Repository: clovaai/dmfont Branch: master Commit: 488a8e823920 Files: 42 Total size: 472.7 KB Directory structure: gitextract_5udlx8oy/ ├── .gitignore ├── LICENSE ├── NOTICE ├── README.md ├── cfgs/ │ ├── kor.yaml │ └── thai.yaml ├── criterions.py ├── datasets/ │ ├── __init__.py │ ├── data_utils.py │ ├── fcdata.py │ ├── kor_dataset.py │ ├── kor_decompose.py │ ├── nonpaired_dataset.py │ ├── samplers.py │ ├── thai_dataset.py │ └── thai_decompose.py ├── evaluator.py ├── inference.py ├── logger.py ├── meta/ │ ├── kor-unrefined.json │ ├── kor_split.json │ └── thai_split.json ├── models/ │ ├── __init__.py │ ├── aux_classifier.py │ ├── comp_encoder.py │ ├── decoder.py │ ├── discriminator.py │ ├── ma_core.py │ ├── memory.py │ └── modules/ │ ├── __init__.py │ ├── blocks.py │ ├── modules.py │ └── self_attention.py ├── requirements.txt ├── scripts/ │ └── prepare_dataset.py ├── ssim.py ├── train.py ├── trainer.py └── utils/ ├── __init__.py ├── utils.py ├── visualize.py └── writer.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *.pkl .ipynb_checkpoints *.ipynb *.png backups/ runs/ __pycache__/ *.swp checkpoints/ logs/ runs-back/ *.log *.sh tags* test* checkpoints data production runs fonts test_cv/ test_us/ ================================================ FILE: LICENSE ================================================ Copyright (c) 2020-present NAVER Corp. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Creative Commons may be contacted at [creativecommons.org](http://creativecommons.org/). ===== jorge-pessoa/pytorch-msssim https://github.com/jorge-pessoa/pytorch-msssim Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ===== ================================================ FILE: README.md ================================================ ## Few-shot Compositional Font Generation with Dual Memory (ECCV'20) **NOTICE: We release the unified few-shot font generation repository ([clovaai/fewshot-font-generation](https://github.com/clovaai/fewshot-font-generation)). If you are interested in using our implementation, please visit the unified repository.**

Official PyTorch implementation of DM-Font | [Paper](https://arxiv.org/abs/2005.10510) | [Video on ECCV 2020](https://youtu.be/VMrMJf21XEA) Junbum Cha, Sanghyuk Chun, Gayoung Lee, Bado Lee, Seonghyeon Kim, Hwalsuk Lee. Clova AI Research, NAVER Corp. In ECCV 2020. ### Abstract Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Despite the remarkable success of existing font generation methods, they have significant drawbacks; they require a large number of reference images to generate a new font set, or they fail to capture detailed styles with a few samples. In this paper, we focus on compositional scripts, a widely used letter system in the world, where each glyph can be decomposed by several components. By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples. We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization compared to the state-of-the-art generation methods in quantitatively and qualitatively. ## Other related repositories You can find more related projects on the few-shot font generation at the following links: - [clovaai/dmfont](https://github.com/clovaai/dmfont) (ECCV'20) | [paper](https://arxiv.org/abs/2005.10510) - [clovaai/lffont](https://github.com/clovaai/lffont) (AAAI'21) | [paper](https://arxiv.org/abs/2009.11042) - [clovaai/mxfont](https://github.com/clovaai/mxfont) (ICCV'21) | [paper](https://arxiv.org/abs/2104.00887) - [clovaai/fewshot-font-generation](https://github.com/clovaai/fewshot-font-generation) The unified few-shot font generation repository ## Installation Install dependencies: ``` pip install -r requirements.txt ``` Note that using different version of required packages can effects the results, especially PyTorch. The implementations are tested on Python 3.6. ## Dataset preparation ### Data sources Korean-handwriting and Thai-printing datasets were built from [UhBee fonts](http://uhbeefont.com/) and [Thai font collection](https://github.com/jeffmcneill/thai-font-collection), respectively. To ensure the style diversity of the dataset, one font was selected for each font family in our experiments. ### Dumping dataset The `scripts/prepare_dataset.py` script renders glyphs from the collected ttf fonts and dumps them into hdf5 files. For the Thai-printing dataset, we rectify the rendering errors using [raqm](https://github.com/HOST-Oman/libraqm). It should be installed before making dataset. - Korean-handwriting ``` python -m scripts.prepare_dataset kor $FONTSDIR meta/kor_split.json $DUMPDIR ``` - Thai-printing ``` python -m scripts.prepare_dataset thai $FONTSDIR meta/thai_split.json $DUMPDIR ``` ## How to run ### Pretrained models For convenience, the minimal size checkpoints are provided by excluding training variables, e.g., momentums in optimizer, discriminator, and non-EMA generator. - [Checkpoint trained on the Korean-handwriting dataset](https://github.com/clovaai/dmfont/releases/download/v1.0.0/korean-handwriting.pth) - [Checkpoint trained on the Thai-printing dataset](https://github.com/clovaai/dmfont/releases/download/v1.0.0/thai-printing.pth) ### Training - Korean-handwriting ``` python train.py $NAME cfgs/kor.yaml ``` - Thai-printing ``` python train.py $NAME cfgs/kor.yaml cfgs/thai.yaml ``` ### Generation & Pixel-level evaluation - Korean-handwriting ``` python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml --mode cv-save ``` - Thai-printing ``` python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml cfgs/thai.yaml --mode cv-save ``` - Korean-unrefined ``` python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml --mode user-study-save ``` ## Citation ``` @inproceedings{cha2020dmfont, title={Few-shot Compositional Font Generation with Dual Memory}, author={Cha, Junbum and Chun, Sanghyuk and Lee, Gayoung and Lee, Bado and Kim, Seonghyeon and Lee, Hwalsuk}, year={2020}, booktitle={European Conference on Computer Vision (ECCV)}, } ``` ## License This project is distributed under [MIT license](LICENSE), except modules.py which is adopted from https://github.com/NVlabs/FUNIT. ``` Copyright (c) 2020-present NAVER Corp. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ``` ================================================ FILE: cfgs/kor.yaml ================================================ # train data_dir: data/fonts-hdf5 data_meta: meta/kor_split.json language: kor batch_size: 12 max_iter: 200000 seed: 2 g_lr: 2e-4 d_lr: 8e-4 n_workers: 2 adam_betas: [0.0, 0.9] init: kaiming # dataset # n_sample is the range of #targets per style chunk (T style chars) dset_args: n_sample_min: 1 n_sample_max: 4 # model C: 32 g_args: comp_enc: norm: none activ: relu pad_type: zero sa: C_qk_ratio: 0.5 n_heads: 2 area: False ffn_mult: 2 dec: norm: IN activ: relu pad_type: zero d_args: w_norm: spectral use_rx: false activ: relu gap_activ: relu pad_type: zero ac_args: norm: IN activ: relu pad_type: zero conv_dropout: 0.3 clf_dropout: 0.2 # losses pixel_w: 0.1 gan_w: 1.0 fm_w: 1.0 ac_w: 0.1 # etc save: all-last print_freq: 1000 val_freq: 10000 save_freq: 50000 tb_freq: 10 ================================================ FILE: cfgs/thai.yaml ================================================ data_dir: data/thai-hdf5-fix2 data_meta: meta/thai_split.json language: thai g_lr: 5e-5 d_lr: 1e-4 max_iter: 250000 batch_size: 10 ================================================ FILE: criterions.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import torch.nn.functional as F def hinge_d_loss(real, fake): return F.relu(1. - real).mean() + F.relu(1. + fake).mean() def hinge_g_loss(real, fake): return -fake.mean() ================================================ FILE: datasets/__init__.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from . import kor_dataset from . import thai_dataset from .fcdata import HDF5Data from .data_utils import ( cyclize, sample, uniform_sample, get_fonts_unionchars, get_union_chars, get_intersection_chars ) def get_ma_dataset(*args, language=None, **kwargs): if language == 'kor': return kor_dataset.get_ma_dataset(*args, **kwargs) elif language == 'thai': return thai_dataset.get_ma_dataset(*args, **kwargs) else: raise ValueError(language) def get_ma_val_dataset(*args, language=None, **kwargs): if language == 'kor': return kor_dataset.get_ma_val_dataset(*args, **kwargs) elif language == 'thai': return thai_dataset.get_ma_val_dataset(*args, **kwargs) else: raise ValueError(language) ================================================ FILE: datasets/data_utils.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import numpy as np def cyclize(loader): """ Cyclize loader """ while True: for x in loader: yield x def rev_dict(l): """ Reverse dict or list """ return {k: i for i, k in enumerate(l)} def uniform_indices(end, n_sample, st=None): """ Sample from [0, end) with (almost) equidistant interval """ if end <= 0: return np.empty(0, dtype=np.int) # NOTE with endpoint=False, np.linspace does not sample the `end` value indices = np.linspace(0, end, num=n_sample, dtype=np.int, endpoint=False) if st is None and end: st = (end-1 - indices[-1]) // 2 return indices + st def sample(population, n_sample, exception=None): """ sampling without replacement N elements from set with exception Params: population: [1d] list or set or np.ndarray Return: np.ndarray """ if exception is not None: population = set(population) - set(exception) if not isinstance(population, np.ndarray): population = np.asarray(list(population)) ids = np.random.choice(len(population), size=n_sample, replace=False) return population[ids] def uniform_sample(population, n_sample, st=None): assert not isinstance(population, set), "population should have order" N = len(population) indices = uniform_indices(N, n_sample, st) if isinstance(population, np.ndarray): return population[indices] elif isinstance(population, list): return [population[idx] for idx in indices] elif isinstance(population, str): return ''.join([population[idx] for idx in indices]) else: raise TypeError(type(population)) def get_fonts(avails): return list(avails.keys()) def get_union_chars(avails): return sorted(set.union(*map(set, avails.values()))) def get_fonts_unionchars(avails): return get_fonts(avails), get_union_chars(avails) def get_intersection_chars(avails): return sorted(set.intersection(*map(set, avails.values()))) ================================================ FILE: datasets/fcdata.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import h5py as h5 from torchvision import transforms from . import thai_decompose as thai class FCData: """ FontChar data Data module provides image access by font_name and char in get method """ def get(self, font_name, char): raise NotImplementedError() class HDF5Data(FCData): def __init__(self, hdf5_paths, transform=None, language='kor'): """ cmap = { font_name: { char: idx, ... }, ... } """ self.paths = hdf5_paths self.transform = transform or transforms.ToTensor() self.language = language self.fn2path = {} self.cmap = {} self.n_items = 0 # chars: union of all chars for path in hdf5_paths: with h5.File(path, 'r') as f: font_name = f['dataset'].attrs['font_name'] self.fn2path[font_name] = path # [:] for batch read char2idx = self.make_char2idx(f['dataset']['chars'][:]) self.cmap[font_name] = char2idx self.n_items += len(char2idx) # indexing self.fonts = list(self.cmap.keys()) def make_char2idx(self, chars): """ Generate char2idx map Args: chars [N] or [N, 4] for kor: [N] for thai: [N, 4] """ if self.language == 'kor': # chars: 1d array [N] char2idx = { chr(ch): i for i, ch in enumerate(chars) } elif self.language == 'thai': # chars: 2d array [N, 4] char2idx = { thai.compose(*ch): i for i, ch in enumerate(chars) } else: raise ValueError(self.language) return char2idx def is_avail(self, font_name, char): if font_name not in self.cmap: return False return char in self.cmap[font_name] def get(self, font_name, char, default=None, transform=None): """ Args: default: if not available (font_name, char) is given, return default. transform: image transform. If not given, use self.transform. """ if default is not None: if not self.is_avail(font_name, char): return default path = self.fn2path[font_name] with h5.File(path, 'r') as f: cidx = self.cmap[font_name][char] image = f['dataset']['images'][cidx] transform = transform or self.transform return transform(image) def get_from_reffont(self, char): """ get character image from reference font """ if self.language == 'kor': font_name = "D2Coding-Ver1.3.2-20180524.ttf" elif self.language == 'thai': font_name = "NotoSansThai-Regular.ttf" return self.get(font_name, char) def get_avail_chars(self, font_name): return list(self.cmap[font_name].keys()) ================================================ FILE: datasets/kor_dataset.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import random from itertools import product import numpy as np import torch from torch.utils.data import Dataset from .kor_decompose import decompose, compose, COMPLETE_SET from .samplers import StyleSampler from .data_utils import rev_dict, sample, get_fonts, get_union_chars class MAStyleFirstDataset(Dataset): """ Sampling style chars first and then generating target chars by combination of style components """ def __init__(self, data, style_sampler, n_sample_min=1, n_sample_max=999, f_mult=800, transform=None, content_font=None): """ Args: style_sampler: style sampler with data source. avail fonts and avail chars are determined by `style_sampler.avails`. n_sample_min: minimum # of target chars per 3 style chars. n_sample_max: maximum # of target chars per 3 style chars. f_mult: #fonts multiplier for full-batch transform: image transform. If not given, use data.transform as default. """ self.data = data self.style_sampler = style_sampler self.avails = style_sampler.avails self.R = style_sampler.R self.n_sample_min = n_sample_min self.n_sample_max = n_sample_max self.f_mult = f_mult self.transform = transform self.content_font = content_font self.fonts = get_fonts(self.avails) self.chars = get_union_chars(self.avails) self.n_fonts = len(self.fonts) self.n_chars = len(self.chars) self.font2idx = rev_dict(self.fonts) self.char2idx = rev_dict(self.chars) self.n_avails = sum(len(chars) for chars in self.avails.values()) def get_avail_chars(self, font_name, style_chars): avail_chars = set(self.avails[font_name]) avail_chars = avail_chars - set(style_chars) return avail_chars def get_component_combinations(self, components, avail_chars, style_comp_ids=None): """ Generate all component combinations within avail_chars Args: style_comp_ids: style component ids for excluding duplication (if needed) """ trg_comp_ids = [] for cho, jung, jong in product(*components): char = compose(cho, jung, jong) if char not in avail_chars: continue ids = np.asarray([cho, jung, jong]) # exclude duplicated components: # exclude target chars which has duplicate source style char # e.g.) src ["성", "공", "해"] => trg ["송"] (duplicated) if ((style_comp_ids == ids).sum(axis=1) >= 2).any(): continue trg_comp_ids.append(ids) return trg_comp_ids def check_and_sample(self, trg_comp_ids): n_sample = len(trg_comp_ids) if n_sample > self.n_sample_max: trg_comp_ids = sample(trg_comp_ids, self.n_sample_max) elif n_sample < self.n_sample_min: return None return trg_comp_ids def __getitem__(self, index): font_idx = index % self.n_fonts font_name = self.fonts[font_idx] while True: #################################################### # 1. sample styles #################################################### style_imgs, style_chars = self.style_sampler.get(font_name, ret_values=True) style_comp_ids = [decompose(char) for char in style_chars] chos, jungs, jongs = list(map(set, zip(*style_comp_ids))) # fullcomb if not (len(chos) == len(jungs) == len(jongs) == self.R): continue style_comp_ids = np.asarray(style_comp_ids) #################################################### # 2. sample targets from style components #################################################### avail_chars = self.get_avail_chars(font_name, style_chars) trg_comp_ids = self.get_component_combinations( (chos, jungs, jongs), avail_chars, style_comp_ids ) trg_comp_ids = np.asarray(trg_comp_ids) trg_comp_ids = self.check_and_sample(trg_comp_ids) if trg_comp_ids is None: continue #################################################### # 3. setup chars, font_ids, char_ids and images #################################################### trg_chars = [compose(*comp_id) for comp_id in trg_comp_ids] trg_imgs = torch.cat([ self.data.get(font_name, char, transform=self.transform) for char in trg_chars ]) style_char_ids = [self.char2idx[ch] for ch in style_chars] trg_char_ids = [self.char2idx[ch] for ch in trg_chars] n_styles = len(style_chars) n_trgs = len(trg_chars) font_idx = torch.as_tensor(font_idx) ret = ( font_idx.repeat(n_styles), torch.as_tensor(style_char_ids), torch.as_tensor(style_comp_ids), style_imgs, font_idx.repeat(n_trgs), torch.as_tensor(trg_char_ids), torch.as_tensor(trg_comp_ids), trg_imgs ) if self.content_font: content_imgs = torch.cat([ self.data.get(self.content_font, char, transform=self.transform) for char in trg_chars ]) ret += (content_imgs,) return ret def __len__(self): return self.n_fonts * self.f_mult @staticmethod def collate_fn(batch): (style_ids, style_char_ids, style_comp_ids, style_imgs, trg_ids, trg_char_ids, trg_comp_ids, trg_imgs, *left) = zip(*batch) ret = ( torch.cat(style_ids), torch.cat(style_char_ids), torch.cat(style_comp_ids), torch.cat(style_imgs).unsqueeze_(1), torch.cat(trg_ids), torch.cat(trg_char_ids), torch.cat(trg_comp_ids), torch.cat(trg_imgs).unsqueeze_(1) ) if left: assert len(left) == 1 content_imgs = left[0] ret += ( torch.cat(content_imgs).unsqueeze_(1), ) return ret class MATargetFirstDataset(Dataset): """ MAStyleFirstDatset samples source style characters first and then determines target characters. In contrast, MATargetFirstDataset samples target characters first and then determines source style characters. """ def __init__(self, target_fc, style_avails, style_data, n_max_match=3, transform=None, ret_targets=False, first_shuffle=False, content_font=None): """ TargetFirstDataset can use out-of-avails target chars, so long as its components could be represented in avail chars. Args: target_fc[font_name] = target_chars style_avails[font_name] = avail_style_chars style_data: style_data getter n_max_match: maximum-allowed matches between style char and target char. n_max_match=3 indicates that style_char == target_char is possible. transform: image transform. If not given, use data.transform as default. ret_targets: return target images also first_shuffle: shuffle item list """ self.target_fc = target_fc self.style_avails = style_avails self.style_avail_comps_list = { fname: [decompose(char) for char in char_list] for fname, char_list in style_avails.items() } self.n_max_match = n_max_match self.transform = transform self.ret_targets = ret_targets self.content_font = content_font self.style_data = style_data self.fcs = [ (font_name, char) for font_name, char_list in target_fc.items() for char in char_list ] if first_shuffle: np.random.shuffle(self.fcs) self.fonts = get_fonts(self.target_fc) self.chars = get_union_chars(self.target_fc) self.font2idx = {font_name: i for i, font_name in enumerate(self.target_fc.keys())} def sample_style_char(self, font_name, trg_char): """ sample style char from target char within avail style chars """ def is_allowed_matches(arr1, arr2): """ check # of matched ids return count(arr1 == arr2) <= self.n_max_match """ if self.n_max_match >= 3: return True n_matched = sum(v1 == v2 for v1, v2 in zip(arr1, arr2)) return n_matched <= self.n_max_match trg_comp_ids = decompose(trg_char) style_chars = [] style_comps_list = [] for i, _ in enumerate(trg_comp_ids): avail_comps_list = list( filter( lambda comp_ids: comp_ids[i] == trg_comp_ids[i] \ and is_allowed_matches(comp_ids, trg_comp_ids), self.style_avail_comps_list[font_name] ) ) style_comp_ids = random.choice(avail_comps_list) style_char = compose(*style_comp_ids) style_chars.append(style_char) style_comps_list.append(style_comp_ids) return style_chars, style_comps_list def __getitem__(self, index): font_name, trg_char = self.fcs[index] font_idx = self.font2idx[font_name] style_chars, style_comp_ids = self.sample_style_char(font_name, trg_char) style_imgs = torch.cat([ self.style_data.get(font_name, char, transform=self.transform) for char in style_chars ]) trg_comp_ids = [decompose(trg_char)] n_styles = len(style_chars) font_idx = torch.as_tensor(font_idx) style_ids = font_idx.repeat(n_styles) trg_ids = font_idx.repeat(1) content_img = self.style_data.get(self.content_font, trg_char, transform=self.transform) ret = ( style_ids, torch.as_tensor(style_comp_ids), style_imgs, trg_ids, torch.as_tensor(trg_comp_ids), content_img ) if self.ret_targets: trg_img = self.style_data.get(font_name, trg_char, transform=self.transform) ret += (trg_img, ) return ret def __len__(self): return len(self.fcs) @staticmethod def collate_fn(batch): style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids, content_imgs, *left = \ list(zip(*batch)) ret = ( torch.cat(style_ids), torch.cat(style_comp_ids), torch.cat(style_imgs).unsqueeze_(1), torch.cat(trg_ids), torch.cat(trg_comp_ids), torch.cat(content_imgs).unsqueeze_(1) ) if left: assert len(left) == 1 trg_imgs = left[0] ret += (torch.cat(trg_imgs).unsqueeze_(1), ) return ret def filter_complete_chars(chars): return sorted(set(chars) & COMPLETE_SET) def get_ma_dataset(hdf5_data, avail_fonts, avail_chars=None, transform=None, **kwargs): if avail_chars: avail_chars = set(avail_chars) R_style = 3 avails = {} for fname in avail_fonts: chars = hdf5_data.get_avail_chars(fname) if avail_chars: chars = set(chars) & avail_chars avails[fname] = filter_complete_chars(chars) style_sampler = StyleSampler(R_style, avails, hdf5_data) dset = MAStyleFirstDataset(hdf5_data, style_sampler, transform=transform, **kwargs) return dset, MAStyleFirstDataset.collate_fn def get_ma_val_dataset(hdf5_data, fonts, chars, style_avails, n_max_match, transform, **kwargs): target_fc = {font_name: chars for font_name in fonts} dset = MATargetFirstDataset( target_fc, style_avails, hdf5_data, n_max_match, transform=transform, **kwargs ) return dset, MATargetFirstDataset.collate_fn ================================================ FILE: datasets/kor_decompose.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ COMPONENT_RANGE = (int('3131', 16), int('3163', 16)) # kr 자음/모음 COMPLETE_RANGE = (int('ac00', 16), int('d7a3', 16)) # kr all complete chars COMPLETE_SET = frozenset(chr(code) for code in range(COMPLETE_RANGE[0], COMPLETE_RANGE[1]+1)) COMPLETE_LIST = sorted(COMPLETE_SET) CHO_LIST = ['ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅉ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ'] JUNG_LIST = ['ㅏ', 'ㅐ', 'ㅑ', 'ㅒ', 'ㅓ', 'ㅔ', 'ㅕ', 'ㅖ', 'ㅗ', 'ㅘ', 'ㅙ', 'ㅚ', 'ㅛ', 'ㅜ', 'ㅝ', 'ㅞ', 'ㅟ', 'ㅠ', 'ㅡ', 'ㅢ', 'ㅣ'] JONG_LIST = [' ', 'ㄱ', 'ㄲ', 'ㄳ', 'ㄴ', 'ㄵ', 'ㄶ', 'ㄷ', 'ㄹ', 'ㄺ', 'ㄻ', 'ㄼ', 'ㄽ', 'ㄾ', 'ㄿ', 'ㅀ', 'ㅁ', 'ㅂ', 'ㅄ', 'ㅅ', 'ㅆ', 'ㅇ', 'ㅈ', 'ㅊ', 'ㅋ', 'ㅌ', 'ㅍ', 'ㅎ'] N_CHO, N_JUNG, N_JONG = len(CHO_LIST), len(JUNG_LIST), len(JONG_LIST) N_COMPONENTS = N_CHO + N_JUNG + N_JONG def compose(cho, jung, jong): """Compose ids to char""" char_id = cho * N_JONG * N_JUNG + jung * N_JONG + jong + COMPLETE_RANGE[0] return chr(char_id) def decompose(char): """Decompose char to ids""" char_code = ord(char) if COMPLETE_RANGE[0] <= char_code <= COMPLETE_RANGE[1]: char_code -= COMPLETE_RANGE[0] jong = char_code % N_JONG jung = (char_code // N_JONG) % N_JUNG cho = char_code // (N_JONG * N_JUNG) char_id = (cho, jung, jong) elif COMPONENT_RANGE[0] <= char_code <= COMPONENT_RANGE[1]: char_code -= COMPONENT_RANGE[0] raise ValueError('Component only ({})'.format(char)) else: raise ValueError('{} is Non kor'.format(char)) return char_id ================================================ FILE: datasets/nonpaired_dataset.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import torch from torch.utils.data import Dataset from . import kor_decompose as kor from . import thai_decompose as thai class EncodeDataset(Dataset): def __init__(self, font_name, chars, data, language, style_id=0, transform=None): self.fname = font_name self.data = data self.chars = chars self.style_id = style_id self.transform = transform self.language = language if language == 'kor': self.decompose = kor.decompose elif language == 'thai': self.decompose = thai.decompose_ids else: raise ValueError(language) def __getitem__(self, index): style_char = self.chars[index] style_comp_ids = self.decompose(style_char) style_img = self.data.get(self.fname, style_char, transform=self.transform) return ( self.style_id, torch.as_tensor(style_comp_ids), style_img ) def __len__(self): return len(self.chars) class DecodeDataset(Dataset): def __init__(self, chars, language, style_id=0): """ Args: chars: target characters language style_id: Use different style id for different reference style set """ self.chars = chars self.language = language self.style_id = style_id if language == 'kor': self.decompose = kor.decompose elif language == 'thai': raise NotImplementedError() else: raise ValueError(language) def __getitem__(self, index): char = self.chars[index] trg_comp_ids = self.decompose(char) return ( self.style_id, torch.as_tensor(trg_comp_ids) ) def __len__(self): return len(self.chars) ================================================ FILE: datasets/samplers.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import torch from .data_utils import sample, uniform_indices class Sampler: def __init__(self, R, avails, data): self.R = R self.avails = avails self.data = data def get_item(self, key, value): raise NotImplementedError() def get(self, key, ex_values=None, ret_values=False): """ Random sampling """ values = self.avails[key] values = sample(values, self.R, ex_values) images = torch.cat([self.get_item(key, value) for value in values]) if ret_values: return images, values return images def get_uniform(self, key, indices=None, st=None): values = self.avails[key] if indices is None: indices = uniform_indices(len(values), self.R, st) images, vals = [], [] for idx in indices: value = values[idx] img = self.get_item(key, value) vals.append(value) images.append(img) return torch.cat(images), vals class StyleSampler(Sampler): """ Return various content (char) but single style (font): which represent style """ def get_item(self, font_name, char): return self.data.get(font_name, char) class ContentSampler(Sampler): """ Return various style (font) but single content (char): which represent content """ def get_item(self, char, font_name): return self.data.get(font_name, char) ================================================ FILE: datasets/thai_dataset.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import random import numpy as np import torch from torch.utils.data import Dataset from . import thai_decompose as thai from .data_utils import rev_dict, get_fonts, get_union_chars def product4_no_dup(consonants, uppers, highests, lowers): for i, co in enumerate(consonants): for j, up in enumerate(uppers): if i == j: continue for k, hi in enumerate(highests): if k in (i, j): continue for l, lo in enumerate(lowers): if l in (i, j, k): continue yield (co, up, hi, lo) class MAStyleFirstDataset(Dataset): def __init__(self, data, fonts, chars, n_sample_min, n_sample_max, f_mult=800, transform=None, content_font=None): self.data = data self.n_sample_min = n_sample_min self.n_sample_max = n_sample_max self.f_mult = f_mult self.transform = transform self.content_font = content_font self.fonts = fonts self.chars = chars self.n_fonts = len(fonts) self.n_chars = len(self.chars) self.font2idx = rev_dict(self.fonts) self.char2idx = rev_dict(self.chars) self.n_avails = self.n_fonts * self.n_chars # for compatibility self.avails = { fname: chars for fname in self.fonts } def sample_style_chars(self, n_styles): # Sampling performance can be improved but which is not the bottleneck, at least now. while True: # sample without replacement => fullcomb consonants = np.random.choice(thai.CONSONANTS, n_styles, replace=False) uppers = np.random.choice(thai.UPPERS, n_styles, replace=False) highests = np.random.choice(thai.HIGHESTS, n_styles, replace=False) lowers = np.random.choice(thai.LOWERS, n_styles, replace=False) style_chars = [] style_ords = [] for c, u, h, l in zip(consonants, uppers, highests, lowers): char = thai.compose(c, u, h, l) if char not in self.chars: break style_chars.append(char) style_ords.append((c, u, h, l)) else: # for-loop is not broken -> all chars available break components = (consonants, uppers, highests, lowers) return style_chars, style_ords, components def sample_trg_chars(self, components, style_chars): combinations = list(product4_no_dup(*components)) np.random.shuffle(combinations) trg_chars = [] trg_ords = [] for c, u, h, l in combinations: char = thai.compose(c, u, h, l) # exclude style chars from target chars if char in style_chars or char not in self.chars: continue trg_chars.append(char) trg_ords.append((c, u, h, l)) if len(trg_chars) >= self.n_sample_max: break return trg_chars, trg_ords def __getitem__(self, index): font_idx = index % self.n_fonts font_name = self.fonts[font_idx] n_styles = 4 while True: # 1. sample style components style_chars, style_ords, components = self.sample_style_chars(n_styles) # 2. sample targets from style components trg_chars, trg_ords = self.sample_trg_chars(components, style_chars) if len(trg_chars) >= self.n_sample_min: break # 3. setup rest style_char_ids = [self.char2idx[ch] for ch in style_chars] style_comp_ids = thai.ord2idx_2d(style_ords) style_imgs = torch.cat([self.data.get(font_name, char) for char in style_chars]) n_trgs = len(trg_chars) trg_char_ids = [self.char2idx[ch] for ch in trg_chars] trg_comp_ids = thai.ord2idx_2d(trg_ords) trg_imgs = torch.cat([self.data.get(font_name, char) for char in trg_chars]) font_idx = torch.as_tensor(font_idx) ret = ( font_idx.repeat(n_styles), torch.as_tensor(style_char_ids), torch.as_tensor(style_comp_ids), style_imgs, font_idx.repeat(n_trgs), torch.as_tensor(trg_char_ids), torch.as_tensor(trg_comp_ids), trg_imgs ) if self.content_font: content_imgs = torch.cat([ self.data.get(self.content_font, char, transform=self.transform) for char in trg_chars ]) ret += (content_imgs,) return ret def __len__(self): return self.n_fonts * self.f_mult @staticmethod def collate_fn(batch): (style_ids, style_char_ids, style_comp_ids, style_imgs, trg_ids, trg_char_ids, trg_comp_ids, trg_imgs, *left) = zip(*batch) ret = ( torch.cat(style_ids), torch.cat(style_char_ids), torch.cat(style_comp_ids), torch.cat(style_imgs).unsqueeze_(1), torch.cat(trg_ids), torch.cat(trg_char_ids), torch.cat(trg_comp_ids), torch.cat(trg_imgs).unsqueeze_(1) ) if left: assert len(left) == 1 content_imgs = left[0] ret += ( torch.cat(content_imgs).unsqueeze_(1), ) return ret class MATargetFirstDataset(Dataset): def __init__(self, target_fc, style_avails, style_data, n_max_match=4, transform=None, ret_targets=False, first_shuffle=False, content_font=None): """ TargetFirstDataset can use out-of-avails target chars, so long as its components could be represented in avail chars. Args: target_fc[font_name] = target_chars style_avails[font_name] = avail_style_chars style_data: style_data getter n_max_match: maximum-allowed matches between style char and target char. n_max_match=4 indicates that style_char == target_char is possible. transform: image transform. If not given, use data.transform as default. ret_targets: return target images also first_shuffle: shuffle item list """ self.target_fc = target_fc self.style_avails = style_avails self.style_avail_comps_list = { fname: [thai.decompose_ords(char) for char in char_list] for fname, char_list in style_avails.items() } self.n_max_match = n_max_match self.transform = transform self.ret_targets = ret_targets self.content_font = content_font self.style_data = style_data self.fcs = [ (font_name, char) for font_name, char_list in target_fc.items() for char in char_list ] if first_shuffle: np.random.shuffle(self.fcs) self.fonts = get_fonts(self.target_fc) self.chars = get_union_chars(self.target_fc) self.font2idx = rev_dict(self.fonts) def sample_style_char(self, font_name, trg_char): """ sample style char from target char within avail style chars """ def is_allowed_matches(arr1, arr2): """ check # of matched ids return count(arr1 == arr2) <= self.n_max_match """ if self.n_max_match >= 4: return True n_matched = sum(v1 == v2 for v1, v2 in zip(arr1, arr2)) return n_matched <= self.n_max_match trg_comp_ords = thai.decompose_ords(trg_char) style_chars = [] style_comps_list = [] for i, _ in enumerate(trg_comp_ords): avail_comps_list = list( filter( lambda comp_ords: comp_ords[i] == trg_comp_ords[i] \ and is_allowed_matches(comp_ords, trg_comp_ords), self.style_avail_comps_list[font_name] ) ) style_comp_ords = random.choice(avail_comps_list) style_char = thai.compose(*style_comp_ords) style_chars.append(style_char) style_comps_list.append(style_comp_ords) return style_chars, thai.ord2idx_2d(style_comps_list) def __getitem__(self, index): font_name, trg_char = self.fcs[index] font_idx = self.font2idx[font_name] style_chars, style_comp_ids = self.sample_style_char(font_name, trg_char) style_imgs = torch.cat([ self.style_data.get(font_name, char, transform=self.transform) for char in style_chars ]) trg_comp_ords = [thai.decompose_ords(trg_char)] trg_comp_ids = thai.ord2idx_2d(trg_comp_ords) n_styles = len(style_chars) font_idx = torch.as_tensor(font_idx) style_ids = font_idx.repeat(n_styles) trg_ids = font_idx.repeat(1) content_img = self.style_data.get(self.content_font, trg_char, transform=self.transform) ret = ( style_ids, torch.as_tensor(style_comp_ids), style_imgs, trg_ids, torch.as_tensor(trg_comp_ids), content_img ) if self.ret_targets: trg_img = self.style_data.get(font_name, trg_char, transform=self.transform) ret += (trg_img, ) return ret def __len__(self): return len(self.fcs) @staticmethod def collate_fn(batch): style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids, content_imgs, *left = \ list(zip(*batch)) ret = ( torch.cat(style_ids), torch.cat(style_comp_ids), torch.cat(style_imgs).unsqueeze_(1), torch.cat(trg_ids), torch.cat(trg_comp_ids), torch.cat(content_imgs).unsqueeze_(1) ) if left: assert len(left) == 1 trg_imgs = left[0] ret += torch.cat(trg_imgs).unsqueeze_(1), return ret def get_ma_dataset(hdf5_data, avail_fonts, avail_chars=None, transform=None, **kwargs): if not avail_chars: avail_chars = list(thai.complete_chars()) dset = MAStyleFirstDataset(hdf5_data, avail_fonts, avail_chars, transform=transform, **kwargs) return dset, MAStyleFirstDataset.collate_fn def get_ma_val_dataset(hdf5_data, fonts, chars, style_avails, n_max_match, transform, **kwargs): target_fc = {font_name: chars for font_name in fonts} dset = MATargetFirstDataset( target_fc, style_avails, hdf5_data, n_max_match, transform=transform, **kwargs ) return dset, MATargetFirstDataset.collate_fn ================================================ FILE: datasets/thai_decompose.