def main(args):
with open('data/vocab.pkl', 'rb') as f:
vocab = pickle.load(f)
encoder = EncoderCNN(256)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(256, 512, len(vocab), 1)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder))
decoder.load_state_dict(torch.load(args.decoder))
measurement_score = test(encoder, decoder, vocab, args.num_samples,
args.num_hints, args.debug, args.c_step,
args.no_avg)
if args.msm == "co":
scores = cocoEval()
scores_u = cocoEval(res='data/captions_val2014_results_u.json')
print(scores)
print(scores_u)
with open(args.filepath, 'w+') as f:
pickle.dump((scores, scores_u), f)
def main(image):
# Configuration for hyper-parameters
config = Config()
# Image Preprocessing
transform = config.test_transform
# Load vocabulary
with open(os.path.join(config.vocab_path, 'vocab.pkl'), 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(config.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(config.embed_size, config.hidden_size, len(vocab),
config.num_layers)
# Load the trained model parameters
encoder.load_state_dict(
torch.load(
os.path.join(config.teacher_cnn_path, config.trained_encoder)))
decoder.load_state_dict(
torch.load(
os.path.join(config.teacher_lstm_path, config.trained_decoder)))
# Prepare Image
image = Image.open(image)
image_tensor = Variable(transform(image).unsqueeze(0))
# Set initial states
state = (Variable(torch.zeros(config.num_layers, 1, config.hidden_size)),
Variable(torch.zeros(config.num_layers, 1, config.hidden_size)))
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
state = [s.cuda() for s in state]
image_tensor = image_tensor.cuda()
# Generate caption from image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature, state)
sampled_ids = sampled_ids.cpu().data.numpy()
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word_id == 96:
sampled_caption.append('<end>')
break
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
# Print out image and generated caption.
print(sentence)
return sentence
def main(args):
# Val images folder
filepath = '/scratch/ys2542/pytorch-tutorial/tutorials/03-advanced/image_captioning/data/resizedval2014'
onlyfiles = [fl for fl in listdir(filepath) if isfile(join(filepath, fl))]
# image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# load vocabulary wrapper pickle file
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
encoder = EncoderCNN(args.embed_size) # build encoder
encoder.eval() # evaluation mode by moving mean and variance
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers) # build decoder
# load the trained CNN and RNN parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Load all images in val folder
for i in onlyfiles:
badsize = 0 # count the unload images
args_image = filepath + '/' # val folder path with image names
args_image = args_image + i
# transform image and wrap it to tensor
image = load_image(args_image, transform)
image_tensor = to_var(image, volatile=True)
if torch.cuda.is_available(): # load GPU
encoder.cuda()
decoder.cuda()
# generate caption from image
try:
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids.cpu().data.numpy()
# decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
# print out image and generated caption without start and end
print('beam_size_1' + '\t' + i + '\t' + sentence[8:-8])
except:
badsize = badsize + 1 # count some wrong images
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.Scale(args.crop_size),
transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
alexnet = models.alexnet(pretrained=True)
alexnet2 = AlexNet2(alexnet)
# Build Models
encoder = EncoderCNN(4096, args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare Image
image = Image.open(args.image)
image_tensor = Variable(transform(image).unsqueeze(0))
# Set initial states
state = (Variable(torch.zeros(args.num_layers, 1, args.hidden_size)),
Variable(torch.zeros(args.num_layers, 1, args.hidden_size)))
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
alexnet2.cuda()
state = [s.cuda() for s in state]
image_tensor = image_tensor.cuda()
# Generate caption from image
alexnet2(image_tensor)
feature = encoder(alexnet2.fc7_value)
sampled_ids = decoder.sample(feature, state)
sampled_ids = sampled_ids.cpu().data.numpy()
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
# Print out image and generated caption.
print(sentence)
def inference_coco(encoder_file: str, decoder_file: str, embed_size: int,
hidden_size: int, from_cpu: bool) -> None:
"""
Displays an original image from coco test dataset and prints its associated caption.
encoder_file: Name of the encoder to load.
decoder_file: Name of the decoder to load.
embed_size: Word embedding size for the encoder.
hidden_size: Hidden layer of the LSTM size.
from_cpu: Whether the model has been saved on CPU.
"""
# Define transform
transform_test = transforms.Compose([
transforms.Resize(256), # smaller edge of image resized to 256
transforms.RandomCrop(224), # get 224x224 crop from random location
transforms.ToTensor(), # convert the PIL Image to a tensor
transforms.Normalize(
(0.485, 0.456, 0.406), # normalize image for pre-trained model
(0.229, 0.224, 0.225))
])
# Device to use fo inference
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Create the data loader.
