ae inference align with master

configs/body_2d_keypoint/associative_embedding/coco/ae_hrnet-w32_8xb24-300e_coco-512x512.py

@@ -96,7 +96,7 @@
         decoder=dict(codec, heatmap_size=codec['input_size'])),
     test_cfg=dict(
         multiscale_test=False,
-        flip_test=True,
+        flip_test=False,
         shift_heatmap=True,
         restore_heatmap_size=True,
         align_corners=False))
@@ -113,9 +113,14 @@
     dict(
         type='BottomupResize',
         input_size=codec['input_size'],
-        size_factor=32,
+        size_factor=64,
         resize_mode='expand'),
-    dict(type='PackPoseInputs')
+    dict(
+        type='PackPoseInputs',
+        meta_keys=('id', 'img_id', 'img_path', 'crowd_index', 'ori_shape',
+                   'img_shape', 'input_size', 'input_center', 'input_scale',
+                   'flip', 'flip_direction', 'flip_indices', 'raw_ann_info',
+                   'skeleton_links'))
 ]
 
 # data loaders
@@ -142,7 +147,7 @@
         type=dataset_type,
         data_root=data_root,
         data_mode=data_mode,
-        ann_file='annotations/person_keypoints_val2017.json',
+        ann_file='annotations/person_keypoints_val2017_tiny_clean.json',
         data_prefix=dict(img='val2017/'),
         test_mode=True,
         pipeline=val_pipeline,
@@ -152,7 +157,8 @@
 # evaluators
 val_evaluator = dict(
     type='CocoMetric',
-    ann_file=data_root + 'annotations/person_keypoints_val2017.json',
+    ann_file=data_root +
+    'annotations/person_keypoints_val2017_tiny_clean.json',
     nms_mode='none',
     score_mode='keypoint',
 )

demo/bottomup_demo.py

@@ -1,6 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+
 import mimetypes
 import os
+import os.path as osp
 import tempfile
 from argparse import ArgumentParser
 
@@ -120,57 +122,64 @@ def main():
     visualizer = VISUALIZERS.build(model.cfg.visualizer)
     visualizer.set_dataset_meta(model.dataset_meta)
 
-    input_type = mimetypes.guess_type(args.input)[0].split('/')[0]
-    if input_type == 'image':
-        pred_instances = process_one_image(
-            args, args.input, model, visualizer, show_interval=0)
-        pred_instances_list = split_instances(pred_instances)
-
-    elif input_type == 'video':
-        tmp_folder = tempfile.TemporaryDirectory()
-        video = mmcv.VideoReader(args.input)
-        progressbar = mmengine.ProgressBar(len(video))
-        video.cvt2frames(tmp_folder.name, show_progress=False)
-        output_root = args.output_root
-        args.output_root = tmp_folder.name
-        pred_instances_list = []
-
-        for frame_id, img_fname in enumerate(os.listdir(tmp_folder.name)):
-            pred_instances = process_one_image(
-                args,
-                f'{tmp_folder.name}/{img_fname}',
-                model,
-                visualizer,
-                show_interval=1)
-            progressbar.update()
-            pred_instances_list.append(
-                dict(
-                    frame_id=frame_id,
-                    instances=split_instances(pred_instances)))
-
-        if output_root:
-            mmcv.frames2video(
-                tmp_folder.name,
-                f'{output_root}/{os.path.basename(args.input)}',
-                fps=video.fps,
-                fourcc='mp4v',
-                show_progress=False)
-        tmp_folder.cleanup()
-
+    if osp.isfile(args.input):
+        inputs = [args.input]
     else:
-        args.save_predictions = False
-        raise ValueError(
-            f'file {os.path.basename(args.input)} has invalid format.')
+        inputs = [osp.join(args.input, fn) for fn in os.listdir(args.input)]
 
