++ 📄 Paper | + 💻 Source Code | + 🤗 Hugging Face +
+ +
+
+
+ +
+ Architecture of the CR-Net model. +
+ +## Introduction + +**CR-Net** is a model enhance the quality of images and videos captured under low-light conditions. +By learning a continuous rendering process, CR-Net effectively improves brightness, producing natural and sharp results even in challenging dark environments. +To learn more about CR-Net, feel free to read our documentation [English](../README.md) | [Tiếng Việt](preview/README-vi.md) | [中文](preview/README-zh.md). + + +### Key Features + +* **Low-light image/video enhancement:** Significantly improves brightness and contrast for images and videos captured in dim lighting. +* **Continuous rendering network:** Employs a novel architecture to deliver smoother and more natural results compared to traditional methods. +* **Flexible applications:** Supports both video processing and directories containing multiple still images. + +## Demo + + + +## Installation and Requirements + +To run this model, you need the proper environment. We recommend the following versions: + +* **Python:** `Python >= 3.10` (Recommended `Python 3.10`) +* **PyTorch:** `PyTorch >= 1.12` (Recommended `PyTorch 2.1.2`) + +**Step 1: Clone the repository** + +```shell + git clone https://github.com/val-utehy/CR-Net.git + cd CR-Net +``` +**Step 2: Install dependencies** + +```shell + pip install -r requirements.txt +``` + +> [!NOTE] +> Make sure you have installed the compatible versions of **torch** and **torchvision** with your **CUDA driver** to leverage GPU. +## Pretrained Models +You can download the pretrained models from this [link](https://huggingface.co/datasets/datnguyentien204/CR-Net). +You can use latest checkpoint `latest_net_G.pth` for inference. +> [!NOTE] +> Please ensure your path to the checkpoint is correct in the script files before running. + +## Usage Guide + +### 1. Model Training + +Training file will be updated soon! + +[//]: # (To train the CR-Net model on your own dataset, follow these steps:) + +[//]: # () +[//]: # (**a. Configure the training script file:**) + +[//]: # () +[//]: # (Open and edit the file `train_scripts/ast_n2h.sh`. In this file, you need to specify important paths such as the dataset path and the checkpoint saving directory.) + +[//]: # () +[//]: # (**b. Run the training script:**) + +[//]: # () +[//]: # (After finishing the configuration, navigate to the project’s root directory and execute the following command:) + +[//]: # () +[//]: # (```shell) + +[//]: # ( bash train_scripts/ast_n2h_dat.sh) + +[//]: # (```) +### 2. Testing and Inference + +**a. Video Processing:** + +#### 1. Configure the script file: +Open and edit the file `test_scripts/ast_inference_video.sh`. Here, you need to provide the path to the trained checkpoint and the input/output video paths. + +#### 2. Run the video processing script: +After completing the configuration, navigate to the project’s root directory and execute the following command: + +```shell + bash test_scripts/ast_inference_video.sh +``` + +**b. Image Directory Processing:** +#### 1. Configure the script file: +Open and edit the file `test_scripts/ast_n2h_dat.sh`. Here, you need to provide the path to the trained checkpoint and the input/output image directory paths. + +#### 2. Run the image directory processing script: +After completing the configuration, navigate to the project’s root directory and execute the following command: + +```shell + bash test_scripts/ast_n2h.sh +``` + +## Citation + + +[//]: # (```bibtex) + +[//]: # (@article{crnet2025,) + +[//]: # ( title={CR-Net: A Continuous Rendering Network for Improving Robustness to Low-illumination},) + +[//]: # ( author={},) + +[//]: # ( journal={},) + +[//]: # ( year={2025}) + +[//]: # (}) + +[//]: # (```) +## References + +1. https://github.com/EndlessSora/TSIT + +2. https://github.com/astra-vision/CoMoGAN + +3. https://github.com/AlienZhang1996/S2WAT + + +## License +This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. \ No newline at end of file diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..502f78972bbbe4d95de8801c47b1b494846a72ac --- /dev/null +++ b/data/__init__.py @@ -0,0 +1,45 @@ +# --- START OF FILE data/__init__.py --- +import importlib +import torch.utils.data +from data.base_dataset import BaseDataset + + +def find_dataset_using_name(dataset_name): + dataset_filename = "data." + dataset_name + "_dataset" + + datasetlib = importlib.import_module(dataset_filename) + + dataset = None + target_dataset_name = dataset_name.replace('_', '') + 'dataset' + for name, cls in datasetlib.__dict__.items(): + if name.lower() == target_dataset_name.lower() \ + and issubclass(cls, BaseDataset): + dataset = cls + + if dataset is None: + raise ValueError("In %s.py, there should be a subclass of BaseDataset " + "with class name that matches %s in lowercase." % + (dataset_filename, target_dataset_name)) + + return dataset + + +def get_option_setter(dataset_name): + dataset_class = find_dataset_using_name(dataset_name) + return dataset_class.modify_commandline_options + + +def create_dataloader(opt): + dataset = find_dataset_using_name(opt.dataset_mode) + instance = dataset(opt) + + print("dataset [%s] of size %d was created" % + (type(instance).__name__, len(instance))) + dataloader = torch.utils.data.DataLoader( + instance, + batch_size=opt.batchSize, + shuffle=not opt.serial_batches, + num_workers=int(opt.nThreads), + drop_last=opt.isTrain + ) + return dataloader \ No newline at end of file diff --git a/data/__pycache__/__init__.cpython-310.pyc b/data/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5bf42bf6b71073b191d2149fc9edb0349f6de676 Binary files /dev/null and b/data/__pycache__/__init__.cpython-310.pyc differ diff --git a/data/__pycache__/base_dataset.cpython-310.pyc b/data/__pycache__/base_dataset.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..835ece09e36eb3e33477c51f116fd447f53e2786 Binary files /dev/null and b/data/__pycache__/base_dataset.cpython-310.pyc differ diff --git a/data/__pycache__/image_folder.cpython-310.pyc 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0000000000000000000000000000000000000000..62489e3f695e4e298b291e53823121668c6a1ec6 --- /dev/null +++ b/data/ade20k_dataset.py @@ -0,0 +1,48 @@ +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class ADE20KDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='resize_and_crop') + if is_train: + parser.set_defaults(load_size=286) + else: + parser.set_defaults(load_size=256) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(label_nc=150) + parser.set_defaults(contain_dontcare_label=True) + parser.set_defaults(cache_filelist_read=False) + parser.set_defaults(cache_filelist_write=False) + parser.set_defaults(no_instance=True) + return parser + + def get_paths(self, opt): + root = opt.croot + phase = 'val' if opt.phase == 'test' else 'train' + + all_images = make_dataset(root, recursive=True, read_cache=False, write_cache=False) + image_paths = [] + label_paths = [] + for p in all_images: + if '_%s_' % phase not in p: + continue + if p.endswith('.jpg'): + image_paths.append(p) + elif p.endswith('.png'): + label_paths.append(p) + + instance_paths = [] # don't use instance map for ade20k + + return label_paths, image_paths, instance_paths + + # In ADE20k, 'unknown' label is of value 0. + # Change the 'unknown' label to the last label to match other datasets. + def postprocess(self, input_dict): + label = input_dict['label'] + label = label - 1 + label[label == -1] = self.opt.label_nc diff --git a/data/base_dataset.py b/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d0d5471c5e9ee2162b969a83360ab187cc452c28 --- /dev/null +++ b/data/base_dataset.py @@ -0,0 +1,125 @@ +import torch.utils.data as data +from PIL import Image +import torchvision.transforms as transforms +import numpy as np +import random + + +class BaseDataset(data.Dataset): + def __init__(self,opt=None): + super(BaseDataset, self).__init__() + if opt is not None: + self.opt = opt + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def initialize(self, opt): + pass + + +def get_params(opt, size): + w, h = size + new_h = h + new_w = w + if opt.preprocess_mode == 'resize_and_crop': + new_h = new_w = opt.load_size + elif opt.preprocess_mode == 'scale_width_and_crop': + new_w = opt.load_size + new_h = opt.load_size * h // w + elif opt.preprocess_mode == 'scale_shortside_and_crop': + ss, ls = min(w, h), max(w, h) # shortside and longside + width_is_shorter = w == ss + ls = int(opt.load_size * ls / ss) + new_w, new_h = (ss, ls) if width_is_shorter else (ls, ss) + + x = random.randint(0, np.maximum(0, new_w - opt.crop_size)) + y = random.randint(0, np.maximum(0, new_h - opt.crop_size)) + + flip = random.random() > 0.5 + return {'crop_pos': (x, y), 'flip': flip} + + +def get_transform(opt, params, method=Image.BICUBIC, normalize=True, toTensor=True): + transform_list = [] + if 'resize' in opt.preprocess_mode: + osize = [opt.load_size, opt.load_size] + transform_list.append(transforms.Resize(osize, interpolation=method)) + elif 'scale_width' in opt.preprocess_mode: + transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method))) + elif 'scale_shortside' in opt.preprocess_mode: + transform_list.append(transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, method))) + + if 'crop' in opt.preprocess_mode: + transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.crop_size))) + + if opt.preprocess_mode == 'none': + base = 32 + transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method))) + + if opt.preprocess_mode == 'fixed': + w = opt.crop_size + h = round(opt.crop_size / opt.aspect_ratio) + transform_list.append(transforms.Lambda(lambda img: __resize(img, w, h, method))) + + if opt.isTrain and not opt.no_flip: + transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip']))) + + if toTensor: + transform_list += [transforms.ToTensor()] + + if normalize: + transform_list += [transforms.Normalize((0.5, 0.5, 0.5), + (0.5, 0.5, 0.5))] + return transforms.Compose(transform_list) + + +def normalize(): + return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + + +def __resize(img, w, h, method=Image.BICUBIC): + return img.resize((w, h), method) + + +def __make_power_2(img, base, method=Image.BICUBIC): + ow, oh = img.size + h = int(round(oh / base) * base) + w = int(round(ow / base) * base) + if (h == oh) and (w == ow): + return img + return img.resize((w, h), method) + + +def __scale_width(img, target_width, method=Image.BICUBIC): + ow, oh = img.size + if (ow == target_width): + return img + w = target_width + h = int(target_width * oh / ow) + return img.resize((w, h), method) + + +def __scale_shortside(img, target_width, method=Image.BICUBIC): + ow, oh = img.size + ss, ls = min(ow, oh), max(ow, oh) # shortside and longside + width_is_shorter = ow == ss + if (ss == target_width): + return img + ls = int(target_width * ls / ss) + nw, nh = (ss, ls) if width_is_shorter else (ls, ss) + return img.resize((nw, nh), method) + + +def __crop(img, pos, size): + ow, oh = img.size + x1, y1 = pos + tw = th = size + return img.crop((x1, y1, x1 + tw, y1 + th)) + + +def __flip(img, flip): + if flip: + return img.transpose(Image.FLIP_LEFT_RIGHT) + return img diff --git a/data/cityscapes_dataset.py b/data/cityscapes_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b41e1c24c81776158d1061507c8c318785b704a4 --- /dev/null +++ b/data/cityscapes_dataset.py @@ -0,0 +1,46 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class CityscapesDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=512) + parser.set_defaults(crop_size=512) + parser.set_defaults(display_winsize=512) + parser.set_defaults(label_nc=35) + parser.set_defaults(aspect_ratio=2.0) + parser.set_defaults(batchSize=16) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + root = opt.croot + phase = 'val' if opt.phase == 'test' else 'train' + + label_dir = os.path.join(root, 'gtFine', phase) + label_paths_all = make_dataset(label_dir, recursive=True) + label_paths = [p for p in label_paths_all if p.endswith('_labelIds.png')] + + image_dir = os.path.join(root, 'leftImg8bit', phase) + image_paths = make_dataset(image_dir, recursive=True) + + if not opt.no_instance: + instance_paths = [p for p in label_paths_all if p.endswith('_instanceIds.png')] + else: + instance_paths = [] + + return label_paths, image_paths, instance_paths + + def paths_match(self, path1, path2): + name1 = os.path.basename(path1) + name2 = os.path.basename(path2) + # compare the first 3 components, [city]_[id1]_[id2] + return '_'.join(name1.split('_')[:3]) == \ + '_'.join(name2.split('_')[:3]) diff --git a/data/custom_dataset.py b/data/custom_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..5a83754832af15a8dd6f77107fcd0a7f511c7360 --- /dev/null +++ b/data/custom_dataset.py @@ -0,0 +1,45 @@ +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class CustomDataset(Pix2pixDataset): + """ Dataset that loads images from directories + Use option --label_dir, --image_dir, --instance_dir to specify the directories. + The images in the directories are sorted in alphabetical order and paired in order. + """ + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='resize_and_crop') + load_size = 286 if is_train else 256 + parser.set_defaults(load_size=load_size) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(label_nc=13) + parser.set_defaults(contain_dontcare_label=False) + + parser.add_argument('--label_dir', type=str, required=True, + help='path to the directory that contains label images') + parser.add_argument('--image_dir', type=str, required=True, + help='path to the directory that contains photo images') + parser.add_argument('--instance_dir', type=str, default='', + help='path to the directory that contains instance maps. Leave black if not exists') + return parser + + def get_paths(self, opt): + label_dir = opt.label_dir + label_paths = make_dataset(label_dir, recursive=False, read_cache=True) + + image_dir = opt.image_dir + image_paths = make_dataset(image_dir, recursive=False, read_cache=True) + + if len(opt.instance_dir) > 0: + instance_dir = opt.instance_dir + instance_paths = make_dataset(instance_dir, recursive=False, read_cache=True) + else: + instance_paths = [] + + assert len(label_paths) == len(image_paths), "The #images in %s and %s do not match. Is there something wrong?" + + return label_paths, image_paths, instance_paths diff --git a/data/day2night_dataset.py b/data/day2night_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8c21291c8d361c94a3640ec2029212d5fd5f42c2 --- /dev/null +++ b/data/day2night_dataset.py @@ -0,0 +1,43 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset + + +class Day2NightDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=512) + parser.set_defaults(crop_size=512) + parser.set_defaults(display_winsize=512) + parser.set_defaults(aspect_ratio=2.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + with open(os.path.join(croot, 'bdd100k_lists/day2night/day_%s.txt' % opt.phase)) as c_list: + c_image_paths_read = c_list.read().splitlines() + c_image_paths = [os.path.join(croot, p) for p in c_image_paths_read if p != ''] + + with open(os.path.join(sroot, 'bdd100k_lists/day2night/night_%s.txt' % opt.phase)) as s_list: + s_image_paths_read = s_list.read().splitlines() + s_image_paths = [os.path.join(sroot, p) for p in s_image_paths_read if p != ''] + + if opt.phase == 'train': + c_image_paths = c_image_paths + c_image_paths + + instance_paths = [] + + length = min(len(c_image_paths), len(s_image_paths)) + c_image_paths = c_image_paths[:length] + s_image_paths = s_image_paths[:length] + return c_image_paths, s_image_paths, instance_paths + + def paths_match(self, path1, path2): + return True \ No newline at end of file diff --git a/data/image_folder.py b/data/image_folder.py new file mode 100644 index 0000000000000000000000000000000000000000..7113f5fe07679bd87def3833baa4d1f0601be5c0 --- /dev/null +++ b/data/image_folder.py @@ -0,0 +1,93 @@ +############################################################################### +# Code from +# https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py +# Modified the original code so that it also loads images from the current +# directory as well as the subdirectories +############################################################################### +import torch.utils.data as data +from PIL import Image +import os + +IMG_EXTENSIONS = [ + '.jpg', '.JPG', '.jpeg', '.JPEG', + '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff', '.webp' +] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def make_dataset_rec(dir, images): + assert os.path.isdir(dir), '%s is not a valid directory' % dir + + for root, dnames, fnames in sorted(os.walk(dir, followlinks=True)): + for fname in fnames: + if is_image_file(fname): + path = os.path.join(root, fname) + images.append(path) + + +def make_dataset(dir, recursive=False, read_cache=False, write_cache=False): + images = [] + + if read_cache: + possible_filelist = os.path.join(dir, 'files.list') + if os.path.isfile(possible_filelist): + with open(possible_filelist, 'r') as f: + images = f.read().splitlines() + return images + + if recursive: + make_dataset_rec(dir, images) + else: + assert os.path.isdir(dir) or os.path.islink(dir), '%s is not a valid directory' % dir + + for root, dnames, fnames in sorted(os.walk(dir)): + for fname in fnames: + if is_image_file(fname): + path = os.path.join(root, fname) + images.append(path) + + if write_cache: + filelist_cache = os.path.join(dir, 'files.list') + with open(filelist_cache, 'w') as f: + for path in images: + f.write("%s\n" % path) + print('wrote filelist cache at %s' % filelist_cache) + + return images + + +def default_loader(path): + return Image.open(path).convert('RGB') + + +class ImageFolder(data.Dataset): + + def __init__(self, root, transform=None, return_paths=False, + loader=default_loader): + imgs = make_dataset(root) + if len(imgs) == 0: + raise(RuntimeError("Found 0 images in: " + root + "\n" + "Supported image extensions are: " + + ",".join(IMG_EXTENSIONS))) + + self.root = root + self.imgs = imgs + self.transform = transform + self.return_paths = return_paths + self.loader = loader + + def __getitem__(self, index): + path = self.imgs[index] + img = self.loader(path) + if self.transform is not None: + img = self.transform(img) + if self.return_paths: + return img, path + else: + return img + + def __len__(self): + return len(self.imgs) diff --git a/data/n2h_dataset.py b/data/n2h_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..1a935a4c82758c5887cdee3830f90c86587faac0 --- /dev/null +++ b/data/n2h_dataset.py @@ -0,0 +1,86 @@ +# --- START OF FILE data/n2h_dataset.py (Sửa lỗi AttributeError) --- +import os +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset +from PIL import Image +import random +from data.base_dataset import get_params, get_transform + + +class N2HDataset(Pix2pixDataset): + + def __init__(self, opt): + """Initialize this dataset class. + + A_paths and B_paths are defined here, and we call the initialize + method of the parent class (Pix2pixDataset) to set up the rest. + """ + # Gọi __init__ của lớp cha gần nhất (Pix2pixDataset) + # Pix2pixDataset không có __init__, nên nó sẽ gọi BaseDataset.__init__(self, opt) + # Điều này là đúng với bản sửa lỗi trước của chúng ta. + super().__init__(opt) + + # Gọi hàm initialize của lớp cha để thiết lập self.label_paths, self.image_paths, và self.dataset_size + self.initialize(opt) + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='resize_and_crop') + parser.set_defaults(load_size=286) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(aspect_ratio=1.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + # Logic này giả định cấu trúc thư mục là croot/trainA, sroot/trainB + c_image_dir = os.path.join(croot, opt.phase + 'A') + s_image_dir = os.path.join(sroot, opt.phase + 'B') + + if not os.path.isdir(c_image_dir): + raise FileNotFoundError(f"Content directory not found: {c_image_dir}") + if not os.path.isdir(s_image_dir): + raise FileNotFoundError(f"Style directory not found: {s_image_dir}") + + c_image_paths = sorted(make_dataset(c_image_dir, recursive=True)) + s_image_paths = sorted(make_dataset(s_image_dir, recursive=True)) + + if opt.phase == 'train' and len(c_image_paths) > 0 and len(s_image_paths) > 0: + if len(c_image_paths) > len(s_image_paths): + s_image_paths = s_image_paths * (len(c_image_paths) // len(s_image_paths) + 1) + elif len(s_image_paths) > len(c_image_paths): + c_image_paths = c_image_paths * (len(s_image_paths) // len(c_image_paths) + 1) + + instance_paths = [] + + return c_image_paths, s_image_paths, instance_paths + + def __getitem__(self, index): + # Lấy ảnh Day (ảnh A - content) + # self.label_paths được gán bằng c_image_paths trong Pix2pixDataset.initialize() + day_path = self.label_paths[index % len(self.label_paths)] + + # Lấy ảnh Night (ảnh B - style) ngẫu nhiên + # self.image_paths được gán bằng s_image_paths trong Pix2pixDataset.initialize() + night_path = self.image_paths[random.randint(0, len(self.image_paths) - 1)] + + day_img = Image.open(day_path).convert('RGB') + night_img = Image.open(night_path).convert('RGB') + + params = get_params(self.opt, day_img.size) + transform = get_transform(self.opt, params) + + day_tensor = transform(day_img) + night_tensor = transform(night_img) + + return {'day': day_tensor, 'night': night_tensor, 'cpath': day_path, 'spath_night': night_path} + + def paths_match(self, path1, path2): + return True \ No newline at end of file diff --git a/data/photo2art_dataset.py b/data/photo2art_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..dce3285dc977d923eb8c77ad5173ac1413c8d5f1 --- /dev/null +++ b/data/photo2art_dataset.py @@ -0,0 +1,43 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class Photo2ArtDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=256) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(aspect_ratio=1.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + c_image_dir = os.path.join(croot, '%sA' % opt.phase) + c_image_paths = sorted(make_dataset(c_image_dir, recursive=True)) + + s_image_dir = os.path.join(sroot, '%sB' % opt.phase) + s_image_paths = sorted(make_dataset(s_image_dir, recursive=True)) + + if opt.phase == 'train': + for i in range(2): + s_image_paths = s_image_paths + s_image_paths + + instance_paths = [] + + length = min(len(c_image_paths), len(s_image_paths)) + c_image_paths = c_image_paths[:length] + s_image_paths = s_image_paths[:length] + return c_image_paths, s_image_paths, instance_paths + + def paths_match(self, path1, path2): + return True \ No newline at end of file diff --git a/data/pix2pix_dataset.py b/data/pix2pix_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..a0dee386e31f77ce0c404a50fcd574a2a565fcef --- /dev/null +++ b/data/pix2pix_dataset.py @@ -0,0 +1,107 @@ +from data.base_dataset import BaseDataset, get_params, get_transform +from PIL import Image +import util.util as util +import os + + +class Pix2pixDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--no_pairing_check', action='store_true', + help='If specified, skip sanity check of correct label-image file pairing') + return parser + + def initialize(self, opt): + self.opt = opt + + label_paths, image_paths, instance_paths = self.get_paths(opt) + + util.natural_sort(label_paths) + util.natural_sort(image_paths) + if not opt.no_instance: + util.natural_sort(instance_paths) + + label_paths = label_paths[:opt.max_dataset_size] + image_paths = image_paths[:opt.max_dataset_size] + instance_paths = instance_paths[:opt.max_dataset_size] + + if not opt.no_pairing_check: + for path1, path2 in zip(label_paths, image_paths): + assert self.paths_match(path1, path2), \ + "The label-image pair (%s, %s) do not look like the right pair because the filenames are quite different. Are you sure about the pairing? Please see data/pix2pix_dataset.py to see what is going on, and use --no_pairing_check to bypass this." % (path1, path2) + + self.label_paths = label_paths + self.image_paths = image_paths + self.instance_paths = instance_paths + + size = len(self.label_paths) + self.dataset_size = size + + def get_paths(self, opt): + label_paths = [] + image_paths = [] + instance_paths = [] + assert False, "A subclass of Pix2pixDataset must override self.get_paths(self, opt)" + return label_paths, image_paths, instance_paths + + def paths_match(self, path1, path2): + filename1_without_ext = os.path.splitext(os.path.basename(path1))[0] + filename2_without_ext = os.path.splitext(os.path.basename(path2))[0] + return filename1_without_ext == filename2_without_ext + + def __getitem__(self, index): + # Label (Content) Image + label_path = self.label_paths[index] + label = Image.open(label_path) + if self.opt.task != 'SIS': + label = label.convert('RGB') + params = get_params(self.opt, label.size) + + if self.opt.task != 'SIS': + transform_label = get_transform(self.opt, params) + label_tensor = transform_label(label) + else: + transform_label = get_transform(self.opt, params, method=Image.NEAREST, normalize=False) + label_tensor = transform_label(label) * 255.0 + label_tensor[label_tensor == 255] = self.opt.label_nc # 'unknown' is opt.label_nc + + # Real (Style) Image + image_path = self.image_paths[index] + assert self.paths_match(label_path, image_path), \ + "The label_path %s and image_path %s don't match." % \ + (label_path, image_path) + image = Image.open(image_path) + image = image.convert('RGB') + + transform_image = get_transform(self.opt, params) + image_tensor = transform_image(image) + + # if using instance maps + if self.opt.no_instance: + instance_tensor = 0 + else: + instance_path = self.instance_paths[index] + instance = Image.open(instance_path) + if instance.mode == 'L': + instance_tensor = transform_label(instance) * 255 + instance_tensor = instance_tensor.long() + else: + instance_tensor = transform_label(instance) + + input_dict = {'label': label_tensor, + 'instance': instance_tensor, + 'image': image_tensor, + 'path': image_path, + 'cpath': label_path + } + + # Give subclasses a chance to modify the final output + self.postprocess(input_dict) + + return input_dict + + def postprocess(self, input_dict): + return input_dict + + def __len__(self): + return self.dataset_size diff --git a/data/single_folder_dataset.py b/data/single_folder_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..dbfb9fd54eb2c50060e9a74efb14de6647c07629 --- /dev/null +++ b/data/single_folder_dataset.py @@ -0,0 +1,51 @@ +# --- START OF FILE data/single_folder_dataset.py --- +from data.base_dataset import BaseDataset, get_transform, get_params +from data.image_folder import make_dataset +from PIL import Image + + +class SingleFolderDataset(BaseDataset): + """ + A dataset class for loading images from a single folder. + Used for testing where only content images are needed. + """ + + @staticmethod + def modify_commandline_options(parser, is_train): + # is_train sẽ là False khi chạy test.py + parser.add_argument('--image_dir', type=str, required=True, + help='path to the directory that contains images') + # Khi test, chúng ta thường không muốn crop ngẫu nhiên, + # nên preprocess_mode='resize' hoặc 'scale_width' là phù hợp. + # Hoặc có thể giữ 'resize_and_crop' và dùng crop_pos=(0,0) + parser.set_defaults(preprocess_mode='resize_and_crop', load_size=256, crop_size=256, no_flip=True) + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + self.image_paths = sorted(make_dataset(opt.image_dir, recursive=True)) + # <<< THAY ĐỔI: Xóa dòng self.transform ở đây >>> + + def __getitem__(self, index): + path = self.image_paths[index] + img = Image.open(path).convert('RGB') + + # <<< THAY ĐỔI: Tạo params và transform cho mỗi ảnh >>> + # Khi test, chúng ta không muốn augmentation ngẫu nhiên. + # Đặt crop_pos=(0,0) và flip=False để đảm bảo tính nhất quán. + params = get_params(self.opt, img.size) + if not self.opt.isTrain: + params['crop_pos'] = (0, 0) + params['flip'] = False + + transform = get_transform(self.opt, params, normalize=True) + img_tensor = transform(img) + # <<< KẾT THÚC THAY ĐỔI >>> + + # Trả về một dict tương thích với những gì model mong đợi ở bước test + # 'day' sẽ được dùng làm content_image. + return {'day': img_tensor, 'cpath': path} + + def __len__(self): + return len(self.image_paths) \ No newline at end of file diff --git a/data/summer2winteryosemite_dataset.py b/data/summer2winteryosemite_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..353faea5289c2b4ebe4ed898a0a77519bfd3750d --- /dev/null +++ b/data/summer2winteryosemite_dataset.py @@ -0,0 +1,42 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset +from data.image_folder import make_dataset + + +class Summer2WinterYosemiteDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=256) + parser.set_defaults(crop_size=256) + parser.set_defaults(display_winsize=256) + parser.set_defaults(aspect_ratio=1.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + c_image_dir = os.path.join(croot, '%sA' % opt.phase) + c_image_paths = sorted(make_dataset(c_image_dir, recursive=True)) + + s_image_dir = os.path.join(sroot, '%sB' % opt.phase) + s_image_paths = sorted(make_dataset(s_image_dir, recursive=True)) + + if opt.phase == 'train': + s_image_paths = s_image_paths + s_image_paths + + instance_paths = [] + + length = min(len(c_image_paths), len(s_image_paths)) + c_image_paths = c_image_paths[:length] + s_image_paths = s_image_paths[:length] + return c_image_paths, s_image_paths, instance_paths + + def paths_match(self, path1, path2): + return True diff --git a/data/sunny2diffweathers_dataset.py b/data/sunny2diffweathers_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..ac8eb2c8f279b58e0f2b6b903ad744762a687101 --- /dev/null +++ b/data/sunny2diffweathers_dataset.py @@ -0,0 +1,54 @@ +import os +from data.pix2pix_dataset import Pix2pixDataset + + +class Sunny2DiffWeathersDataset(Pix2pixDataset): + + @staticmethod + def modify_commandline_options(parser, is_train): + parser = Pix2pixDataset.modify_commandline_options(parser, is_train) + parser.add_argument('--test_mode', type=str, default='all', + help='specify style mode to control multi-modal image synthesis (MMIS) during test phase:' + 'night | cloudy | rainy | snowy | all') + parser.set_defaults(preprocess_mode='fixed') + parser.set_defaults(load_size=512) + parser.set_defaults(crop_size=512) + parser.set_defaults(display_winsize=512) + parser.set_defaults(aspect_ratio=2.0) + opt, _ = parser.parse_known_args() + if hasattr(opt, 'num_upsampling_layers'): + parser.set_defaults(num_upsampling_layers='more') + return parser + + + def get_paths(self, opt): + croot = opt.croot + sroot = opt.sroot + + with open(os.path.join(croot, 'bdd100k_lists/sunny2diffweathers/sunny_%s.txt' % opt.phase)) as c_list: + c_image_paths_read = c_list.read().splitlines() + c_image_paths = [os.path.join(croot, p) for p in c_image_paths_read if p != ''] + + if opt.phase == 'train' or opt.test_mode == 'all': + mode_list = ['night', 'cloudy', 'rainy', 'snowy'] + else: + mode_list = [opt.test_mode] + s_image_paths = [] + for mode in mode_list: + with open(os.path.join(sroot, 'bdd100k_lists/sunny2diffweathers/%s_%s.txt' % (mode, opt.phase))) as s_list: + s_image_paths_read = s_list.read().splitlines() + s_image_paths_mode = [os.path.join(sroot, p) for p in s_image_paths_read if p != ''] + s_image_paths.extend(s_image_paths_mode) + + while len(s_image_paths) < len(c_image_paths): + s_image_paths = s_image_paths + s_image_paths + + instance_paths = [] + + length = min(len(c_image_paths), len(s_image_paths)) + c_image_paths = c_image_paths[:length] + s_image_paths = s_image_paths[:length] + return c_image_paths, s_image_paths, instance_paths + + def paths_match(self, path1, path2): + return True \ No newline at end of file diff --git a/data/unaligned_day_night_dataset.py b/data/unaligned_day_night_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..d2534a5cacbe42eb871bcf283f4a49fdb7f0c103 --- /dev/null +++ b/data/unaligned_day_night_dataset.py @@ -0,0 +1,53 @@ +import os.path +from data.base_dataset import BaseDataset, get_transform, get_params +from data.image_folder import make_dataset +from PIL import Image +import random + +class UnalignedDayNightDataset(BaseDataset): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--dataroot', required=True, + help='path to images (should have subfolders train/val containing day/night)') + parser.set_defaults(preprocess_mode='resize_and_crop', load_size=286, crop_size=256) + if not is_train: + parser.set_defaults(no_flip=True) + return parser + + def __init__(self, opt): + BaseDataset.