data_loader = get_loader(transform=transform_test, mode='test')
# Obtain sample image
_, image = next(iter(data_loader))
# The size of the vocabulary.
vocab_size = len(data_loader.dataset.vocab)
# Initialize the encoder and decoder, and set each to inference mode.
encoder = EncoderCNN(embed_size)
encoder.eval()
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
decoder.eval()
# Load the trained weights.
if from_cpu:
encoder.load_state_dict(
torch.load(os.path.join('./models', encoder_file),
map_location='cpu'))
decoder.load_state_dict(
torch.load(os.path.join('./models', decoder_file),
map_location='cpu'))
else:
encoder.load_state_dict(
torch.load(os.path.join('./models', encoder_file)))
decoder.load_state_dict(
torch.load(os.path.join('./models', decoder_file)))
# Move models to GPU if CUDA is available.
encoder.to(device)
decoder.to(device)
get_prediction(encoder, decoder, data_loader, device)
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare Image
image = load_image(args.image, transform)
image_tensor = to_var(image, volatile=True)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Generate caption from image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids.cpu().data.numpy()
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
# Print out image and generated caption.
sentence = sentence.replace('<start> ',
'').replace(' <end>', '').replace('.',
'').strip()
translator = Translator()
sentence_indo = translator.translate(sentence, dest='id').text
print('This is an image of: ' + sentence_indo)
tts = gTTS(sentence_indo, 'id')
tts.save('result.mp3')
playsound('result.mp3')
image = Image.open(args.image)
plt.imshow(np.asarray(image))
plt.show()
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare Image
#image = load_image(args.image, transform)
#image_tensor = to_var(image, volatile=True)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
data = []
try:
img_path = args.image
# Prepare Image
image = load_image(img_path, transform)
image_tensor = to_var(image, volatile=True)
# Generate caption from image
feature = encoder(image_tensor)
#pdb.set_trace()
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids.cpu().data.numpy()
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
if word == '<start>':
continue
if word == '<end>':
break
sampled_caption.append(word)
sentence = ' '.join(sampled_caption)
# Print out image and generated caption.
print(sentence)
data.append({'key': img_path.split('/')[-1], 'sentence': sentence})
except:
print(img_path)
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size,
len(vocab), args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Prepare Image
image_dir = args.image
images = os.listdir(image_dir)
for image_id in images:
if not image_id.endswith('.jpg'):
continue
image = os.path.join(image_dir, image_id)
image = load_image(image, transform)
image_tensor = to_var(image, volatile=True)
# Generate caption from image
try:
feature, cnn_features = encoder(image_tensor)
sampled_ids = decoder.sample(feature, cnn_features)
sampled_ids = sampled_ids.cpu().data.numpy()
except:
continue
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
# Print out image and generated caption.
print (image_id + '\t' + sentence)
def main(args):
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size,
len(vocab), args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare Image
image = load_image(args.image, transform)
image_tensor = to_var(image, volatile=True)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Generate caption from image
feature = encoder(image_tensor)
sentence = decode(feature,[],decoder,vocab)
print (sentence)
user_input = raw_input("Does it make sense to you?(y/n)\n")
if str(user_input) == "n":
f = open('data/step_1/caption_1.txt','r')
ground_true = f.read()
teach_wordid = []
teach_wordid.append(vocab.word2idx["<start>"])
while(True):
print "This is the ground true:\n"+ground_true+"\n"+\
"###################################################\n"
reference = ground_true.split()
hypothesis = sentence.split()
BLEUscore = nltk.translate.bleu_score.sentence_bleu([reference], hypothesis)
print "Current BLEU score is "+str(BLEUscore)
word = raw_input("next word:\n")
word_idx = vocab.word2idx[word]
teach_wordid.append(word_idx)
sentence = decode(feature,teach_wordid,decoder,vocab)
print "###################################################\n"
print "Current Translated sentence is: \n"+sentence+"\n"
def main():
st.title('Image Captioning App')
st.markdown(STYLE, unsafe_allow_html=True)
file = st.file_uploader("Upload file", type=["png", "jpg", "jpeg"])
show_file = st.empty()
if not file:
show_file.info("Please upload a file of type: " +
", ".join(["png", "jpg", "jpeg"]))
return
content = file.getvalue()
show_file.image(file)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
encoder_file = 'encoder-5-batch-128-hidden-256-epochs-5.pkl'
decoder_file = 'decoder-5-batch-128-hidden-256-epochs-5.pkl'
embed_size = 300
hidden_size = 256
vocab_size, word2idx, idx2word = get_vocab()
encoder = EncoderCNN(embed_size)
encoder.eval()
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
decoder.eval()
encoder.load_state_dict(torch.load(os.path.join('./models', encoder_file)))
decoder.load_state_dict(torch.load(os.path.join('./models', decoder_file)))
encoder.to(device)
decoder.to(device)
transform_test = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
PIL_image = Image.open(file).convert('RGB')
orig_image = np.array(PIL_image)
image = transform_test(PIL_image)
image = image.to(device).unsqueeze(0)
features = encoder(image).unsqueeze(1)
output = decoder.sample(features)
sentence = clean_sentence(output, idx2word)
st.info("Generated caption --> " + sentence)
file.close()
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare Image
image = load_image(args.image, transform)
image_tensor = to_var(image, volatile=True)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
bar = Bar('Processing', max=100)
for i in range(100):
bar.next()
# Generate caption from image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids.cpu().data.numpy()
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
bar.finish()
