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tester.py
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import argparse
import torch
import torchvision.transforms as transforms
import pickle
from utils.load_data import get_loader
from utils.models import EncoderCNN, Impression_Decoder, Atten_Sen_Decoder
from metrics import compute_metrics, generate_text_file
from IUdata.build_vocab import JsonReader, Vocabulary
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
import os
def test(img_en_path, imp_de_path, fin_de_path, args):
""""load trained models and generate impressions and findings for evaluating"""
test_transforms = transforms.Compose([
transforms.Resize(args.resize_size),
transforms.CenterCrop(args.crop_size),
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)
vocab_size = len(vocab)
# testing dataset loader
eval_data_loader = get_loader(args.image_dir, args.eval_json_dir,
vocab, args.eval_batch_size,
args.num_workers, args.max_impression_len,
args.max_sen_num, args.max_single_sen_len, shuffle=False,
device = device)
# Models
image_encoder = EncoderCNN().eval().to(device)
impression_decoder = Impression_Decoder(args.embed_size, args.hidden_size,
vocab_size, args.imp_layers_num,
args.num_global_features, args.num_conv1d_out,
args.teach_rate, args.max_impression_len).eval().to(device)
finding_decoder = Atten_Sen_Decoder(args.embed_size, args.hidden_size, vocab_size,
args.fin_num_layers, args.sen_enco_num_layers,
args.num_global_features, args.num_regions, args.num_conv1d_out,
args.teach_rate, args.max_single_sen_len, args.max_sen_num).eval().to(device)
# load trained model weights
image_encoder.load_state_dict(torch.load(img_en_path))
impression_decoder.load_state_dict(torch.load(imp_de_path))
finding_decoder.load_state_dict(torch.load(fin_de_path))
# Generate impressions and findings
pre_imps_lst, pre_fins_lst, img_id_list = [], [], []
for i, (images, images_ids, _, _, _, _) in enumerate(eval_data_loader):
frontal_imgs = images.to(device)
global_feas = image_encoder(frontal_imgs)
predicted_imps, global_topic_vec = impression_decoder.sampler(global_feas, args.max_impression_len)
predicted_fins = finding_decoder.sampler(global_feas, global_topic_vec, args.max_single_sen_len,
args.max_sen_num)
img_id_list.append(images_ids)
pre_imps_lst.append(predicted_imps)
pre_fins_lst.append(predicted_fins)
return pre_imps_lst, pre_fins_lst, img_id_list
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# paths
parser.add_argument('--model_path', type=str, default='model_weights', help='path for weights')
parser.add_argument('--vocab_path', type=str, default='IUdata/IUdata_vocab_0threshold.pkl', help='path for vocabulary wrapper')
parser.add_argument('--image_dir', type=str, default='IUdata/NLMCXR_Frontal', help='directory for X-ray images')
parser.add_argument('--eval_json_dir', type=str, default='IUdata/IUdata_test.json', help='the path for json file')
# model parameters
parser.add_argument('--eval_batch_size', type=int, default=75, help='batch size for loading data')
parser.add_argument('--num_workers', type=int, default=0, help='multi-process data loading')
parser.add_argument('--max_impression_len', type=int, default=15,
help='The maximum length of the impression (one or several sentences)')
parser.add_argument('--max_single_sen_len', type=int, default=15,
help='The maximum length of the each sentence in the finding')
parser.add_argument('--max_sen_num', type=int, default=7, help='The maximum number of sentences in the finding')
parser.add_argument('--single_punc', type=bool, default=True,
help='Take punctuation as a single word: If true, generate sentences such as: Hello , world .')
parser.add_argument('--imp_fin_only', type=bool, default=False, help='Only evaluate on Impression+Finding')
#################################################################################################################################
#################################################################################################################################
# not changed parameters
parser.add_argument('--resize_size', type=int, default=256, help='The resize size of the X-ray image')
parser.add_argument('--crop_size', type=int, default=224, help='The crop size of the X-ray image')
parser.add_argument('--embed_size', type=int, default=512, help='The embed_size for vocabulary and images')
parser.add_argument('--hidden_size', type=int, default=512, help='The number of hidden states in LSTM layers')
# 将特征features由2048改为512
parser.add_argument('--num_global_features', type=int, default=512,
help='The number of global features for image encoder')
parser.add_argument('--imp_layers_num', type=int, default=1, help='The number of LSTM layers in impression decoder')
parser.add_argument('--fin_num_layers', type=int, default=2, help='The number of LSTM layers in finding decoder ')
parser.add_argument('--sen_enco_num_layers', type=int, default=3,
help='The number of convolutional layer in topic encoder')
parser.add_argument('--num_local_features', type=int, default=512,
help='The channel number of local features for image encoder')
parser.add_argument('--num_regions', type=int, default=49, help='The number of sub-regions for local features')
parser.add_argument('--num_conv1d_out', type=int, default=1024,
help='The number of output channels for 1d convolution of sentence encoder')
parser.add_argument('--teach_rate', type=float, default=0.0, help='No teach force is used in testing')
parser.add_argument('--log_step', type=int, default=100, help='The interval of displaying the loss and perplexity')
parser.add_argument('--save_step', type=int, default=1000, help='The interval of saving weights of models')
#################################################################################################################################
#################################################################################################################################
args = parser.parse_args()
print(args)
img_en_path, imp_de_path, fin_de_path = None, None, None
num_ckpt = 0
for path in os.listdir(args.model_path):
if path.split(".")[-1] == 'ckpt':
num_ckpt += 1
if 'image' in path:
img_en_path = os.path.join(args.model_path, path)
elif 'impression' in path:
imp_de_path = os.path.join(args.model_path, path)
elif 'finding' in path:
fin_de_path = os.path.join(args.model_path, path)
""""Please only keep one combination of weights in the model_weights folder"""
"""请在model_weights文件夹中只保留一个权重组合"""
assert num_ckpt == 2 or num_ckpt == 3
num_run = "test"
predicted_imps_lst, predicted_fins_lst, image_id_list = test(img_en_path, imp_de_path, fin_de_path, args)
_, _, _, _ = compute_metrics(predicted_imps_lst, predicted_fins_lst, image_id_list, args)
generate_text_file(predicted_imps_lst, predicted_fins_lst, image_id_list, num_run, args)