_

LICENSE

@@ -0,0 +1,25 @@
+ABINet for non-commercial purposes
+
+Copyright (c) 2021, USTC
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

@@ -1,13 +1,142 @@
----
-title: Dvatch Captcha Sneedium
-emoji: 🌍
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
+
+The official code of [ABINet](https://arxiv.org/pdf/2103.06495.pdf) (CVPR 2021, Oral).
+
+ABINet uses a vision model and an explicit language model to recognize text in the wild, which are trained in end-to-end way. The language model (BCN) achieves bidirectional language representation in simulating cloze test, additionally utilizing iterative correction strategy.
+
+![framework](./figs/framework.png)
+
+## Runtime Environment
+
+- We provide a pre-built docker image using the Dockerfile from `docker/Dockerfile`
+
+- Running in Docker
+    ```
+    $ [email protected]:FangShancheng/ABINet.git
+    $ docker run --gpus all --rm -ti --ipc=host -v $(pwd)/ABINet:/app fangshancheng/fastai:torch1.1 /bin/bash
+    ```
+- (Untested) Or using the dependencies
+    ```
+    pip install -r requirements.txt
+    ```
+
+## Datasets
+
+- Training datasets
+
+    1. [MJSynth](http://www.robots.ox.ac.uk/~vgg/data/text/) (MJ): 
+        - Use `tools/create_lmdb_dataset.py` to convert images into LMDB dataset
+        - [LMDB dataset BaiduNetdisk(passwd:n23k)](https://pan.baidu.com/s/1mgnTiyoR8f6Cm655rFI4HQ)
+    2. [SynthText](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) (ST):
+        - Use `tools/crop_by_word_bb.py` to crop images from original [SynthText](http://www.robots.ox.ac.uk/~vgg/data/scenetext/) dataset, and convert images into LMDB dataset by `tools/create_lmdb_dataset.py`
+        - [LMDB dataset BaiduNetdisk(passwd:n23k)](https://pan.baidu.com/s/1mgnTiyoR8f6Cm655rFI4HQ)
+    3. [WikiText103](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip), which is only used for pre-trainig language models:
+        - Use `notebooks/prepare_wikitext103.ipynb` to convert text into CSV format.
+        - [CSV dataset BaiduNetdisk(passwd:dk01)](https://pan.baidu.com/s/1yabtnPYDKqhBb_Ie9PGFXA)
+
+- Evaluation datasets, LMDB datasets can be downloaded from [BaiduNetdisk(passwd:1dbv)](https://pan.baidu.com/s/1RUg3Akwp7n8kZYJ55rU5LQ), [GoogleDrive](https://drive.google.com/file/d/1dTI0ipu14Q1uuK4s4z32DqbqF3dJPdkk/view?usp=sharing).
+    1. ICDAR 2013 (IC13)
+    2. ICDAR 2015 (IC15)
+    3. IIIT5K Words (IIIT)
+    4. Street View Text (SVT)
+    5. Street View Text-Perspective (SVTP)
+    6. CUTE80 (CUTE)
+
+
+- The structure of `data` directory is
+    ```
+    data
+    ├── charset_36.txt
+    ├── evaluation
+    │   ├── CUTE80
+    │   ├── IC13_857
+    │   ├── IC15_1811
+    │   ├── IIIT5k_3000
+    │   ├── SVT
+    │   └── SVTP
+    ├── training
+    │   ├── MJ
+    │   │   ├── MJ_test
+    │   │   ├── MJ_train
+    │   │   └── MJ_valid
+    │   └── ST
+    ├── WikiText-103.csv
+    └── WikiText-103_eval_d1.csv
+    ```
+
+### Pretrained Models
+
+Get the pretrained models from [BaiduNetdisk(passwd:kwck)](https://pan.baidu.com/s/1b3vyvPwvh_75FkPlp87czQ), [GoogleDrive](https://drive.google.com/file/d/1mYM_26qHUom_5NU7iutHneB_KHlLjL5y/view?usp=sharing). Performances of the pretrained models are summaried as follows:
+
+|Model|IC13|SVT|IIIT|IC15|SVTP|CUTE|AVG|
+|-|-|-|-|-|-|-|-|
+|ABINet-SV|97.1|92.7|95.2|84.0|86.7|88.5|91.4|
+|ABINet-LV|97.0|93.4|96.4|85.9|89.5|89.2|92.7|
+
+## Training
+
+1. Pre-train vision model
+    ```
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_vision_model.yaml
+    ```
+2. Pre-train language model
+    ```
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/pretrain_language_model.yaml
+    ```
+3. Train ABINet
+    ```
+    CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --config=configs/train_abinet.yaml
+    ```
+Note:
+- You can set the `checkpoint` path for vision and language models separately for specific pretrained model, or set to `None` to train from scratch
+
+
+## Evaluation
+
+```
+CUDA_VISIBLE_DEVICES=0 python main.py --config=configs/train_abinet.yaml --phase test --image_only
+```
+Additional flags:
+- `--checkpoint /path/to/checkpoint` set the path of evaluation model 
+- `--test_root /path/to/dataset` set the path of evaluation dataset
+- `--model_eval [alignment|vision]` which sub-model to evaluate
+- `--image_only` disable dumping visualization of attention masks
+
+## Web Demo
+
+Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/tomofi/ABINet-OCR)
+
+## Run Demo
+
+```
+python demo.py --config=configs/train_abinet.yaml --input=figs/test
+```
+Additional flags:
+- `--config /path/to/config` set the path of configuration file 
+- `--input /path/to/image-directory` set the path of image directory or wildcard path, e.g, `--input='figs/test/*.png'`
+- `--checkpoint /path/to/checkpoint` set the path of trained model
+- `--cuda [-1|0|1|2|3...]` set the cuda id, by default -1 is set and stands for cpu
+- `--model_eval [alignment|vision]` which sub-model to use
+- `--image_only` disable dumping visualization of attention masks
+
+## Visualization
+Successful and failure cases on low-quality images:
+
+![cases](./figs/cases.png)
+
+## Citation
+If you find our method useful for your reserach, please cite
+```bash 
+@article{fang2021read,
+  title={Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition},
+  author={Fang, Shancheng and Xie, Hongtao and Wang, Yuxin and Mao, Zhendong and Zhang, Yongdong},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year={2021}
+}
+ ```
+
+ ## License
+
+This project is only free for academic research purposes, licensed under the 2-clause BSD License - see the LICENSE file for details.
+
+Feel free to contact [email protected] if you have any questions.

app.py

@@ -0,0 +1,122 @@
+import argparse
+import logging
+import os
+import glob
+import tqdm
+import torch, re
+import PIL
+import cv2
+import numpy as np
+import torch.nn.functional as F
+from torchvision import transforms
+from utils import Config, Logger, CharsetMapper
+
+def get_model(config):
+    import importlib
+    names = config.model_name.split('.')
+    module_name, class_name = '.'.join(names[:-1]), names[-1]
+    cls = getattr(importlib.import_module(module_name), class_name)
+    model = cls(config)
+    logging.info(model)
+    model = model.eval()
+    return model
+
+def preprocess(img, width, height):
+    img = cv2.resize(np.array(img), (width, height))
+    img = transforms.ToTensor()(img).unsqueeze(0)
+    mean = torch.tensor([0.485, 0.456, 0.406])
+    std  = torch.tensor([0.229, 0.224, 0.225])
+    return (img-mean[...,None,None]) / std[...,None,None]
+
+def postprocess(output, charset, model_eval):
+    def _get_output(last_output, model_eval):
+        if isinstance(last_output, (tuple, list)): 
+            for res in last_output:
+                if res['name'] == model_eval: output = res
+        else: output = last_output
+        return output
+
+    def _decode(logit):
+        """ Greed decode """
+        out = F.softmax(logit, dim=2)
+        pt_text, pt_scores, pt_lengths = [], [], []
+        for o in out:
+            text = charset.get_text(o.argmax(dim=1), padding=False, trim=False)
+            text = text.split(charset.null_char)[0]  # end at end-token
+            pt_text.append(text)
+            pt_scores.append(o.max(dim=1)[0])
+            pt_lengths.append(min(len(text) + 1, charset.max_length))  # one for end-token
+        return pt_text, pt_scores, pt_lengths
+
+    output = _get_output(output, model_eval)
+    logits, pt_lengths = output['logits'], output['pt_lengths']
+    pt_text, pt_scores, pt_lengths_ = _decode(logits)
+    
+    return pt_text, pt_scores, pt_lengths_
+
+def load(model, file, device=None, strict=True):
+    if device is None: device = 'cpu'
+    elif isinstance(device, int): device = torch.device('cuda', device)
+    assert os.path.isfile(file)
+    state = torch.load(file, map_location=device)
+    if set(state.keys()) == {'model', 'opt'}:
+        state = state['model']
+    model.load_state_dict(state, strict=strict)
+    return model
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, default='configs/train_abinet.yaml',
+                        help='path to config file')
+    parser.add_argument('--input', type=str, default='figs/test')
+    parser.add_argument('--cuda', type=int, default=-1)
+    parser.add_argument('--checkpoint', type=str, default='workdir/train-abinet/best-train-abinet.pth')
+    parser.add_argument('--model_eval', type=str, default='alignment', 
+                        choices=['alignment', 'vision', 'language'])
+    args = parser.parse_args()
+    config = Config(args.config)
+    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
+    if args.model_eval is not None: config.model_eval = args.model_eval
+    config.global_phase = 'test'
+    config.model_vision_checkpoint, config.model_language_checkpoint = None, None
+    device = 'cpu' if args.cuda < 0 else f'cuda:{args.cuda}'
+
+    Logger.init(config.global_workdir, config.global_name, config.global_phase)
+    Logger.enable_file()
+    logging.info(config)
+
+    logging.info('Construct model.')
