def train():
# load training dataset (6K)
filename = 'Flickr_8k.trainImages.txt'
train = util.load_ids(filename)
print('Dataset: %d' % len(train))
train_captions = util.load_clean_captions('descriptions.txt', train)
print('Captions: train number=%d' % len(train_captions))
# photo features
train_features = util.load_photo_features('features.pkl', train)
print('Photos: train=%d' % len(train_features))
# prepare tokenizer
tokenizer = load(open('tokenizer.pkl', 'rb'))
vocab_size = len(tokenizer.word_index) + 1
print('Vocabulary Size: %d' % vocab_size)
# determine the maximum sequence length
max_len = util.get_max_length(train_captions)
print('Description Length: %d' % max_len)
# define the model
model = caption_model(vocab_size, max_len)
# train the model, run epochs manually and save after each epoch
epochs = 20
steps = len(train_captions)
for i in range(epochs):
# create the data generator
generator = data_generator(train_captions, train_features, tokenizer, max_len)
# fit for one epoch
model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
# save model
model.save('model_' + str(i) + '.h5')
def generate_caption_run():
image_file = 'Flickr_8k.testImages.txt'
ids = util.load_ids(image_file)
features = util.load_photo_features('features.pkl', ids)
nlp_model = load_model('model_19.h5')
tokenizer = load(open('tokenizer.pkl', 'rb'))
caption = generate_caption(nlp_model, tokenizer,
features['3596131692_91b8a05606'], 40)
print('+++++++++++ caption is: ', caption, '+++++++++++++++++++')
def evaluate_check():
filename = 'Flickr_8k.testImages.txt'
test = util.load_ids(filename)
test_features = util.load_photo_features('features3.pkl', test)
print("Photos: test=%d" % len(test_features))
# load the model
model_name = 'model_1.h5'
model = load_model(model_name)
tokenizer = load(open('tokenizer.pkl', 'rb'))
captions = util.load_clean_captions('descriptions.txt', test)
evaluate_model(model, captions, test_features, tokenizer)
def evaluate_model_run():
model = load_model('model_19.h5')
filename = 'Flickr_8k.testImages.txt'
test = util.load_ids(filename)
# test play as "index" role, just from description.txt and featute.pkl to
# load the special info which define in "index"
test_caption = util.load_clean_captions('descriptions.txt', test)
test_features = util.load_photo_features('features.pkl', test)
tokenizer = load(open('tokenizer.pkl', 'rb'))
bleu1, bleu2, bleu3, bleu4 = evaluate_model(model, test_caption,
test_features, tokenizer)
print('BLEU-1: %f' % bleu1)
print('BLEU-2: %f' % bleu2)
print('BLEU-3: %f' % bleu3)
print('BLEU-4: %f' % bleu4)
def train():
# load training dataset (6K)
filename = 'Flickr_8k.trainImages.txt'
train = util.load_ids(filename)
print('Dataset: %d' % len(train))
train_captions = util.load_clean_captions('descriptions.txt', train)
print('Captions: train number=%d' % len(train_captions))
# photo features
train_features = util.load_photo_features('features.pkl', train)
print('Photos: train=%d' % len(train_features))
# prepare tokenizer
tokenizer = load(open('tokenizer.pkl', 'rb'))
vocab_size = len(tokenizer.word_index) + 1
print('Vocabulary Size: %d' % vocab_size)
# determine the maximum sequence length
max_len = util.get_max_length(train_captions)
print('Description Length: %d' % max_len)
# define the model
model = caption_model(vocab_size, max_len)
# train the model, run epochs manually and save after each epoch
epochs = 5
steps = len(train_captions)
for i in range(epochs):
# create the data generator
generator = data_generator(train_captions, train_features, tokenizer,
max_len)