# Print out image and generated caption.
print("\n")
print(sentence)
image = Image.open(args.image)
imgplot = plt.imshow(np.asarray(image))
plt.show()
def load_coco_encoder(self):
# Hard-code embedding size to that used in pretrained model at path
init = torch.load('/data/rxdh/conventions_data/encoder-5-3000.pkl')
encoder = EncoderCNN(256).to('cuda')
encoder.eval()
encoder.load_state_dict(init)
self.transform = transforms.Compose([
transforms.CenterCrop(self.imsize),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
return encoder
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.Scale(args.crop_size),
#transforms.CenterCrop(args.crop_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare Image
image = load_image(args.image, transform)
image_tensor = to_var(image, volatile=True)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
image_tensor = image_tensor.cuda()
# Generate caption from image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature, args.length)
sampled_ids = sampled_ids.cpu().data.numpy()
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
if word != '<start>' and word != '<end>':
sampled_caption.append(word)
if word == '<end>':
break
sentence = ''.join(sampled_caption)
# Print out image and generated caption.
print(sentence)
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args.embed_size)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size,
len(vocab), args.num_layers)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare Image
image = load_image(args.image, transform)
image_tensor = to_var(image, volatile=True)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Generate caption from image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids.cpu().data.numpy()
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
# Print out image and generated caption.
print (sentence)
image = Image.open(args.image)
plt.imshow(np.asarray(image))
def encode(img,vocab):
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
encoder = EncoderCNN(256)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
encoder.load_state_dict(torch.load('../models/encoder-4-3000.pkl'))
image = load_image(img, transform)
image_tensor = to_var(image, volatile=True)
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
feature = encoder(image_tensor)
return feature
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build models
encoder = EncoderCNN(
args.embed_size) # eval mode (batchnorm uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
encoder = encoder.to(device)
decoder = decoder.to(device)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
encoder.eval()
decoder.load_state_dict(torch.load(args.decoder_path))
# Prepare an image
image = load_image(args.image, transform)
image_tensor = image.to(device)
# Generate an caption from the image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids[0].cpu().numpy(
) # (1, max_seq_length) -> (max_seq_length)
# Convert word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
# Print out the image and the generated caption
print(sentence)
image = Image.open(args.image)
plt.imshow(np.asarray(image))
def generatecaption(image):
# Image preprocessing
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
# Load vocabulary wrapper
with open('/root/ImageCaptioning/data/vocab.pkl', 'rb') as f:
vocab = pickle.load(f)
# Build models
encoder = EncoderCNN(256).eval() # eval mode (batchnorm uses moving mean/variance)
decoder = DecoderRNN(256, 512, len(vocab), 1)
encoder = encoder.to(device)
decoder = decoder.to(device)
# Load the trained model parameters
encoder.load_state_dict(torch.load('models/encoder-5-3000.pkl', map_location='cpu'))
decoder.load_state_dict(torch.load('models/decoder-5-3000.pkl', map_location='cpu'))
encoder.eval()
decoder.eval()
# Prepare an image
image = load_image(image, transform)
image_tensor = image.to(device)
# Generate an caption from the image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids[0].cpu().numpy() # (1, max_seq_length) -> (max_seq_length)
# Convert word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
self.sentence = ' '.join(sampled_caption)
# Print out the image and the generated caption
self.Entry1.delete(0, END)
self.Entry1.insert(0,self.sentence[7:-5])
def get_text_caption(image):
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build models
encoder = EncoderCNN(
args.embed_size, args.model_type,
args.mode) # eval mode (batchnorm uses moving mean/variance)
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
encoder = encoder.to(device)
decoder = decoder.to(device)
# Load the trained model parameters
encoder.load_state_dict(
torch.load(args.model_path + "_" + args.model_type + "/encoder.pt"))
encoder.eval()
decoder.load_state_dict(
torch.load(args.model_path + "_" + args.model_type + "/decoder.pt"))
decoder.eval()
# Prepare an image
image_tensor = image.to(device)
# Generate an caption from the image
feature = encoder(image_tensor)
sampled_ids = decoder.sample(feature)
sampled_ids = sampled_ids[0].cpu().numpy(
) # (1, max_seq_length) -> (max_seq_length)
print(sampled_ids)
# Convert word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
return (sentence.split("<start> ")[1].split(" <end>")[0]
[:-2].capitalize().replace(" , ", ", "))
class Annotator():
def __init__(self):
self.transform = transforms.Compose([
transforms.Resize(256), # smaller edge of image resized to 256
transforms.CenterCrop(224), # get 224x224 crop from the center
transforms.ToTensor(), # convert the PIL Image to a tensor
transforms.Normalize((0.485, 0.456, 0.406), # normalize image for pre-trained model
(0.229, 0.224, 0.225))])
# Load cherckpoint with best model
self.checkpoint = torch.load(os.path.join('./models', 'best-model.pkl'), 'cpu')
# Specify values for embed_size and hidden_size - we use the same values as in training step
self.embed_size = 512
self.hidden_size = 512
# Get the vocabulary and its size
self.vocab = Vocabulary(None, './vocab.pkl', "<start>", "<end>", "<unk>", "<pad>", "", "", True)
self.vocab_size = len(self.vocab)
# Initialize the encoder and decoder, and set each to inference mode
self.encoder = EncoderCNN(self.embed_size)
self.encoder.eval()
self.decoder = DecoderRNN(self.embed_size, self.hidden_size, self.vocab_size)
self.decoder.eval()
# Load the pre-trained weights
self.encoder.load_state_dict(self.checkpoint['encoder'])
self.decoder.load_state_dict(self.checkpoint['decoder'])
