def test(hparams): model = Autoencoder(hparams) model.encoder = torch.load("trained_models/train_all/encoder.pt") model.decoder = torch.load("trained_models/train_all/decoder.pt") #print(model) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) #from torchsummary import summary #summary(model, (1, 64, 192)) model.encoder.eval() model.decoder.eval() output_dir = "output/{}".format( os.path.basename(hparams.image_list).split('.')[0]) with open(hparams.image_list) as f: image_files = f.read().splitlines() play_thermal(image_files, hparams, output_dir, encoder=model.encoder.to('cpu'), decoder=model.decoder.to('cpu'), norm=hparams.norm, n_channels=hparams.nc, show=False, save=False) if not len(image_files) > 0: print("did not find any files")
def test(hparams): model = Autoencoder(hparams) model.encoder = torch.load("encoder.pt") model.decoder = torch.load("decoder.pt") model.encoder.eval() model.decoder.eval() folders = sorted( [y for y in glob(os.path.join(hparams.data_root, '* - *'))]) for folder in folders[2:3]: #create output folder output_dir = 'output' if not os.path.exists(output_dir): os.mkdir(output_dir) folder_dir = os.path.join(output_dir, folder.split('/')[-1]) if not os.path.exists(folder_dir): os.mkdir(folder_dir) # list images frame_list = sorted( [y for y in glob(os.path.join(folder, 'img_*.jpg'))]) if not len(frame_list) > 0: print("did not find any files") return play_thermal(frame_list, hparams, folder_dir, encoder=model.encoder.to('cpu'), decoder=model.decoder.to('cpu'), norm=hparams.norm, n_channels=hparams.nc)