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from .data_utils import rev_dict CONSONANTS = [3585, 3586, 3587, 3588, 3589, 3590, 3591, 3592, 3593, 3594, 3595, 3596, 3597, 3598, 3599, 3600, 3601, 3602, 3603, 3604, 3605, 3606, 3607, 3608, 3609, 3610, 3611, 3612, 3613, 3614, 3615, 3616, 3617, 3618, 3619, 3621, 3623, 3624, 3625, 3626, 3627, 3628, 3629, 3630] UPPERS = [3633, 3635, 3636, 3637, 3638, 3639, 0] HIGHESTS = [3655, 3656, 3657, 3658, 3659, 3660, 3661, 3662, 0] LOWERS = [3640, 3641, 3642, 0] N_CONSONANTS = len(CONSONANTS) N_UPPERS = len(UPPERS) N_HIGHESTS = len(HIGHESTS) N_LOWERS = len(LOWERS) N_COMPONENTS = N_CONSONANTS + N_UPPERS + N_HIGHESTS + N_LOWERS def _ord2idx(ords): dic = { ord_num: i for i, ord_num in enumerate(ords) } return dic CONSONANTS_ORD2IDX = _ord2idx(CONSONANTS) UPPERS_ORD2IDX = _ord2idx(UPPERS) HIGHESTS_ORD2IDX = _ord2idx(HIGHESTS) LOWERS_ORD2IDX = _ord2idx(LOWERS) def compose(consonant, upper, highest, lower): """ Compose ords to char """ s = chr(consonant) \ + (chr(upper) if upper else '') \ + (chr(highest) if highest else '') \ + (chr(lower) if lower else '') return s def compose_ids(consonant, upper, highest, lower): """ Compose ids to char """ consonant = CONSONANTS[consonant] upper = UPPERS[upper] highest = HIGHESTS[highest] lower = LOWERS[lower] return compose(consonant, upper, highest, lower) def complete_chars(): for consonant in CONSONANTS: for upper in UPPERS: for highest in HIGHESTS: for lower in LOWERS: char = compose(consonant, upper, highest, lower) yield char ALLCHARS = list(complete_chars()) CHAR2IDX = rev_dict(ALLCHARS) def ord2idx_2d(ords): """ Args: ords: 2d [[consonant, upper, highest, lower], ...] array Return: 2d [[consonant_idx, upper_idx, highest_idx, lower_idx], ...] array """ ret = [] for consonant, upper, highest, lower in ords: consonant_idx = CONSONANTS_ORD2IDX[consonant] upper_idx = UPPERS_ORD2IDX[upper] highest_idx = HIGHESTS_ORD2IDX[highest] lower_idx = LOWERS_ORD2IDX[lower] ret.append((consonant_idx, upper_idx, highest_idx, lower_idx)) return ret def decompose_ords(char): """ Decompose char into ords """ if not char: return 0, 0, 0, 0 consonant = ord(char[0]) upper = highest = lower = 0 for ch in char[1:]: ch = ord(ch) if ch in UPPERS: upper = ch elif ch in HIGHESTS: highest = ch elif ch in LOWERS: lower = ch else: raise ValueError(char) return consonant, upper, highest, lower def decompose_ids(char): """ Decompose char into ids """ index = CHAR2IDX[char] return decompose_index(index) def decompose_index(index): """ Decompose char index to component indices Note: char index come from `complete_chars()`. """ lower_idx = index % N_LOWERS index //= N_LOWERS highest_idx = index % N_HIGHESTS index //= N_HIGHESTS upper_idx = index % N_UPPERS index //= N_UPPERS consonant_idx = index return consonant_idx, upper_idx, highest_idx, lower_idx ================================================ FILE: evaluator.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from itertools import chain from pathlib import Path import json import argparse import random import numpy as np import torch import torch.nn.functional as F from torchvision import transforms from tqdm import tqdm from sconf import Config import utils from logger import Logger from models import MACore from datasets import uniform_sample from datasets import kor_decompose as kor from datasets import thai_decompose as thai from inference import ( infer, get_val_loader, infer_2stage, get_val_encode_loader, get_val_decode_loader ) from ssim import SSIM, MSSSIM def torch_eval(val_fn): @torch.no_grad() def decorated(self, gen, *args, **kwargs): gen.eval() ret = val_fn(self, gen, *args, **kwargs) gen.train() return ret return decorated class Evaluator: """DMFont evaluator. The evaluator provides pixel-level evaluation and glyphs generation from the reference style samples. """ def __init__(self, data, trn_avails, logger, writer, batch_size, transform, content_font, language, meta, val_loaders, n_workers=2): self.data = data self.logger = logger self.writer = writer self.batch_size = batch_size self.transform = transform self.n_workers = n_workers self.unify_resize_method = True self.trn_avails = trn_avails self.val_loaders = val_loaders self.content_font = content_font self.language = language if self.language == 'kor': self.n_comp_types = 3 elif self.language == 'thai': self.n_comp_types = 4 else: raise ValueError() # setup cross-validation self.SSIM = SSIM().cuda() weights = [0.25, 0.3, 0.3, 0.15] self.MSSSIM = MSSSIM(weights=weights).cuda() n_batches = [len(loader) for loader in self.val_loaders.values()] self.n_cv_batches = min(n_batches) self.logger.info("# of cross-validation batches = {}".format(self.n_cv_batches)) # the number of chars/fonts for CV visualization n_chars = 16 n_fonts = 16 seen_chars = uniform_sample(meta['train']['chars'], n_chars//2) unseen_chars = uniform_sample(meta['valid']['chars'], n_chars//2) unseen_fonts = uniform_sample(meta['valid']['fonts'], n_fonts) self.cv_comparable_fonts = unseen_fonts self.cv_comparable_chars = seen_chars + unseen_chars allchars = meta['train']['chars'] + meta['valid']['chars'] self.cv_comparable_avails = { font: allchars for font in self.cv_comparable_fonts } def validation(self, gen, step, extra_tag=''): self.comparable_validset_validation(gen, step, True, 'comparable_val'+extra_tag) plot_dic = {} for tag, loader in self.val_loaders.items(): tag = tag + extra_tag l1, ssim, msssim = self.cross_validation( gen, step, loader, tag, n_batches=self.n_cv_batches ) plot_dic[f'val/{tag}/l1'] = l1 plot_dic[f'val/{tag}/ssim'] = ssim plot_dic[f'val/{tag}/ms-ssim'] = msssim if not np.isnan(msssim) else 0. self.writer.add_scalars(plot_dic, step) return plot_dic @torch_eval def comparable_validset_validation(self, gen, step, compare_inputs=False, tag='comparable_val'): """Comparable validation on validation set from CV""" comparable_grid = self.comparable_validation( gen, self.cv_comparable_avails, self.cv_comparable_fonts, self.cv_comparable_chars, n_max_match=1, compare_inputs=compare_inputs ) self.writer.add_image(tag, comparable_grid, global_step=step) @torch_eval def comparable_validation(self, gen, style_avails, target_fonts, target_chars, n_max_match=3, compare_inputs=False): """Compare horizontally for target fonts and chars""" # infer loader = get_val_loader( self.data, target_fonts, target_chars, style_avails, B=self.batch_size, n_max_match=n_max_match, transform=self.transform, content_font=self.content_font, language=self.language, n_workers=self.n_workers ) out = infer(gen, loader) # [B, 1, 128, 128] # ref original chars refs = self.get_charimages(target_fonts, target_chars) compare_batches = [refs, out] if compare_inputs: compare_batches += self.get_inputimages(loader) nrow = len(target_chars) comparable_grid = utils.make_comparable_grid(*compare_batches, nrow=nrow) return comparable_grid @torch_eval def cross_validation(self, gen, step, loader, tag, n_batches, n_log=64, save_dir=None): """Validation using splitted cross-validation set Args: n_log: # of images to log save_dir: if given, images are saved to save_dir """ if save_dir: save_dir = Path(save_dir) save_dir.mkdir(parents=True, exist_ok=True) outs = [] trgs = [] n_accum = 0 losses = utils.AverageMeters("l1", "ssim", "msssim") for i, (style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids, content_imgs, trg_imgs) in enumerate(loader): if i == n_batches: break style_ids = style_ids.cuda() style_comp_ids = style_comp_ids.cuda() style_imgs = style_imgs.cuda() trg_ids = trg_ids.cuda() trg_comp_ids = trg_comp_ids.cuda() trg_imgs = trg_imgs.cuda() gen.encode_write(style_ids, style_comp_ids, style_imgs) out = gen.read_decode(trg_ids, trg_comp_ids) B = len(out) # log images if n_accum < n_log: trgs.append(trg_imgs) outs.append(out) n_accum += B if n_accum >= n_log: # log results outs = torch.cat(outs)[:n_log] trgs = torch.cat(trgs)[:n_log] self.merge_and_log_image(tag, outs, trgs, step) l1, ssim, msssim = self.get_pixel_losses(out, trg_imgs, self.unify_resize_method) losses.updates({ "l1": l1.item(), "ssim": ssim.item(), "msssim": msssim.item() }, B) # save images if save_dir: font_ids = trg_ids.detach().cpu().numpy() images = out.detach().cpu() # [B, 1, 128, 128] char_comp_ids = trg_comp_ids.detach().cpu().numpy() # [B, n_comp_types] for font_id, image, comp_ids in zip(font_ids, images, char_comp_ids): font_name = loader.dataset.fonts[font_id] # name.ttf font_name = Path(font_name).stem # remove ext (save_dir / font_name).mkdir(parents=True, exist_ok=True) if self.language == 'kor': char = kor.compose(*comp_ids) elif self.language == 'thai': char = thai.compose_ids(*comp_ids) uni = "".join([f'{ord(each):04X}' for each in char]) path = save_dir / font_name / "{}_{}.png".format(font_name, uni) utils.save_tensor_to_image(image, path) self.logger.info( " [Valid] {tag:30s} | Step {step:7d} L1 {L.l1.avg:7.4f} SSIM {L.ssim.avg:7.4f}" " MSSSIM {L.msssim.avg:7.4f}" .format(tag=tag, step=step, L=losses)) return losses.l1.avg, losses.ssim.avg, losses.msssim.avg def get_pixel_losses(self, out, trg_imgs, unify): """ Args: out: generated images trg_imgs: target GT images unify: if True is given, unify glyph size and resize method before evaluation. This option give us the fair evaluation setting, which is used in the paper. """ def unify_resize_method(img): # Unify various glyph size and resize method for fair evaluation size = img.size(-1) if size == 128: transform = transforms.Compose([ transforms.ToPILImage(), transforms.Resize([64, 64]), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ]) img = torch.stack([transform(_img) for _img in img.cpu()]).cuda() img = F.interpolate(img, scale_factor=2.0, mode='bicubic', align_corners=True) return img if unify: out = unify_resize_method(out) trg_imgs = unify_resize_method(trg_imgs) l1 = F.l1_loss(out, trg_imgs) ssim = self.SSIM(out, trg_imgs) msssim = self.MSSSIM(out, trg_imgs) return l1, ssim, msssim @torch_eval def handwritten_validation_2stage(self, gen, step, fonts, style_chars, target_chars, comparable=False, save_dir=None, tag='hw_validation_2stage'): """2-stage handwritten validation Args: fonts: [font_name1, font_name2, ...] save_dir: if given, do not write image grid, instead save every image into save_dir """ if save_dir is not None: save_dir = Path(save_dir) save_dir.mkdir(parents=True, exist_ok=True) outs = [] for font_name in tqdm(fonts): encode_loader = get_val_encode_loader( self.data, font_name, style_chars, self.language, self.transform ) decode_loader = get_val_decode_loader(target_chars, self.language) out = infer_2stage(gen, encode_loader, decode_loader) outs.append(out) if save_dir: for char, glyph in zip(target_chars, out): uni = "".join([f'{ord(each):04X}' for each in char]) path = save_dir / font_name / "{}_{}.png".format(font_name, uni) path.parent.mkdir(parents=True, exist_ok=True) utils.save_tensor_to_image(glyph, path) if save_dir: # do not write grid return out = torch.cat(outs) if comparable: # ref original chars refs = self.get_charimages(fonts, target_chars) nrow = len(target_chars) grid = utils.make_comparable_grid(refs, out, nrow=nrow) else: grid = utils.to_grid(out, 'torch', nrow=len(target_chars)) tag = tag + target_chars[:4] self.writer.add_image(tag, grid, global_step=step) def get_inputimages(self, val_loader): # integrate style images inputs = [] for style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids, content_imgs \ in val_loader: inputs.append(style_imgs) inputs = torch.cat(inputs) shape = inputs.shape inputs = inputs.view(shape[0]//self.n_comp_types, self.n_comp_types, *shape[1:]) batches = [inputs[:, i] for i in range(self.n_comp_types)] return batches def get_charimages(self, fonts, chars, empty_header=False, as_tensor=True): """ get char images from self.data Return: 2d list of charimages or 5d tensor: [ [charimage1, charimage2, ...] (font1), ... ] or Tensor [n_fonts, n_chars, 1, 128, 128] """ empty_box = torch.ones(1, 128, 128) charimages = [ [self.data.get(font_name, char, empty_box) for char in chars] for font_name in fonts ] if empty_header: header = [empty_box for _ in chars] charimages.insert(0, header) if as_tensor: charimages = torch.stack(list(chain.from_iterable(charimages))) return charimages def merge_and_log_image(self, name, out, target, step): """ Merge out and target into 2-column grid and log it """ merge = utils.make_merged_grid([out, target], merge_dim=2) self.writer.add_image(name, merge, global_step=step) def eval_ckpt(): from train import ( setup_language_dependent, setup_data, setup_cv_dset_loader, get_dset_loader ) logger = Logger.get() parser = argparse.ArgumentParser('MaHFG-eval') parser.add_argument( "name", help="name is used for directory name of the user-study generation results" ) parser.add_argument("resume") parser.add_argument("img_dir") parser.add_argument("config_paths", nargs="+") parser.add_argument("--show", action="store_true", default=False) parser.add_argument( "--mode", default="eval", help="eval (default) / cv-save / user-study / user-study-save. " "`eval` generates comparable grid and computes pixel-level CV scores. " "`cv-save` generates and saves all target characters in CV. " "`user-study` generates comparable grid for the ramdomly sampled target characters. " "`user-study-save` generates and saves all target characters in user-study." ) parser.add_argument("--deterministic", default=False, action="store_true") parser.add_argument("--debug", default=False, action="store_true") args, left_argv = parser.parse_known_args() cfg = Config(*args.config_paths) cfg.argv_update(left_argv) torch.backends.cudnn.benchmark = True cfg['data_dir'] = Path(cfg['data_dir']) if args.show: exit() # seed np.random.seed(cfg['seed']) torch.manual_seed(cfg['seed']) random.seed(cfg['seed']) if args.deterministic: torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True cfg['n_workers'] = 0 logger.info("#" * 80) logger.info("# Deterministic option is activated !") logger.info("# Deterministic evaluator only ensure the deterministic cross-validation") logger.info("#" * 80) else: torch.backends.cudnn.benchmark = True if args.mode.startswith('mix'): assert cfg['g_args']['style_enc']['use'], \ "Style mixing is only available with style encoder model" ##################################### # Dataset #################################### # setup language dependent values content_font, n_comp_types, n_comps = setup_language_dependent(cfg) # setup transform transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]) ]) # setup data hdf5_data, meta = setup_data(cfg, transform) # setup dataset trn_dset, loader = get_dset_loader( hdf5_data, meta['train']['fonts'], meta['train']['chars'], transform, True, cfg, content_font=content_font ) val_loaders = setup_cv_dset_loader( hdf5_data, meta, transform, n_comp_types, content_font, cfg ) ##################################### # Model #################################### # setup generator only g_kwargs = cfg.get('g_args', {}) gen = MACore( 1, cfg['C'], 1, **g_kwargs, n_comps=n_comps, n_comp_types=n_comp_types, language=cfg['language'] ) gen.cuda() ckpt = torch.load(args.resume) logger.info("Use EMA generator as default") gen.load_state_dict(ckpt['generator_ema']) step = ckpt['epoch'] loss = ckpt['loss'] logger.info("Resumed checkpoint from {} (Step {}, Loss {:7.3f})".format( args.resume, step, loss)) writer = utils.DiskWriter(args.img_dir, 0.6) evaluator = Evaluator( hdf5_data, trn_dset.avails, logger, writer, cfg['batch_size'], content_font=content_font, transform=transform, language=cfg['language'], val_loaders=val_loaders, meta=meta ) evaluator.n_cv_batches = -1 logger.info("Update n_cv_batches = -1 to evaluate about full data") if args.debug: evaluator.n_cv_batches = 10 logger.info("!!! DEBUG MODE: n_cv_batches = 10 !!!") if args.mode == 'eval': logger.info("Start validation ...") dic = evaluator.validation(gen, step) logger.info("Validation is done. Result images are saved to {}".format(args.img_dir)) elif args.mode.startswith('user-study'): meta = json.load(open('meta/kor-unrefined.json')) target_chars = meta['target_chars'] style_chars = meta['style_chars'] fonts = meta['fonts'] if args.mode == 'user-study': sampled_target_chars = uniform_sample(target_chars, 20) logger.info("Start generation kor-unrefined ...") logger.info("Sampled chars = {}".format(sampled_target_chars)) evaluator.handwritten_validation_2stage( gen, step, fonts, style_chars, sampled_target_chars, comparable=True, tag='userstudy-{}'.format(args.name) ) elif args.mode == 'user-study-save': logger.info("Start generation & saving kor-unrefined ...") save_dir = Path(args.img_dir) / "{}-{}".format(args.name, step) evaluator.handwritten_validation_2stage( gen, step, fonts, style_chars, target_chars, comparable=True, save_dir=save_dir ) logger.info("Validation is done. Result images are saved to {}".format(args.img_dir)) elif args.mode == 'cv-save': save_dir = Path(args.img_dir) / "cv_images_{}".format(step) logger.info("Save CV results to {} ...".format(save_dir)) utils.rm(save_dir) for tag, loader in val_loaders.items(): l1, ssim, msssim = evaluator.cross_validation( gen, step, loader, tag, n_batches=evaluator.n_cv_batches, save_dir=(save_dir / tag) ) else: raise ValueError(args.mode) if __name__ == "__main__": eval_ckpt() ================================================ FILE: inference.