__init__(self) + self.opt = opt + + root = opt.dataroot + phase = opt.phase + + self.dir_A = os.path.join(root, phase, 'day') + self.dir_B = os.path.join(root, phase, 'night') + + self.A_paths = sorted(make_dataset(self.dir_A, recursive=True)) + self.B_paths = sorted(make_dataset(self.dir_B, recursive=True)) + + self.A_size = len(self.A_paths) + self.B_size = len(self.B_paths) + + if self.A_size == 0 or self.B_size == 0: + raise (RuntimeError(f"Found 0 images in one of the data directories: {self.dir_A} or {self.dir_B}")) + + def __getitem__(self, index): + A_path = self.A_paths[index % self.A_size] + index_B = random.randint(0, self.B_size - 1) + B_path = self.B_paths[index_B] + + A_img = Image.open(A_path).convert('RGB') + B_img = Image.open(B_path).convert('RGB') + + params = get_params(self.opt, A_img.size) + transform = get_transform(self.opt, params) + + A = transform(A_img) + B = transform(B_img) + + return {'day': A, 'night': B, 'cpath': A_path, 'spath_night': B_path} + + def __len__(self): + return max(self.A_size, self.B_size) \ No newline at end of file diff --git a/datasets/bdd100k_lists/day2night/day_test.txt b/datasets/bdd100k_lists/day2night/day_test.txt new file mode 100644 index 0000000000000000000000000000000000000000..3d60c0942ff98b186d3c5dbb442d0da0eed93713 --- /dev/null +++ b/datasets/bdd100k_lists/day2night/day_test.txt @@ -0,0 +1,1764 @@ +images/100k/val/b1cebfb7-284f5117.jpg +images/100k/val/b1d10d08-c35503b8.jpg +images/100k/val/b1d7b3ac-36f2d3b7.jpg +images/100k/val/b1d7b3ac-5744370e.jpg +images/100k/val/b1d7b3ac-5af8623b.jpg +images/100k/val/b1d7b3ac-995f9d8a.jpg +images/100k/val/b1dac7f7-6b2e0382.jpg 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+images/100k/train/fde816b0-1b0f1a85.jpg +images/100k/train/fde816b0-7d366afb.jpg +images/100k/train/fdebe7ab-8409a734.jpg +images/100k/train/fdf465ad-f9f80730.jpg +images/100k/train/fdf83cde-c0898074.jpg +images/100k/train/fdfda543-18fb3fc9.jpg +images/100k/train/fe03fc8a-5dabe7d4.jpg +images/100k/train/fe0b200a-b9c280a1.jpg +images/100k/train/fe160d48-d8ef6858.jpg diff --git a/datasets/prepare_ade20k.sh b/datasets/prepare_ade20k.sh new file mode 100644 index 0000000000000000000000000000000000000000..66bbfaca633265a87d84238ed4e26448145435a6 --- /dev/null +++ b/datasets/prepare_ade20k.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash + +set -x + +DATAROOT=$1 + +ln -s $DATAROOT ./datasets diff --git a/datasets/prepare_bdd100k.sh b/datasets/prepare_bdd100k.sh new file mode 100644 index 0000000000000000000000000000000000000000..163319105732726a50a89629ad1aa23c65f951f0 --- /dev/null +++ b/datasets/prepare_bdd100k.sh @@ -0,0 +1,8 @@ +#!/usr/bin/env bash + +set -x + +DATAROOT=$1 + +cp -r ./datasets/bdd100k_lists $DATAROOT +ln -s $DATAROOT ./datasets diff --git a/datasets/prepare_cityscapes.sh b/datasets/prepare_cityscapes.sh new file mode 100644 index 0000000000000000000000000000000000000000..66bbfaca633265a87d84238ed4e26448145435a6 --- /dev/null +++ b/datasets/prepare_cityscapes.sh @@ -0,0 +1,7 @@ +#!/usr/bin/env bash + +set -x + +DATAROOT=$1 + +ln -s $DATAROOT ./datasets diff --git a/datasets/prepare_photo2art.sh b/datasets/prepare_photo2art.sh new file mode 100644 index 0000000000000000000000000000000000000000..f3746dde90c206e404e34c69df8dabf4b83b1ce2 --- /dev/null +++ b/datasets/prepare_photo2art.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env bash + +set -x + +cd ./datasets + +wget --no-check-certificate https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/cezanne2photo.zip +unzip cezanne2photo.zip +rm -f cezanne2photo.zip + +wget --no-check-certificate https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip +unzip monet2photo.zip +rm -f monet2photo.zip + +wget --no-check-certificate https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/ukiyoe2photo.zip +unzip ukiyoe2photo.zip +rm -f ukiyoe2photo.zip + +wget --no-check-certificate https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/vangogh2photo.zip +unzip vangogh2photo.zip +rm -f vangogh2photo.zip + +mkdir -p photo2art/testA +mkdir -p photo2art/testB +mkdir -p photo2art/trainA +mkdir -p photo2art/trainB + +for img in cezanne2photo/testB/* +do + mv "$img" photo2art/testA/ +done + +for img in $(ls cezanne2photo/testA) +do + mv 'cezanne2photo/testA/'$img 'photo2art/testB/cezanne_'$img +done + +for img in $(ls monet2photo/testA) +do + mv 'monet2photo/testA/'$img 'photo2art/testB/monet_'$img +done + +for img in $(ls ukiyoe2photo/testA) +do + mv 'ukiyoe2photo/testA/'$img 'photo2art/testB/ukiyoe_'$img +done + +for img in $(ls vangogh2photo/testA) +do + mv 'vangogh2photo/testA/'$img 'photo2art/testB/vangogh_'$img +done + +for img in cezanne2photo/trainB/* +do + mv "$img" photo2art/trainA/ +done + +for img in $(ls cezanne2photo/trainA) +do + mv 'cezanne2photo/trainA/'$img 'photo2art/trainB/cezanne_'$img +done + +for img in $(ls monet2photo/trainA) +do + mv 'monet2photo/trainA/'$img 'photo2art/trainB/monet_'$img +done + +for img in $(ls ukiyoe2photo/trainA) +do + mv 'ukiyoe2photo/trainA/'$img 'photo2art/trainB/ukiyoe_'$img +done + +for img in $(ls vangogh2photo/trainA) +do + mv 'vangogh2photo/trainA/'$img 'photo2art/trainB/vangogh_'$img +done + +rm -rf cezanne2photo +rm -rf monet2photo +rm -rf ukiyoe2photo +rm -rf vangogh2photo + +cd .. diff --git a/datasets/prepare_summer2winteryosemite.sh b/datasets/prepare_summer2winteryosemite.sh new file mode 100644 index 0000000000000000000000000000000000000000..5ad5b52decaab44e4570b740a904e830e42313e6 --- /dev/null +++ b/datasets/prepare_summer2winteryosemite.sh @@ -0,0 +1,9 @@ +#!/usr/bin/env bash + +set -x + +cd ./datasets +wget --no-check-certificate https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/summer2winter_yosemite.zip +unzip summer2winter_yosemite.zip +rm -f summer2winter_yosemite.zip +cd .. diff --git a/inference_video.py b/inference_video.py new file mode 100644 index 0000000000000000000000000000000000000000..9f34e69518b211067edffe50269c742aa8414885 --- /dev/null +++ b/inference_video.py @@ -0,0 +1,175 @@ +import os +import torch +import cv2 +import numpy as np +import math +from tqdm import tqdm +import argparse +import pickle + +try: + from moviepy import VideoFileClip + MOVIEPY_AVAILABLE = True +except ImportError: + MOVIEPY_AVAILABLE = False + +from options.base_options import BaseOptions +from models.pix2pix_model import Pix2PixModel +from util import util +import torchvision.transforms as transforms +from PIL import Image + +def load_options_from_checkpoint(opt): + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + opt_file_path = os.path.join(expr_dir, 'opt.pkl') + if not os.path.exists(opt_file_path): + opt_file_path = os.path.join(expr_dir, 'opt.txt') + if not os.path.exists(opt_file_path): + raise FileNotFoundError(f"Không tìm thấy file option (opt.pkl hoặc opt.txt) trong {expr_dir}") + print(f"Đang tải options từ: {opt_file_path}") + try: + with open(opt_file_path, 'rb') as f: + loaded_opt = pickle.load(f) + except Exception: + loaded_opt = BaseOptions().load_options(opt) + cmd_opt_vars = vars(opt) + loaded_opt_vars = vars(loaded_opt) + loaded_opt_vars.update(cmd_opt_vars) + final_opt = argparse.Namespace(**loaded_opt_vars) + final_opt.isTrain = False + final_opt.phase = 'test' + return final_opt + +def get_safe_resolution(width, height, max_height=720): + if height <= max_height and width % 16 == 0 and height % 16 == 0: + return width, height + ratio = max_height / height + new_width = int(width * ratio) + new_height = max_height + if new_width % 16 != 0: + new_width = new_width - (new_width % 16) + if new_height % 16 != 0: + new_height = new_height - (new_height % 16) + return new_width, new_height + +def preprocess_frame(frame_pil): + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + return transform(frame_pil).unsqueeze(0) + +def main(): + parser = argparse.ArgumentParser(description='Video inference script for Day-to-Night model.') + parser.add_argument('--name', type=str, required=True, help='Tên của experiment đã train.') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='Thư mục chứa các checkpoints.') + parser.add_argument('--which_epoch', type=str, default='latest', help='Epoch muốn sử dụng để test.') + parser.add_argument('--gpu_ids', type=str, default='0', help='GPU IDs. Dùng -1 cho CPU.') + parser.add_argument('--video_path', type=str, required=True, help='Đường dẫn đến video đầu vào.') + parser.add_argument('--output_dir', type=str, default='./results', help='Thư mục gốc để lưu video kết quả.') + parser.add_argument('--no_audio', action='store_true', help='Không giữ lại âm thanh từ video gốc.') + parser.add_argument('--max_resolution_height', type=int, default=720, help='Chiều cao tối đa của video để xử lý.') + parser.add_argument('--degree_step', type=float, default=5.0, help='Số độ sẽ tăng lên sau mỗi frame.') + + opt = parser.parse_args() + opt = load_options_from_checkpoint(opt) + opt.preprocess_mode = 'none' + + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id = int(str_id) + if id >= 0: + opt.gpu_ids.append(id) + if len(opt.gpu_ids) > 0: + torch.cuda.set_device(opt.gpu_ids[0]) + + model = Pix2PixModel(opt) + model.eval() + print(f"Model {opt.name} epoch {opt.which_epoch} loaded successfully.") + + video_in = cv2.VideoCapture(opt.video_path) + if not video_in.isOpened(): + raise IOError(f"Không thể mở video: {opt.video_path}") + + fps = video_in.get(cv2.CAP_PROP_FPS) + total_frames = int(video_in.get(cv2.CAP_PROP_FRAME_COUNT)) + original_width = int(video_in.get(cv2.CAP_PROP_FRAME_WIDTH)) + original_height = int(video_in.get(cv2.CAP_PROP_FRAME_HEIGHT)) + + proc_width, proc_height = get_safe_resolution(original_width, original_height, opt.max_resolution_height) + print(f"Original Video Info: {original_width}x{original_height}, {fps:.2f} FPS, {total_frames} frames.") + if (proc_width, proc_height) != (original_width, original_height): + print(f"Video will be resized to {proc_width}x{proc_height} for processing.") + + processed_frames_np = [] + + for frame_idx in tqdm(range(total_frames), desc="Processing Video Frames"): + ret, frame = video_in.read() + if not ret: + break + frame_resized = cv2.resize(frame, (proc_width, proc_height), interpolation=cv2.INTER_AREA) + frame_rgb = cv2.cvtColor(frame_resized, cv2.COLOR_BGR2RGB) + frame_pil = Image.fromarray(frame_rgb) + input_tensor = preprocess_frame(frame_pil) + data_i = {'day': input_tensor, 'cpath': ['dummy_path']} + + cycle_length = 360 / opt.degree_step + half_cycle = int(cycle_length / 2) + idx_in_cycle = frame_idx % int(cycle_length) + if idx_in_cycle < half_cycle: + current_degree = 180 + idx_in_cycle * opt.degree_step + else: + current_degree = (idx_in_cycle - half_cycle) * opt.degree_step + + phi_val = math.radians(current_degree) + phi_tensor_1d = torch.tensor([phi_val], device=model.device) + model.opt.phi = phi_tensor_1d + + with torch.no_grad(): + generated_tensor = model(data_i, mode='inference', arbitrary_input=True) + generated_numpy = util.tensor2im(generated_tensor)[0] + processed_frame_bgr = cv2.cvtColor(generated_numpy, cv2.COLOR_RGB2BGR) + processed_frames_np.append(processed_frame_bgr) + + video_in.release() + print("Finished processing all frames.") + + output_video_dir = os.path.join(opt.output_dir, opt.name, f'{opt.phase}_{opt.which_epoch}') + os.makedirs(output_video_dir, exist_ok=True) + video_basename = os.path.basename(opt.video_path) + video_name, video_ext = os.path.splitext(video_basename) + video_size, model_name=str(opt.max_resolution_height), str(opt.name) + output_path = os.path.join(output_video_dir, f"{video_name}_cycle_by_degree{video_ext}_{video_size}_{model_name}.mp4") + print(f"Saving output video to: {output_path}") + + temp_video_path = output_path + ".tmp.mp4" + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + video_out = cv2.VideoWriter(temp_video_path, fourcc, fps, (proc_width, proc_height)) + for frame in tqdm(processed_frames_np, desc="Writing video file"): + video_out.write(frame) + video_out.release() + + if not opt.no_audio and MOVIEPY_AVAILABLE: + print("Adding audio from original video...") + try: + original_clip = VideoFileClip(opt.video_path) + generated_clip = VideoFileClip(temp_video_path) + final_clip = generated_clip.set_audio(original_clip.audio) + final_clip.write_videofile(output_path, codec='libx264', audio_codec='aac', logger='bar') + original_clip.close() + generated_clip.close() + os.remove(temp_video_path) + print("Audio added successfully.") + except Exception as e: + print(f"\nWarning: Could not add audio due to an error: {e}") + print("The video without audio has been saved.") + os.rename(temp_video_path, output_path) + else: + if not MOVIEPY_AVAILABLE and not opt.no_audio: + print("\nWarning: 'moviepy' is not installed. Cannot process audio. Skipping.") + os.rename(temp_video_path, output_path) + print(f"Video processing complete. Output saved to {output_path}") + +if __name__ == '__main__': + main() diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e895b4bc509ade61d8e2a454e86817b50e85ac33 --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,39 @@ +import importlib +import torch + + +def find_model_using_name(model_name): + # Given the option --model [modelname], + # the file "models/modelname_model.py" + # will be imported. + model_filename = "models." + model_name + "_model" + modellib = importlib.import_module(model_filename) + + # In the file, the class called ModelNameModel() will + # be instantiated. It has to be a subclass of torch.nn.Module, + # and it is case-insensitive. + model = None + target_model_name = model_name.replace('_', '') + 'model' + for name, cls in modellib.__dict__.items(): + if name.lower() == target_model_name.lower() \ + and issubclass(cls, torch.nn.Module): + model = cls + + if model is None: + print("In %s.py, there should be a subclass of torch.nn.Module with class name that matches %s in lowercase." % (model_filename, target_model_name)) + exit(0) + + return model + + +def get_option_setter(model_name): + model_class = find_model_using_name(model_name) + return model_class.modify_commandline_options + + +def create_model(opt): + model = find_model_using_name(opt.model) + instance = model(opt) + print("model [%s] was created" % (type(instance).__name__)) + + return instance diff --git a/models/__pycache__/__init__.cpython-310.pyc b/models/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4670d6aad14974c26d916c0d9e96577b16d79c9c Binary files /dev/null and b/models/__pycache__/__init__.cpython-310.pyc differ diff --git a/models/__pycache__/pix2pix_model.cpython-310.pyc b/models/__pycache__/pix2pix_model.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e88938fe1dc6cfaa4a57a868795ab81306637aa1 Binary files /dev/null and b/models/__pycache__/pix2pix_model.cpython-310.pyc differ diff --git a/models/networks/AdaIN/function.py b/models/networks/AdaIN/function.py new file mode 100644 index 0000000000000000000000000000000000000000..9da189136db169d9f09f2b3535f5072c60837a7d --- /dev/null +++ b/models/networks/AdaIN/function.py @@ -0,0 +1,98 @@ +import torch +import torch.nn.functional as F + + +# The code was inspired from https://github.com/naoto0804/pytorch-AdaIN. +def calc_mean_std(feat, eps=1e-5, mask=None): + # eps is a small value added to the variance to avoid divide-by-zero. + size = feat.size() + assert (len(size) == 4) + if mask is not None: + msize = mask.size() + assert (msize[0] == size[0] and msize[2] == size[2] and msize[3] == size[3]) + N, C = size[:2] + if mask is not None: + cnt = mask.view(N, 1, -1).sum(2) + mf = mask * feat + mf = mf.view(N, C, -1).sum(2) + mf /= cnt + feat_mean = mf.view(N, C, 1, 1) + vf = ((feat - feat_mean) ** 2) * mask + vf = vf.view(N, C, -1).sum(2) + vf = vf / (cnt - 1) + vf += eps + feat_std = vf.sqrt().view(N, C, 1, 1) + else: + feat_var = feat.view(N, C, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(N, C, 1, 1) + feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat, c_mask=None, s_mask=None): + assert (content_feat.size()[:2] == style_feat.size()[:2]) + + size = content_feat.size() + H, W = size[2], size[3] + msk = None + if c_mask is not None: + msk = F.interpolate(c_mask, (H, W)) + + s_size = style_feat.size() + s_H, s_W = s_size[2], s_size[3] + s_msk = None + if s_mask is not None: + s_msk = F.interpolate(s_mask, (s_H, s_W)) + + style_mean, style_std = calc_mean_std(style_feat, mask=s_msk) + content_mean, content_std = calc_mean_std(content_feat, mask=msk) + + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size) + + if msk is not None: + return (normalized_feat * style_std.expand(size) + style_mean.expand(size)) * msk + content_feat * (1 - msk) + else: + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +def _calc_feat_flatten_mean_std(feat): + # takes 3D feat (C, H, W), return mean and std of array within channels + assert (feat.size()[0] == 3) + assert (isinstance(feat, torch.FloatTensor)) + feat_flatten = feat.view(3, -1) + mean = feat_flatten.mean(dim=-1, keepdim=True) + std = feat_flatten.std(dim=-1, keepdim=True) + return feat_flatten, mean, std + + +def _mat_sqrt(x): + U, D, V = torch.svd(x) + return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t()) + + +def coral(source, target): + # assume both source and target are 3D array (C, H, W) + # Note: flatten -> f + source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source) + source_f_norm = (source_f - source_f_mean.expand_as( + source_f)) / source_f_std.expand_as(source_f) + source_f_cov_eye = \ + torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3) + + target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target) + target_f_norm = (target_f - target_f_mean.expand_as( + target_f)) / target_f_std.expand_as(target_f) + target_f_cov_eye = \ + torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3) + + source_f_norm_transfer = torch.mm( + _mat_sqrt(target_f_cov_eye), + torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)), + source_f_norm) + ) + + source_f_transfer = source_f_norm_transfer * \ + target_f_std.expand_as(source_f_norm) + \ + target_f_mean.expand_as(source_f_norm) + + return source_f_transfer.view(source.size()) diff --git a/models/networks/DenseArchitecture.py b/models/networks/DenseArchitecture.py new file mode 100644 index 0000000000000000000000000000000000000000..26fb1c2109bd3f3b3e7b22d34278875264df6631 --- /dev/null +++ b/models/networks/DenseArchitecture.py @@ -0,0 +1,98 @@ +import re +from collections import OrderedDict +from typing import List, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +import torch.nn.utils.spectral_norm as spectral_norm + +class _DenseLayer(nn.Module): + def __init__( + self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False + ) -> None: + super().__init__() + # self.norm1 = spectral_norm(num_input_features) + self.relu1 = nn.ReLU(inplace=True) + self.conv1 = spectral_norm(nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)) + + # self.norm2 = spectral_norm(bn_size * growth_rate) + self.relu2 = nn.ReLU(inplace=True) + self.conv2 = spectral_norm(nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)) + + self.drop_rate = float(drop_rate) + self.memory_efficient = memory_efficient + + def bn_function(self, inputs: List[Tensor]) -> Tensor: + concated_features = torch.cat(inputs, 1) + bottleneck_output = self.conv1(self.relu1(concated_features)) # noqa: T484 + return bottleneck_output + + # todo: rewrite when torchscript supports any + def any_requires_grad(self, input: List[Tensor]) -> bool: + for tensor in input: + if tensor.requires_grad: + return True + return False + + # torchscript does not yet support *args, so we overload method + # allowing it to take either a List[Tensor] or single Tensor + def forward(self, input: Tensor) -> Tensor: # noqa: F811 + if isinstance(input, Tensor): + prev_features = [input] + else: + prev_features = input + + if self.memory_efficient and self.any_requires_grad(prev_features): + if torch.jit.is_scripting(): + raise Exception("Memory Efficient not supported in JIT") + + bottleneck_output = self.call_checkpoint_bottleneck(prev_features) + else: + bottleneck_output = self.bn_function(prev_features) + + new_features = self.conv2(self.relu2(bottleneck_output)) + if self.drop_rate > 0: + new_features = F.dropout(new_features, p=self.drop_rate, training=self.training) + return new_features + + +class _DenseBlock(nn.ModuleDict): + _version = 2 + + def __init__( + self, + num_layers: int, + num_input_features: int, + bn_size: int, + growth_rate: int, + drop_rate: float, + memory_efficient: bool = False, + ) -> None: + super().__init__() + for i in range(num_layers): + layer = _DenseLayer( + num_input_features + i * growth_rate, + growth_rate=growth_rate, + bn_size=bn_size, + drop_rate=drop_rate, + memory_efficient=memory_efficient, + ) + self.add_module("denselayer%d" % (i + 1), layer) + + def forward(self, init_features: Tensor) -> Tensor: + features = [init_features] + for name, layer in self.items(): + new_features = layer(features) + features.append(new_features) + return torch.cat(features, 1) + + +class _Transition(nn.Sequential): + def __init__(self, num_input_features: int, num_output_features: int) -> None: + super().__init__() + self.norm = nn.InstanceNorm2d(num_input_features) + self.relu = nn.ReLU(inplace=True) + self.conv = nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False) + self.pool = nn.AvgPool2d(kernel_size=2, stride=2) diff --git a/models/networks/__init__.py b/models/networks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6152d4c3e1f236881098ad869f0fb55b116ca16a --- /dev/null +++ b/models/networks/__init__.py @@ -0,0 +1,240 @@ +import torch +from models.networks.base_network import BaseNetwork +from models.networks.loss import * +from models.networks.discriminator import * +from models.networks.encoder import ConvEncoder +from models.networks.generator import TSITGenerator, Pix2PixHDGenerator +from models.networks.rafael_generator import RafaelGenerator +import torch.nn as nn +from .style_encoder import SimpleStyleEncoder + +SWIN_COND_D_IMPORTED = False +SwinTransformerConditionalDiscriminator = None +try: + from models.networks.swin_discriminator import SwinTransformerConditionalDiscriminator + SWIN_COND_D_IMPORTED = True + print("Successfully imported SwinTransformerConditionalDiscriminator.") +except ImportError as e: + print(f"Warning: Could not import SwinTransformerConditionalDiscriminator: {e}. Swin D will not be available.") +except Exception as e: + print(f"Warning: An unexpected error occurred while importing SwinTransformerConditionalDiscriminator: {e}") + + +def find_class_in_module(target_cls_name, module_globals): + target_cls_name_lower_no_underscore = target_cls_name.lower().replace('_', '') + for name, cls_obj in module_globals.items(): + if isinstance(cls_obj, type) and name.lower().replace('_', '') == target_cls_name_lower_no_underscore: + return cls_obj + + available_classes = [name for name, obj in module_globals.items() if isinstance(obj, type)] + print( + f"Error: Class '{target_cls_name_lower_no_underscore}' (derived from '{target_cls_name}') not found in module_globals.") + print(f"Available classes (case-insensitive, no underscore): {[ac.lower().replace('_', '') for ac in available_classes]}") + print(f"Original available classes: {available_classes}") + raise ValueError( + f"In current module, there should be a class named '{target_cls_name}' (comparison is case-insensitive and ignores underscores)." + ) + + +def modify_commandline_options(parser, is_train): + opt, _ = parser.parse_known_args() + + # Generator options + if hasattr(opt, 'netG') and opt.netG: + try: + netG_cls = find_class_in_module(opt.netG, globals()) + if hasattr(netG_cls, 'modify_commandline_options'): + parser = netG_cls.modify_commandline_options(parser, is_train) + print(f"DEBUG: Called modify_commandline_options for netG: {opt.netG}") + except ValueError as e: + print(f"Warning: Could not find class for netG '{opt.netG}' to call modify_commandline_options. {e}") + + + # Discriminator options + if is_train and hasattr(opt, 'netD') and opt.netD: + netD_cls_to_modify = None + netD_lower_no_underscore = opt.netD.lower().replace('_','') + + if netD_lower_no_underscore == 'swintransformerconditionaldiscriminator' and SWIN_COND_D_IMPORTED: + netD_cls_to_modify = SwinTransformerConditionalDiscriminator + print(f"DEBUG: networks.modify_commandline_options trying SwinTransformerConditionalDiscriminator for netD '{opt.netD}'.") + elif netD_lower_no_underscore.startswith('swin') and SWIN_COND_D_IMPORTED: + netD_cls_to_modify = SwinTransformerConditionalDiscriminator + print(f"DEBUG: networks.modify_commandline_options trying SwinTransformerConditionalDiscriminator for netD '{opt.netD}' by prefix.") + else: + try: + cls_cand = find_class_in_module(opt.netD, globals()) + if issubclass(cls_cand, BaseNetwork): + netD_cls_to_modify = cls_cand + print(f"DEBUG: networks.modify_commandline_options trying {cls_cand.__name__} for netD '{opt.netD}'.") + except ValueError: + pass + + if netD_cls_to_modify and hasattr(netD_cls_to_modify, 'modify_commandline_options'): + parser = netD_cls_to_modify.modify_commandline_options(parser, is_train) + print(f"DEBUG: Called modify_commandline_options for netD: {netD_cls_to_modify.__name__}") + elif netD_lower_no_underscore.startswith('swin') and not netD_cls_to_modify: + print(f"Warning: netD '{opt.netD}' looks like Swin, but class/modify_commandline_options not found/called.") + elif not netD_cls_to_modify: + print(f"Warning: Could not find a specific class for netD '{opt.netD}' to call modify_commandline_options. Standard D options might not be fully parsed if this D type has specific ones.") + + + # VAE Encoder options (original TSIT) + if hasattr(opt, 'use_vae') and opt.use_vae: + if not hasattr(opt, 'netG') or opt.netG != 'RafaelGenerator': # VAE not used with RafaelGenerator in current setup + if hasattr(ConvEncoder, 'modify_commandline_options'): + parser = ConvEncoder.modify_commandline_options(parser, is_train) + print("DEBUG: Called modify_commandline_options for ConvEncoder (netE).") + return parser + + +def define_G(opt): + # opt.netG should be the string name of the generator class (e.g., "RafaelGenerator") + # find_class_in_module will find it in the `globals()` of this file. + print(f"Attempting to define Generator: {opt.netG}") + netG_cls = find_class_in_module(opt.netG, globals()) + netG = netG_cls(opt) + + print_network(netG) + if len(opt.gpu_ids) > 0: + assert (torch.cuda.is_available()) + netG.cuda(opt.gpu_ids[0]) # .cuda() is deprecated, use .to(device) + # netG.to(torch.device(f'cuda:{opt.gpu_ids[0]}')) + netG.init_weights(opt.init_type, opt.init_variance) + return netG + + +def define_D(opt): + netD_cls = None + netD_name_lower_no_underscore = opt.netD.lower().replace('_', '') + + print(f"Attempting to define Discriminator for opt.netD = '{opt.netD}'") + + if netD_name_lower_no_underscore == 'swintransformerconditionaldiscriminator' and SWIN_COND_D_IMPORTED: + print(f"Selected SwinTransformerConditionalDiscriminator for netD by full name: '{opt.netD}'.") + netD_cls = SwinTransformerConditionalDiscriminator + elif netD_name_lower_no_underscore.startswith("swin") and SWIN_COND_D_IMPORTED: + print(f"Selected SwinTransformerConditionalDiscriminator for netD by prefix: '{opt.netD}'.") + netD_cls = SwinTransformerConditionalDiscriminator + elif opt.netD in ['MultiscaleDiscriminator', 'NLayerDiscriminator']: # Add other known D types here + print(f"Selected {opt.netD} for netD (classic type).") + netD_cls = find_class_in_module(opt.netD, globals()) + else: + # Fallback for any other D specified, or if the above specific checks failed + try: + print(f"Attempting to find class for netD '{opt.netD}' by general search in globals()...") + netD_cls = find_class_in_module(opt.netD, globals()) # This will raise ValueError if not found + print(f"Found and selected {netD_cls.__name__} for netD via general search.") + except ValueError as e: + # This will now properly raise an error if opt.netD is not recognized. + raise ValueError(f"Unknown or unimported Discriminator type specified by --netD: {opt.netD}. Error: {e}") + + + if netD_cls is None: # Should ideally not be reached if find_class_in_module or the specific checks work + raise ValueError(f"Could not assign a class for Discriminator type: {opt.netD}. Check imports and naming.") + + netD = netD_cls(opt) + print_network(netD) + if len(opt.gpu_ids) > 0 and torch.cuda.is_available(): + netD.cuda(opt.gpu_ids[0]) + # netD.to(torch.device(f'cuda:{opt.gpu_ids[0]}')) + + netD.init_weights(opt.init_type, opt.init_variance) + return netD + + +def define_E(opt): + print("Attempting to define Encoder (ConvEncoder for VAE)") + netE = ConvEncoder(opt) + print_network(netE) + if len(opt.gpu_ids) > 0: + assert (torch.cuda.is_available()) + netE.cuda(opt.gpu_ids[0]) + # netE.to(torch.device(f'cuda:{opt.gpu_ids[0]}')) + netE.init_weights(opt.init_type, opt.init_variance) + return netE + + +def print_network(net): + if isinstance(net, list): + actual_net_to_print = net[0] + print(f"Printing network info for the first model in a list of {len(net)} models.") + else: + actual_net_to_print = net + + num_params = 0 + for param in actual_net_to_print.parameters(): + num_params += param.numel() + + print('Network [%s] was created. Total number of parameters: %.1f million. ' + 'To see the architecture, do print(network).' + % (type(actual_net_to_print).__name__, num_params / 1000000)) + + +class Identity(torch.nn.Module): + def forward(self, x): + return x + + +def get_norm_layer(opt, norm_type='instance'): + # norm_type is a string like 'spectralinstance' or 'instance' or 'batch' + def get_out_channel(layer): + if hasattr(layer, 'out_channels'): + return getattr(layer, 'out_channels') + if hasattr(layer, 'out_features'): # For Linear layers + return getattr(layer, 'out_features') + if hasattr(layer, 'weight') and layer.weight.dim() > 1: # Conv, Linear + return layer.weight.size(0) + raise ValueError(f"Cannot get out_channel for layer {type(layer)}") + + def add_norm_layer(layer_instance): # layer_instance is e.g. nn.Conv2d(...) + nonlocal norm_type # Use norm_type from the outer scope + current_norm_type = norm_type + + # Spectral normalization part + if current_norm_type.startswith('spectral'): + # Apply spectral norm to the layer (e.g., nn.Conv2d) + # torch.nn.utils.spectral_norm returns the spectrally_normed_layer + layer_with_spec_norm = torch.nn.utils.spectral_norm(layer_instance) + # The rest of the norm_type string is the actual normalization (e.g., 'instance') + subnorm_type = current_norm_type[len('spectral'):] + else: + layer_with_spec_norm = layer_instance # No spectral norm + subnorm_type = current_norm_type + + if subnorm_type == 'none' or len(subnorm_type) == 0: + return layer_with_spec_norm # Only spectral norm (if any) or just the layer + + # For other normalizations, remove bias from the layer_with_spec_norm + # as it's cancelled by the normalization layer's learnable/non-learnable beta. + if getattr(layer_with_spec_norm, 'bias', None) is not None: + delattr(layer_with_spec_norm, 'bias') + layer_with_spec_norm.register_parameter('bias', None) + + out_channels = get_out_channel(layer_instance) # Get channels from original layer + + if subnorm_type == 'batch': + norm_layer_instance = nn.BatchNorm2d(out_channels, affine=True) + elif subnorm_type == 'syncbatch': + # Assuming SynchronizedBatchNorm2d is available in this scope + # from models.networks.sync_batchnorm import SynchronizedBatchNorm2d # Or import globally + try: + from models.networks.sync_batchnorm import SynchronizedBatchNorm2d + norm_layer_instance = SynchronizedBatchNorm2d(out_channels, affine=True) + except ImportError: + print("Warning: SynchronizedBatchNorm2d not found, falling back to BatchNorm2d for 'syncbatch'.") + norm_layer_instance = nn.BatchNorm2d(out_channels, affine=True) + elif subnorm_type == 'instance': + norm_layer_instance = nn.InstanceNorm2d(out_channels, affine=False) # Original TSIT used affine=False + elif subnorm_type.startswith('fade'): # FADE is handled differently, usually within the block + print( + f"Warning: Norm type '{subnorm_type}' looks like FADE. " + "This generic get_norm_layer is not for FADE. FADE should be part of specific blocks. 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files /dev/null and b/models/networks/__pycache__/time_utils.cpython-310.pyc differ diff --git a/models/networks/architecture.py b/models/networks/architecture.py new file mode 100644 index 0000000000000000000000000000000000000000..4758c9a6ad120484f5b3c85fd44c1981a00bb179 --- /dev/null +++ b/models/networks/architecture.py @@ -0,0 +1,202 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision +import torch.nn.utils.spectral_norm as spectral_norm +from models.networks.normalization import FADE +from models.networks.sync_batchnorm import SynchronizedBatchNorm2d +from torchvision.models import vgg19, VGG19_Weights + + +# ResNet block that uses FADE. +# It differs from the ResNet block of SPADE in that +# it takes in the feature map as input, learns the skip connection if necessary. +# This architecture seemed like a standard architecture for unconditional or +# class-conditional GAN architecture using residual block. +# The code was inspired from https://github.com/LMescheder/GAN_stability +# and https://github.com/NVlabs/SPADE. +class FADEResnetBlock(nn.Module): + def __init__(self, fin, fout, opt): + super().