Misc

.gitignore

@@ -18,6 +18,7 @@ data/*
 *.cu
 *.pt
 *.pth
+*.pdf
 
 # template
 

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (torch_nightly)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (torch18)" project-jdk-type="Python SDK" />
 </project>

.idea/mmsr.iml

@@ -9,7 +9,7 @@
       <excludeFolder url="file://$MODULE_DIR$/results" />
       <excludeFolder url="file://$MODULE_DIR$/tb_logger" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.8 (torch_nightly)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (torch18)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">

codes/data/image_folder_dataset.py

@@ -5,10 +5,14 @@ import random
 import cv2
 import kornia
 import numpy as np
+import pytorch_ssim
 import torch
 import os
 
+import torchvision
+from torch.utils.data import DataLoader
 from torchvision.transforms import Normalize
+from tqdm import tqdm
 
 from data import util
 # Builds a dataset created from a simple folder containing a list of training/test/validation images.
@@ -140,7 +144,7 @@ class ImageFolderDataset:
         if self.normalize:
             hq = self.normalize(hq)
 
-        out_dict = {'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
+        out_dict = {'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item], 'has_alt': False}
 
         if self.fetch_alt_image:
             # This works by assuming a specific filename structure as would produced by ffmpeg. ex:
@@ -165,6 +169,7 @@ class ImageFolderDataset:
                     alt_hq = util.read_img(None, next_img, rgb=True)
                     alt_hs = self.resize_hq([alt_hq])
                     alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hs[0], (2, 0, 1)))).float()
+                    out_dict['has_alt'] = True
                     if not self.skip_lq:
                         for_lq.append(alt_hs[0])
                 except:
@@ -200,33 +205,25 @@ class ImageFolderDataset:
 if __name__ == '__main__':
     opt = {
         'name': 'amalgam',
-        'paths': ['F:\\4k6k\\datasets\\images\\youtube\\4k_quote_unquote\\images'],
+        'paths': ['E:\\4k6k\\datasets\\ns_images\\256_unsupervised'],
         'weights': [1],
         'target_size': 256,
-        'force_multiple': 32,
+        'force_multiple': 1,
         'scale': 2,
-        'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
+        'corrupt_before_downsize': True,
-        'random_corruptions': ['noise-5', 'none'],
-        'num_corrupts_per_image': 1,
-        'corrupt_before_downsize': False,
         'fetch_alt_image': True,
-        #'labeler': {
+        'disable_flip': True,
-        #    'type': 'patch_labels',
+        'fixed_corruptions': [ 'jpeg-broad' ],
-        #    'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories_new.json'
+        'num_corrupts_per_image': 0,
-        #}
+        'corruption_blur_scale': 0
     }
 
-    ds = ImageFolderDataset(opt)
+    ds = DataLoader(ImageFolderDataset(opt), shuffle=True, num_workers=2)
     import os
-    os.makedirs("debug", exist_ok=True)
+    output_path = 'E:\\4k6k\\datasets\\ns_images\\128_unsupervised'
-    for i in range(0, len(ds)):
+    os.makedirs(output_path, exist_ok=True)
-        o = ds[random.randint(0, len(ds)-1)]
+    for i, d in tqdm(enumerate(ds)):
-        hq = o['lq']
+        lq = d['lq']
-        #masked = (o['labels_mask'] * .5 + .5) * hq
+        torchvision.utils.save_image(lq[:,:,16:-16,:], f'{output_path}\\{i+500000}.png')
-        import torchvision
+        if i >= 200000:
-        torchvision.utils.save_image(hq.unsqueeze(0), "debug/%i_lq.png" % (i,))
+            break
-        torchvision.utils.save_image(o['alt_lq'].unsqueeze(0), "debug/%i_lq_alt.png" % (i,))
-        #if len(o['labels'].unique()) > 1:
-        #    randlbl = np.random.choice(o['labels'].unique()[1:])
-        #    moremask = hq * ((1*(o['labels'] == randlbl))*.5+.5)
-        #    torchvision.utils.save_image(moremask.unsqueeze(0), "debug/%i_%s.png" % (i, o['label_strings'][randlbl]))

codes/scripts/byol/tsne_torch.py

@@ -208,7 +208,7 @@ def run_tsne_instance_level():
     print("Run Y = tsne.tsne(X, no_dims, perplexity) to perform t-SNE on your dataset.")
 