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import torch from torch.utils.data import DataLoader from datasets import get_ma_val_dataset from datasets.nonpaired_dataset import EncodeDataset, DecodeDataset def infer(gen, loader): outs = [] for style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids, content_imgs \ in loader: style_ids = style_ids.cuda() style_comp_ids = style_comp_ids.cuda() style_imgs = style_imgs.cuda() trg_ids = trg_ids.cuda() trg_comp_ids = trg_comp_ids.cuda() gen.encode_write(style_ids, style_comp_ids, style_imgs) out = gen.read_decode(trg_ids, trg_comp_ids) outs.append(out.detach().cpu()) return torch.cat(outs) # [B, 1, 128, 128]; B = #fonts * #chars def get_val_loader(data, fonts, chars, style_avails, transform, content_font, language, B=32, n_max_match=3, n_workers=2): val_dset, collate_fn = get_ma_val_dataset( data, fonts, chars, style_avails, n_max_match, transform=transform, content_font=content_font, language=language ) loader = DataLoader(val_dset, batch_size=B, shuffle=False, num_workers=n_workers, collate_fn=collate_fn) return loader def infer_2stage(gen, encode_loader, decode_loader, reset_memory=True): """ 2-stage infer; encode first, decode second """ # stage 1. encode if reset_memory: gen.reset_dynamic_memory() for style_ids, style_comp_ids, style_imgs in encode_loader: style_ids = style_ids.cuda() style_comp_ids = style_comp_ids.cuda() style_imgs = style_imgs.cuda() gen.encode_write(style_ids, style_comp_ids, style_imgs, reset_dynamic_memory=False) # stage 2. decode outs = [] for trg_ids, trg_comp_ids in decode_loader: trg_ids = trg_ids.cuda() trg_comp_ids = trg_comp_ids.cuda() out = gen.read_decode(trg_ids, trg_comp_ids) outs.append(out.detach().cpu()) return torch.cat(outs) def get_val_encode_loader(data, font_name, encode_chars, language, transform, B=32, num_workers=2, style_id=0): encode_dset = EncodeDataset( font_name, encode_chars, data, language=language, transform=transform, style_id=style_id ) loader = DataLoader(encode_dset, batch_size=B, shuffle=False, num_workers=num_workers) return loader def get_val_decode_loader(chars, language, B=32, num_workers=2, style_id=0): decode_dset = DecodeDataset(chars, language=language, style_id=style_id) loader = DataLoader(decode_dset, batch_size=B, shuffle=False, num_workers=num_workers) return loader ================================================ FILE: logger.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import sys import logging log_lv = { 'critical': logging.CRITICAL, 'error': logging.ERROR, 'warning': logging.WARNING, 'info': logging.INFO, 'debug': logging.DEBUG } class ColorFormatter(logging.Formatter): color_dic = { 'DEBUG' : 37, # white 'INFO' : 36, # cyan 'WARNING' : 33, # yellow 'ERROR' : 31, # red 'CRITICAL': 41, # white on red bg } def format(self, record): color = self.color_dic.get(record.levelname, 37) # default white record.levelname = "\033[{}m{}\033[0m".format(color, record.levelname) return logging.Formatter.format(self, record) class Logger(logging.Logger): NAME = 'SingletonLogger' @classmethod def get(cls, file_path=None, level='info', colorize=True): logging.setLoggerClass(cls) logger = logging.getLogger(cls.NAME) logging.setLoggerClass(logging.Logger) # restore logger.setLevel(log_lv[level]) if logger.hasHandlers(): # If logger already got all handlers (# handlers == 2), use the logger. # else, re-set handlers. if len(logger.handlers) == 2: return logger logger.handlers.clear() log_format = '%(levelname)s::%(asctime)s | %(message)s' date_format = '%m/%d %H:%M:%S' if colorize: formatter = ColorFormatter(log_format, date_format) else: formatter = logging.Formatter(log_format, date_format) # standard output handler # NOTE as default, StreamHandler use stderr stream instead of stdout stream. # Use StreamHandler(sys.stdout) for stdout stream. stream_handler = logging.StreamHandler(sys.stdout) stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) if file_path: # file output handler file_handler = logging.FileHandler(file_path) file_handler.setFormatter(formatter) logger.addHandler(file_handler) logger.propagate = False return logger def nofmt(self, msg, *args, level='info', **kwargs): level = log_lv[level] formatters = self.remove_formats() super().log(level, msg, *args, **kwargs) self.set_formats(formatters) def remove_formats(self): """ Remove all formats from logger """ formatters = [] for handler in self.handlers: formatters.append(handler.formatter) handler.setFormatter(logging.Formatter("%(message)s")) return formatters def set_formats(self, formatters): """ Set formats to every handler of logger """ for handler, formatter in zip(self.handlers, formatters): handler.setFormatter(formatter) def set_file_handler(self, file_path): file_handler = logging.FileHandler(file_path) formatter = self.handlers[0].formatter file_handler.setFormatter(formatter) self.addHandler(file_handler) ================================================ FILE: meta/kor-unrefined.json ================================================ {"target_chars": "\uac00\uace0\uacfe\uad18\uad53\uadf8\uae68\uaf86\uaf9c\ub044\ub04c\ub0d0\ub0f5\ub1cc\ub204\ub247\ub290\ub29c\ub338\ub3d2\ub46c\ub488\ub4b3\ub4dc\ub5a0\ub610\ub63e\ub6f0\ub728\ub73c\ub808\ub878\ub8c6\ub958\ub974\ub98c\uba70\ubafc\ubb6a\ubbc0\ubbdc\ubcd8\ubd2c\ubd67\ubd9c\ube0c\ube10\ube45\ubf40\ubf62\ubfb0\ubfd3\uc058\uc090\uc0ac\uc0d1\uc1a8\uc1ea\uc218\uc2a4\uc2b0\uc314\uc33d\uc42c\uc49c\uc4c7\uc4f0\uc500\uc57c\uc636\uc6cc\uc704\uc733\uc73c\uc7e4\uc815\uc870\uc96c\uc988\uc9bb\uca4c\uca81\ucad8\ucb0f\ucbd4\ucbf0\uccb4\uccd1\ucd40\ucd5c\ucdb2\uce20\uce24\ucf1c\ucf3d\ucfc4\ucfe7\ucffc\ud06c\ud074\ud184\ud1a0\ud1c6\ud22c\ud26f\ud2b8\ud319\ud408\ud44e\ud4b0\ud504\ud514\ud53c\ud558\ud585\ud68c\ud718\ud747\ud750\ud764", "style_chars": "\uac1d\uadfc\uae89\uaf48\uafbf\ub22a\ub361\ub4ec\ub5cd\ub71f\ub839\ub9a7\ubaa5\ubafb\ubcf6\ube65\uc060\uc25b\uc48e\uc5a9\uc750\uc8a2\uc9a0\ucb2a\ucd7b\ucf5a\ud2c4\ud2f9\ud4db\ud6f2", "fonts": ["Handwritten-10113", "Handwritten-10483", "Handwritten-10518", "Handwritten-10637", "Handwritten-10789", "Handwritten-10803", "Handwritten-11218", "Handwritten-11266", "Handwritten-11282", "Handwritten-11441", "Handwritten-11564", "Handwritten-11997", "Handwritten-13026", "Handwritten-15959", "Handwritten-16510", "Handwritten-16921", "Handwritten-17143", "Handwritten-19035", "Handwritten-19211", "Handwritten-19651", "Handwritten-20590", "Handwritten-22536", "Handwritten-23081", "Handwritten-2337", "Handwritten-24513", "Handwritten-2532", "Handwritten-2939", "Handwritten-3086", "Handwritten-3103", "Handwritten-321", "Handwritten-3246", "Handwritten-3336", "Handwritten-3487", "Handwritten-3491", "Handwritten-3675", "Handwritten-3678", "Handwritten-3787", "Handwritten-3839", "Handwritten-3861", "Handwritten-3874", "Handwritten-3889", "Handwritten-3926", "Handwritten-3941", "Handwritten-3996", "Handwritten-4124", "Handwritten-4125", "Handwritten-4342", "Handwritten-4419", "Handwritten-4487", "Handwritten-4506", "Handwritten-4668", "Handwritten-4679", "Handwritten-4771", "Handwritten-4892", "Handwritten-5073", "Handwritten-5141", "Handwritten-5536", "Handwritten-6095", "Handwritten-6114", "Handwritten-6198", "Handwritten-6544", "Handwritten-6555", "Handwritten-6564", "Handwritten-6604", "Handwritten-6758", "Handwritten-6793", "Handwritten-687", "Handwritten-7154", "Handwritten-7242", "Handwritten-7319", "Handwritten-7815", "Handwritten-7980", "Handwritten-8190", "Handwritten-8221", "Handwritten-8249", "Handwritten-8290", "Handwritten-8295", "Handwritten-8357", "Handwritten-8440", "Handwritten-8450", "Handwritten-872", "Handwritten-8812", "Handwritten-8852", "Handwritten-9211", "Handwritten-9220", "Handwritten-936", "Handwritten-952", "Handwritten-9641"]} ================================================ FILE: meta/kor_split.json ================================================ {"train": {"fonts": ["UhBee Rami.ttf", "UhBee DoolDool.ttf", "UhBee ddolyoung.ttf", "UhBee Jisyuk jisyuk.ttf", "UhBee My Husband.ttf", "UhBee Sunhong.ttf", "UhBee song.ttf", "UhBee chae-eun.ttf", "UhBee mingi calli.ttf", "UhBee Jin-gun.ttf", "UhBee KOR.ttf", "UhBee RingRing.ttf", "UhBee UZ.ttf", "UhBee HYUNJUNG.ttf", "UhBee freak researcher minani.ttf", "UhBee UJong.ttf", "UhBee JJIBBABBA.ttf", "UhBee Yeonie.ttf", "UhBee jung.ttf", "UhBee DDOYO.ttf", "UhBee Jiyoung.ttf", "UhBee KeongKeong.ttf", "UhBee hye mong.ttf", "UhBee Jjipungding.ttf", "UhBee dami.ttf", "UhBee ann.ttf", "UhBee Hyeki.ttf", "UhBee Namjieun.ttf", "UhBee EUN KYUNG.ttf", "UhBee Kang-Ja.ttf", "UhBee Soyun.ttf", "UhBee ARyong.ttf", "UhBee TOMO.ttf", "UhBee mysen.ttf", "UhBee puding.ttf", "UhBee Banynany.ttf", "UhBee Ryuga.ttf", "UhBee Yiseul.ttf", "UhBee namsoyoung.ttf", "UhBee MinAhBang.ttf", "UhBee Rice.ttf", "UhBee yul.ttf", "UhBee Se_hyun.ttf", "UhBee wocked woman.ttf", "UhBee QUEEN J.ttf", "UhBee BongSik.ttf", "UhBee Mardyu.ttf", "UhBee kairu mischief.ttf", "UhBee matsuko.ttf", "UhBee Motomi.ttf", "UhBee Minidori.ttf", "UhBee Hanbee.ttf", "UhBee baby.ttf", "UhBee mun seul ye.ttf", "UhBee ibuson.ttf", "UhBee Simple girl.ttf", "UhBee Joker.ttf", "UhBee yoonjaa.ttf", "UhBee BEOJJI.ttf", "UhBee Miwan.ttf", "UhBee Tokki.ttf", "UhBee ZIGLE.ttf", "UhBee DongKyung.ttf", "UhBee swit.ttf", "UhBee yoongdi.ttf", "UhBee Gmin2.ttf", "UhBee Strawberry.ttf", "UhBee yehee.ttf"], "chars": ["\ud4cc", "\ubda4", "\ud4df", "\uca00", "\ub000", "\uc5f6", "\ub385", "\ud37c", "\ucd18", "\uce0c", "\ucda4", "\ub879", "\ub7a9", "\uc800", "\ucde8", "\uc3e0", "\uaca0", "\uc885", "\uc6e9", "\ud69f", "\uc501", "\uaecc", "\ub053", "\ud391", "\ucdfd", "\ub215", "\ub791", "\uccf0", "\uc245", "\ud658", "\uc9ca", "\uc069", "\ub648", "\uc0dd", "\uacb9", "\uc7c0", "\ud044", "\ub82c", "\ud2c8", "\ub450", "\uc740", "\uc0b0", "\ud39c", "\ub7f4", "\ud2ac", "\uc2ac", "\ub144", "\ub975", "\ud45c", "\uc234", "\uacec", "\ub530", "\uc204", "\uc704", "\uba69", "\ud460", "\ub268", "\ud79b", "\ucf11", "\uc6c0", "\ud3c4", "\uac04", "\uc314", "\uc737", "\ub54c", "\ucacd", "\uc388", "\uc5ff", "\ucd6d", "\ub134", "\uc880", "\uc5ed", "\uacb8", "\uadf9", "\ub46c", "\ud140", "\ubdd8", "\ubb36", "\ud62d", "\ubc25", "\ud78c", "\ubee0", "\uad8c", "\ud33b", "\ubcf4", "\ub2ef", "\ub08f", "\uba4d", "\ub19c", "\ub0b4", "\ud300", "\uc65c", "\uce31", "\ub0ae", "\uaf0d", "\ubba8", "\ub611", "\uae5c", "\uce94", "\uaebd", "\ub458", "\ud64b", "\uc49c", "\ud2f4", "\ubc9a", "\ud6d4", "\uc12d", "\ubf1c", "\ubc99", "\uac08", "\ud0ec", "\ub123", "\ubd18", "\ucc3d", "\uc448", "\ub808", "\ub110", "\uc21f", "\ucf70", "\uc1f0", "\uc258", "\ub2dd", "\ub41c", "\uc4b8", "\uc1f3", "\ubb3b", "\ub2f4", "\ub9e8", "\uc790", "\ucc75", "\uc625", "\uc9f0", "\uc27f", "\ub7a0", "\ucb50", "\uc570", "\ud482", "\ucc66", "\ub35c", "\ub4e4", "\ud3a0", "\ubc44", "\ub18b", "\uccec", "\uc7a3", "\ucdc4", "\uc164", "\uc9c1", "\uc82d", "\uc324", "\uc571", "\ubfdc", "\ubcb4", "\uae4e", "\ub0e0", "\uc66f", "\uc068", "\ud018", "\uc480", "\ub9f9", "\uc981", "\ud55c", "\uc6fd", "\ub0d0", "\uac9c", "\ub463", "\ub9f4", "\ubc49", "\uc6dc", "\ucca9", "\uc0d3", "\ub35f", "\ucb20", "\uc5fd", "\ub365", "\ucad9", "\ub8f0", "\ub792", "\uc630", "\ub08c", "\uc20f", "\ubcbc", "\ud0d4", "\ud480", "\ub5a1", "\ud6fd", "\ub9f5", "\ud281", "\ud540", "\ub748", "\uc1d7", "\ud5d0", "\ud1a8", "\ud380", "\ubee4", "\ucad3", "\ucd64", "\uae34", "\ud004", "\ud398", "\ub205", "\uc7d8", "\ud790", "\uac5c", "\uba4b", "\ub004", "\ub31c", "\ucf85", "\uc5a9", "\ub18d", "\ucf19", "\uce69", "\uc568", "\uaff8", "\ub5b4", "\uc637", "\uc0a0", "\uc6f8", "\uaff0", "\uc724", "\uc840", "\ud53c", "\ud145", "\uc044", "\ucc58", "\ub5d1", "\ud639", "\uc9ed", "\ud76c", "\ud5cc", "\ud6d9", "\ucb79", "\ub2cc", "\uca54", "\uafd8", "\ud32c", "\ub54d", "\ud587", "\ucf69", "\ub4e6", "\ucef9", "\ud390", "\uac84", "\uac38", "\ud0dd", "\ucef4", "\uc6df", "\ub951", "\ub773", "\ubc31", "\ud649", "\ucabd", "\uc6c5", "\uc1a1", "\ucc68", "\ub1cc", "\uae09", "\ub614", "\ub518", "\uc14c", "\ubcf6", "\ub9d2", "\uc11d", "\ubf55", "\uc58f", "\uc104", "\ub381", "\uc26c", "\ud620", "\ucd95", "\ub141", "\uae61", "\ud4e8", "\ud050", "\ubc1c", "\uac2d", "\uaebe", "\ucd5d", "\ud30c", "\ub0e5", "\uc2fb", "\uc290", "\uac83", "\uc3e2", "\uc138", "\ub839", "\uc329", "\ucb78", "\uac14", "\uc4d4", "\uc883", "\uc12c", "\uc6b8", "\uad74", "\ucac0", "\uca60", "\ub525", "\ubb58", "\ubfc5", "\ucc3e", "\uc92c", "\ubb6c", "\ube5b", "\uc82c", "\ud320", "\ud284", "\ub220", "\ud551", "\ub9d1", "\ub5d8", "\uadfc", "\uace7", "\ucfe0", "\ud601", "\ud585", "\uae46", "\ubd8a", "\ucaec", "\ub3e8", "\uc77d", "\ud5e5", "\ud15c", "\ub451", "\uc05c", "\ub2f7", "\uc59c", "\uae60", "\ub969", "\ud0d0", "\uaf3c", "\uad7b", "\uafc0", "\uc5d8", "\uc600", "\uc19f", "\uac09", "\ub540", "\ud651", "\ucc70", "\ud3fc", "\uc5e3", "\uaca9", "\uac10", "\uc560", "\uca14", "\ubb47", "\uc36c", "\uaed0", "\ub04c", "\ubf40", "\ud15f", "\uc27d", "\uacb0", "\uc624", "\uafa4", "\ub768", "\uc7a1", "\ub028", "\ud329", "\ub584", "\uc060", "\ubb0f", "\uca0b", "\uc2e4", "\uc158", "\ub7fc", "\ud17c", "\ub0bc", "\ucfe4", "\uc911", "\uc61b", "\uafb9", "\uacc4", "\uba87", "\uc561", "\ud64d", "\ud610", "\uc881", "\uba58", "\ub0a9", "\ubfb0", "\ud645", "\ubab8", "\uad2d", "\uba48", "\ub2c8", "\uc318", "\uc5c5", "\uaf32", "\uc97c", "\uc379", "\ud035", "\ud2b8", "\uc5cc", "\ubc85", "\ucd24", "\ub1df", "\ub315", "\ube1f", "\ub380", "\ubed7", "\ubdd4", "\ucae0", "\uce6d", "\ud6bd", "\ucc38", "\uac90", "\uc5e0", "\uba53", "\ubcc0", "\ucd0c", "\ub775", "\uc774", "\ud3fd", "\ud0e4", "\uc11e", "\ucd98", "\ub0d4", "\uafb1", "\uc004", "\ud654", "\uc10b", "\uad2c", "\ucc9c", "\ud5c8", "\uc694", "\ubcd1", "\uadc0", "\uce04", "\ube70", "\ud3ec", "\ub3d9", "\ubcdc", "\ud2d4", "\ube44", "\ub0b3", "\uacc8", "\uc6dd", "\ud569", "\uc091", "\ucec1", "\ud4f8", "\uc479", "\ub5a8", "\uac20", "\ub4e3", "\ubdc0", "\uc464", "\uadd1", "\uad81", "\ub8e8", "\uc2eb", "\uac8a", "\uaf2d", "\uc2a8", "\uce78", "\uba01", "\ub0c5", "\ub985", "\ub2aa", "\ub5b1", "\uad44", "\uad18", "\ub95c", "\uc988", "\uc8e1", "\ud720", "\uad6d", "\uc640", 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"\ucacc", "\uaf64", "\ube98", "\uacac", "\uc3f5", "\ud648", "\ucad1", "\ubd58", "\uc5c4", "\uc78a", "\ud4d0", "\ucbb8", "\uc514", "\ud718", "\uc57d", "\ud6c8", "\uc0bf", "\ud264", "\ubc17", "\uac2f", "\uc9c7", "\ud2c0", "\uc00d", "\uc75c", "\ud685", "\ud408", "\uc154", "\ubb38", "\ub8c1", "\ubd40", "\uba84", "\uc3a0", "\uc168", "\ud401", "\ubc1b", "\uacf5", "\uce29", "\uc906", "\uc58d", "\ud670", "\uc475", "\ud69d", "\ub515", "\ud655", "\ub189", "\uccb5", "\uc155", "\ud614", "\uaf84", "\uc958", "\uac3c", "\uc9ec", "\uac85", "\ucbe9", "\uc824", "\uba00", "\ubc45", "\uba39", "\ucff1", "\ud0d5", "\uafcd", "\ud0a5", "\uc370", "\ud568", "\ucefc", "\uc88b", "\ube59", "\uc0b4", "\uaf08", "\uc4f0", "\ubb20", "\ud47c", "\uad04", "\ub429", "\uc2f1", "\ud46d", "\ubca7", "\uc298", "\ub729", "\uc7d0", "\ub878", "\uc796", "\ucb59", "\uafe8", "\ub370", "\uc0b5", "\ub5bb", "\ucc98", "\uac16", "\uc7a0", "\uc0d9", "\uc100", "\ub560", "\uc31f", "\uac77", "\ub761", "\ubc30", "\uad47", "\uc0ec", "\ub36e", 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MIT license """ from .ma_core import MACore from .discriminator import Discriminator from .aux_classifier import AuxClassifier ================================================ FILE: models/aux_classifier.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from functools import partial import torch.nn as nn from .modules import ResBlock, Flatten class AuxClassifier(nn.Module): def __init__(self, C, C_out, norm='BN', activ='relu', pad_type='reflect', conv_dropout=0., clf_dropout=0.): super().