__init__() + # attributes + self.learned_shortcut = (fin != fout) + fmiddle = fin + + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + + # apply spectral norm if specified + if 'spectral' in opt.norm_G: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + + # define normalization layers + fade_config_str = opt.norm_G.replace('spectral', '') + self.norm_0 = FADE(fade_config_str, fin, fin) + self.norm_1 = FADE(fade_config_str, fmiddle, fmiddle) + if self.learned_shortcut: + self.norm_s = FADE(fade_config_str, fin, fin) + + # Note the resnet block with FADE also takes in |feat|, + # the feature map as input + def forward(self, x, feat): + x_s = self.shortcut(x, feat) + + dx = self.conv_0(self.actvn(self.norm_0(x, feat))) + dx = self.conv_1(self.actvn(self.norm_1(dx, feat))) + + out = x_s + dx + + return out + + def shortcut(self, x, feat): + if self.learned_shortcut: + x_s = self.conv_s(self.norm_s(x, feat)) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) + + +class StreamResnetBlock(nn.Module): + def __init__(self, fin, fout, opt): + super().__init__() + # attributes + self.learned_shortcut = (fin != fout) + fmiddle = fin + + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + + # apply spectral norm if specified + if 'spectral' in opt.norm_S: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + + # define normalization layers + subnorm_type = opt.norm_S.replace('spectral', '') + if subnorm_type == 'batch': + self.norm_layer_in = nn.BatchNorm2d(fin, affine=True) + self.norm_layer_out= nn.BatchNorm2d(fout, affine=True) + if self.learned_shortcut: + self.norm_layer_s = nn.BatchNorm2d(fout, affine=True) + elif subnorm_type == 'syncbatch': + self.norm_layer_in = SynchronizedBatchNorm2d(fin, affine=True) + self.norm_layer_out= SynchronizedBatchNorm2d(fout, affine=True) + if self.learned_shortcut: + self.norm_layer_s = SynchronizedBatchNorm2d(fout, affine=True) + elif subnorm_type == 'instance': + self.norm_layer_in = nn.InstanceNorm2d(fin, affine=False) + self.norm_layer_out= nn.InstanceNorm2d(fout, affine=False) + if self.learned_shortcut: + self.norm_layer_s = nn.InstanceNorm2d(fout, affine=False) + else: + raise ValueError('normalization layer %s is not recognized' % subnorm_type) + + def forward(self, x): + x_s = self.shortcut(x) + + dx = self.actvn(self.norm_layer_in(self.conv_0(x))) + dx = self.actvn(self.norm_layer_out(self.conv_1(dx))) + + out = x_s + dx + + return out + + def shortcut(self,x): + if self.learned_shortcut: + x_s = self.actvn(self.norm_layer_s(self.conv_s(x))) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) + + +# ResNet block used in pix2pixHD +# We keep the same architecture as pix2pixHD. +class ResnetBlock(nn.Module): + def __init__(self, dim, norm_layer, activation=nn.ReLU(False), kernel_size=3): + super().__init__() + + pw = (kernel_size - 1) // 2 + self.conv_block = nn.Sequential( + nn.ReflectionPad2d(pw), + norm_layer(nn.Conv2d(dim, dim, kernel_size=kernel_size)), + activation, + nn.ReflectionPad2d(pw), + norm_layer(nn.Conv2d(dim, dim, kernel_size=kernel_size)) + ) + + def forward(self, x): + y = self.conv_block(x) + out = x + y + return out + + +# A fully connected mapping network to create latent code of style image +class FCMapping(nn.Module): + def __init__(self, opt): + super().__init__() + self.fc1 = nn.Linear(3 * opt.crop_size * opt.crop_size, 1024) + self.fc2 = nn.Linear(1024, 1024) + self.fc3 = nn.Linear(1024, 512) + self.fc4 = nn.Linear(512, 256) + self.fc5 = nn.Linear(256, opt.latent_dim) + self.actvn = nn.LeakyReLU(2e-1) + + def forward(self, x): + x = x.view(x.size(0), -1) + x = self.actvn(self.fc1(x)) + x = self.actvn(self.fc2(x)) + x = self.actvn(self.fc3(x)) + x = self.actvn(self.fc4(x)) + x = self.fc5(x) + return x + + +# VGG architecture, used for the perceptual loss using a pretrained VGG network +class VGG19(torch.nn.Module): + def __init__(self, requires_grad=False): + super().__init__() + vgg_pretrained_features = vgg19(weights=VGG19_Weights.DEFAULT).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + for x in range(2): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(2, 7): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(7, 12): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(12, 21): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(21, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + h_relu1 = self.slice1(X) + h_relu2 = self.slice2(h_relu1) + h_relu3 = self.slice3(h_relu2) + h_relu4 = self.slice4(h_relu3) + h_relu5 = self.slice5(h_relu4) + out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] + return out diff --git a/models/networks/base_network.py b/models/networks/base_network.py new file mode 100644 index 0000000000000000000000000000000000000000..2eecf7d67c5c22400f59e00d2af1bf7e8740bec5 --- /dev/null +++ b/models/networks/base_network.py @@ -0,0 +1,54 @@ +import torch.nn as nn +from torch.nn import init + + +class BaseNetwork(nn.Module): + def __init__(self): + super(BaseNetwork, self).__init__() + + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def print_network(self): + if isinstance(self, list): + self = self[0] + num_params = 0 + for param in self.parameters(): + num_params += param.numel() + print('Network [%s] was created. Total number of parameters: %.1f million. ' + 'To see the architecture, do print(network).' + % (type(self).__name__, num_params / 1000000)) + + def init_weights(self, init_type='normal', gain=0.02): + def init_func(m): + classname = m.__class__.__name__ + if classname.find('BatchNorm2d') != -1: + if hasattr(m, 'weight') and m.weight is not None: + init.normal_(m.weight.data, 1.0, gain) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'xavier_uniform': + init.xavier_uniform_(m.weight.data, gain=1.0) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + elif init_type == 'none': # uses pytorch's default init method + m.reset_parameters() + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + + self.apply(init_func) + + # propagate to children + for m in self.children(): + if hasattr(m, 'init_weights'): + m.init_weights(init_type, gain) diff --git a/models/networks/discriminator.py b/models/networks/discriminator.py new file mode 100644 index 0000000000000000000000000000000000000000..b4b854f149e7695759f1d6796daadd8870b5244b --- /dev/null +++ b/models/networks/discriminator.py @@ -0,0 +1,119 @@ +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +from models.networks.normalization import get_norm_layer +import util.util as util + + +class MultiscaleDiscriminator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--netD_subarch', type=str, default='n_layer', + help='architecture of each discriminator') + parser.add_argument('--num_D', type=int, default=2, + help='number of discriminators to be used in multiscale') + opt, _ = parser.parse_known_args() + + # define properties of each discriminator of the multiscale discriminator + subnetD = util.find_class_in_module(opt.netD_subarch + 'discriminator', + 'models.networks.discriminator') + subnetD.modify_commandline_options(parser, is_train) + + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + + for i in range(opt.num_D): + subnetD = self.create_single_discriminator(opt) + self.add_module('discriminator_%d' % i, subnetD) + + def create_single_discriminator(self, opt): + subarch = opt.netD_subarch + if subarch == 'n_layer': + netD = NLayerDiscriminator(opt) + else: + raise ValueError('unrecognized discriminator subarchitecture %s' % subarch) + return netD + + def downsample(self, input): + return F.avg_pool2d(input, kernel_size=3, + stride=2, padding=[1, 1], + count_include_pad=False) + + # Returns list of lists of discriminator outputs. + # The final result is of size opt.num_D x opt.n_layers_D + def forward(self, input): + result = [] + get_intermediate_features = not self.opt.no_ganFeat_loss + for name, D in self.named_children(): + out = D(input) + if not get_intermediate_features: + out = [out] + result.append(out) + input = self.downsample(input) + + return result + + +# Defines the PatchGAN discriminator with the specified arguments. +class NLayerDiscriminator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--n_layers_D', type=int, default=4, + help='# layers in each discriminator') + return parser + + def __init__(self, opt): + super().__init__() + if opt.task != 'SIS': + opt.n_layers_D = 3 + self.opt = opt + + kw = 4 + padw = int(np.ceil((kw - 1.0) / 2)) + nf = opt.ndf + input_nc = 6 + + norm_layer = get_norm_layer(opt, opt.norm_D) + sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), + nn.LeakyReLU(0.2, False)]] + + for n in range(1, opt.n_layers_D): + nf_prev = nf + nf = min(nf * 2, 512) + stride = 1 if n == 3 else 2 + sequence += [[norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, + stride=stride, padding=padw)), + nn.LeakyReLU(0.2, False) + ]] + + sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]] + + # We divide the layers into groups to extract intermediate layer outputs + for n in range(len(sequence)): + self.add_module('model' + str(n), nn.Sequential(*sequence[n])) + + def compute_D_input_nc(self, opt): + input_nc = opt.output_nc + if opt.task == 'SIS': + input_nc += opt.label_nc + if opt.contain_dontcare_label: + input_nc += 1 + if not opt.no_instance: + input_nc += 1 + return input_nc + + def forward(self, input): + results = [input] + for submodel in self.children(): + intermediate_output = submodel(results[-1]) + results.append(intermediate_output) + + get_intermediate_features = not self.opt.no_ganFeat_loss + if get_intermediate_features: + return results[1:] + else: + return results[-1] diff --git a/models/networks/dstream.py b/models/networks/dstream.py new file mode 100644 index 0000000000000000000000000000000000000000..9432921f82cd08c00709043e6777563ffaf2600a --- /dev/null +++ b/models/networks/dstream.py @@ -0,0 +1,101 @@ +import torch +import torch.nn.functional as F +import torch.nn as nn +from models.networks.base_network import BaseNetwork +from models.networks.DenseArchitecture import _DenseBlock, _Transition +from collections import OrderedDict +import torch.nn.utils.spectral_norm as spectral_norm + +# Content/style stream. +# The two streams are symmetrical with the same network structure, +# aiming at extracting corresponding feature representations in different levels. +class Stream(BaseNetwork): + def __init__(self, opt): + super().__init__() + self.opt = opt + growth_rate = 32 + bn_size = 4 + drop_rate = 0.0 + block_config = [2, 4, 8, 16, 16] + num_init_features = 64 + self.features = nn.ModuleList() + + # First convolution + self.block0 = nn.Sequential( + OrderedDict( + [ + ("conv0", spectral_norm(nn.Conv2d(3, num_init_features, kernel_size=3, stride=1, padding=1, bias=False))), + # ("norm0", nn.InstanceNorm2d(num_init_features)), + ("relu0", nn.ReLU(inplace=True)), + ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), + ] + ) + ) + + + # Each denseblock + num_features = num_init_features + for i, num_layers in enumerate(block_config): + block = _DenseBlock( + num_layers=num_layers, + num_input_features=num_features, + bn_size=bn_size, + growth_rate=growth_rate, + drop_rate=drop_rate, + memory_efficient=False, + ) + self.features.add_module("denseblock%d" % (i + 1), block) + # print(f"{num_features} + {num_layers} * {growth_rate} = {num_features + num_layers * growth_rate}") + num_features = num_features + num_layers * growth_rate + if i != len(block_config) - 1: + trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2) + self.features.add_module("transition%d" % (i + 1), trans) + num_features = num_features // 2 + # Final instance norm + # self.features.add_module("norm5", nn.InstanceNorm2d(num_features)) + + # Official init from torch repo. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.constant_(m.bias, 0) + + def down(self, input): + return F.interpolate(input, scale_factor=0.5) + + def forward(self,input): + x0 = self.block0(input) + + x1 = self.features.denseblock1(x0) + x1 = self.features.transition1(x1) + + x2 = self.features.denseblock2(x1) + x2 = self.features.transition2(x2) + + x3 = self.features.denseblock3(x2) + x3 = self.features.transition3(x3) + + x4 = self.features.denseblock4(x3) + x4 = self.features.transition4(x4) + + x5 = self.features.denseblock5(x4) + x5 = self.down(x5) + + + return [x0, x1, x2, x3, x4, x5] + +# Test above architecture with random input. +if __name__ == "__main__": + import torch + from options.train_options import TrainOptions + + # parse options + opt = TrainOptions().parse() + stream = Stream(opt=opt) + input = torch.randn(1, 3, 512, 512) + x0, x1, x2, x3, x4, x5 = stream(input) + print(x0.shape, x1.shape, x2.shape, x3.shape, x4.shape, x5.shape) diff --git a/models/networks/encoder.py b/models/networks/encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..ade9c060f14df5baf4573978b67401d0843da1eb --- /dev/null +++ b/models/networks/encoder.py @@ -0,0 +1,49 @@ +import torch.nn as nn +import numpy as np +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +from models.networks.normalization import get_norm_layer + + +class ConvEncoder(BaseNetwork): + """ Same architecture as the image discriminator """ + + def __init__(self, opt): + super().__init__() + self.opt = opt + kw = 3 + pw = int(np.ceil((kw - 1.0) / 2)) + ndf = opt.ngf + norm_layer = get_norm_layer(opt, opt.norm_E) + self.layer1 = norm_layer(nn.Conv2d(3, ndf, kw, stride=2, padding=pw)) + self.layer2 = norm_layer(nn.Conv2d(ndf * 1, ndf * 2, kw, stride=2, padding=pw)) + self.layer3 = norm_layer(nn.Conv2d(ndf * 2, ndf * 4, kw, stride=2, padding=pw)) + self.layer4 = norm_layer(nn.Conv2d(ndf * 4, ndf * 8, kw, stride=2, padding=pw)) + self.layer5 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw)) + if opt.crop_size >= 256: + self.layer6 = norm_layer(nn.Conv2d(ndf * 8, ndf * 8, kw, stride=2, padding=pw)) + + self.so = s0 = 4 + self.fc_mu = nn.Linear(ndf * 8 * s0 * s0, 256) + self.fc_var = nn.Linear(ndf * 8 * s0 * s0, 256) + + self.actvn = nn.LeakyReLU(0.2, False) + + def forward(self, x): + if x.size(2) != 256 or x.size(3) != 256: + x = F.interpolate(x, size=(256, 256), mode='bilinear') + + x = self.layer1(x) + x = self.layer2(self.actvn(x)) + x = self.layer3(self.actvn(x)) + x = self.layer4(self.actvn(x)) + x = self.layer5(self.actvn(x)) + if self.opt.crop_size >= 256: + x = self.layer6(self.actvn(x)) + x = self.actvn(x) + + x = x.view(x.size(0), -1) + mu = self.fc_mu(x) + logvar = self.fc_var(x) + + return mu, logvar diff --git a/models/networks/generator.py b/models/networks/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..66a72dfcd68b53e417c6219afcabef8a15b2f353 --- /dev/null +++ b/models/networks/generator.py @@ -0,0 +1,176 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +from models.networks.normalization import AdaptiveInstanceNorm +from models.networks.architecture import ResnetBlock as ResnetBlock +from models.networks.architecture import FADEResnetBlock as FADEResnetBlock +from models.networks.dstream import Stream as Dstream +from models.networks.architecture import FCMapping + +class TSITGenerator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.set_defaults(norm_G='spectralfadesyncbatch3x3') + parser.add_argument('--num_upsampling_layers', + choices=('normal', 'more', 'most'), default='more', + help="If 'more', adds upsampling layer between the two middle resnet blocks." + "If 'most', also add one more upsampling + resnet layer at the end of the generator." + "We only use 'more' as the default setting.") + + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + nf = opt.ngf + self.content_stream = Dstream(self.opt) + self.fc_mapping = FCMapping(self.opt) + self.block_config = [16, 16, 8, 4 ,2] + self.sw, self.sh = self.compute_latent_vector_size(opt) + total_feat = 856 + growth_rate = 32 + self.fader_feats = nn.ModuleList() + self.norms = nn.ModuleList() + + num_features = total_feat + for i, num_layers in enumerate(self.block_config): + out_feat = num_features - (num_layers * growth_rate) + if out_feat < 0: out_feat = num_features * 2 - (num_layers * growth_rate) + if out_feat == 0: out_feat = num_features + fader_resnet_block = FADEResnetBlock(num_features, out_feat, opt) + norm = AdaptiveInstanceNorm(num_features, opt) + self.fader_feats.add_module("up_%d" % (i), fader_resnet_block) + self.norms.add_module("adain_%d" % (i), norm) + num_features = out_feat + + # last one more layer + final = FADEResnetBlock(num_features, out_feat, opt) + self.fader_feats.add_module("up_%d" % (i+1), final) + fnorm = AdaptiveInstanceNorm(num_features, opt) + self.norms.add_module("adain_%d" % (i + 1), fnorm) + if opt.use_vae: + # In case of VAE, we will sample from random z vector + self.fc = nn.Linear(opt.z_dim, 16 * nf * self.sw * self.sh) + else: + # Otherwise, we make the network deterministic by starting with + # downsampled segmentation map (content) instead of random z + self.fc = nn.Conv2d(self.opt.semantic_nc, total_feat, 3, padding=1) + + self.conv_img = nn.Conv2d(num_features, 3, 3, padding=1) + + self.up = nn.Upsample(scale_factor=2) + + def compute_latent_vector_size(self, opt, num_blocks=5): + + sw = opt.crop_size // (2**(num_blocks+1)) + sh = round(sw / opt.aspect_ratio) + + return sw, sh + + + def forward(self, input, real, z=None): + content = input + style = real + ft0, ft1, ft2, ft3, ft4, ft5 = self.content_stream(content) + style_latent = self.fc_mapping(style) + # style_latent = None + + # sample random noise + x = torch.randn(content.size(0), 3, self.sw, self.sh, dtype=torch.float32, device=content.get_device()) + x = self.fc(x) + + # get alpha from options (should be random during training) + alpha = self.opt.alpha + + x = self.norms.adain_0(x, style_latent, alpha=alpha) + x = self.fader_feats.up_0(x, ft5) + x = self.up(x) + + x = self.norms.adain_1(x, style_latent, alpha=alpha) + x = self.fader_feats.up_1(x, ft4) + x = self.up(x) + + x = self.norms.adain_2(x, style_latent, alpha=alpha) + x = self.fader_feats.up_2(x, ft3) + x = self.up(x) + + x = self.norms.adain_3(x, style_latent, alpha=alpha) + x = self.fader_feats.up_3(x, ft2) + x = self.up(x) + + x = self.norms.adain_4(x, style_latent, alpha=alpha) + x = self.fader_feats.up_4(x, ft1) + x = self.up(x) + + x = self.norms.adain_5(x, style_latent, alpha=alpha) + x = self.fader_feats.up_5(x, ft0) + x = self.up(x) + + x = self.conv_img(F.leaky_relu(x, 2e-1)) + x = torch.tanh(x) + return x + + +class Pix2PixHDGenerator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--resnet_n_downsample', type=int, default=4, help='number of downsampling layers in netG') + parser.add_argument('--resnet_n_blocks', type=int, default=9, help='number of residual blocks in the global generator network') + parser.add_argument('--resnet_kernel_size', type=int, default=3, + help='kernel size of the resnet block') + parser.add_argument('--resnet_initial_kernel_size', type=int, default=7, + help='kernel size of the first convolution') + parser.set_defaults(norm_G='instance') + return parser + + def __init__(self, opt): + super().__init__() + input_nc = opt.label_nc + (1 if opt.contain_dontcare_label else 0) + (0 if opt.no_instance else 1) + + norm_layer = self.get_norm_layer(opt, opt.norm_G) + activation = nn.ReLU(False) + + model = [] + + # initial conv + model += [nn.ReflectionPad2d(opt.resnet_initial_kernel_size // 2), + norm_layer(nn.Conv2d(input_nc, opt.ngf, + kernel_size=opt.resnet_initial_kernel_size, + padding=0)), + activation] + + # downsample + mult = 1 + for i in range(opt.resnet_n_downsample): + model += [norm_layer(nn.Conv2d(opt.ngf * mult, opt.ngf * mult * 2, + kernel_size=3, stride=2, padding=1)), + activation] + mult *= 2 + + # resnet blocks + for i in range(opt.resnet_n_blocks): + model += [ResnetBlock(opt.ngf * mult, + norm_layer=norm_layer, + activation=activation, + kernel_size=opt.resnet_kernel_size)] + + # upsample + for i in range(opt.resnet_n_downsample): + nc_in = int(opt.ngf * mult) + nc_out = int((opt.ngf * mult) / 2) + model += [norm_layer(nn.ConvTranspose2d(nc_in, nc_out, + kernel_size=3, stride=2, + padding=1, output_padding=1)), + activation] + mult = mult // 2 + + # final output conv + model += [nn.ReflectionPad2d(3), + nn.Conv2d(nc_out, opt.output_nc, kernel_size=7, padding=0), + nn.Tanh()] + + self.model = nn.Sequential(*model) + + def forward(self, input, z=None): + return self.model(input) diff --git a/models/networks/loss.py b/models/networks/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..0d86b1ba9437a57ccc8e4ab7698462738cc9c85c --- /dev/null +++ b/models/networks/loss.py @@ -0,0 +1,431 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from models.networks.architecture import VGG19 +from torch.nn import init +from torchvision import models +import numpy as np +from pytorch_msssim import ssim, ms_ssim +from torchvision.models import vgg16, VGG16_Weights + + +class LaplacianLoss(nn.Module): + def __init__(self, device): + super(LaplacianLoss, self).__init__() + self.device = device + self.kernel = torch.tensor([[[[0, 1, 0], [1, -4, 1], [0, 1, 0]]]], dtype=torch.float32, device=self.device) + + def rgb_to_grayscale(self, x): + return (0.299 * x[:, 0, :, :] + 0.587 * x[:, 1, :, :] + 0.114 * x[:, 2, :, :]).unsqueeze(1) + + def forward(self, pred, target): + pred_gray = self.rgb_to_grayscale(pred) + target_gray = self.rgb_to_grayscale(target) + pred_lap = F.conv2d(pred_gray, self.kernel, padding=1) + target_lap = F.conv2d(target_gray, self.kernel, padding=1) + return F.l1_loss(pred_lap, target_lap) + +class GANLoss(nn.Module): + def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0, + tensor=torch.FloatTensor, opt=None): + super(GANLoss, self).__init__() + self.real_label = target_real_label + self.fake_label = target_fake_label + self.real_label_tensor = None + self.fake_label_tensor = None + self.zero_tensor = None + self.Tensor = tensor + self.gan_mode = gan_mode + self.opt = opt + if gan_mode not in ['ls', 'original', 'w', 'hinge', 'non_saturating']: + raise ValueError('Unexpected gan_mode {}'.format(gan_mode)) + + def get_target_tensor(self, input, target_is_real): + if target_is_real: + if self.real_label_tensor is None: + self.real_label_tensor = self.Tensor(1).fill_(self.real_label) + self.real_label_tensor.requires_grad_(False) + return self.real_label_tensor.expand_as(input) + else: + if self.fake_label_tensor is None: + self.fake_label_tensor = self.Tensor(1).fill_(self.fake_label) + self.fake_label_tensor.requires_grad_(False) + return self.fake_label_tensor.expand_as(input) + + def get_zero_tensor(self, input): + if self.zero_tensor is None: + self.zero_tensor = torch.zeros(1, device='cuda') + self.zero_tensor.requires_grad_(False) + return self.zero_tensor.expand_as(input) + + def loss(self, input, target_is_real, for_discriminator=True): + if self.gan_mode == 'non_saturating': + if for_discriminator: + if target_is_real: + return F.softplus(-input).mean() + else: + return F.softplus(input).mean() + else: + return F.softplus(-input).mean() + + elif self.gan_mode == 'original': + target_tensor = self.get_target_tensor(input, target_is_real) + loss = F.binary_cross_entropy_with_logits(input, target_tensor) + return loss + elif self.gan_mode == 'ls': + target_tensor = self.get_target_tensor(input, target_is_real) + return F.mse_loss(input, target_tensor) + elif self.gan_mode == 'hinge': + if for_discriminator: + if target_is_real: + minval = torch.min(input - 1, self.get_zero_tensor(input)) + loss = -torch.mean(minval) + else: + minval = torch.min(-input - 1, self.get_zero_tensor(input)) + loss = -torch.mean(minval) + else: + assert target_is_real, "The generator's hinge loss must be aiming for real" + loss = -torch.mean(input) + return loss + else: + if target_is_real: + return -input.mean() + else: + return input.mean() + + def __call__(self, input, target_is_real, for_discriminator=True): + if isinstance(input, list): + loss = 0 + for pred_i in input: + if isinstance(pred_i, list): + pred_i = pred_i[-1] + loss_tensor = self.loss(pred_i, target_is_real, for_discriminator) + bs = 1 if len(loss_tensor.size()) == 0 else loss_tensor.size(0) + new_loss = torch.mean(loss_tensor.view(bs, -1), dim=1) + loss += new_loss + return loss / len(input) + else: + return self.loss(input, target_is_real, for_discriminator) + + +class VGGLoss(nn.Module): + def __init__(self, gpu_ids): + super(VGGLoss, self).__init__() + self.vgg = VGG19().cuda() + self.criterion = nn.L1Loss() + self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0] + + def forward(self, x, y): + x_vgg, y_vgg = self.vgg(x), self.vgg(y) + loss = 0 + for i in range(len(x_vgg)): + loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) + return loss + + +class KLDLoss(nn.Module): + def forward(self, mu, logvar): + return -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp()) + + +class PatchSim(nn.Module): + def __init__(self, patch_nums=256, patch_size=None, norm=True): + super(PatchSim, self).__init__() + self.patch_nums = patch_nums + self.patch_size = patch_size + self.use_norm = norm + + def forward(self, feat, patch_ids=None): + B, C, W, H = feat.size() + feat = feat - feat.mean(dim=[-2, -1], keepdim=True) + feat = F.normalize(feat, dim=1) if self.use_norm else feat / np.sqrt(C) + query, key, patch_ids = self.select_patch(feat, patch_ids=patch_ids) + patch_sim = query.bmm(key) if self.use_norm else torch.tanh(query.bmm(key) / 10) + if patch_ids is not None: + patch_sim = patch_sim.view(B, len(patch_ids), -1) + + return patch_sim, patch_ids + + def select_patch(self, feat, patch_ids=None): + B, C, W, H = feat.size() + pw, ph = self.patch_size, self.patch_size + feat_reshape = feat.permute(0, 2, 3, 1).flatten(1, 2) + if self.patch_nums > 0: + if patch_ids is None: + patch_ids = torch.randperm(feat_reshape.size(1), device=feat.device) + patch_ids = patch_ids[:int(min(self.patch_nums, patch_ids.size(0)))] + feat_query = feat_reshape[:, patch_ids, :] + feat_key = [] + Num = feat_query.size(1) + if pw < W and ph < H: + pos_x, pos_y = patch_ids // W, patch_ids % W + left, top = pos_x - int(pw / 2), pos_y - int(ph / 2) + left, top = torch.where(left > 0, left, torch.zeros_like(left)), torch.where(top > 0, top, + torch.zeros_like(top)) + start_x = torch.where(left > (W - pw), (W - pw) * torch.ones_like(left), left) + start_y = torch.where(top > (H - ph), (H - ph) * torch.ones_like(top), top) + for i in range(Num): + feat_key.append(feat[:, :, start_x[i]:start_x[i] + pw, start_y[i]:start_y[i] + ph]) + feat_key = torch.stack(feat_key, dim=0).permute(1, 0, 2, 3, 4) + feat_key = feat_key.reshape(B * Num, C, pw * ph) + feat_query = feat_query.reshape(B * Num, 1, C) + else: + feat_key = feat.reshape(B, C, W * H) + else: + feat_query = feat.reshape(B, C, H * W).permute(0, 2, 1) + feat_key = feat.reshape(B, C, H * W) + + return feat_query, feat_key, patch_ids + + +class SSIMLoss(nn.Module): + def __init__(self, data_range=2.0, size_average=True, channel=3, nonnegative_ssim=False): + super(SSIMLoss, self).__init__() + self.data_range = data_range + self.size_average = size_average + self.channel = channel + self.nonnegative_ssim = nonnegative_ssim + + def forward(self, img1, img2): + ssim_value = ssim(img1, img2, data_range=self.data_range, size_average=self.size_average, + nonnegative_ssim=self.nonnegative_ssim) + loss = 1.0 - ssim_value + return loss + + +class SpatialCorrelativeLoss(nn.Module): + def __init__(self, loss_mode='cos', patch_nums=256, patch_size=32, norm=True, use_conv=True, + init_type='normal', init_gain=0.02, gpu_ids=[], T=0.1): + super(SpatialCorrelativeLoss, self).__init__() + self.patch_sim = PatchSim(patch_nums=patch_nums, patch_size=patch_size, norm=norm) + self.patch_size = patch_size + self.patch_nums = patch_nums + self.norm = norm + self.use_conv = use_conv + self.conv_init = False + self.init_type = init_type + self.init_gain = init_gain + self.gpu_ids = gpu_ids + self.loss_mode = loss_mode + self.T = T + self.criterion = nn.L1Loss() if norm else nn.SmoothL1Loss() + self.cross_entropy_loss = nn.CrossEntropyLoss() + + def update_init_(self): + self.conv_init = True + + def create_conv(self, feat, layer): + input_nc = feat.size(1) + output_nc = max(32, input_nc // 4) + conv = nn.Sequential(*[nn.Conv2d(input_nc, output_nc, kernel_size=1), + nn.ReLU(), + nn.Conv2d(output_nc, output_nc, kernel_size=1)]) + conv.to(feat.device) + setattr(self, 'conv_%d' % layer, conv) + init_net(conv, self.init_type, self.init_gain, self.gpu_ids) + + def cal_sim(self, f_src, f_tgt, f_other=None, layer=0, patch_ids=None): + if self.use_conv: + if not self.conv_init: + self.create_conv(f_src, layer) + conv = getattr(self, 'conv_%d' % layer) + f_src, f_tgt = conv(f_src), conv(f_tgt) + f_other = conv(f_other) if f_other is not None else None + sim_src, patch_ids = self.patch_sim(f_src, patch_ids) + sim_tgt, patch_ids = self.patch_sim(f_tgt, patch_ids) + if f_other is not None: + sim_other, _ = self.patch_sim(f_other, patch_ids) + else: + sim_other = None + + return sim_src, sim_tgt, sim_other + + def compare_sim(self, sim_src, sim_tgt, sim_other): + B, Num, N = sim_src.size() + if self.loss_mode == 'info' or sim_other is not None: + sim_src = F.normalize(sim_src, dim=-1) + sim_tgt = F.normalize(sim_tgt, dim=-1) + sim_other = F.normalize(sim_other, dim=-1) + sam_neg1 = (sim_src.bmm(sim_other.permute(0, 2, 1))).view(-1, Num) / self.T + sam_neg2 = (sim_tgt.bmm(sim_other.permute(0, 2, 1))).view(-1, Num) / self.T + sam_self = (sim_src.bmm(sim_tgt.permute(0, 2, 1))).view(-1, Num) / self.T + sam_self = torch.cat([sam_self, sam_neg1, sam_neg2], dim=-1) + loss = self.cross_entropy_loss(sam_self, torch.arange(0, sam_self.size(0), dtype=torch.long, + device=sim_src.device) % (Num)) + else: + tgt_sorted, _ = sim_tgt.sort(dim=-1, descending=True) + num = int(N / 4) + src = torch.where(sim_tgt < tgt_sorted[:, :, num:num + 1], 0 * sim_src, sim_src) + tgt = torch.where(sim_tgt < tgt_sorted[:, :, num:num + 1], 0 * sim_tgt, sim_tgt) + if self.loss_mode == 'l1': + loss = self.criterion((N / num) * src, (N / num) * tgt) + elif self.loss_mode == 'cos': + sim_pos = F.cosine_similarity(src, tgt, dim=-1) + loss = self.criterion(torch.ones_like(sim_pos), sim_pos) + else: + raise NotImplementedError('padding [%s] is not implemented' % self.loss_mode) + + return loss + + def loss(self, f_src, f_tgt, f_other=None, layer=0): + sim_src, sim_tgt, sim_other = self.cal_sim(f_src, f_tgt, f_other, layer) + loss = self.compare_sim(sim_src, sim_tgt, sim_other) + return loss + + +class Normalization(nn.Module): + def __init__(self, device): + super(Normalization, self).__init__() + mean = torch.tensor([0.485, 0.456, 0.406]).to(device) + std = torch.tensor([0.229, 0.224, 0.225]).to(device) + self.mean = mean.view(-1, 1, 1) + self.std = std.view(-1, 1, 1) + + def forward(self, img): + return (img - self.mean) / self.std + + +def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[], debug=False, initialize_weights=True): + if len(gpu_ids) > 0: + assert (torch.cuda.is_available()) + net.to(gpu_ids[0]) + if initialize_weights: + init_weights(net, init_type, init_gain=init_gain, debug=debug) + return net + + +def init_weights(net, init_type='normal', init_gain=0.02, debug=False): + def init_func(m): + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if debug: + print(classname) + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, init_gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=init_gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=init_gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: + init.normal_(m.weight.data, 1.0, init_gain) + init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + + +class VGG16(nn.Module): + def __init__(self): + super(VGG16, self).