# Move models to GPU if CUDA is available.
#if torch.cuda.is_available():
# encoder.cuda()
# decoder.cuda()
def annotate(self, image):
transformed = self.transform(image).unsqueeze(0)
features = self.encoder(transformed).unsqueeze(1)
# Pass the embedded image features through the model to get a predicted caption.
output = self.decoder.sample_beam_search(features)
print('example output:', output)
sentence = clean_sentence(output[0], self.vocab)
print('example sentence:', sentence)
return sentence
def get_caption(self, img_tensor):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
print("running")
# Models
encoder_file = 'legit_model/encoder_1.pkl'
decoder_file = 'legit_model/decoder_1.pkl'
# Embed and hidden
embed_size = 512
hidden_size = 512
# The size of the vocabulary.
vocab_size = 8856
# Initialize the encoder and decoder, and set each to inference mode.
encoder = EncoderCNN(embed_size)
encoder.eval()
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
decoder.eval()
# Load the trained weights.
encoder.load_state_dict(
torch.load(os.path.join('./models', encoder_file)))
decoder.load_state_dict(
torch.load(os.path.join('./models', decoder_file)))
# Move models to GPU if CUDA is available.
encoder.to(device)
decoder.to(device)
img_d = img_tensor.to(device)
# Obtain the embedded image features.
features = encoder(img_d).unsqueeze(1)
# Pass the embedded image features through the model to get a predicted caption.
img_output = decoder.sample(features)
sentence = self.clean_sentence(img_output)
return sentence
def initialize():
checkpoint = torch.load(os.path.join('./models', 'best-model.pkl'), map_location=torch.device('cpu'))
embed_size = 256
hidden_size = 512
with open('./vocab.pkl', "rb") as f:
vocab = pickle.load(f)
vocab_size = len(vocab)
encoder = EncoderCNN(embed_size)
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
encoder.eval()
decoder.eval()
encoder.load_state_dict(checkpoint['encoder'])
decoder.load_state_dict(checkpoint['decoder'])
return encoder, decoder, vocab
def get_model(device,vocab_size):
# model weights file
encoder_file = "models/encoder-3.pkl"
decoder_file = "models/decoder-3.pkl"
embed_size = 512
hidden_size = 512
# Initialize the encoder and decoder, and set each to inference mode.
encoder = EncoderCNN(embed_size)
encoder.eval()
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
decoder.eval()
# Load the trained weights.
#print(torch.load(encoder_file))
encoder.load_state_dict(torch.load(encoder_file))
decoder.load_state_dict(torch.load(decoder_file))
# Move models to GPU if CUDA is available.
encoder.to(device)
decoder.to(device)
return encoder,decoder
# The size of the vocabulary.
vocab_size = len(vocab)
# Initialize the encoder and decoder.
encoder = EncoderCNN(embed_size)
decoder = DecoderRNN(embed_size, hidden_size, vocab_size)
device = torch.device("cpu")
# encoder.to(device)
# decoder.to(device)
# Load the pretrained model
encoder.load_state_dict(torch.load(PRETRAINED_MODEL_PATH.format('encoder')))
decoder.load_state_dict(torch.load(PRETRAINED_MODEL_PATH.format('decoder')))
encoder.eval()
decoder.eval()
images, conv_images = next(iter(data_loader))
features = encoder(conv_images).unsqueeze(1)
output = decoder.sample(features, max_len=max_len)
word_list = []
for word_idx in output:
if word_idx == vocab.word2idx[vocab.start_word]:
continue
if word_idx == vocab.word2idx[vocab.end_word]:
break
word_list.append(vocab.idx2word[word_idx])
print(' '.join(word_list))
def main():
# Configuration for hyper-parameters
config = Config()
# Image preprocessing
transform = config.train_transform
# Load vocabulary wrapper
with open(os.path.join(config.vocab_path, 'vocab.pkl'), 'rb') as f:
vocab = pickle.load(f)
# Build data loader
image_path = os.path.join(config.image_path, 'train2014')
json_path = os.path.join(config.caption_path, 'captions_train2014.json')
train_loader = get_data_loader(image_path, json_path, vocab,
transform, config.batch_size,
shuffle=True, num_workers=config.num_threads)
total_step = len(train_loader)
# Build Models
teachercnn = EncoderCNN(config.embed_size)
teachercnn.eval()
studentcnn = StudentCNN_Model1(config.embed_size)
#Load the best teacher model
teachercnn.load_state_dict(torch.load(os.path.join('../TrainedModels/TeacherCNN', config.trained_encoder)))
studentlstm = DecoderRNN(config.embed_size, config.hidden_size/2,
len(vocab), config.num_layers/2)
if torch.cuda.is_available():
teachercnn.cuda()
studentcnn.cuda()
studentlstm.cuda()
# Loss and Optimizer
criterion_lstm = nn.CrossEntropyLoss()
criterion_cnn = nn.MSELoss()
params = list(studentlstm.parameters()) + list(studentcnn.parameters())
optimizer_lstm = torch.optim.Adam(params, lr=config.learning_rate)
optimizer_cnn = torch.optim.Adam(studentcnn.parameters(), lr=config.cnn_learningrate)
print('entering in to training loop')
# Train the Models
for epoch in range(config.num_epochs):
for i, (images, captions, lengths, img_ids) in enumerate(train_loader):
images = Variable(images)
captions = Variable(captions)
if torch.cuda.is_available():
images = images.cuda()
captions = captions.cuda()
targets = pack_padded_sequence(captions, lengths, batch_first=True)[0]