-    if args.save_predictions:
-        with open(args.pred_save_path, 'w') as f:
-            json.dump(
-                dict(
-                    meta_info=model.dataset_meta,
-                    instance_info=pred_instances_list),
-                f,
-                indent='\t')
-        print(f'predictions have been saved at {args.pred_save_path}')
+    for fn in inputs:
+
+        input_type = mimetypes.guess_type(fn)[0].split('/')[0]
+        if input_type == 'image':
+            pred_instances = process_one_image(
+                args, fn, model, visualizer, show_interval=0)
+            pred_instances_list = split_instances(pred_instances)
+
+        elif input_type == 'video':
+            tmp_folder = tempfile.TemporaryDirectory()
+            video = mmcv.VideoReader(fn)
+            progressbar = mmengine.ProgressBar(len(video))
+            video.cvt2frames(tmp_folder.name, show_progress=False)
+            output_root = args.output_root
+            args.output_root = tmp_folder.name
+            pred_instances_list = []
+
+            for frame_id, img_fname in enumerate(os.listdir(tmp_folder.name)):
+                pred_instances = process_one_image(
+                    args,
+                    f'{tmp_folder.name}/{img_fname}',
+                    model,
+                    visualizer,
+                    show_interval=1)
+                progressbar.update()
+                pred_instances_list.append(
+                    dict(
+                        frame_id=frame_id,
+                        instances=split_instances(pred_instances)))
+
+            if output_root:
+                mmcv.frames2video(
+                    tmp_folder.name,
+                    f'{output_root}/{os.path.basename(fn)}',
+                    fps=video.fps,
+                    fourcc='mp4v',
+                    show_progress=False)
+            tmp_folder.cleanup()
+
+        else:
+            args.save_predictions = False
+            raise ValueError(
+                f'file {os.path.basename(fn)} has invalid format.')
+
+        if args.save_predictions:
+            with open(args.pred_save_path, 'w') as f:
+                json.dump(
+                    dict(
+                        meta_info=model.dataset_meta,
+                        instance_info=pred_instances_list),
+                    f,
+                    indent='\t')
+            print(f'predictions have been saved at {args.pred_save_path}')
 
 
 if __name__ == '__main__':

mmpose/codecs/associative_embedding.py

@@ -1,10 +1,12 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from collections import namedtuple
+from copy import deepcopy
 from itertools import product
 from typing import Any, List, Optional, Tuple
 
 import numpy as np
 import torch
+from mmengine import dump
 from munkres import Munkres
 from torch import Tensor
 
@@ -75,7 +77,9 @@ def _init_group():
             tag_list=[])
         return _group
 
-    for i in keypoint_order:
+    group_history = []
+
+    for idx, i in enumerate(keypoint_order):
         # Get all valid candidate of the i-th keypoints
         valid = vals[i] > val_thr
         if not valid.any():
@@ -87,12 +91,22 @@ def _init_group():
 
         if len(groups) == 0:  # Initialize the group pool
             for tag, val, loc in zip(tags_i, vals_i, locs_i):
+
+                # Check if the keypoint belongs to existing groups
+                if len(groups):
+                    prev_tags = np.stack([g.tag_list[0] for g in groups])
+                    dists = np.linalg.norm(prev_tags - tag, ord=2, axis=1)
+                    if dists.min() < 1:
+                        continue
+
                 group = _init_group()
                 group.kpts[i] = loc
                 group.scores[i] = val
                 group.tag_list.append(tag)
 
                 groups.append(group)
+            costs_copy = None
+            matches = None
 
         else:  # Match keypoints to existing groups
             groups = groups[:max_groups]
@@ -101,17 +115,18 @@ def _init_group():
             # Calculate distance matrix between group tags and tag candidates
             # of the i-th keypoint
             # Shape: (M', 1, L) , (1, G, L) -> (M', G, L)
-            diff = tags_i[:, None] - np.array(group_tags)[None]
+            diff = (tags_i[:, None] -
+                    np.array(group_tags)[None]).astype(np.float64)
             dists = np.linalg.norm(diff, ord=2, axis=2)
             num_kpts, num_groups = dists.shape[:2]
 
-            # Experimental cost function for keypoint-group matching
+            # Experimental cost function for keypoint-group matching2
             costs = np.round(dists) * 100 - vals_i[..., None]
+
             if num_kpts > num_groups:
-                padding = np.full((num_kpts, num_kpts - num_groups),
-                                  1e10,
-                                  dtype=np.float32)
+                padding = np.full((num_kpts, num_kpts - num_groups), 1e10)
                 costs = np.concatenate((costs, padding), axis=1)
+            costs_copy = costs.copy()
 
             # Match keypoints and groups by Munkres algorithm
             matches = munkres.compute(costs)
@@ -121,13 +136,30 @@ def _init_group():
                     # Add the keypoint to the matched group
                     group = groups[group_idx]
                 else:
-                    # Initialize a new group with unmatched keypoint
-                    group = _init_group()
-                    groups.append(group)
-
-                group.kpts[i] = locs_i[kpt_idx]
-                group.scores[i] = vals_i[kpt_idx]
-                group.tag_list.append(tags_i[kpt_idx])
+                    # if dists[kpt_idx].min() < 0.2:
+                    if False:
+                        group = None
+                    else:
+                        # Initialize a new group with unmatched keypoint
+                        group = _init_group()
+                        groups.append(group)
+                if group is not None:
+                    group.kpts[i] = locs_i[kpt_idx]
+                    group.scores[i] = vals_i[kpt_idx]
+                    group.tag_list.append(tags_i[kpt_idx])
+
+        out = {
+            'idx': idx,
+            'i': i,
+            'costs': costs_copy,
+            'matches': matches,
+            'kpts': np.array([g.kpts for g in groups]),
+            'scores': np.array([g.scores for g in groups]),
+            'tag_list': [np.array(g.tag_list) for g in groups],
+        }
+        group_history.append(deepcopy(out))
+
+    dump(group_history, 'group_history.pkl')
 
