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| [settings] | ||
| known_third_party = PIL,cog,cv2,detectron2,grade,graphviz,h5py,matplotlib,mmcv,mmdet,numpy,packaging,panopticapi,pycocotools,setuptools,six,terminaltables,torch,torchvision,tqdm,xmltodict | ||
| known_third_party = PIL,cog,cv2,dataset,detectron2,evaluator,grade,graphviz,h5py,matplotlib,mmcv,mmdet,numpy,packaging,panopticapi,pycocotools,setuptools,six,terminaltables,torch,torchvision,tqdm,trainer,xmltodict |
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| # CE7454 Assignment 1: PSG Classification | ||
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| ## Get Started | ||
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| We specially build the tiny codebase here (in this directory) to help our students to quickly get started. | ||
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| First off, let's clone or fork the codebase and enter in the `ce7454` directory. Don't forget to star the repo if you find the assignment is interesting and instructive. | ||
| Then we download the data [here](https://entuedu-my.sharepoint.com/:f:/g/personal/jingkang001_e_ntu_edu_sg/El0TuNPJWyVJqw6agAzd7l0BU_Tr9gJEgUzLbZggaAKyHg?e=cFPqNF) and unzip the data at the correct place. Eventually, your `ce7454` folder should looks like this: | ||
| ``` | ||
| ce7454 | ||
| ├── checkpoints | ||
| ├── data | ||
| │ ├── coco | ||
| │ │ ├── panoptic_train2017 | ||
| │ │ ├── panoptic_val2017 | ||
| │ │ ├── train2017 | ||
| │ │ └── val2017 | ||
| │ └── psg | ||
| │ ├── psg_cls_basic.json | ||
| │ └── psg_val_advanced.json | ||
| ├── results | ||
| ├── dataset.py | ||
| ├── evaluator.py | ||
| ├── ... | ||
| ``` | ||
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| We provide 4500 training data, 500 validation data, and 500 test data. | ||
| Notice that there might not be exactly 4500 training images (so are val/test images) as some images are annotated twice, and we consider one annotation as one sample. | ||
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| Then, we need to setup the environment. We use `conda` to manage our dependencies. The code is heavily dependent on PyTorch. | ||
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| ```bash | ||
| conda install python=3.7 pytorch=1.7.0 torchvision=0.8.0 torchaudio==0.7.0 cudatoolkit=10.1 | ||
| pip install tqdm | ||
| ``` | ||
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| Finally, make sure your working directory is `ce7454`, and let's train the model! | ||
| ```bash | ||
| python main.py | ||
| ``` | ||
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| You can explore the project by reading from the `main.py` and dive in. Good Luck!! |
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| import io | ||
| import json | ||
| import logging | ||
| import os | ||
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| import torch | ||
| import torchvision.transforms as trn | ||
| from PIL import Image, ImageFile | ||
| from torch.utils.data import Dataset | ||
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| # to fix "OSError: image file is truncated" | ||
| ImageFile.LOAD_TRUNCATED_IMAGES = True | ||
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| class Convert: | ||
| def __init__(self, mode='RGB'): | ||
| self.mode = mode | ||
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| def __call__(self, image): | ||
| return image.convert(self.mode) | ||
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| def get_transforms(stage: str): | ||
| mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5] | ||
| if stage == 'train': | ||
| return trn.Compose([ | ||
| Convert('RGB'), | ||
| trn.Resize((1333, 800)), | ||
| trn.RandomHorizontalFlip(), | ||
| trn.RandomCrop((1333, 800), padding=4), | ||
| trn.ToTensor(), | ||
| trn.Normalize(mean, std), | ||
| ]) | ||
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| elif stage in ['val', 'test']: | ||
| return trn.Compose([ | ||
| Convert('RGB'), | ||
| trn.Resize((1333, 800)), | ||
| trn.ToTensor(), | ||
| trn.Normalize(mean, std), | ||
| ]) | ||
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| class PSGClsDataset(Dataset): | ||
| def __init__( | ||
| self, | ||
| stage, | ||
| root='./data/coco/', | ||
| num_classes=56, | ||
| ): | ||
| super(PSGClsDataset, self).__init__() | ||
| with open('./data/psg/psg_cls_basic.json') as f: | ||
| dataset = json.load(f) | ||
| self.imglist = [ | ||
| d for d in dataset['data'] | ||
| if d['image_id'] in dataset[f'{stage}_image_ids'] | ||
| ] | ||
| self.root = root | ||
| self.transform_image = get_transforms(stage) | ||
| self.num_classes = num_classes | ||
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| def __len__(self): | ||
| return len(self.imglist) | ||
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| def __getitem__(self, index): | ||
| sample = self.imglist[index] | ||
| path = os.path.join(self.root, sample['file_name']) | ||
| try: | ||
| with open(path, 'rb') as f: | ||
| content = f.read() | ||
| filebytes = content | ||
| buff = io.BytesIO(filebytes) | ||
| image = Image.open(buff).convert('RGB') | ||
| sample['data'] = self.transform_image(image) | ||
| except Exception as e: | ||
| logging.error('Error, cannot read [{}]'.format(path)) | ||
| raise e | ||
| # Generate Soft Label | ||
| soft_label = torch.Tensor(self.num_classes) | ||