     limit = 4000
-    X, files = torch.load('results.pth')
+    X, files = torch.load('../results_instance_resnet.pth')
     zipped = list(zip(X, files))
     shuffle(zipped)
     X, files = zip(*zipped)
@@ -242,7 +242,7 @@ def run_tsne_instance_level():
 # Uses the results from the calculation above to create a **massive** pdf plot that shows 1/8 size images on the tsne
 # spectrum.
 def plot_instance_level_results_as_image_graph():
-    Y, files = torch.load('tsne_output.pth')
+    Y, files = torch.load('../tsne_output.pth')
     fig, ax = pyplot.subplots()
     fig.set_size_inches(200,200,forward=True)
     ax.update_datalim(np.column_stack([Y[:, 0], Y[:, 1]]))
@@ -250,7 +250,7 @@ def plot_instance_level_results_as_image_graph():
 
     for b in tqdm(range(Y.shape[0])):
         im = pyplot.imread(files[b])
-        im = OffsetImage(im, zoom=1/8)
+        im = OffsetImage(im, zoom=1/2)
         ab = AnnotationBbox(im, (Y[b, 0], Y[b, 1]), xycoords='data', frameon=False)
         ax.add_artist(ab)
     ax.scatter(Y[:, 0], Y[:, 1])
@@ -277,7 +277,7 @@ def run_tsne_pixel_level():
     '''
 
     # For resnet-style latent tuples
-    X, files = torch.load('../results.pth')
+    X, files = torch.load('../../results/2021-4-8-imgset-latent-dict.pth')
     zipped = list(zip(X, files))
     shuffle(zipped)
     X, files = zip(*zipped)
@@ -347,8 +347,8 @@ def plot_pixel_level_results_as_image_graph():
 if __name__ == "__main__":
     # For use with instance-level results (e.g. from byol_resnet_playground.py)
     #run_tsne_instance_level()
-    #plot_instance_level_results_as_image_graph()
+    plot_instance_level_results_as_image_graph()
 
     # For use with pixel-level results (e.g. from byol_uresnet_playground)
     #run_tsne_pixel_level()
-    plot_pixel_level_results_as_image_graph()
+    #plot_pixel_level_results_as_image_graph()

codes/scripts/extract_square_images.py

@@ -19,9 +19,9 @@ def main():
     # compression time. If read raw images during training, use 0 for faster IO speed.
 
     opt['dest'] = 'file'
-    opt['input_folder'] = ['E:\\4k6k\\datasets\\images\\faces\\CelebAMask-HQ\\CelebA-HQ-img']
+    opt['input_folder'] = ['E:\\4k6k\\datasets\\images\\lsun\\lsun\\cats']
-    opt['save_folder'] = 'E:\\4k6k\\datasets\\images\\faces\\CelebAMask-HQ\\256px'
+    opt['save_folder'] = 'E:\\4k6k\\datasets\\images\\lsun\\lsun\\cats\\256_4_by_3'
-    opt['imgsize'] = 256
+    opt['imgsize'] = (256,192)
     opt['bottom_crop'] = 0
     opt['keep_folder'] = False
 
@@ -66,15 +66,25 @@ class TiledDataset(data.Dataset):
 
         h, w, c = img.shape
         # Uncomment to filter any image that doesnt meet a threshold size.
-        if min(h,w) < self.opt['imgsize']:
+        imgsz_w, imgsz_h = self.opt['imgsize']
+        if w < imgsz_w or h < imgsz_h:
             print("Skipping due to threshold")
             return None
 