__init__() ResBlk = partial(ResBlock, norm=norm, activ=activ, pad_type=pad_type, dropout=conv_dropout) self.layers = nn.ModuleList([ ResBlk(C, C*2, 3, 1, downsample=True), ResBlk(C*2, C*2, 3, 1), nn.AdaptiveAvgPool2d(1), Flatten(1), nn.Dropout(clf_dropout), nn.Linear(C*2, C_out) ]) def forward(self, x): for layer in self.layers: x = layer(x) return x ================================================ FILE: models/comp_encoder.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from functools import partial import torch.nn as nn import torch from .modules import ConvBlock, ResBlock, GCBlock, SAFFNBlock class ComponentEncoder(nn.Module): """ Component image decomposer Encode the glyph into each component-wise features """ def __init__(self, C_in, C, norm='none', activ='relu', pad_type='reflect', sa=None, n_comp_types=3): super().__init__() self.n_heads = n_comp_types ConvBlk = partial(ConvBlock, norm=norm, activ=activ, pad_type=pad_type) ResBlk = partial(ResBlock, norm=norm, activ=activ, pad_type=pad_type) SAFFNBlk = partial(SAFFNBlock, **sa) self.shared = nn.ModuleList([ ConvBlk(C_in, C, 3, 1, 1, norm='none', activ='none'), # 128x128 ConvBlk(C*1, C*2, 3, 1, 1, downsample=True), # 64x64 GCBlock(C*2), ConvBlk(C*2, C*4, 3, 1, 1, downsample=True), # 32x32 SAFFNBlk(C*4, size=32, rel_pos=True), ]) self.heads = nn.ModuleList([ nn.ModuleList([ ResBlk(C*4, C*4, 3, 1), SAFFNBlk(C*4, size=32, rel_pos=False), ResBlk(C*4, C*4, 3, 1), ResBlk(C*4, C*8, 3, 1, downsample=True), # 16x16 SAFFNBlk(C*8, size=16, rel_pos=False), ResBlk(C*8, C*8) ]) for _ in range(self.n_heads) ]) # skip featuremap indices. Since shared featuremap is idx=0, # featuremap index starts from 1 in each head. self.skip_layers = [3] # final output shape self.final_shape = (C*8, 16, 16) def forward(self, x): # foward in shared for layer in self.shared: x = layer(x) # forward in heads feats = [x] xs = [x] * self.n_heads n_layers = len(self.heads[0]) for layer_idx in range(n_layers): for head_idx, head in enumerate(self.heads): layer = head[layer_idx] xs[head_idx] = layer(xs[head_idx]) comp_feature = torch.stack(xs, dim=1) # [B, n_comp_types, C, H, W] feats.append(comp_feature) return feats def filter_skips(self, feats): if self.skip_layers is None: return None return [feats[i] for i in self.skip_layers] ================================================ FILE: models/decoder.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from functools import partial import torch import torch.nn as nn from .modules import ConvBlock, ResBlock, HourGlass class Integrator(nn.Module): """Integrate component type-wise features""" def __init__(self, C, n_comps=3, norm='none', activ='none', C_in=None): super().__init__() C_in = (C_in or C) * n_comps self.integrate_layer = ConvBlock(C_in, C, 1, 1, 0, norm=norm, activ=activ) def forward(self, comps): """ Args: comps [B, n_comps, C, H, W]: component features """ inputs = comps.flatten(1, 2) out = self.integrate_layer(inputs) return out class Decoder(nn.Module): def __init__(self, C, C_out, size, norm='IN', activ='relu', pad_type='reflect', n_comp_types=3): super().__init__() ConvBlk = partial(ConvBlock, norm=norm, activ=activ, pad_type=pad_type) ResBlk = partial(ResBlock, norm=norm, activ=activ, pad_type=pad_type) # Hourglass block downsamples the featuremap to 1x1 where IN makes trouble. HGBlk = partial(HourGlass, size=size, norm='BN', activ=activ, pad_type=pad_type) IntegrateBlk = partial( Integrator, norm='none', activ='none', n_comps=n_comp_types ) self.layers = nn.ModuleList([ IntegrateBlk(C*8), HGBlk(C*8, C*16, n_downs=4), ResBlk(C*8, C*8, 3, 1), ResBlk(C*8, C*8, 3, 1), ConvBlk(C*8, C*4, 3, 1, 1, upsample=True), # 32x32 ConvBlk(C*12, C*8, 3, 1, 1), # enc-skip ConvBlk(C*8, C*8, 3, 1, 1), ConvBlk(C*8, C*4, 3, 1, 1), ConvBlk(C*4, C*2, 3, 1, 1, upsample=True), # 64x64 ConvBlk(C*2, C*1, 3, 1, 1, upsample=True), # 128x128 ConvBlk(C*1, C_out, 3, 1, 1) ]) self.skip_indices = [5] self.skip_layers = nn.ModuleList([IntegrateBlk(C*8, C_in=C*4)]) self.out = nn.Tanh() def forward(self, comps, skips=None): """ Args: comps [B, n_comps, C, H, W]: component features skips: skip features """ if skips is not None: assert len(skips) == 1 skip_idx = self.skip_indices[0] skip_layer = self.skip_layers[0] skip_feat = skips[0] x = comps for i, layer in enumerate(self.layers): if i == skip_idx: skip_feat = skip_layer(skip_feat) # integrate skip features x = torch.cat([x, skip_feat], dim=1) x = layer(x) return self.out(x) ================================================ FILE: models/discriminator.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from functools import partial import torch import torch.nn as nn from .modules import ResBlock, ConvBlock, w_norm_dispatch, activ_dispatch class MultitaskDiscriminator(nn.Module): def __init__(self, C, n_fonts, n_chars, use_rx=True, w_norm='spectral', activ='none'): super().__init__() self.use_rx = use_rx self.activ = activ_dispatch(activ)() w_norm = w_norm_dispatch(w_norm) self.font_emb = w_norm(nn.Embedding(n_fonts, C)) self.char_emb = w_norm(nn.Embedding(n_chars, C)) if use_rx: self.rx = w_norm(nn.Conv2d(C, 1, kernel_size=1, padding=0)) def forward(self, x, font_indices, char_indices): """ Args: x: [B, C, H, W] font_indices: [B] char_indices: [B] Return: [rx_logit, font_logit, char_logit]; [B, 1, H, W] """ x = self.activ(x) font_emb = self.font_emb(font_indices) # [B, C] char_emb = self.char_emb(char_indices) # [B, (3), C] if hasattr(self, "rx"): rx_out = self.rx(x) # [B, 1, H, W] ret = [rx_out] else: ret = [torch.as_tensor(0.0)] # dummy font_out = torch.einsum('bchw,bc->bhw', x, font_emb).unsqueeze(1) char_out = torch.einsum('bchw,bc->bhw', x, char_emb).unsqueeze(1) ret += [font_out, char_out] return ret class Discriminator(nn.Module): def __init__(self, C, n_fonts, n_chars, activ='relu', gap_activ='relu', w_norm='spectral', use_rx=False, pad_type='reflect'): super().__init__() ConvBlk = partial(ConvBlock, w_norm=w_norm, activ=activ, pad_type=pad_type) ResBlk = partial(ResBlock, w_norm=w_norm, activ=activ, pad_type=pad_type) feats = [ ConvBlk(1, C, stride=2, activ='none'), # 64x64 (stirde==2) ResBlk(C*1, C*2, downsample=True), # 32x32 ResBlk(C*2, C*4, downsample=True), # 16x16 ResBlk(C*4, C*8, downsample=True), # 8x8 ResBlk(C*8, C*16, downsample=False), # 8x8 ResBlk(C*16, C*32, downsample=False), # 8x8 ResBlk(C*32, C*32, downsample=False), # 8x8 ] self.feats = nn.ModuleList(feats) gap_activ = activ_dispatch(gap_activ) self.gap = nn.Sequential( gap_activ(), nn.AdaptiveAvgPool2d(1) ) self.projD = MultitaskDiscriminator(C*32, n_fonts, n_chars, use_rx=use_rx, w_norm=w_norm) def forward(self, x, font_indices, char_indices, out_feats=False): feats = [] for layer in self.feats: x = layer(x) feats.append(x) x = self.gap(x) # final features ret = self.projD(x, font_indices, char_indices) if out_feats: ret.append(feats) return ret @property def use_rx(self): return self.projD.use_rx ================================================ FILE: models/ma_core.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import torch.nn as nn from .comp_encoder import ComponentEncoder from .decoder import Decoder from .memory import Memory class MACore(nn.Module): """ Memory-augmented HFG """ def __init__(self, C_in, C, C_out, comp_enc, dec, n_comps, n_comp_types, language): """ Args: C_in: 1 C: unit of channel size C_out: 1 comp_enc: component encoder configs dec: decoder configs n_comps: # of total component instances. n_comp_types: # of component types. kor=3, thai=4. """ super().__init__() self.component_encoder = ComponentEncoder( C_in, C, **comp_enc, n_comp_types=n_comp_types ) self.mem_shape = self.component_encoder.final_shape self.memory = Memory(self.mem_shape, n_comps, persistent=True, language=language) # setup skip memory if self.component_encoder.skip_layers is not None: # use dynamic memory only self.skip_memory = Memory(self.mem_shape, n_comps, persistent=False, language=language) skip_layers = self.component_encoder.skip_layers assert skip_layers is None or len(skip_layers) == 1, "Only supports #skip_layers <= 1" self.decoder = Decoder( C, C_out, self.mem_shape[-1], **dec, n_comp_types=n_comp_types ) def reset_dynamic_memory(self): self.memory.reset_dynamic() if hasattr(self, 'skip_memory'): self.skip_memory.reset_dynamic() def encode_write(self, style_ids, comp_ids, style_imgs, reset_dynamic_memory=True): """ Encode feature from input data and write it to memory Args: # batch size B can be different with infer phase style_ids [B]: style index comp_ids [B, n_comp_types]: component ids of style chars style_imgs [B, 1, 128, 128]: eq_fonts """ if reset_dynamic_memory: # reset dynamic memory before write self.reset_dynamic_memory() # encode & write component feature feats = self.component_encoder(style_imgs) # [B, n_comp_types, C, H, W] comp_feats = feats[-1] skips = self.component_encoder.filter_skips(feats) # filter skip features self.memory.write(style_ids, comp_ids, comp_feats) if hasattr(self, 'skip_memory'): self.skip_memory.write(style_ids, comp_ids, skips[0]) return comp_feats def read_decode(self, target_style_ids, target_comp_ids): """ Read feature from memory and decode it Args: # batch size B can be different with write phase target_style_ids: [B] target_comp_ids: [B, n_comp_types] """ # read memory w/ or w/o persistent memory # [B, n_comp_types, C, H, W] comp_feats = self.memory.read(target_style_ids, target_comp_ids) skips = None if hasattr(self, 'skip_memory'): skip_feats = self.skip_memory.read(target_style_ids, target_comp_ids) skips = [skip_feats] out = self.decoder(comp_feats, skips) return out ================================================ FILE: models/memory.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import numpy as np import torch import torch.nn as nn from models.modules import split_dim, ConvBlock import datasets.kor_decompose as kor import datasets.thai_decompose as thai def comp_id_to_addr(ids, language): """ Component id to memory address converter Args: ids [B, 3 or 4], torch.tensor: [B, 3] -> kor, [B, 4] -> thai. """ ids = ids.clone() if language == 'kor': ids[:, 1] += kor.N_CHO ids[:, 2] += kor.N_CHO + kor.N_JUNG elif language == 'thai': ids[:, 1] += thai.N_CONSONANTS ids[:, 2] += thai.N_CONSONANTS + thai.N_UPPERS ids[:, 3] += thai.N_CONSONANTS + thai.N_UPPERS + thai.N_HIGHESTS else: raise ValueError(language) return ids class DynamicMemory(nn.Module): # NOTE the dynamic memory can be accelerated by using torch tensor instead of python dict. def __init__(self): super().__init__() self.memory = {} self.reset() def write(self, style_ids, comp_addrs, comp_feats): """ Batch write Args: style_ids: [B] comp_addrs: [B, 3] comp_feats: [B, 3, mem_shape] """ assert len(style_ids) == len(comp_addrs) == len(comp_feats), "Input sizes are different" # batch iter for style_id, comp_addrs_per_char, comp_feats_per_char in zip(style_ids, comp_addrs, comp_feats): # comp iter for comp_addr, comp_feat in zip(comp_addrs_per_char, comp_feats_per_char): self.write_point(style_id, comp_addr, comp_feat) def read(self, style_ids, comp_addrs, reduction='mean'): """ Batch read Args: style_ids: [B] comp_addrs: [B, 3] reduction: reduction method if multiple features exist in sample memory address: ['mean' (default), 'first', 'rand', 'none'] """ out = [] for style_id, comp_addrs_per_char in zip(style_ids, comp_addrs): char_feats = [] for comp_addr in comp_addrs_per_char: comp_feat = self.read_point(style_id, comp_addr, reduction) char_feats.append(comp_feat) char_feats = torch.stack(char_feats) # [3, mem_shape] out.append(char_feats) out = torch.stack(out) # [B, 3, mem_shape] return out def write_point(self, style_id, comp_addr, data): self.memory.setdefault(style_id.item(), {}) \ .setdefault(comp_addr.item(), []) \ .append(data) def read_point(self, style_id, comp_addr, reduction='mean'): """ Point read """ comp_feats = self.memory[style_id.item()][comp_addr.item()] return self.reduce_features(comp_feats, reduction) def reduce_features(self, feats, reduction='mean'): if len(feats) == 1: return feats[0] if reduction == 'mean': return torch.stack(feats).mean(dim=0) elif reduction == 'first': return feats[0] elif reduction == 'rand': return np.random.choice(feats) elif reduction == 'none': return feats else: raise ValueError(reduction) def reset(self): self.memory = {} def reset_batch(self, style_ids, comp_addrs): for style_id, comp_addrs_per_char in zip(style_ids, comp_addrs): for comp_addr in comp_addrs_per_char: self.reset_point(style_id, comp_addr) def reset_point(self, style_id, comp_addr): self.memory[style_id.item()].pop(comp_addr.item()) class PersistentMemory(nn.Module): def __init__(self, n_comps, mem_shape): """ Args: mem_shape: (C, H, W) tuple (3-elem) """ super().__init__() self.shape = mem_shape self.bias = nn.Parameter(torch.randn(n_comps, *mem_shape)) C = mem_shape[0] self.hypernet = nn.Sequential( ConvBlock(C, C), ConvBlock(C, C), ConvBlock(C, C) ) def read(self, comp_addrs): b = self.bias[comp_addrs] # [B, 3, mem_shape] return b def forward(self, x, comp_addrs): """ Args: x: [B, 3, *mem_shape] comp_addr: [B, 3] """ b = self.read(comp_addrs) # [B, 3, *mem_shape] * 2 B = b.size(0) b = b.flatten(0, 1) b = self.hypernet(b) b = split_dim(b, 0, B) return x + b class Memory(nn.Module): # n_components: # of total comopnents. 19 + 21 + 28 = 68 in kr. STYLE_ADDR = -1 def __init__(self, mem_shape, n_comps, persistent, language): """ Args: mem_shape (tuple [3]): memory shape in (C, H, W) tuple, which is same as encoded feature shape n_comps: # of total components, which identify persistent memory size """ super().__init__() self.dynamic_memory = DynamicMemory() self.mem_shape = mem_shape self.persistent = persistent self.language = language if persistent: self.persistent_memory = PersistentMemory(n_comps, mem_shape) def write(self, style_ids, comp_ids, comp_feats): """ Write data into dynamic memory """ comp_addrs = comp_id_to_addr(comp_ids, self.language) self.dynamic_memory.write(style_ids, comp_addrs, comp_feats) def read(self, style_ids, comp_ids): """ Read data from memory (dynamic w/ or w/o persistent) Args: comp_ids [B, 3] """ # memory shape = [B, 3, mem_shape] comp_addrs = comp_id_to_addr(comp_ids, self.language) mem = self.dynamic_memory.read(style_ids, comp_addrs) if self.persistent: mem = self.persistent_memory(mem, comp_addrs) return mem def reset_style(self, style_ids): style_addrs = self.get_style_addr(len(style_ids)) self.dynamic_memory.reset_batch(style_ids, style_addrs) def write_style(self, style_ids, style_codes): style_addrs = self.get_style_addr(len(style_ids)) self.dynamic_memory.write(style_ids, style_addrs, style_codes.unsqueeze(1)) def read_style(self, style_ids): style_addrs = self.get_style_addr(len(style_ids)) return self.dynamic_memory.read(style_ids, style_addrs).squeeze(1) def get_style_addr(self, N): return torch.full([N, 1], self.STYLE_ADDR, dtype=torch.long) def reset_dynamic(self): """ Reset dynamic memory """ self.dynamic_memory.reset() ================================================ FILE: models/modules/__init__.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from .modules import * from .blocks import * from .self_attention import * ================================================ FILE: models/modules/blocks.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from functools import partial import torch import torch.nn as nn import torch.nn.functional as F from .modules import spectral_norm class Flatten(nn.Module): def __init__(self, start_dim=1, end_dim=-1): super(Flatten, self).__init__() self.start_dim = start_dim self.end_dim = end_dim def forward(self, input): return input.flatten(self.start_dim, self.end_dim) def dispatcher(dispatch_fn): def decorated(key, *args): if callable(key): return key if key is None: key = 'none' return dispatch_fn(key, *args) return decorated @dispatcher def norm_dispatch(norm): return { 'none': nn.Identity, 'in': partial(nn.InstanceNorm2d, affine=False), # false as default 'bn': nn.BatchNorm2d, }[norm.lower()] @dispatcher def w_norm_dispatch(w_norm): # Unlike other dispatcher, w_norm is function, not class. return { 'spectral': spectral_norm, 'none': lambda x: x }[w_norm.lower()] @dispatcher def activ_dispatch(activ): return { "none": nn.Identity, "relu": nn.ReLU, "lrelu": partial(nn.LeakyReLU, negative_slope=0.2) }[activ.lower()] @dispatcher def pad_dispatch(pad_type): return { "zero": nn.ZeroPad2d, "replicate": nn.ReplicationPad2d, "reflect": nn.ReflectionPad2d }[pad_type.lower()] class LinearBlock(nn.Module): """Pre-activate linear block""" def __init__(self, C_in, C_out, norm='none', activ='relu', bias=True, w_norm='none', dropout=0.): super().__init__() activ = activ_dispatch(activ) if norm.lower() == 'bn': norm = nn.BatchNorm1d elif norm.lower() == 'none': norm = nn.Identity else: raise ValueError(f"LinearBlock supports BN only (but {norm} is given)") w_norm = w_norm_dispatch(w_norm) self.norm = norm(C_in) self.activ = activ() if dropout > 0.: self.dropout = nn.Dropout(p=dropout) self.linear = w_norm(nn.Linear(C_in, C_out, bias)) def forward(self, x): x = self.norm(x) x = self.activ(x) if hasattr(self, 'dropout'): x = self.dropout(x) return self.linear(x) class ConvBlock(nn.Module): """Pre-activate conv block""" def __init__(self, C_in, C_out, kernel_size=3, stride=1, padding=1, norm='none', activ='relu', bias=True, upsample=False, downsample=False, w_norm='none', pad_type='zero', dropout=0.): if kernel_size == 1: assert padding == 0 super().