Revert "Misc"

This reverts commit 0e3ea63a14.

# Conflicts:
#	codes/test.py
#	codes/train.py

codes/test.py

@@ -1,42 +1,151 @@
+import os.path as osp
+import logging
+import time
+import argparse
+from collections import OrderedDict
+
+import os
+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, save_attention_to_image_rgb
+from switched_conv import compute_attention_specificity
+from data import create_dataset, create_dataloader
+from models import create_model
+from tqdm import tqdm
 import torch
+import models.networks as networks
 
 
-class CheckpointFunction(torch.autograd.Function):
+# Concepts: Swap transformations around. Normalize attention. Disable individual switches, both randomly and one at
-    @staticmethod
+# a time, starting at the last switch. Pick random regions in an image and print out the full attention vector for
-    def forward(ctx, run_function, length, *args):
+# each switch. Yield an output directory name for each alteration and None when last alteration is completed.
-        ctx.run_function = run_function
+def alter_srg(srg: srg.ConfigurableSwitchedResidualGenerator2):
-        ctx.input_tensors = list(args[:length])
+    # First alteration, strip off switches one at a time.
-        ctx.input_params = list(args[length:])
+    yield "naked"
-        with torch.no_grad():
+
-            output_tensors = ctx.run_function(*ctx.input_tensors)
+    '''
-        return output_tensors
+    for i in range(1, len(srg.switches)):
+        srg.switches = srg.switches[:-i]
+        yield "stripped-%i" % (i,)
+    '''
+
+    for sw in srg.switches:
+        sw.set_temperature(.001)
+    yield "specific"
+
+    for sw in srg.switches:
+        sw.set_temperature(1000)
+    yield "normalized"
+
+    for sw in srg.switches:
+        sw.set_temperature(1)
+        sw.switch.attention_norm = None
+    yield "no_anorm"
+    return None
+
+def analyze_srg(srg: srg.ConfigurableSwitchedResidualGenerator2, path, alteration_suffix):
+    mean_hists = [compute_attention_specificity(att, 2) for att in srg.attentions]
+    means = [i[0] for i in mean_hists]
+    hists = [torch.histc(i[1].clone().detach().cpu().flatten().float(), bins=srg.transformation_counts) for i in mean_hists]
+    hists = [h / torch.sum(h) for h in hists]
+    for i in range(len(means)):
+        print("%s - switch_%i_specificity" % (alteration_suffix, i), means[i])
+        print("%s - switch_%i_histogram" % (alteration_suffix, i), hists[i])
+
+    [save_attention_to_image_rgb(path, srg.attentions[i], srg.transformation_counts, alteration_suffix, i) 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()
+
+    visuals = model.get_current_visuals(need_GT)['rlt'].cpu()
+    fea_loss = 0
+    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')
+
+        if need_GT:
+            fea_loss += model.compute_fea_loss(visuals[i], data['GT'][i])
+
+        util.save_img(sr_img, save_img_path)
+    return fea_loss
 
-    @staticmethod
-    def backward(ctx, *output_grads):
-        for i in range(len(ctx.input_tensors)):
-            temp = ctx.input_tensors[i]
-            ctx.input_tensors[i] = temp.detach()
-            ctx.input_tensors[i].requires_grad = True
-        with torch.enable_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        print("Backpropping")
-        input_grads = torch.autograd.grad(output_tensors, ctx.input_tensors + ctx.input_params, output_grads, allow_unused=True)
-        return (None, None) + input_grads
 
-from models.archs.arch_util import ConvGnSilu
-import torch.nn as nn
 if __name__ == "__main__":
-    model = nn.Sequential(ConvGnSilu(3, 64, 3, norm=False),
+    #### options
-                          ConvGnSilu(64, 3, 3, norm=False)
+    torch.backends.cudnn.benchmark = True
-                          )
+    srg_analyze = False
-    model.train()
+    parser = argparse.ArgumentParser()
-    seed = torch.randn(1,3,32,32)
+    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/srgan_compute_feature.yml')
-    recurrent = seed
+    opt = option.parse(parser.parse_args().opt, is_train=False)
-    outs = []
+    opt = option.dict_to_nonedict(opt)
-    for i in range(10):
-        args = (recurrent, ) + tuple(model.parameters())
-        recurrent = CheckpointFunction.apply(model, 1, *args)
-        outs.append(recurrent)
 
-    l = nn.L1Loss()(recurrent, torch.randn(1,3,32,32))
+    util.mkdirs(
-    l.backward()
+        (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)
+    fea_loss = 0
+    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):
+                    alt_path = osp.join(dataset_dir, alteration_suffix)
+                    img_path = data['GT_path'][0] if need_GT else data['LQ_path'][0]
+                    img_name = osp.splitext(osp.basename(img_path))[0] + opt['name']
+                    alteration_suffix += img_name
+                    os.makedirs(alt_path, exist_ok=True)
+                    forward_pass(model, dataset_dir, alteration_suffix)
+                    analyze_srg(model_copy, alt_path, 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:
+                fea_loss += forward_pass(model, dataset_dir, opt['name'])
+
+        # log
+        logger.info('# Validation # Fea: {:.4e}'.format(fea_loss / len(test_loader)))