From c6d6b7560746b06d122f584e4caeb9d7b76afd83 Mon Sep 17 00:00:00 2001 From: liuxin1 Date: Tue, 4 Aug 2020 19:55:19 +0800 Subject: [PATCH 1/7] add OneHand10K dataset --- mmpose/datasets/__init__.py | 6 +- mmpose/datasets/datasets/__init__.py | 6 +- mmpose/datasets/datasets/top_down/__init__.py | 5 +- .../top_down/topdown_onehand10k_dataset.py | 244 ++++++++ tests/data/OneHand10K/160.jpg | Bin 0 -> 10492 bytes tests/data/OneHand10K/2251.jpg | Bin 0 -> 5216054 bytes tests/data/OneHand10K/5.jpg | Bin 0 -> 122973 bytes tests/data/OneHand10K/75.jpg | Bin 0 -> 18606 bytes tests/data/OneHand10K/test_onehand10k.json | 541 ++++++++++++++++++ tests/test_datasets/test_top_down_dataset.py | 47 ++ 10 files changed, 843 insertions(+), 6 deletions(-) create mode 100644 mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py create mode 100755 tests/data/OneHand10K/160.jpg create mode 100755 tests/data/OneHand10K/2251.jpg create mode 100755 tests/data/OneHand10K/5.jpg create mode 100755 tests/data/OneHand10K/75.jpg create mode 100755 tests/data/OneHand10K/test_onehand10k.json diff --git a/mmpose/datasets/__init__.py b/mmpose/datasets/__init__.py index 10782d27de..b1eb343456 100644 --- a/mmpose/datasets/__init__.py +++ b/mmpose/datasets/__init__.py @@ -1,12 +1,12 @@ from .builder import build_dataloader, build_dataset from .datasets import (BottomUpCocoDataset, TopDownCocoDataset, - TopDownMpiiTrbDataset) + TopDownMpiiTrbDataset, TopDownOneHand10KDataset) from .pipelines import Compose from .registry import DATASETS, PIPELINES from .samplers import DistributedSampler __all__ = [ 'TopDownCocoDataset', 'BottomUpCocoDataset', 'TopDownMpiiTrbDataset', - 'build_dataloader', 'build_dataset', 'Compose', 'DistributedSampler', - 'DATASETS', 'PIPELINES' + 'TopDownOneHand10KDataset', 'build_dataloader', 'build_dataset', 'Compose', + 'DistributedSampler', 'DATASETS', 'PIPELINES' ] diff --git a/mmpose/datasets/datasets/__init__.py b/mmpose/datasets/datasets/__init__.py index 143a547349..0893aa3455 100644 --- a/mmpose/datasets/datasets/__init__.py +++ b/mmpose/datasets/datasets/__init__.py @@ -1,6 +1,8 @@ from .bottom_up import BottomUpCocoDataset -from .top_down import TopDownCocoDataset, TopDownMpiiTrbDataset +from .top_down import (TopDownCocoDataset, TopDownMpiiTrbDataset, + TopDownOneHand10KDataset) __all__ = [ - 'TopDownCocoDataset', 'BottomUpCocoDataset', 'TopDownMpiiTrbDataset' + 'TopDownCocoDataset', 'BottomUpCocoDataset', 'TopDownMpiiTrbDataset', + 'TopDownOneHand10KDataset' ] diff --git a/mmpose/datasets/datasets/top_down/__init__.py b/mmpose/datasets/datasets/top_down/__init__.py index 75ac18b1d3..c85ae6d0d5 100644 --- a/mmpose/datasets/datasets/top_down/__init__.py +++ b/mmpose/datasets/datasets/top_down/__init__.py @@ -1,4 +1,7 @@ from .topdown_coco_dataset import TopDownCocoDataset from .topdown_mpii_trb_dataset import TopDownMpiiTrbDataset +from .topdown_onehand10k_dataset import TopDownOneHand10KDataset -__all__ = ['TopDownCocoDataset', 'TopDownMpiiTrbDataset'] +__all__ = [ + 'TopDownCocoDataset', 'TopDownMpiiTrbDataset', 'TopDownOneHand10KDataset' +] diff --git a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py new file mode 100644 index 0000000000..23b685e7b5 --- /dev/null +++ b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py @@ -0,0 +1,244 @@ +import copy as cp +import os +from collections import OrderedDict + +import json_tricks as