-        # We must convert the image into a square.
+        # We must first center-crop the image to the proper aspect ratio
-        dim = min(h, w)
+        aspect_ratio = imgsz_h / imgsz_w
-        # Crop the image so that only the center is left, since this is often the most salient part of the image.
+        if h < w * aspect_ratio:
-        img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
+            hdim = h
-        img = cv2.resize(img, (self.opt['imgsize'], self.opt['imgsize']), interpolation=cv2.INTER_AREA)
+            wdim = int(h / aspect_ratio)
+        elif w * aspect_ratio < h:
+            hdim = int(w * aspect_ratio)
+            wdim = w
+        else:
+            hdim = h
+            wdim = w
+        img = img[(h - hdim) // 2:hdim + (h - hdim) // 2, (w - wdim) // 2:wdim + (w - wdim) // 2, :]
+
+        img = cv2.resize(img, (imgsz_w, imgsz_h), interpolation=cv2.INTER_AREA)
         output_folder = self.opt['save_folder']
         if self.opt['keep_folder']:
             # Attempt to find the folder name one level above opt['input_folder'] and use that.

codes/train2.py

@@ -1,325 +0,0 @@
-import os
-import math
-import argparse
-import random
-import logging
-from tqdm import tqdm
-
-import torch
-from data.data_sampler import DistIterSampler
-from trainer.eval.evaluator import create_evaluator
-
-from utils import util, options as option
-from data import create_dataloader, create_dataset
-from trainer.ExtensibleTrainer import ExtensibleTrainer
-from time import time
-
-def init_dist(backend, **kwargs):
-    # These packages have globals that screw with Windows, so only import them if needed.
-    import torch.distributed as dist
-    import torch.multiprocessing as mp
-
-    """initialization for distributed training"""
-    if mp.get_start_method(allow_none=True) != 'spawn':
-        mp.set_start_method('spawn')
-    rank = int(os.environ['RANK'])
-    num_gpus = torch.cuda.device_count()
-    torch.cuda.set_device(rank % num_gpus)
-    dist.init_process_group(backend=backend, **kwargs)
-
-class Trainer:
-
-    def init(self, opt, launcher, all_networks={}):
-        self._profile = False
-        self.val_compute_psnr = opt['eval']['compute_psnr'] if 'compute_psnr' in opt['eval'].keys() else True
-        self.val_compute_fea = opt['eval']['compute_fea'] if 'compute_fea' in opt['eval'].keys() else True
-
-        #### loading resume state if exists
-        if opt['path'].get('resume_state', None):
-            # distributed resuming: all load into default GPU
-            device_id = torch.cuda.current_device()
-            resume_state = torch.load(opt['path']['resume_state'],
-                                      map_location=lambda storage, loc: storage.cuda(device_id))
-            option.check_resume(opt, resume_state['iter'])  # check resume options
-        else:
-            resume_state = None
-
-        #### mkdir and loggers
-        if self.rank <= 0:  # normal training (self.rank -1) OR distributed training (self.rank 0)
-            if resume_state is None:
-                util.mkdir_and_rename(
-                    opt['path']['experiments_root'])  # rename experiment folder if exists
-                util.mkdirs(
-                    (path for key, path in opt['path'].items() if not key == 'experiments_root' and path is not None
-                     and 'pretrain_model' not in key and 'resume' not in key))
-
-            # config loggers. Before it, the log will not work
-            util.setup_logger('base', opt['path']['log'], 'train_' + opt['name'], level=logging.INFO,
-                              screen=True, tofile=True)
-            self.logger = logging.getLogger('base')
-            self.logger.info(option.dict2str(opt))
-            # tensorboard logger
-            if opt['use_tb_logger'] and 'debug' not in opt['name']:
-                self.tb_logger_path = os.path.join(opt['path']['experiments_root'], 'tb_logger')
-                version = float(torch.__version__[0:3])
-                if version >= 1.1:  # PyTorch 1.1
-                    from torch.utils.tensorboard import SummaryWriter
-                else:
-                    self.self.logger.info(