+    model = get_model(config).to(device)
+    model = load(model, config.model_checkpoint, device=device)
+    charset = CharsetMapper(filename=config.dataset_charset_path,
+                            max_length=config.dataset_max_length + 1)
+
+    if os.path.isdir(args.input):
+        paths = [os.path.join(args.input, fname) for fname in os.listdir(args.input)]
+    else:
+        paths = glob.glob(os.path.expanduser(args.input))
+        assert paths, "The input path(s) was not found"
+    paths = sorted(paths)
+
+
+    count = 0
+    checks = 0
+    print(tqdm.tqdm(paths))
+    for path in tqdm.tqdm(paths):
+        img = PIL.Image.open(path).convert('RGB')
+        img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
+        img = img.to(device)
+        res = model(img)
+        pt_text, _, __ = postprocess(res, charset, config.model_eval)
+        a = re.findall(r'(\d{6}).png', path)[0]
+        # print(a)
+        # print(pt_text[0], "Lol")
+        # a = re.findall(r'base/(.*).pn', path)[0]
+        checks += 1
+        if a.lower() != pt_text[0].lower():
+            count += 1
+            print(f'label:{a.lower()} ||| guess:{pt_text[0]} ||| count_fails:{str(count)}/{str(checks)}')
+
+if __name__ == '__main__':
+    main()

callbacks.py

@@ -0,0 +1,360 @@
+import logging
+import shutil
+import time
+
+import editdistance as ed
+import torchvision.utils as vutils
+from fastai.callbacks.tensorboard import (LearnerTensorboardWriter,
+                                          SummaryWriter, TBWriteRequest,
+                                          asyncTBWriter)
+from fastai.vision import *
+from torch.nn.parallel import DistributedDataParallel
+from torchvision import transforms
+
+import dataset
+from utils import CharsetMapper, Timer, blend_mask
+
+
+class IterationCallback(LearnerTensorboardWriter):
+    "A `TrackerCallback` that monitor in each iteration."
+    def __init__(self, learn:Learner, name:str='model', checpoint_keep_num=5,
+                 show_iters:int=50, eval_iters:int=1000, save_iters:int=20000,
+                 start_iters:int=0, stats_iters=20000):
+        #if self.learn.rank is not None: time.sleep(self.learn.rank)  # keep all event files
+        super().__init__(learn, base_dir='.', name=learn.path, loss_iters=show_iters, 
+                        stats_iters=stats_iters, hist_iters=stats_iters)
+        self.name, self.bestname = Path(name).name, f'best-{Path(name).name}'
+        self.show_iters = show_iters
+        self.eval_iters = eval_iters
+        self.save_iters = save_iters
+        self.start_iters = start_iters
+        self.checpoint_keep_num = checpoint_keep_num
+        self.metrics_root = 'metrics/'  # rewrite
+        self.timer = Timer()
+        self.host = self.learn.rank is None or self.learn.rank == 0
+
+    def _write_metrics(self, iteration:int, names:List[str], last_metrics:MetricsList)->None:
+        "Writes training metrics to Tensorboard."
+        for i, name in enumerate(names):
+            if last_metrics is None or len(last_metrics) < i+1: return
+            scalar_value = last_metrics[i]
+            self._write_scalar(name=name, scalar_value=scalar_value, iteration=iteration)
+
+    def _write_sub_loss(self, iteration:int, last_losses:dict)->None:
+        "Writes sub loss to Tensorboard."
+        for name, loss in last_losses.items():
+            scalar_value = to_np(loss)
+            tag = self.metrics_root + name
+            self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
+
+    def _save(self, name):
+        if isinstance(self.learn.model, DistributedDataParallel):
+            tmp = self.learn.model
+            self.learn.model = self.learn.model.module
+            self.learn.save(name)
+            self.learn.model = tmp
+        else: self.learn.save(name)
+
+    def _validate(self, dl=None, callbacks=None, metrics=None, keeped_items=False):
+        "Validate on `dl` with potential `callbacks` and `metrics`."
+        dl = ifnone(dl, self.learn.data.valid_dl)
+        metrics = ifnone(metrics, self.learn.metrics)
+        cb_handler = CallbackHandler(ifnone(callbacks, []), metrics)
+        cb_handler.on_train_begin(1, None, metrics); cb_handler.on_epoch_begin()
+        if keeped_items: cb_handler.state_dict.update(dict(keeped_items=[]))
+        val_metrics = validate(self.learn.model, dl, self.loss_func, cb_handler)
+        cb_handler.on_epoch_end(val_metrics)
+        if keeped_items: return cb_handler.state_dict['keeped_items']
+        else: return cb_handler.state_dict['last_metrics']
+
+    def jump_to_epoch_iter(self, epoch:int, iteration:int)->None:
+        try:
+            self.learn.load(f'{self.name}_{epoch}_{iteration}', purge=False)
+            logging.info(f'Loaded {self.name}_{epoch}_{iteration}')
+        except: logging.info(f'Model {self.name}_{epoch}_{iteration} not found.')
+
+    def on_train_begin(self, n_epochs, **kwargs):
+        # TODO: can not write graph here
+        # super().on_train_begin(**kwargs)
+        self.best = -float('inf')
+        self.timer.tic()
+        if self.host:
+            checkpoint_path = self.learn.path/'checkpoint.yaml'
+            if checkpoint_path.exists():
+                os.remove(checkpoint_path)
+            open(checkpoint_path, 'w').close()
+        return {'skip_validate': True, 'iteration':self.start_iters}  # disable default validate
+
+    def on_batch_begin(self, **kwargs:Any)->None:
+        self.timer.toc_data()
+        super().on_batch_begin(**kwargs)
+
+    def on_batch_end(self, iteration, epoch, last_loss, smooth_loss, train, **kwargs):
+        super().on_batch_end(last_loss, iteration, train, **kwargs)
+        if iteration == 0: return
+
+        if iteration % self.loss_iters == 0:
+            last_losses = self.learn.loss_func.last_losses
+            self._write_sub_loss(iteration=iteration, last_losses=last_losses)
+            self.tbwriter.add_scalar(tag=self.metrics_root + 'lr',
+                scalar_value=self.opt.lr, global_step=iteration)
+
+        if iteration % self.show_iters == 0:
+            log_str = f'epoch {epoch} iter {iteration}: loss = {last_loss:6.4f},  ' \
+                      f'smooth loss = {smooth_loss:6.4f}'
+            logging.info(log_str)
+            # log_str = f'data time = {self.timer.data_diff:.4f}s, runing time = {self.timer.running_diff:.4f}s'
+            # logging.info(log_str)
+
+        if iteration % self.eval_iters == 0:
+            # TODO: or remove time to on_epoch_end
+            # 1. Record time 
+            log_str = f'average data time = {self.timer.average_data_time():.4f}s, ' \
+                      f'average running time = {self.timer.average_running_time():.4f}s'
+            logging.info(log_str)
+
+            # 2. Call validate
+            last_metrics = self._validate()
+            self.learn.model.train()
+            log_str = f'epoch {epoch} iter {iteration}: eval loss = {last_metrics[0]:6.4f},  ' \
+                      f'ccr = {last_metrics[1]:6.4f},  cwr = {last_metrics[2]:6.4f},  ' \
+                      f'ted = {last_metrics[3]:6.4f},  ned = {last_metrics[4]:6.4f},  ' \
+                      f'ted/w = {last_metrics[5]:6.4f}, '
+            logging.info(log_str)
+            names = ['eval_loss', 'ccr', 'cwr', 'ted', 'ned', 'ted/w']
+            self._write_metrics(iteration, names, last_metrics)
+
+            # 3. Save best model
+            current = last_metrics[2]
+            if current is not None and current > self.best:
+                logging.info(f'Better model found at epoch {epoch}, '\
+                             f'iter {iteration} with accuracy value: {current:6.4f}.')
+                self.best = current
+                self._save(f'{self.bestname}')
+
+        if iteration % self.save_iters == 0 and self.host:
+            logging.info(f'Save model {self.name}_{epoch}_{iteration}')
+            filename = f'{self.name}_{epoch}_{iteration}'
+            self._save(filename)
+
+            checkpoint_path = self.learn.path/'checkpoint.yaml'
+            if not checkpoint_path.exists():
+                open(checkpoint_path, 'w').close()
+            with open(checkpoint_path, 'r') as file:
+                checkpoints = yaml.load(file, Loader=yaml.FullLoader) or dict()
+            checkpoints['all_checkpoints'] = (
+                checkpoints.get('all_checkpoints') or list())
+            checkpoints['all_checkpoints'].insert(0, filename)
+            if len(checkpoints['all_checkpoints']) > self.checpoint_keep_num:
+                removed_checkpoint = checkpoints['all_checkpoints'].pop()
+                removed_checkpoint =  self.learn.path/self.learn.model_dir/f'{removed_checkpoint}.pth'
+                os.remove(removed_checkpoint)
+            checkpoints['current_checkpoint'] = filename
+            with open(checkpoint_path, 'w') as file:
+                yaml.dump(checkpoints, file)
+
+
+        self.timer.toc_running()
+
+    def on_train_end(self, **kwargs):
+        #self.learn.load(f'{self.bestname}', purge=False)
+        pass
+    
+    def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None:
+        self._write_embedding(iteration=iteration)
+
+
+class TextAccuracy(Callback):
+    _names = ['ccr', 'cwr', 'ted', 'ned', 'ted/w']
+    def __init__(self, charset_path, max_length, case_sensitive, model_eval):
+        self.charset_path = charset_path
+        self.max_length = max_length
+        self.case_sensitive = case_sensitive
+        self.charset = CharsetMapper(charset_path, self.max_length)
+        self.names = self._names
+
+        self.model_eval = model_eval or 'alignment'
+        assert self.model_eval in ['vision', 'language', 'alignment']
+ 
+    def on_epoch_begin(self, **kwargs):
+        self.total_num_char = 0.
+        self.total_num_word = 0.
+        self.correct_num_char = 0.
+        self.correct_num_word = 0.
+        self.total_ed = 0.
+        self.total_ned = 0.