json +import numpy as np + +from mmpose.datasets.builder import DATASETS +from .topdown_base_dataset import TopDownBaseDataset + + +@DATASETS.register_module() +class TopDownOneHand10KDataset(TopDownBaseDataset): + """OneHand10K dataset for top-down hand pose estimation. + + The dataset loads raw features and apply specified transforms + to return a dict containing the image tensors and other information. + + OneHand10K keypoint indexes:: + + 0: 'wrist', + 1: 'thumb1', + 2: 'thumb2', + 3: 'thumb3', + 4: 'thumb4', + 5: 'forefinger1', + 6: 'forefinger2', + 7: 'forefinger3', + 8: 'forefinger4', + 9: 'middle_finger1', + 10: 'middle_finger2', + 11: 'middle_finger3', + 12: 'middle_finger4', + 13: 'ring_finger1', + 14: 'ring_finger2', + 15: 'ring_finger3', + 16: 'ring_finger4', + 17: 'pinky_finger1', + 18: 'pinky_finger2', + 19: 'pinky_finger3', + 20: 'pinky_finger4' + + Args: + ann_file (str): Path to the annotation file. + img_prefix (str): Path to a directory where images are held. + Default: None. + data_cfg (dict): config + pipeline (list[dict | callable]): A sequence of data transforms. + test_mode (bool): Store True when building test or + validation dataset. Default: False. + """ + + def __init__(self, + ann_file, + img_prefix, + data_cfg, + pipeline, + test_mode=False): + + super().__init__( + ann_file, img_prefix, data_cfg, pipeline, test_mode=test_mode) + + self.ann_info['flip_pairs'] = [] + + self.ann_info['use_different_joints_weight'] = False + self.ann_info['joints_weight'] = \ + np.ones(21, dtype=np.float32).reshape( + (self.ann_info['num_joints'], 1)) + + self.db = self._get_db(ann_file) + self.image_set = set([x['image_file'] for x in self.db]) + self.num_images = len(self.image_set) + + print('=> num_images: {}'.format(self.num_images)) + print('=> load {} samples'.format(len(self.db))) + + def _get_db(self, ann_file): + """Load dataset.""" + with open(ann_file, 'r') as f: + data = json.load(f) + tmpl = dict( + image_file=None, + center=None, + scale=None, + rotation=0, + joints_3d=None, + joints_3d_visible=None, + dataset='OneHand10K') + + imid2info = {x['id']: x for x in data['images']} + + num_joints = self.ann_info['num_joints'] + gt_db = [] + + for anno in data['annotations']: + newitem = cp.deepcopy(tmpl) + image_id = anno['image_id'] + newitem['image_file'] = os.path.join( + self.img_prefix, imid2info[image_id]['file_name']) + + if max(anno['keypoints']) == 0: + continue + + joints_3d = np.zeros((num_joints, 3), dtype=np.float) + joints_3d_visible = np.zeros((num_joints, 3), dtype=np.float) + + for ipt in range(num_joints): + joints_3d[ipt, 0] = anno['keypoints'][ipt * 3 + 0] + joints_3d[ipt, 1] = anno['keypoints'][ipt * 3 + 1] + joints_3d[ipt, 2] = 0 + t_vis = anno['keypoints'][ipt * 3 + 2] + if t_vis > 1: + t_vis = 1 + joints_3d_visible[ipt, 0] = t_vis + joints_3d_visible[ipt, 1] = t_vis + joints_3d_visible[ipt, 2] = 0 + + center, scale = self._box2cs(anno['bbox'][:4]) + newitem['center'] = center + newitem['scale'] = scale + newitem['joints_3d'] = joints_3d + newitem['joints_3d_visible'] = joints_3d_visible + gt_db.append(newitem) + + return gt_db + + def _box2cs(self, box): + """Get box center & scale given box (x, y, w, h).""" + x, y, w, h = box[:4] + return self._xywh2cs(x, y, w, h) + + def _xywh2cs(self, x, y, w, h): + """This encodes bbox(x,y,w,w) into (center, scale) + + Args: + x, y, w, h + + Returns: + center (np.ndarray[float32](2,)): center of the bbox (x, y). + scale (np.ndarray[float32](2,)): scale of the bbox w & h. + """ + aspect_ratio = self.ann_info['image_size'][0] / self.ann_info[ + 'image_size'][1] + center = np.array([x + w * 0.5, y + h * 0.5], dtype=np.float32) + + if (not self.test_mode) and np.random.rand() < 0.3: + center += 0.4 * (np.random.rand(2) - 0.5) * [w, h] + + if w > aspect_ratio * h: + h = w * 1.0 / aspect_ratio + elif w < aspect_ratio * h: + w = h * aspect_ratio + + # pixel std is 200.0 + scale = np.array([w / 200.0, h / 200.0], dtype=np.float32) + + scale = scale * 1.25 + + return center, scale + + def evaluate_kernal(self, pred, joints_3d, joints_3d_visible, bbox): + """Evaluate one example. + + ||pre[i] - joints_3d[i]|| < 0.2 * max(w, h) + """ + num_joints = self.ann_info['num_joints'] + bbox = np.array(bbox) + threshold = np.max(bbox[2:]) * 0.2 + hit = np.zeros(num_joints, dtype=np.float32) + exist = np.zeros(num_joints, dtype=np.float32) + + for i in range(num_joints): + pred_pt = pred[i] + gt_pt = joints_3d[i] + vis = joints_3d_visible[i][0] + if vis: + exist[i] = 1 + else: + continue + distance = np.linalg.norm(pred_pt[:2] - gt_pt[:2]) + if distance < threshold: + hit[i] = 1 + return hit, exist + + def evaluate(self, outputs, res_folder, metrics='PCK', **kwargs): + """Evaluate OneHand10K keypoint results.""" + res_file = os.path.join(res_folder, 'result_keypoints.json') + + all_preds, all_boxes, all_image_path = list(map(list, zip(*outputs))) + + kpts = [] + + for idx, kpt in enumerate(all_preds): + kpts.append({ + 'keypoints': + kpt[0], + 'center': + all_boxes[idx][0][0:2], + 'scale': + all_boxes[idx][0][2:4], + 'area': + all_boxes[idx][0][4], + 'score': + all_boxes[idx][0][5], + 'image_id': + int(''.join(all_image_path[idx]).split('/')[-1][:-4]), + }) + + self._write_keypoint_results(kpts, res_file) + info_str = self._report_metric(res_file) + name_value = OrderedDict(info_str) + + return name_value + + def _write_keypoint_results(self, keypoints, res_file): + """Write results into a json file.""" + + with open(res_file, 'w') as f: + json.dump(keypoints, f, sort_keys=True, indent=4) + + def _report_metric(self, res_file): + """Keypoint evaluation. + + Report Mean Acc of skeleton, contour and all joints. + """ + num_joints = self.ann_info['num_joints'] + hit = np.zeros(num_joints) + exist = np.zeros(num_joints) + + with open(res_file, 'r') as fin: + preds = json.load(fin) + + assert len(preds) == len(self.db) + for pred, item in zip(preds, self.db): + h, e = self.evaluate_kernal(pred['keypoints'], item['joints_3d'], + item['joints_3d_visible'], + item['headbox']) + hit += h + exist += e + p_all = np.sum(hit) / np.sum(exist) + + info_str = [] + info_str.append(('p_acc', p_all)) + return info_str diff --git a/tests/data/OneHand10K/160.jpg b/tests/data/OneHand10K/160.jpg new file mode 100755 index 0000000000000000000000000000000000000000..891919b576812dc925b8fa6981d84ebd47bc6afa GIT binary patch literal 10492 zcmb`L1yo$g*Wh1w<8Hw$fsx7Ajz>A`+ zG_?m@>7M^7r{e&8Bmfv;SJ0rQ{zv|QMYxu3P)`5=X~4C4t*kvP;rtTLPClM)_j0)1 z0Krqodj=!iGZbzhoQdz*_D^Q}L+4L^e9u>`zdz;;?oUQJ;1J19#y{zF3QGs)~ zx4pFwoJZkI=j7#V59f0@6F6I2cmMzb#=YFr+R_frTyVyK>S#&BSrhM0r@yhM z^)tAg03hw^<_opAwezHAvSg;_78VwwRn?S z6S!~Q{v&((9sp{?0DySv9~s>T0Kk3;05yZZy$|7iyx4hqx;^6L^zrfGu(!74xOeE^ z<^QPgx8{F_-{#}EpYLzqQOj7{Sa>;kQr|n(($&e;3rg+bW?