/
tester_stackgan.py
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/
tester_stackgan.py
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import torch
import json
from models.Encoder import baseEncoder, baseEncoderv2, harchyEncoder
from models.StackGAN import G_NET
from torch import nn
from utils.MIMICDataSet import MIMICDataset2
from utils.OpeniDataSet import OpeniDataset2
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from utils import get_time, matplotlib_imshow, deNorm, Rescale, ToTensor, Equalize
from tqdm import tqdm
from torchvision.utils import save_image
import os
import numpy as np
class Tester:
def __init__(self):
self.cfg_json = "config/MIMIC_StackGANtest.json"
self.cfg = self.pare_cfg(self.cfg_json)
self.exp_name = self.cfg["EXPER_NAME"]
self.encoder_resume = self.cfg["RESUME_ENCODER"]
self.decoder_resume_F = self.cfg["RESUME_DECODER_F"]
self.decoder_resume_L = self.cfg["RESUME_DECODER_L"]
self.word_dict = self.cfg["DICTIONARY"]
self.text_csv = self.cfg["TEXT_CSV"]
self.img_csv = self.cfg["IMG_CSV"]
self.data_root = self.cfg["DATA_ROOT"]
self.image_size = tuple(self.cfg["IMAGE_SIZE"])
self.name = self.cfg["EXPER_NAME"]
self.test_csv = self.cfg["TEST_CSV"]
self.save_img_dir = './save_image/MIMIC_Stack256'
if os.path.exists(self.save_img_dir)==False:
os.mkdir(self.save_img_dir)
self.ENCODERS = {
"baseENCODER": baseEncoder,
"baseENCODERv2": baseEncoderv2,
"harchyENCODER": harchyEncoder
}
self.dataset = {
"OPENI": OpeniDataset2,
"MIMIC-CXR": MIMICDataset2
}
##################################################
################# Dataset Setup ##################
##################################################
self.t2i_dataset = self.dataset[self.cfg["DATASET"]](csv_txt=self.text_csv,
csv_img=self.img_csv,
root=self.data_root,
word_dict=self.word_dict,
transform=transforms.Compose([
Rescale(self.image_size),
Equalize(),
ToTensor()
]))
self.testset = self.dataset[self.cfg["DATASET"]](csv_txt=self.test_csv,
csv_img=self.img_csv,
root=self.data_root,
word_dict=self.word_dict,
transform=transforms.Compose([
Rescale(self.image_size),
Equalize(),
ToTensor()
]))
self.testset = self.t2i_dataset
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
s_gpus = self.cfg["GPU_ID"].split(',')
self.gpus = [int(ix) for ix in s_gpus]
self.num_gpus = len(self.gpus)
self.test_dataloader = DataLoader(self.testset,
batch_size=12,
shuffle=False,
num_workers=8,
drop_last=True)
self.base_size = self.image_size[0]
self.base_ratio = int(np.log2(self.base_size))
#########################################
############ Network Init ###############
#########################################
self.define_nets()
self.decoder_L= self.define_nets()
self.decoder_F= self.define_nets()
self.encoder = self.ENCODERS[self.cfg["ENCODER"]](vocab_size=self.t2i_dataset.vocab_size,
embed_size=self.cfg["E_EMBED_SIZE"],
hidden_size=self.cfg["E_HIDEN_SIZE"],
max_len=[self.t2i_dataset.max_len_finding,
self.t2i_dataset.max_len_impression],
unit=self.cfg["RNN_CELL"],
feature_base_dim=self.cfg["D_CHANNEL_SIZE"]
).to(self.device)
self.encoder = nn.DataParallel(self.encoder, device_ids=self.gpus)
def define_nets(self):
netG = G_NET().to(self.device)
# netG.apply(weights_init)
netG = nn.DataParallel(netG, device_ids=self.gpus).to(self.device)
print(netG)
return netG
def load_model(self):
print("Model Loading.............")
self.encoder.load_state_dict(torch.load(self.encoder_resume))
self.decoder_F.load_state_dict(torch.load(self.decoder_resume_F))
self.decoder_L.load_state_dict(torch.load(self.decoder_resume_L))
def test(self):
self.load_model()
self.encoder.eval()
self.decoder_F.eval()
self.decoder_L.eval()
print("Start generating")
for idx, batch in enumerate(tqdm(self.test_dataloader)):
finding = batch['finding'].to(self.device)
impression = batch['impression'].to(self.device)
txt_emded, hidden = self.encoder(finding, impression)
pre_image_f, mu, logvar = self.decoder_F(txt_emded)
pre_image_l, mu, logvar = self.decoder_L(txt_emded)
pre_image_f = deNorm(pre_image_f[-1]).data.cpu()
pre_image_l = deNorm(pre_image_l[-1]).data.cpu()
subject_id = batch['subject_id'].data.cpu().numpy()
for i in range(pre_image_f.shape[0]):
save_image(pre_image_f[i],'{}/{}_f.png'.format(self.save_img_dir,subject_id[i]))
save_image(pre_image_l[i],'{}/{}_l.png'.format(self.save_img_dir,subject_id[i]))
def pare_cfg(self, cfg_json):
with open(cfg_json) as f:
cfg = f.read()
print(cfg)
print("Config Loaded")
return json.loads(cfg)
def main():
trainer = Tester()
trainer.test()
if __name__ == "__main__":
main()