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ridasalam · Apr 11, 2019 · 366b075 · 366b075
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diff --git a/README.md b/README.md
@@ -1,2 +1,111 @@
-# Graphonomy-Universal-Human-Parsing-via-Graph-Transfer-Learning
-coming soon.
+# Graphonomy: Universal Human Parsing via Graph Transfer Learning
+
+This repository contains the code for the paper:
+
+[**Graphonomy: Universal Human Parsing via Graph Transfer Learning**](https://arxiv.org/abs/1904.04536)
+,Ke Gong, Yiming Gao, Xiaodan Liang, Xiaohui Shen, Meng Wang, Liang Lin.
+
+# Environment and installation
++ Pytorch = 0.4.0
++ torchvision
++ scipy
++ tensorboardX
++ numpy
++ opencv-python
++ matplotlib
++ networkx
+
+   you can install above package by using `pip install -r requirements.txt`
+
+# Getting Started
+### Data Preparation
++ You need to download the human parsing dataset, prepare the images and store in `/data/datasets/dataset_name/`.
+We recommend to symlink the path to the dataets to `/data/dataset/` as follows
+
+```
+# symlink the Pascal-Person-Part dataset for example
+ln -s /path_to_Pascal_Person_Part/* data/datasets/pascal/
+```
++ The file structure should look like:
+```
+/Graphonomy
+  /data
+    /datasets
+      /pascal
+        /JPEGImages
+        /list
+        /SegmentationPart
+      /CIHP_4w
+        /Images
+        /lists
+        ...  
+```
+
+### Inference
+We provide a simply script to get the visualization result on the CIHP dataset using [trained](https://drive.google.com/file/d/1O9YD4kHgs3w2DUcWxtHiEFyWjCBeS_Vc/view?usp=sharing)
+ models as follows :
+```shell
+# Example of inference
+python exp/inference/inference.py  \
+--loadmodel /path_to_inference_model \
+--img_path ./img/messi.jpg \
+--output_path ./img/ \
+--output_name /output_file_name
+``` 
+
+### Training
+#### Transfer learning
+1. Download the Pascal pretrained model(avaliable soon).
+2. Run the `sh train_transfer_cihp.sh`.
+3. The results and models are saved in exp/transfer/run/.
+4. Evaluation and visualization script is eval_cihp.sh. You only need to change the attribute of `--loadmodel` before you run it.
+
+#### Universal training
+1. Download the [pretrained](https://drive.google.com/file/d/18WiffKnxaJo50sCC9zroNyHjcnTxGCbk/view?usp=sharing) model and store in /data/pretrained_model/.
+2. Run the `sh train_universal.sh`.
+3. The results and models are saved in exp/universal/run/.
+
+### Testing 
+If you want to evaluate the performance of a pre-trained model on PASCAL-Person-Part or CIHP val/test set, 
+simply run the script: `sh eval_cihp/pascal.sh`.
+Specify the specific model. And we provide the final model that you can download and store it in /data/pretrained_model/.
+
+### Models
+**Pascal-Person-Part trained model**
+
+|Model|Google Cloud|Baidu Yun|
+|--------|--------------|-----------|
+|Graphonomy(CIHP)| [Download](https://drive.google.com/file/d/1cwEhlYEzC7jIShENNLnbmcBR0SNlZDE6/view?usp=sharing)| Avaliable soon|
+
+**CIHP trained model**
+
+|Model|Google Cloud|Baidu Yun|
+|--------|--------------|-----------|
+|Graphonomy(PASCAL)| [Download](https://drive.google.com/file/d/1O9YD4kHgs3w2DUcWxtHiEFyWjCBeS_Vc/view?usp=sharing)| Avaliable soon|
+
+**Universal trained model**
+
+|Model|Google Cloud|Baidu Yun|
+|--------|--------------|-----------|
+|Universal|Avaliable soon|Avaliable soon|
+
+### Todo:
+- [ ] release pretrained and trained models
+- [ ] update universal eval code&script
+
+# Citation
+
+```
+@inproceedings{Gong2019Graphonomy,
+author = {Ke Gong and Yiming Gao and Xiaodan Liang and Xiaohui Shen and Meng Wang and Liang Lin},
+title = {Graphonomy: Universal Human Parsing via Graph Transfer Learning},
+booktitle = {CVPR},
+year = {2019},
+}
+
+```
+
+# Contact
+if you have any questions about this repo, please feel free to contact 
+[[email protected]](mailto:[email protected]).