__init__() + features = vgg16(weights=VGG16_Weights.DEFAULT).features + self.relu1_1 = torch.nn.Sequential() + self.relu1_2 = torch.nn.Sequential() + + self.relu2_1 = torch.nn.Sequential() + self.relu2_2 = torch.nn.Sequential() + + self.relu3_1 = torch.nn.Sequential() + self.relu3_2 = torch.nn.Sequential() + self.relu3_3 = torch.nn.Sequential() + + self.relu4_1 = torch.nn.Sequential() + self.relu4_2 = torch.nn.Sequential() + self.relu4_3 = torch.nn.Sequential() + + self.relu5_1 = torch.nn.Sequential() + self.relu5_2 = torch.nn.Sequential() + self.relu5_3 = torch.nn.Sequential() + + for x in range(2): + self.relu1_1.add_module(str(x), features[x]) + + for x in range(2, 4): + self.relu1_2.add_module(str(x), features[x]) + + for x in range(4, 7): + self.relu2_1.add_module(str(x), features[x]) + + for x in range(7, 9): + self.relu2_2.add_module(str(x), features[x]) + + for x in range(9, 12): + self.relu3_1.add_module(str(x), features[x]) + + for x in range(12, 14): + self.relu3_2.add_module(str(x), features[x]) + + for x in range(14, 16): + self.relu3_3.add_module(str(x), features[x]) + + for x in range(16, 18): + self.relu4_1.add_module(str(x), features[x]) + + for x in range(18, 21): + self.relu4_2.add_module(str(x), features[x]) + + for x in range(21, 23): + self.relu4_3.add_module(str(x), features[x]) + + for x in range(23, 26): + self.relu5_1.add_module(str(x), features[x]) + + for x in range(26, 28): + self.relu5_2.add_module(str(x), features[x]) + + for x in range(28, 30): + self.relu5_3.add_module(str(x), features[x]) + + def forward(self, x, layers=None, encode_only=False, resize=False): + relu1_1 = self.relu1_1(x) + relu1_2 = self.relu1_2(relu1_1) + + relu2_1 = self.relu2_1(relu1_2) + relu2_2 = self.relu2_2(relu2_1) + + relu3_1 = self.relu3_1(relu2_2) + relu3_2 = self.relu3_2(relu3_1) + relu3_3 = self.relu3_3(relu3_2) + + relu4_1 = self.relu4_1(relu3_3) + relu4_2 = self.relu4_2(relu4_1) + relu4_3 = self.relu4_3(relu4_2) + + relu5_1 = self.relu5_1(relu4_3) + relu5_2 = self.relu5_2(relu5_1) + relu5_3 = self.relu5_3(relu5_2) + + out = { + 'relu1_1': relu1_1, + 'relu1_2': relu1_2, + + 'relu2_1': relu2_1, + 'relu2_2': relu2_2, + + 'relu3_1': relu3_1, + 'relu3_2': relu3_2, + 'relu3_3': relu3_3, + + 'relu4_1': relu4_1, + 'relu4_2': relu4_2, + 'relu4_3': relu4_3, + + 'relu5_1': relu5_1, + 'relu5_2': relu5_2, + 'relu5_3': relu5_3, + } + if encode_only: + if len(layers) > 0: + feats = [] + for layer, key in enumerate(out): + if layer in layers: + feats.append(out[key]) + return feats + else: + return out['relu3_1'] + return out \ No newline at end of file diff --git a/models/networks/normalization.py b/models/networks/normalization.py new file mode 100644 index 0000000000000000000000000000000000000000..a14daa36d82d2b4d9d9f21ca6c135439c451d8b8 --- /dev/null +++ b/models/networks/normalization.py @@ -0,0 +1,119 @@ +import re +import torch.nn as nn +from models.networks.sync_batchnorm import SynchronizedBatchNorm2d +import torch.nn.utils.spectral_norm as spectral_norm +import torch + + +# Returns a function that creates a standard normalization function +def get_norm_layer(opt, norm_type='instance'): + # helper function to get # output channels of the previous layer + def get_out_channel(layer): + if hasattr(layer, 'out_channels'): + return getattr(layer, 'out_channels') + return layer.weight.size(0) + + # this function will be returned + def add_norm_layer(layer): + nonlocal norm_type + if norm_type.startswith('spectral'): + layer = spectral_norm(layer) + subnorm_type = norm_type[len('spectral'):] + + if subnorm_type == 'none' or len(subnorm_type) == 0: + return layer + + # remove bias in the previous layer, which is meaningless + # since it has no effect after normalization + if getattr(layer, 'bias', None) is not None: + delattr(layer, 'bias') + layer.register_parameter('bias', None) + + if subnorm_type == 'batch': + norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True) + elif subnorm_type == 'syncbatch': + norm_layer = SynchronizedBatchNorm2d(get_out_channel(layer), affine=True) + elif subnorm_type == 'instance': + norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) + else: + raise ValueError('normalization layer %s is not recognized' % subnorm_type) + + return nn.Sequential(layer, norm_layer) + + return add_norm_layer + + +# Creates FADE normalization layer based on the given configuration +class FADE(nn.Module): + def __init__(self, config_text, norm_nc, label_nc): + super().__init__() + + assert config_text.startswith('fade') + parsed = re.search('fade(\D+)(\d)x\d', config_text) + param_free_norm_type = str(parsed.group(1)) + ks = int(parsed.group(2)) + + if param_free_norm_type == 'instance': + self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) + elif param_free_norm_type == 'syncbatch': + self.param_free_norm = SynchronizedBatchNorm2d(norm_nc, affine=False) + elif param_free_norm_type == 'batch': + self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False) + else: + raise ValueError('%s is not a recognized param-free norm type in FADE' + % param_free_norm_type) + + pw = ks // 2 + self.mlp_gamma = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw) + self.mlp_beta = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw) + + def forward(self, x, feat): + # Step 1. generate parameter-free normalized activations + normalized = self.param_free_norm(x) + + # Step 2. produce scale and bias conditioned on feature map + gamma = self.mlp_gamma(feat) + beta = self.mlp_beta(feat) + + # Step 3. apply scale and bias + out = normalized * (1 + gamma) + beta + + return out + +class AdaptiveInstanceNorm(nn.Module): + def __init__(self, in_channel, opt): + super().__init__() + self.norm = nn.InstanceNorm2d(in_channel) + self.gamma = nn.Conv2d(in_channel, in_channel, 1) + self.beta = nn.Conv2d(in_channel, in_channel, 1) + self.conv_l = nn.Conv2d(2 * opt.latent_dim, in_channel, 1) + self.relu = nn.ReLU() + def forward(self, input, latent_code, alpha): + size = latent_code.size() + alpha = alpha.expand(size) + latent_code = torch.cat([latent_code, alpha], 1) + latent_code = latent_code.unsqueeze(2).unsqueeze(3) + latent_code = self.relu(self.conv_l(latent_code)) + gamma = self.gamma(latent_code) + beta = self.beta(latent_code) + out = self.norm(input) + out = out * (1 + gamma) + beta + return out + +# class AdaptiveInstanceNorm(nn.Module): +# def __init__(self, in_channel, opt): +# super().__init__() +# self.norm = nn.InstanceNorm2d(in_channel) +# self.gamma = nn.Conv2d(in_channel, in_channel, 1) +# self.beta = nn.Conv2d(in_channel, in_channel, 1) +# self.conv1x1 = nn.Conv2d(in_channel, in_channel, 1) +# self.relu = nn.ReLU() +# def forward(self, input, style, alpha): +# size = input.size() +# alpha = alpha.expand(size) +# gamma = self.gamma(alpha) +# beta = self.beta(alpha) +# normalized = self.norm(input) +# out = normalized * gamma + beta +# out = self.relu(self.conv1x1(out)) +# return out diff --git a/models/networks/rafael_generator.py b/models/networks/rafael_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..1453d818e19bbcbf269cff85fbabcfc30c522510 --- /dev/null +++ b/models/networks/rafael_generator.py @@ -0,0 +1,248 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import os +import math +import random +from models.networks.base_network import BaseNetwork +from models.networks.rafael_generator_module.rafael import RAFAEL as RAFAEL_Content_Encoder +from .time_utils import get_day_night_weights + + +class AdaINLayer(nn.Module): + def __init__(self): + super().__init__() + self.instance_norm = None + + def forward(self, content_feat, style_gamma, style_beta): + if content_feat is None or style_gamma is None or style_beta is None: return content_feat + if content_feat.numel() == 0: return content_feat + if self.instance_norm is None or self.instance_norm.num_features != content_feat.size(1): + if content_feat.size(1) == 0: return content_feat + self.instance_norm = nn.InstanceNorm2d(content_feat.size(1), affine=False, track_running_stats=False).to( + content_feat.device) + normalized_content = self.instance_norm(content_feat) + return style_gamma * normalized_content + style_beta + + +class AdaINResBlockWithLatent(nn.Module): + def __init__(self, channels, latent_dim, use_reflection_pad=True): + super().__init__() + self.use_reflection_pad = use_reflection_pad + self.mlp_style1 = nn.Linear(latent_dim, channels * 2) + self.pad1 = nn.ReflectionPad2d(1) if use_reflection_pad else nn.Identity() + self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=0) + self.relu1 = nn.ReLU() + self.mlp_style2 = nn.Linear(latent_dim, channels * 2) + self.pad2 = nn.ReflectionPad2d(1) if use_reflection_pad else nn.Identity() + self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=0) + self.adain1 = AdaINLayer() + self.adain2 = AdaINLayer() + + def forward(self, x, z_style): + skip_connection = x + style_params1 = self.mlp_style1(z_style) + gamma1 = style_params1[:, :x.size(1)].unsqueeze(-1).unsqueeze(-1) + beta1 = style_params1[:, x.size(1):].unsqueeze(-1).unsqueeze(-1) + stylized_x = self.adain1(x, gamma1, beta1) + h = self.pad1(stylized_x) + h = self.relu1(self.conv1(h)) + style_params2 = self.mlp_style2(z_style) + gamma2 = style_params2[:, :h.size(1)].unsqueeze(-1).unsqueeze(-1) + beta2 = style_params2[:, h.size(1):].unsqueeze(-1).unsqueeze(-1) + stylized_h = self.adain2(h, gamma2, beta2) + h = self.pad2(stylized_h) + h = self.conv2(h) + return F.relu(h + skip_connection) + + +class AdaINSynthesisNetworkWithLatent(BaseNetwork): + def __init__(self, opt, content_encoder_output_dim, initial_content_spatial_res, style_latent_dim): + super().__init__() + self.opt = opt + current_channels = content_encoder_output_dim + self.initial_conv = nn.Sequential(nn.ReflectionPad2d(1), + nn.Conv2d(content_encoder_output_dim, current_channels, kernel_size=3, + padding=0), nn.ReLU()) + res_blocks_list = [] + for _ in range(opt.synth_num_res_blocks): res_blocks_list.append( + AdaINResBlockWithLatent(current_channels, style_latent_dim)) + self.res_blocks = nn.ModuleList(res_blocks_list) + upsample_blocks_list = [] + target_h, current_h = opt.rafael_img_size, initial_content_spatial_res[0] + num_upsamples_needed = 3 + if current_h > 0 and target_h > current_h: + ratio = target_h / current_h + if ratio == int(ratio) and (int(ratio) & (int(ratio) - 1)) == 0: num_upsamples_needed = int( + math.log2(ratio)) + for i in range(num_upsamples_needed): + out_channels = current_channels // 2 if current_channels > opt.ngf * 2 else opt.ngf + if out_channels < opt.ngf and opt.ngf > 0: out_channels = opt.ngf + if out_channels <= 0: out_channels = opt.ngf if opt.ngf > 0 else 3 + upsample_blocks_list.extend([nn.Upsample(scale_factor=2, mode='nearest'), nn.ReflectionPad2d(1), + nn.Conv2d(current_channels, out_channels, kernel_size=3, padding=0), nn.ReLU(), + AdaINResBlockWithLatent(out_channels, style_latent_dim)]) + current_channels = out_channels + self.upsample_blocks = nn.ModuleList(upsample_blocks_list) + self.final_conv = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(current_channels, 3, kernel_size=7, padding=0), + nn.Tanh()) + + def forward(self, content_features_seq, content_spatial_res_tuple, z_style1, z_style2=None, mixing_prob=0.0, + arbitrary_input=False): + B, L, C_enc = content_features_seq.shape + H_feat, W_feat = content_spatial_res_tuple + x = content_features_seq.permute(0, 2, 1).contiguous().view(B, C_enc, H_feat, W_feat) + x = self.initial_conv(x) + num_adain_blocks = len(self.res_blocks) + len( + [op for op in self.upsample_blocks if isinstance(op, AdaINResBlockWithLatent)]) + mixing_point = num_adain_blocks + 1 + if z_style2 is not None and mixing_prob > 0 and random.random() < mixing_prob: mixing_point = torch.randint(1, + num_adain_blocks, + ( + 1,)).item() + current_adain_block_idx = 0 + for block in self.res_blocks: + z_current = z_style2 if current_adain_block_idx >= mixing_point else z_style1 + x = block(x, z_current) + current_adain_block_idx += 1 + upsample_op_idx = 0 + while upsample_op_idx < len(self.upsample_blocks): + x = self.upsample_blocks[upsample_op_idx](x); + upsample_op_idx += 1 + x = self.upsample_blocks[upsample_op_idx](x); + upsample_op_idx += 1 + x = self.upsample_blocks[upsample_op_idx](x); + upsample_op_idx += 1 + x = self.upsample_blocks[upsample_op_idx](x); + upsample_op_idx += 1 + res_block = self.upsample_blocks[upsample_op_idx] + z_current = z_style2 if current_adain_block_idx >= mixing_point else z_style1 + x = res_block(x, z_current) + upsample_op_idx += 1 + current_adain_block_idx += 1 + return self.final_conv(x) + + +class RafaelGenerator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--rafael_img_size', type=int, default=256) + parser.add_argument('--rafael_patch_size', type=int, default=2) + parser.add_argument('--rafael_embed_dim', type=int, default=192) + parser.add_argument('--rafael_depths', type=str, default='2,2,2') + parser.add_argument('--rafael_nheads', type=str, default='3,6,12') + parser.add_argument('--rafael_strip_widths', type=str, default='2,4,7') + parser.add_argument('--rafael_use_checkpoint', action='store_true') + parser.add_argument('--style_enc_latent_dim', type=int, default=256, help='Output latent z dimension') + parser.add_argument('--synth_num_res_blocks', type=int, default=4) + if is_train: + parser.add_argument('--style_mixing_prob', type=float, default=0.9) + + parser.add_argument('--light_map_channels', type=int, default=1, + help='Số kênh cho bản đồ hướng sáng (1 hoặc 2).') + parser.add_argument('--add_phi_to_latent', action='store_true', help='Thêm (cos(phi), sin(phi)) vào z_style') + + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + + rafael_depths = [int(d) for d in opt.rafael_depths.split(',') if d.strip()] + rafael_nheads = [int(h) for h in opt.rafael_nheads.split(',') if h.strip()] + rafael_strip_widths_str = opt.rafael_strip_widths.split(',') + if len(rafael_strip_widths_str) == 1 and rafael_strip_widths_str[0].strip(): + rafael_strip_widths = [int(rafael_strip_widths_str[0])] * len(rafael_depths) + else: + rafael_strip_widths = [int(sw) for sw in rafael_strip_widths_str if sw.strip()] + + content_encoder_in_chans = 3 + opt.light_map_channels + + self.rafael_content_encoder = RAFAEL_Content_Encoder( + img_size=opt.rafael_img_size, patch_size=opt.rafael_patch_size, + in_chans=content_encoder_in_chans, + embed_dim=opt.rafael_embed_dim, depths=rafael_depths, nhead=rafael_nheads, + strip_width=rafael_strip_widths, use_checkpoint=opt.rafael_use_checkpoint + ) + + num_downsamples_in_enc = len(rafael_depths) - 1 + content_encoder_output_dim = opt.rafael_embed_dim * (2 ** num_downsamples_in_enc) + + initial_spatial_res_h = opt.rafael_img_size // opt.rafael_patch_size + initial_spatial_res_w = opt.rafael_img_size // opt.rafael_patch_size + self.content_encoder_spatial_res = ( + initial_spatial_res_h // (2 ** num_downsamples_in_enc), + initial_spatial_res_w // (2 ** num_downsamples_in_enc) + ) + + self.latent_dim = opt.style_enc_latent_dim + synthesis_latent_dim = self.latent_dim + 2 if opt.add_phi_to_latent else self.latent_dim + self.z_day = nn.Parameter(torch.randn(1, self.latent_dim)) + self.z_night = nn.Parameter(torch.randn(1, self.latent_dim)) + + self.synthesis_network = AdaINSynthesisNetworkWithLatent( + opt=opt, + content_encoder_output_dim=content_encoder_output_dim, + initial_content_spatial_res=self.content_encoder_spatial_res, + style_latent_dim=synthesis_latent_dim + ) + + def forward(self, content_image_with_light, w_day, w_night, cos_phi, sin_phi, phi2=None, arbitrary_input=False): + current_device = content_image_with_light.device + batch_size = content_image_with_light.size(0) + + f_c_tuple = self.rafael_content_encoder(content_image_with_light, arbitrary_input=arbitrary_input) + content_features_seq, content_spatial_res = f_c_tuple[0], f_c_tuple[2] + + z_day_learned = self.z_day.expand(batch_size, -1) + z_night_learned = self.z_night.expand(batch_size, -1) + + w_day1_expanded = w_day.view(-1, 1).expand_as(z_day_learned) + w_night1_expanded = w_night.view(-1, 1).expand_as(z_night_learned) + z_style1_base = w_day1_expanded * z_day_learned + w_night1_expanded * z_night_learned + + if self.opt.add_phi_to_latent: + z_style1 = torch.cat([z_style1_base, cos_phi.view(-1, 1), sin_phi.view(-1, 1)], dim=1) + else: + z_style1 = z_style1_base + + z_style2 = None + if phi2 is not None and self.training: + phi2_tensor = phi2.to(current_device, dtype=z_day_learned.dtype) + + w_day2, w_night2 = get_day_night_weights(phi2_tensor, self.opt.daylight_curve_steepness) + + w_day2_expanded = w_day2.view(-1, 1).expand_as(z_day_learned) + w_night2_expanded = w_night2.view(-1, 1).expand_as(z_night_learned) + z_style2_base = w_day2_expanded * z_day_learned + w_night2_expanded * z_night_learned + + if self.opt.add_phi_to_latent: + cos_phi2, sin_phi2 = torch.cos(phi2_tensor), torch.sin(phi2_tensor) + z_style2 = torch.cat([z_style2_base, cos_phi2.view(-1, 1), sin_phi2.view(-1, 1)], dim=1) + else: + z_style2 = z_style2_base + + mixing_prob = self.opt.style_mixing_prob if self.training else 0.0 + i_cs = self.synthesis_network(content_features_seq, content_spatial_res, z_style1, z_style2, mixing_prob, + arbitrary_input=arbitrary_input) + + generator_returns = {} + + if self.training: + z_day_for_identity = z_day_learned + z_night_for_identity = z_night_learned + if self.opt.add_phi_to_latent: + day_phi_components = torch.tensor([[-1.0, 0.0]], device=current_device).expand(batch_size, -1) + night_phi_components = torch.tensor([[1.0, 0.0]], device=current_device).expand(batch_size, -1) + z_day_for_identity = torch.cat([z_day_learned, day_phi_components], dim=1) + z_night_for_identity = torch.cat([z_night_learned, night_phi_components], dim=1) + + i_cc = self.synthesis_network(content_features_seq, content_spatial_res, z_day_for_identity, + mixing_prob=0.0, arbitrary_input=arbitrary_input) + i_nn = self.synthesis_network(content_features_seq, content_spatial_res, z_night_for_identity, + mixing_prob=0.0, arbitrary_input=arbitrary_input) + generator_returns['identity_content'] = i_cc + generator_returns['identity_night'] = i_nn + + return i_cs, generator_returns + diff --git a/models/networks/rafael_generator_module/__init__.py 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0000000000000000000000000000000000000000..0cbf44d4cb15702d85f99449772bc8ebb25fc85f --- /dev/null +++ b/models/networks/rafael_generator_module/rafael.py @@ -0,0 +1,564 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from models.networks.rafael_generator_module.rafael_trans_tools import to_2tuple, to_ntuple +from models.networks.rafael_generator_module.rafael_transformer_components import Mlp, Attention, DropPath + + + +def split_int(num): + """Split an integer into 2 integers evenly + Args: + num (int): The input integer + Returns: + num_1 (int) + num_2 (int) + """ + if num % 2 == 0: + num_1 = num_2 = num // 2 + else: + num_1 = num // 2 + num_2 = num_1 + 1 + return num_1, num_2 + + +def unpad2D(input, pad): + """Crop the input tensor according to pad.(Inverse operation for padding) + Args: + input (Tensor): (B, C, H, W) + pad (Tuple of int): (left, right, top, bottom) + Returns: + output (Tensor): (B, C, new_H, new_W) + """ + pad_W_left, pad_W_right, pad_H_top, pad_H_bottom = pad + if pad_H_top == 0 and pad_H_bottom == 0 and not (pad_W_left == 0 and pad_W_right == 0): + output = input[:, :, :, pad_W_left:-pad_W_right] + elif pad_W_left == 0 and pad_W_right == 0 and not (pad_H_top == 0 and pad_H_bottom == 0): + output = input[:, :, pad_H_top:-pad_H_bottom, :] + elif pad_H_top == 0 and pad_H_bottom == 0 and pad_W_left == 0 and pad_W_right == 0: + output = input + else: + output = input[:, :, pad_H_top:-pad_H_bottom, pad_W_left:-pad_W_right] + return output + + +def seq_padding(x, dividable_size, input_resolution, pad_mode='constant'): + """Padding for sequential data + Args: + x (Tensor): (B, L, C) + dividable_size (Tuple | int): dividable size (div_H, div_W) + input_resolution (Tuple): resolution of x (H_orig, W_orig) + Returns: + x_padded (Tensor): (B, new_L, C) where new_L = H_padded * W_padded + output_resolution (Tuple): (H_padded, W_padded) + pad_dims (Tuple of int): (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom) + """ + H_orig, W_orig = input_resolution + B, L_orig, C = x.shape + assert L_orig == H_orig * W_orig, f'seq_padding: Input L_orig {L_orig} does not match H_orig*W_orig {H_orig * W_orig} ({H_orig}x{W_orig})' + + dividable_size_H, dividable_size_W = to_2tuple(dividable_size) + + x_img_domain = x.permute(0, 2, 1).reshape(B, C, H_orig, W_orig) + + rema_H, rema_W = H_orig % dividable_size_H, W_orig % dividable_size_W + + pad_H_total = dividable_size_H - rema_H if rema_H != 0 else 0 + pad_W_total = dividable_size_W - rema_W if rema_W != 0 else 0 + + pad_H_top, pad_H_bottom = split_int(pad_H_total) + pad_W_left, pad_W_right = split_int(pad_W_total) + + pad_dims = (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom) + + needs_padding = pad_H_total > 0 or pad_W_total > 0 + if needs_padding: + x_padded_img_domain = F.pad(x_img_domain, pad_dims, pad_mode, 0) + else: + x_padded_img_domain = x_img_domain + + H_padded, W_padded = x_padded_img_domain.shape[-2:] + x_padded_seq_domain = x_padded_img_domain.reshape(B, C, -1).permute(0, 2, 1) + + assert x_padded_seq_domain.shape[1] == H_padded * W_padded, \ + f"seq_padding: Padded L {x_padded_seq_domain.shape[1]} != H_padded*W_padded {H_padded * W_padded} ({H_padded}x{W_padded})" + + return x_padded_seq_domain, (H_padded, W_padded), pad_dims + + +def seq_unpad(x, padded_resolution, pad_dims): + """Unpadding for sequential data + Args: + x (Tensor): (B, L_padded, C) + padded_resolution (Tuple): (H_padded, W_padded) + pad_dims (Tuple of int): (pad_W_left, pad_W_right, pad_H_top, pad_H_bottom) + Returns: + x_unpadded (Tensor): (B, new_L_orig, C) + output_resolution (Tuple): (H_orig, W_orig) + """ + H_padded, W_padded = padded_resolution + B, L_padded, C = x.shape + assert L_padded == H_padded * W_padded, \ + f'seq_unpad: Input L_padded {L_padded} does not match H_padded*W_padded {H_padded * W_padded} ({H_padded}x{W_padded})' + + x_img_domain = x.permute(0, 2, 1).reshape(B, C, H_padded, W_padded) + + needs_unpadding = any(p > 0 for p in pad_dims) + if needs_unpadding: + x_unpadded_img_domain = unpad2D(x_img_domain, pad=pad_dims) + else: + x_unpadded_img_domain = x_img_domain + + H_orig, W_orig = x_unpadded_img_domain.shape[-2:] + x_unpadded_seq_domain = x_unpadded_img_domain.reshape(B, C, -1).permute(0, 2, 1) + + assert x_unpadded_seq_domain.shape[1] == H_orig * W_orig, \ + f"seq_unpad: Unpadded L {x_unpadded_seq_domain.shape[1]} != H_orig*W_orig {H_orig * W_orig} ({H_orig}x{W_orig})" + + return x_unpadded_seq_domain, (H_orig, W_orig) + + +########################################## Basic components ########################################## + +def window_partition(x, window_size): + win_H, win_W = to_2tuple(window_size) + B, H, W, C = x.shape + assert H % win_H == 0 and W % win_W == 0, \ + f"window_partition: H({H}) or W({W}) not divisible by window_size ({win_H}, {win_W}). This should be handled by padding." + n_win_H = H // win_H + n_win_W = W // win_W + x = x.view(B, n_win_H, win_H, n_win_W, win_W, C) + windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, win_H, win_W, C) + return windows + + +def window_reverse(windows, window_size, H, W): + win_H, win_W = to_2tuple(window_size) + assert H % win_H == 0 and W % win_W == 0, \ + f"window_reverse: H({H}) or W({W}) not divisible by window_size ({win_H}, {win_W})." + n_win_H = H // win_H + n_win_W = W // win_W + num_total_windows = n_win_H * n_win_W + assert windows.shape[0] % num_total_windows == 0, "window_reverse: Incorrect number of windows." + B = windows.shape[0] // num_total_windows + x = windows.view(B, n_win_H, n_win_W, win_H, win_W, -1) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class PatchEmbed_Kai(nn.Module): + def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None, flatten=True): + super().__init__() + patch_size = to_2tuple(patch_size) + self.patch_size = patch_size + self.in_chans = in_chans + self.flatten = flatten + self.proj = nn.Conv2d(self.in_chans, embed_dim, kernel_size=patch_size, stride=patch_size) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + B, C, H_img, W_img = x.shape + assert C == self.in_chans, 'Input image need to have same numbers of channels with the initialed.' + x_projected = self.proj(x) + H_feat, W_feat = x_projected.shape[2], x_projected.shape[3] + if self.flatten: + x_flattened = x_projected.flatten(2).transpose(1, 2) + else: + x_flattened = x_projected + x_normed = self.norm(x_flattened) + return x_normed, (H_feat, W_feat) + + +class PatchMerging_Kai(nn.Module): + def __init__(self, input_resolution, d_model, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = to_2tuple(input_resolution) + self.d_model = d_model + self.reduction = nn.Linear(4 * d_model, 2 * d_model, bias=False) + self.norm = norm_layer(4 * d_model) + + def forward(self, x_tuple): + current_tensor = x_tuple[0] + arbitrary_input = x_tuple[1] + + if arbitrary_input: + H_feat, W_feat = x_tuple[2] + else: + H_feat, W_feat = self.input_resolution + + B, L_feat, C_feat = current_tensor.shape + assert L_feat == H_feat * W_feat, \ + f"PatchMerging_Kai: Input L_feat {L_feat} does not match H_feat*W_feat {H_feat * W_feat} ({H_feat}x{W_feat}). Arbitrary: {arbitrary_input}. Expected H,W by PMK for fixed: {self.input_resolution}" + + x_img_domain = current_tensor.view(B, H_feat, W_feat, C_feat) + H_eff_for_merge, W_eff_for_merge = H_feat, W_feat + temp_x_img_domain = x_img_domain + + if H_feat % 2 != 0: + temp_x_img_domain = temp_x_img_domain[:, 0:-1, :, :] + H_eff_for_merge -= 1 + if W_feat % 2 != 0: + temp_x_img_domain = temp_x_img_domain[:, :, 0:-1, :] + W_eff_for_merge -= 1 + + x0 = temp_x_img_domain[:, 0::2, 0::2, :] + x1 = temp_x_img_domain[:, 1::2, 0::2, :] + x2 = temp_x_img_domain[:, 0::2, 1::2, :] + x3 = temp_x_img_domain[:, 1::2, 1::2, :] + + x_cat = torch.cat([x0, x1, x2, x3], -1) + H_merged, W_merged = x_cat.shape[1], x_cat.shape[2] + x_reshaped_seq = x_cat.view(B, -1, 4 * C_feat) + assert x_reshaped_seq.shape[ + 1] == H_merged * W_merged, "PatchMerging_Kai: Logic error in patch count after cat and reshape." + normed_x = self.norm(x_reshaped_seq) + reduced_x = self.reduction(normed_x) + return reduced_x, arbitrary_input, (H_merged, W_merged) + + +class WindowAttention_Kai(nn.Module): + def __init__(self, d_model, window_size, nhead, qkv_bias=True, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.window_size_H, self.window_size_W = to_2tuple(window_size) + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + self.relative_position_bias_table = nn.Parameter( + torch.zeros((2 * self.window_size_H - 1) * (2 * self.window_size_W - 1), nhead) + ) + coords_h = torch.arange(self.window_size_H) + coords_w = torch.arange(self.window_size_W) + coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.window_size_H - 1 + relative_coords[:, :, 1] += self.window_size_W - 1 + relative_coords[:, :, 0] *= 2 * self.window_size_W - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index) + self.qkv = nn.Linear(d_model, d_model * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + nn.init.trunc_normal_(self.relative_position_bias_table, std=.02) + + def forward(self, x, feat_resolution_shape): + H_feat, W_feat = feat_resolution_shape + B, N_feat, C = x.size() + assert N_feat == H_feat * W_feat, \ + f"WindowAttention_Kai: Input N_feat {N_feat} != H_feat*W_feat {H_feat * W_feat} ({H_feat}x{W_feat})" + assert H_feat % self.window_size_H == 0 and W_feat % self.window_size_W == 0, \ + f"WindowAttention_Kai: H_feat({H_feat}) or W_feat({W_feat}) not divisible by window_size ({self.window_size_H},{self.window_size_W})." + x_img_domain = x.reshape(B, H_feat, W_feat, C) + x_windows = window_partition(x_img_domain, (self.window_size_H, self.window_size_W)) + x_windows_seq = x_windows.reshape(-1, self.window_size_H * self.window_size_W, C) + B_win, N_win, _ = x_windows_seq.shape + qkv = self.qkv(x_windows_seq).reshape(B_win, N_win, 3, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) + attn = (q @ k.transpose(-2, -1)) * self.scale + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( + N_win, N_win, self.nhead) + relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() + attn = attn + relative_position_bias.unsqueeze(0) + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + x_attn_out_win_seq = (attn @ v).transpose(1, 2).reshape(B_win, N_win, C) + x_projected_win_seq = self.proj(x_attn_out_win_seq) + x_dropped_win_seq = self.proj_drop(x_projected_win_seq) + x_dropped_windows = x_dropped_win_seq.reshape(-1, self.window_size_H, self.window_size_W, C) + x_img_domain_reconstructed = window_reverse(x_dropped_windows, (self.window_size_H, self.window_size_W), H_feat, + W_feat) + x_final_seq = x_img_domain_reconstructed.reshape(B, N_feat, C) + assert x_final_seq.shape[1] == N_feat, "WindowAttention_Kai: Output patch count mismatch." + return x_final_seq + + +class LePEAttention(nn.Module): + def __init__(self, d_model, nhead=8, strip_width=7, is_vertical=False, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + self.d_model = d_model + self.strip_width = strip_width + self.is_vertical = is_vertical + self.attn = Attention( + d_model=d_model, nhead=nhead, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=proj_drop + ) + + def forward(self, x, feat_resolution_shape): + H_feat, W_feat = feat_resolution_shape + B, N_feat, C = x.size() + assert N_feat == H_feat * W_feat, \ + f"LePEAttention Input: x patches {N_feat} != H_feat*W_feat {H_feat * W_feat} ({H_feat}x{W_feat})" + x_img_domain = x.reshape(B, H_feat, W_feat, C) + current_strip_window_H, current_strip_window_W = 0, 0 + num_patches_per_strip = 0 + if self.is_vertical: + assert W_feat % self.strip_width == 0, \ + f"LePEAttention (vertical): W_feat({W_feat}) not divisible by strip_width ({self.strip_width})." + current_strip_window_H, current_strip_window_W = H_feat, self.strip_width + x_strips = window_partition(x_img_domain, (current_strip_window_H, current_strip_window_W)) + num_patches_per_strip = H_feat * self.strip_width + else: + assert H_feat % self.strip_width == 0, \ + f"LePEAttention (horizontal): H_feat({H_feat}) not divisible by strip_width ({self.strip_width})." + current_strip_window_H, current_strip_window_W = self.strip_width, W_feat + x_strips = window_partition(x_img_domain, (current_strip_window_H, current_strip_window_W)) + num_patches_per_strip = self.strip_width * W_feat + x_strips_seq = x_strips.reshape(-1, num_patches_per_strip, C) + attn_out_strips_seq = self.attn(x_strips_seq) + attn_out_strips = attn_out_strips_seq.reshape(-1, current_strip_window_H, current_strip_window_W, C) + x_img_domain_reconstructed = window_reverse(attn_out_strips, (current_strip_window_H, current_strip_window_W), + H_feat, W_feat) + x_final_seq = x_img_domain_reconstructed.reshape(B, N_feat, C) + assert x_final_seq.shape[1] == N_feat, "LePEAttention: Output patch count mismatch." + return x_final_seq + + +class LePEAttentionBlock(nn.Module): + def __init__(self, d_model, input_resolution, nhead=8, strip_width=7, + mlp_ratio=4, qkv_bias=False, drop=0., attn_drop=0., drop_path=0., + act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.d_model = d_model + self.input_resolution_fixed = to_2tuple(input_resolution) # input_resolution cho fixed path + self.strip_width = strip_width + self.norm1 = norm_layer(d_model) + self.attn1 = LePEAttention( + d_model=d_model, nhead=nhead, strip_width=strip_width, is_vertical=False, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop + ) + self.attn2 = LePEAttention( + d_model=d_model, nhead=nhead, strip_width=strip_width, is_vertical=True, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop + ) + self.attn3 = WindowAttention_Kai( + d_model=d_model, window_size=(strip_width * 2, strip_width * 2), nhead=nhead, + qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop + ) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(d_model) + mlp_hidden_dim = int(d_model * mlp_ratio) + self.mlp = Mlp(d_model, hidden_features=mlp_hidden_dim, out_features=d_model, act_layer=act_layer, drop=drop) + + def forward(self, x_tuple): + current_tensor = x_tuple[0] + arbitrary_input = x_tuple[1] + H_orig, W_orig = x_tuple[2] + + assert current_tensor.shape[1] == H_orig * W_orig, \ + f"LePEAttentionBlock Input: tensor patches {current_tensor.shape[1]} != H_orig*W_orig {H_orig * W_orig} ({H_orig}x{W_orig})" + + dividable_size_for_padding = self.strip_width * 2 + + # --- SỬA ĐỔI Ở ĐÂY --- + # Luôn thực hiện padding nếu H_orig hoặc W_orig không chia hết, bất kể arbitrary_input + # Điều này đảm bảo các lớp attention bên trong nhận được kích thước chia hết. + needs_padding_for_block = (H_orig % dividable_size_for_padding != 0) or \ + (W_orig % dividable_size_for_padding != 0) or \ + (H_orig % self.strip_width != 0) or \ + (W_orig % self.strip_width != 0) # Thêm kiểm tra cho strip_width đơn lẻ + + if needs_padding_for_block: + # Tính toán dividable_size tổng thể cho cả strip_width và strip_width*2 + # lcm(strip_width, strip_width*2) = strip_width*2 + actual_dividable_size = self.strip_width * 2 + + padded_x, (H_padded, W_padded), pad_dims = seq_padding( + current_tensor, + dividable_size=actual_dividable_size, # Đảm bảo chia hết cho cả sw và sw*2 + input_resolution=(H_orig, W_orig), + pad_mode='constant' + ) + else: # Không cần padding, H_orig, W_orig đã chia hết + H_padded, W_padded = H_orig, W_orig + padded_x = current_tensor + pad_dims = (0, 0, 0, 0) + # ----------------------- + + shortcut = padded_x + normed_x = self.norm1(padded_x) + + x1 = self.attn1(normed_x, feat_resolution_shape=(H_padded, W_padded)) + x2 = self.attn2(normed_x, feat_resolution_shape=(H_padded, W_padded)) + x3 = self.attn3(normed_x, feat_resolution_shape=(H_padded, W_padded)) + + assert x1.shape == normed_x.shape, f"Shape mismatch after attn1: {x1.shape} vs {normed_x.shape}" + assert x2.shape == normed_x.shape, f"Shape mismatch after attn2: {x2.shape} vs {normed_x.shape}" + assert x3.shape == normed_x.shape, f"Shape mismatch after attn3: {x3.shape} vs {normed_x.shape}" + + q_for_combine = normed_x.unsqueeze(dim=2) + k_v_for_combine = torch.stack([normed_x, x1, x2, x3], dim=2) + attn_on_attn_results = (q_for_combine @ k_v_for_combine.transpose(-1, -2)).softmax(dim=-1) + x_after_combine_method = (attn_on_attn_results @ k_v_for_combine).squeeze(dim=2) + + assert x_after_combine_method.shape == shortcut.shape, \ + f"Shape mismatch after combining attentions: {x_after_combine_method.shape} vs {shortcut.shape}" + + x_residual_after_attn = shortcut + self.drop_path(x_after_combine_method) + x_after_ffn = x_residual_after_attn + self.drop_path(self.mlp(self.norm2(x_residual_after_attn))) + + output_tensor_before_unpad = x_after_ffn + final_output_resolution_before_unpad = (H_padded, W_padded) + + # Luôn unpad nếu có padding đã được áp dụng (pad_dims != (0,0,0,0)) + if needs_padding_for_block: # Hoặc đơn giản là if any(p > 0 for p in pad_dims): + unpadded_x, (H_final_orig, W_final_orig) = seq_unpad( + output_tensor_before_unpad, + padded_resolution=(H_padded, W_padded), # Kích thước của tensor trước khi unpad + pad_dims=pad_dims + ) + # H_final_orig, W_final_orig phải bằng H_orig, W_orig ban đầu + assert H_final_orig == H_orig and W_final_orig == W_orig, \ + f"Unpadding error: Original ({H_orig},{W_orig}) vs Unpadded ({H_final_orig},{W_final_orig})" + output_tensor_final = unpadded_x + final_output_resolution = (H_final_orig, W_final_orig) + else: + output_tensor_final = output_tensor_before_unpad + final_output_resolution = final_output_resolution_before_unpad # (H_orig, W_orig) + + return (output_tensor_final, arbitrary_input, final_output_resolution) + + +class BasicLayer_SA(nn.Module): + def __init__(self, d_model, input_resolution, depth, nhead, strip_width, + mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., + drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False): + super().