+
+    def _get_output(self, last_output):
+        if isinstance(last_output, (tuple, list)): 
+            for res in last_output:
+                if res['name'] == self.model_eval: output = res
+        else: output = last_output
+        return output
+    
+    def _update_output(self, last_output, items):
+        if isinstance(last_output, (tuple, list)): 
+            for res in last_output:
+                if res['name'] == self.model_eval: res.update(items)
+        else: last_output.update(items)
+        return last_output
+
+    def on_batch_end(self, last_output, last_target, **kwargs):
+        output = self._get_output(last_output)
+        logits, pt_lengths = output['logits'], output['pt_lengths']
+        pt_text, pt_scores, pt_lengths_ = self.decode(logits)
+        assert (pt_lengths == pt_lengths_).all(), f'{pt_lengths} != {pt_lengths_} for {pt_text}'
+        last_output = self._update_output(last_output, {'pt_text':pt_text, 'pt_scores':pt_scores})
+
+        pt_text = [self.charset.trim(t) for t in pt_text]
+        label = last_target[0]
+        if label.dim() == 3: label = label.argmax(dim=-1)  # one-hot label
+        gt_text = [self.charset.get_text(l, trim=True) for l in label]
+        
+        for i in range(len(gt_text)):
+            if not self.case_sensitive:
+                gt_text[i], pt_text[i] = gt_text[i].lower(), pt_text[i].lower()
+            distance = ed.eval(gt_text[i], pt_text[i])
+            self.total_ed += distance
+            self.total_ned += float(distance) / max(len(gt_text[i]), 1)
+
+            if gt_text[i] == pt_text[i]:
+                self.correct_num_word += 1
+            self.total_num_word += 1
+            
+            for j in range(min(len(gt_text[i]), len(pt_text[i]))):
+                if gt_text[i][j] == pt_text[i][j]:
+                    self.correct_num_char += 1
+            self.total_num_char += len(gt_text[i])
+
+        return {'last_output': last_output}
+
+    def on_epoch_end(self, last_metrics, **kwargs):
+        mets = [self.correct_num_char / self.total_num_char,
+                self.correct_num_word / self.total_num_word,
+                self.total_ed,
+                self.total_ned,
+                self.total_ed / self.total_num_word]
+        return add_metrics(last_metrics, mets)
+
+    def decode(self, logit):
+        """ Greed decode """
+        # TODO: test running time and decode on GPU
+        out = F.softmax(logit, dim=2)
+        pt_text, pt_scores, pt_lengths = [], [], []
+        for o in out:
+            text = self.charset.get_text(o.argmax(dim=1), padding=False, trim=False)
+            text = text.split(self.charset.null_char)[0]  # end at end-token
+            pt_text.append(text)
+            pt_scores.append(o.max(dim=1)[0])
+            pt_lengths.append(min(len(text) + 1, self.max_length))  # one for end-token
+        pt_scores = torch.stack(pt_scores)
+        pt_lengths = pt_scores.new_tensor(pt_lengths, dtype=torch.long)
+        return pt_text, pt_scores, pt_lengths
+
+
+class TopKTextAccuracy(TextAccuracy):
+    _names = ['ccr', 'cwr']
+    def __init__(self, k, charset_path, max_length, case_sensitive, model_eval):
+        self.k = k
+        self.charset_path = charset_path
+        self.max_length = max_length
+        self.case_sensitive = case_sensitive
+        self.charset = CharsetMapper(charset_path, self.max_length)
+        self.names = self._names
+ 
+    def on_epoch_begin(self, **kwargs):
+        self.total_num_char = 0.
+        self.total_num_word = 0.
+        self.correct_num_char = 0.
+        self.correct_num_word = 0.
+
+    def on_batch_end(self, last_output, last_target, **kwargs):
+        logits, pt_lengths = last_output['logits'], last_output['pt_lengths']
+        gt_labels, gt_lengths = last_target[:]
+
+        for logit, pt_length, label, length in zip(logits, pt_lengths, gt_labels, gt_lengths):
+            word_flag = True
+            for i in range(length):
+                char_logit = logit[i].topk(self.k)[1]
+                char_label = label[i].argmax(-1)
+                if char_label in char_logit: self.correct_num_char += 1
+                else: word_flag = False
+                self.total_num_char += 1
+            if pt_length == length and word_flag:
+                self.correct_num_word += 1
+            self.total_num_word += 1
+
+    def on_epoch_end(self, last_metrics, **kwargs):
+        mets = [self.correct_num_char / self.total_num_char,
+                self.correct_num_word / self.total_num_word,
+                0., 0., 0.]
+        return add_metrics(last_metrics, mets)
+
+
+class DumpPrediction(LearnerCallback):
+
+    def __init__(self, learn, dataset, charset_path, model_eval, image_only=False, debug=False):
+        super().__init__(learn=learn)
+        self.debug = debug
+        self.model_eval = model_eval or 'alignment'
+        self.image_only = image_only
+        assert self.model_eval in ['vision', 'language', 'alignment']
+
+        self.dataset, self.root = dataset, Path(self.learn.path)/f'{dataset}-{self.model_eval}'
+        self.attn_root = self.root/'attn'
+        self.charset = CharsetMapper(charset_path)
+        if self.root.exists(): shutil.rmtree(self.root)
+        self.root.mkdir(), self.attn_root.mkdir()
+
+        self.pil = transforms.ToPILImage()
+        self.tensor = transforms.ToTensor()
+        size = self.learn.data.img_h, self.learn.data.img_w
+        self.resize = transforms.Resize(size=size, interpolation=0)
+        self.c = 0
+
+    def on_batch_end(self, last_input, last_output, last_target, **kwargs):
+        if isinstance(last_output, (tuple, list)):
+            for res in last_output:
+                if res['name'] == self.model_eval: pt_text = res['pt_text']
+                if res['name'] == 'vision': attn_scores = res['attn_scores'].detach().cpu()
+                if res['name'] == self.model_eval: logits = res['logits']
+        else:
+            pt_text = last_output['pt_text']
+            attn_scores = last_output['attn_scores'].detach().cpu()
+            logits = last_output['logits']
+
+        images = last_input[0] if isinstance(last_input, (tuple, list)) else last_input
+        images = images.detach().cpu()
+        pt_text = [self.charset.trim(t) for t in pt_text]
+        gt_label = last_target[0]
+        if gt_label.dim() == 3: gt_label = gt_label.argmax(dim=-1)  # one-hot label
+        gt_text = [self.charset.get_text(l, trim=True) for l in gt_label]
+
+        prediction, false_prediction = [], []
+        for gt, pt, image, attn, logit in zip(gt_text, pt_text, images, attn_scores, logits):
+            prediction.append(f'{gt}\t{pt}\n')
+            if gt != pt:
+                if self.debug:
+                    scores = torch.softmax(logit, dim=-1)[:max(len(pt), len(gt)) + 1]
+                    logging.info(f'{self.c} gt {gt}, pt {pt}, logit {logit.shape}, scores {scores.topk(5, dim=-1)}')
+                false_prediction.append(f'{gt}\t{pt}\n')
+
+            image = self.learn.data.denorm(image)
+            if not self.image_only:
+                image_np = np.array(self.pil(image))
+                attn_pil = [self.pil(a) for a in attn[:, None, :, :]]
+                attn = [self.tensor(self.resize(a)).repeat(3, 1, 1) for a in attn_pil]
+                attn_sum = np.array([np.array(a) for a in attn_pil[:len(pt)]]).sum(axis=0)
+                blended_sum = self.tensor(blend_mask(image_np, attn_sum))
+                blended = [self.tensor(blend_mask(image_np, np.array(a))) for a in attn_pil]
+                save_image = torch.stack([image] + attn + [blended_sum] + blended)
+                save_image = save_image.view(2, -1, *save_image.shape[1:])
+                save_image = save_image.permute(1, 0, 2, 3, 4).flatten(0, 1)
+                vutils.save_image(save_image, self.attn_root/f'{self.c}_{gt}_{pt}.jpg', 
+                                nrow=2, normalize=True, scale_each=True)
+            else:
+                self.pil(image).save(self.attn_root/f'{self.c}_{gt}_{pt}.jpg')
+            self.c += 1
+
+        with open(self.root/f'{self.model_eval}.txt', 'a') as f: f.writelines(prediction)
+        with open(self.root/f'{self.model_eval}-false.txt', 'a') as f: f.writelines(false_prediction)  

commands.sh

@@ -0,0 +1,3 @@
+python tools/create_lmdb_dataset.py
+python demo.py --config=configs/train_abinet.yaml --input=base/
+python main.py --config=configs/train_abinet.yaml 

config.yaml

@@ -0,0 +1,71 @@
+global:
+  name: train-abinet
+  phase: train
+  stage: train-super
+  workdir: workdir
+  seed: ~
+ 
+dataset:
+  train: {
+    roots: [
+            'output_tbell_dataset2/',
+            # 'data/training/MJ/MJ_train/', 
+            # 'data/training/MJ/MJ_test/', 
+            # 'data/training/MJ/MJ_valid/', 
+            # 'data/training/ST'
+            ],
+    batch_size: 20
+  }
+  test: {
+    roots: [
+            'output_tbell_dataset2/'
+            ],
+    batch_size: 2
+  }
+  data_aug: True
+  multiscales: False
+  num_workers: 1
+
+training:
+  epochs: 100000
+  show_iters: 50
+  eval_iters: 600
+  # save_iters: 3000
+
+optimizer:
+  type: AdamW
+  true_wd: False
+  wd: 0.0
+  bn_wd: False
+  clip_grad: 20
+  lr: 0.0001
+  args: {
+    betas: !!python/tuple [0.9, 0.999], # for default Adam 
+  }
+  scheduler: {
+    periods: [6, 4],
+    gamma: 0.1,
+  }
+
+model:
+  name: 'modules.model_abinet_iter.ABINetIterModel'
+  iter_size: 3
+  ensemble: ''
+  use_vision: True
+  vision: {
+    checkpoint: workdir/pretrain-vision-model/best-pretrain-vision-model.pth,
+    loss_weight: 1.,
+    attention: 'position',
+    backbone: 'transformer',
+    backbone_ln: 3,
+  }
+  language: {
+    checkpoint:  workdir/pretrain-language-model/pretrain-language-model.pth,
+    num_layers: 4,
+    loss_weight: 1.,
+    detach: True,
+    use_self_attn: False
+  }
+  alignment: {
+    loss_weight: 1.,
+  }

dataset.py

@@ -0,0 +1,278 @@
+import logging
+import re
+
+import cv2
+import lmdb
+import six
+from fastai.vision import *
+from torchvision import transforms
+
+from transforms import CVColorJitter, CVDeterioration, CVGeometry
+from utils import CharsetMapper, onehot
+
+
+class ImageDataset(Dataset):
+    "`ImageDataset` read data from LMDB database."
