name="D3") # D1 Coarse d_model4 = discriminator(image_shape_coarse, label_shape_coarse, ndf, n_layers, n_downsampling=1, name="D4") # D2 Coarse # define generator models g_coarse_model = coarse_generator(img_shape=image_shape_coarse, n_downsampling=2, n_blocks=9, n_channels=1) g_fine_model = fine_generator(x_coarse_shape=image_shape_xglobal, input_shape=image_shape_fine, nff=nff, n_blocks=3) # define fundus2angio gan_model = aagan(g_fine_model, g_coarse_model, d_model1, d_model2, d_model3, d_model4, image_shape_fine, image_shape_coarse, image_shape_xglobal, label_shape_fine, label_shape_coarse) # train model train(d_model1, d_model2, d_model3, d_model4, g_coarse_model, g_fine_model, gan_model,
image_shape_fine = (128,128,3) mask_shape_fine = (128,128,1) label_shape_fine = (128,128,1) image_shape_x_coarse = (64,64,128) image_shape_coarse = (64,64,3) mask_shape_coarse = (64,64,1) label_shape_coarse = (64,64,1) img_shape_g = (64,64,3) ndf=64 ncf=128 nff=128 ## Load models K.clear_session() opt = Adam() g_local_model = fine_generator(x_coarse_shape=image_shape_x_coarse,input_shape=image_shape_fine,mask_shape=mask_shape_fine,nff=nff) g_local_model.load_weights(args.weight_name_local) g_local_model.compile(loss='mse', optimizer=opt) g_global_model = coarse_generator(img_shape=image_shape_coarse,mask_shape=mask_shape_coarse,ncf=ncf) g_global_model.load_weights(args.weight_name_global) g_global_model.compile(loss='mse',optimizer=opt) ## Create Output Directory out_path = args.out_dir directories = [out_path,out_path+'/Coarse',out_path+'/Fine'] for directory in directories: if not os.path.exists(directory): os.makedirs(directory) ## Find file numbers,paths or names