-                        'You are using PyTorch {}. Tensorboard will use [tensorboardX]'.format(version))
-                    from tensorboardX import SummaryWriter
-                self.tb_logger = SummaryWriter(log_dir=self.tb_logger_path)
-        else:
-            util.setup_logger('base', opt['path']['log'], 'train', level=logging.INFO, screen=True)
-            self.logger = logging.getLogger('base')
-
-        # convert to NoneDict, which returns None for missing keys
-        opt = option.dict_to_nonedict(opt)
-        self.opt = opt
-
-        #### wandb init
-        if opt['wandb']:
-            import wandb
-            os.makedirs(os.path.join(opt['path']['log'], 'wandb'), exist_ok=True)
-            wandb.init(project=opt['name'], dir=opt['path']['log'])
-
-        #### random seed
-        seed = opt['train']['manual_seed']
-        if seed is None:
-            seed = random.randint(1, 10000)
-        if self.rank <= 0:
-            self.logger.info('Random seed: {}'.format(seed))
-        seed += self.rank  # Different multiprocessing instances should behave differently.
-        util.set_random_seed(seed)
-
-        torch.backends.cudnn.benchmark = True
-        # torch.backends.cudnn.deterministic = True
-        # torch.autograd.set_detect_anomaly(True)
-
-        # Save the compiled opt dict to the global loaded_options variable.
-        util.loaded_options = opt
-
-        #### create train and val dataloader
-        dataset_ratio = 1  # enlarge the size of each epoch
-        for phase, dataset_opt in opt['datasets'].items():
-            if phase == 'train':
-                self.train_set = create_dataset(dataset_opt)
-                train_size = int(math.ceil(len(self.train_set) / dataset_opt['batch_size']))
-                total_iters = int(opt['train']['niter'])
-                self.total_epochs = int(math.ceil(total_iters / train_size))
-                if opt['dist']:
-                    self.train_sampler = DistIterSampler(self.train_set, self.world_size, self.rank, dataset_ratio)
-                    self.total_epochs = int(math.ceil(total_iters / (train_size * dataset_ratio)))
-                else:
-                    self.train_sampler = None
-                self.train_loader = create_dataloader(self.train_set, dataset_opt, opt, self.train_sampler)
-                if self.rank <= 0:
-                    self.logger.info('Number of train images: {:,d}, iters: {:,d}'.format(
-                        len(self.train_set), train_size))
-                    self.logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
-                        self.total_epochs, total_iters))
-            elif phase == 'val':
-                self.val_set = create_dataset(dataset_opt)
-                self.val_loader = create_dataloader(self.val_set, dataset_opt, opt, None)
-                if self.rank <= 0:
-                    self.logger.info('Number of val images in [{:s}]: {:d}'.format(
-                        dataset_opt['name'], len(self.val_set)))
-            else:
-                raise NotImplementedError('Phase [{:s}] is not recognized.'.format(phase))
-        assert self.train_loader is not None
-
-        #### create model
-        self.model = ExtensibleTrainer(opt, cached_networks=all_networks)
-
-        ### Evaluators
-        self.evaluators = []
-        if 'evaluators' in opt['eval'].keys():
-            for ev_key, ev_opt in opt['eval']['evaluators'].items():
-                self.evaluators.append(create_evaluator(self.model.networks[ev_opt['for']],
-                                                        ev_opt, self.model.env))
-
-        #### resume training
-        if resume_state:
-            self.logger.info('Resuming training from epoch: {}, iter: {}.'.format(
-                resume_state['epoch'], resume_state['iter']))
-
-            self.start_epoch = resume_state['epoch']
-            self.current_step = resume_state['iter']
-            self.model.resume_training(resume_state, 'amp_opt_level' in opt.keys())  # handle optimizers and schedulers
-        else:
-            self.current_step = -1 if 'start_step' not in opt.keys() else opt['start_step']
-            self.start_epoch = 0