| soft_label.fill_(0) | ||
| soft_label[sample['relations']] = 1 | ||
| sample['soft_label'] = soft_label | ||
| del sample['relations'] | ||
| return sample |
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| import numpy as np | ||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| from torch.utils.data import DataLoader | ||
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| class Evaluator: | ||
| def __init__( | ||
| self, | ||
| net: nn.Module, | ||
| k: int, | ||
| ): | ||
| self.net = net | ||
| self.k = k | ||
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| def eval_recall( | ||
| self, | ||
| data_loader: DataLoader, | ||
| ): | ||
| self.net.eval() | ||
| loss_avg = 0.0 | ||
| pred_list, gt_list = [], [] | ||
| with torch.no_grad(): | ||
| for batch in data_loader: | ||
| data = batch['data'].cuda() | ||
| logits = self.net(data) | ||
| prob = torch.sigmoid(logits) | ||
| target = batch['soft_label'].cuda() | ||
| loss = F.binary_cross_entropy(prob, target, reduction='sum') | ||
| loss_avg += float(loss.data) | ||
| # gather prediction and gt | ||
| pred = torch.topk(prob.data, self.k)[1] | ||
| pred = pred.cpu().detach().tolist() | ||
| pred_list.extend(pred) | ||
| for soft_label in batch['soft_label']: | ||
| gt_label = (soft_label == 1).nonzero(as_tuple=True)[0]\ | ||
| .cpu().detach().tolist() | ||
| gt_list.append(gt_label) | ||
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| # compute mean recall | ||
| score_list = np.zeros([56, 2], dtype=int) | ||
| for gt, pred in zip(gt_list, pred_list): | ||
| for gt_id in gt: | ||
| # pos 0 for counting all existing relations | ||
| score_list[gt_id][0] += 1 | ||
| if gt_id in pred: | ||
| # pos 1 for counting relations that is recalled | ||
| score_list[gt_id][1] += 1 | ||
| score_list = score_list[6:] | ||
| # to avoid nan | ||
| score_list[:, 0][score_list[:, 0] == 0] = 1 | ||
| meanrecall = np.mean(score_list[:, 1] / score_list[:, 0]) | ||
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| metrics = {} | ||
| metrics['test_loss'] = loss_avg / len(data_loader) | ||
| metrics['mean_recall'] = meanrecall | ||
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| return metrics | ||
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| def submit( | ||
| self, | ||
| data_loader: DataLoader, | ||
| ): | ||
| self.net.eval() | ||
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| pred_list = [] | ||
| with torch.no_grad(): | ||
| for batch in data_loader: | ||
| data = batch['data'].cuda() | ||
| logits = self.net(data) | ||
| prob = torch.sigmoid(logits) | ||
| pred = torch.topk(prob.data, self.k)[1] | ||
| pred = pred.cpu().detach().tolist() | ||
| pred_list.extend(pred) | ||
| return pred_list |
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| import argparse | ||
| import os | ||
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| import numpy as np | ||
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| def compute_recall(gt_list, pred_list): | ||
| score_list = np.zeros([56, 2], dtype=int) | ||
| for gt, pred in zip(gt_list, pred_list): | ||
| for gt_id in gt: | ||
| # pos 0 for counting all existing relations | ||
| score_list[gt_id][0] += 1 | ||
| if gt_id in pred: | ||
| # pos 1 for counting relations that is recalled | ||
| score_list[gt_id][1] += 1 | ||
| score_list = score_list[6:] | ||
| # to avoid nan, but test set does not have empty predict | ||
| # score_list[:,0][score_list[:,0] == 0] = 1 | ||
| meanrecall = np.mean(score_list[:, 1] / score_list[:, 0]) | ||
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| return meanrecall | ||
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| def parse_args(): | ||
| parser = argparse.ArgumentParser(description='MMDet eval a model') | ||
| parser.add_argument('input_path', help='input file path') | ||
| parser.add_argument('output_path', help='output file path') | ||
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| args = parser.parse_args() | ||
| return args | ||
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| def main(): | ||
| args = parse_args() | ||
| submit_dir = os.path.join(args.input_path, 'res') | ||
| groundtruth_dir = os.path.join(args.input_path, 'ref') | ||
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| gt_list = [] | ||
| with open(os.path.join(groundtruth_dir, 'psg_cls_gt.txt'), 'r') as reader: | ||
| for line in reader.readlines(): | ||
| gt_list.append( | ||
| [int(label) for label in line.strip('/n').split(' ')]) | ||
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| pred_list = [] | ||
| with open(os.path.join(submit_dir, 'result.txt'), 'r') as reader: | ||
| for line in reader.readlines(): | ||
| pred_list.append( | ||
| [int(label) for label in line.strip('/n').split(' ')]) | ||
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| assert np.array(pred_list).shape == ( | ||
| 500, 3), 'make sure the submitted file is 500 x 3' | ||
| result = compute_recall(gt_list, pred_list) | ||
| output_filename = os.path.join(args.output_path, 'scores.txt') | ||
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| with open(output_filename, 'w') as f3: | ||
| f3.write('score: {}\n'.format(result)) | ||
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| if __name__ == '__main__': | ||
| main() |
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