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main.py
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# =============================================================================
# Import required libraries
# =============================================================================
import argparse
import os
import numpy as np
import torch
from torch import nn
from datasets import *
from utils import *
from models import *
from models_attention import *
from beam_search import *
from engine import Engine
# checking the availability of GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# =============================================================================
# Define hyperparameters
# =============================================================================
parser = argparse.ArgumentParser(
description='PyTorch Training for Automatic Image Annotation')
parser.add_argument('--seed', default=1, type=int,
help='seed for initializing training')
parser.add_argument('--data_root_dir', default='./Corel-5k/', type=str)
parser.add_argument('--image-size', default=448, type=int)
parser.add_argument('--epochs', default=80, type=int)
parser.add_argument('--batch-size', default=32, type=int)
parser.add_argument('--encoder-lr', default=1e-4, type=float)
parser.add_argument('--decoder-lr', default=1e-4, type=float)
parser.add_argument('--num-workers', default=2, type=int,
help='number of data loading workers (default: 2)')
parser.add_argument('--beam-width', default=10, type=int)
parser.add_argument('--max-seq-len', metavar='NAME', type=int,
help='maximum sequence length (e.g. 5)')
parser.add_argument('--method', metavar='NAME', help='method name (e.g. RIA)')
parser.add_argument('--order-free', metavar='NAME')
parser.add_argument('--sort', dest='sort', action='store_true',
help='sorting labels by frequency')
parser.add_argument('--is_glove', dest='is_glove', action='store_true',
help='utilizing GLOVE pre-trained weights in the embedding matrix')
parser.add_argument('--evaluate', dest='evaluate', action='store_true',
help='evaluation of the model on the validation set')
parser.add_argument(
'--save_dir', default='./checkpoints/', type=str, help='save path')
def main(args):
if args.seed is not None:
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
is_train = True if not args.evaluate else False
train_loader, validation_loader, classes, word_map = make_data_loader(args)
#
if (args.method == 'RIA' or args.method == 'SR-CNN-RNN'):
input_dim = 260+2
hidden_dim = 2048
output_dim = 260+1
cnn = TResNet(args, pretrained=is_train, num_classes=len(classes))
elif args.method == 'Attention':
input_dim = 260+2
hidden_dim = 2048
output_dim = 260+1
attention_dim = 1024
cnn = TResNet_att(args, pretrained=is_train)
#
if args.is_glove:
emb_dim = 300
if os.path.exists('./glove/Corel-5k_glove.pkl'):
glove_weights = torch.load('./glove/Corel-5k_glove.pkl')
else:
# download "glove.6B.300d.txt" and place it in glove folder
glove_weights = word_embedding(
'./glove/glove.6B.300d.txt', classes)
torch.save(glove_weights, './glove/Corel-5k_glove.pkl')
#
if (args.method == 'RIA' or args.method == 'SR-CNN-RNN'):
lstm = Anotator(args,
input_size=input_dim,
hidden_size=hidden_dim,
output_size=output_dim,
num_classes=len(classes),
emb_size=emb_dim,
is_glove=args.is_glove,
glove_weights=glove_weights)
elif args.method == 'Attention':
lstm = Anotator_att(args,
input_size=input_dim,
hidden_size=hidden_dim,
output_size=output_dim,
attention_size=attention_dim,
emb_size=emb_dim,
is_glove=args.is_glove,
glove_weights=glove_weights)
else:
emb_dim = 1024
if (args.method == 'RIA' or args.method == 'SR-CNN-RNN'):
lstm = Anotator(args,
input_size=input_dim,
hidden_size=hidden_dim,
output_size=output_dim,
num_classes=len(classes),
emb_size=emb_dim,
is_glove=args.is_glove)
elif args.method == 'Attention':
lstm = Anotator_att(args,
input_size=input_dim,
hidden_size=hidden_dim,
output_size=output_dim,
attention_size=attention_dim,
emb_size=emb_dim,
is_glove=args.is_glove)
criterion_1 = nn.CrossEntropyLoss()
criterion_2 = nn.MultiLabelSoftMarginLoss()
criterion = (criterion_1, criterion_2)
engine = Engine(args,
cnn,
lstm,
criterion,
train_loader,
validation_loader,
classes,
word_map)
if is_train:
engine.initialization()
engine.train_iteration()
else:
engine.initialization()
engine.load_model()
engine.validation(dataloader=validation_loader)
#
print('Applying beam search algorithm')
engine.beam_search_validation(dataloader=validation_loader,
beam_width=args.beam_width)
# show images and predicted labels
images, binary_annotations, _, _ = iter(validation_loader).next()
images = images.to(device)
#
if args.method == 'Attention':
for i in range(0, 32, 2):
output, alphas = annotate_image_beam_search(cnn,
lstm,
images[i],
word_map,
args.beam_width)
visualize_att(args,
images[i],
output,
alphas,
classes,
word_map,
smooth=False)
#
outputs = annotate_batch_beam_search(args,
cnn,
lstm,
images,
word_map,
args.beam_width)
batch_plot(args,
images,
outputs,
binary_annotations,
classes,
word_map)
if __name__ == "__main__":
args = parser.parse_args()
main(args)