdbDataset( args.alphabet,root=args.train_2,
transform=dataset.resizeNormalize((args.imgW, args.imgH)))
assert train_dataset_2
train_dataset = torch.utils.data.ConcatDataset([train_dataset, train_dataset_2])
if args.train_3!=None:
train_dataset_3 = dataset.lmdbDataset( args.alphabet,root=args.train_3,
transform=dataset.resizeNormalize((args.imgW, args.imgH)))
assert train_dataset_3
train_dataset = torch.utils.data.ConcatDataset([train_dataset, train_dataset_3])
# 该接口主要用来将自定义的数据读取接口的输出或者PyTorch已有的数据读取接口的输入按照batch size封装成Tensor
# train_loader = torch.utils.data.DataLoader(
# train_dataset, batch_size=args.batchSize,
# shuffle=False,sampler=dataset.randomSequentialSampler(train_dataset, args.batchSize),
# num_workers=int(args.workers))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batchSize,
shuffle=False,
num_workers=int(args.workers))
return train_loader
def Load_test_data(dataset_name):
dataset = dataset.lmdbDataset( args.alphabet1,test=True,root=dataset_name,
transform=dataset.resizeNormalize((args.imgW, args.imgH)))
test_loader = torch.utils.data.DataLoader(
dataset, shuffle=False, batch_size=args.batchSize, num_workers=int(args.workers))
return test_loader
def set_random_seed(random_seed):
random.seed(random_seed)
np.random.seed(random_seed)
torch.manual_seed(random_seed)
# print(random.seed)
def train_batch():
data = train_iter.next()
cpu_images = data[0]
cpu_labels = data[1]
utils.loadData(image, cpu_images)
utils.loadData(ori_label, cpu_labels)
label = utils.label_convert(ori_label, nclass) ## 进行 one-hot 编码
print("label size", label.shape)
preds = MODEL(image)
cost_pred = criterion(preds, label)
cost = cost_pred
loss_avg.add(cost)
MODEL.zero_grad()
cost.backward()
optimizer.step()
args = parse_args() print(args) set_random_seed(args.random_seed) train_loader = Load_train_data(args) if args.test_1!=None: test_loader1 = Load_train_data(args.test_1) if args.test_2!=None: test_loader2 = Load_train_data(args.test_2) if args.test_3!=None: test_loader3 = Load_train_data(args.test_3) nclass = len(args.alphabet.split(args.sep)) criterion = torch.nn.CrossEntropyLoss() MODEL = MODEL(nclass) image = torch.FloatTensor(args.batchSize, 1, args.imgH, args.imgW) ori_label = torch.IntTensor(args.batchSize) label = torch.IntTensor(args.batchSize, nclass) if args.cuda: MODEL.cuda() MODEL = torch.nn.DataParallel(MODEL, device_ids=range(args.ngpu)) image = image.cuda() ori_label = ori_label.cuda() label = label.cuda() criterion = criterion.cuda() # loss averager loss_pred = utils.averager()
import numpy as np
import cv2
from models.model import MODEL
from models.transformer.Constants import UNK,PAD,BOS,EOS,PAD_WORD,UNK_WORD,BOS_WORD,EOS_WORD
import os,sys,pdb
model_path = sys.argv[1]
img_path = sys.argv[2]
img_name = img_path.split('.')[0].split('/')[-1]
alphabet = '0 1 2 3 4 5 6 7 8 9 a b c d e f g h i j k l m n o p q r s t u v w x y z A B C D E F G H I J K L M N O P Q R S T U V W X Y Z ! " \' # $ % & ( ) * + , - . / : ; < = > ? @ [ \\ ] _ ` ~'
n_bm = 5
imgW = 160
imgH = 48
nclass = len(alphabet.split(' '))
MODEL = MODEL(n_bm, nclass)
if torch.cuda.is_available():
MODEL = MODEL.cuda()
print('loading pretrained model from %s' % model_path)
state_dict = torch.load(model_path)
MODEL_state_dict_rename = OrderedDict()
for k, v in state_dict.items():
name = k.replace("module.", "") # remove `module.`
MODEL_state_dict_rename[name] = v
MODEL.load_state_dict(MODEL_state_dict_rename)
for p in MODEL.parameters():
p.requires_grad = False
print(args)
set_random_seed(args.random_seed)
train_loader = Load_train_data(args)
if args.test_1 != None:
test_loader1 = Load_test_data(args, args.test_1)
if args.test_2 != None:
test_loader2 = Load_test_data(args, args.test_2)
if args.test_3 != None:
test_loader3 = Load_test_data(args, args.test_3)
nclass = 2
criterion = torch.nn.CrossEntropyLoss()
MODEL = MODEL()
image = torch.FloatTensor(args.batchSize, 1, args.imgH, args.imgW)
ori_label = torch.LongTensor(args.batchSize)
if args.cuda:
MODEL.cuda()
# MODEL = torch.nn.DataParallel(MODEL, device_ids=range(args.ngpu))
image = image.cuda()
ori_label = ori_label.cuda()
criterion = criterion.cuda()
# # loss averager
loss = utils.averager()
optimizer = torch.optim.Adadelta(filter(lambda p: p.requires_grad,
MODEL.parameters()),