__init__() + self.d_model = d_model + self.input_resolution_fixed = to_2tuple(input_resolution) + self.depth = depth + if isinstance(strip_width, list): + assert len(strip_width) == depth, "strip_width list length must match layer depth" + self.block_strip_widths = strip_width + else: + self.block_strip_widths = [strip_width] * depth + self.use_checkpoint = use_checkpoint + self.blocks = nn.ModuleList([ + LePEAttentionBlock( + d_model=d_model, + input_resolution=self.input_resolution_fixed, + nhead=nhead, + strip_width=self.block_strip_widths[i], + mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop, + drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, + norm_layer=norm_layer + ) for i in range(self.depth) + ]) + if downsample is not None: + self.downsample = downsample(self.input_resolution_fixed, d_model=d_model, norm_layer=norm_layer) + else: + self.downsample = None + + def forward(self, x_tuple_input): + _tensor_in_bsl, _arb_in_bsl, (_H_in_bsl, _W_in_bsl) = x_tuple_input + if not _arb_in_bsl: + assert (_H_in_bsl, _W_in_bsl) == self.input_resolution_fixed, \ + f"BasicLayer_SA (Fixed): Input tuple H,W ({_H_in_bsl},{_W_in_bsl}) != self.input_resolution_fixed {self.input_resolution_fixed}" + assert _tensor_in_bsl.shape[1] == self.input_resolution_fixed[0] * self.input_resolution_fixed[1], \ + f"BasicLayer_SA (Fixed): Input tensor patches {_tensor_in_bsl.shape[1]} != expected {self.input_resolution_fixed[0] * self.input_resolution_fixed[1]}" + else: + assert _tensor_in_bsl.shape[1] == _H_in_bsl * _W_in_bsl, \ + f"BasicLayer_SA (Arbitrary): Input tensor patches {_tensor_in_bsl.shape[1]} != H*W from tuple {_H_in_bsl * _W_in_bsl}" + + current_processing_tuple = x_tuple_input + for blk in self.blocks: + if self.use_checkpoint: + current_processing_tuple = blk(current_processing_tuple) + + else: + current_processing_tuple = blk(current_processing_tuple) + + if self.downsample is not None: + current_processing_tuple = self.downsample(current_processing_tuple) + return current_processing_tuple + + +class RAFAEL(nn.Module): + def __init__(self, img_size=224, patch_size=4, in_chans=3, + embed_dim=96, depths=[2, 2, 6, 2], nhead=[3, 6, 12, 24], + strip_width=7, mlp_ratio=4., qkv_bias=True, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1, + norm_layer=nn.LayerNorm, ape=False, patch_norm=True, + use_checkpoint=False): + super().__init__() + self.img_size_fixed = to_2tuple(img_size) + self.patch_size = to_2tuple(patch_size) + self.num_layers = len(depths) + if isinstance(strip_width, (list, tuple)): + assert len( + strip_width) == self.num_layers, "strip_width list/tuple length must match num_layers (depths length)" + self.layer_strip_widths = list(strip_width) + else: + self.layer_strip_widths = [strip_width] * self.num_layers + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.patch_embed = PatchEmbed_Kai( + patch_size=self.patch_size, in_chans=in_chans, embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None + ) + self.patches_resolution_fixed = (self.img_size_fixed[0] // self.patch_size[0], + self.img_size_fixed[1] // self.patch_size[1]) + self.num_patches_fixed = self.patches_resolution_fixed[0] * self.patches_resolution_fixed[1] + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, self.num_patches_fixed, embed_dim)) + nn.init.trunc_normal_(self.absolute_pos_embed, std=.02) + self.pos_drop = nn.Dropout(drop_rate) + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] + self.layers = nn.ModuleList() + current_d_model = self.embed_dim + current_H_fixed = self.patches_resolution_fixed[0] + current_W_fixed = self.patches_resolution_fixed[1] + for i in range(self.num_layers): + layer_input_resolution_fixed = (current_H_fixed, current_W_fixed) + layer = BasicLayer_SA( + d_model=current_d_model, input_resolution=layer_input_resolution_fixed, + depth=depths[i], nhead=nhead[i], strip_width=self.layer_strip_widths[i], + mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])], norm_layer=norm_layer, + downsample=PatchMerging_Kai if (i < self.num_layers - 1) else None, + use_checkpoint=use_checkpoint + ) + self.layers.append(layer) + if (i < self.num_layers - 1): + current_d_model *= 2 + current_H_fixed //= 2 + current_W_fixed //= 2 + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.trunc_normal_(m.weight, std=.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'absolute_pos_embed'} + + @torch.jit.ignore + def no_weight_decay_keywords(self): + return {'relative_position_bias_table'} + + def forward_features(self, x_img_tensor, arbitrary_input_flag, initial_img_resolution_tuple_unused): + x_after_embed, (H_feat, W_feat) = self.patch_embed(x_img_tensor) + if self.ape: + if not arbitrary_input_flag and (H_feat * W_feat == self.num_patches_fixed): + x_after_embed = x_after_embed + self.absolute_pos_embed + elif arbitrary_input_flag: + pass + x_after_pos_drop = self.pos_drop(x_after_embed) + current_processing_tuple = (x_after_pos_drop, arbitrary_input_flag, (H_feat, W_feat)) + for layer in self.layers: + current_processing_tuple = layer(current_processing_tuple) + return current_processing_tuple + + def forward(self, x_img, arbitrary_input=False): + img_H_orig, img_W_orig = x_img.shape[2], x_img.shape[3] + output_tuple = self.forward_features(x_img, arbitrary_input, (img_H_orig, img_W_orig)) + return output_tuple \ No newline at end of file diff --git a/models/networks/rafael_generator_module/rafael_configuration.py b/models/networks/rafael_generator_module/rafael_configuration.py new file mode 100644 index 0000000000000000000000000000000000000000..4c9ab2eeee1bb500a44b53b8f23a1ed03b552093 --- /dev/null +++ b/models/networks/rafael_generator_module/rafael_configuration.py @@ -0,0 +1,28 @@ +import torch.nn as nn + +class TransModule_Config(): + def __init__( + self, + nlayer=3, + d_model=768, + nhead=8, + mlp_ratio=4, + qkv_bias=False, + attn_drop=0., + drop=0., + drop_path=0., + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + norm_first=False + ): + self.nlayer = nlayer + self.d_model = d_model + self.nhead = nhead + self.mlp_ratio = mlp_ratio + self.qkv_bias = qkv_bias + self.attn_drop = attn_drop + self.drop = drop + self.drop_path = drop_path + self.act_layer = act_layer + self.norm_layer = norm_layer + self.norm_first = norm_first \ No newline at end of file diff --git a/models/networks/rafael_generator_module/rafael_net.py b/models/networks/rafael_generator_module/rafael_net.py new file mode 100644 index 0000000000000000000000000000000000000000..f18badbca3d1f9e1c68c713f275ac0511a93b381 --- /dev/null +++ b/models/networks/rafael_generator_module/rafael_net.py @@ -0,0 +1,215 @@ +import torch.nn as nn +from models.networks.rafael_generator_module.rafael_configuration import TransModule_Config +from models.networks.rafael_generator_module.rafael_transformer_components import TransformerDecoderLayer + +########################################## VGG & components ########################################## + +vgg = nn.Sequential( + nn.Conv2d(3, 3, (1, 1)), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(3, 64, (3, 3)), + nn.ReLU(), # relu1-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 64, (3, 3)), + nn.ReLU(), # relu1-2 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 128, (3, 3)), + nn.ReLU(), # relu2-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 128, (3, 3)), + nn.ReLU(), # relu2-2 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 256, (3, 3)), + nn.ReLU(), # relu3-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), # relu3-2 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), # relu3-3 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), # relu3-4 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 512, (3, 3)), + nn.ReLU(), # relu4-1, this is the last layer used + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu4-2 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu4-3 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu4-4 + nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu5-1 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu5-2 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU(), # relu5-3 + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 512, (3, 3)), + nn.ReLU() # relu5-4 +) + + +# compute channel-wise means and variances of features +def calc_mean_std(feat, eps=1e-5): + size = feat.size() + assert len(size) == 4, 'The shape of feature needs to be a tuple with length 4.' + B, C = size[:2] + feat_mean = feat.reshape(B, C, -1).mean(dim=2).reshape(B, C, 1, 1) + feat_std = (feat.reshape(B, C, -1).var(dim=2) + eps).sqrt().reshape(B, C, 1, 1) + return feat_mean, feat_std + + +# normalize features +def mean_variance_norm(feat): + size = feat.size() + mean, std = calc_mean_std(feat) + normalized_feat = (feat - mean.expand(size)) / std.expand(size) + return normalized_feat + + +########################################## Transfer Module ########################################## + +class TransModule(nn.Module): + """The Transfer Module of Style Transfer via Transformer + + Taking Transformer Decoder as the transfer module. + + Args: + config: The configuration of the transfer module + """ + + def __init__(self, config: TransModule_Config = None): + super(TransModule, self).__init__() + self.layers = nn.ModuleList([ + TransformerDecoderLayer( + d_model=config.d_model, + nhead=config.nhead, + mlp_ratio=config.mlp_ratio, + qkv_bias=config.qkv_bias, + attn_drop=config.attn_drop, + drop=config.drop, + drop_path=config.drop_path, + act_layer=config.act_layer, + norm_layer=config.norm_layer, + norm_first=config.norm_first + ) \ + for i in range(config.nlayer) + ]) + + def forward(self, content_feature, style_feature): + """ + Args: + content_feature: Content features，for producing Q sequences. Similar to tgt sequences in pytorch. (Tensor,[Batch,sequence,dim]) + style_feature : Style features，for producing K,V sequences.Similar to memory sequences in pytorch.(Tensor,[Batch,sequence,dim]) + + Returns: + Tensor with shape (Batch,sequence,dim) + """ + for layer in self.layers: + content_feature = layer(content_feature, style_feature) + + return content_feature + + + +########################################## Decoder ########################################## + +decoder_stem = nn.Sequential( + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(512, 256, (3, 3)), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 256, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(256, 128, (3, 3)), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 128, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(128, 64, (3, 3)), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 64, (3, 3)), + nn.ReLU(), + nn.ReflectionPad2d((1, 1, 1, 1)), + nn.Conv2d(64, 3, (3, 3)), +) + + +class Decoder_MVGG(nn.Module): + def __init__(self, d_model=768, seq_input=False): + super(Decoder_MVGG, self).__init__() + self.d_model = d_model + self.seq_input = seq_input + self.decoder = nn.Sequential( + # Proccess Layer 1 + + # Upsample Layer 2 + nn.ReflectionPad2d(1), + nn.Conv2d(int(self.d_model), 256, 3, 1, 0), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d(1), + nn.Conv2d(256, 256, 3, 1, 0), + nn.ReLU(), + nn.ReflectionPad2d(1), + nn.Conv2d(256, 256, 3, 1, 0), + nn.ReLU(), + nn.ReflectionPad2d(1), + nn.Conv2d(256, 256, 3, 1, 0), + nn.ReLU(), + + # Upsample Layer 3 + nn.ReflectionPad2d(1), + nn.Conv2d(256, 128, 3, 1, 0), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d(1), + nn.Conv2d(128, 128, 3, 1, 0), + nn.ReLU(), + + # Upsample Layer 4 + nn.ReflectionPad2d(1), + nn.Conv2d(128, 64, 3, 1, 0), + nn.ReLU(), + nn.Upsample(scale_factor=2, mode='nearest'), + nn.ReflectionPad2d(1), + nn.Conv2d(64, 64, 3, 1, 0), + nn.ReLU(), + + # Channel to 3 + nn.ReflectionPad2d(1), + nn.Conv2d(64, 3, 3, 1, 0), + ) + + def forward(self, x, input_resolution): + if self.seq_input == True: + B, N, C = x.size() + # H, W = math.ceil(self.img_H//self.patch_size), math.ceil(self.img_W//self.patch_size) + (H, W) = input_resolution + x = x.permute(0, 2, 1).reshape(B, C, H, W) + x = self.decoder(x) + return x diff --git a/models/networks/rafael_generator_module/rafael_trans_tools.py b/models/networks/rafael_generator_module/rafael_trans_tools.py new file mode 100644 index 0000000000000000000000000000000000000000..b9d81f4accdd4e0ace5a6f07df1de507939caf50 --- /dev/null +++ b/models/networks/rafael_generator_module/rafael_trans_tools.py @@ -0,0 +1,44 @@ +import torch.nn as nn +from itertools import repeat +from typing import Iterable + + +def _ntuple(n): + """Copy item to be a tuple with n length (Implemented as timm) + """ + + def parse(x): + if isinstance(x, Iterable): + return x + else: + return tuple(repeat(x, n)) + + return parse + + +to_2tuple = _ntuple(2) +to_ntuple = _ntuple + + +class DropPath(nn.Module): + """Stochasticly zero channels of data.(Implemented as timm) + """ + + def __init__(self, drop=0.5, scale=True): + super().__init__() + self.drop = drop + self.scale = scale + + def forward(self, x): + return self.drop_path(x, self.drop, self.training, self.scale) + + def drop_path(self, x, drop=0.5, training=True, scale=True): + if drop == 0. or not training: + return x + drop_p = 1 - drop + shape = (x.shape[0],) + (1,) * (x.ndim - 1) + random_tensor = x.new_empty(shape).bernoulli_(drop_p) + if drop_p > 0. and scale: + random_tensor.div_(drop_p) + + return x * random_tensor \ No newline at end of file diff --git a/models/networks/rafael_generator_module/rafael_transformer_components.py b/models/networks/rafael_generator_module/rafael_transformer_components.py new file mode 100644 index 0000000000000000000000000000000000000000..f4ac6d8d00470f215c2a3ebc811ddba243b92acc --- /dev/null +++ b/models/networks/rafael_generator_module/rafael_transformer_components.py @@ -0,0 +1,168 @@ +import torch.nn as nn +from models.networks.rafael_generator_module.rafael_trans_tools import DropPath, to_2tuple + + +class Mlp(nn.Module): + """MLP as implemented in timm + """ + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + drops = to_2tuple(drop) + + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.drop1 = nn.Dropout(drops[0]) + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop2 = nn.Dropout(drops[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.fc2(x) + x = self.drop2(x) + return x + + +class Attention(nn.Module): + """Self Attention as implemented in timm + """ + + def __init__(self, d_model, nhead=8, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + self.to_qkv = nn.Linear(d_model, d_model * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.size() + qkv = self.to_qkv(x).reshape(B, N, 3, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + q, k, v = qkv.unbind(0) + + attn = (q @ k.transpose(-1, -2)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Attention_Cross(nn.Module): + """Attention for decoder layer.Some palce may be called "inter attention" + """ + + def __init__(self, d_model, nhead=8, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + assert d_model % nhead == 0, 'd_model needs to be divisible by nhead' + self.nhead = nhead + self.scale = (d_model // nhead) ** -0.5 + + self.to_q = nn.Linear(d_model, d_model, bias=qkv_bias) + self.to_kv = nn.Linear(d_model, d_model * 2, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(d_model, d_model) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, y): + """ + Args: + x: output of the former layer + y: memery of the encoder layer + """ + B, Nx, C = x.size() + _, Ny, _ = y.size() + q = self.to_q(x).reshape(B, Nx, self.nhead, C // self.nhead).permute(0, 2, 1, 3) + kv = self.to_kv(y).reshape(B, Ny, 2, self.nhead, C // self.nhead).permute(2, 0, 3, 1, 4) + k, v = kv.unbind(0) + + attn = (q @ k.transpose(-1, -2)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, Nx, C) + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class TransformerEncoderLayer(nn.Module): + """Implemented as vit block in timm + """ + + def __init__(self, d_model, nhead=8, mlp_ratio=4, qkv_bias=False, attn_drop=0., + drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_first=False): + super().__init__() + mlp_hidden_dim = int(d_model * mlp_ratio) + + self.attn = Attention(d_model, nhead=nhead, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) + self.mlp = Mlp(d_model, hidden_features=mlp_hidden_dim, out_features=d_model, act_layer=act_layer, drop=drop) + + self.norm_first = norm_first + self.norm1 = norm_layer(d_model) + self.norm2 = norm_layer(d_model) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + def forward(self, x): + if self.norm_first == True: + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + x = self.norm1(x + self.drop_path(self.attn(x))) + x = self.norm2(x + self.drop_path(self.mlp(x))) + return x + + +class TransformerDecoderLayer(nn.Module): + """Transformer Decoder Layer + """ + + def __init__(self, d_model, nhead=8, mlp_ratio=4, qkv_bias=False, attn_drop=0., + drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, norm_first=False): + super().__init__() + mlp_hidden_dim = int(d_model * mlp_ratio) + + self.attn1 = Attention(d_model, nhead=nhead, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) + self.attn2 = Attention_Cross(d_model, nhead=nhead, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop) + self.mlp = Mlp(d_model, hidden_features=mlp_hidden_dim, out_features=d_model, act_layer=act_layer, drop=drop) + + self.norm_first = norm_first + self.norm1 = norm_layer(d_model) + self.norm2 = norm_layer(d_model) + self.norm3 = norm_layer(d_model) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + def forward(self, x, y): + """ + Args: + x: output of the former layer + y: memery of the encoder layer + """ + if self.norm_first == True: + x = x + self.drop_path(self.attn1(self.norm1(x))) + x = x + self.drop_path(self.attn2(self.norm2(x), y)) + x = x + self.drop_path(self.mlp(self.norm3(x))) + else: + x = self.norm1(x + self.drop_path(self.attn1(x))) + x = self.norm2(x + self.drop_path(self.attn2(x, y))) + x = self.norm3(x + self.drop_path(self.mlp(x))) + return x + +# Example Decoder_Layer +# import torch +# model = TransformerDecoderLayer(768, nhead=8, norm_first=True) +# tgt = torch.randn(1, 256, 768) +# memory = torch.randn(1, 196, 768) +# output = model(tgt, memory) +# output.shape \ No newline at end of file diff --git a/models/networks/stream.py b/models/networks/stream.py new file mode 100644 index 0000000000000000000000000000000000000000..632b69809a1031038cbc094296a06cd95b6580a1 --- /dev/null +++ b/models/networks/stream.py @@ -0,0 +1,52 @@ +import torch.nn.functional as F +from models.networks.base_network import BaseNetwork +from models.networks.architecture import StreamResnetBlock as StreamResnetBlock + + +# Content/style stream. +# The two streams are symmetrical with the same network structure, +# aiming at extracting corresponding feature representations in different levels. +class Stream(BaseNetwork): + def __init__(self, opt): + super().__init__() + self.opt = opt + nf = opt.ngf + self.res_0 = StreamResnetBlock(opt.semantic_nc, 1 * nf, opt) # 64-ch feature + self.res_1 = StreamResnetBlock(1 * nf, 2 * nf, opt) # 128-ch feature + self.res_2 = StreamResnetBlock(2 * nf, 4 * nf, opt) # 256-ch feature + self.res_3 = StreamResnetBlock(4 * nf, 8 * nf, opt) # 512-ch feature + self.res_4 = StreamResnetBlock(8 * nf, 16 * nf, opt) # 1024-ch feature + self.res_5 = StreamResnetBlock(16 * nf, 16 * nf, opt) # 1024-ch feature + self.res_6 = StreamResnetBlock(16 * nf, 16 * nf, opt) # 1024-ch feature + self.res_7 = StreamResnetBlock(16 * nf, 16 * nf, opt) # 1024-ch feature + + def down(self, input): + return F.interpolate(input, scale_factor=0.5) + + def forward(self,input): + # assume that input shape is (n,c,256,512) + + x0 = self.res_0(input) # (n,64,256,512) + + x1 = self.down(x0) + x1 = self.res_1(x1) # (n,128,128,256) + + x2 = self.down(x1) + x2 = self.res_2(x2) # (n,256,64,128) + + x3 = self.down(x2) + x3 = self.res_3(x3) # (n,512,32,64) + + x4 = self.down(x3) + x4 = self.res_4(x4) # (n,1024,16,32) + + x5 = self.down(x4) + x5 = self.res_5(x5) # (n,1024,8,16) + + x6 = self.down(x5) + x6 = self.res_6(x6) # (n,1024,4,8) + + x7 = self.down(x6) + x7 = self.res_7(x7) # (n,1024,2,4) + + return [x0, x1, x2, x3, x4, x5, x6, x7] diff --git a/models/networks/style_encoder.py b/models/networks/style_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..84d187c3831acbd7acbfcc00cfe5adf1a493d4c9 --- /dev/null +++ b/models/networks/style_encoder.py @@ -0,0 +1,65 @@ +import torch +import torch.nn as nn +from models.networks.base_network import BaseNetwork +import functools + + + +class SimpleStyleEncoder(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + kw = 4 + padw = 1 + use_bias_for_conv = True + norm_layers_list = [] + if opt.norm_SE == 'instance': + norm_layer_class = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False) + use_bias_for_conv = True + elif opt.norm_SE == 'batch': + norm_layer_class = functools.partial(nn.BatchNorm2d, affine=True) + use_bias_for_conv = False + elif opt.norm_SE == 'none' or opt.norm_SE is None: + norm_layer_class = None + else: + raise NotImplementedError(f"Normalization layer {opt.norm_SE} is not implemented for SimpleStyleEncoder") + + sequence = [ + nn.Conv2d(opt.style_enc_nc, opt.style_enc_nef, kernel_size=kw, stride=2, padding=padw, + bias=use_bias_for_conv), + ] + if norm_layer_class: sequence.append(norm_layer_class(opt.style_enc_nef)) + sequence.append(nn.ReLU(True)) + + nf_mult = 1 + for n in range(1, opt.style_enc_n_downsampling): + nf_mult_prev = nf_mult + nf_mult = min(2 ** n, 8) + sequence.append( + nn.Conv2d(opt.style_enc_nef * nf_mult_prev, opt.style_enc_nef * nf_mult, + kernel_size=kw, stride=2, padding=padw, bias=use_bias_for_conv) + ) + if norm_layer_class: sequence.append(norm_layer_class(opt.style_enc_nef * nf_mult)) + sequence.append(nn.ReLU(True)) + + final_channels = opt.style_enc_nef * nf_mult + + sequence += [ + nn.Conv2d(final_channels, final_channels, kernel_size=3, stride=1, padding=1, bias=use_bias_for_conv), + ] + if norm_layer_class: sequence.append(norm_layer_class(final_channels)) + sequence.append(nn.ReLU(True)) + sequence += [nn.AdaptiveAvgPool2d(1)] # Output: [B, final_channels, 1, 1] + + self.cnn_body = nn.Sequential(*sequence) + self.fc_out = nn.Linear(final_channels, opt.style_enc_latent_dim) # opt.style_enc_latent_dim + + def forward(self, input_image): + features = self.cnn_body(input_image) + features_flat = features.view(features.size(0), -1) # Flatten + latent_code = self.fc_out(features_flat) + return latent_code \ No newline at end of file diff --git a/models/networks/swin_discriminator.py b/models/networks/swin_discriminator.py new file mode 100644 index 0000000000000000000000000000000000000000..d4adddf9d979810e1fdf1b357929269d537a9c7e --- /dev/null +++ b/models/networks/swin_discriminator.py @@ -0,0 +1,251 @@ +import torch +import torch.nn as nn +import timm +import torch.nn.functional as F +from torch.nn import init + +from models.networks.base_network import BaseNetwork + + +def _get_swin_d_init_func_helper(it_type, it_gain): + """Helper function to get the initialization function.""" + + def init_func_local(m): + classname = m.__class__.__name__ + if classname.find('BatchNorm2d') != -1 or classname.find('InstanceNorm2d') != -1: # Handle InstanceNorm too + if hasattr(m, 'weight') and m.weight is not None: init.normal_(m.weight.data, 1.0, it_gain) + if hasattr(m, 'bias') and m.bias is not None: init.constant_(m.bias.data, 0.0) + elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if it_type == 'normal': + init.normal_(m.weight.data, 0.0, it_gain) + elif it_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=it_gain) + elif it_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif it_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=it_gain) + elif it_type == 'none': + pass + else: + raise NotImplementedError(f'initialization method [{it_type}] is not implemented') + if hasattr(m, 'bias') and m.bias is not None: init.constant_(m.bias.data, 0.0) + + return init_func_local + + +class ConditionEncoder(nn.Module): + def __init__(self, input_nc=3, output_dim=None, num_downsamplings=3, ngf=64): + super().__init__() + layers = [nn.Conv2d(input_nc, ngf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)] + mult = 1 + for i in range(1, num_downsamplings): + mult_prev = mult + mult = min(2 ** i, 8) + layers.extend([ + nn.Conv2d(ngf * mult_prev, ngf * mult, kernel_size=4, stride=2, padding=1, bias=False), + nn.InstanceNorm2d(ngf * mult), + nn.LeakyReLU(0.2, True) + ]) + final_cnn_channels = ngf * mult + if output_dim is not None and output_dim != final_cnn_channels: + layers.append(nn.Conv2d(final_cnn_channels, output_dim, kernel_size=1, stride=1, padding=0)) + self.final_channels = output_dim + else: + self.final_channels = final_cnn_channels + self.encoder = nn.Sequential(*layers) + + def forward(self, x): + return self.encoder(x) + + +class CrossAttentionLayer(nn.Module): + def __init__(self, dim, num_heads, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + assert dim % num_heads == 0, f"dim ({dim}) must be divisible by num_heads ({num_heads})" + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = head_dim ** -0.5 + self.q_proj = nn.Linear(dim, dim, bias=qkv_bias) + self.k_proj = nn.Linear(dim, dim, bias=qkv_bias) + self.v_proj = nn.Linear(dim, dim, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, query, key_value): + B_q, N_q, C_q = query.shape + B_kv, N_kv, C_kv = key_value.shape + assert C_q == C_kv, f"Query channels {C_q} and Key-Value channels {C_kv} must match" + dim = C_q + q = self.q_proj(query).reshape(B_q, N_q, self.num_heads, dim // self.num_heads).permute(0, 2, 1, 3) + k = self.k_proj(key_value).reshape(B_kv, N_kv, self.num_heads, dim // self.num_heads).permute(0, 2, 1, 3) + v = self.v_proj(key_value).reshape(B_kv, N_kv, self.num_heads, dim // self.num_heads).permute(0, 2, 1, 3) + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + x = (attn @ v).transpose(1, 2).reshape(B_q, N_q, dim) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class CrossAttentionBlock(nn.Module): + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + self.norm_q = norm_layer(dim) + self.norm_kv = norm_layer(dim) + self.cross_attn = CrossAttentionLayer(dim, num_heads, qkv_bias, attn_drop, proj_drop=drop) + self.drop_path = nn.Identity() + + def forward(self, query_feat, condition_feat): + attn_out = self.cross_attn(self.norm_q(query_feat), self.norm_kv(condition_feat)) + query_feat = query_feat + self.drop_path(attn_out) + return query_feat + + +class SwinTransformerConditionalDiscriminator(BaseNetwork): + @staticmethod + def modify_commandline_options(parser, is_train): + parser.add_argument('--swin_d_model_name', type=str, default='swinv2_tiny_window8_256.ms_in1k', + help="Name of the Swin model from timm.") + parser.add_argument('--swin_d_pretrained', action='store_true', help="Use pretrained Swin backbone.") + parser.add_argument('--swin_d_output_style', type=str, default='patch', choices=['patch', 'global'], + help="Output style: 'patch' for patch-wise scores, 'global' for a single score.") + parser.add_argument('--swin_d_num_conv_head', type=int, default=2, + help="Number of conv layers in the patch-output head.") + parser.add_argument('--swin_d_use_sigmoid', action='store_true', help="Apply sigmoid to the final D output.") + parser.add_argument('--cond_enc_input_nc', type=int, default=3, help='Input channels for condition encoder.') + parser.add_argument('--cond_enc_ngf', type=int, default=64, + help='Base number of filters for condition encoder.') + parser.add_argument('--cond_enc_num_downs', type=int, default=3, + help='Number of downsampling layers in condition encoder.') + parser.add_argument('--d_cross_attn_num_heads', type=int, default=4, + help='Number of heads for cross attention in D.') + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + self.output_style = opt.swin_d_output_style + swin_input_nc = 3 + + self.user_target_input_size = (opt.crop_size, opt.crop_size) + + self.backbone = timm.create_model( + opt.swin_d_model_name, + pretrained=opt.swin_d_pretrained, + num_classes=0, + in_chans=swin_input_nc, + img_size=self.user_target_input_size + ) + self.expected_input_size = self.user_target_input_size + + self.final_swin_embed_dim = self.backbone.num_features + assert self.final_swin_embed_dim > 0, "Could not determine backbone's feature dimension." + + with torch.no_grad(): + dummy_b_in = torch.randn(1, swin_input_nc, self.expected_input_size[0], self.expected_input_size[1]) + dummy_b_feat_raw = self.backbone.forward_features(dummy_b_in) + + # Timm models for Swin output (B, H, W, C) or (B, L, C) + if dummy_b_feat_raw.dim() == 4 and dummy_b_feat_raw.shape[-1] == self.final_swin_embed_dim: # NHWC + self.H_feat_approx = dummy_b_feat_raw.shape[1] + self.W_feat_approx = dummy_b_feat_raw.shape[2] + elif dummy_b_feat_raw.dim() == 3 and dummy_b_feat_raw.shape[-1] == self.final_swin_embed_dim: # BLC + L_approx = dummy_b_feat_raw.shape[1] + # Infer H, W from L, assuming a square-like feature map + self.H_feat_approx = self.W_feat_approx = int(L_approx ** 0.5) + if self.H_feat_approx * self.W_feat_approx != L_approx: + print(f"Warning: Cannot infer square feature map size from L={L_approx}. Fallback needed.") + else: + raise RuntimeError( + f"Unexpected Swin backbone output shape: {dummy_b_feat_raw.shape}. Expected NHWC or BLC format.") + + self.condition_encoder = ConditionEncoder( + input_nc=opt.cond_enc_input_nc, + output_dim=self.final_swin_embed_dim, + num_downsamplings=opt.cond_enc_num_downs, + ngf=opt.cond_enc_ngf) + assert self.condition_encoder.final_channels == self.final_swin_embed_dim, f"ConditionEncoder output_dim ({self.condition_encoder.final_channels}) mismatch with Swin embed_dim ({self.final_swin_embed_dim})" + + self.final_cross_attention_block = CrossAttentionBlock( + dim=self.final_swin_embed_dim, + num_heads=opt.d_cross_attn_num_heads) + + self.num_features_for_head = self.final_swin_embed_dim + + if self.output_style == 'patch': + patch_head_layers = [] + current_channels_head = self.num_features_for_head + for i in range(opt.swin_d_num_conv_head - 1): + out_ch_head = current_channels_head // 2 + patch_head_layers.extend([ + nn.Conv2d(current_channels_head, out_ch_head, kernel_size=3, padding=1, bias=False), + nn.InstanceNorm2d(out_ch_head), + nn.LeakyReLU(0.2, True) + ]) + current_channels_head = out_ch_head + patch_head_layers.append(nn.Conv2d(current_channels_head, 1, kernel_size=3, padding=1)) + if opt.swin_d_use_sigmoid: patch_head_layers.append(nn.Sigmoid()) + self.head = nn.Sequential(*patch_head_layers) + elif self.output_style == 'global': + self.global_pool = nn.AdaptiveAvgPool1d(1) + fc1_out = self.num_features_for_head // 2 + self.head = nn.Sequential( + nn.Linear(self.num_features_for_head, fc1_out), + nn.LeakyReLU(0.2, True), + nn.Linear(fc1_out, 1) + ) + if opt.swin_d_use_sigmoid: self.head.add_module("sigmoid", nn.Sigmoid()) + + def init_weights(self, init_type='xavier', gain=0.02): + pass + + def forward(self, main_img, condition_img): + if main_img.shape[2:] != self.expected_input_size: + main_img = F.interpolate(main_img, size=self.expected_input_size, mode='bilinear', align_corners=False) + if condition_img.shape[2:] != self.expected_input_size: + condition_img = F.interpolate(condition_img, size=self.expected_input_size, mode='bilinear', + align_corners=False) + + features_main_raw = self.backbone.forward_features(main_img) + + if features_main_raw.dim() == 4: # NHWC format + B, Hf, Wf, C_swin = features_main_raw.shape + features_main_seq = features_main_raw.reshape(B, Hf * Wf, C_swin) + actual_H_feat_main, actual_W_feat_main = Hf, Wf + elif features_main_raw.dim() == 3: # BLC format + features_main_seq = features_main_raw + actual_H_feat_main, actual_W_feat_main = self.H_feat_approx, self.W_feat_approx + else: + raise ValueError(f"Unexpected Swin output shape: {features_main_raw.shape}") + + cond_feat_map = self.condition_encoder(condition_img) + cond_feat_seq = cond_feat_map.flatten(2).transpose(1, 2).contiguous() + + fused_features = self.final_cross_attention_block(features_main_seq, cond_feat_seq) + + if hasattr(self.backbone, 'norm') and self.backbone.norm is not None: + final_features_for_head = self.backbone.norm(fused_features) + else: + final_features_for_head = fused_features + + output = None + features_for_loss = None + B_ff, L_ff, C_ff = final_features_for_head.shape + + if self.output_style == 'patch': + features_2d = final_features_for_head.permute(0, 2, 1).reshape(B_ff, C_ff, actual_H_feat_main, + actual_W_feat_main) + output = self.head(features_2d) + features_for_loss = features_2d + elif self.output_style == 'global': + pooled_features = self.global_pool(final_features_for_head.transpose(1, 2)).squeeze(-1) + output = self.head(pooled_features) + features_for_loss = pooled_features + + if output is None: + raise RuntimeError("Discriminator output failed to compute.") + + return features_for_loss, output \ No newline at end of file diff --git a/models/networks/sync_batchnorm/__init__.py b/models/networks/sync_batchnorm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6d9b36c74b1808b56ded68cf080a689db7e0ee4e --- /dev/null +++ b/models/networks/sync_batchnorm/__init__.py @@ -0,0 +1,14 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import set_sbn_eps_mode +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .batchnorm import patch_sync_batchnorm, convert_model +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/models/networks/sync_batchnorm/__pycache__/__init__.cpython-310.pyc b/models/networks/sync_batchnorm/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cd71fa01fb049639af0df2388d98a278526f5ff8 Binary files /dev/null and b/models/networks/sync_batchnorm/__pycache__/__init__.cpython-310.pyc differ diff --git a/models/networks/sync_batchnorm/__pycache__/batchnorm.cpython-310.pyc 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differ diff --git a/models/networks/sync_batchnorm/batchnorm.py b/models/networks/sync_batchnorm/batchnorm.py new file mode 100644 index 0000000000000000000000000000000000000000..bf8d7a7325b474771a11a137053971fd40426079 --- /dev/null +++ b/models/networks/sync_batchnorm/batchnorm.py @@ -0,0 +1,412 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections +import contextlib + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm + +try: + from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast +except ImportError: + ReduceAddCoalesced = Broadcast = None + +try: + from jactorch.parallel.comm import SyncMaster + from jactorch.parallel.data_parallel import JacDataParallel as DataParallelWithCallback +except ImportError: + from .comm import SyncMaster + from .replicate import DataParallelWithCallback + +__all__ = [ + 'set_sbn_eps_mode', + 'SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d', + 'patch_sync_batchnorm', 'convert_model' +] + + +SBN_EPS_MODE = 'clamp' + + +def set_sbn_eps_mode(mode): + global SBN_EPS_MODE + assert mode in ('clamp', 'plus') + SBN_EPS_MODE = mode + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dimensions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True): + assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.' + + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine, + track_running_stats=track_running_stats) + + if not self.track_running_stats: + import warnings + warnings.warn('track_running_stats=False is not supported by the SynchronizedBatchNorm.') + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + assert input.size(1) == self.num_features, 'Channel size mismatch: got {}, expect {}.'.format(input.size(1), self.num_features) + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + if hasattr(torch, 'no_grad'): + with torch.no_grad(): + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + else: + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + + if SBN_EPS_MODE == 'clamp': + return mean, bias_var.clamp(self.eps) ** -0.5 + elif SBN_EPS_MODE == 'plus': + return mean, (bias_var + self.eps) ** -0.5 + else: + raise ValueError('Unknown EPS mode: {}.'.format(SBN_EPS_MODE)) + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + + +@contextlib.contextmanager +def patch_sync_batchnorm(): + import torch.nn as nn + + backup = nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d + + nn.BatchNorm1d = SynchronizedBatchNorm1d + nn.BatchNorm2d = SynchronizedBatchNorm2d + nn.BatchNorm3d = SynchronizedBatchNorm3d + + yield + + nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d = backup + + +def convert_model(module): + """Traverse the input module and its child recursively + and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d + to SynchronizedBatchNorm*N*d + + Args: + module: the input module needs to be convert to SyncBN model + + Examples: + >>> import torch.nn as nn + >>> import torchvision + >>> # m is a standard pytorch model + >>> m = torchvision.models.resnet18(True) + >>> m = nn.DataParallel(m) + >>> # after convert, m is using SyncBN + >>> m = convert_model(m) + """ + if isinstance(module, torch.nn.DataParallel): + mod = module.module + mod = convert_model(mod) + mod = DataParallelWithCallback(mod, device_ids=module.device_ids) + return mod + + mod = module + for pth_module, sync_module in zip([torch.nn.modules.batchnorm.BatchNorm1d, + torch.nn.modules.batchnorm.BatchNorm2d, + torch.nn.modules.batchnorm.BatchNorm3d], + [SynchronizedBatchNorm1d, + SynchronizedBatchNorm2d, + SynchronizedBatchNorm3d]): + if isinstance(module, pth_module): + mod = sync_module(module.num_features, module.eps, module.momentum, module.affine) + mod.running_mean = module.running_mean + mod.running_var = module.running_var + if module.affine: + mod.weight.data = module.weight.data.clone().detach() + mod.bias.data = module.bias.data.clone().detach() + + for name, child in module.named_children(): + mod.add_module(name, convert_model(child)) + + return mod diff --git a/models/networks/sync_batchnorm/batchnorm_reimpl.py b/models/networks/sync_batchnorm/batchnorm_reimpl.py new file mode 100644 index 0000000000000000000000000000000000000000..18145c3353e13d482c492ae46df91a537669fca0 --- /dev/null +++ b/models/networks/sync_batchnorm/batchnorm_reimpl.py @@ -0,0 +1,74 @@ +#! /usr/bin/env python3 +# -*- coding: utf-8 -*- +# File : batchnorm_reimpl.py +# Author : acgtyrant +# Date : 11/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import torch +import torch.nn as nn +import torch.nn.init as init + +__all__ = ['BatchNorm2dReimpl'] + + +class BatchNorm2dReimpl(nn.Module): + """ + A re-implementation of batch normalization, used for testing the numerical + stability. + + Author: acgtyrant + See also: + https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14 + """ + def __init__(self, num_features, eps=1e-5, momentum=0.1): + super().__init__() + + self.num_features = num_features + self.eps = eps + self.momentum = momentum + self.weight = nn.Parameter(torch.empty(num_features)) + self.bias = nn.Parameter(torch.empty(num_features)) + self.register_buffer('running_mean', torch.zeros(num_features)) + self.register_buffer('running_var', torch.ones(num_features)) + self.reset_parameters() + + def reset_running_stats(self): + self.running_mean.zero_() + self.running_var.fill_(1) + + def reset_parameters(self): + self.reset_running_stats() + init.uniform_(self.weight) + init.zeros_(self.bias) + + def forward(self, input_): + batchsize, channels, height, width = input_.size() + numel = batchsize * height * width + input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel) + sum_ = input_.sum(1) + sum_of_square = input_.pow(2).sum(1) + mean = sum_ / numel + sumvar = sum_of_square - sum_ * mean + + self.running_mean = ( + (1 - self.momentum) * self.running_mean + + self.momentum * mean.detach() + ) + unbias_var = sumvar / (numel - 1) + self.running_var = ( + (1 - self.momentum) * self.running_var + + self.momentum * unbias_var.detach() + ) + + bias_var = sumvar / numel + inv_std = 1 / (bias_var + self.eps).pow(0.5) + output = ( + (input_ - mean.unsqueeze(1)) * inv_std.unsqueeze(1) * + self.weight.unsqueeze(1) + self.bias.unsqueeze(1)) + + return output.view(channels, batchsize, height, width).permute(1, 0, 2, 3).contiguous() + diff --git a/models/networks/sync_batchnorm/comm.py b/models/networks/sync_batchnorm/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b --- /dev/null +++ b/models/networks/sync_batchnorm/comm.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {'master_callback': self._master_callback} + + def __setstate__(self, state): + self.__init__(state['master_callback']) + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/models/networks/sync_batchnorm/replicate.py b/models/networks/sync_batchnorm/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06 --- /dev/null +++ b/models/networks/sync_batchnorm/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/models/networks/sync_batchnorm/unittest.py b/models/networks/sync_batchnorm/unittest.py new file mode 100644 index 0000000000000000000000000000000000000000..998223a0e0242dc4a5b2fcd74af79dc7232794da --- /dev/null +++ b/models/networks/sync_batchnorm/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest +import torch + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, x, y): + adiff = float((x - y).abs().max()) + if (y == 0).all(): + rdiff = 'NaN' + else: + rdiff = float((adiff / y).abs().max()) + + message = ( + 'Tensor close check failed\n' + 'adiff={}\n' + 'rdiff={}\n' + ).format(adiff, rdiff) + self.assertTrue(torch.allclose(x, y, atol=1e-5, rtol=1e-3), message) + diff --git a/models/networks/time_utils.py b/models/networks/time_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..be2edbe8b7ad392df71fc19669a2ee6bf13f0fce --- /dev/null +++ b/models/networks/time_utils.py @@ -0,0 +1,13 @@ +import torch +import math + + +def get_day_night_weights(phi, steepness=1.0, min_w=0, max_w=1): + w_day_raw = (-torch.cos(phi) + 1.0) / 2.0 + + if steepness != 1.0: + w_day_raw = torch.pow(w_day_raw, steepness) + + w_day = w_day_raw * (max_w - min_w) + min_w + + return w_day, 1.0 - w_day \ No newline at end of file diff --git a/models/pix2pix_model.py b/models/pix2pix_model.py new file mode 100644 index 0000000000000000000000000000000000000000..b2630f6bd344586f629daaa2823d37c7a9977fdf --- /dev/null +++ b/models/pix2pix_model.py @@ -0,0 +1,471 @@ +import torch +import models.networks as networks +from models.networks.loss import VGG16, SpatialCorrelativeLoss, SSIMLoss, LaplacianLoss +from models.networks.time_utils import get_day_night_weights +import util.util as util +import torch.nn as nn +import os +import torch.nn.functional as F +import math +import random + + +class TVLoss(nn.Module): + def __init__(self): + super(TVLoss, self).__init__() + + def forward(self, x): + batch_size, c, h, w = x.size() + tv_h = torch.pow(x[:, :, 1:, :] - x[:, :, :-1, :], 2).sum() + tv_w = torch.pow(x[:, :, :, 1:] - x[:, :, :, :-1], 2).sum() + return (tv_h + tv_w) / (batch_size * c * h * w) + +class ThresholdedL1Loss(nn.Module): + def __init__(self, threshold): + super().__init__() + self.threshold = threshold + + def forward(self, input_tensor): + avg_brightness = torch.mean(input_tensor, dim=[1, 2, 3]) + loss = F.relu(avg_brightness - self.threshold) + return torch.mean(loss) + + +def hour_to_rad(hour): + return (hour / 12.0) * math.pi + + +def create_light_direction_map(phi, size, num_channels, device, w_day): + B = phi.shape[0] + H, W = size + x_coords = torch.linspace(-1, 1, W, device=device) + y_coords = torch.linspace(-1, 1, H, device=device) + y_grid, x_grid = torch.meshgrid(y_coords, x_coords, indexing='ij') + x_grid = x_grid.unsqueeze(0).expand(B, -1, -1) + y_grid = y_grid.unsqueeze(0).expand(B, -1, -1) + phi_adjusted = phi - math.pi / 2 + light_vec_x = torch.cos(phi_adjusted).view(B, 1, 1) + light_vec_y = torch.sin(phi_adjusted).view(B, 1, 1) + + directional_intensity = w_day.view(B, 1, 1) + + if num_channels == 1: + light_map = (x_grid * light_vec_x + y_grid * light_vec_y) * directional_intensity + return light_map.unsqueeze(1) + elif num_channels == 2: + light_map_x = x_grid * light_vec_x * directional_intensity + light_map_y = y_grid * light_vec_y * directional_intensity + return torch.stack([light_map_x, light_map_y], dim=1) + else: + raise ValueError("num_channels for light map must be 1 or 2") + + +class Pix2PixModel(torch.nn.Module): + @staticmethod + def modify_commandline_options(parser, is_train): + networks.modify_commandline_options(parser, is_train) + + parser.add_argument('--daylight_curve_steepness', type=float, default=0.75) + parser.add_argument('--lambda_vgg_interp', type=float, default=15.0) + parser.add_argument('--lambda_tv', type=float, default=5.0) + + parser.add_argument('--attn_layers', type=str, default='4,7,9') + parser.add_argument('--patch_nums', type=float, default=128) + parser.add_argument('--patch_size', type=int, default=32) + parser.add_argument('--loss_mode', type=str, default='cos') + parser.add_argument('--use_norm', action='store_true') + parser.add_argument('--learned_attn', action='store_true', default=False) + parser.add_argument('--T', type=float, default=0.07) + parser.add_argument('--lambda_spatial', type=float, default=10.0) + parser.add_argument('--lambda_G', type=float, default=1.0) + if is_train: + parser.add_argument('--night_loss_warmup_iters', type=int, default=10000) + parser.add_argument('--use_thresholded_blackout', action='store_true') + parser.add_argument('--blackout_threshold', type=float, default=-0.9) + parser.add_argument('--lambda_thresholded_blackout', type=float, default=20.0) + + parser.add_argument('--lambda_content_focus', type=float, default=5.0) + parser.add_argument('--lambda_continuity', type=float, default=10.0) + parser.add_argument('--lambda_style', type=float, default=30.0) + parser.add_argument('--lambda_identity', type=float, default=10.0) + parser.add_argument('--lambda_style_recon', type=float, default=15.0) + parser.add_argument('--lambda_r1', type=float, default=10.0) + parser.add_argument('--lambda_latent_diversity', type=float, default=1.0) + parser.add_argument('--use_patch_vgg_loss', action='store_true') + parser.add_argument('--lambda_patch_vgg', type=float, default=10.0) + parser.add_argument('--patch_vgg_num_patches', type=int, default=64) + parser.add_argument('--patch_vgg_size', type=int, default=64) + parser.add_argument('--lambda_ppl', type=float, default=2.0) + parser.add_argument('--ppl_reg_every', type=int, default=4) + parser.add_argument('--r1_reg_every', type=int, default=16) + + parser.add_argument('--lambda_laplacian', type=float, default=5.0) + parser.add_argument('--lambda_ssim', type=float, default=1.0) + + if not hasattr(parser.parse_known_args()[0], 'lambda_z_reg'): + parser.add_argument('--lambda_z_reg', type=float, default=0.0) + if not hasattr(parser.parse_known_args()[0], 'lambda_night_content'): + parser.add_argument('--lambda_night_content', type=float, default=0.0) + return parser + + def __init__(self, opt): + super().__init__() + self.opt = opt + self.attn_layers = [int(i) for i in self.opt.attn_layers.split(',')] + self.device = self._get_current_device_for_loss_init() + self.netG, self.netD, self.netD2, self.netE = self.initialize_networks(opt) + if opt.isTrain: + self.criterionGAN = networks.GANLoss(opt.gan_mode, + tensor=torch.cuda.FloatTensor if self.use_gpu() else torch.FloatTensor, + opt=self.opt) + self.criterionFeat = torch.nn.L1Loss() + self.criterionIdentity = torch.nn.L1Loss() + if (not opt.no_vgg_loss and opt.lambda_vgg > 0) or \ + (hasattr(opt, 'lambda_style') and opt.lambda_style > 0) or \ + (hasattr(opt, 'lambda_vgg_interp') and opt.lambda_vgg_interp > 0) or \ + (hasattr(opt, 'lambda_night_content') and opt.lambda_night_content > 0) or \ + (hasattr(opt, 'lambda_continuity') and opt.lambda_continuity > 0): + self.criterionVGG = networks.VGGLoss(self.opt.gpu_ids) + if self.use_gpu(): self.criterionVGG.to(self.device) + + if opt.lambda_tv > 0: + self.criterionTV = TVLoss().to(self.device) + + if opt.lambda_ssim > 0: + self.criterionSSIM = SSIMLoss().to(self.device) + if opt.lambda_laplacian > 0: + self.criterionLaplacian = LaplacianLoss(self.device).to(self.device) + + if opt.use_thresholded_blackout: + self.criterionThresholdedBlackout = ThresholdedL1Loss(threshold=opt.blackout_threshold).to(self.device) + + self.vgg_for_spatial = None + self.criterionSpatial = None + if opt.lambda_spatial > 0: + self.vgg_for_spatial = VGG16().to(self.device) + self.vgg_for_spatial.eval() + for param in self.vgg_for_spatial.parameters(): param.requires_grad = False + self.criterionSpatial = SpatialCorrelativeLoss(loss_mode=opt.loss_mode, patch_nums=opt.patch_nums, + patch_size=opt.patch_size, norm=opt.use_norm, + use_conv=opt.learned_attn, T=opt.T).to(self.device) + self.g_reg_interval = opt.ppl_reg_every + self.d_reg_interval = opt.r1_reg_every + self.pl_mean = torch.tensor(0.0, device=self.device) + + def _get_current_device_for_loss_init(self): + return torch.device(f'cuda:{self.opt.gpu_ids[0]}' if self.use_gpu() and self.opt.gpu_ids else 'cpu') + + def forward(self, data, mode, iter_count=0, arbitrary_input=False): + content_image_orig = data['day'].to(self.device) + night_image = data.get('night', content_image_orig).to(self.device) + phi2_for_mixing = data.get('phi2', None) + phi_for_interp = self.opt.phi.to(self.device, dtype=content_image_orig.dtype) + + w_day, w_night = get_day_night_weights(phi_for_interp, self.opt.daylight_curve_steepness) + cos_phi = torch.cos(phi_for_interp) + sin_phi = torch.sin(phi_for_interp) + + _, _, H, W = content_image_orig.shape + light_map = create_light_direction_map(phi_for_interp, size=(H, W), num_channels=self.opt.light_map_channels, + device=self.device, w_day=w_day) + content_image_with_light = torch.cat([content_image_orig, light_map], dim=1) + + net_g_module = self.netG.module if isinstance(self.netG, torch.nn.DataParallel) else self.netG + if hasattr(net_g_module, 'opt'): net_g_module.opt.phi = self.opt.phi + + if mode == 'inference': + with torch.no_grad(): + fake_image, _ = net_g_module(content_image_with_light, w_day, w_night, cos_phi, sin_phi, phi2=None, + arbitrary_input=arbitrary_input) + return fake_image + + w_day_pix = w_day.view(-1, 1, 1, 1).expand_as(content_image_orig) + style_image_target = w_day_pix * content_image_orig + (1.0 - w_day_pix) * night_image + + if mode == 'generator': + g_loss_for_backward, g_losses_for_display, generated_images = self.compute_generator_loss( + content_image_with_light, content_image_orig, style_image_target, + night_image, w_day, w_night, cos_phi, sin_phi, iter_count, phi2_for_mixing, + arbitrary_input=arbitrary_input + ) + generated_images['style_target_interpolated'] = style_image_target + generated_images['light_map_viz'] = light_map + return g_loss_for_backward, g_losses_for_display, generated_images + elif mode == 'discriminator': + d1_losses, d2_losses = self.compute_discriminator_loss( + content_image_with_light, content_image_orig, style_image_target, + w_day, w_night, cos_phi, sin_phi, iter_count, phi2_for_mixing, arbitrary_input=arbitrary_input + ) + return d1_losses, d2_losses + else: + raise ValueError(f"|mode| is invalid: {mode}") + + def g_main_loss(self, content_with_light, content_orig, style_target, night_image, w_day, w_night, cos_phi, sin_phi, + phi2_for_mixing, arbitrary_input, iter_count): + G_losses = {} + fake_image, identity_images_dict = self.netG(content_with_light, w_day, w_night, cos_phi, sin_phi, + phi2=phi2_for_mixing, arbitrary_input=arbitrary_input) + if fake_image is None: raise RuntimeError("Generator output is None.") + + warmup_factor = min(1.0, iter_count / self.opt.night_loss_warmup_iters) + G_losses['Night_Loss_Warmup'] = torch.tensor(warmup_factor, device=self.device) + + if self.opt.lambda_vgg_interp > 0 and self.criterionVGG is not None: + with torch.no_grad(): + vgg_feats_day = self.criterionVGG.vgg(content_orig) + soft_night_anchor_img = (content_orig * 0.1) - 0.85 + vgg_feats_soft_night = self.criterionVGG.vgg(soft_night_anchor_img) + hard_night_anchor_img = torch.full_like(content_orig, -0.9, device=self.device) + vgg_feats_hard_night = self.criterionVGG.vgg(hard_night_anchor_img) + extreme_night_anchor_img = torch.full_like(content_orig, -1.0, device=self.device) + vgg_feats_extreme_night = self.criterionVGG.vgg(extreme_night_anchor_img) + + vgg_feats_fake = self.criterionVGG.vgg(fake_image) + vgg_interp_loss = 0 + + phi_deg = self.opt.phi * (180.0 / math.pi) + phi_deg = phi_deg.view(-1, 1, 1, 1) + phi_deg_sym = torch.where(phi_deg > 180.0, 360.0 - phi_deg, phi_deg) + + alpha_p1 = (phi_deg_sym / 15.0).clamp(0.0, 1.0) + alpha_p2 = ((phi_deg_sym - 15.0) / 30.0).clamp(0.0, 1.0) + alpha_p3 = ((phi_deg_sym - 45.0) / 135.0).clamp(0.0, 1.0) + + for i in range(len(vgg_feats_fake)): + interp_p1 = (1.0 - alpha_p1) * vgg_feats_extreme_night[i] + alpha_p1 * vgg_feats_hard_night[i] + interp_p2 = (1.0 - alpha_p2) * vgg_feats_hard_night[i] + alpha_p2 * vgg_feats_soft_night[i] + interp_p3 = (1.0 - alpha_p3) * vgg_feats_soft_night[i] + alpha_p3 * vgg_feats_day[i] + + mask_p1 = (phi_deg_sym <= 15.0).expand_as(vgg_feats_fake[i]) + mask_p2 = ((phi_deg_sym > 15.0) & (phi_deg_sym <= 45.0)).expand_as(vgg_feats_fake[i]) + + target_feat = torch.where(mask_p1, interp_p1, + torch.where(mask_p2, interp_p2, + interp_p3)) + + vgg_interp_loss += self.criterionFeat(vgg_feats_fake[i], target_feat.detach()) + + G_losses['VGG_Interp'] = vgg_interp_loss * self.opt.lambda_vgg_interp + + with torch.no_grad(): + hard_night_anchor = torch.full_like(content_orig, -1.0, device=self.device) + soft_night_anchor = (content_orig * 0.1) - 0.85 + phi_deg_blend = self.opt.phi * (180.0 / math.pi) + blend_weight = (F.relu(20.0 - phi_deg_blend) / 20.0).clamp(max=1.0).view(-1, 1, 1, 1) + night_anchor_image = blend_weight * hard_night_anchor + (1.0 - blend_weight) * soft_night_anchor + + if self.netD2 is not None: + pred_fake_d2_features, _ = self.discriminate(fake_image, content_orig, 'D2') + G_losses['GAN_D2'] = self.criterionGAN(pred_fake_d2_features, True, + for_discriminator=False) * self.opt.lambda_G + if not self.opt.no_ganFeat_loss and self.opt.lambda_feat > 0: + real_target_for_d = w_day.view(-1, 1, 1, 1) * content_orig + w_night.view(-1, 1, 1, + 1) * night_anchor_image + with torch.no_grad(): + pred_real_d2_features, _ = self.discriminate(real_target_for_d, content_orig, 'D2') + G_losses['GAN_Feat_D2'] = self.feat_matching_loss(pred_fake_d2_features, + pred_real_d2_features) * self.opt.lambda_feat + + if self.opt.lambda_identity > 0: + i_cc = identity_images_dict.get('identity_content') + if i_cc is not None: + loss_identity = self.criterionIdentity(i_cc, content_orig) + G_losses['Identity'] = (loss_identity * w_day).mean() * self.opt.lambda_identity + + if warmup_factor > 0: + if self.opt.lambda_style_recon > 0: + i_nn = identity_images_dict.get('identity_night') + if i_nn is not None: + loss_style_recon = self.criterionIdentity(i_nn, night_anchor_image) + G_losses['Style_Recon'] = ( + loss_style_recon * w_night).mean() * self.opt.lambda_style_recon * warmup_factor + + if self.opt.lambda_laplacian > 0 and hasattr(self, 'criterionLaplacian'): + loss_lap = self.criterionLaplacian(fake_image, content_orig) + G_losses['Laplacian'] = (loss_lap * w_night).mean() * self.opt.lambda_laplacian * warmup_factor + + if self.opt.lambda_tv > 0 and hasattr(self, 'criterionTV'): + loss_tv = self.criterionTV(fake_image) + G_losses['TV'] = (loss_tv * w_night).mean() * self.opt.lambda_tv * warmup_factor + + if self.opt.use_thresholded_blackout and hasattr(self, 'criterionThresholdedBlackout'): + loss_thresh_blackout = self.criterionThresholdedBlackout(fake_image) + G_losses['Thresh_Blackout'] = ( + loss_thresh_blackout * w_night).mean() * self.opt.lambda_thresholded_blackout * warmup_factor + + if hasattr(self.opt, 'lambda_z_reg') and self.opt.lambda_z_reg > 0: + netG_module = self.netG.module if isinstance(self.netG, torch.nn.DataParallel) else self.netG + if hasattr(netG_module, 'z_day') and hasattr(netG_module, 'z_night'): + z_day, z_night = netG_module.z_day, netG_module.z_night + G_losses['Z_Reg'] = self.criterionFeat(z_day, z_night) * self.opt.lambda_z_reg + + total_loss = sum(l for l in G_losses.values() if l is not None and isinstance(l, torch.Tensor)) + return total_loss, G_losses, fake_image, identity_images_dict + + def compute_generator_loss(self, content_with_light, content_orig, style_target, night_image, w_day, w_night, + cos_phi, sin_phi, iter_count, + phi2_for_mixing, arbitrary_input=False): + if self.netD2: + for p in self.netD2.parameters(): p.requires_grad = False + + g_loss_main, g_losses_for_display, fake_image_1, identity_images_dict = self.g_main_loss( + content_with_light, content_orig, style_target, night_image, w_day, w_night, cos_phi, sin_phi, + phi2_for_mixing, arbitrary_input=arbitrary_input, iter_count=iter_count + ) + + if self.opt.lambda_continuity > 0: + netG_module = self.netG.module if isinstance(self.netG, torch.nn.DataParallel) else self.netG + + phi_1 = self.opt.phi.clone() + delta_phi = (torch.rand_like(phi_1) * 0.2) - 0.1 + phi_2 = (phi_1 + delta_phi) + + w_day_2, w_night_2 = get_day_night_weights(phi_2, self.opt.daylight_curve_steepness) + cos_phi_2 = torch.cos(phi_2) + sin_phi_2 = torch.sin(phi_2) + + _, _, H, W = content_orig.shape + light_map_2 = create_light_direction_map(phi_2, (H, W), self.opt.light_map_channels, self.device, w_day_2) + content_with_light_2 = torch.cat([content_orig, light_map_2], dim=1) + + with torch.no_grad(): + fake_image_2, _ = netG_module(content_with_light_2, w_day_2, w_night_2, cos_phi_2, sin_phi_2, phi2=None, + arbitrary_input=arbitrary_input) + + loss_l1 = self.criterionIdentity(fake_image_1, fake_image_2.detach()) + loss_vgg = self.criterionVGG(fake_image_1, + fake_image_2.detach()) if self.criterionVGG is not None else torch.tensor(0.0, + device=self.device) + + continuity_loss = loss_l1 + loss_vgg + + g_losses_for_display["Continuity"] = continuity_loss.clone().detach() + g_loss_main += continuity_loss * self.opt.lambda_continuity + + if self.opt.lambda_ppl > 0 and (iter_count + 1) % self.g_reg_interval == 0: + g_losses_for_display["PPL"] = torch.tensor(0.0, device=self.device) + else: + g_losses_for_display["PPL"] = torch.tensor(0.0, device=self.device) + + g_losses_for_display['G_total'] = g_loss_main.clone().detach() + return g_loss_main, g_losses_for_display, {**identity_images_dict, 'synthesized_image': fake_image_1} + + def d_main_loss(self, content_with_light, content_orig, style_target, w_day, w_night, cos_phi, sin_phi, + phi2_for_mixing, arbitrary_input=False): + if not self.netD2: return torch.tensor(0.0, device=self.device), {} + for p in self.netD2.parameters(): p.requires_grad = True + D2_losses = {} + with torch.no_grad(): + fake_image, _ = self.netG(content_with_light, w_day, w_night, cos_phi, sin_phi, phi2=phi2_for_mixing, + arbitrary_input=arbitrary_input) + fake_image = fake_image.detach() + pred_fake_d2_full, _ = self.discriminate(fake_image, content_orig, 'D2') + D2_losses['D2_Fake'] = self.criterionGAN(pred_fake_d2_full, False, for_discriminator=True) + pred_real_d2_full, _ = self.discriminate(style_target, content_orig, 'D2') + D2_losses['D2_real'] = self.criterionGAN(pred_real_d2_full, True, for_discriminator=True) + return sum(D2_losses.values()), D2_losses + + def compute_discriminator_loss(self, content_with_light, content_orig, style_target, w_day, w_night, cos_phi, + sin_phi, iter_count, phi2_for_mixing, arbitrary_input=False): + if not self.netD2: return {}, {} + d_loss_main, d2_losses = self.d_main_loss(content_with_light, content_orig, style_target, w_day, w_night, + cos_phi, sin_phi, phi2_for_mixing, arbitrary_input=arbitrary_input) + if self.opt.lambda_r1 > 0 and (iter_count + 1) % self.d_reg_interval == 0: + r1_loss = self.d_r1_regularize(style_target.clone(), content_orig) + d2_losses['D2_R1'] = r1_loss.clone().detach() + weighted_r1_loss = self.opt.lambda_r1 / 2 * r1_loss * self.d_reg_interval + d_loss_main += weighted_r1_loss + else: + d2_losses['D2_R1'] = torch.tensor(0.0, device=self.device) + return {}, d2_losses + + def d_r1_regularize(self, style_target, content_orig): + if not self.netD2: return torch.tensor(0.0, device=self.device) + style_target.requires_grad = True + _, pred_real_d2_logit = self.discriminate(style_target, content_orig, 'D2') + r1_loss = self.r1_penalty(pred_real_d2_logit, style_target) + return r1_loss + + def create_optimizers(self, opt): + G_params = list(self.netG.parameters()) + optimizer_G = torch.optim.Adam(G_params, lr=opt.lr, betas=(opt.beta1, opt.beta2)) + D_params = list(self.netD2.parameters()) if self.netD2 else [] + optimizer_D = torch.optim.Adam(D_params, lr=opt.lr, betas=(opt.beta1, opt.beta2)) if D_params else None + return optimizer_G, optimizer_D, optimizer_D + + def r1_penalty(self, real_pred, real_img): + grad_real = torch.autograd.grad(outputs=real_pred.sum(), inputs=real_img, create_graph=True, retain_graph=True)[ + 0] + return grad_real.pow(2).reshape(real_img.shape[0], -1).sum(1).mean() + + def discriminate(self, image_to_judge, condition, type): + target_D = self.netD2 + if 'swin' in self.opt.netD.lower(): + return target_D(image_to_judge, condition) + else: + d_input = torch.cat((image_to_judge, condition), dim=1) + discriminator_out = target_D(d_input) + if isinstance(discriminator_out, list): + logit = discriminator_out[-1][-1] if isinstance(discriminator_out[-1], list) else discriminator_out[-1] + return discriminator_out, logit + else: + return [discriminator_out], discriminator_out + + def feat_matching_loss(self, pred_fake_scales, pred_real_scales): + loss = 0.0 + for fake_feat_scale, real_feat_scale in zip(pred_fake_scales, pred_real_scales): + if isinstance(fake_feat_scale, list): + for f, r in zip(fake_feat_scale[:-1], real_feat_scale[:-1]): loss += self.criterionFeat(f, r.detach()) + else: + loss += self.criterionFeat(fake_feat_scale, real_feat_scale.detach()) + return loss + + def use_gpu(self): + return len(self.opt.gpu_ids) > 0 + + def save(self, epoch): + util.save_network(self.netG, 'G', epoch, self.opt) + if self.netD2: util.save_network(self.netD2, 'D2', epoch, self.opt) + + def initialize_networks(self, opt): + netG = networks.define_G(opt) + netD2 = networks.define_D(opt) if opt.isTrain else None + if not opt.isTrain or opt.continue_train: + netG = util.load_network(netG, 'G', opt.which_epoch, opt) + if opt.isTrain: netD2 = util.load_network(netD2, 'D2', opt.which_epoch, opt) + return netG, None, netD2, None + + def calculate_patch_vgg_loss(self, fake, real): + if not (hasattr(self.opt, + 'use_patch_vgg_loss') and self.opt.use_patch_vgg_loss and self.opt.lambda_patch_vgg > 0 and self.criterionVGG is not None): return torch.tensor( + 0.0, device=fake.device) + patch_size, num_patches = self.opt.patch_vgg_size, self.opt.patch_vgg_num_patches + B, C, H, W = fake.shape + if patch_size > H or patch_size > W: return torch.tensor(0.0, device=fake.device) + rand_h = torch.randint(0, H - patch_size + 