+
+    def __init__(self,
+                 path:PathOrStr,
+                 is_training:bool=True,
+                 img_h:int=32,
+                 img_w:int=100,
+                 max_length:int=25,
+                 check_length:bool=True,
+                 case_sensitive:bool=False,
+                 charset_path:str='data/charset_36.txt',
+                 convert_mode:str='RGB',
+                 data_aug:bool=True,
+                 deteriorate_ratio:float=0.,
+                 multiscales:bool=True,
+                 one_hot_y:bool=True,
+                 return_idx:bool=False,
+                 return_raw:bool=False,
+                 **kwargs):
+        self.path, self.name = Path(path), Path(path).name
+        assert self.path.is_dir() and self.path.exists(), f"{path} is not a valid directory."
+        self.convert_mode, self.check_length = convert_mode, check_length
+        self.img_h, self.img_w = img_h, img_w
+        self.max_length, self.one_hot_y = max_length, one_hot_y
+        self.return_idx, self.return_raw = return_idx, return_raw
+        self.case_sensitive, self.is_training = case_sensitive, is_training
+        self.data_aug, self.multiscales = data_aug, multiscales
+        self.charset = CharsetMapper(charset_path, max_length=max_length+1)
+        self.c = self.charset.num_classes
+
+        self.env = lmdb.open(str(path), readonly=True, lock=False, readahead=False, meminit=False)
+        assert self.env, f'Cannot open LMDB dataset from {path}.'
+        with self.env.begin(write=False) as txn:
+            self.length = int(txn.get('num-samples'.encode()))
+
+        if self.is_training and self.data_aug:
+            self.augment_tfs = transforms.Compose([
+                CVGeometry(degrees=45, translate=(0.0, 0.0), scale=(0.5, 2.), shear=(45, 15), distortion=0.5, p=0.5),
+                CVDeterioration(var=20, degrees=6, factor=4, p=0.25),
+                CVColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1, p=0.25)
+            ])
+        self.totensor = transforms.ToTensor()
+    
+    def __len__(self): return self.length
+
+    def _next_image(self, index):
+        next_index = random.randint(0, len(self) - 1)
+        return self.get(next_index)
+
+    def _check_image(self, x, pixels=6):
+        if x.size[0] <= pixels or x.size[1] <= pixels: return False
+        else: return True
+
+    def resize_multiscales(self, img, borderType=cv2.BORDER_CONSTANT): 
+        def _resize_ratio(img, ratio, fix_h=True):
+            if ratio * self.img_w < self.img_h:
+                if fix_h: trg_h = self.img_h
+                else: trg_h = int(ratio * self.img_w)
+                trg_w = self.img_w
+            else: trg_h, trg_w = self.img_h, int(self.img_h / ratio)
+            img = cv2.resize(img, (trg_w, trg_h))
+            pad_h, pad_w = (self.img_h - trg_h) / 2, (self.img_w - trg_w) / 2
+            top, bottom = math.ceil(pad_h), math.floor(pad_h)
+            left, right = math.ceil(pad_w), math.floor(pad_w)
+            img = cv2.copyMakeBorder(img, top, bottom, left, right, borderType)
+            return img
+        
+        if self.is_training: 
+            if random.random() < 0.5:
+                base, maxh, maxw = self.img_h, self.img_h, self.img_w
+                h, w = random.randint(base, maxh), random.randint(base, maxw)
+                return _resize_ratio(img, h/w)
+            else: return _resize_ratio(img, img.shape[0] / img.shape[1])  # keep aspect ratio
+        else:  return _resize_ratio(img, img.shape[0] / img.shape[1])  # keep aspect ratio
+
+    def resize(self, img):
+        if self.multiscales: return self.resize_multiscales(img, cv2.BORDER_REPLICATE)
+        else: return cv2.resize(img, (self.img_w, self.img_h))
+         
+    def get(self, idx):
+        with self.env.begin(write=False) as txn:
+            image_key, label_key = f'image-{idx+1:09d}', f'label-{idx+1:09d}'
+            try:
+                label = str(txn.get(label_key.encode()), 'utf-8')  # label
+                label = re.sub('[^0-9a-zA-Z]+', '', label)
+                if self.check_length and self.max_length > 0:
+                    if len(label) > self.max_length or len(label) <= 0:
+                        #logging.info(f'Long or short text image is found: {self.name}, {idx}, {label}, {len(label)}')
+                        return self._next_image(idx)
+                label = label[:self.max_length]
+
+                imgbuf = txn.get(image_key.encode())  # image
+                buf = six.BytesIO()
+                buf.write(imgbuf)
+                buf.seek(0)
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin
+                    image = PIL.Image.open(buf).convert(self.convert_mode)
+                if self.is_training and not self._check_image(image):
+                    #logging.info(f'Invalid image is found: {self.name}, {idx}, {label}, {len(label)}')
+                    return self._next_image(idx)
+            except:
+                import traceback
+                traceback.print_exc()
+                logging.info(f'Corrupted image is found: {self.name}, {idx}, {label}, {len(label)}')
+                return self._next_image(idx)
+            return image, label, idx
+
+    def _process_training(self, image):
+        if self.data_aug: image = self.augment_tfs(image)
+        image = self.resize(np.array(image))
+        return image
+
+    def _process_test(self, image):
+        return self.resize(np.array(image)) # TODO:move is_training to here
+
+    def __getitem__(self, idx):
+        image, text, idx_new = self.get(idx)
+        if not self.is_training: assert idx == idx_new, f'idx {idx} != idx_new {idx_new} during testing.'
+
+        if self.is_training: image = self._process_training(image)
+        else: image = self._process_test(image)
+        if self.return_raw: return image, text
+        image = self.totensor(image)
+
+        length = tensor(len(text) + 1).to(dtype=torch.long)  # one for end token
+        label = self.charset.get_labels(text, case_sensitive=self.case_sensitive)
+        label = tensor(label).to(dtype=torch.long)
+        if self.one_hot_y: label = onehot(label, self.charset.num_classes)
+
+        if self.return_idx: y = [label, length, idx_new]
+        else: y = [label, length]
+        return image, y
+
+
+class TextDataset(Dataset):
+    def __init__(self,
+                 path:PathOrStr, 
+                 delimiter:str='\t',
+                 max_length:int=25, 
+                 charset_path:str='data/charset_36.txt', 
+                 case_sensitive=False, 
+                 one_hot_x=True,
+                 one_hot_y=True,
+                 is_training=True,
+                 smooth_label=False,
+                 smooth_factor=0.2,
+                 use_sm=False,
+                 **kwargs):
+        self.path = Path(path)
+        self.case_sensitive, self.use_sm = case_sensitive, use_sm
+        self.smooth_factor, self.smooth_label = smooth_factor, smooth_label
+        self.charset = CharsetMapper(charset_path, max_length=max_length+1)
+        self.one_hot_x, self.one_hot_y, self.is_training = one_hot_x, one_hot_y, is_training
+        if self.is_training and self.use_sm: self.sm = SpellingMutation(charset=self.charset)
+
+        dtype = {'inp': str, 'gt': str}
+        self.df = pd.read_csv(self.path, dtype=dtype, delimiter=delimiter, na_filter=False)
+        self.inp_col, self.gt_col = 0, 1
+
+    def __len__(self): return len(self.df)
+
+    def __getitem__(self, idx):
+        text_x = self.df.iloc[idx, self.inp_col]
+        text_x = re.sub('[^0-9a-zA-Z]+', '', text_x)
+        if not self.case_sensitive: text_x = text_x.lower()
+        if self.is_training and self.use_sm: text_x = self.sm(text_x)
+
+        length_x = tensor(len(text_x) + 1).to(dtype=torch.long)  # one for end token
+        label_x = self.charset.get_labels(text_x, case_sensitive=self.case_sensitive)
+        label_x = tensor(label_x)
+        if self.one_hot_x:
+            label_x = onehot(label_x, self.charset.num_classes)
+            if self.is_training and self.smooth_label: 
+                label_x = torch.stack([self.prob_smooth_label(l) for l in label_x])
+        x =  [label_x, length_x]
+    
+        text_y = self.df.iloc[idx, self.gt_col]
+        text_y = re.sub('[^0-9a-zA-Z]+', '', text_y)
+        if not self.case_sensitive: text_y = text_y.lower()
+        length_y = tensor(len(text_y) + 1).to(dtype=torch.long)  # one for end token
+        label_y = self.charset.get_labels(text_y, case_sensitive=self.case_sensitive)
+        label_y = tensor(label_y)
+        if self.one_hot_y: label_y = onehot(label_y, self.charset.num_classes)
+        y = [label_y, length_y]
+
+        return x, y
+
+    def prob_smooth_label(self, one_hot):
+        one_hot = one_hot.float()
+        delta = torch.rand([]) * self.smooth_factor
+        num_classes = len(one_hot)
+        noise = torch.rand(num_classes)
+        noise = noise / noise.sum() * delta
+        one_hot = one_hot * (1 - delta) + noise
+        return one_hot
+
+
+class SpellingMutation(object):
+    def __init__(self, pn0=0.7, pn1=0.85, pn2=0.95, pt0=0.7, pt1=0.85, charset=None):
+        """ 
+        Args:
+            pn0: the prob of not modifying characters is (pn0)
+            pn1: the prob of modifying one characters is (pn1 - pn0)
+            pn2: the prob of modifying two characters is (pn2 - pn1), 
+                 and three (1 - pn2)
+            pt0: the prob of replacing operation is pt0.