__init__() self.C_in = C_in self.C_out = C_out activ = activ_dispatch(activ) norm = norm_dispatch(norm) w_norm = w_norm_dispatch(w_norm) pad = pad_dispatch(pad_type) self.upsample = upsample self.downsample = downsample self.norm = norm(C_in) self.activ = activ() if dropout > 0.: self.dropout = nn.Dropout2d(p=dropout) self.pad = pad(padding) self.conv = w_norm(nn.Conv2d(C_in, C_out, kernel_size, stride, bias=bias)) def forward(self, x): x = self.norm(x) x = self.activ(x) if self.upsample: x = F.interpolate(x, scale_factor=2) if hasattr(self, 'dropout'): x = self.dropout(x) x = self.conv(self.pad(x)) if self.downsample: x = F.avg_pool2d(x, 2) return x class ResBlock(nn.Module): """Pre-activate residual block""" def __init__(self, C_in, C_out, kernel_size=3, padding=1, upsample=False, downsample=False, norm='none', w_norm='none', activ='relu', pad_type='zero', dropout=0.): assert not (upsample and downsample) super().__init__() w_norm = w_norm_dispatch(w_norm) self.C_in = C_in self.C_out = C_out self.upsample = upsample self.downsample = downsample self.conv1 = ConvBlock(C_in, C_out, kernel_size, 1, padding, norm, activ, upsample=upsample, w_norm=w_norm, pad_type=pad_type, dropout=dropout) self.conv2 = ConvBlock(C_out, C_out, kernel_size, 1, padding, norm, activ, w_norm=w_norm, pad_type=pad_type, dropout=dropout) if C_in != C_out or upsample or downsample: self.skip = w_norm(nn.Conv2d(C_in, C_out, 1)) def forward(self, x): out = x out = self.conv1(out) out = self.conv2(out) if self.downsample: out = F.avg_pool2d(out, 2) if hasattr(self, 'skip'): if self.upsample: x = F.interpolate(x, scale_factor=2) x = self.skip(x) if self.downsample: x = F.avg_pool2d(x, 2) out = out + x return out class Upsample1x1(nn.Module): """Upsample 1x1 to 2x2 using Linear""" def __init__(self, C_in, C_out, norm='none', activ='relu', w_norm='none'): assert norm.lower() != 'in', 'Do not use instance norm for 1x1 spatial size' super().__init__() self.C_in = C_in self.C_out = C_out self.proj = ConvBlock( C_in, C_out*4, 1, 1, 0, norm=norm, activ=activ, w_norm=w_norm ) def forward(self, x): # x: [B, C_in, 1, 1] x = self.proj(x) # [B, C_out*4, 1, 1] B, C = x.shape[:2] return x.view(B, C//4, 2, 2) class HourGlass(nn.Module): """U-net like hourglass module""" def __init__(self, C_in, C_max, size, n_downs, n_mids=1, norm='none', activ='relu', w_norm='none', pad_type='zero'): """ Args: C_max: maximum C_out of left downsampling block's output """ super().__init__() assert size == n_downs ** 2, "HGBlock assume that the spatial size is downsampled to 1x1." self.C_in = C_in ConvBlk = partial(ConvBlock, norm=norm, activ=activ, w_norm=w_norm, pad_type=pad_type) self.lefts = nn.ModuleList() c_in = C_in for i in range(n_downs): c_out = min(c_in*2, C_max) self.lefts.append(ConvBlk(c_in, c_out, downsample=True)) c_in = c_out # 1x1 conv for mids self.mids = nn.Sequential( *[ ConvBlk(c_in, c_out, kernel_size=1, padding=0) for _ in range(n_mids) ] ) self.rights = nn.ModuleList() for i, lb in enumerate(self.lefts[::-1]): c_out = lb.C_in c_in = lb.C_out channel_in = c_in*2 if i else c_in # for channel concat if i == 0: block = Upsample1x1(channel_in, c_out, norm=norm, activ=activ, w_norm=w_norm) else: block = ConvBlk(channel_in, c_out, upsample=True) self.rights.append(block) def forward(self, x): features = [] for lb in self.lefts: x = lb(x) features.append(x) assert x.shape[-2:] == torch.Size((1, 1)) for i, (rb, lf) in enumerate(zip(self.rights, features[::-1])): if i: x = torch.cat([x, lf], dim=1) x = rb(x) return x ================================================ FILE: models/modules/modules.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. """ import torch.nn as nn def split_dim(x, dim, n_chunks): shape = x.shape assert shape[dim] % n_chunks == 0 return x.view(*shape[:dim], n_chunks, shape[dim] // n_chunks, *shape[dim+1:]) def weights_init(init_type='default'): """Adopted and modified from FUNIT Copyright (C) 2019 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode). """ def init_fun(m): classname = m.__class__.__name__ if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'): if init_type == 'gaussian': nn.init.normal_(m.weight.data, 0.0, 0.02) elif init_type == 'xavier': nn.init.xavier_normal_(m.weight.data, gain=2**0.5) elif init_type == 'kaiming': nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': nn.init.orthogonal_(m.weight.data, gain=2**0.5) elif init_type == 'default': pass else: assert 0, "Unsupported initialization: {}".format(init_type) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) return init_fun def spectral_norm(module): """ init & apply spectral norm """ nn.init.xavier_uniform_(module.weight, 2 ** 0.5) if hasattr(module, 'bias') and module.bias is not None: module.bias.data.zero_() return nn.utils.spectral_norm(module) ================================================ FILE: models/modules/self_attention.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import torch import torch.nn as nn import torch.nn.functional as F from .modules import split_dim from .blocks import w_norm_dispatch, ConvBlock, norm_dispatch class Attention(nn.Module): def __init__(self, C_in_q, C_in_kv, C_qk, C_v, w_norm='none', scale=False, n_heads=1, down_kv=False, rel_pos_size=None): """ Args: C_in_q: query source (encoder feature x) C_in_kv: key/value source (decoder feature y) C_qk: inner query/key dim, which should be same C_v: inner value dim, which same as output dim down_kv: Area attention for lightweight self-attention w/ mean pooling. rel_pos_size: height & width for relative positional embedding. If None or 0 is given, do not use relative positional embedding. """ super().__init__() self.n_heads = n_heads self.down_kv = down_kv w_norm = w_norm_dispatch(w_norm) self.q_proj = w_norm(nn.Conv1d(C_in_q, C_qk, 1)) self.k_proj = w_norm(nn.Conv1d(C_in_kv, C_qk, 1)) self.v_proj = w_norm(nn.Conv1d(C_in_kv, C_v, 1)) self.out = w_norm(nn.Conv2d(C_v, C_v, 1)) if scale: self.scale = 1. / (C_qk ** 0.5) if rel_pos_size: C_h_qk = C_qk // n_heads self.rel_pos = RelativePositionalEmbedding2d( C_h_qk, rel_pos_size, rel_pos_size, down_kv=down_kv ) def forward(self, x, y): """ Attend from x (decoder) to y (encoder) Args: x: decoder feature y: encoder feature """ B, C, H, W = x.shape flat_x = x.flatten(start_dim=2) # [B, C, H*W] if not self.down_kv: flat_y = y.flatten(start_dim=2) else: y_down = F.avg_pool2d(y, 2) flat_y = y_down.flatten(2) # [B, C, H*W/4] query = self.q_proj(flat_x) # [B, C_qk, H*W] key = self.k_proj(flat_y) # [B, C_qk, H*W] value = self.v_proj(flat_y) # [B, C, H*W] query = split_dim(query, 1, self.n_heads) key = split_dim(key, 1, self.n_heads) value = split_dim(value, 1, self.n_heads) attn_score = torch.einsum('bhcq,bhck->bhqk', query, key) # [B, n_heads, H*W, H*W] if hasattr(self, 'rel_pos'): attn_score += self.rel_pos(query) if hasattr(self, 'scale'): attn_score *= self.scale attn_w = F.softmax(attn_score, dim=-1) attn_out = torch.einsum('bhqk,bhck->bhcq', attn_w, value).reshape(B, C, H, W) out = self.out(attn_out) return out class AttentionFFNBlock(nn.Module): """ Transformer-like attention + ffn block """ def __init__(self, C_in_q, C_in_kv, C_qk, C_v, size, scale=True, norm='ln', dropout=0.1, activ='relu', n_heads=1, ffn_mult=4, area=False, rel_pos=False): super().__init__() self.C_out = C_v if rel_pos: rel_pos = size self.attn = Attention( C_in_q, C_in_kv, C_qk, C_v, scale=scale, n_heads=n_heads, down_kv=area, rel_pos_size=rel_pos ) self.dropout = nn.Dropout2d(dropout) self.ffn = nn.Sequential( ConvBlock(C_v, C_v*ffn_mult, 1, 1, 0, activ='none'), nn.Dropout2d(dropout), ConvBlock(C_v*ffn_mult, C_v, 1, 1, 0, activ=activ) ) if norm == 'ln': self.norm = nn.LayerNorm([C_v, size, size]) else: norm = norm_dispatch(norm) self.norm = norm(C_v) def forward(self, x, y): skip = x x = self.norm(x) x = self.attn(x, y) x = self.dropout(x) x = self.ffn(x) x += skip return x class SAFFNBlock(AttentionFFNBlock): def __init__(self, C, size, C_qk_ratio=0.25, scale=True, norm='ln', dropout=0.1, activ='relu', n_heads=1, ffn_mult=4, area=False, rel_pos=False): C_in_q = C C_in_kv = C C_qk = int(C * C_qk_ratio) C_v = C super().__init__( C_in_q, C_in_kv, C_qk, C_v, size, scale, norm, dropout, activ, n_heads, ffn_mult, area, rel_pos ) self.C_in = C def forward(self, x): return super().forward(x, x) class GlobalContext(nn.Module): """ Global-context """ def __init__(self, C, bottleneck_ratio=0.25, w_norm='none'): super().__init__() C_bottleneck = int(C * bottleneck_ratio) w_norm = w_norm_dispatch(w_norm) self.k_proj = w_norm(nn.Conv2d(C, 1, 1)) self.transform = nn.Sequential( w_norm(nn.Linear(C, C_bottleneck)), nn.LayerNorm(C_bottleneck), nn.ReLU(), w_norm(nn.Linear(C_bottleneck, C)) ) def forward(self, x): # x: [B, C, H, W] context_logits = self.k_proj(x) # [B, 1, H, W] context_weights = F.softmax(context_logits.flatten(1), dim=1) # [B, HW] context = torch.einsum('bci,bi->bc', x.flatten(2), context_weights) out = self.transform(context) return out[..., None, None] class GCBlock(nn.Module): """ Global-context block """ def __init__(self, C, bottleneck_ratio=0.25, w_norm='none'): super().__init__() self.gc = GlobalContext(C, bottleneck_ratio, w_norm) def forward(self, x): gc = self.gc(x) return x + gc class RelativePositionalEmbedding2d(nn.Module): """ Learned relative positional embedding return Q * (R_x + R_y) for input Q and learned embedding R """ def __init__(self, emb_dim, H, W, down_kv=False): super().__init__() self.H = H self.W = W self.down_kv = down_kv self.h_emb = nn.Embedding(H*2-1, emb_dim) self.w_emb = nn.Embedding(W*2-1, emb_dim) rel_y, rel_x = self.rel_grid() self.register_buffer('rel_y', rel_y) self.register_buffer('rel_x', rel_x) def rel_grid(self): # rel_y in [-(H-1), (H-1)] # rel_x in [-(W-1), (W-1)] y, x = torch.meshgrid(torch.arange(self.H), torch.arange(self.W)) # rel_y[i, j] = j_y - i_y # rel_x[i, j] = j_x - i_x rel_y = y.reshape(1, -1) - y.reshape(-1, 1) rel_x = x.reshape(1, -1) - x.reshape(-1, 1) if self.down_kv: def down(x): n_q, n_k = x.shape x = x.view(n_q, 1, int(n_k**0.5), int(n_k**0.5)) return (F.avg_pool2d(x.float(), 2) - 0.5).flatten(1).long() rel_y = down(rel_y) rel_x = down(rel_x) # shifting negative values to semi-positive values (>=0) rel_y += (self.H-1) rel_x += (self.W-1) return rel_y, rel_x def forward(self, query): """ Args: query: [B, n_heads, C_qk, H*W] return: [B, n_heads, H*W, H*W] """ r_x = self.w_emb(self.rel_x) # [H*W, H*W, C_qk] r_y = self.h_emb(self.rel_y) # [H*W, H*W, C_qk] S_rel = torch.einsum('bhci,ijc->bhij', query, r_x + r_y) return S_rel ================================================ FILE: requirements.txt ================================================ torch==1.1.0 torchvision==0.2.2 h5py==2.9.0 numpy==1.17.2 Pillow==6.2.0 sconf==0.2.1 tqdm==4.47.0 tensorboard==2.0.0 tensorboardX==1.9 fontTools==4.12.1 fire==0.2.1 ================================================ FILE: scripts/prepare_dataset.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import os import json from itertools import chain from functools import reduce from pathlib import Path from tqdm import tqdm import h5py as h5 import fire import numpy as np from PIL import Image, ImageDraw, ImageFont, features from fontTools.ttLib import TTFont from logger import Logger from datasets import thai_decompose as thai CODE_RANGE = { 'kor': [[0x0021, 0x007E], [0x3131, 0x3163], [0xAC00, 0xD7A3]], 'thai': [[0x0E01, 0x0E3A], [0x0E3F, 0x0E5B]] } def get_code_points(language): codes = set() code_range = CODE_RANGE[language] for rangemin, rangemax in code_range: for codepoint in range(rangemin, rangemax+1): codes.add(chr(codepoint)) return codes def dump_to_hdf5(dump_path, font_name, images, chars, compression=None): with h5.File(dump_path, 'w') as f: dset = f.create_group('dataset') dset.attrs['font_name'] = font_name N = len(images) dset.create_dataset('images', (N, 128, 128), np.uint8, compression=compression, data=np.stack(images)) data = np.array(chars) dset.create_dataset('chars', data.shape, np.int, compression=compression, data=np.array(chars)) class FontProcessor(object): def __init__(self, language, resize_method="bilinear", font_size_factor=2, sample_size=128): if language == 'thai': assert features.check('raqm'), 'Please install raqm first for thai font rendering' self.logger = Logger.get(file_path='preparedata.log', level='error') self.language = language self.targetcodes = get_code_points(self.language) if resize_method == 'bilinear': self.resize_method = Image.BILINEAR else: raise ValueError('Invalid resize method: {}'.format(resize_method)) self.sample_size = sample_size self.font_size = self.sample_size * font_size_factor def ord(self, char): if self.language == 'kor': return ord(char) elif self.language == 'thai': return thai.decompose_ords(char) else: raise ValueError(self.language) def is_renderable_char(self, font, ch): ch = self.fix_char_order_if_thai(ch) try: size = reduce(lambda x, y: x * y, font.getsize(ch)) except OSError: self.logger.warning('{}, "{}" ({}) cannot be opened'.format(font, ch, self.ord(ch))) return False if not size: self.logger.warning('{}, "{}" ({}) has size {}'.format( font, ch, self.ord(ch), font.getsize(ch)) ) return False return True def avail_chars(self, targetfontpath, pilfont): ttfont = TTFont(targetfontpath) existing_chars = {chr(key) for table in ttfont['cmap'].tables for key in table.cmap.keys()} rendercheckedchars = {ch for ch in existing_chars if self.is_renderable_char(pilfont, ch)} return rendercheckedchars def get_charsize(self, char, font): char = self.fix_char_order_if_thai(char) size_x, size_y = font.getsize(char) offset_x, offset_y = font.getoffset(char) return size_x-offset_x, size_y-offset_y def render_center_no_offset(self, char, font, fontmaxsize, size=128, margin=0): char = self.fix_char_order_if_thai(char) size_x, size_y = font.getsize(char) offset_x, offset_y = font.getoffset(char) roi_w = size_x-offset_x roi_h = size_y-offset_y img = Image.new('L', (roi_w, roi_h), 255) draw = ImageDraw.Draw(img) draw.text((-offset_x, -offset_y), char, font=font) if img.size[0] == 0 or img.size[1] == 0: self.logger.warning( '{}, "{}" ({}) is empty (size=0)'.format(font, char, self.ord(char)) ) return False npimg = 255 - np.array(img) if not npimg.sum(): self.logger.warning( '{}, "{}" ({}) is empty (no black)'.format(font, char, self.ord(char)) ) return False wmin = npimg.sum(0).nonzero()[0].min() wmax = npimg.sum(0).nonzero()[0].max() hmin = npimg.sum(1).nonzero()[0].min() hmax = npimg.sum(1).nonzero()[0].max() npimg = 255 - npimg[hmin:hmax+1, wmin:wmax+1] canvas_size = int(fontmaxsize*(1+margin)) left_margin = (canvas_size - roi_w)//2 right_margin = canvas_size - roi_w - left_margin top_margin = (canvas_size - roi_h)//2 bottom_margin = canvas_size - roi_h - top_margin npimg = np.pad(npimg, ((top_margin, bottom_margin), (left_margin, right_margin)), 'constant', constant_values=255) img = Image.fromarray(npimg).resize((size, size), resample=self.resize_method) return img def dump_fonts(self, fonts, dump_dir, compression=None): """ calculates maximum size of available codepoints target text character is rendered accordingly relative size within each font are maintained proportionally relative size across fonts are adjusted (Maximum size codepoint to be maintained in the canvas) """ self.logger.info('# Font candidates: {}'.format(len(fonts))) dump_dir = Path(dump_dir) dump_dir.mkdir(parents=True, exist_ok=True) assert dump_dir.is_dir() n_fonts = len(fonts) for i, targetfontpath in enumerate(fonts): targetfontname = os.path.basename(targetfontpath) # w/ ext font_name = os.path.splitext(targetfontname)[0] # w/o ext hdf5_name = "{}.hdf5".format(font_name) dump_path = dump_dir / hdf5_name if dump_path.exists(): continue font = ImageFont.truetype(targetfontpath, self.font_size) codepoints = self.avail_chars(targetfontpath, font) # available & desired fonts codepoints = codepoints & self.targetcodes # avail chars if self.language == 'kor': if len(codepoints) == 0: self.logger.error("Font {} don't have any valid chars".format(targetfontname)) continue elif self.language == 'thai': if codepoints != self.targetcodes: self.logger.error("Font {} don't have full components ({}, {})".format( targetfontname, len(codepoints), len(self.targetcodes))) continue codepoints = list(thai.complete_chars()) else: raise ValueError(self.language) # max rendered size sizes = [self.get_charsize(codepoint, font) for codepoint in codepoints] fontmaxsize = max(chain(*sizes)) images = [] chars = [] c = 0 for codepoint in tqdm(codepoints, desc=f"{i+1}. {font_name}"): if not codepoint: self.logger.error("Wrong codepoint: {}".format(codepoint)) raise ValueError(codepoint) img = self.render_center_no_offset(codepoint, font, fontmaxsize, size=self.sample_size, margin=0) if not img: continue images.append(img) chars.append(self.ord(codepoint)) dump_to_hdf5(dump_path, targetfontname, images, chars, compression=compression) self.logger.info("[{:3d}/{:3d}] {} has {} valid chars and {} images...".format( i+1, n_fonts, font_name, len(codepoints), len(images))) def fix_char_order_if_thai(self, char): """ Fix character component order for correct rendering consonant - upper - highest - lower => consonant - lower - upper - highest """ if self.language == 'thai': ords = thai.decompose_ords(char) char = thai.compose(ords[0], ords[3], ords[1], ords[2]) return char def main(language, fonts_dir, meta_path, dump_dir): """ Args: language: kor / thai fonts_dir: font directory that has ttf files meta_path: meta file path dump_dir: dataset dir """ fonts_dir = Path(fonts_dir) meta = json.load(open(meta_path)) allfonts = set(meta['train']['fonts'] + meta['valid']['fonts']) fonts = [ str(fname) for fname in fonts_dir.rglob("*.ttf") if fname.name in allfonts ] assert len(allfonts) == len(fonts) processor = FontProcessor(language) processor.dump_fonts(fonts, dump_dir) if __name__ == '__main__': fire.Fire(main) ================================================ FILE: ssim.py ================================================ from math import exp import torch import torch.nn.functional as F ############################################################################### # SSIM and MS-SSIM # from https://github.com/jorge-pessoa/pytorch-msssim ############################################################################### def gaussian(window_size, sigma): gauss = torch.Tensor( [exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)] ) return gauss/gauss.sum() def create_window(window_size, channel=1): _1D_window = gaussian(window_size, 1.5).unsqueeze(1) _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) window = _2D_window.expand(channel, 1, window_size, window_size).contiguous() return window def ssim(img1, img2, window_size=11, window=None, size_average=True, full=False, val_range=None): # Value range can be different from 255. Other common ranges are 1 (sigmoid) and 2 (tanh). if val_range is None: if torch.max(img1) > 128: max_val = 255 else: max_val = 1 if torch.min(img1) < -0.5: min_val = -1 else: min_val = 0 L = max_val - min_val else: L = val_range padd = 0 (_, channel, height, width) = img1.size() if window is None: real_size = min(window_size, height, width) window = create_window(real_size, channel=channel).to(img1.device) mu1 = F.conv2d(img1, window, padding=padd, groups=channel) mu2 = F.conv2d(img2, window, padding=padd, groups=channel) mu1_sq = mu1.pow(2) mu2_sq = mu2.pow(2) mu1_mu2 = mu1 * mu2 sigma1_sq = F.conv2d(img1 * img1, window, padding=padd, groups=channel) - mu1_sq sigma2_sq = F.conv2d(img2 * img2, window, padding=padd, groups=channel) - mu2_sq sigma12 = F.conv2d(img1 * img2, window, padding=padd, groups=channel) - mu1_mu2 C1 = (0.01 * L) ** 2 C2 = (0.03 * L) ** 2 v1 = 2.0 * sigma12 + C2 v2 = sigma1_sq + sigma2_sq + C2 cs = torch.mean(v1 / v2) # contrast sensitivity ssim_map = ((2 * mu1_mu2 + C1) * v1) / ((mu1_sq + mu2_sq + C1) * v2) if size_average: ret = ssim_map.mean() else: ret = ssim_map.mean(1).mean(1).mean(1) if full: return ret, cs return ret def msssim(img1, img2, weights=None, window_size=11, window=None, size_average=True, val_range=None, normalize=False): if weights is None: weights = torch.FloatTensor([0.0448, 0.2856, 0.3001, 0.2363, 0.1333]).to(img1.device) levels = weights.size(0) mssim = [] mcs = [] for _ in range(levels): sim, cs = ssim( img1, img2, window_size=window_size, window=window, size_average=size_average, full=True, val_range=val_range ) mssim.append(sim) mcs.append(cs) img1 = F.avg_pool2d(img1, (2, 2)) img2 = F.avg_pool2d(img2, (2, 2)) mssim = torch.stack(mssim) mcs = torch.stack(mcs) # Normalize (to avoid NaNs during training unstable models, not compliant with original definition) if normalize: mssim = (mssim + 1) / 2 mcs = (mcs + 1) / 2 pow1 = mcs ** weights pow2 = mssim ** weights # From Matlab implementation https://ece.uwaterloo.ca/~z70wang/research/iwssim/ output = torch.prod(pow1[:-1] * pow2[-1]) return output # Classes to re-use window class SSIM(torch.nn.Module): def __init__(self, window_size=11, size_average=True, val_range=None, channel=1): super(SSIM, self).__init__() self.window_size = window_size self.size_average = size_average self.val_range = val_range self.channel = channel window = create_window(window_size, channel) self.register_buffer('window', window) def forward(self, img1, img2): assert self.channel == img1.size(1) return ssim( img1, img2, window=self.window, window_size=self.window_size, size_average=self.size_average, val_range=self.val_range ) class MSSSIM(torch.nn.Module): def __init__(self, weights=None, window_size=11, size_average=True, val_range=None, channel=1, normalize=False): super(MSSSIM, self).__init__() self.window_size = window_size self.size_average = size_average self.val_range = val_range self.channel = channel self.normalize = normalize if weights is None: weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333] self.register_buffer('weights', torch.as_tensor(weights)) window = create_window(window_size, channel) self.register_buffer('window', window) def forward(self, img1, img2): assert img1.size(1) == self.channel return msssim( img1, img2, weights=self.weights, window_size=self.window_size, window=self.window, size_average=self.size_average, val_range=self.val_range, normalize=self.normalize ) ================================================ FILE: train.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import sys import json from pathlib import Path import argparse import random import torch from torch.utils.data import DataLoader import torch.optim as optim from torchvision import transforms import numpy as np from sconf import Config, dump_args from logger import Logger from models import MACore, Discriminator, AuxClassifier from models.modules import weights_init from datasets import HDF5Data, get_ma_dataset, get_ma_val_dataset import datasets.kor_decompose as kor import datasets.thai_decompose as thai import utils from trainer import Trainer, load_checkpoint from evaluator import Evaluator def get_dset_loader(data, avail_fonts, avail_chars, transform, shuffle, cfg, content_font=None): dset, collate_fn = get_ma_dataset( data, avail_fonts, avail_chars=avail_chars, transform=transform, **cfg.get('dset_args', {}), content_font=content_font, language=cfg['language'] ) loader = DataLoader(dset, batch_size=cfg['batch_size'], shuffle=shuffle, num_workers=cfg['n_workers'], collate_fn=collate_fn) return dset, loader def get_val_dset_loader(data, avail_fonts, avail_chars, trn_avail_chars, transform, batch_size, n_workers=2, n_max_match=3, content_font=None, language=None): style_avails = { font_name: trn_avail_chars for font_name in avail_fonts } dset, collate_fn = get_ma_val_dataset( data, avail_fonts, avail_chars, style_avails, n_max_match=n_max_match, transform=transform, ret_targets=True, first_shuffle=True, content_font=content_font, language=language ) loader = DataLoader(dset, batch_size=batch_size, shuffle=False, num_workers=n_workers, collate_fn=collate_fn) return dset, loader def setup_args_and_config(): parser = argparse.ArgumentParser('MaHFG') parser.add_argument("name") parser.add_argument("config_paths", nargs="+") parser.add_argument("--show", action="store_true", default=False) parser.add_argument("--resume", default=None) parser.add_argument("--log_lv", default='info') parser.add_argument("--debug", default=False, action="store_true") parser.add_argument("--tb-image", default=False, action="store_true", help="Write image log to tensorboard") parser.add_argument("--deterministic", default=False, action="store_true") args, left_argv = parser.parse_known_args() assert not args.name.endswith(".yaml") cfg = Config(*args.config_paths, colorize_modified_item=True) cfg.argv_update(left_argv) if args.debug: cfg['print_freq'] = 1 cfg['tb_freq'] = 1 cfg['max_iter'] = 10 # cfg['save'] = 'last' cfg['val_freq'] = 5 cfg['save_freq'] = 10 args.name += "_debug" args.tb_image = True args.log_lv = 'debug' cfg['data_dir'] = Path(cfg['data_dir']) assert cfg['save_freq'] % cfg['val_freq'] == 0 return args, cfg def setup_language_dependent(cfg): if cfg['language'] == 'kor': content_font = "NanumBarunpenR.ttf" n_comp_types = 3 # cho, jung, jong n_comps = kor.N_COMPONENTS elif cfg['language'] == 'thai': content_font = "NotoSansThai-Regular.ttf" n_comp_types = 4 # consonant, upper, highest, lower n_comps = thai.N_COMPONENTS else: raise ValueError(cfg['language']) return content_font, n_comp_types, n_comps def setup_data(cfg, val_transform): """ setup data, meta_data, and check cross-validation flag Return (tuple): (data, meta_data) data (HDF5Data) meta_data (dict) """ hdf5_paths = list(cfg['data_dir'].glob("*.hdf5")) hdf5_data = HDF5Data(hdf5_paths, val_transform, language=cfg['language']) # setup meta data meta = json.load(open(cfg['data_meta'])) return hdf5_data, meta def setup_cv_dset_loader(hdf5_data, meta, val_transform, n_comp_types, content_font, cfg): trn_chars = meta['train']['chars'] batch_size = cfg['batch_size'] * 3 n_workers = cfg['n_workers'] n_max_match = n_comp_types # for validation dset # seen fonts, unseen chars -> same as original unseen validation sfuc_dset, sfuc_loader = get_val_dset_loader( hdf5_data, meta['train']['fonts'], meta['valid']['chars'], trn_chars, val_transform, batch_size, n_workers, n_max_match, content_font, cfg['language'] ) # unseen fonts, seen chars ufsc_dset, ufsc_loader = get_val_dset_loader( hdf5_data, meta['valid']['fonts'], meta['train']['chars'], trn_chars, val_transform, batch_size, n_workers, n_max_match, content_font, cfg['language'] ) # unseen fonts, unseen chars ufuc_dset, ufuc_loader = get_val_dset_loader( hdf5_data, meta['valid']['fonts'], meta['valid']['chars'], trn_chars, val_transform, batch_size, n_workers, n_max_match, content_font, cfg['language'] ) # setup val_loaders val_loaders = { "SeenFonts-UnseenChars": sfuc_loader, "UnseenFonts-SeenChars": ufsc_loader, "UnseenFonts-UnseenChars": ufuc_loader } return val_loaders def main(): ############################ # argument setup ############################ args, cfg = setup_args_and_config() if args.show: print("### Run Argv:\n> {}".format(' '.join(sys.argv))) print("### Run Arguments:") s = dump_args(args) print(s + '\n') print("### Configs:") print(cfg.dumps()) sys.exit() timestamp = utils.timestamp() unique_name = "{}_{}".format(timestamp, args.name) cfg['unique_name'] = unique_name # for save directory cfg['name'] = args.name utils.makedirs('logs') utils.makedirs(Path('checkpoints', unique_name)) # logger logger_path = Path('logs', f"{unique_name}.log") logger = Logger.get(file_path=logger_path, level=args.log_lv, colorize=True) # writer image_scale = 0.6 writer_path = Path('runs', unique_name) if args.tb_image: writer = utils.TBWriter(writer_path, scale=image_scale) else: image_path = Path('images', unique_name) writer = utils.TBDiskWriter(writer_path, image_path, scale=image_scale) # log default informations args_str = dump_args(args) logger.info("Run Argv:\n> {}".format(' '.join(sys.argv))) logger.info("Args:\n{}".format(args_str)) logger.info("Configs:\n{}".format(cfg.dumps())) logger.info("Unique name: {}".format(unique_name)) # seed np.random.seed(cfg['seed']) torch.manual_seed(cfg['seed']) random.seed(cfg['seed']) if args.deterministic: # https://discuss.pytorch.org/t/how-to-get-deterministic-behavior/18177/16 # https://pytorch.org/docs/stable/notes/randomness.html torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True cfg['n_workers'] = 0 logger.info("#" * 80) logger.info("# Deterministic option is activated !") logger.info("#" * 80) else: torch.backends.cudnn.benchmark = True ############################ # setup dataset & loader ############################ logger.info("Get dataset ...") # setup language dependent values content_font, n_comp_types, n_comps = setup_language_dependent(cfg) # setup transform transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.5], [0.5]) ]) # setup data hdf5_data, meta = setup_data(cfg, transform) # setup dataset trn_dset, loader = get_dset_loader( hdf5_data, meta['train']['fonts'], meta['train']['chars'], transform, True, cfg, content_font=content_font ) logger.info("### Training dataset ###") logger.info("# of avail fonts = {}".format(trn_dset.n_fonts)) logger.info(f"Total {len(loader)} iterations per epochs") logger.info("# of avail items = {}".format(trn_dset.n_avails)) logger.info(f"#fonts = {trn_dset.n_fonts}, #chars = {trn_dset.n_chars}") val_loaders = setup_cv_dset_loader( hdf5_data, meta, transform, n_comp_types, content_font, cfg ) sfuc_loader = val_loaders['SeenFonts-UnseenChars'] sfuc_dset = sfuc_loader.dataset ufsc_loader = val_loaders['UnseenFonts-SeenChars'] ufsc_dset = ufsc_loader.dataset ufuc_loader = val_loaders['UnseenFonts-UnseenChars'] ufuc_dset = ufuc_loader.dataset logger.info("### Cross-validation datasets ###") logger.info( "Seen fonts, Unseen chars | " "#items = {}, #fonts = {}, #chars = {}, #steps = {}".format( len(sfuc_dset), len(sfuc_dset.fonts), len(sfuc_dset.chars), len(sfuc_loader))) logger.info( "Unseen fonts, Seen chars | " "#items = {}, #fonts = {}, #chars = {}, #steps = {}".format( len(ufsc_dset), len(ufsc_dset.fonts), len(ufsc_dset.chars), len(ufsc_loader))) logger.info( "Unseen fonts, Unseen chars | " "#items = {}, #fonts = {}, #chars = {}, #steps = {}".format( len(ufuc_dset), len(ufuc_dset.fonts), len(ufuc_dset.chars), len(ufuc_loader))) ############################ # build model ############################ logger.info("Build model ...") # generator g_kwargs = cfg.get('g_args', {}) gen = MACore( 1, cfg['C'], 1, **g_kwargs, n_comps=n_comps, n_comp_types=n_comp_types, language=cfg['language'] ) gen.cuda() gen.apply(weights_init(cfg['init'])) d_kwargs = cfg.get('d_args', {}) disc = Discriminator(cfg['C'], trn_dset.n_fonts, trn_dset.n_chars, **d_kwargs) disc.cuda() disc.apply(weights_init(cfg['init'])) if cfg['ac_w'] > 0.: C = gen.mem_shape[0] aux_clf = AuxClassifier(C, n_comps, **cfg['ac_args']) aux_clf.cuda() aux_clf.apply(weights_init(cfg['init'])) else: aux_clf = None assert cfg['ac_gen_w'] == 0., "ac_gen loss is only available with ac loss" # setup optimizer g_optim = optim.Adam(gen.parameters(), lr=cfg['g_lr'], betas=cfg['adam_betas']) d_optim = optim.Adam(disc.parameters(), lr=cfg['d_lr'], betas=cfg['adam_betas']) ac_optim = optim.Adam(aux_clf.parameters(), lr=cfg['g_lr'], betas=cfg['adam_betas']) \ if aux_clf is not None else None # resume checkpoint st_step = 1 if args.resume: st_step, loss = load_checkpoint(args.resume, gen, disc, aux_clf, g_optim, d_optim, ac_optim) logger.info("Resumed checkpoint from {} (Step {}, Loss {:7.3f})".format( args.resume, st_step-1, loss)) ############################ # setup validation ############################ evaluator = Evaluator( hdf5_data, trn_dset.avails, logger, writer, cfg['batch_size'], content_font=content_font, transform=transform, language=cfg['language'], val_loaders=val_loaders, meta=meta ) if args.debug: evaluator.n_cv_batches = 10 logger.info("Change CV batches to 10 for debugging") ############################ # start training ############################ trainer = Trainer( gen, disc, g_optim, d_optim, aux_clf, ac_optim, writer, logger, evaluator, cfg ) trainer.train(loader, st_step) if __name__ == "__main__": main() ================================================ FILE: trainer.