+
diff --git a/dataloaders/__init__.py b/dataloaders/__init__.py
diff --git a/dataloaders/atr.py b/dataloaders/atr.py
@@ -0,0 +1,109 @@
+from __future__ import print_function, division
+import os
+from PIL import Image
+from torch.utils.data import Dataset
+from .mypath_atr import Path
+import random
+from PIL import ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+class VOCSegmentation(Dataset):
+    """
+    ATR dataset
+    """
+
+    def __init__(self,
+                 base_dir=Path.db_root_dir('atr'),
+                 split='train',
+                 transform=None,
+                 flip=False,
+                 ):
+        """
+        :param base_dir: path to ATR dataset directory
+        :param split: train/val
+        :param transform: transform to apply
+        """
+        super(VOCSegmentation).__init__()
+        self._flip_flag = flip
+
+        self._base_dir = base_dir
+        self._image_dir = os.path.join(self._base_dir, 'JPEGImages')
+        self._cat_dir = os.path.join(self._base_dir, 'SegmentationClassAug')
+        self._flip_dir = os.path.join(self._base_dir,'SegmentationClassAug_rev')
+
+        if isinstance(split, str):
+            self.split = [split]
+        else:
+            split.sort()
+            self.split = split
+
+        self.transform = transform
+
+        _splits_dir = os.path.join(self._base_dir, 'list')
+
+        self.im_ids = []
+        self.images = []
+        self.categories = []
+        self.flip_categories = []
+
+        for splt in self.split:
+            with open(os.path.join(os.path.join(_splits_dir, splt + '_id.txt')), "r") as f:
+                lines = f.read().splitlines()
+
+            for ii, line in enumerate(lines):
+
+                _image = os.path.join(self._image_dir, line+'.jpg' )
+                _cat = os.path.join(self._cat_dir, line +'.png')
+                _flip = os.path.join(self._flip_dir,line + '.png')
+                # print(self._image_dir,_image)
+                assert os.path.isfile(_image)
+                # print(_cat)
+                assert os.path.isfile(_cat)
+                assert os.path.isfile(_flip)
+                self.im_ids.append(line)
+                self.images.append(_image)
+                self.categories.append(_cat)
+                self.flip_categories.append(_flip)
+
+
+        assert (len(self.images) == len(self.categories))
+        assert len(self.flip_categories) == len(self.categories)
+
+        # Display stats
+        print('Number of images in {}: {:d}'.format(split, len(self.images)))
+
+    def __len__(self):
+        return len(self.images)
+
+
+    def __getitem__(self, index):
+        _img, _target= self._make_img_gt_point_pair(index)
+        sample = {'image': _img, 'label': _target}
+
+        if self.transform is not None:
+            sample = self.transform(sample)
+
+        return sample
+
+    def _make_img_gt_point_pair(self, index):
+        # Read Image and Target
+        # _img = np.array(Image.open(self.images[index]).convert('RGB')).astype(np.float32)
+        # _target = np.array(Image.open(self.categories[index])).astype(np.float32)
+
+        _img = Image.open(self.images[index]).convert('RGB')  # return is RGB pic
+        if self._flip_flag:
+            if random.random() < 0.5:
+                _target = Image.open(self.flip_categories[index])
+                _img = _img.transpose(Image.FLIP_LEFT_RIGHT)
+            else:
+                _target = Image.open(self.categories[index])
+        else:
+            _target = Image.open(self.categories[index])
+
+        return _img, _target
+
+    def __str__(self):
+        return 'ATR(split=' + str(self.split) + ')'
+
+
+
diff --git a/dataloaders/cihp.py b/dataloaders/cihp.py
@@ -0,0 +1,107 @@
+from __future__ import print_function, division
+import os
+from PIL import Image
+from torch.utils.data import Dataset
+from .mypath_cihp import Path
+import random
+
+class VOCSegmentation(Dataset):
+    """
+    CIHP dataset
+    """
+
+    def __init__(self,
+                 base_dir=Path.db_root_dir('cihp'),
+                 split='train',
+                 transform=None,
+                 flip=False,
+                 ):
+        """
+        :param base_dir: path to CIHP dataset directory
+        :param split: train/val/test
+        :param transform: transform to apply
+        """
+        super(VOCSegmentation).__init__()
+        self._flip_flag = flip
+
+        self._base_dir = base_dir
+        self._image_dir = os.path.join(self._base_dir, 'Images')
+        self._cat_dir = os.path.join(self._base_dir, 'Category_ids')
+        self._flip_dir = os.path.join(self._base_dir,'Category_rev_ids')
+
+        if isinstance(split, str):
+            self.split = [split]
+        else:
+            split.sort()
+            self.split = split
+
+        self.transform = transform
+
+        _splits_dir = os.path.join(self._base_dir, 'lists')
+
+        self.im_ids = []
+        self.images = []
+        self.categories = []
+        self.flip_categories = []
+
+        for splt in self.split:
+            with open(os.path.join(os.path.join(_splits_dir, splt + '_id.txt')), "r") as f:
+                lines = f.read().splitlines()
+
+            for ii, line in enumerate(lines):
+
+                _image = os.path.join(self._image_dir, line+'.jpg' )
+                _cat = os.path.join(self._cat_dir, line +'.png')
+                _flip = os.path.join(self._flip_dir,line + '.png')
+                # print(self._image_dir,_image)
+                assert os.path.isfile(_image)
+                # print(_cat)
+                assert os.path.isfile(_cat)
+                assert os.path.isfile(_flip)
+                self.im_ids.append(line)
+                self.images.append(_image)
+                self.categories.append(_cat)
+                self.flip_categories.append(_flip)
+
+
+        assert (len(self.images) == len(self.categories))
+        assert len(self.flip_categories) == len(self.categories)
+
+        # Display stats
+        print('Number of images in {}: {:d}'.format(split, len(self.images)))
+
+    def __len__(self):
+        return len(self.images)
+
+
+    def __getitem__(self, index):
+        _img, _target= self._make_img_gt_point_pair(index)
+        sample = {'image': _img, 'label': _target}
+
+        if self.transform is not None:
+            sample = self.transform(sample)
+
+        return sample
+
+    def _make_img_gt_point_pair(self, index):
+        # Read Image and Target
+        # _img = np.array(Image.open(self.images[index]).convert('RGB')).astype(np.float32)
+        # _target = np.array(Image.open(self.categories[index])).astype(np.float32)
+
+        _img = Image.open(self.images[index]).convert('RGB')  # return is RGB pic
+        if self._flip_flag:
+            if random.random() < 0.5:
+                _target = Image.open(self.flip_categories[index])
+                _img = _img.transpose(Image.FLIP_LEFT_RIGHT)
+            else:
+                _target = Image.open(self.categories[index])
+        else:
+            _target = Image.open(self.categories[index])
+
+        return _img, _target
+
+    def __str__(self):
+        return 'CIHP(split=' + str(self.split) + ')'
+
+
+