+            pt1: the prob of inserting operation is (pt1 - pt0),
+                 and deleting operation is (1 - pt1)
+        """
+        super().__init__()
+        self.pn0, self.pn1, self.pn2 = pn0, pn1, pn2
+        self.pt0, self.pt1 = pt0, pt1
+        self.charset = charset
+        logging.info(f'the probs: pn0={self.pn0}, pn1={self.pn1} ' + 
+                     f'pn2={self.pn2}, pt0={self.pt0}, pt1={self.pt1}')
+
+    def is_digit(self, text, ratio=0.5):
+        length = max(len(text), 1)
+        digit_num = sum([t in self.charset.digits for t in text])
+        if digit_num / length < ratio: return False
+        return True
+
+    def is_unk_char(self, char):
+        # return char == self.charset.unk_char
+        return (char not in self.charset.digits) and (char not in self.charset.alphabets)
+
+    def get_num_to_modify(self, length):
+        prob = random.random()
+        if prob < self.pn0: num_to_modify = 0
+        elif prob < self.pn1: num_to_modify = 1
+        elif prob < self.pn2: num_to_modify = 2
+        else: num_to_modify = 3
+        
+        if length <= 1: num_to_modify = 0
+        elif length >= 2 and length <= 4: num_to_modify = min(num_to_modify, 1)
+        else: num_to_modify = min(num_to_modify, length // 2)  # smaller than length // 2
+        return num_to_modify
+
+    def __call__(self, text, debug=False):
+        if self.is_digit(text): return text
+        length = len(text)
+        num_to_modify = self.get_num_to_modify(length)
+        if num_to_modify <= 0: return text
+
+        chars = []
+        index = np.arange(0, length)
+        random.shuffle(index)
+        index = index[: num_to_modify]
+        if debug: self.index = index
+        for i, t in enumerate(text):
+            if i not in index: chars.append(t)
+            elif self.is_unk_char(t): chars.append(t)
+            else:
+                prob = random.random()
+                if prob < self.pt0: # replace
+                    chars.append(random.choice(self.charset.alphabets))
+                elif prob < self.pt1: # insert
+                    chars.append(random.choice(self.charset.alphabets))
+                    chars.append(t)
+                else: # delete
+                    continue
+        new_text = ''.join(chars[: self.charset.max_length-1])
+        return new_text if len(new_text) >= 1 else text

losses.py

@@ -0,0 +1,72 @@
+from fastai.vision import *
+
+from modules.model import Model
+
+
+class MultiLosses(nn.Module):
+    def __init__(self, one_hot=True):
+        super().__init__()
+        self.ce = SoftCrossEntropyLoss() if one_hot else torch.nn.CrossEntropyLoss()
+        self.bce = torch.nn.BCELoss()
+
+    @property
+    def last_losses(self):
+        return self.losses
+
+    def _flatten(self, sources, lengths):
+        return torch.cat([t[:l] for t, l in zip(sources, lengths)])
+
+    def _merge_list(self, all_res):
+        if not isinstance(all_res, (list, tuple)):
+            return all_res
+        def merge(items):
+            if isinstance(items[0], torch.Tensor): return torch.cat(items, dim=0)
+            else: return items[0]
+        res = dict()
+        for key in all_res[0].keys():
+            items = [r[key] for r in all_res]
+            res[key] = merge(items)
+        return res
+
+    def _ce_loss(self, output, gt_labels, gt_lengths, idx=None, record=True):
+        loss_name = output.get('name')
+        pt_logits, weight = output['logits'], output['loss_weight']
+
+        assert pt_logits.shape[0] % gt_labels.shape[0] == 0
+        iter_size = pt_logits.shape[0] // gt_labels.shape[0]
+        if iter_size > 1:
+            gt_labels = gt_labels.repeat(3, 1, 1)
+            gt_lengths = gt_lengths.repeat(3)
+        flat_gt_labels = self._flatten(gt_labels, gt_lengths)
+        flat_pt_logits = self._flatten(pt_logits, gt_lengths)
+
+        nll = output.get('nll')
+        if nll is not None:
+            loss = self.ce(flat_pt_logits, flat_gt_labels, softmax=False) * weight
+        else:
+            loss = self.ce(flat_pt_logits, flat_gt_labels) * weight
+        if record and loss_name is not None: self.losses[f'{loss_name}_loss'] = loss
+
+        return loss
+
+    def forward(self, outputs, *args):
+        self.losses = {}
+        if isinstance(outputs, (tuple, list)):
+            outputs = [self._merge_list(o) for o in outputs]
+            return sum([self._ce_loss(o, *args) for o in outputs if o['loss_weight'] > 0.])
+        else:
+            return self._ce_loss(outputs, *args, record=False)
+
+
+class SoftCrossEntropyLoss(nn.Module):
+    def __init__(self, reduction="mean"):
+        super().__init__()
+        self.reduction = reduction
+
+    def forward(self, input, target, softmax=True):
+        if softmax: log_prob = F.log_softmax(input, dim=-1)
+        else: log_prob = torch.log(input)
+        loss = -(target * log_prob).sum(dim=-1)
+        if self.reduction == "mean": return loss.mean()
+        elif self.reduction == "sum": return loss.sum()
+        else: return loss

main.py

@@ -0,0 +1,246 @@
+import argparse
+import logging
+import os
+import random
+
+import torch
+from fastai.callbacks.general_sched import GeneralScheduler, TrainingPhase
+from fastai.distributed import *
+from fastai.vision import *
+from torch.backends import cudnn
+
+from callbacks import DumpPrediction, IterationCallback, TextAccuracy, TopKTextAccuracy
+from dataset import ImageDataset, TextDataset
+from losses import MultiLosses
+from utils import Config, Logger, MyDataParallel, MyConcatDataset
+
+
+def _set_random_seed(seed):
+    if seed is not None:
+        random.seed(seed)
+        torch.manual_seed(seed)
+        cudnn.deterministic = True
+        logging.warning('You have chosen to seed training. '
+                        'This will slow down your training!')
+
+def _get_training_phases(config, n):
+    lr = np.array(config.optimizer_lr)
+    periods = config.optimizer_scheduler_periods
+    sigma = [config.optimizer_scheduler_gamma ** i for i in range(len(periods))]
+    phases = [TrainingPhase(n * periods[i]).schedule_hp('lr', lr * sigma[i])
+                for i in range(len(periods))]
+    return phases
+
+def _get_dataset(ds_type, paths, is_training, config, **kwargs):
+    kwargs.update({
+        'img_h': config.dataset_image_height,
+        'img_w': config.dataset_image_width,
+        'max_length': config.dataset_max_length,
+        'case_sensitive': config.dataset_case_sensitive,
+        'charset_path': config.dataset_charset_path,
+        'data_aug': config.dataset_data_aug,
+        'deteriorate_ratio': config.dataset_deteriorate_ratio,
+        'is_training': is_training,
+        'multiscales': config.dataset_multiscales,
+        'one_hot_y': config.dataset_one_hot_y,
+    })
+    datasets = [ds_type(p, **kwargs) for p in paths]
+    if len(datasets) > 1: return MyConcatDataset(datasets)
+    else: return datasets[0]
+
+
+def _get_language_databaunch(config):
+    kwargs = {
+        'max_length': config.dataset_max_length,
+        'case_sensitive': config.dataset_case_sensitive,
+        'charset_path': config.dataset_charset_path,
+        'smooth_label': config.dataset_smooth_label,
+        'smooth_factor': config.dataset_smooth_factor,
+        'one_hot_y': config.dataset_one_hot_y,
+        'use_sm': config.dataset_use_sm,
+    }
+    train_ds = TextDataset(config.dataset_train_roots[0], is_training=True, **kwargs)
+    valid_ds = TextDataset(config.dataset_test_roots[0], is_training=False, **kwargs)
+    data = DataBunch.create(
+        path=train_ds.path,
+        train_ds=train_ds,
+        valid_ds=valid_ds,
+        bs=config.dataset_train_batch_size,
+        val_bs=config.dataset_test_batch_size,
+        num_workers=config.dataset_num_workers,
+        pin_memory=config.dataset_pin_memory)
+    logging.info(f'{len(data.train_ds)} training items found.')
+    if not data.empty_val:
+        logging.info(f'{len(data.valid_ds)} valid items found.')
+    return data
+
+def _get_databaunch(config):
+    # An awkward way to reduce loadding data time during test
+    if config.global_phase == 'test': config.dataset_train_roots = config.dataset_test_roots
+    train_ds = _get_dataset(ImageDataset, config.dataset_train_roots, True, config)
+    valid_ds = _get_dataset(ImageDataset, config.dataset_test_roots, False, config)
+    data = ImageDataBunch.create(
+        train_ds=train_ds,
+        valid_ds=valid_ds,
+        bs=config.dataset_train_batch_size,
+        val_bs=config.dataset_test_batch_size,
+        num_workers=config.dataset_num_workers,
+        pin_memory=config.dataset_pin_memory).normalize(imagenet_stats)
+    ar_tfm = lambda x: ((x[0], x[1]), x[1])  # auto-regression only for dtd
+    data.add_tfm(ar_tfm)
+
+    logging.info(f'{len(data.train_ds)} training items found.')
+    if not data.empty_val:
+        logging.info(f'{len(data.valid_ds)} valid items found.')
+    
+    return data
+
+def _get_model(config):
+    import importlib
+    names = config.model_name.split('.')
