import os.path as osp import logging import time import argparse from collections import OrderedDict import options.options as option import utils.util as util from data.util import bgr2ycbcr import models.archs.SwitchedResidualGenerator_arch as srg from switched_conv_util import save_attention_to_image from data import create_dataset, create_dataloader from models import create_model from tqdm import tqdm import torch import models.networks as networks # Concepts: Swap transformations around. Normalize attention. Disable individual switches, both randomly and one at # a time, starting at the last switch. Pick random regions in an image and print out the full attention vector for # each switch. Yield an output directory name for each alteration and None when last alteration is completed. def alter_srg(srg: srg.ConfigurableSwitchedResidualGenerator2): # First alteration, strip off switches one at a time. yield "naked" for i in range(1, len(srg.switches)): srg.switches = srg.switches[:-i] yield "stripped-%i" % (i,) return None def analyze_srg(srg: srg.ConfigurableSwitchedResidualGenerator2, path, alteration_suffix): [save_attention_to_image(path, srg.attentions[i], srg.transformation_counts, i, "attention_" + alteration_suffix, l_mult=5) for i in range(len(srg.attentions))] def forward_pass(model, output_dir, alteration_suffix=''): model.feed_data(data, need_GT=need_GT) model.test() if isinstance(model.fake_GenOut[0], tuple): visuals = model.fake_GenOut[0][0].detach().float().cpu() else: visuals = model.fake_GenOut[0].detach().float().cpu() for i in range(visuals.shape[0]): img_path = data['GT_path'][i] if need_GT else data['LQ_path'][i] img_name = osp.splitext(osp.basename(img_path))[0] sr_img = util.tensor2img(visuals[i]) # uint8 # save images suffix = alteration_suffix if suffix: save_img_path = osp.join(output_dir, img_name + suffix + '.png') else: save_img_path = osp.join(output_dir, img_name + '.png') util.save_img(sr_img, save_img_path) if __name__ == "__main__": #### options torch.backends.cudnn.benchmark = True want_just_images = True srg_analyze = True parser = argparse.ArgumentParser() parser.add_argument('-opt', type=str, help='Path to options YMAL file.', default='../options/analyze_srg.yml') opt = option.parse(parser.parse_args().opt, is_train=False) opt = option.dict_to_nonedict(opt) util.mkdirs( (path for key, path in opt['path'].items() if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key)) util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO, screen=True, tofile=True) logger = logging.getLogger('base') logger.info(option.dict2str(opt)) #### Create test dataset and dataloader test_loaders = [] for phase, dataset_opt in sorted(opt['datasets'].items()): test_set = create_dataset(dataset_opt) test_loader = create_dataloader(test_set, dataset_opt) logger.info('Number of test images in [{:s}]: {:d}'.format(dataset_opt['name'], len(test_set))) test_loaders.append(test_loader) model = create_model(opt) for test_loader in test_loaders: test_set_name = test_loader.dataset.opt['name'] logger.info('\nTesting [{:s}]...'.format(test_set_name)) test_start_time = time.time() dataset_dir = osp.join(opt['path']['results_root'], test_set_name) util.mkdir(dataset_dir) test_results = OrderedDict() test_results['psnr'] = [] test_results['ssim'] = [] test_results['psnr_y'] = [] test_results['ssim_y'] = [] tq = tqdm(test_loader) for data in tq: need_GT = False if test_loader.dataset.opt['dataroot_GT'] is None else True if srg_analyze: orig_model = model.netG model_copy = networks.define_G(opt).to(model.device) model_copy.load_state_dict(orig_model.state_dict()) model.netG = model_copy for alteration_suffix in alter_srg(model_copy): img_path = data['GT_path'][0] if need_GT else data['LQ_path'][0] img_name = osp.splitext(osp.basename(img_path))[0] alteration_suffix += img_name forward_pass(model, dataset_dir, alteration_suffix) analyze_srg(model_copy, dataset_dir, alteration_suffix) # Reset model and do next alteration. model_copy = networks.define_G(opt).to(model.device) model_copy.load_state_dict(orig_model.state_dict()) model.netG = model_copy else: forward_pass(model, dataset_dir)