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import copy from pathlib import Path import torch import torch.nn as nn import torch.nn.functional as F import utils from datasets import cyclize from models.memory import comp_id_to_addr from criterions import hinge_g_loss, hinge_d_loss def has_bn(model): for m in model.modules(): if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)): return True return False def load_checkpoint(path, gen, disc, aux_clf, g_optim, d_optim, ac_optim): ckpt = torch.load(path) gen.load_state_dict(ckpt['generator']) g_optim.load_state_dict(ckpt['optimizer']) if disc is not None: disc.load_state_dict(ckpt['discriminator']) d_optim.load_state_dict(ckpt['d_optimizer']) if aux_clf is not None: aux_clf.load_state_dict(ckpt['aux_clf']) ac_optim.load_state_dict(ckpt['ac_optimizer']) # NOTE epoch is step st_epoch = ckpt['epoch'] + 1 loss = ckpt['loss'] return st_epoch, loss class Trainer: def __init__(self, gen, disc, g_optim, d_optim, aux_clf, ac_optim, writer, logger, evaluator, cfg): self.gen = gen self.gen_ema = copy.deepcopy(self.gen) self.is_bn_gen = has_bn(self.gen) self.disc = disc self.g_optim = g_optim self.d_optim = d_optim self.aux_clf = aux_clf self.ac_optim = ac_optim self.writer = writer self.logger = logger self.evaluator = evaluator self.cfg = cfg self.step = 1 self.language = cfg['language'] self.g_losses = {} self.d_losses = {} self.ac_losses = {} def clear_losses(self): """ Integrate & clear loss dict """ # g losses loss_dic = {k: v.item() for k, v in self.g_losses.items()} loss_dic['g_total'] = sum(loss_dic.values()) # d losses loss_dic.update({k: v.item() for k, v in self.d_losses.items()}) # ac losses loss_dic.update({k: v.item() for k, v in self.ac_losses.items()}) self.g_losses = {} self.d_losses = {} self.ac_losses = {} return loss_dic def accum_g(self, decay=0.999): par1 = dict(self.gen_ema.named_parameters()) par2 = dict(self.gen.named_parameters()) for k in par1.keys(): par1[k].data.mul_(decay).add_(1 - decay, par2[k].data) def sync_g_ema(self, style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids): """ update running stats for BN & update max singular value for SN """ org_train_mode = self.gen_ema.training with torch.no_grad(): self.gen_ema.train() self.gen_ema.encode_write(style_ids, style_comp_ids, style_imgs) self.gen_ema.read_decode(trg_ids, trg_comp_ids) self.gen_ema.train(org_train_mode) def train(self, loader, st_step=1, val=None): val = val or {} self.gen.train() self.disc.train() # loss stats losses = utils.AverageMeters("g_total", "pixel", "disc", "gen", "fm", "ac", "ac_gen") # discriminator stats discs = utils.AverageMeters("real", "fake", "real_font", "real_char", "fake_font", "fake_char", "real_acc", "fake_acc", "real_font_acc", "real_char_acc", "fake_font_acc", "fake_char_acc") # etc stats stats = utils.AverageMeters("B_style", "B_target", "ac_acc", "ac_gen_acc") self.step = st_step self.clear_losses() self.logger.info("Start training ...") for (style_ids, style_char_ids, style_comp_ids, style_imgs, trg_ids, trg_char_ids, trg_comp_ids, trg_imgs, *content_imgs) in cyclize(loader): B = trg_imgs.size(0) stats.updates({ "B_style": style_imgs.size(0), "B_target": B }) style_ids = style_ids.cuda() # style_char_ids = style_char_ids.cuda() style_comp_ids = style_comp_ids.cuda() style_imgs = style_imgs.cuda() trg_ids = trg_ids.cuda() trg_char_ids = trg_char_ids.cuda() trg_comp_ids = trg_comp_ids.cuda() trg_imgs = trg_imgs.cuda() # infer comp_feats = self.gen.encode_write(style_ids, style_comp_ids, style_imgs) out = self.gen.read_decode(trg_ids, trg_comp_ids) # D loss real, real_font, real_char, real_feats = self.disc( trg_imgs, trg_ids, trg_char_ids, out_feats=True ) fake, fake_font, fake_char = self.disc(out.detach(), trg_ids, trg_char_ids) self.add_gan_d_loss(real, real_font, real_char, fake, fake_font, fake_char) self.d_optim.zero_grad() self.d_backward() self.d_optim.step() # G loss fake, fake_font, fake_char, fake_feats = self.disc( out, trg_ids, trg_char_ids, out_feats=True ) self.add_gan_g_loss(real, real_font, real_char, fake, fake_font, fake_char) # feature matching loss self.add_fm_loss(real_feats, fake_feats) # disc stats racc = lambda x: (x > 0.).float().mean().item() facc = lambda x: (x < 0.).float().mean().item() discs.updates({ "real": real.mean().item(), "fake": fake.mean().item(), "real_font": real_font.mean().item(), "real_char": real_char.mean().item(), "fake_font": fake_font.mean().item(), "fake_char": fake_char.mean().item(), 'real_acc': racc(real), 'fake_acc': facc(fake), 'real_font_acc': racc(real_font), 'real_char_acc': racc(real_char), 'fake_font_acc': facc(fake_font), 'fake_char_acc': facc(fake_char) }, B) # pixel loss self.add_pixel_loss(out, trg_imgs) self.g_optim.zero_grad() # NOTE ac loss generates & leaves grads to G. # so g_optim.zero_grad() should place in front of ac loss and # g_backward() should follow ac loss. if self.aux_clf is not None: self.add_ac_losses_and_update_stats( comp_feats, style_comp_ids, out, trg_comp_ids, stats ) self.ac_optim.zero_grad() self.ac_backward(retain_graph=True) self.ac_optim.step() self.g_backward() self.g_optim.step() loss_dic = self.clear_losses() losses.updates(loss_dic, B) # generator EMA self.accum_g() if self.is_bn_gen: self.sync_g_ema(style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids) # after step if self.step % self.cfg['tb_freq'] == 0: self.plot(losses, discs, stats) if self.step % self.cfg['print_freq'] == 0: self.log(losses, discs, stats) losses.resets() discs.resets() stats.resets() if self.step % self.cfg['val_freq'] == 0: epoch = self.step / len(loader) self.logger.info("Validation at Epoch = {:.3f}".format(epoch)) self.evaluator.merge_and_log_image('d1', out, trg_imgs, self.step) self.evaluator.validation(self.gen, self.step) # if non-BN generator, sync max singular value of spectral norm. if not self.is_bn_gen: self.sync_g_ema(style_ids, style_comp_ids, style_imgs, trg_ids, trg_comp_ids) self.evaluator.validation(self.gen_ema, self.step, extra_tag='_EMA') # save freq == val freq self.save( loss_dic['g_total'], self.cfg['save'], self.cfg.get('save_freq', self.cfg['val_freq']) ) if self.step >= self.cfg['max_iter']: self.logger.info("Iteration finished.") break self.step += 1 def add_pixel_loss(self, out, target): loss = F.l1_loss(out, target, reduction='mean') * self.cfg['pixel_w'] self.g_losses['pixel'] = loss return loss def add_gan_g_loss(self, real, real_font, real_char, fake, fake_font, fake_char): if self.cfg['gan_w'] == 0.: return 0. g_loss = hinge_g_loss(real_font.detach(), fake_font) + \ hinge_g_loss(real_char.detach(), fake_char) if self.disc.use_rx: g_loss += hinge_g_loss(real.detach(), fake) g_loss *= self.cfg['gan_w'] self.g_losses['gen'] = g_loss return g_loss def add_gan_d_loss(self, real, real_font, real_char, fake, fake_font, fake_char): if self.cfg['gan_w'] == 0.: return 0. d_loss = hinge_d_loss(real_font, fake_font) + \ hinge_d_loss(real_char, fake_char) if self.disc.use_rx: d_loss += hinge_d_loss(real, fake) d_loss *= self.cfg['gan_w'] self.d_losses['disc'] = d_loss return d_loss def add_fm_loss(self, real_feats, fake_feats): if self.cfg['fm_w'] == 0.: return 0. fm_loss = 0. for real_f, fake_f in zip(real_feats, fake_feats): fm_loss += F.l1_loss(real_f.detach(), fake_f) fm_loss = fm_loss / len(real_feats) * self.cfg['fm_w'] self.g_losses['fm'] = fm_loss return fm_loss def add_ac_losses_and_update_stats(self, comp_feats, style_comp_ids, generated, trg_comp_ids, stats): # 1. ac(enc(x)) loss loss, acc = self.infer_ac(comp_feats, style_comp_ids) self.ac_losses['ac'] = loss * self.cfg['ac_w'] stats.ac_acc.update(acc, style_comp_ids.numel()) # 2. ac(enc(fake)) loss # Freeze second encoder to prevent cheating by encoder with utils.temporary_freeze(self.gen.component_encoder): feats = self.gen.component_encoder(generated) gen_comp_feats = feats[-1] loss, acc = self.infer_ac(gen_comp_feats, trg_comp_ids) self.ac_losses['ac_gen'] = loss * self.cfg['ac_w'] stats.ac_gen_acc.update(acc, trg_comp_ids.numel()) def infer_ac(self, comp_feats, comp_ids): """ Auxiliary classifier loss on style or output features """ comp_addrs = comp_id_to_addr(comp_ids, self.language) comp_feats_flat = comp_feats.flatten(0, 1) comp_addrs_flat = comp_addrs.flatten(0, 1) aux_out = self.aux_clf(comp_feats_flat) loss = F.cross_entropy(aux_out, comp_addrs_flat) acc = utils.accuracy(aux_out, comp_addrs_flat) return loss, acc def d_backward(self): with utils.temporary_freeze(self.gen): d_loss = sum(self.d_losses.values()) d_loss.backward() def g_backward(self): with utils.temporary_freeze(self.disc): g_loss = sum(self.g_losses.values()) g_loss.backward() def ac_backward(self, retain_graph): if self.aux_clf is None: return org_grads = utils.freeze(self.gen.memory.persistent_memory) if 'ac' in self.ac_losses: self.ac_losses['ac'].backward(retain_graph=retain_graph) if 'ac_gen' in self.ac_losses: with utils.temporary_freeze(self.aux_clf): self.ac_losses['ac_gen'].backward(retain_graph=retain_graph) utils.unfreeze(self.gen.memory.persistent_memory, org_grads) def save(self, cur_loss, method, save_freq=None): """ Args: method: all / last all: save checkpoint by step last: save checkpoint to 'last.pth' all-last: save checkpoint by step per save_freq and save checkpoint to 'last.pth' always """ if method not in ['all', 'last', 'all-last']: return step_save = False last_save = False if method == 'all' or (method == 'all-last' and self.step % save_freq == 0): step_save = True if method in ('last', 'all-last'): last_save = True assert step_save or last_save save_dic = { 'generator': self.gen.state_dict(), 'generator_ema': self.gen_ema.state_dict(), 'discriminator': self.disc.state_dict(), 'd_optimizer': self.d_optim.state_dict(), 'optimizer': self.g_optim.state_dict(), 'epoch': self.step, 'loss': cur_loss } if self.aux_clf is not None: save_dic['aux_clf'] = self.aux_clf.state_dict() save_dic['ac_optimizer'] = self.ac_optim.state_dict() ckpt_dir = Path("checkpoints", self.cfg['unique_name']) step_ckpt_name = "{:06d}-{}.pth".format(self.step, self.cfg['name']) last_ckpt_name = "last.pth" step_ckpt_path = ckpt_dir / step_ckpt_name last_ckpt_path = ckpt_dir / last_ckpt_name log = "" if step_save: torch.save(save_dic, str(step_ckpt_path)) log = "Checkpoint is saved to {}".format(step_ckpt_path) if last_save: utils.rm(last_ckpt_path) last_ckpt_path.symlink_to(step_ckpt_path.absolute()) log += " w/ {} symlink".format(last_ckpt_name) if not step_save and last_save: utils.rm(last_ckpt_path) torch.save(save_dic, str(last_ckpt_path)) log = "Checkpoint is saved to {}".format(last_ckpt_path) self.logger.info("{}\n".format(log)) def plot(self, losses, discs, stats): tag_scalar_dic = { 'train/g_total_loss': losses.g_total.val, 'train/pixel_loss': losses.pixel.val, 'train/d_loss': losses.disc.val, 'train/g_loss': losses.gen.val, 'train/d_real_font': discs.real_font.val, 'train/d_real_char': discs.real_char.val, 'train/d_fake_font': discs.fake_font.val, 'train/d_fake_char': discs.fake_char.val, 'train/d_real_font_acc': discs.real_font_acc.val, 'train/d_real_char_acc': discs.real_char_acc.val, 'train/d_fake_font_acc': discs.fake_font_acc.val, 'train/d_fake_char_acc': discs.fake_char_acc.val } if self.disc.use_rx: tag_scalar_dic.update({ 'train/d_real': discs.real.val, 'train/d_fake': discs.fake.val, 'train/d_real_acc': discs.real_acc.val, 'train/d_fake_acc': discs.fake_acc.val }) if self.cfg['fm_w'] > 0.: tag_scalar_dic['train/feature_matching'] = losses.fm.val if self.aux_clf is not None: tag_scalar_dic.update({ 'train/ac_loss': losses.ac.val, 'train/ac_acc': stats.ac_acc.val, 'train/ac_gen_loss': losses.ac_gen.val, 'train/ac_gen_acc': stats.ac_gen_acc.val }) self.writer.add_scalars(tag_scalar_dic, self.step) def log(self, losses, discs, stats): self.logger.info( " Step {step:7d}: L1 {L.pixel.avg:7.4f} D {L.disc.avg:7.3f} G {L.gen.avg:7.3f}" " FM {L.fm.avg:7.3f} AC {S.ac_acc.avg:5.1%} AC_gen {S.ac_gen_acc.avg:5.1%}" " R {D.real_acc.avg:7.3f} F {D.fake_acc.avg:7.3f}" " R_font {D.real_font_acc.avg:7.3f} F_font {D.fake_font_acc.avg:7.3f}" " R_char {D.real_char_acc.avg:7.3f} F_char {D.fake_char_acc.avg:7.3f}" " B_stl {S.B_style.avg:5.1f} B_trg {S.B_target.avg:5.1f}" .format(step=self.step, L=losses, D=discs, S=stats)) ================================================ FILE: utils/__init__.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from .utils import * from .visualize import * from .writer import * ================================================ FILE: utils/utils.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import os import shutil from contextlib import contextmanager from datetime import datetime import errno class AverageMeter(): """ Computes and stores the average and current value """ def __init__(self): self.reset() def reset(self): """ Reset all statistics """ self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): """ Update statistics """ self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def __repr__(self): return "{:.3f} (val={:.3f}, count={})".format(self.avg, self.val, self.count) class AverageMeters(): def __init__(self, *keys): self.keys = keys for k in keys: setattr(self, k, AverageMeter()) def resets(self): for k in self.keys: getattr(self, k).reset() def updates(self, dic, n=1): for k, v in dic.items(): getattr(self, k).update(v, n) def __repr__(self): return " ".join(["{}: {}".format(k, str(getattr(self, k))) for k in self.keys]) def accuracy(out, target): pred = out.max(1)[1] corr = (pred == target).sum().item() B = len(target) acc = float(corr) / B return acc @contextmanager def temporary_freeze(module): org_grads = freeze(module) yield unfreeze(module, org_grads) def freeze(module): if module is None: return None org = [] module.eval() for p in module.parameters(): org.append(p.requires_grad) p.requires_grad_(False) return org def unfreeze(module, org=None): if module is None: return module.train() if org is not None: org = iter(org) for p in module.parameters(): grad = next(org) if org else True p.requires_grad_(grad) def makedirs(path): if not os.path.exists(path): try: os.makedirs(path) except OSError as exc: if exc.errno != errno.EEXIST: raise def rm(path): """ remove dir recursively """ if os.path.isdir(path): shutil.rmtree(path) elif os.path.exists(path): os.remove(path) def cp(src, dst): shutil.copy2(src, dst) def timestamp(fmt="%y%m%d_%H-%M-%S"): return datetime.now().strftime(fmt) ================================================ FILE: utils/visualize.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ import torch from torchvision import utils as tv_utils from PIL import Image def make_merged_grid(batchs, merge_dim, **kwargs): """ Generate grid for each batch and integrate them with sep bar Args: merge_dim: 1 => height-wise merge, 2=> width-wise merge. """ sep_bar_size = 12 out = [] for batch in batchs: grid = to_grid(batch, 'torch', **kwargs).cpu() shape = list(grid.size()) shape[merge_dim] = sep_bar_size sep_bar = torch.zeros(*shape) out += [grid, sep_bar] # remove last sep_bar return torch.cat(out[:-1], dim=merge_dim) def make_comparable_grid(*batches, nrow): assert all(len(batches[0]) == len(batch) for batch in batches[1:]) N = len(batches[0]) grids = [] for i in range(0, N, nrow): rows = [batch[i:i+nrow] for batch in batches] row = torch.cat(rows) grid = to_grid(row, 'torch', nrow=nrow) grids.append(grid) C, _H, W = grid.shape sep_bar = torch.zeros(C, 10, W) grids.append(sep_bar) return torch.cat(grids[:-1], dim=1) def normalize(tensor, eps=1e-5): """ Normalize tensor to [0, 1] """ # eps=1e-5 is same as make_grid in torchvision. minv, maxv = tensor.min(), tensor.max() tensor = (tensor - minv) / (maxv - minv + eps) return tensor def to_grid(tensor, to, **kwargs): """ Integrated functions of make_grid and save_image Possible conversions: torch: torch tensor [0, 1] numpy: numpy ndarr [0, 255] pil: PIL image """ to = to.lower() grid = tv_utils.make_grid(tensor, **kwargs, normalize=True) if to == 'torch': return grid ndarr = grid.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy() if to == 'numpy': return ndarr im = Image.fromarray(ndarr) if to == 'pil': return im raise ValueError("Not supported target format `{}`".format(to)) def save_tensor_to_image(tensor, filepath, scale=None): """ Save torch tensor to filepath Same as torchvision.save_image; only scale factor is difference. """ tensor = normalize(tensor) ndarr = tensor.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy() if ndarr.shape[-1] == 1: ndarr = ndarr.squeeze(-1) im = Image.fromarray(ndarr) if scale: size = tuple(map(lambda v: int(v*scale), im.size)) im = im.resize(size, resample=Image.BILINEAR) im.save(filepath) def textboxes(chars, data): images = [data.get_from_reffont(char) for char in chars] return images ================================================ FILE: utils/writer.py ================================================ """ DMFont Copyright (c) 2020-present NAVER Corp. MIT license """ from pathlib import Path import torch.nn.functional as F from . import save_tensor_to_image class Writer: def add_scalars(self, tag_scalar_dic, global_step): raise NotImplementedError() def add_image(self, tag, img_tensor, global_step): raise NotImplementedError() class DiskWriter(Writer): def __init__(self, img_path, scale=None): self.img_dir = Path(img_path) self.img_dir.mkdir(parents=True, exist_ok=True) self.scale = scale def add_scalars(self, tag_scalar_dic, global_step): pass def add_image(self, tag, img_tensor, global_step): path = self.img_dir / "{:07d}-{}.png".format(global_step, tag) save_tensor_to_image(img_tensor, path, self.scale) class TBWriter(Writer): def __init__(self, dir_path, scale=None): from tensorboardX import SummaryWriter self.writer = SummaryWriter(dir_path, flush_secs=30) self.scale = scale def add_scalars(self, tag_scalar_dic, global_step): for tag, scalar in tag_scalar_dic.items(): self.writer.add_scalar(tag, scalar, global_step) def add_image(self, tag, img_tensor, global_step): if self.scale: img_tensor = F.interpolate( img_tensor.unsqueeze(0), scale_factor=self.scale, mode='bilinear', align_corners=False ).squeeze(0) self.writer.add_image(tag, img_tensor, global_step) class TBDiskWriter(TBWriter): def __init__(self, dir_path, img_path, scale=None): super().__init__(dir_path) # composition self._disk_writer = DiskWriter(img_path, scale) def add_image(self, tag, img_tensor, global_step): return self._disk_writer.add_image(tag, img_tensor, global_step)