+    module_name, class_name = '.'.join(names[:-1]), names[-1]
+    cls = getattr(importlib.import_module(module_name), class_name)
+    model = cls(config)
+    logging.info(model)
+    return model
+
+
+def _get_learner(config, data, model, local_rank=None):
+    strict = ifnone(config.model_strict, True)
+    if config.global_stage == 'pretrain-language':
+        metrics = [TopKTextAccuracy(
+            k=ifnone(config.model_k, 5),
+            charset_path=config.dataset_charset_path,
+            max_length=config.dataset_max_length + 1,
+            case_sensitive=config.dataset_eval_case_sensisitves,
+            model_eval=config.model_eval)] 
+    else:
+        metrics = [TextAccuracy(
+            charset_path=config.dataset_charset_path,
+            max_length=config.dataset_max_length + 1,
+            case_sensitive=config.dataset_eval_case_sensisitves,
+            model_eval=config.model_eval)]
+    opt_type = getattr(torch.optim, config.optimizer_type)
+    learner = Learner(data, model, silent=True, model_dir='.',
+        true_wd=config.optimizer_true_wd, 
+        wd=config.optimizer_wd,
+        bn_wd=config.optimizer_bn_wd,
+        path=config.global_workdir,
+        metrics=metrics,
+        opt_func=partial(opt_type, **config.optimizer_args or dict()), 
+        loss_func=MultiLosses(one_hot=config.dataset_one_hot_y))
+    learner.split(lambda m: children(m))
+
+    if config.global_phase == 'train':
+        num_replicas = 1 if local_rank is None else torch.distributed.get_world_size()
+        phases = _get_training_phases(config, len(learner.data.train_dl)//num_replicas)
+        learner.callback_fns += [
+            partial(GeneralScheduler, phases=phases),
+            partial(GradientClipping, clip=config.optimizer_clip_grad),
+            partial(IterationCallback, name=config.global_name,
+                    show_iters=config.training_show_iters,
+                    eval_iters=config.training_eval_iters,
+                    save_iters=config.training_save_iters,
+                    start_iters=config.training_start_iters,
+                    stats_iters=config.training_stats_iters)]
+    else:
+        learner.callbacks += [
+            DumpPrediction(learn=learner,
+                    dataset='-'.join([Path(p).name for p in config.dataset_test_roots]),charset_path=config.dataset_charset_path,
+                    model_eval=config.model_eval,
+                    debug=config.global_debug,
+                    image_only=config.global_image_only)]
+
+    learner.rank = local_rank
+    if local_rank is not None:
+        logging.info(f'Set model to distributed with rank {local_rank}.')
+        learner.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(learner.model)
+        learner.model.to(local_rank)
+        learner = learner.to_distributed(local_rank)
+
+    if torch.cuda.device_count() > 1 and local_rank is None:
+        logging.info(f'Use {torch.cuda.device_count()} GPUs.')
+        learner.model = MyDataParallel(learner.model)
+
+    if config.model_checkpoint:
+        if Path(config.model_checkpoint).exists():
+            with open(config.model_checkpoint, 'rb') as f:
+                buffer = io.BytesIO(f.read())
+            learner.load(buffer, strict=strict)
+        else:
+            from distutils.dir_util import copy_tree
+            src = Path('/data/fangsc/model')/config.global_name
+            trg = Path('/output')/config.global_name
+            if src.exists(): copy_tree(str(src), str(trg))
+            learner.load(config.model_checkpoint, strict=strict)
+        logging.info(f'Read model from {config.model_checkpoint}')
+    elif config.global_phase == 'test':
+        learner.load(f'best-{config.global_name}', strict=strict)
+        logging.info(f'Read model from best-{config.global_name}')
+
+    if learner.opt_func.func.__name__ == 'Adadelta':    # fastai bug, fix after 1.0.60
+        learner.fit(epochs=0, lr=config.optimizer_lr)
+        learner.opt.mom = 0.
+
+    return learner
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, required=True,
+                        help='path to config file')
+    parser.add_argument('--phase', type=str, default=None, choices=['train', 'test'])
+    parser.add_argument('--name', type=str, default=None)
+    parser.add_argument('--checkpoint', type=str, default=None)
+    parser.add_argument('--test_root', type=str, default=None)
+    parser.add_argument("--local_rank", type=int, default=None)
+    parser.add_argument('--debug', action='store_true', default=None)
+    parser.add_argument('--image_only', action='store_true', default=None)
+    parser.add_argument('--model_strict', action='store_false', default=None)
+    parser.add_argument('--model_eval', type=str, default=None, 
+                        choices=['alignment', 'vision', 'language'])
+    args = parser.parse_args()
+    config = Config(args.config)
+    if args.name is not None: config.global_name = args.name
+    if args.phase is not None: config.global_phase = args.phase
+    if args.test_root is not None: config.dataset_test_roots = [args.test_root]
+    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
+    if args.debug is not None: config.global_debug = args.debug
+    if args.image_only is not None: config.global_image_only = args.image_only
+    if args.model_eval is not None: config.model_eval = args.model_eval
+    if args.model_strict is not None: config.model_strict = args.model_strict
+
+    Logger.init(config.global_workdir, config.global_name, config.global_phase)
+    Logger.enable_file()
+    _set_random_seed(config.global_seed)
+    logging.info(config)
+
+    if args.local_rank is not None:
+        logging.info(f'Init distribution training at device {args.local_rank}.')
+        torch.cuda.set_device(args.local_rank)
+        torch.distributed.init_process_group(backend='nccl', init_method='env://')
+
+    logging.info('Construct dataset.')
+    if config.global_stage == 'pretrain-language': data = _get_language_databaunch(config)
+    else: data = _get_databaunch(config)
+
+    logging.info('Construct model.')
+    model = _get_model(config)
+
+    logging.info('Construct learner.')
+    learner = _get_learner(config, data, model, args.local_rank)
+
+    if config.global_phase == 'train':
+        logging.info('Start training.')
+        learner.fit(epochs=config.training_epochs,
+                    lr=config.optimizer_lr)
+    else:
+        logging.info('Start validate')
+        last_metrics = learner.validate()
+        log_str = f'eval loss = {last_metrics[0]:6.3f},  ' \
+                  f'ccr = {last_metrics[1]:6.3f},  cwr = {last_metrics[2]:6.3f},  ' \
+                  f'ted = {last_metrics[3]:6.3f},  ned = {last_metrics[4]:6.0f},  ' \
+                  f'ted/w = {last_metrics[5]:6.3f}, '
+        logging.info(log_str)
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,9 @@
+torch
+torchvision
+fastai==1.0.60
+LMDB
+Pillow
+opencv-python
+tensorboardX
+PyYAML
+gdown

transforms.py

@@ -0,0 +1,329 @@
+import math
+import numbers
+import random
+
+import cv2
+import numpy as np
+from PIL import Image
+from torchvision import transforms
+from torchvision.transforms import Compose
+
+
+def sample_asym(magnitude, size=None):
+    return np.random.beta(1, 4, size) * magnitude
+
+def sample_sym(magnitude, size=None):
+    return (np.random.beta(4, 4, size=size) - 0.5) * 2 * magnitude
+
+def sample_uniform(low, high, size=None):
+    return np.random.uniform(low, high, size=size)
+
+def get_interpolation(type='random'):
+    if type == 'random':
+        choice = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA]
+        interpolation = choice[random.randint(0, len(choice)-1)]
+    elif type == 'nearest': interpolation = cv2.INTER_NEAREST
+    elif type == 'linear': interpolation = cv2.INTER_LINEAR
+    elif type == 'cubic': interpolation = cv2.INTER_CUBIC
+    elif type == 'area': interpolation = cv2.INTER_AREA
+    else: raise TypeError('Interpolation types only nearest, linear, cubic, area are supported!')
+    return interpolation
+
+class CVRandomRotation(object):
+    def __init__(self, degrees=15):
+        assert isinstance(degrees, numbers.Number), "degree should be a single number."
+        assert degrees >= 0, "degree must be positive."
+        self.degrees = degrees
+
+    @staticmethod
+    def get_params(degrees):
+        return sample_sym(degrees)
+    
+    def __call__(self, img):
+        angle = self.get_params(self.degrees)
+        src_h, src_w = img.shape[:2]
+        M = cv2.getRotationMatrix2D(center=(src_w/2, src_h/2), angle=angle, scale=1.0)
+        abs_cos, abs_sin = abs(M[0,0]), abs(M[0,1])
+        dst_w = int(src_h * abs_sin + src_w * abs_cos)
+        dst_h = int(src_h * abs_cos + src_w * abs_sin)
+        M[0, 2] += (dst_w - src_w)/2
+        M[1, 2] += (dst_h - src_h)/2
+        
+        flags = get_interpolation()
+        return cv2.warpAffine(img, M, (dst_w, dst_h), flags=flags, borderMode=cv2.BORDER_REPLICATE)
+
+class CVRandomAffine(object):
+    def __init__(self, degrees, translate=None, scale=None, shear=None):
+        assert isinstance(degrees, numbers.Number), "degree should be a single number."
+        assert degrees >= 0, "degree must be positive."
+        self.degrees = degrees
+
+        if translate is not None:
+            assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
+                "translate should be a list or tuple and it must be of length 2."
+            for t in translate:
+                if not (0.0 <= t <= 1.0):
+                    raise ValueError("translation values should be between 0 and 1")
+        self.translate = translate
+
+        if scale is not None:
+            assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
+                "scale should be a list or tuple and it must be of length 2."
+            for s in scale:
+                if s <= 0:
+                    raise ValueError("scale values should be positive")
+        self.scale = scale
+
+        if shear is not None:
+            if isinstance(shear, numbers.Number):
+                if shear < 0:
+                    raise ValueError("If shear is a single number, it must be positive.")
+                self.shear = [shear]
+            else:
+                assert isinstance(shear, (tuple, list)) and (len(shear) == 2), \
+                    "shear should be a list or tuple and it must be of length 2."