^Yf&Hi75_F#RS&rT<>^zc{TcUghPVGw|EmJW!XLpN_O{gbVreZM zYD+Ju_dUaZ6ZaJ&fCgX#1ON#@3D5#e06V|~2m&I&V?Y*A0@MK=zyL4>ECD;f8Gr&l zKmhOp2nV8ow?HD00%QU?Kp{{DQ~c0SAOny&$QI-R@&W~bLP1d=7$^nw5mX2&2i1dGLEWGs z&?IO9v;jH<;z=zXZpC-+{BhCEyxxE4UXt z2A&6RflnX+1RX*Mp@FbNgdoxob%-Iv1_FfyK_VeZkSs_Uqyf?e8G+10wjmb?NC@}{ 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--git a/tests/test_datasets/test_top_down_dataset.py b/tests/test_datasets/test_top_down_dataset.py index 7841658ce3..5f8b73e81e 100644 --- a/tests/test_datasets/test_top_down_dataset.py +++ b/tests/test_datasets/test_top_down_dataset.py @@ -67,3 +67,50 @@ def test_top_down_COCO_dataset(): image_id = 785 assert image_id in custom_dataset.image_set_index assert len(custom_dataset.image_set_index) == 4 + + +def test_top_down_OneHand10K_dataset(): + dataset = 'TopDownOneHand10KDataset' + # test COCO datasets + dataset_class = DATASETS.get(dataset) + + channel_cfg = dict( + num_output_channels=21, + dataset_joints=21, + dataset_channel=[ + [ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, + 18, 19, 20 + ], + ], + inference_channel=[ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20 + ]) + + data_cfg = dict( + image_size=[256, 256], + heatmap_size=[64, 64], + num_output_channels=channel_cfg['num_output_channels'], + num_joints=channel_cfg['dataset_joints'], + dataset_channel=channel_cfg['dataset_channel'], + inference_channel=channel_cfg['inference_channel']) + # Test + data_cfg_copy = copy.deepcopy(data_cfg) + _ = dataset_class( + ann_file='tests/data/OneHand10K/test_onehand10k.json', + img_prefix='tests/data/OneHand10K/', + data_cfg=data_cfg_copy, + pipeline=[], + test_mode=True) + + custom_dataset = dataset_class( + ann_file='tests/data/OneHand10K/test_onehand10k.json', + img_prefix='tests/data/OneHand10K/', + data_cfg=data_cfg_copy, + pipeline=[], + test_mode=False) + + assert custom_dataset.test_mode is False + + assert custom_dataset.num_images == 4 From 0724440c79a4b7bfa6e1e3c43645faa3cbdef7da Mon Sep 17 00:00:00 2001 From: liuxin1 Date: Wed, 5 Aug 2020 17:33:15 +0800 Subject: [PATCH 2/7] resolve comments --- .../top_down/topdown_onehand10k_dataset.py | 59 ++++++++----------- 1 file changed, 24 insertions(+), 35 deletions(-) diff --git a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py index 23b685e7b5..34646f7dce 100644 --- a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py +++ b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py @@ -1,5 +1,6 @@ import copy as cp import os +import os.path as osp from collections import OrderedDict import json_tricks as json @@ -63,16 +64,16 @@ def __init__(self, self.ann_info['flip_pairs'] = [] self.ann_info['use_different_joints_weight'] = False - self.ann_info['joints_weight'] = \ - np.ones(21, dtype=np.float32).reshape( - (self.ann_info['num_joints'], 1)) + assert self.ann_info['num_joints'] == 21 + self.ann_info['joints_weight'] = \ + np.ones((self.ann_info['num_joints'], 1), dtype=np.float32) self.db = self._get_db(ann_file) self.image_set = set([x['image_file'] for x in self.db]) self.num_images = len(self.image_set) - print('=> num_images: {}'.format(self.num_images)) - print('=> load {} samples'.format(len(self.db))) + print(f'=> num_images: {self.num_images}') + print(f'=> load {len(self.db)} samples') def _get_db(self, ann_file): """Load dataset.""" @@ -111,11 +112,9 @@ def _get_db(self, ann_file): t_vis = anno['keypoints'][ipt * 3 + 2] if t_vis > 1: t_vis = 1 - joints_3d_visible[ipt, 0] = t_vis - joints_3d_visible[ipt, 1] = t_vis - joints_3d_visible[ipt, 2] = 0 + joints_3d_visible[ipt, :] = (t_vis, t_vis, 0) - center, scale = self._box2cs(anno['bbox'][:4]) + center, scale = self._xywh2cs(*anno['bbox'][:4]) newitem['center'] = center newitem['scale'] = scale newitem['joints_3d'] = joints_3d @@ -124,11 +123,6 @@ def _get_db(self, ann_file): return gt_db - def _box2cs(self, box): - """Get box center & scale given box (x, y, w, h).""" - x, y, w, h = box[:4] - return self._xywh2cs(x, y, w, h) - def _xywh2cs(self, x, y, w, h): """This encodes bbox(x,y,w,w) into (center, scale) @@ -158,7 +152,7 @@ def _xywh2cs(self, x, y, w, h): return center, scale - def evaluate_kernal(self, pred, joints_3d, joints_3d_visible, bbox): + def _evaluate_kernel(self, pred, joints_3d, joints_3d_visible, bbox): """Evaluate one example. ||pre[i] - joints_3d[i]|| < 0.2 * max(w, h) @@ -186,24 +180,19 @@ def evaluate(self, outputs, res_folder, metrics='PCK', **kwargs): """Evaluate OneHand10K keypoint results.""" res_file = os.path.join(res_folder, 'result_keypoints.json') - all_preds, all_boxes, all_image_path = list(map(list, zip(*outputs))) - kpts = [] - for idx, kpt in enumerate(all_preds): + for preds, boxes, image_path in outputs: + str_image_path = ''.join(image_path) + image_id = int(osp.basename(osp.splitext(str_image_path)[0])) + kpts.append({ - 'keypoints': - kpt[0], - 'center': - all_boxes[idx][0][0:2], - 'scale': - all_boxes[idx][0][2:4], - 'area': - all_boxes[idx][0][4], - 'score': - all_boxes[idx][0][5], - 'image_id': - int(''.join(all_image_path[idx]).split('/')[-1][:-4]), + 'keypoints': list(preds[0].astype(np.float32)), + 'center': list(boxes[0][0:2].astype(np.float32)), + 'scale': list(boxes[0][2:4].astype(np.float32)), + 'area': float(boxes[0][4]), + 'score': float(boxes[0][5]), + 'image_id': image_id, }) self._write_keypoint_results(kpts, res_file) @@ -224,21 +213,21 @@ def _report_metric(self, res_file): Report Mean Acc of skeleton, contour and all joints. """ num_joints = self.ann_info['num_joints'] - hit = np.zeros(num_joints) - exist = np.zeros(num_joints) + hit = np.zeros(num_joints, dtype = np.float32) + exist = np.zeros(num_joints, dtype = np.float32) with open(res_file, 'r') as fin: preds = json.load(fin) assert len(preds) == len(self.db) for pred, item in zip(preds, self.db): - h, e = self.evaluate_kernal(pred['keypoints'], item['joints_3d'], + h, e = self._evaluate_kernel(pred['keypoints'], item['joints_3d'], item['joints_3d_visible'], item['headbox']) hit += h exist += e - p_all = np.sum(hit) / np.sum(exist) + pck = np.sum(hit) / np.sum(exist) info_str = [] - info_str.append(('p_acc', p_all)) + info_str.append(('PCK', pck)) return info_str From 24339b9951da8d999164909638eea9682c9d5904 Mon Sep 17 00:00:00 2001 From: liuxin1 Date: Wed, 5 Aug 2020 17:37:28 +0800 Subject: [PATCH 3/7] pre commit --- .../datasets/top_down/topdown_onehand10k_dataset.