# Forward, Backward and Optimize
optimizer_lstm.zero_grad()
optimizer_cnn.zero_grad()
features_tr = teachercnn(images)
features_st = studentcnn(images)
outputs = studentlstm(features_st, captions, lengths)
loss = criterion(features_st, features_tr.detach()) + criterion_lstm(outputs, targets)
loss.backward()
optimizer_cnn.step()
optimizer_lstm.step()
# Print log info
if i % config.log_step == 0:
print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f'
%(epoch, config.num_epochs, i, total_step,
loss.data[0], np.exp(loss.data[0])))
# Save the Model
if (i+1) % config.save_step == 0:
torch.save(studentlstm.state_dict(),
os.path.join(config.student_lstm_path,
'decoder-%d-%d.pkl' %(epoch+1, i+1)))
torch.save(studentcnn.state_dict(),
os.path.join(config.student_cnn_path,
'encoder-%d-%d.pkl' %(epoch+1, i+1)))
if checkpoints:
for cp in checkpoints:
name, num = cp[:-4].split('_')
num = int(num)
if name == model_name and model_idx == num:
state_dict = torch.load(
'checkpoint/{}_{}.tar'.format(model_name, num))
encoder.load_state_dict(state_dict['encoder_state_dict'])
decoder.load_state_dict(state_dict['decoder_state_dict'])
#optimizer.load_state_dict(state_dict['optimizer_state_dict'])
print('model_{}_{} is being used'.format(name,state_dict['epoch']))
break
# test
decoder.eval()
encoder.eval()
with torch.no_grad():
all_ref = []
all_pred = []
#print('to device finish')
for i, (images, batch_captions) in enumerate(BLEU4loader):
if i >= 40:
continue
all_ref.extend(batch_captions)
images = images.to(device)
#all_ref.extend(batch_captions)
# Generate an caption from the image
feature = encoder(images)
all_pred.extend(decoder.beam_search(feature))
def main(args):
# Create model directory
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
# Load vocabulary wrapper.
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Image preprocessing
# For normalization, see https://github.com/pytorch/vision#models
transform = transforms.Compose([
transforms.RandomCrop(args.crop_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
val_loader = get_loader('./data/val_resized2014/',
'./data/annotations/captions_val2014.json', vocab,
transform, 1, False, 1)
start_epoch = 0
encoder_state = args.encoder
decoder_state = args.decoder
# Build the models
encoder = EncoderCNN(args.embed_size)
if not args.train_encoder:
encoder.eval()
decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab),
args.num_layers)
if args.restart:
encoder_state, decoder_state = 'new', 'new'
if encoder_state == '': encoder_state = 'new'
if decoder_state == '': decoder_state = 'new'
if decoder_state != 'new':
start_epoch = int(decoder_state.split('-')[1])
print("Using encoder: {}".format(encoder_state))
print("Using decoder: {}".format(decoder_state))
# Build data loader
data_loader = get_loader(args.image_dir,
args.caption_path,
vocab,
transform,
args.batch_size,
shuffle=True,
num_workers=args.num_workers)
""" Make logfile and log output """
with open(args.model_path + args.logfile, 'a+') as f:
f.write("Training on vanilla loss (using new model). Started {} .\n".
format(str(datetime.now())))
f.write("Using encoder: new\nUsing decoder: new\n\n")
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
params = list(decoder.parameters()) + list(
encoder.linear.parameters()) + list(encoder.bn.parameters())
optimizer = torch.optim.Adam(params, lr=args.learning_rate)
batch_loss = []
batch_acc = []
# Train the Models
total_step = len(data_loader)
for epoch in range(start_epoch, args.num_epochs):
for i, (images, captions, lengths, _, _) in enumerate(data_loader):
# Set mini-batch dataset
images = to_var(images, volatile=True)
captions = to_var(captions)
targets = pack_padded_sequence(captions, lengths,
batch_first=True)[0]
# Forward, Backward and Optimize
decoder.zero_grad()
encoder.zero_grad()
features = encoder(images)
out = decoder(features, captions, lengths)
loss = criterion(out, targets)
batch_loss.append(loss.data[0])
loss.backward()
optimizer.step()
# # Print log info
# if i % args.log_step == 0:
# print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f, Val: %.5f, %.5f'
# %(epoch, args.num_epochs, i, total_step,
# loss.data[0], np.exp(loss.data[0]), acc, gt_acc))
# with open(args.model_path + args.logfile, 'a') as f:
# f.write('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f, Val: %.5f, %.5f\n'
# %(epoch, args.num_epochs, i, total_step,
# loss.data[0], np.exp(loss.data[0]), acc, gt_acc))
# Save the models
if (i + 1) % args.save_step == 0:
torch.save(
decoder.state_dict(),
os.path.join(args.model_path,
'decoder-%d-%d.pkl' % (epoch + 1, i + 1)))
torch.save(
encoder.state_dict(),
os.path.join(args.model_path,
'encoder-%d-%d.pkl' % (epoch + 1, i + 1)))
with open(args.model_path + 'training_loss.pkl', 'w+') as f:
pickle.dump(batch_loss, f)
with open(args.model_path + 'training_val.pkl', 'w+') as f:
pickle.dump(batch_acc, f)
with open(args.model_path + args.logfile, 'a') as f:
f.write("Training finished at {} .\n\n".format(str(datetime.now())))
def _main():
parser = argparse.ArgumentParser()
parser.add_argument("filename", help="(optional) path to photograph, for which a caption will be generated", nargs = "?")