+                self.shear = shear
+        else:
+            self.shear = shear
+            
+    def _get_inverse_affine_matrix(self, center, angle, translate, scale, shear):
+        # https://github.com/pytorch/vision/blob/v0.4.0/torchvision/transforms/functional.py#L717
+        from numpy import sin, cos, tan
+        
+        if isinstance(shear, numbers.Number):
+            shear = [shear, 0]
+
+        if not isinstance(shear, (tuple, list)) and len(shear) == 2:
+            raise ValueError(
+                "Shear should be a single value or a tuple/list containing " +
+                "two values. Got {}".format(shear))
+
+        rot = math.radians(angle)
+        sx, sy = [math.radians(s) for s in shear]
+
+        cx, cy = center
+        tx, ty = translate
+
+        # RSS without scaling
+        a = cos(rot - sy) / cos(sy)
+        b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
+        c = sin(rot - sy) / cos(sy)
+        d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)
+
+        # Inverted rotation matrix with scale and shear
+        # det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
+        M = [d, -b, 0,
+             -c, a, 0]
+        M = [x / scale for x in M]
+
+        # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
+        M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
+        M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)
+
+        # Apply center translation: C * RSS^-1 * C^-1 * T^-1
+        M[2] += cx
+        M[5] += cy
+        return M
+
+    @staticmethod
+    def get_params(degrees, translate, scale_ranges, shears, height):        
+        angle = sample_sym(degrees)
+        if translate is not None:
+            max_dx = translate[0] * height
+            max_dy = translate[1] * height
+            translations = (np.round(sample_sym(max_dx)), np.round(sample_sym(max_dy)))
+        else:
+            translations = (0, 0)
+
+        if scale_ranges is not None:
+            scale = sample_uniform(scale_ranges[0], scale_ranges[1])
+        else:
+            scale = 1.0
+
+        if shears is not None:
+            if len(shears) == 1:
+                shear = [sample_sym(shears[0]), 0.]
+            elif len(shears) == 2:
+                shear = [sample_sym(shears[0]), sample_sym(shears[1])]
+        else:
+            shear = 0.0
+
+        return angle, translations, scale, shear
+    
+    
+    def __call__(self, img):
+        src_h, src_w = img.shape[:2]
+        angle, translate, scale, shear = self.get_params(
+                self.degrees, self.translate, self.scale, self.shear, src_h)
+
+        M = self._get_inverse_affine_matrix((src_w/2, src_h/2), angle, (0, 0), scale, shear)
+        M = np.array(M).reshape(2,3)
+        
+        startpoints = [(0, 0), (src_w - 1, 0), (src_w - 1, src_h - 1), (0, src_h - 1)]
+        project = lambda x, y, a, b, c: int(a*x + b*y + c)
+        endpoints = [(project(x, y, *M[0]), project(x, y, *M[1])) for x, y in startpoints]
+        
+        rect = cv2.minAreaRect(np.array(endpoints))
+        bbox = cv2.boxPoints(rect).astype(dtype=np.int)
+        max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
+        min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
+
+        dst_w = int(max_x - min_x)
+        dst_h = int(max_y - min_y)
+        M[0, 2] += (dst_w - src_w) / 2
+        M[1, 2] += (dst_h - src_h) / 2
+        
+        # add translate
+        dst_w += int(abs(translate[0]))
+        dst_h += int(abs(translate[1]))
+        if translate[0] < 0: M[0, 2] += abs(translate[0])
+        if translate[1] < 0: M[1, 2] += abs(translate[1])
+            
+        flags = get_interpolation()
+        return cv2.warpAffine(img, M, (dst_w , dst_h), flags=flags, borderMode=cv2.BORDER_REPLICATE)
+
+class CVRandomPerspective(object):
+    def __init__(self, distortion=0.5):
+        self.distortion = distortion
+
+    def get_params(self, width, height, distortion):
+        offset_h = sample_asym(distortion * height / 2, size=4).astype(dtype=np.int)
+        offset_w = sample_asym(distortion * width / 2, size=4).astype(dtype=np.int)
+        topleft  = (            offset_w[0],              offset_h[0])
+        topright = (width - 1 - offset_w[1],              offset_h[1])
+        botright = (width - 1 - offset_w[2], height - 1 - offset_h[2])
+        botleft  = (            offset_w[3], height - 1 - offset_h[3])
+
+        startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1), (0, height - 1)]
+        endpoints = [topleft, topright, botright, botleft]
+        return np.array(startpoints, dtype=np.float32), np.array(endpoints, dtype=np.float32)
+    
+    def __call__(self, img):
+        height, width = img.shape[:2]
+        startpoints, endpoints = self.get_params(width, height, self.distortion)
+        M = cv2.getPerspectiveTransform(startpoints, endpoints)
+
+        # TODO: more robust way to crop image
+        rect = cv2.minAreaRect(endpoints)
+        bbox = cv2.boxPoints(rect).astype(dtype=np.int)
+        max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
+        min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
+        min_x, min_y = max(min_x, 0), max(min_y, 0)
+
+        flags = get_interpolation() 
+        img = cv2.warpPerspective(img, M, (max_x, max_y), flags=flags, borderMode=cv2.BORDER_REPLICATE)
+        img = img[min_y:, min_x:]
+        return img
+
+class CVRescale(object):
+    
+    def __init__(self, factor=4, base_size=(128, 512)):
+        """ Define image scales using gaussian pyramid and rescale image to target scale.
+        
+        Args:
+            factor: the decayed factor from base size, factor=4 keeps target scale by default.
+            base_size: base size the build the bottom layer of pyramid
+        """
+        if isinstance(factor, numbers.Number):
+            self.factor = round(sample_uniform(0, factor))
+        elif isinstance(factor, (tuple, list)) and len(factor) == 2:
+            self.factor = round(sample_uniform(factor[0], factor[1]))
+        else:
+            raise Exception('factor must be number or list with length 2')
+        # assert factor is valid
+        self.base_h, self.base_w = base_size[:2]
+
+    def __call__(self, img):
+        if self.factor == 0: return img 
+        src_h, src_w = img.shape[:2]
+        cur_w, cur_h = self.base_w, self.base_h        
+        scale_img = cv2.resize(img, (cur_w, cur_h), interpolation=get_interpolation())
+        for _ in range(self.factor): 
+            scale_img = cv2.pyrDown(scale_img)
+        scale_img = cv2.resize(scale_img, (src_w, src_h), interpolation=get_interpolation())
+        return scale_img
+
+class CVGaussianNoise(object):
+    def __init__(self, mean=0, var=20):
+        self.mean = mean
+        if isinstance(var, numbers.Number):
+            self.var = max(int(sample_asym(var)), 1)
+        elif isinstance(var, (tuple, list)) and len(var) == 2:
+            self.var = int(sample_uniform(var[0], var[1]))
+        else:
+            raise Exception('degree must be number or list with length 2')
+        
+    def __call__(self, img):
+        noise = np.random.normal(self.mean, self.var**0.5, img.shape)
+        img = np.clip(img + noise, 0, 255).astype(np.uint8)
+        return img
+
+class CVMotionBlur(object):
+    def __init__(self, degrees=12, angle=90):
+        if isinstance(degrees, numbers.Number):
+            self.degree = max(int(sample_asym(degrees)), 1)
+        elif isinstance(degrees, (tuple, list)) and len(degrees) == 2:
+            self.degree = int(sample_uniform(degrees[0], degrees[1]))
+        else:
+            raise Exception('degree must be number or list with length 2')
+        self.angle = sample_uniform(-angle, angle)
+
+    def __call__(self, img):
+        M = cv2.getRotationMatrix2D((self.degree // 2, self.degree // 2), self.angle, 1)
+        motion_blur_kernel = np.zeros((self.degree, self.degree))
+        motion_blur_kernel[self.degree // 2, :] = 1
+        motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M, (self.degree, self.degree))
+        motion_blur_kernel = motion_blur_kernel / self.degree
+        img = cv2.filter2D(img, -1, motion_blur_kernel)
+        img = np.clip(img, 0, 255).astype(np.uint8)
+        return img
+
+class CVGeometry(object):
+    def __init__(self, degrees=15, translate=(0.3, 0.3), scale=(0.5, 2.), 
+                       shear=(45, 15), distortion=0.5, p=0.5):
+        self.p = p
+        type_p = random.random()
+        if type_p < 0.33:
+            self.transforms = CVRandomRotation(degrees=degrees)
+        elif type_p < 0.66:
+            self.transforms = CVRandomAffine(degrees=degrees, translate=translate, scale=scale, shear=shear)
+        else:
+            self.transforms = CVRandomPerspective(distortion=distortion)
+
+    def __call__(self, img):
+        if random.random() < self.p:
+            img = np.array(img)
+            return Image.fromarray(self.transforms(img))
+        else: return img
+
+class CVDeterioration(object):
+    def __init__(self, var, degrees, factor, p=0.5):
+        self.p = p
+        transforms = []
+        if var is not None:
+            transforms.append(CVGaussianNoise(var=var))
+        if degrees is not None:
+            transforms.append(CVMotionBlur(degrees=degrees))
+        if factor is not None:
+            transforms.append(CVRescale(factor=factor))
+            
+        random.shuffle(transforms)
+        transforms = Compose(transforms)
+        self.transforms =  transforms
+        
+    def __call__(self, img):
+        if random.random() < self.p:
+            img = np.array(img)
+            return Image.fromarray(self.transforms(img))
+        else: return img
+
+
+class CVColorJitter(object):
+    def __init__(self, brightness=0.5, contrast=0.5, saturation=0.5, hue=0.1, p=0.5):
+        self.p = p
+        self.transforms = transforms.ColorJitter(brightness=brightness, contrast=contrast, 
+                                                 saturation=saturation, hue=hue)
+
+    def __call__(self, img):
+        if random.random() < self.p: return self.transforms(img)
+        else: return img

utils.py

@@ -0,0 +1,304 @@
+import logging
+import os
+import time
+
+import cv2
+import numpy as np
+import torch
+import yaml
+from matplotlib import colors
+from matplotlib import pyplot as plt
+from torch import Tensor, nn
+from torch.utils.data import ConcatDataset
+
+class CharsetMapper(object):
+    """A simple class to map ids into strings.