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py index 34646f7dce..4c7d5e05be 100644 --- a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py +++ b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py @@ -213,8 +213,8 @@ def _report_metric(self, res_file): Report Mean Acc of skeleton, contour and all joints. """ num_joints = self.ann_info['num_joints'] - hit = np.zeros(num_joints, dtype = np.float32) - exist = np.zeros(num_joints, dtype = np.float32) + hit = np.zeros(num_joints, dtype=np.float32) + exist = np.zeros(num_joints, dtype=np.float32) with open(res_file, 'r') as fin: preds = json.load(fin) @@ -222,8 +222,8 @@ def _report_metric(self, res_file): assert len(preds) == len(self.db) for pred, item in zip(preds, self.db): h, e = self._evaluate_kernel(pred['keypoints'], item['joints_3d'], - item['joints_3d_visible'], - item['headbox']) + item['joints_3d_visible'], + item['headbox']) hit += h exist += e pck = np.sum(hit) / np.sum(exist) From 62144f57680bc5f132674ace245c42a42af0b019 Mon Sep 17 00:00:00 2001 From: liuxin1 Date: Wed, 5 Aug 2020 18:25:23 +0800 Subject: [PATCH 4/7] resolve comments --- .../datasets/datasets/top_down/topdown_onehand10k_dataset.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py index 4c7d5e05be..ef8cf4096c 100644 --- a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py +++ b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py @@ -109,9 +109,7 @@ def _get_db(self, ann_file): joints_3d[ipt, 0] = anno['keypoints'][ipt * 3 + 0] joints_3d[ipt, 1] = anno['keypoints'][ipt * 3 + 1] joints_3d[ipt, 2] = 0 - t_vis = anno['keypoints'][ipt * 3 + 2] - if t_vis > 1: - t_vis = 1 + t_vis = min(anno['keypoints'][ipt * 3 + 2], 1) joints_3d_visible[ipt, :] = (t_vis, t_vis, 0) center, scale = self._xywh2cs(*anno['bbox'][:4]) From 19d784ed4d3a6526c67f2ccce032437299a610ad Mon Sep 17 00:00:00 2001 From: liuxin1 Date: Thu, 6 Aug 2020 14:39:09 +0800 Subject: [PATCH 5/7] add onehand10k config and fix bug --- .../onehand10k/res50_onehand10k_256x256.py | 131 ++++++++++++++++++ .../top_down/topdown_onehand10k_dataset.py | 12 +- 2 files changed, 138 insertions(+), 5 deletions(-) create mode 100644 configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py diff --git a/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py b/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py new file mode 100644 index 0000000000..a2533ae6b8 --- /dev/null +++ b/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py @@ -0,0 +1,131 @@ +log_level = 'INFO' +load_from = None +resume_from = None +dist_params = dict(backend='nccl') +workflow = [('train', 1)] +checkpoint_config = dict(interval=10) +evaluation = dict(interval=1, metric='PCK') + +optimizer = dict( + type='Adam', + lr=5e-4, +) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[170, 200]) +total_epochs = 210 +log_config = dict( + interval=20, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) + +channel_cfg = dict( + num_output_channels=21, + dataset_joints=21, + dataset_channel=[ + [ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, + 19, 20 + ], + ], + inference_channel=[ + 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, + 20 + ]) + +# model settings +model = dict( + type='TopDown', + pretrained='models/pytorch/imagenet/resnet50-19c8e357.pth', + backbone=dict(type='ResNet', depth=50), + keypoint_head=dict( + type='TopDownSimpleHead', + in_channels=2048, + out_channels=channel_cfg['num_output_channels'], + ), + train_cfg=dict(), + test_cfg=dict( + flip_test=True, + post_process=True, + shift_heatmap=True, + unbiased_decoding=False, + modulate_kernel=11), + loss_pose=dict(type='JointsMSELoss', use_target_weight=True)) + +data_cfg = dict( + image_size=[256, 256], + heatmap_size=[64, 