1, (num_patches,), device=fake.device) + rand_w = torch.randint(0, W - patch_size + 1, (num_patches,), device=fake.device) + fake_patches, real_patches = [], [] + for i in range(num_patches): + for b in range(B): + fake_patches.append( + fake[b:b + 1, :, rand_h[i]:rand_h[i] + patch_size, rand_w[i]:rand_w[i] + patch_size]) + real_patches.append( + real[b:b + 1, :, rand_h[i]:rand_h[i] + patch_size, rand_w[i]:rand_w[i] + patch_size]) + if not fake_patches: return torch.tensor(0.0, device=fake.device) + return self.criterionVGG(torch.cat(fake_patches, dim=0), torch.cat(real_patches, dim=0).detach()) + + def calculate_style_loss(self, fake, target): + if self.criterionVGG is None: return torch.tensor(0.0, device=fake.device) + fake_vgg_feats = self.criterionVGG.vgg(fake) + target_vgg_feats = self.criterionVGG.vgg(target) + style_loss = 0.0 + for f_f, f_t in zip(fake_vgg_feats, target_vgg_feats): + style_loss += self.criterionFeat(self.gram_matrix(f_f), self.gram_matrix(f_t).detach()) + return style_loss + + def gram_matrix(self, input_tensor): + b, c, h, w = input_tensor.size() + features = input_tensor.view(b, c, h * w) + features_t = features.transpose(1, 2) + G = features.bmm(features_t) + return G.div(c * h * w) \ No newline at end of file diff --git a/options/__init__.py b/options/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/options/__pycache__/__init__.cpython-310.pyc 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0000000000000000000000000000000000000000..c06bc19f7c01bab8697bba694be30e1681f4e01d Binary files /dev/null and b/options/__pycache__/train_options.cpython-310.pyc differ diff --git a/options/base_options.py b/options/base_options.py new file mode 100644 index 0000000000000000000000000000000000000000..605c7f2725018a3d4def01a4025d58965ca0795f --- /dev/null +++ b/options/base_options.py @@ -0,0 +1,227 @@ +import sys +import argparse +import os +from util import util +import torch +import models +import data +import pickle + + +class BaseOptions(): + def __init__(self): + self.initialized = False + + def initialize(self, parser): + # experiment specifics + parser.add_argument('--name', type=str, default='ast_s2wat_default_experiment', + help='name of the experiment. It decides where to store samples and models') + parser.add_argument('--task', type=str, default='AST', help='task type: AST | SIS | MMIS') + + parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') + parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') + parser.add_argument('--model', type=str, default='pix2pix', + help='which model to use (pix2pix is the main model structure)') + parser.add_argument('--norm_G', type=str, default='spectralinstance', + help='instance normalization or batch normalization for G parts not using FADE/S2WAT norms') + parser.add_argument('--norm_D', type=str, default='spectralinstance', + help='instance normalization or batch normalization for D') + parser.add_argument('--norm_S', type=str, default='spectralinstance', + help='instance normalization or batch normalization for Stream (original TSIT content/style stream)') + parser.add_argument('--norm_E', type=str, default='spectralinstance', + help='instance normalization or batch normalization for E (TSIT VAE encoder)') + parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc') + + + # input/output sizes + parser.add_argument('--batchSize', type=int, default=1, help='input batch size') + parser.add_argument('--preprocess_mode', type=str, default='scale_width_and_crop', + help='scaling and cropping of images at load time.', + choices=("resize_and_crop", "crop", "scale_width", "scale_width_and_crop", + "scale_shortside", "scale_shortside_and_crop", "fixed", "none")) + parser.add_argument('--load_size', type=int, default=286, + help='Scale images to this size. The final image will be cropped to --crop_size.') + parser.add_argument('--crop_size', type=int, default=256, + help='Crop to the width of crop_size (after initially scaling the images to load_size.)') + parser.add_argument('--aspect_ratio', type=float, default=1.0, + help='The ratio width/height. The final height of the load image will be crop_size/aspect_ratio') + parser.add_argument('--label_nc', type=int, default=3, + help='# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.') + parser.add_argument('--contain_dontcare_label', action='store_true', + help='if the label map contains dontcare label (dontcare=255)') + parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels') + + # for setting inputs + parser.add_argument('--dataset_mode', type=str, default='summer2winteryosemite') + parser.add_argument('--croot', type=str, default='./datasets/summer2winter_yosemite/', help='content dataroot') + parser.add_argument('--sroot', type=str, default='./datasets/summer2winter_yosemite/', help='style dataroot') + parser.add_argument('--serial_batches', action='store_true', + help='if true, takes images in order to make batches, otherwise takes them randomly') + parser.add_argument('--no_flip', action='store_true', + help='if specified, do not flip the images for data argumentation') + parser.add_argument('--nThreads', default=0, type=int, help='# threads for loading data') + parser.add_argument('--max_dataset_size', type=int, default=sys.maxsize, + help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') + parser.add_argument('--load_from_opt_file', action='store_true', + help='load the options from checkpoints and use that as default') + parser.add_argument('--cache_filelist_write', action='store_true', + help='saves the current filelist into a text file, so that it loads faster') + parser.add_argument('--cache_filelist_read', action='store_true', help='reads from the file list cache') + + # for displays + parser.add_argument('--display_winsize', type=int, default=256, help='display window size') + + # for generator + parser.add_argument('--netG', type=str, default='RafaelGenerator', + help='selects model to use for netG (tsit | pix2pixhd | RafaelGenerator)') + parser.add_argument('--ngf', type=int, default=64, + help='# of gen filters in first conv layer (used by original TSIT, S2WAT uses embed_dim)') + parser.add_argument('--init_type', type=str, default='xavier', + help='network initialization [normal|xavier|kaiming|orthogonal]') + parser.add_argument('--init_variance', type=float, default=0.02, + help='variance of the initialization distribution') + + # <<< THAY ĐỔI: Đổi tên alpha thành phi + parser.add_argument('--phi', type=float, default=0.0, + help='The parameter that controls the degree of stylization cyclically (angle in radians, 0 to 2*pi)') + + # for instance-wise features (original TSIT) + parser.add_argument('--no_instance', action='store_true', + help='if specified, do *not* add instance map as input (for SIS task)') + parser.add_argument('--use_vae', action='store_true', + help='enable training with an image encoder (TSIT original VAE).') + + self.initialized = True + return parser + + def gather_options(self): + """ + Gathers all options, initializes the parser, and parses arguments. + This version is corrected to avoid UnboundLocalError. + """ + # Always create a parser instance. The initialize() method will add arguments to it. + parser = argparse.ArgumentParser( + formatter_class=argparse.ArgumentDefaultsHelpFormatter) + + # The initialize method adds all options and sets self.initialized = True + parser = self.initialize(parser) + + # The rest of the original logic can now run safely + opt, unknown = parser.parse_known_args() + + # Add model and dataset specific options + model_name = opt.model + model_option_setter = models.get_option_setter(model_name) + parser = model_option_setter(parser, self.isTrain) + + dataset_mode = opt.dataset_mode + dataset_option_setter = data.get_option_setter(dataset_mode) + parser = dataset_option_setter(parser, self.isTrain) + + # Re-parse to get all options, including model/dataset specific ones + opt, _ = parser.parse_known_args() + + # Load options from file if requested + if opt.load_from_opt_file: + parser = self.update_options_from_file(parser, opt) + + # Final parse + opt = parser.parse_args() + self.parser = parser + return opt + + def print_options(self, opt): + message = '' + message += '----------------- Options ---------------\n' + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + message += '{:>35}: {:<30}{}\n'.format(str(k), str(v), comment) + message += '----------------- End -------------------' + print(message) + + def option_file_path(self, opt, makedir=False): + expr_dir = os.path.join(opt.checkpoints_dir, opt.name) + if makedir: + util.mkdirs(expr_dir) + file_name = os.path.join(expr_dir, 'opt') + return file_name + + def save_options(self, opt): + file_name = self.option_file_path(opt, makedir=True) + with open(file_name + '.txt', 'wt') as opt_file: + for k, v in sorted(vars(opt).items()): + comment = '' + default = self.parser.get_default(k) + if v != default: + comment = '\t[default: %s]' % str(default) + opt_file.write('{:>35}: {:<30}{}\n'.format(str(k), str(v), comment)) + + with open(file_name + '.pkl', 'wb') as opt_file: + pickle.dump(opt, opt_file) + + def update_options_from_file(self, parser, opt): + new_opt = self.load_options(opt) + for k, v in sorted(vars(opt).items()): + if hasattr(new_opt, k) and v != getattr(new_opt, k): + new_val = getattr(new_opt, k) + parser.set_defaults(**{k: new_val}) + return parser + + def load_options(self, opt): + file_name = self.option_file_path(opt, makedir=False) + new_opt_path = file_name + '.pkl' + if not os.path.exists(new_opt_path): + print(f"Warning: Option file {new_opt_path} not found. Using command line options.") + return opt + new_opt = pickle.load(open(new_opt_path, 'rb')) + return new_opt + + def parse(self, save=True): + opt = self.gather_options() + opt.isTrain = self.isTrain + + if opt.netG == 'RafaelGenerator': + if hasattr(opt, 'rafael_img_size') and opt.rafael_img_size != opt.crop_size: + print(f"Warning: crop_size ({opt.crop_size}) and rafael_img_size ({opt.rafael_img_size}) differ. " + f"Setting rafael_img_size to crop_size ({opt.crop_size}) for consistency during training.") + opt.rafael_img_size = opt.crop_size + + if opt.use_vae: + if not hasattr(opt, 'latent_dim'): + print("Warning: --latent_dim not found, VAE might not be configured correctly. ") + + assert opt.task == 'AST' or opt.task == 'SIS' or opt.task == 'MMIS', \ + f'Task type should be: AST | SIS | MMIS, but got {opt.task}.' + + if opt.task == 'SIS': + opt.semantic_nc = opt.label_nc + \ + (1 if opt.contain_dontcare_label else 0) + \ + (0 if opt.no_instance else 1) + if opt.netG == 'RafaelGenerator': + print( + "Warning: Using RafaelGenerator for SIS task. Ensure rafael encoder can handle semantic maps or a conversion layer exists.") + else: + opt.semantic_nc = 3 + + self.print_options(opt) + if opt.isTrain and save: + self.save_options(opt) + + str_ids = opt.gpu_ids.split(',') + opt.gpu_ids = [] + for str_id in str_ids: + id_val = int(str_id) + if id_val >= 0: + opt.gpu_ids.append(id_val) + if len(opt.gpu_ids) > 0: + torch.cuda.set_device(opt.gpu_ids[0]) + + assert len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0, \ + "Batch size %d is wrong. It must be a multiple of # GPUs %d." \ + % (opt.batchSize, len(opt.gpu_ids)) + + self.opt = opt + return self.opt \ No newline at end of file diff --git a/options/test_options.py b/options/test_options.py new file mode 100644 index 0000000000000000000000000000000000000000..c5c68dec9e351c0026e67393b97fdeebc0f6ffc9 --- /dev/null +++ b/options/test_options.py @@ -0,0 +1,20 @@ +from .base_options import BaseOptions + + +class TestOptions(BaseOptions): + def initialize(self, parser): + BaseOptions.initialize(self, parser) + parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.') + parser.add_argument('--which_epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--how_many', type=int, default=float("inf"), help='how many test images to run') + parser.add_argument('--show_input', action='store_true', help='show input images with the synthesized image') + + + parser.set_defaults(dataset_mode='single_folder') + + parser.set_defaults(preprocess_mode='scale_width_and_crop', crop_size=256, load_size=256, display_winsize=256) + parser.set_defaults(serial_batches=True) + parser.set_defaults(no_flip=True) + parser.set_defaults(phase='test') + self.isTrain = False + return parser \ No newline at end of file diff --git a/options/train_options.py b/options/train_options.py new file mode 100644 index 0000000000000000000000000000000000000000..f476b66d1e28af63546301b0b42c0cb85cabfb6d --- /dev/null +++ b/options/train_options.py @@ -0,0 +1,52 @@ +from .base_options import BaseOptions + + +class TrainOptions(BaseOptions): + def initialize(self, parser): + BaseOptions.initialize(self, parser) + parser.add_argument('--display_freq', type=int, default=100, + help='frequency of showing training results on screen') + parser.add_argument('--print_freq', type=int, default=100, + help='frequency of showing training results on console') + parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results') + parser.add_argument('--save_epoch_freq', type=int, default=10, + help='frequency of saving checkpoints at the end of epochs') + parser.add_argument('--no_html', action='store_true', + help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/') + parser.add_argument('--debug', action='store_true', help='only do one epoch and displays at each iteration') + parser.add_argument('--tf_log', action='store_true', + help='if specified, use tensorboard logging. Requires tensorflow installed') + + parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') + parser.add_argument('--which_epoch', type=str, default='latest', + help='which epoch to load? set to latest to use latest cached model') + parser.add_argument('--niter', type=int, default=50, + help='# of iter at starting learning rate. This is NOT the total #epochs. Totla #epochs is niter + niter_decay') + parser.add_argument('--niter_decay', type=int, default=0, + help='# of iter to linearly decay learning rate to zero') + parser.add_argument('--optimizer', type=str, default='adam') + parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam') + parser.add_argument('--beta2', type=float, default=0.999, help='momentum term of adam') + parser.add_argument('--lr', type=float, default=0.0002, help='initial learning rate for adam') + parser.add_argument('--D_steps_per_G', type=int, default=1, + help='number of discriminator iterations per generator iterations.') + + parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in first conv layer') + parser.add_argument('--lambda_feat', type=float, default=10.0, help='weight for feature matching loss') + parser.add_argument('--lambda_vgg', type=float, default=10.0, help='weight for vgg loss') + parser.add_argument('--no_ganFeat_loss', action='store_true', + help='if specified, do *not* use discriminator feature matching loss') + parser.add_argument('--no_vgg_loss', action='store_true', + help='if specified, do *not* use VGG feature matching loss') + parser.add_argument('--gan_mode', type=str, default='hinge', help='(ls|original|hinge)') + parser.add_argument('--netD', type=str, + help='(NLayerDiscriminator|MultiscaleDiscriminator|swin_transformer_conditional_discriminator)') + parser.add_argument('--no_TTUR', action='store_true', help='Use TTUR training scheme') + parser.add_argument('--lambda_kld', type=float, default=0.05) + + parser.add_argument('--lambda_z_reg', type=float, default=0.0, help='weight for latent z-vector regularization') + parser.add_argument('--lambda_night_content', type=float, default=0.0, + help='weight for VGG content loss specifically at night') + + self.isTrain = True + return parser diff --git a/preview/README-vi.md b/preview/README-vi.md new file mode 100644 index 0000000000000000000000000000000000000000..5484fc4c8ba04975f4e9f2c3efff3f2d5a4dd8c4 --- /dev/null +++ b/preview/README-vi.md @@ -0,0 +1,135 @@ +# CR-Net: Mạng kết xuất liên tục giúp tăng cường khả năng xử lý trong môi trường ánh sáng yếu + +
+ 📄 Paper | + 💻 Mã nguồn | + 🤗 Hugging Face +
+ + +
+ 
+
+ +
+ Kiến trúc của mô hình CR-Net. +
+ + +## Giới thiệu + +**CR-Net** là một model để cải thiện chất lượng hình ảnh và video trong điều kiện ánh sáng yếu. +Bằng cách học một quy trình kết xuất liên tục, CR-Net có khả năng tăng cường độ sáng một cách hiệu quả, tạo ra kết quả tự nhiên và rõ nét ngay cả trong những môi trường thiếu sáng. +Để tìm hiểu thêm về CR-Net, vui lòng đọc tài liệu của chúng tôi [English](../README.md) | [Tiếng Việt](README-vi.md) | [中文](README-zh.md). + + +### Các tính năng chính + +* **Tăng cường chất lượng ảnh/video thiếu sáng:** Cải thiện đáng kể độ sáng và độ tương phản của hình ảnh và video được quay trong điều kiện ánh sáng yếu. +* **Mạng kết xuất liên tục:** Áp dụng một kiến trúc mới lạ để mang lại kết quả mượt mà và tự nhiên hơn so với các phương pháp truyền thống. +* **Ứng dụng linh hoạt:** Hỗ trợ xử lý cho cả video và thư mục chứa nhiều hình ảnh tĩnh. +## Demo + + + + +## Cài đặt và Yêu cầu + +Để chạy mô hình này, bạn cần có môi trường phù hợp. Chúng tôi khuyến nghị sử dụng các phiên bản sau: + +* **Python:** `Python >= 3.10` (Khuyến nghị `Python 3.10`) +* **PyTorch:** `PyTorch >= 1.12` (Khuyến nghị `PyTorch 2.1.2`) + +**Bước 1: Clone repository** + +```shell + git clone https://github.com/val-utehy/CR-Net.git + cd CR-Net +``` + +**Bước 2: Cài đặt các phụ thuộc** + +```shell + pip install -r requirements.txt +``` + +> [!NOTE] +> Hãy chắc chắn rằng bạn đã cài đặt phiên bản **torch** và **torchvision** tương thích với **CUDA driver** để tận dụng GPU. + + +## Hướng dẫn sử dụng + +### 1. Huấn luyện mô hình (Training) + +Training file will be updated soon! + +[//]: # (Để huấn luyện mô hình CR-Net trên bộ dữ liệu của riêng bạn, hãy làm theo các bước sau:) + +[//]: # () +[//]: # (**a. Cấu hình file script huấn luyện:**) + +[//]: # () +[//]: # (Mở và chỉnh sửa file `train_scripts/ast_n2h_dat.sh`. Trong file này, bạn cần chỉ định các đường dẫn quan trọng như đường dẫn đến bộ dữ liệu và nơi lưu checkpoint.) + +[//]: # () +[//]: # (**b. Chạy script huấn luyện:**) + +[//]: # () +[//]: # (Sau khi đã cấu hình xong, di chuyển đến thư mục gốc của dự án và thực thi lệnh sau:) + +[//]: # () +[//]: # (```shell) + +[//]: # ( bash train_scripts/ast_n2h_dat.sh) + +[//]: # (```) + +### 2. Thử nghiệm và Inference (Testing and Inference) +**a. Xử lý video:** +#### 1. Cấu hình file scripts: +Mở và chỉnh sửa file test_scripts/ast_inference_video_dat.sh. Tại đây, bạn cần cung cấp đường dẫn đến checkpoint đã huấn luyện và video đầu vào/đầu ra. +#### 2. Chạy script xử lý video: +Sau khi đã cấu hình xong, di chuyển đến thư mục gốc của dự án và thực thi lệnh sau: +```shell + bash test_scripts/ast_inference_video.sh +``` + +**b. Xử lý thư mục ảnh:** +#### 1. Cấu hình file scripts: +Mở và chỉnh sửa file test_scripts/ast_n2h_dat.sh. Tại đây, bạn cần cung cấp đường dẫn đến checkpoint đã huấn luyện và thư mục ảnh đầu vào/đầu ra. + +#### 2. Chạy script xử lý thư mục ảnh: +Sau khi đã cấu hình xong, di chuyển đến thư mục gốc của dự án và thực thi lệnh sau: +```shell + bash test_scripts/ast_n2h.sh +``` + + +## Trích dẫn (Citation) + +[//]: # (```bibtex) + +[//]: # (@article{crnet2025,) + +[//]: # ( title={CR-Net: A Continuous Rendering Network for Improving Robustness to Low-illumination},) + +[//]: # ( author={Tên tác giả 1 and Tên tác giả 2 and ...},) + +[//]: # ( journal={Tên hội nghị hoặc tạp chí},) + +[//]: # ( year={2025}) + +[//]: # (}) + +[//]: # (```) +## Tham khaor + +1. https://github.com/EndlessSora/TSIT + +2. https://github.com/astra-vision/CoMoGAN + +3. https://github.com/AlienZhang1996/S2WAT + +## Giấy phép (License) +CR-Net được cấp phép dưới giấy phép MIT. Vui lòng xem file [LICENSE](../LICENSE) để biết thêm chi tiết. + diff --git a/preview/README-zh.md b/preview/README-zh.md new file mode 100644 index 0000000000000000000000000000000000000000..b58b8a32ec68694d9623ba12c9108b3e25d734c5 --- /dev/null +++ b/preview/README-zh.md @@ -0,0 +1,146 @@ +# CR-Net: 一种连续渲染网络，用于增强低光环境下的处理能力 + +
+ 📄 论文 | + 💻 源码 | + 🤗 Hugging Face +
+ +
+
+
+ +
+ CR-Net 模型的架构。 +
+ + + +## 介绍 + +**CR-Net** 是一个深度学习模型，旨在提升在低光条件下的图像和视频质量。通过学习一个连续渲染过程，CR-Net 能够有效地恢复细节、减少噪声并增强亮度，从而在具有挑战性的弱光环境中生成自然清晰的结果。 要了解更多关于 CR-Net 的信息，请阅读我们的文档 [English](../README.md) | [Tiếng Việt](README-vi.md) | [中文](README-zh.md)。 + + +### 主要特性 + +* **增强低光图像/视频质量：** 显著改善在低光条件下拍摄的图像和视频的亮度与对比度。 +* **连续渲染网络：** 采用新颖的架构，比传统方法生成更加平滑、自然的效果。 +* **灵活应用：** 支持处理视频和包含多张静态图像的文件夹。 + +## 演示 + + + + + +## 安装与要求 + +要运行该模型，您需要合适的环境。我们推荐使用以下版本： + +* **Python:** `Python >= 3.10`（推荐使用 `Python 3.10`） +* **PyTorch:** `PyTorch >= 1.12`（推荐使用 `PyTorch 2.1.2`） + +**步骤 1: 克隆仓库** + +```shell + git clone https://github.com/val-utehy/CR-Net.git + cd CR-Net + +``` + +**步骤 2: 安装依赖** + +```shell + pip install -r requirements.txt +``` + +> [!注意] +> 请确保您已安装与 **CUDA 驱动** 兼容的 **torch** 和 **torchvision** 版本，以充分利用 GPU。 + + + +## 使用指南 + +### 1. 模型训练 (Training) + +Training file will be updated soon! + +[//]: # (要在您自己的数据集上训练 CR-Net，请按照以下步骤操作：) + +[//]: # () +[//]: # (**a. 配置训练脚本文件：**) + +[//]: # () +[//]: # (打开并编辑文件 `train_scripts/ast_n2h_dat.sh`。在该文件中，您需要指定重要路径，例如数据集路径和保存 checkpoint 的位置。) + +[//]: # () +[//]: # (**b. 运行训练脚本：**) + +[//]: # () +[//]: # (完成配置后，进入项目根目录并执行以下命令：) + +[//]: # () +[//]: # (```shell) + +[//]: # ( bash train_scripts/ast_n2h_dat.sh) + +[//]: # () +[//]: # (```) + +### 2. 测试与推理 (Testing and Inference) + +**a. 视频处理：** + +#### 1. 配置脚本文件： +打开并编辑文件 `test_scripts/ast_inference_video_dat.sh`。在此文件中，您需要提供训练好的 checkpoint 路径，以及输入/输出视频的路径。 + +#### 2. 运行视频处理脚本： +完成配置后，进入项目根目录并执行以下命令： + +```shell + bash test_scripts/ast_inference_video.sh +``` + +**b. 图像文件夹处理：** + +#### 1. 配置脚本文件： +打开并编辑文件 `test_scripts/ast_n2h.sh`。在此文件中，您需要提供训练好的 checkpoint 路径，以及输入/输出图像文件夹的路径。 + +#### 2. 运行图像文件夹处理脚本： +完成配置后，进入项目根目录并执行以下命令： + +```shell + bash test_scripts/ast_n2h.sh +``` + + +## 引用 (Citation) + +[//]: # (```bibtex) + +[//]: # (@article{crnet2025,) + +[//]: # ( title={CR-Net: A Continuous Rendering Network for Improving Robustness to Low-illumination},) + +[//]: # ( author={作者姓名1 and 作者姓名2 and ...},) + +[//]: # ( journal={会议或期刊名称},) + +[//]: # ( year={2025}) + +[//]: # (}) + +[//]: # (```) + +## References + +1. https://github.com/EndlessSora/TSIT + +2. https://github.com/astra-vision/CoMoGAN + +3. https://github.com/AlienZhang1996/S2WAT + +## 许可证 (License) +CR-Net 在 MIT 许可证下发布。请参阅 [LICENSE](../LICENSE) 文件以获取更多详细信息。 + + diff --git a/preview/structures.jpg b/preview/structures.jpg new file mode 100644 index 0000000000000000000000000000000000000000..7add034d4b18daada715632f7c7f2965887c51dd --- /dev/null +++ b/preview/structures.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:62c41bf52531071c62f3f17645de8a7ec25c8590c9d171d6cf3774bcf3f53197 +size 121812 diff --git a/preview/video_demo.gif b/preview/video_demo.gif new file mode 100644 index 0000000000000000000000000000000000000000..aba63d7a4449a2031f2e125dae5c7c02167b7a54 --- /dev/null +++ b/preview/video_demo.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:325fbbacf989d0b4e9bb81a134d8490418857048531b33b7fd122015890f37d6 +size 100259931 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..1f8abd327cbc32c141c60c9034093bbb4121265a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,28 @@ +diffusers<=0.34.0 +graphviz<=0.21 +torch>=2.1.0 +h5py<=3.14.0 +keras<=3.10.0 +lightgbm<=4.6.0 +lpips<=0.1.4 +matplotlib<=3.7.5 +networkx<=3.4.2 +nvidia-ml-py<=12.575.51 +nvitop<=1.5.1 +opencv-python<=4.11.0.86 +pandas<=2.2.1 +psutil<=7.0.0 +rich<=14.0.0 +scikit-image<=0.22.0 +scikit-learn<=1.2.2 +seaborn<=0.13.2 +sympy<=1.14.0 +tensorboard +tensorboardX +pytorch-fid +torchinfo +pytorch-msssim +timm<=1.0.16 +transformers<=4.49.0 +triton<=2.1.0 +numpy<=2.0.0 \ No newline at end of file diff --git a/test.py b/test.py new file mode 100644 index 0000000000000000000000000000000000000000..12bc2ee0cc162f80f8239aae3ed8a1142eced54f --- /dev/null +++ b/test.py @@ -0,0 +1,164 @@ +import os +import torch +import cv2 +import numpy as np +import math +from tqdm import tqdm +from PIL import Image +import torchvision.transforms as transforms +import argparse +from options.test_options import TestOptions +from models.pix2pix_model import Pix2PixModel +from util import util + + +def get_safe_resolution(width, height, max_height): + if height <= max_height and width % 16 == 0 and height % 16 == 0: + return width, height + + if height > max_height: + ratio = max_height / float(height) + new_width = int(width * ratio) + new_height = max_height + else: + new_width = width + new_height = height + + safe_width = new_width - (new_width % 16) + safe_height = new_height - (new_height % 16) + + if safe_width == 0: safe_width = 16 + if safe_height == 0: safe_height = 16 + + return safe_width, safe_height + + +class CustomTestOptions(TestOptions): + def initialize(self, parser): + parser = super().initialize(parser) + parser.add_argument('--hours', type=float, nargs='+', required=True, + help='List of hours to apply (e.g., 8.5 13 18.75). Range: 0h-24h.') + parser.add_argument('--add_timestamp', action='store_true', + help='If enabled, a timestamp (HH:MM) will be added at the top-left corner of the image.') + parser.add_argument('--max_height', type=int, default=720, + help='Maximum height for processed images. If set, images will be resized. Example: 720.') + parser.set_defaults(preprocess_mode='none') + return parser + + +def main(): + opt = CustomTestOptions().parse() + + model = Pix2PixModel(opt) + model.eval() + print(f"Model {opt.name} epoch {opt.which_epoch} has been successfully loaded.") + + image_extensions = ['.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'] + try: + image_files = sorted( + [f for f in os.listdir(opt.image_dir) if os.path.splitext(f)[1].lower() in image_extensions]) + except FileNotFoundError: + raise IOError(f"Error: Cannot find input image directory: {opt.image_dir}") + + if not image_files: + raise IOError(f"No image files found in directory: {opt.image_dir}") + + if opt.how_many < len(image_files): + image_files = image_files[:int(opt.how_many)] + print(f"Processing the first {len(image_files)} images according to --how_many parameter.") + + output_dir = os.path.join(opt.results_dir, opt.name, f'{opt.phase}_{opt.which_epoch}') + os.makedirs(output_dir, exist_ok=True) + print(f"Results will be saved to: {output_dir}") + + total_iterations = len(image_files) * len(opt.hours) + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + + printed_resize_message = False + + with tqdm(total=total_iterations, desc="Processing images") as pbar: + for image_name in image_files: + image_path = os.path.join(opt.image_dir, image_name) + + try: + input_image_pil = Image.open(image_path).convert('RGB') + original_width, original_height = input_image_pil.size + image_to_process = input_image_pil + + if opt.max_height is not None and opt.max_height > 0: + proc_width, proc_height = get_safe_resolution(original_width, original_height, opt.max_height) + if (proc_width, proc_height) != (original_width, original_height): + image_to_process = input_image_pil.resize((proc_width, proc_height), Image.Resampling.LANCZOS) + if not printed_resize_message: + print( + f"Note: Large images will be resized. Example: {original_width}x{original_height} -> {proc_width}x{proc_height}.") + printed_resize_message = True + + input_tensor = transform(image_to_process).unsqueeze(0) + data_i = {'day': input_tensor, 'cpath': [image_path]} + + for hour in opt.hours: + angle_degrees = hour * 15.0 + phi_val = math.radians(angle_degrees) + phi_tensor_1d = torch.tensor([phi_val], device=model.device) + model.opt.phi = phi_tensor_1d + + with torch.no_grad(): + generated_tensor = model(data_i, mode='inference', arbitrary_input=True) + + generated_numpy_rgb = util.tensor2im(generated_tensor)[0] + + if image_to_process.size != input_image_pil.size: + generated_pil = Image.fromarray(generated_numpy_rgb) + generated_pil_resized = generated_pil.resize(input_image_pil.size, Image.Resampling.LANCZOS) + generated_numpy_rgb = np.array(generated_pil_resized) + + generated_bgr = cv2.cvtColor(generated_numpy_rgb, cv2.COLOR_RGB2BGR) + + if opt.add_timestamp: + h = int(hour) + m = int((hour - h) * 60) + timestamp_text = f"{h:02d}:{m:02d}" + + font = cv2.FONT_HERSHEY_SIMPLEX + font_scale = 0.6 + thickness = 1 + padding = 8 + + (text_width, text_height), baseline = cv2.getTextSize(timestamp_text, font, font_scale, + thickness) + + margin = 10 + rect_tl = (margin, margin) + rect_br = (margin + text_width + padding * 2, margin + text_height + padding * 2) + + text_origin = (margin + padding, margin + text_height + padding) + + cv2.rectangle(generated_bgr, rect_tl, rect_br, (0, 0, 0), cv2.FILLED) + + cv2.putText(generated_bgr, timestamp_text, text_origin, font, font_scale, + (255, 255, 255), thickness, cv2.LINE_AA) + base_name, ext = os.path.splitext(image_name) + output_filename = f"{base_name}_hour_{hour}{ext}" + output_path = os.path.join(output_dir, output_filename) + cv2.imwrite(output_path, generated_bgr) + + pbar.update(1) + + except Exception as e: + print(f"\nError while processing image {image_name}: {e}") + if 'cuda' in str(e).lower(): + torch.cuda.empty_cache() + remaining_hours = len(opt.hours) - (pbar.n % len(opt.hours)) + if remaining_hours < len(opt.hours): + pbar.update(remaining_hours) + + print(f"\nProcessing completed. Results are saved in: {output_dir}") + + +if __name__ == '__main__': + main() diff --git a/test_scripts/ast_day2night.sh b/test_scripts/ast_day2night.sh new file mode 100644 index 0000000000000000000000000000000000000000..d9a53aad240a9b125bc2b2fcbf17aa0b5d711f81 --- /dev/null +++ b/test_scripts/ast_day2night.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_day2night' +TASK='AST' +DATA='day2night' +CROOT='./datasets/bdd100k' +SROOT='./datasets/bdd100k' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/ast_inference_video.sh b/test_scripts/ast_inference_video.sh new file mode 100644 index 0000000000000000000000000000000000000000..3d367947c7c62b8ef74d9d96031737955be7cec1 --- /dev/null +++ b/test_scripts/ast_inference_video.sh @@ -0,0 +1,23 @@ +#!/bin/bash +set -e + +EXPERIMENT_NAME='ast_rafael_v32_decrease_night_v2' +WHICH_EPOCH=8 +GPU_ID=0 +INPUT_VIDEO='/home/share/RAFAEL_7_Dark/HangzhouDemo.mp4' +OUTPUT_VIDEO='/workspace/RAFAEL_7_Dark/output_vid/output_full_resolution.mp4' +CHECKPOINTS_DIR='./checkpoints' +CYCLE_DURATION=20 +MAX_HEIGHT=720 +# ------------------------------------ + +python inference_video.py \ + --name "${EXPERIMENT_NAME}" \ + --which_epoch "${WHICH_EPOCH}" \ + --gpu_ids "${GPU_ID}" \ + --video_path "${INPUT_VIDEO}" \ + --max_resolution_height ${MAX_HEIGHT} \ + --degree_step 1 \ + # --no_audio + +echo "Video inference finished." \ No newline at end of file diff --git a/test_scripts/ast_n2h.sh b/test_scripts/ast_n2h.sh new file mode 100644 index 0000000000000000000000000000000000000000..29c6293276c36debcf8b433b85c8568a9341126b --- /dev/null +++ b/test_scripts/ast_n2h.sh @@ -0,0 +1,34 @@ +#!/bin/bash +set -e + +EXPERIMENT_NAME='ast_rafael_v1_sharpening' +IMAGE_FOLDER='/home/share/RAFAEL_8/VOC_2012_dark_YOLO_format/images/trainval' +WHICH_EPOCH='latest' +MAX_IMG_HEIGHT=960 +GPU_ID="0" + +#@HOURS="16.0" +HOURS="16.0 17.0 18.0 19.0 20.0 21.0 22.0" +#HOURS="17.0 17.5 18.0 18.5 19.0 19.5 20.0" + +ADD_MARK=false + +RESULTS_DIR='./results_v2' +CHECKPOINTS_DIR='./checkpoints_v2' +TIMESTAMP_FLAG="" +if [ "$ADD_MARK" = true ]; then + TIMESTAMP_FLAG="--add_timestamp" +fi +python test.py \ + --name "${EXPERIMENT_NAME}" \ + --image_dir "${IMAGE_FOLDER}" \ + --checkpoints_dir "${CHECKPOINTS_DIR}" \ + --results_dir "${RESULTS_DIR}" \ + --which_epoch "${WHICH_EPOCH}" \ + --gpu_ids "${GPU_ID}" \ + --hours ${HOURS} \ + --load_from_opt_file \ + --max_height "${MAX_IMG_HEIGHT}" \ + ${TIMESTAMP_FLAG} + +echo "Testing finished. Results are in ${RESULTS_DIR}/${EXPERIMENT_NAME}/" \ No newline at end of file diff --git a/test_scripts/ast_photo2art.sh b/test_scripts/ast_photo2art.sh new file mode 100644 index 0000000000000000000000000000000000000000..19ac4222dce3339e1d5a29fc46f98069ac3254b8 --- /dev/null +++ b/test_scripts/ast_photo2art.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_photo2art' +TASK='AST' +DATA='photo2art' +CROOT='./datasets/photo2art' +SROOT='./datasets/photo2art' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/ast_summer2winteryosemite.sh b/test_scripts/ast_summer2winteryosemite.sh new file mode 100644 index 0000000000000000000000000000000000000000..ab28480512b443a5d0c884709f9d86c18f64e07e --- /dev/null +++ b/test_scripts/ast_summer2winteryosemite.sh @@ -0,0 +1,31 @@ +#!