-        if 'force_start_step' in opt.keys():
-            self.current_step = opt['force_start_step']
-        opt['current_step'] = self.current_step
-
-    def do_step(self, train_data):
-        if self._profile:
-            print("Data fetch: %f" % (time() - _t))
-            _t = time()
-
-        opt = self.opt
-        self.current_step += 1
-        #### update learning rate
-        self.model.update_learning_rate(self.current_step, warmup_iter=opt['train']['warmup_iter'])
-
-        #### training
-        if self._profile:
-            print("Update LR: %f" % (time() - _t))
-            _t = time()
-        self.model.feed_data(train_data, self.current_step)
-        self.model.optimize_parameters(self.current_step)
-        if self._profile:
-            print("Model feed + step: %f" % (time() - _t))
-            _t = time()
-
-        #### log
-        if self.current_step % opt['logger']['print_freq'] == 0 and self.rank <= 0:
-            logs = self.model.get_current_log(self.current_step)
-            message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(self.epoch, self.current_step)
-            for v in self.model.get_current_learning_rate():
-                message += '{:.3e},'.format(v)
-            message += ')] '
-            for k, v in logs.items():
-                if 'histogram' in k:
-                    self.tb_logger.add_histogram(k, v, self.current_step)
-                elif isinstance(v, dict):
-                    self.tb_logger.add_scalars(k, v, self.current_step)
-                else:
-                    message += '{:s}: {:.4e} '.format(k, v)
-                    # tensorboard logger
-                    if opt['use_tb_logger'] and 'debug' not in opt['name']:
-                        self.tb_logger.add_scalar(k, v, self.current_step)
-            if opt['wandb']:
-                import wandb
-                wandb.log(logs)
-            self.logger.info(message)
-
-        #### save models and training states
-        if self.current_step % opt['logger']['save_checkpoint_freq'] == 0:
-            if self.rank <= 0:
-                self.logger.info('Saving models and training states.')
-                self.model.save(self.current_step)
-                self.model.save_training_state(self.epoch, self.current_step)
-            if 'alt_path' in opt['path'].keys():
-                import shutil
-                print("Synchronizing tb_logger to alt_path..")
-                alt_tblogger = os.path.join(opt['path']['alt_path'], "tb_logger")
-                shutil.rmtree(alt_tblogger, ignore_errors=True)
-                shutil.copytree(self.tb_logger_path, alt_tblogger)
-
-        #### validation
-        if opt['datasets'].get('val', None) and self.current_step % opt['train']['val_freq'] == 0:
-            if opt['model'] in ['sr', 'srgan', 'corruptgan', 'spsrgan',
-                                'extensibletrainer'] and self.rank <= 0:  # image restoration validation
-                avg_psnr = 0.
-                avg_fea_loss = 0.
-                idx = 0
-                val_tqdm = tqdm(self.val_loader)
-                for val_data in val_tqdm:
-                    idx += 1
-                    for b in range(len(val_data['HQ_path'])):
-                        img_name = os.path.splitext(os.path.basename(val_data['HQ_path'][b]))[0]
-                        img_dir = os.path.join(opt['path']['val_images'], img_name)
-
-                        util.mkdir(img_dir)
-
-                        self.model.feed_data(val_data, self.current_step)
-                        self.model.test()
-
-                        visuals = self.model.get_current_visuals()
-                        if visuals is None:
-                            continue
-
-                        sr_img = util.tensor2img(visuals['rlt'][b])  # uint8
-                        # calculate PSNR
-                        if self.val_compute_psnr:
-                            gt_img = util.tensor2img(visuals['hq'][b])  # uint8
-                            sr_img, gt_img = util.crop_border([sr_img, gt_img], opt['scale'])
-                            avg_psnr += util.calculate_psnr(sr_img, gt_img)
-
-                        # calculate fea loss