parser.add_argument("--host", help="(optional) host to start a webserver on. Default: 0.0.0.0", nargs = "?", default = "0.0.0.0")
parser.add_argument("--port", help="(optional) port to start a webserver on. http://hostname:port/query", nargs = "?", type = int, default = 1985)
parser.add_argument("--verbose", "-v", help="print verbose query information", action="store_true")
global _args
_args = parser.parse_args()
if not _args.filename and not _args.port:
parser.print_help()
sys.exit(-1)
global _device
_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("PyTorch device = ", _device)
# Load the vocabulary dictionary
vocab_threshold = None,
vocab_file = "./vocab.pkl"
start_word = "<start>"
end_word = "<end>"
unk_word = "<unk>"
load_existing_vocab = True
#annotations_file = "/opt/cocoapi/annotations/captions_train2014.json"
annotations_file = None
print("Loading vocabulary...")
global _vocab
_vocab = Vocabulary(vocab_threshold, vocab_file, start_word, end_word, unk_word, annotations_file, load_existing_vocab)
vocab_size = len (_vocab)
print("Vocabulary contains %d words" % vocab_size)
# Load pre-trained models:
# encoder (Resnet + embedding layers)
# decoder (LSTM)
global _encoder
global _decoder
encoder_path = os.path.join("./models/", _encoder_file)
decoder_path = os.path.join("./models/", _decoder_file)
print("Loading ", encoder_path)
_encoder = EncoderCNN(_embed_size)
_encoder.load_state_dict(torch.load(encoder_path))
_encoder.eval()
_encoder.to(_device)
print("Loading ", decoder_path)
_decoder = DecoderRNN(_embed_size, _hidden_size, vocab_size, _num_layers)
_decoder.load_state_dict(torch.load(decoder_path))
_decoder.eval()
_decoder.to(_device)
# Caption the photo, or start a web server if no photo specified
if _args.filename:
_get_prediction_from_file(_args.filename)
else:
global _app
global _api
_app = Flask(__name__)
_api = Api(_app)
_api.add_resource(ImageCaptionResource,
"/v1/caption",
"/v1/caption/")
_app.run(host = _args.host, port = _args.port)
def extract(args):
# Image preprocessing
transform = transforms.Compose([
transforms.Resize(SIZE),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
# Load vocabulary wrapper
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build models
encoder = EncoderCNN(args.embed_size).eval(
) # eval mode (batchnorm uses moving mean/variance)
# decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab), args.num_layers)
dissection.retain_layers(encoder, [
('resnet.7.2.relu', 'final_layer'),
])
encoder = encoder.to(device)
# decoder = decoder.to(device)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
# decoder.load_state_dict(torch.load(args.decoder_path))
encoder.eval()
encoder = encoder.to(device)
# decoder = decoder.to(device)
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder_path))
# decoder.load_state_dict(torch.load(args.decoder_path))
# Load data
data_loader = get_loader(args.image_dir,
args.caption_path,
vocab,
transform,
args.batch_size,
shuffle=False,
num_workers=args.num_workers)
# Run the models
with torch.no_grad():
total_step = len(data_loader)
os.makedirs(os.path.join(PARENT_DIR, 'results', 'activations'),
exist_ok=True)
path = os.path.join(PARENT_DIR, 'results', 'samples.txt')
with open(path, 'w') as results_file:
start = time.time()
for batch, (images, captions, lengths) in enumerate(data_loader):
# Set mini-batch dataset
images = images.to(device)
# captions = captions.to(device)
# targets = pack_padded_sequence(captions, lengths, batch_first=True)[0]
# Forward, backward and optimize
features = encoder(images)
# outputs = decoder(features, captions, lengths)
# loss = criterion(outputs, targets)
# decoder.zero_grad()
# encoder.zero_grad()
# loss.backward()
# optimizer.step()
activations = encoder.retained['final_layer']
images = dissection.ReverseNormalize(
(0.485, 0.456, 0.406), (0.229, 0.224, 0.225))(images)
images = images.cpu().numpy().transpose([0, 2, 3, 1])
activations = activations.cpu().numpy()
scores = np.max(activations, axis=(-1, -2))
samples = np.argmax(scores, axis=-1)
gathered = activations[np.arange(len(samples)),
samples].transpose([1, 2, 0])
mask = cv2.resize(gathered, SIZE).transpose([2, 0, 1])
k = int(0.8 * mask.size)
threshhold = np.partition(mask, k, axis=None)[k]
mask = mask >= threshhold
mask = np.expand_dims(mask, axis=-1)
outimg = np.concatenate((images, (1 + mask) / 2.), axis=-1)
# outimg = outimg * mask
activations = outimg
for i, sample in enumerate(samples):
i += args.batch_size * batch
results_file.write('{} {}\n'.format(i, sample))
for i, activation in enumerate(activations):
i += args.batch_size * batch
path = os.path.join(PARENT_DIR, 'results', 'activations',
'{}.png'.format(i))
outactivation = skimage.img_as_ubyte(activation)