# fit for one epoch
# generator just return two dimenstion data, the first means X, which has two data
# first is the featur of the pic, second is the surfix words; second means Y, the
# word of predict for Next.
# At first I don't kown why generator will return three value(feature, surfix, the next word)
# but the model just has two input, later I got first tow means X and will go into the model,
# the third is means Y, the reponse variance.
# Don't need fear the generator will be executed forever, for the super-parameter epoches & steps_per_epoch
# has limited the time of invoking generator, which is epochs * steps_per_epoch
# Generator, is magical!
model.fit_generator(generator,
epochs=1,
steps_per_epoch=steps,
verbose=1)
# save model
model.save('model' + os.sep + 'model_my' + str(i) + '.h5')
def generate_caption_run():
# load test set
filename = "Flickr_8k.testImages.txt"
test = util.load_ids(filename)
# photo features
test_features = util.load_photo_features('features.pkl', test)
print('Photos: test=%d' % len(test_features))
# load the model
filename = 'model_19.h5'
model = load_model(filename)
tokenizer = load(open('tokenizer.pkl', 'rb'))
caption = generate_caption(model, tokenizer,
test_features['3596131692_91b8a05606'], 40)
print('Generated caption is:' + caption)
return caption
def evaluate_model_run(model_name):
# load test set
filename = '../Flickr8k_text/Flickr_8k.testImages.txt'
# test = util.load_ids(filename)
test = task3.load_image_names(filename)
test_captions = util.load_clean_captions('../task3/descriptions.txt', test)
# photo feaatures
test_features = util.load_photo_features('../task2/features.pkl', test)
print('Photos: test=%d' % len(test_features))
# load the model
model = load_model(model_name)
tokenizer = load(open('../task3/tokenizer.pkl', 'rb'))
print(evaluate_model(model, test_captions, test_features, tokenizer))
def generate_caption_run():
# load test set
filename = '../Flickr8k_text/Flickr_8k.testImages.txt'
# filename = '../Flickr8k_text/Flickr_8k.trainImages.txt'
# test = util.load_ids(filename)
test = task3.load_image_names(filename)
# photo feaatures
test_features = util.load_photo_features('../task2/features.pkl', test)
print('Photos: test=%d' % len(test_features))
#load the model
filename = '../task4/model_8.h5'
model = load_model(filename)
tokenizer = load(open('../task3/tokenizer.pkl', 'rb'))
caption = generate_caption(model, tokenizer, test_features['2194286203_5dc620006a.jpg'], 38)
return caption
def evalueateModel(img_path):
# load training dataset (6K)
filename = 'Flickr_8k.testImages.txt'
test = util.load_ids(filename)
# photo features
test_features = util.load_photo_features('features.pkl', test)
print("Photos ：test =%d" % len(test_features))
model_name = 'model_8.h5'
model = load_model(model_name)
tokenizer = load(open('tokenizer.pkl', 'rb'))
expected = generate_caption(
model, tokenizer, test_features[img_path.split("/")[-1].split(".")[0]],
40)
img = Image.open(img_path)
plt.imshow(img)
plt.xlabel(expected[9:][:-6], fontsize=15, color='red')
plt.show()
return expected
def evaluate_model_run():
# load test set
filename = 'Flickr_8k.testImages.txt'
test = util.load_ids(filename)
print('number of test images: %d' % len(test))
test_captions = util.load_clean_captions('descriptions.txt', test)
# photo features
test_features = util.load_photo_features('features.pkl', test)
# load the model
filename = 'model_19.h5'
model = load_model(filename)
tokenizer = load(open('tokenizer.pkl', 'rb'))
# evaluate model
bleu1, bleu2, bleu3, bleu4 = evaluate_model(model, test_captions,
test_features, tokenizer, 40)
print('BLEU-1: %f' % bleu1)
print('BLEU-2: %f' % bleu2)
print('BLEU-3: %f' % bleu3)
print('BLEU-4: %f' % bleu4)
def generate_caption_check(img_path):
filename = 'Flickr_8k.testImages.txt'
test = util.load_ids(filename)
test_features = util.load_photo_features('features3.pkl', test)
print("Photos: test=%d" % len(test_features))
# load the model
model_name = 'model_1.h5'
model = load_model(model_name)
tokenizer = load(open('tokenizer.pkl', 'rb'))
expected = generate_caption(
model, tokenizer, test_features[img_path.split("/")[1].split(".")[0]],
40)
img = Image.open(img_path)
plt.imshow(img)
plt.xlabel(expected[9:-6], fontsize=15, color="red")
#plt.xlabel("WoW, You are so beautiful.", fontsize=15, color="red")
plt.show()
return expected
filename = 'Flickr_8k.trainImages.txt'
train = util.load_ids(filename) # 返回了一个{}，包含了文件名（去除.jpg）
des_path = r'E:/AI资源计算机视觉/JM07 - TXXY - CV2期/02.资料/homework-master-7fc833414b95225130c323c278230bc388af5c6b/homework1/task5/descriptions.txt'
train_captions = util.load_clean_captions(des_path, train)
# print('Captions: train number=%d' % len(train_captions))
# print(train_captions["3227594168_3351722aae"]) # ['startseq Two blonde ladies wearing sunglasses lounge on the grass with a dacshund . endseq',
# 'startseq Two blonde young women hang out in the grass with a brown dog . endseq',
# 'startseq Two blond women sit in grass with a small dog . endseq',
# 'startseq Two women laying on grass with a dog . endseq',
# 'startseq Two women on a grassy hill lit by the sun ; one looks at a dachshund , the other looks to the side . endseq']
# photo features
feature_path = r"E:/AI资源计算机视觉/JM07 - TXXY - CV2期/02.资料/homework-master-7fc833414b95225130c323c278230bc388af5c6b/homework1/features.pkl"
train_features = util.load_photo_features(feature_path, train)
# print('Photos: train=%d' % len(train_features))
# print(len(train_features["3585117340_73e96b6173"][0])) # 4096
# prepare tokenizer
tokenizer = load(open('tokenizer.pkl', 'rb'))
vocab_size = len(tokenizer.word_index) + 1 # 词汇表大小
print('Vocabulary Size: %d' % vocab_size) # 7378
# determine the maximum sequence length
max_len = util.get_max_length(train_captions)
print('Description Length: %d' % max_len) # 40
# define the model
model = caption_model(vocab_size, max_len)
print(model)