64], + num_output_channels=channel_cfg['num_output_channels'], + num_joints=channel_cfg['dataset_joints'], + dataset_channel=channel_cfg['dataset_channel'], + inference_channel=channel_cfg['inference_channel']) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='TopDownRandomFlip', flip_prob=0.5), + dict( + type='TopDownGetRandomScaleRotation', rot_factor=20, scale_factor=0.3), + dict(type='TopDownAffine'), + dict(type='ToTensor'), + dict( + type='NormalizeTensor', + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + dict(type='TopDownGenerateTarget', sigma=2), + dict( + type='Collect', + keys=['img', 'target', 'target_weight'], + meta_keys=[ + 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', + 'rotation', 'flip_pairs' + ]), +] + +valid_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='TopDownAffine'), + dict(type='ToTensor'), + dict( + type='NormalizeTensor', + mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]), + dict( + type='Collect', + keys=[ + 'img', + ], + meta_keys=['image_file', 'center', 'scale', 'rotation', 'flip_pairs']), +] + +test_pipeline = valid_pipeline + +data_root = 'data/onehand10k' +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type='TopDownOneHand10KDataset', + ann_file=f'{data_root}/annotations/train.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=train_pipeline), + val=dict( + type='TopDownOneHand10KDataset', + ann_file=f'{data_root}/annotations/test.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=valid_pipeline), + test=dict( + type='TopDownOneHand10KDataset', + ann_file=f'{data_root}/annotations/test.json', + img_prefix=f'{data_root}/', + data_cfg=data_cfg, + pipeline=valid_pipeline), +) diff --git a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py index ef8cf4096c..d2364e8523 100644 --- a/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py +++ b/mmpose/datasets/datasets/top_down/topdown_onehand10k_dataset.py @@ -86,6 +86,7 @@ def _get_db(self, ann_file): rotation=0, joints_3d=None, joints_3d_visible=None, + bbox=None, dataset='OneHand10K') imid2info = {x['id']: x for x in data['images']} @@ -117,6 +118,7 @@ def _get_db(self, ann_file): newitem['scale'] = scale newitem['joints_3d'] = joints_3d newitem['joints_3d_visible'] = joints_3d_visible + newitem['bbox'] = anno['bbox'][:4] gt_db.append(newitem) return gt_db @@ -185,9 +187,9 @@ def evaluate(self, outputs, res_folder, metrics='PCK', **kwargs): image_id = int(osp.basename(osp.splitext(str_image_path)[0])) kpts.append({ - 'keypoints': list(preds[0].astype(np.float32)), - 'center': list(boxes[0][0:2].astype(np.float32)), - 'scale': list(boxes[0][2:4].astype(np.float32)), + 'keypoints': preds[0].tolist(), + 'center': boxes[0][0:2].tolist(), + 'scale': boxes[0][2:4].tolist(), 'area': float(boxes[0][4]), 'score': float(boxes[0][5]), 'image_id': image_id, @@ -221,11 +223,11 @@ def _report_metric(self, res_file): for pred, item in zip(preds, self.db): h, e = self._evaluate_kernel(pred['keypoints'], item['joints_3d'], item['joints_3d_visible'], - item['headbox']) + item['bbox']) hit += h exist += e pck = np.sum(hit) / np.sum(exist) info_str = [] - info_str.append(('PCK', pck)) + info_str.append(('PCK', pck.item())) return info_str From e20b804abea65b294c80319f20b536d76ffcb3de Mon Sep 17 00:00:00 2001 From: jinsheng Date: Thu, 13 Aug 2020 17:01:06 +0800 Subject: [PATCH 6/7] add onehand10k models --- configs/top_down/resnet/README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/configs/top_down/resnet/README.md