imageio.imwrite(path, outactivation)
clock = time.time()
delay = clock - start
start = clock
max_batch = 100
# print('Step {}/{}: Time = {:.2f}'.format(batch, len(data_loader), delay))
print('Step {}/{}: Time = {:.2f}'.format(
batch, max_batch, delay))
if batch == max_batch:
break
def main():
#write predicted caption
if not os.path.exists(args['generate_caption_path']):
os.makedirs(args['generate_caption_path'])
caption_string = os.path.join(args['generate_caption_path'], "caption_ncrt_class5.txt")
#mode = "a" if os.path.exists(caption_string) else "w"
fp =open(caption_string, "w+")
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.9638, 0.9638, 0.9638),
(0.1861, 0.1861, 0.1861))])
# Load vocabulary wrapper
with open(args['vocab_path'], 'rb') as f:
vocab = pickle.load(f)
# Build Models
encoder = EncoderCNN(args['embed_size'])
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(args['embed_size'], args['hidden_size'],
len(vocab), args['num_layers'], max_seq_length=50)
decoder.eval()
# Load the trained model parameters
encoder.load_state_dict(torch.load(args['encoder_path']))
decoder.load_state_dict(torch.load(args['decoder_path']))
# If use gpu
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Prepare Image
image_dir = args['image_path']
images = os.listdir(image_dir)
i = 1
for image_id in images:
#print('i->',i)
#i = i+1
if not image_id.endswith('.jpg'):
continue
image = os.path.join(image_dir, image_id)
image = load_image(image, transform)
image_tensor = image.cuda()
# Generate caption from image
try:
feature, cnn_features = encoder(image_tensor)
sampled_ids = decoder.sample(feature, cnn_features)
sampled_ids = sampled_ids.cpu().data.numpy()
except:
continue
#print('image_ids->',image_id)
# Decode word_ids to words
sampled_caption = []
for word_id in sampled_ids:
word = vocab.idx2word[word_id]
sampled_caption.append(word)
if word == '<end>':
break
sentence = ' '.join(sampled_caption)
print ('i->', i, image_id + '\t' + sentence)
fp.write(image_id)
fp.write('\t')
fp.write(sentence)
if i<398:
fp.write("\n")
i = i+1
fp.close()
def main(args):
# Image preprocessing
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
'''
# Load vocabulary wrapper
with open(args.inverse_object_id_mapping, 'rb') as f:
inverse_object_id_mapping = pickle.load(f)
num_objects = len(inverse_object_id_mapping.keys())
'''
# Build models
encoderCNN = EncoderCNN(args.embed_size).eval(
) # eval mode (batchnorm uses moving mean/variance)
encoderRNN = EncoderRNN(num_objects, args.embed_size, args.hidden_size)
model = Model(num_objects, args.embed_size)
encoderCNN = encoderCNN.to(device)
encoderRNN = encoderRNN.to(device)
model = model.to(device)
encoderCNN.eval()
encoderRNN.eval()
model.eval()
# Load the trained model parameters
encoderCNN.load_state_dict(torch.load(args.encoderCNN_path))
encoderRNN.load_state_dict(torch.load(args.encoderRNN_path))
model.load_state_dict(torch.load(args.model_path))
# Prepare an image
image = load_image(args.image, transform)
image_tensor = image.to(device)
# Generate an caption from the image
image_features = encoderCNN(image_tensor)
input = torch.LongTensor([[[1]]]).to(device)
h0 = torch.zeros((1, 1, args.hidden_size)).to(device)
c0 = torch.zeros((1, 1, args.hidden_size)).to(device)
max_seqlen = 10
result = []
K = 3
all_candidates = [([1], 1.0, h0, c0) for i in range(K)]
for i in range(max_seqlen):
Q = []
for _k in range(K):
if i == 0 and _k == 1: # first word
break
hashtag_features, (h0,
c0), Ul = encoderRNN(input[_k],
all_candidates[_k][2],
all_candidates[_k][3])
outputs = model(image_features, hashtag_features, Ul)
prob, topk = torch.topk(outputs, 20, dim=1)
tup = list(zip(topk[0].cpu().tolist(), prob[0].cpu().tolist()))
topk = [a for a in tup if a[0] not in all_candidates[_k][0]]
try:
topk.remove(1)
topk.remove(0)
except:
pass
for _k_ in range(K):
Q.append((all_candidates[_k][0] + [topk[_k_][0]],
abs(all_candidates[_k][1] * topk[_k_][1]), h0, c0))
all_candidates = sorted(Q, key=lambda x: x[1], reverse=True)[:K]
input = []
for _k in range(K):
input.append([[all_candidates[_k][0][-1]]])
input = torch.LongTensor(input).to(device)
#result.append(top1.cpu().numpy()[0][0])
result = sorted(all_candidates, key=lambda x: x[1], reverse=True)
result = [i[0] for i in result]
print(result)
for i in range(K):
tmp = [inverse_object_id_mapping[j] for j in result[i]]
final = zip([j['name'] for j in tmp],
[j['supercategory'] for j in tmp])
for j in final:
print(j)
print("-" * 50)
image = Image.open(args.image)
plt.imshow(np.asarray(image))
def main(args):
with open('./data/vocab.pkl', 'rb') as f:
vocab = pickle.load(f)
encoder = EncoderCNN(256)
encoder.eval() # evaluation mode (BN uses moving mean/variance)
