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import time
import pickle
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
from PIL import Image
import cv2
from models import *
from dataset import *
from loss import *
from build_tag import *
from build_vocab import *
class CaptionSampler(object):
def __init__(self):
# Default configuration values
self.args = {
"model_dir": "model/",
"image_dir": "",
"caption_json": "",
"vocab_path": "vocab.pkl",
"file_lists": "",
"load_model_path": "train_best_loss.pth.tar",
"resize": 224,
"cam_size": 224,
"generate_dir": "cam",
"result_path": "results",
"result_name": "debug",
"momentum": 0.1,
"visual_model_name": "densenet201",
"pretrained": False,
"classes": 210,
"sementic_features_dim": 512,
"k": 10,
"attention_version": "v4",
"embed_size": 512,
"hidden_size": 512,
"sent_version": "v1",
"sentence_num_layers": 2,
"dropout": 0.1,
"word_num_layers": 1,
"s_max": 10,
"n_max": 30,
"batch_size": 8,
"lambda_tag": 10000,
"lambda_stop": 10,
"lambda_word": 1,
"cuda": False # Keep CUDA disabled by default
}
self.vocab = self.__init_vocab()
self.tagger = self.__init_tagger()
self.transform = self.__init_transform()
self.model_state_dict = self.__load_mode_state_dict()
self.extractor = self.__init_visual_extractor()
self.mlc = self.__init_mlc()
self.co_attention = self.__init_co_attention()
self.sentence_model = self.__init_sentence_model()
self.word_model = self.__init_word_word()
self.ce_criterion = self._init_ce_criterion()
self.mse_criterion = self._init_mse_criterion()
@staticmethod
def _init_ce_criterion():
return nn.CrossEntropyLoss(size_average=False, reduce=False)
@staticmethod
def _init_mse_criterion():
return nn.MSELoss()
def sample(self, image_file):
self.extractor.eval()
self.mlc.eval()
self.co_attention.eval()
self.sentence_model.eval()
self.word_model.eval()
imageData = self.transform(image_file)
imageData = imageData.unsqueeze_(0)
image = self.__to_var(imageData, requires_grad=False)
visual_features, avg_features = self.extractor.forward(image)
tags, semantic_features = self.mlc(avg_features)
sentence_states = None
prev_hidden_states = self.__to_var(torch.zeros(image.shape[0], 1, self.args["hidden_size"]))
pred_sentences = []
for i in range(self.args["s_max"]):
ctx, alpha_v, alpha_a = self.co_attention.forward(avg_features, semantic_features, prev_hidden_states)
topic, p_stop, hidden_state, sentence_states = self.sentence_model.forward(ctx,
prev_hidden_states,
sentence_states)
p_stop = p_stop.squeeze(1)
p_stop = torch.max(p_stop, 1)[1].unsqueeze(1)
start_tokens = np.zeros((topic.shape[0], 1))
start_tokens[:, 0] = self.vocab('<start>')
start_tokens = self.__to_var(torch.Tensor(start_tokens).long(), requires_grad=False)
sampled_ids = self.word_model.sample(topic, start_tokens)
prev_hidden_states = hidden_state
sampled_ids = sampled_ids * p_stop.numpy()
pred_sentences.append(self.__vec2sent(sampled_ids[0]))
return pred_sentences
def __init_cam_path(self, image_file):
generate_dir = os.path.join(self.args["model_dir"], self.args["generate_dir"])
if not os.path.exists(generate_dir):
os.makedirs(generate_dir)
image_dir = os.path.join(generate_dir, image_file)
if not os.path.exists(image_dir):
os.makedirs(image_dir)
return image_dir
def __save_json(self, result):
result_path = os.path.join(self.args["model_dir"], self.args["result_path"])
if not os.path.exists(result_path):
os.makedirs(result_path)
with open(os.path.join(result_path, '{}.json'.format(self.args["result_name"])), 'w') as f:
json.dump(result, f)
def __load_mode_state_dict(self):
try:
model_state_dict = torch.load(os.path.join(self.args["model_dir"], self.args["load_model_path"]), map_location=torch.device('cpu'))
print("[Load Model-{} Succeed!]".format(self.args["load_model_path"]))
print("Load From Epoch {}".format(model_state_dict['epoch']))
return model_state_dict
except Exception as err:
print("[Load Model Failed] {}".format(err))
raise err
def __init_tagger(self):
return Tag()
def __vec2sent(self, array):
sampled_caption = []
for word_id in array:
word = self.vocab.get_word_by_id(word_id)
if word == '<start>':
continue
if word == '<end>' or word == '<pad>':
break
sampled_caption.append(word)
return ' '.join(sampled_caption)
def __init_vocab(self):
with open('vocab.pkl', 'rb') as f:
vocab = pickle.load(f)
print(vocab)
return vocab
def __init_data_loader(self, file_list):
data_loader = get_loader(image_dir=self.args.image_dir,
caption_json=self.args.caption_json,
file_list=file_list,
vocabulary=self.vocab,
transform=self.transform,
batch_size=self.args.batch_size,
s_max=self.args.s_max,
n_max=self.args.n_max,
shuffle=False)
return data_loader
def __init_transform(self):
transform = transforms.Compose([
transforms.Resize((self.args["resize"], self.args["resize"])),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
return transform
def __to_var(self, x, requires_grad=True):
if self.args["cuda"]:
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
def __init_visual_extractor(self):
model = VisualFeatureExtractor(model_name=self.args["visual_model_name"],
pretrained=self.args["pretrained"])
if self.model_state_dict is not None:
print("Visual Extractor Loaded!")
model.load_state_dict(self.model_state_dict['extractor'])
if self.args["cuda"]:
model = model.cuda()
return model
def __init_mlc(self):
model = MLC(classes=self.args["classes"],
sementic_features_dim=self.args["sementic_features_dim"],
fc_in_features=self.extractor.out_features,
k=self.args["k"])
if self.model_state_dict is not None:
print("MLC Loaded!")
model.load_state_dict(self.model_state_dict['mlc'])
if self.args["cuda"]:
model = model.cuda()
return model
def __init_co_attention(self):
model = CoAttention(version=self.args["attention_version"],
embed_size=self.args["embed_size"],
hidden_size=self.args["hidden_size"],
visual_size=self.extractor.out_features,
k=self.args["k"],
momentum=self.args["momentum"])
if self.model_state_dict is not None:
print("Co-Attention Loaded!")
model.load_state_dict(self.model_state_dict['co_attention'])
if self.args["cuda"]:
model = model.cuda()
return model
def __init_sentence_model(self):
model = SentenceLSTM(version=self.args["sent_version"],
embed_size=self.args["embed_size"],
hidden_size=self.args["hidden_size"],
num_layers=self.args["sentence_num_layers"],
dropout=self.args["dropout"],
momentum=self.args["momentum"])
if self.model_state_dict is not None:
print("Sentence Model Loaded!")
model.load_state_dict(self.model_state_dict['sentence_model'])
if self.args["cuda"]:
model = model.cuda()
return model
def __init_word_word(self):
model = WordLSTM(vocab_size=len(self.vocab),
embed_size=self.args["embed_size"],
hidden_size=self.args["hidden_size"],
num_layers=self.args["word_num_layers"],
n_max=self.args["n_max"])
if self.model_state_dict is not None:
print("Word Model Loaded!")
model.load_state_dict(self.model_state_dict['word_model'])
if self.args["cuda"]:
model = model.cuda()
return model
def main(image):
sampler = CaptionSampler()
# image = 'sample_images/CXR195_IM-0618-1001.png'
caption = sampler.sample(image)
print(caption[0])
return caption[0]
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