120 lines
4.8 KiB
Python
120 lines
4.8 KiB
Python
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)
|