decoder = DecoderRNN(256, 512, len(vocab), 1)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Load the trained model parameters
encoder.load_state_dict(torch.load(args.encoder))
decoder.load_state_dict(torch.load(args.decoder))
if args.test_prop0:
decoder.test_h_from_c()
return
if args.test_c_step:
data_points = test(encoder, decoder, vocab, args.num_samples,
args.num_hints)
with open(args.filepath, 'w+') as f:
pickle.dump(data_points, f)
print("Done sampling for c_step evaluation. Data saved to {}".format(
args.filepath))
return
measurement_score = test(encoder, decoder, vocab, args.num_samples,
args.num_hints, args.debug, args.c_step,
args.no_avg)
if args.msm == "ps":
if not args.no_avg:
print "ground truth prediction score without update\n" + str(
measurement_score[0])
print "ground truth prediction score with update\n" + str(
measurement_score[1])
print "Difference\n" + str(measurement_score[1] -
measurement_score[0])
else:
with open(args.filepath, 'w+') as f:
pickle.dump(measurement_score, f)
print "Done. Data saved to {}".format(args.filepath)
elif args.msm == "ce":
if not args.no_avg:
print "Cross Entropy Loss without update\n" + str(
measurement_score[0])
print "Cross Entropy Loss with update\n" + str(
measurement_score[1])
print "Difference\n" + str(measurement_score[1] -
measurement_score[0])
else:
with open(args.filepath, 'w+') as f:
pickle.dump(measurement_score, f)
print "Done. Data saved to {}".format(args.filepath)
elif args.msm == "co":
scores = cocoEval()
scores_u = cocoEval(res='data/captions_val2014_results_u.json')
print(scores)
print(scores_u)
def main():
# Configuration for hyper-parameters
torch.cuda.set_device(0)
config = Config()
# Image preprocessing
transform = config.train_transform
# Load vocabulary wrapper
with open(os.path.join(config.vocab_path, 'vocab.pkl'), 'rb') as f:
vocab = pickle.load(f)
# Build data loader
train_image_path = os.path.join(config.image_path, 'train2017')
json_path = os.path.join(config.caption_path, 'captions_train2017.json')
train_loader = get_data_loader(train_image_path,
json_path,
vocab,
transform,
config.batch_size,
shuffle=False,
num_workers=config.num_threads)
val_image_path = os.path.join(config.image_path, 'val2017')
json_path = os.path.join(config.caption_path, 'captions_val2017.json')
val_loader = get_data_loader(val_image_path,
json_path,
vocab,
transform,
config.batch_size,
shuffle=False,
num_workers=config.num_threads)
total_step = len(train_loader)
# Build Models
encoder = EncoderCNN(config.embed_size)
encoder.eval()
decoder = DecoderRNN(config.embed_size, config.hidden_size, len(vocab),
config.num_layers)
if torch.cuda.is_available():
encoder.cuda()
decoder.cuda()
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
params = list(decoder.parameters()) + list(encoder.resnet.fc.parameters())
optimizer = torch.optim.Adam(params, lr=config.learning_rate)
print('entering in to training loop')
# Train the Models
with open('train1_log.txt', 'w') as logfile:
logfile.write('Validation Error,Training Error')
for epoch in range(0, 25):
for i, (images, captions, lengths,
img_ids) in enumerate(train_loader):
images = Variable(images)
captions = Variable(captions)
if torch.cuda.is_available():
images = images.cuda()
captions = captions.cuda()
targets = pack_padded_sequence(captions,
lengths,
batch_first=True)[0]
# Forward, Backward and Optimize
optimizer.zero_grad()
features = encoder(images)
outputs = decoder(features, captions, lengths)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
# Print log info
if i % config.log_step == 0:
print(
'Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f'
% (epoch, config.num_epochs, i, total_step,
loss.data[0], np.exp(loss.data[0])))
# Save the Model
if (i + 1) % config.save_step == 0:
torch.save(
encoder.state_dict(),
os.path.join(config.teacher_cnn_path,
'encoder-%d-%d.pkl' % (epoch + 1, i + 1)))
torch.save(
decoder.state_dict(),
os.path.join(config.teacher_lstm_path,
'decoder-%d-%d.pkl' % (epoch + 1, i + 1)))
print('Just Completed an Epoch, Initite Validation Error Test')
avgvalloss = 0
for j, (images, captions, lengths,
img_ids) in enumerate(val_loader):
images = Variable(images)
captions = Variable(captions)
if torch.cuda.is_available():
images = images.cuda()
captions = captions.cuda()
targets = pack_padded_sequence(captions,
lengths,
batch_first=True)[0]
optimizer.zero_grad()
features = encoder(images)
outputs = decoder(features, captions, lengths)
valloss = criterion(outputs, targets)
if j == 0:
avgvalloss = valloss.data[0]
avgvalloss = (avgvalloss + valloss.data[0]) / 2
if ((j + 1) % 1000 == 0):
print('Average Validation Loss: %.4f' % (avgvalloss))
logfile.write(
str(avgvalloss) + ',' + str(loss.data[0]) + str('\n'))
break