/usr/bin/env bash + +set -x + +NAME='ast_summer2winteryosemite' +TASK='AST' +DATA='summer2winteryosemite' +CROOT='./datasets/summer2winter_yosemite' +SROOT='./datasets/summer2winter_yosemite' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/mmis_sunny2diffweathers.sh b/test_scripts/mmis_sunny2diffweathers.sh new file mode 100644 index 0000000000000000000000000000000000000000..d7058f4fc3627c19380ba71e20603dc0dfdfc18f --- /dev/null +++ b/test_scripts/mmis_sunny2diffweathers.sh @@ -0,0 +1,33 @@ +#!/usr/bin/env bash + +set -x + +NAME='mmis_sunny2diffweathers' +TASK='MMIS' +DATA='sunny2diffweathers' +CROOT='./datasets/bdd100k' +SROOT='./datasets/bdd100k' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' +MODE=${1:-'all'} + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --no_pairing_check \ + --no_instance \ + --num_upsampling_layers more \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input \ + --test_mode $MODE diff --git a/test_scripts/sis_ade20k.sh b/test_scripts/sis_ade20k.sh new file mode 100644 index 0000000000000000000000000000000000000000..5932236cefe63b48cc2d9eebbdc20a1b392d5c22 --- /dev/null +++ b/test_scripts/sis_ade20k.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -x + +NAME='sis_ade20k' +TASK='SIS' +DATA='ade20k' +CROOT='./datasets/ADEChallengeData2016' +SROOT='./datasets/ADEChallengeData2016' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --num_upsampling_layers more \ + --use_vae \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/test_scripts/sis_cityscapes.sh b/test_scripts/sis_cityscapes.sh new file mode 100644 index 0000000000000000000000000000000000000000..ba5ec6472d01237343a47a09437f6235002dd05f --- /dev/null +++ b/test_scripts/sis_cityscapes.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +set -x + +NAME='sis_cityscapes' +TASK='SIS' +DATA='cityscapes' +CROOT='./datasets/Cityscapes' +SROOT='./datasets/Cityscapes' +CKPTROOT='./checkpoints' +WORKER=4 +RESROOT='./results' +EPOCH='latest' + +python test.py \ + --name $NAME \ + --task $TASK \ + --gpu_ids 0 \ + --checkpoints_dir $CKPTROOT \ + --batchSize 1 \ + --dataset_mode $DATA \ + --croot $CROOT \ + --sroot $SROOT \ + --nThreads $WORKER \ + --num_upsampling_layers more \ + --use_vae \ + --alpha 1.0 \ + --results_dir $RESROOT \ + --which_epoch $EPOCH \ + --show_input diff --git a/trainers/__init__.py b/trainers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/trainers/__pycache__/__init__.cpython-310.pyc b/trainers/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0a69b7882ba8b316e946c374e5c6b8a70dee3c3e Binary files /dev/null and b/trainers/__pycache__/__init__.cpython-310.pyc differ diff --git a/trainers/__pycache__/pix2pix_trainer.cpython-310.pyc b/trainers/__pycache__/pix2pix_trainer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..88ea94a68ad6d0693aa6fba8eed65616d06464f7 Binary files /dev/null and b/trainers/__pycache__/pix2pix_trainer.cpython-310.pyc differ diff --git a/trainers/pix2pix_trainer.py b/trainers/pix2pix_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..50d8704e2b966b18f8492b3ee387d744e95432c7 --- /dev/null +++ b/trainers/pix2pix_trainer.py @@ -0,0 +1,92 @@ +from models.networks.sync_batchnorm import DataParallelWithCallback +from models.pix2pix_model import Pix2PixModel +import torch + + +class Pix2PixTrainer(): + """ + Trainer creates the model and optimizers, and uses them to + updates the weights of the network while reporting losses + and the latest visuals to visualize the progress in training. + """ + + def __init__(self, opt): + self.opt = opt + self.pix2pix_model = Pix2PixModel(opt) + if len(opt.gpu_ids) > 1: + self.pix2pix_model = DataParallelWithCallback(self.pix2pix_model, device_ids=opt.gpu_ids) + self.pix2pix_model_on_one_gpu = self.pix2pix_model.module + else: + self.pix2pix_model.to(f'cuda:{opt.gpu_ids[0]}' if opt.gpu_ids else 'cpu') + self.pix2pix_model_on_one_gpu = self.pix2pix_model + + self.generated = None + if opt.isTrain: + self.optimizer_G, _, self.optimizer_D = self.pix2pix_model_on_one_gpu.create_optimizers(opt) + self.old_lr = opt.lr + + def run_generator_one_step(self, data, iter_count): + self.optimizer_G.zero_grad() + g_loss_for_backward, g_losses_for_display, generated = self.pix2pix_model(data, mode='generator', + iter_count=iter_count) + if g_loss_for_backward is not None: + g_loss_for_backward.mean().backward() + torch.nn.utils.clip_grad_norm_(self.pix2pix_model_on_one_gpu.netG.parameters(), 1.0) + self.optimizer_G.step() + self.g_losses = g_losses_for_display + self.generated = generated + + def run_discriminator_one_step(self, data, iter_count): + if self.optimizer_D is None: + self.d_losses = {} + return + + self.optimizer_D.zero_grad() + _, d2_losses = self.pix2pix_model(data, mode='discriminator', iter_count=iter_count) + + d_loss = sum(d2_losses.values()).mean() + + d_loss.backward() + self.optimizer_D.step() + + self.d_losses = d2_losses + + def get_latest_losses(self): + return {**self.g_losses, **self.d_losses} + + def get_latest_generated(self): + return self.generated + + def save(self, epoch): + self.pix2pix_model_on_one_gpu.save(epoch) + + def update_learning_rate(self, epoch): + if epoch > self.opt.niter: + if self.opt.niter_decay > 0: + lrd = self.opt.lr / self.opt.niter_decay + new_lr = self.old_lr - lrd + else: + new_lr = self.old_lr + else: + new_lr = self.old_lr + + if new_lr != self.old_lr: + if self.opt.no_TTUR: + new_lr_G = new_lr + new_lr_D = new_lr + else: + new_lr_G = new_lr + new_lr_D = new_lr / 2 + + print(f'Updating learning rate: old_base={self.old_lr:.6f} -> new_base={new_lr:.6f}') + print(f'New LR_G: {new_lr_G:.6f}, New LR_D: {new_lr_D:.6f}') + + if self.optimizer_D: + for param_group in self.optimizer_D.param_groups: + param_group['lr'] = new_lr_D + + if self.optimizer_G: + for param_group in self.optimizer_G.param_groups: + param_group['lr'] = new_lr_G + + self.old_lr = new_lr \ No newline at end of file diff --git a/util/__init__.py b/util/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/util/__pycache__/__init__.cpython-310.pyc b/util/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..87d21e03be8aca88de465b0a83ec3a346c0df640 Binary files /dev/null and b/util/__pycache__/__init__.cpython-310.pyc differ diff --git a/util/__pycache__/coco.cpython-310.pyc b/util/__pycache__/coco.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4c0a143c345ed6e0f2c522644232d39497cced85 Binary files /dev/null and b/util/__pycache__/coco.cpython-310.pyc differ diff --git a/util/__pycache__/html.cpython-310.pyc b/util/__pycache__/html.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..721927eb372924d85357a16529d5927bc05a305a Binary files /dev/null and b/util/__pycache__/html.cpython-310.pyc differ diff --git a/util/__pycache__/iter_counter.cpython-310.pyc b/util/__pycache__/iter_counter.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f549a906faa7618a2842e9ba469555b773895c80 Binary files /dev/null and b/util/__pycache__/iter_counter.cpython-310.pyc differ diff --git a/util/__pycache__/util.cpython-310.pyc b/util/__pycache__/util.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4f8fbe94123616c58108a465044d680f8e27faf2 Binary files /dev/null and b/util/__pycache__/util.cpython-310.pyc differ diff --git a/util/__pycache__/visualizer.cpython-310.pyc b/util/__pycache__/visualizer.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7f18c8bc548ba83530d6a433787324bed58928f2 Binary files /dev/null and b/util/__pycache__/visualizer.cpython-310.pyc differ diff --git a/util/coco.py b/util/coco.py new file mode 100644 index 0000000000000000000000000000000000000000..804339e7458e2ca03a155ab590df992015967a86 --- /dev/null +++ b/util/coco.py @@ -0,0 +1,193 @@ +def id2label(id): + if id == 182: + id = 0 + else: + id = id + 1 + labelmap = \ + {0: 'unlabeled', + 1: 'person', + 2: 'bicycle', + 3: 'car', + 4: 'motorcycle', + 5: 'airplane', + 6: 'bus', + 7: 'train', + 8: 'truck', + 9: 'boat', + 10: 'traffic light', + 11: 'fire hydrant', + 12: 'street sign', + 13: 'stop sign', + 14: 'parking meter', + 15: 'bench', + 16: 'bird', + 17: 'cat', + 18: 'dog', + 19: 'horse', + 20: 'sheep', + 21: 'cow', + 22: 'elephant', + 23: 'bear', + 24: 'zebra', + 25: 'giraffe', + 26: 'hat', + 27: 'backpack', + 28: 'umbrella', + 29: 'shoe', + 30: 'eye glasses', + 31: 'handbag', + 32: 'tie', + 33: 'suitcase', + 34: 'frisbee', + 35: 'skis', + 36: 'snowboard', + 37: 'sports ball', + 38: 'kite', + 39: 'baseball bat', + 40: 'baseball glove', + 41: 'skateboard', + 42: 'surfboard', + 43: 'tennis racket', + 44: 'bottle', + 45: 'plate', + 46: 'wine glass', + 47: 'cup', + 48: 'fork', + 49: 'knife', + 50: 'spoon', + 51: 'bowl', + 52: 'banana', + 53: 'apple', + 54: 'sandwich', + 55: 'orange', + 56: 'broccoli', + 57: 'carrot', + 58: 'hot dog', + 59: 'pizza', + 60: 'donut', + 61: 'cake', + 62: 'chair', + 63: 'couch', + 64: 'potted plant', + 65: 'bed', + 66: 'mirror', + 67: 'dining table', + 68: 'window', + 69: 'desk', + 70: 'toilet', + 71: 'door', + 72: 'tv', + 73: 'laptop', + 74: 'mouse', + 75: 'remote', + 76: 'keyboard', + 77: 'cell phone', + 78: 'microwave', + 79: 'oven', + 80: 'toaster', + 81: 'sink', + 82: 'refrigerator', + 83: 'blender', + 84: 'book', + 85: 'clock', + 86: 'vase', + 87: 'scissors', + 88: 'teddy bear', + 89: 'hair drier', + 90: 'toothbrush', + 91: 'hair brush', # Last class of Thing + 92: 'banner', # Beginning of Stuff + 93: 'blanket', + 94: 'branch', + 95: 'bridge', + 96: 'building-other', + 97: 'bush', + 98: 'cabinet', + 99: 'cage', + 100: 'cardboard', + 101: 'carpet', + 102: 'ceiling-other', + 103: 'ceiling-tile', + 104: 'cloth', + 105: 'clothes', + 106: 'clouds', + 107: 'counter', + 108: 'cupboard', + 109: 'curtain', + 110: 'desk-stuff', + 111: 'dirt', + 112: 'door-stuff', + 113: 'fence', + 114: 'floor-marble', + 115: 'floor-other', + 116: 'floor-stone', + 117: 'floor-tile', + 118: 'floor-wood', + 119: 'flower', + 120: 'fog', + 121: 'food-other', + 122: 'fruit', + 123: 'furniture-other', + 124: 'grass', + 125: 'gravel', + 126: 'ground-other', + 127: 'hill', + 128: 'house', + 129: 'leaves', + 130: 'light', + 131: 'mat', + 132: 'metal', + 133: 'mirror-stuff', + 134: 'moss', + 135: 'mountain', + 136: 'mud', + 137: 'napkin', + 138: 'net', + 139: 'paper', + 140: 'pavement', + 141: 'pillow', + 142: 'plant-other', + 143: 'plastic', + 144: 'platform', + 145: 'playingfield', + 146: 'railing', + 147: 'railroad', + 148: 'river', + 149: 'road', + 150: 'rock', + 151: 'roof', + 152: 'rug', + 153: 'salad', + 154: 'sand', + 155: 'sea', + 156: 'shelf', + 157: 'sky-other', + 158: 'skyscraper', + 159: 'snow', + 160: 'solid-other', + 161: 'stairs', + 162: 'stone', + 163: 'straw', + 164: 'structural-other', + 165: 'table', + 166: 'tent', + 167: 'textile-other', + 168: 'towel', + 169: 'tree', + 170: 'vegetable', + 171: 'wall-brick', + 172: 'wall-concrete', + 173: 'wall-other', + 174: 'wall-panel', + 175: 'wall-stone', + 176: 'wall-tile', + 177: 'wall-wood', + 178: 'water-other', + 179: 'waterdrops', + 180: 'window-blind', + 181: 'window-other', + 182: 'wood'} + if id in labelmap: + return labelmap[id] + else: + return 'unknown' diff --git a/util/html.py b/util/html.py new file mode 100644 index 0000000000000000000000000000000000000000..50f67c74420e871aab4783cdd03ad13cb1ff33de --- /dev/null +++ b/util/html.py @@ -0,0 +1,71 @@ +import datetime +import dominate +from dominate.tags import * +import os + + +class HTML: + def __init__(self, web_dir, title, refresh=0): + if web_dir.endswith('.html'): + web_dir, html_name = os.path.split(web_dir) + else: + web_dir, html_name = web_dir, 'index.html' + self.title = title + self.web_dir = web_dir + self.html_name = html_name + self.img_dir = os.path.join(self.web_dir, 'images') + if len(self.web_dir) > 0 and not os.path.exists(self.web_dir): + os.makedirs(self.web_dir) + if len(self.web_dir) > 0 and not os.path.exists(self.img_dir): + os.makedirs(self.img_dir) + + self.doc = dominate.document(title=title) + with self.doc: + h1(datetime.datetime.now().strftime("%I:%M%p on %B %d, %Y")) + if refresh > 0: + with self.doc.head: + meta(http_equiv="refresh", content=str(refresh)) + + def get_image_dir(self): + return self.img_dir + + def add_header(self, str): + with self.doc: + h3(str) + + def add_table(self, border=1): + self.t = table(border=border, style="table-layout: fixed;") + self.doc.add(self.t) + + def add_images(self, ims, txts, links, width=512): + self.add_table() + with self.t: + with tr(): + for im, txt, link in zip(ims, txts, links): + with td(style="word-wrap: break-word;", halign="center", valign="top"): + with p(): + with a(href=os.path.join('images', link)): + img(style="width:%dpx" % (width), src=os.path.join('images', im)) + br() + p(txt.encode('utf-8')) + + def save(self): + html_file = os.path.join(self.web_dir, self.html_name) + f = open(html_file, 'wt') + f.write(self.doc.render()) + f.close() + + +if __name__ == '__main__': + html = HTML('web/', 'test_html') + html.add_header('hello world') + + ims = [] + txts = [] + links = [] + for n in range(4): + ims.append('image_%d.jpg' % n) + txts.append('text_%d' % n) + links.append('image_%d.jpg' % n) + html.add_images(ims, txts, links) + html.save() diff --git a/util/iter_counter.py b/util/iter_counter.py new file mode 100644 index 0000000000000000000000000000000000000000..1d79d9793a4d3a79343b72fec776a67abd9e6528 --- /dev/null +++ b/util/iter_counter.py @@ -0,0 +1,69 @@ +import os +import time +import numpy as np + + +# Helper class that keeps track of training iterations +class IterationCounter(): + def __init__(self, opt, dataset_size): + self.opt = opt + self.dataset_size = dataset_size + + self.first_epoch = 1 + self.total_epochs = opt.niter + opt.niter_decay + self.epoch_iter = 0 # iter number within each epoch + self.iter_record_path = os.path.join(self.opt.checkpoints_dir, self.opt.name, 'iter.txt') + if opt.isTrain and opt.continue_train: + try: + self.first_epoch, self.epoch_iter = np.loadtxt( + self.iter_record_path, delimiter=',', dtype=int) + print('Resuming from epoch %d at iteration %d' % (self.first_epoch, self.epoch_iter)) + except: + print('Could not load iteration record at %s. Starting from beginning.' % + self.iter_record_path) + + self.total_steps_so_far = (self.first_epoch - 1) * dataset_size + self.epoch_iter + + # return the iterator of epochs for the training + def training_epochs(self): + return range(self.first_epoch, self.total_epochs + 1) + + def record_epoch_start(self, epoch): + self.epoch_start_time = time.time() + self.epoch_iter = 0 + self.last_iter_time = time.time() + self.current_epoch = epoch + + def record_one_iteration(self): + current_time = time.time() + + # the last remaining batch is dropped (see data/__init__.py), + # so we can assume batch size is always opt.batchSize + self.time_per_iter = (current_time - self.last_iter_time) / self.opt.batchSize + self.last_iter_time = current_time + self.total_steps_so_far += self.opt.batchSize + self.epoch_iter += self.opt.batchSize + + def record_epoch_end(self): + current_time = time.time() + self.time_per_epoch = current_time - self.epoch_start_time + print('End of epoch %d / %d \t Time Taken: %d sec' % + (self.current_epoch, self.total_epochs, self.time_per_epoch)) + if self.current_epoch % self.opt.save_epoch_freq == 0: + np.savetxt(self.iter_record_path, (self.current_epoch + 1, 0), + delimiter=',', fmt='%d') + print('Saved current iteration count at %s.' % self.iter_record_path) + + def record_current_iter(self): + np.savetxt(self.iter_record_path, (self.current_epoch, self.epoch_iter), + delimiter=',', fmt='%d') + print('Saved current iteration count at %s.' % self.iter_record_path) + + def needs_saving(self): + return (self.total_steps_so_far % self.opt.save_latest_freq) < self.opt.batchSize + + def needs_printing(self): + return (self.total_steps_so_far % self.opt.print_freq) < self.opt.batchSize + + def needs_displaying(self): + return (self.total_steps_so_far % self.opt.display_freq) < self.opt.batchSize diff --git a/util/util.py b/util/util.py new file mode 100644 index 0000000000000000000000000000000000000000..fcac87161378b18ae5e76c5db61cd9c4930880db --- /dev/null +++ b/util/util.py @@ -0,0 +1,295 @@ +import re +import importlib +import torch +from argparse import Namespace +import numpy as np +from PIL import Image +import os +import argparse +import dill as pickle +import util.coco + + +def save_obj(obj, name): + with open(name, 'wb') as f: + pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL) + + +def load_obj(name): + with open(name, 'rb') as f: + return pickle.load(f) + + +# returns a configuration for creating a generator +# |default_opt| should be the opt of the current experiment +# |**kwargs|: if any configuration should be overriden, it can be specified here +def copyconf(default_opt, **kwargs): + conf = argparse.Namespace(**vars(default_opt)) + for key in kwargs: + print(key, kwargs[key]) + setattr(conf, key, kwargs[key]) + return conf + + +def tile_images(imgs, picturesPerRow=4): + """ Code borrowed from + https://stackoverflow.com/questions/26521365/cleanly-tile-numpy-array-of-images-stored-in-a-flattened-1d-format/26521997 + """ + + # Padding + if imgs.shape[0] % picturesPerRow == 0: + rowPadding = 0 + else: + rowPadding = picturesPerRow - imgs.shape[0] % picturesPerRow + if rowPadding > 0: + imgs = np.concatenate([imgs, np.zeros((rowPadding, *imgs.shape[1:]), dtype=imgs.dtype)], axis=0) + + # Tiling Loop (The conditionals are not necessary anymore) + tiled = [] + for i in range(0, imgs.shape[0], picturesPerRow): + tiled.append(np.concatenate([imgs[j] for j in range(i, i + picturesPerRow)], axis=1)) + + tiled = np.concatenate(tiled, axis=0) + return tiled + + +# Converts a Tensor into a Numpy array +# |imtype|: the desired type of the converted numpy array +def tensor2im(image_tensor, imtype=np.uint8, normalize=True, tile=False): + if isinstance(image_tensor, list): + image_numpy = [] + for i in range(len(image_tensor)): + image_numpy.append(tensor2im(image_tensor[i], imtype, normalize)) + return image_numpy + + if image_tensor.dim() == 4: + # transform each image in the batch + images_np = [] + for b in range(image_tensor.size(0)): + one_image = image_tensor[b] + one_image_np = tensor2im(one_image) + images_np.append(one_image_np.reshape(1, *one_image_np.shape)) + images_np = np.concatenate(images_np, axis=0) + if tile: + images_tiled = tile_images(images_np) + return images_tiled + else: + return images_np + + if image_tensor.dim() == 2: + image_tensor = image_tensor.unsqueeze(0) + image_numpy = image_tensor.detach().cpu().float().numpy() + if normalize: + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 + else: + image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0 + image_numpy = np.clip(image_numpy, 0, 255) + if image_numpy.shape[2] == 1: + image_numpy = image_numpy[:, :, 0] + return image_numpy.astype(imtype) + + +# Converts a one-hot tensor into a colorful label map +def tensor2label(label_tensor, n_label, imtype=np.uint8, tile=False): + if label_tensor.dim() == 4: + # transform each image in the batch + images_np = [] + for b in range(label_tensor.size(0)): + one_image = label_tensor[b] + one_image_np = tensor2label(one_image, n_label, imtype) + images_np.append(one_image_np.reshape(1, *one_image_np.shape)) + images_np = np.concatenate(images_np, axis=0) + if tile: + images_tiled = tile_images(images_np) + return images_tiled + else: + images_np = images_np[0] + return images_np + + if label_tensor.dim() == 1: + return np.zeros((64, 64, 3), dtype=np.uint8) + if n_label == 0: + return tensor2im(label_tensor, imtype) + label_tensor = label_tensor.cpu().float() + if label_tensor.size()[0] > 1: + label_tensor = label_tensor.max(0, keepdim=True)[1] + label_tensor = Colorize(n_label)(label_tensor) + label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0)) + result = label_numpy.astype(imtype) + return result + + +def save_image(image_numpy, image_path, create_dir=False): + if create_dir: + os.makedirs(os.path.dirname(image_path), exist_ok=True) + if len(image_numpy.shape) == 2: + image_numpy = np.expand_dims(image_numpy, axis=2) + if image_numpy.shape[2] == 1: + image_numpy = np.repeat(image_numpy, 3, 2) + image_pil = Image.fromarray(image_numpy) + + # save to png + image_pil.save(image_path.replace('.jpg', '.png')) + + +def mkdirs(paths): + if isinstance(paths, list) and not isinstance(paths, str): + for path in paths: + mkdir(path) + else: + mkdir(paths) + + +def mkdir(path): + if not os.path.exists(path): + os.makedirs(path) + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split('(\d+)', text)] + + +def natural_sort(items): + items.sort(key=natural_keys) + + +def str2bool(v): + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + + +def find_class_in_module(target_cls_name, module): + target_cls_name = target_cls_name.replace('_', '').lower() + clslib = importlib.import_module(module) + cls = None + for name, clsobj in clslib.__dict__.items(): + if name.lower() == target_cls_name: + cls = clsobj + + if cls is None: + print("In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module, target_cls_name)) + exit(0) + + return cls + + +def save_network(net, label, epoch, opt): + save_filename = '%s_net_%s.pth' % (epoch, label) + save_path = os.path.join(opt.checkpoints_dir, opt.name, save_filename) + torch.save(net.cpu().state_dict(), save_path) + if len(opt.gpu_ids) and torch.cuda.is_available(): + net.cuda() + + +def load_network(net, label, epoch, opt, strict=True): # Added strict parameter + save_filename = '%s_net_%s.pth' % (epoch, label) + save_dir = os.path.join(opt.checkpoints_dir, opt.name) + save_path = os.path.join(save_dir, save_filename) + + # Determine map_location + if len(opt.gpu_ids) > 0 and torch.cuda.is_available(): + # Sử dụng device string trực tiếp + map_location = f'cuda:{opt.gpu_ids[0]}' + else: + map_location = 'cpu' + + if not os.path.exists(save_path): + print(f"Warning: Checkpoint file not found at {save_path}. Network {label} will not be loaded.") + return net + + print(f"Loading network {label} from {save_path} with map_location='{map_location}'") # Thêm log + weights = torch.load(save_path, map_location=map_location) # Truyền device string + + try: + + model_to_load = net.module if isinstance(net, torch.nn.DataParallel) else net + model_to_load.load_state_dict(weights, strict=strict) + print(f"Successfully loaded network {label} (strict={strict})") + except RuntimeError as e: + print(f"Error loading state_dict for network {label} (strict={strict}): {e}") + if strict: + print( + f"Consider using --continue_train with a compatible checkpoint or ensure the model architecture matches the checkpoint. Or, if this is for testing an older model, the loading might need to be non-strict or keys adjusted.") + + return net + + +############################################################################### +# Code from +# https://github.com/ycszen/pytorch-seg/blob/master/transform.py +# Modified so it complies with the Citscape label map colors +############################################################################### +def uint82bin(n, count=8): + """returns the binary of integer n, count refers to amount of bits""" + return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)]) + + +def labelcolormap(N): + if N == 35: # cityscape + cmap = np.array([(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (111, 74, 0), (81, 0, 81), + (128, 64, 128), (244, 35, 232), (250, 170, 160), (230, 150, 140), (70, 70, 70), (102, 102, 156), (190, 153, 153), + (180, 165, 180), (150, 100, 100), (150, 120, 90), (153, 153, 153), (153, 153, 153), (250, 170, 30), (220, 220, 0), + (107, 142, 35), (152, 251, 152), (70, 130, 180), (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), + (0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100), (0, 0, 230), (119, 11, 32), (0, 0, 142)], + dtype=np.uint8) + else: + cmap = np.zeros((N, 3), dtype=np.uint8) + for i in range(N): + r, g, b = 0, 0, 0 + id = i + 1 # let's give 0 a color + for j in range(7): + str_id = uint82bin(id) + r = r ^ (np.uint8(str_id[-1]) << (7 - j)) + g = g ^ (np.uint8(str_id[-2]) << (7 - j)) + b = b ^ (np.uint8(str_id[-3]) << (7 - j)) + id = id >> 3 + cmap[i, 0] = r + cmap[i, 1] = g + cmap[i, 2] = b + + if N == 182: # COCO + important_colors = { + 'sea': (54, 62, 167), + 'sky-other': (95, 219, 255), + 'tree': (140, 104, 47), + 'clouds': (170, 170, 170), + 'grass': (29, 195, 49) + } + for i in range(N): + name = util.coco.id2label(i) + if name in important_colors: + color = important_colors[name] + cmap[i] = np.array(list(color)) + + return cmap + + +class Colorize(object): + def __init__(self, n=35): + self.cmap = labelcolormap(n) + self.cmap = torch.from_numpy(self.cmap[:n]) + + def __call__(self, gray_image): + size = gray_image.size() + color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0) + + for label in range(0, len(self.cmap)): + mask = (label == gray_image[0]).cpu() + color_image[0][mask] = self.cmap[label][0] + color_image[1][mask] = self.cmap[label][1] + color_image[2][mask] = self.cmap[label][2] + + return color_image diff --git a/util/visualizer.py b/util/visualizer.py new file mode 100644 index 0000000000000000000000000000000000000000..fa212dd311af4df44bcb064c9f1fe8f8a800fe55 --- /dev/null +++ b/util/visualizer.py @@ -0,0 +1,227 @@ +import os +import ntpath +import time +from . import util +from . import html +import numpy as np +from PIL import Image as PILImage +import torch +from collections import OrderedDict + +try: + from StringIO import StringIO +except ImportError: + from io import BytesIO + + +class Visualizer(): + def __init__(self, opt): + self.opt = opt + self.tf_log = opt.isTrain and opt.tf_log + self.use_html = opt.isTrain and not opt.no_html + self.win_size = opt.display_winsize + self.name = opt.name + if self.tf_log: + import tensorflow as tf + self.tf = tf + self.log_dir = os.path.join(opt.checkpoints_dir, opt.name, 'logs') + self.writer = tf.summary.FileWriter(self.log_dir) + + if self.use_html: + self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web') + self.img_dir = os.path.join(self.web_dir, 'images') + print('create web directory %s...' % self.web_dir) + util.mkdirs([self.web_dir, self.img_dir]) + + if opt.isTrain: + self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt') + with open(self.log_name, "a") as log_file: + now = time.strftime("%c") + log_file.write('================ Training Loss (%s) ================\n' % now) + + def convert_map_to_numpy(self, data_map): + if data_map is None or not isinstance(data_map, torch.Tensor): + return None + if data_map.dim() == 4: + data_map = data_map[0] + if data_map.size(0) > 1: + data_map = data_map[0, :, :].unsqueeze(0) + map_numpy = data_map.cpu().float().numpy() + min_val, max_val = np.min(map_numpy), np.max(map_numpy) + if max_val - min_val > 1e-6: + map_numpy = (map_numpy - min_val) / (max_val - min_val) + else: + map_numpy = np.zeros_like(map_numpy) + map_numpy = (map_numpy * 255.0).astype(np.uint8) + if map_numpy.shape[0] == 1: + map_numpy = np.transpose(map_numpy, (1, 2, 0)) + map_numpy = np.repeat(map_numpy, 3, axis=2) + else: + map_numpy = np.stack((map_numpy,) * 3, axis=-1) + return map_numpy + + def display_current_results(self, visuals, epoch, step): + visuals_np = OrderedDict() + for label, image in visuals.items(): + if image is None: + continue + if 'light_map' in label: + image_numpy = self.convert_map_to_numpy(image) + elif 'input_label' in label: + image_numpy = util.tensor2label(image, self.opt.label_nc, tile=False) + else: + image_numpy = util.tensor2im(image, tile=False) + + if image_numpy.ndim == 4: + image_numpy = image_numpy[0] + + visuals_np[label] = image_numpy + + if self.tf_log: + img_summaries = [] + for label, image_numpy in visuals_np.items(): + if image_numpy is None: continue + try: + s = BytesIO() + pil_img = PILImage.fromarray(image_numpy) + pil_img.save(s, format="jpeg") + img_sum = self.tf.Summary.Image(encoded_image_string=s.getvalue(), height=image_numpy.shape[0], + width=image_numpy.shape[1]) + img_summaries.append(self.tf.Summary.Value(tag=f'epoch_{epoch}/{label}', image=img_sum)) + except Exception as e: + print(f"Could not write image {label} to TF logs: {e}") + + if img_summaries: + summary = self.tf.Summary(value=img_summaries) + self.writer.add_summary(summary, step) + + if self.use_html: + webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=0) + webpage.add_header('Epoch [%d] Iteration [%d]' % (epoch, step)) + + visuals_for_html = [] + labels_for_html = [] + + standard_height = self.opt.crop_size + + for label, image_numpy in visuals_np.items(): + if image_numpy is None: continue + + pil_img = PILImage.fromarray(image_numpy) + + if pil_img.height != standard_height: + aspect_ratio = pil_img.width / pil_img.height + new_width = int(standard_height * aspect_ratio) + pil_img = pil_img.resize((new_width, standard_height), PILImage.LANCZOS) + + visuals_for_html.append(np.array(pil_img)) + labels_for_html.append(label) + + if not visuals_for_html: + return + + try: + concatenated_image = np.concatenate(visuals_for_html, axis=1) + + image_name = 'epoch%.3d_iter%.7d_combined.png' % (epoch, step) + save_path = os.path.join(self.img_dir, image_name) + util.save_image(concatenated_image, save_path) + + webpage.add_images([image_name], [' | '.join(labels_for_html)], [image_name], + width=self.win_size * len(visuals_for_html)) + webpage.save() + + except ValueError as e: + print(f"Error during HTML image concatenation for step {step}: {e}") + print("Skipping HTML log for this step. Image shapes might be incompatible even after resizing.") + + def plot_current_errors(self, errors, step): + if self.tf_log: + for tag, value in errors.items(): + if isinstance(value, torch.Tensor): + value_to_log = value.mean().float().item() + elif isinstance(value, (float, int)): + value_to_log = float(value) + else: + continue + summary = self.tf.Summary(value=[self.tf.Summary.Value(tag=tag, simple_value=value_to_log)]) + self.writer.add_summary(summary, step) + + def print_current_errors(self, epoch, i, errors, t): + message = '(epoch: %d, iters: %d, time: %.3f) ' % (epoch, i, t) + for k, v_orig in errors.items(): + v_to_print = v_orig + if isinstance(v_orig, torch.Tensor): + if v_orig.numel() > 0: + v_to_print = v_orig.mean().item() + else: + v_to_print = 0.0 + elif not isinstance(v_orig, (float, int)): + continue + + message += '%s: %.3f ' % (k, float(v_to_print)) + + print(message) + with open(self.log_name, "a") as log_file: + log_file.write('%s\n' % message) + + def save_images(self, webpage, visuals, image_path_list, alpha=1.0): + visuals_np = OrderedDict() + for label, image in visuals.items(): + if 'light_map' in label: + visuals_np[label] = self.convert_map_to_numpy(image) + else: + visuals_np[label] = util.tensor2im(image) + + base_image_dir = webpage.get_image_dir() + image_path_str = image_path_list[0] if isinstance(image_path_list, (list, tuple)) else image_path_list + short_path = ntpath.basename(image_path_str) + name_prefix = os.path.splitext(short_path)[0] + + current_alpha_float = alpha + if isinstance(current_alpha_float, torch.Tensor): + current_alpha_float = current_alpha_float.mean().item() + elif not isinstance(current_alpha_float, (float, int)): + try: + current_alpha_float = float(current_alpha_float) + except ValueError: + current_alpha_float = 1.0 + + alpha_folder_name = "alpha_{:.3f}".format(current_alpha_float).replace('.', '_') + specific_alpha_image_dir = os.path.join(base_image_dir, alpha_folder_name) + util.mkdirs(specific_alpha_image_dir) + + image_name_final = '%s.png' % (name_prefix) + save_path = os.path.join(specific_alpha_image_dir, image_name_final) + + images_to_concatenate = [] + for label, image_numpy in visuals_np.items(): + img_to_add = image_numpy + if image_numpy.ndim == 4 and image_numpy.shape[0] == 1: + img_to_add = image_numpy.squeeze(0) + elif image_numpy.ndim != 2 and image_numpy.ndim != 3: + continue + + if img_to_add.ndim == 2: + img_to_add = np.stack((img_to_add,) * 3, axis=-1) + if img_to_add.ndim == 3 and img_to_add.shape[2] == 1: + img_to_add = np.concatenate([img_to_add] * 3, axis=2) + + if img_to_add.shape[2] == 3: + images_to_concatenate.append(img_to_add) + + if not images_to_concatenate: + return + + try: + image_concatenated_horizontally = np.concatenate(images_to_concatenate, axis=1) + util.save_image(image_concatenated_horizontally, save_path, create_dir=True) + except ValueError as e: + print(f"Error concatenating images for {save_path}: {e}") + print("Concatenated images list content (shapes):") + for idx, vis_np_item in enumerate(images_to_concatenate): + print(f" Visual {idx}: shape {vis_np_item.shape if hasattr(vis_np_item, 'shape') else 'N/A'}") + + relative_image_path_for_html = os.path.join(alpha_folder_name, image_name_final) + webpage.add_images([relative_image_path_for_html], [f"{name_prefix}_alpha_{current_alpha_float:.3f}"], + [relative_image_path_for_html], width=self.win_size * len(images_to_concatenate)) \ No newline at end of file