-                        if self.val_compute_fea:
-                            avg_fea_loss += self.model.compute_fea_loss(visuals['rlt'][b], visuals['hq'][b])
-
-                        # Save SR images for reference
-                        img_base_name = '{:s}_{:d}.png'.format(img_name, self.current_step)
-                        save_img_path = os.path.join(img_dir, img_base_name)
-                        util.save_img(sr_img, save_img_path)
-
-                avg_psnr = avg_psnr / idx
-                avg_fea_loss = avg_fea_loss / idx
-
-                # log
-                self.logger.info('# Validation # PSNR: {:.4e} Fea: {:.4e}'.format(avg_psnr, avg_fea_loss))
-
-                # tensorboard logger
-                if opt['use_tb_logger'] and 'debug' not in opt['name'] and self.rank <= 0:
-                    self.tb_logger.add_scalar('val_psnr', avg_psnr, self.current_step)
-                    self.tb_logger.add_scalar('val_fea', avg_fea_loss, self.current_step)
-
-        if len(self.evaluators) != 0 and self.current_step % opt['train']['val_freq'] == 0 and self.rank <= 0:
-            eval_dict = {}
-            for eval in self.evaluators:
-                eval_dict.update(eval.perform_eval())
-            if self.rank <= 0:
-                print("Evaluator results: ", eval_dict)
-                for ek, ev in eval_dict.items():
-                    self.tb_logger.add_scalar(ek, ev, self.current_step)
-
-    def do_training(self):
-        self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step))
-        for epoch in range(self.start_epoch, self.total_epochs + 1):
-            self.epoch = epoch
-            if opt['dist']:
-                self.train_sampler.set_epoch(epoch)
-            tq_ldr = tqdm(self.train_loader)
-
-            _t = time()
-            for train_data in tq_ldr:
-                self.do_step(train_data)
-
-    def create_training_generator(self, index):
-        self.logger.info('Start training from epoch: {:d}, iter: {:d}'.format(self.start_epoch, self.current_step))
-        for epoch in range(self.start_epoch, self.total_epochs + 1):
-            self.epoch = epoch
-            if self.opt['dist']:
-                self.train_sampler.set_epoch(epoch)
-            tq_ldr = tqdm(self.train_loader, position=index)
-
-            _t = time()
-            for train_data in tq_ldr:
-                yield self.model
-                self.do_step(train_data)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_pixpro_3.yml')
-    parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
-    parser.add_argument('--local_rank', type=int, default=0)
-    args = parser.parse_args()
-    opt = option.parse(args.opt, is_train=True)
-    if args.launcher != 'none':
-        # export CUDA_VISIBLE_DEVICES for running in distributed mode.
-        if 'gpu_ids' in opt.keys():
-            gpu_list = ','.join(str(x) for x in opt['gpu_ids'])
-            os.environ['CUDA_VISIBLE_DEVICES'] = gpu_list
-            print('export CUDA_VISIBLE_DEVICES=' + gpu_list)
-    trainer = Trainer()
-
-    #### distributed training settings
-    if args.launcher == 'none':  # disabled distributed training
-        opt['dist'] = False
-        trainer.rank = -1
-        if len(opt['gpu_ids']) == 1:
-            torch.cuda.set_device(opt['gpu_ids'][0])
-        print('Disabled distributed training.')
-    else:
-        opt['dist'] = True
-        init_dist('nccl')
-        trainer.world_size = torch.distributed.get_world_size()
-        trainer.rank = torch.distributed.get_rank()
-
-    trainer.init(opt, args.launcher)
-    trainer.do_training()

recipes/stylegan/README.md

@@ -0,0 +1,10 @@
+# StyleGAN Implementations
+DLAS supports two different StyleGAN2 implementations:
+
+- [@rosinality implementation](https://github.com/rosinality/stylegan2-pytorch/commits/master)
+  Designed to reach parity with the nVidia reference implementation in TF1.5
+- [@lucidrains implementation](https://github.com/lucidrains/stylegan2-pytorch)
+  Designed with simplicity and readability in mind.
+  
+I prefer the readability of @lucidrains implementation, but you cannot (yet) use pretrained weights
+with it. I'm working on that.