b/configs/top_down/resnet/README.md index 58d8b7ac33..0a5089a179 100644 --- a/configs/top_down/resnet/README.md +++ b/configs/top_down/resnet/README.md @@ -29,3 +29,10 @@ | Arch | Input Size | Skeleton Acc | Contour Acc | Mean Acc | ckpt | log | | :--- | :--------: | :------: | :------: |:------: |:------: |:------: | | [pose_resnet_50](/configs/top_down/resnet/mpii_trb/res50_mpii_trb_256x256.py) | 256x256 | 0.884 | 0.855 | 0.865 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_mpii_trb_256x256-f0305d2e_20200727.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_mpii_trb_256x256_20200727.log.json) | + + +### Results on OneHand10K val set. + +| Arch | Input Size | PCK@0.2 | ckpt | log | +| :--- | :--------: | :------: | :------: |:------: | +| [pose_resnet_50](/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py) | 256x256 | 0.985 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_onehand10k_256x256-e67998f6_20200813.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/top_down/resnet/res50_onehand10k_256x256_20200813.log.json) | From a5703706c5bfcc9a798399df29532b7da5182b36 Mon Sep 17 00:00:00 2001 From: jinsheng Date: Thu, 13 Aug 2020 17:22:41 +0800 Subject: [PATCH 7/7] update readme for onehand10k --- .../onehand10k/res50_onehand10k_256x256.py | 6 ++-- docs/getting_started.md | 28 +++++++++++++++++++ 2 files changed, 31 insertions(+), 3 deletions(-) diff --git a/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py b/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py index a2533ae6b8..583a2bda73 100644 --- a/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py +++ b/configs/top_down/resnet/onehand10k/res50_onehand10k_256x256.py @@ -112,19 +112,19 @@ workers_per_gpu=2, train=dict( type='TopDownOneHand10KDataset', - ann_file=f'{data_root}/annotations/train.json', + ann_file=f'{data_root}/annotations/onehand10k_train.json', img_prefix=f'{data_root}/', data_cfg=data_cfg, pipeline=train_pipeline), val=dict( type='TopDownOneHand10KDataset', - ann_file=f'{data_root}/annotations/test.json', + ann_file=f'{data_root}/annotations/onehand10k_test.json', img_prefix=f'{data_root}/', data_cfg=data_cfg, pipeline=valid_pipeline), test=dict( type='TopDownOneHand10KDataset', - ann_file=f'{data_root}/annotations/test.json', + ann_file=f'{data_root}/annotations/onehand10k_test.json', img_prefix=f'{data_root}/', data_cfg=data_cfg, pipeline=valid_pipeline), diff --git a/docs/getting_started.md b/docs/getting_started.md index 835a13f34a..5f8c9d6083 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -86,6 +86,34 @@ mmpose ``` +**For OneHand10K data**, please download from [OneHand10K Dataset](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html). +Please download the annotation files from [onehand10k_annotations](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/datasets/onehand10k_annotations.tar). +Extract them under {MMPose}/data, and make them look like this: + +``` +mmpose +├── mmpose +├── docs +├── tests +├── tools +├── configs +`── data + │── onehand10k + |── annotations + | |── onehand10k_train.json + | |── onehand10k_test.json + `── Train + | |── source + | |── 0.jpg + | |── 1.jpg + | ... + `── Test + |── source + |── 0.jpg + |── 1.jpg + +``` + For using custom datasets, please refer to [Tutorial 2: Adding New Dataset](tutorials/new_dataset.md) ## Prepare Pretrained Models

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