+
+    It works only when the character set is 1:1 mapping between individual
+    characters and individual ids.
+    """
+
+    def __init__(self,
+                 filename='',
+                 max_length=30,
+                 null_char=u'\u2591'):
+        """Creates a lookup table.
+
+        Args:
+          filename: Path to charset file which maps characters to ids.
+          max_sequence_length: The max length of ids and string.
+          null_char: A unicode character used to replace '<null>' character.
+            the default value is a light shade block '░'.
+        """
+        self.null_char = null_char
+        self.max_length = max_length
+
+        self.label_to_char = self._read_charset(filename)
+        self.char_to_label = dict(map(reversed, self.label_to_char.items()))
+        self.num_classes = len(self.label_to_char)
+ 
+    def _read_charset(self, filename):
+        """Reads a charset definition from a tab separated text file.
+
+        Args:
+          filename: a path to the charset file.
+
+        Returns:
+          a dictionary with keys equal to character codes and values - unicode
+          characters.
+        """
+        import re
+        pattern = re.compile(r'(\d+)\t(.+)')
+        charset = {}
+        self.null_label = 0
+        charset[self.null_label] = self.null_char
+        with open(filename, 'r') as f:
+            for i, line in enumerate(f):
+                m = pattern.match(line)
+                assert m, f'Incorrect charset file. line #{i}: {line}'
+                label = int(m.group(1)) + 1
+                char = m.group(2)
+                charset[label] = char
+        return charset
+
+    def trim(self, text):
+        assert isinstance(text, str)
+        return text.replace(self.null_char, '')
+
+    def get_text(self, labels, length=None, padding=True, trim=False):
+        """ Returns a string corresponding to a sequence of character ids.
+        """
+        length = length if length else self.max_length
+        labels = [l.item() if isinstance(l, Tensor) else int(l) for l in labels]
+        if padding:
+            labels = labels + [self.null_label] * (length-len(labels))
+        text = ''.join([self.label_to_char[label] for label in labels])
+        if trim: text = self.trim(text)
+        return text
+
+    def get_labels(self, text, length=None, padding=True, case_sensitive=False):
+        """ Returns the labels of the corresponding text.
+        """
+        length = length if length else self.max_length
+        if padding:
+            text = text + self.null_char * (length - len(text))
+        if not case_sensitive:
+            text = text.lower()
+        labels = [self.char_to_label[char] for char in text]
+        return labels
+
+    def pad_labels(self, labels, length=None):
+        length = length if length else self.max_length
+
+        return labels + [self.null_label] * (length - len(labels))
+
+    @property
+    def digits(self):
+        return '0123456789'
+
+    @property
+    def digit_labels(self):
+        return self.get_labels(self.digits, padding=False)
+
+    @property
+    def alphabets(self):
+        all_chars = list(self.char_to_label.keys())
+        valid_chars = []
+        for c in all_chars:
+            if c in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ':
+                valid_chars.append(c)
+        return ''.join(valid_chars)
+
+    @property
+    def alphabet_labels(self):
+        return self.get_labels(self.alphabets, padding=False)
+
+
+class Timer(object):
+    """A simple timer."""
+    def __init__(self):
+        self.data_time = 0.
+        self.data_diff = 0.
+        self.data_total_time = 0.
+        self.data_call = 0
+        self.running_time = 0.
+        self.running_diff = 0.
+        self.running_total_time = 0.
+        self.running_call = 0
+
+    def tic(self):
+        self.start_time = time.time()
+        self.running_time = self.start_time
+
+    def toc_data(self):
+        self.data_time = time.time()
+        self.data_diff = self.data_time - self.running_time
+        self.data_total_time += self.data_diff
+        self.data_call += 1
+
+    def toc_running(self):
+        self.running_time = time.time()
+        self.running_diff = self.running_time - self.data_time
+        self.running_total_time += self.running_diff
+        self.running_call += 1
+
+    def total_time(self):
+        return self.data_total_time + self.running_total_time
+
+    def average_time(self):
+        return  self.average_data_time() + self.average_running_time()
+
+    def average_data_time(self):
+        return self.data_total_time / (self.data_call or 1)
+
+    def average_running_time(self):
+        return self.running_total_time / (self.running_call or 1)
+
+
+class Logger(object):
+    _handle = None
+    _root = None
+
+    @staticmethod
+    def init(output_dir, name, phase):
+        format = '[%(asctime)s %(filename)s:%(lineno)d %(levelname)s {}] ' \
+                '%(message)s'.format(name)
+        logging.basicConfig(level=logging.INFO, format=format)
+
+        try: os.makedirs(output_dir)
+        except: pass
+        config_path = os.path.join(output_dir, f'{phase}.txt')
+        Logger._handle = logging.FileHandler(config_path)
+        Logger._root = logging.getLogger()
+
+    @staticmethod
+    def enable_file():
+        if Logger._handle is None or Logger._root is None:
+            raise Exception('Invoke Logger.init() first!')
+        Logger._root.addHandler(Logger._handle)
+
+    @staticmethod
+    def disable_file():
+        if Logger._handle is None or Logger._root is None:
+            raise Exception('Invoke Logger.init() first!')
+        Logger._root.removeHandler(Logger._handle)
+
+
+class Config(object):
+
+    def __init__(self, config_path, host=True):
+        def __dict2attr(d, prefix=''):
+            for k, v in d.items():
+                if isinstance(v, dict):
+                    __dict2attr(v, f'{prefix}{k}_')
+                else:
+                    if k == 'phase':
+                        assert v in ['train', 'test']
+                    if k == 'stage':
+                        assert v in ['pretrain-vision', 'pretrain-language',
+                                     'train-semi-super', 'train-super']
+                    self.__setattr__(f'{prefix}{k}', v)
+
+        assert os.path.exists(config_path), '%s does not exists!' % config_path
+        with open(config_path) as file:
+            config_dict = yaml.load(file, Loader=yaml.FullLoader)
+        with open('configs/template.yaml') as file:
+            default_config_dict = yaml.load(file, Loader=yaml.FullLoader)
+        __dict2attr(default_config_dict)
+        __dict2attr(config_dict)
+        self.global_workdir = os.path.join(self.global_workdir, self.global_name)
+
+    def __getattr__(self, item):
+        attr = self.__dict__.get(item)
+        if attr is None:
+            attr = dict()
+            prefix = f'{item}_'
+            for k, v in self.__dict__.items():
+                if k.startswith(prefix):
+                    n = k.replace(prefix, '')
+                    attr[n] = v
+            return attr if len(attr) > 0 else None
+        else:
+            return attr
+
+    def __repr__(self):
+        str = 'ModelConfig(\n'
+        for i, (k, v) in enumerate(sorted(vars(self).items())):
+            str += f'\t({i}): {k} = {v}\n'
+        str += ')'
+        return str
+
+def blend_mask(image, mask, alpha=0.5, cmap='jet', color='b', color_alpha=1.0):
+    # normalize mask
+    mask = (mask-mask.min()) / (mask.max() - mask.min() + np.finfo(float).eps)
+    if mask.shape != image.shape:
+        mask = cv2.resize(mask,(image.shape[1], image.shape[0]))
+    # get color map
+    color_map = plt.get_cmap(cmap)
+    mask = color_map(mask)[:,:,:3]
+    # convert float to uint8
+    mask = (mask * 255).astype(dtype=np.uint8)
+
+    # set the basic color
+    basic_color = np.array(colors.to_rgb(color)) * 255 
+    basic_color = np.tile(basic_color, [image.shape[0], image.shape[1], 1]) 
+    basic_color = basic_color.astype(dtype=np.uint8)
+    # blend with basic color
+    blended_img = cv2.addWeighted(image, color_alpha, basic_color, 1-color_alpha, 0)
+    # blend with mask
+    blended_img = cv2.addWeighted(blended_img, alpha, mask, 1-alpha, 0)
+
+    return blended_img
+
+def onehot(label, depth, device=None):
+    """ 
+    Args:
+        label: shape (n1, n2, ..., )
+        depth: a scalar
+
+    Returns:
+        onehot: (n1, n2, ..., depth)
+    """
+    if not isinstance(label, torch.Tensor):
+        label = torch.tensor(label, device=device)
+    onehot = torch.zeros(label.size() + torch.Size([depth]), device=device)
+    onehot = onehot.scatter_(-1, label.unsqueeze(-1), 1)
+
+    return onehot
+
+class MyDataParallel(nn.DataParallel):
+
+    def gather(self, outputs, target_device):
+        r"""
+        Gathers tensors from different GPUs on a specified device
+        (-1 means the CPU).
+        """
+        def gather_map(outputs):
+            out = outputs[0]
+            if isinstance(out, (str, int, float)):
+                return out
+            if isinstance(out, list) and isinstance(out[0], str):
+                return [o for out in outputs for o in out]
+            if isinstance(out, torch.Tensor):
+                return torch.nn.parallel._functions.Gather.apply(target_device, self.dim, *outputs)
+            if out is None:
+                return None
+            if isinstance(out, dict):
+                if not all((len(out) == len(d) for d in outputs)):
+                    raise ValueError('All dicts must have the same number of keys')
+                return type(out)(((k, gather_map([d[k] for d in outputs]))
+                                for k in out))
+            return type(out)(map(gather_map, zip(*outputs)))
+
+        # Recursive function calls like this create reference cycles.
+        # Setting the function to None clears the refcycle.
+        try:
+            res = gather_map(outputs)
+        finally:
+            gather_map = None
+        return res
+
+
+class MyConcatDataset(ConcatDataset):
+    def __getattr__(self, k): 
+        return getattr(self.datasets[0], k)