     groups = groups[:max_groups]
     if groups:
@@ -210,7 +242,7 @@ def __init__(
         decode_gaussian_kernel: int = 3,
         decode_keypoint_thr: float = 0.1,
         decode_tag_thr: float = 1.0,
-        decode_topk: int = 20,
+        decode_topk: int = 30,
         decode_max_instances: Optional[int] = None,
     ) -> None:
         super().__init__()
@@ -336,6 +368,12 @@ def _get_batch_topk(self, batch_heatmaps: Tensor, batch_tags: Tensor,
         B, K, H, W = batch_heatmaps.shape
         L = batch_tags.shape[1] // K
 
+        # Heatmap NMS
+        dump(batch_heatmaps.cpu().numpy(), 'heatmaps.pkl')
+        batch_heatmaps = batch_heatmap_nms(batch_heatmaps,
+                                           self.decode_nms_kernel)
+        dump(batch_heatmaps.cpu().numpy(), 'heatmaps_nms.pkl')
+
         # shape of topk_val, top_indices: (B, K, TopK)
         topk_vals, topk_indices = batch_heatmaps.flatten(-2, -1).topk(
             k, dim=-1)
@@ -433,9 +471,8 @@ def _fill_missing_keypoints(self, keypoints: np.ndarray,
                 cost_map = np.round(dist_map) * 100 - heatmaps[k]  # H, W
                 y, x = np.unravel_index(np.argmin(cost_map), shape=(H, W))
                 keypoints[n, k] = [x, y]
-                keypoint_scores[n, k] = heatmaps[k, y, x]
 
-        return keypoints, keypoint_scores
+        return keypoints
 
     def batch_decode(self, batch_heatmaps: Tensor, batch_tags: Tensor
                      ) -> Tuple[List[np.ndarray], List[np.ndarray]]:
@@ -457,15 +494,12 @@ def batch_decode(self, batch_heatmaps: Tensor, batch_tags: Tensor
                 batch, each is in shape (N, K). It usually represents the
                 confidience of the keypoint prediction
         """
+
         B, _, H, W = batch_heatmaps.shape
         assert batch_tags.shape[0] == B and batch_tags.shape[2:4] == (H, W), (
             f'Mismatched shapes of heatmap ({batch_heatmaps.shape}) and '
             f'tagging map ({batch_tags.shape})')
 
-        # Heatmap NMS
-        batch_heatmaps = batch_heatmap_nms(batch_heatmaps,
-                                           self.decode_nms_kernel)
-
         # Get top-k in each heatmap and and convert to numpy
         batch_topk_vals, batch_topk_tags, batch_topk_locs = to_numpy(
             self._get_batch_topk(
@@ -489,7 +523,7 @@ def batch_decode(self, batch_heatmaps: Tensor, batch_tags: Tensor
 
             if keypoints.size > 0:
                 # identify missing keypoints
-                keypoints, scores = self._fill_missing_keypoints(
+                keypoints = self._fill_missing_keypoints(
                     keypoints, scores, heatmaps, tags)
 
                 # refine keypoint coordinates according to heatmap distribution
@@ -500,6 +534,8 @@ def batch_decode(self, batch_heatmaps: Tensor, batch_tags: Tensor
                         blur_kernel_size=self.decode_gaussian_kernel)
                 else:
                     keypoints = refine_keypoints(keypoints, heatmaps)
+                    # keypoints += 0.75
+                    keypoints += 0.5
 
             batch_keypoints.append(keypoints)
             batch_keypoint_scores.append(scores)

mmpose/datasets/transforms/bottomup_transforms.py

@@ -478,6 +478,7 @@ def transform(self, results: Dict) -> Optional[dict]:
                     output_size=actual_input_size)
             else:
                 center = np.array([img_w / 2, img_h / 2], dtype=np.float32)
+                center = np.round(center)
                 scale = np.array([
                     img_w * padded_input_size[0] / actual_input_size[0],
                     img_h * padded_input_size[1] / actual_input_size[1]
@@ -489,11 +490,18 @@ def transform(self, results: Dict) -> Optional[dict]:
                     rot=0,
                     output_size=padded_input_size)
 
-            _img = cv2.warpAffine(
-                img, warp_mat, padded_input_size, flags=cv2.INTER_LINEAR)
+            _img = cv2.warpAffine(img, warp_mat, padded_input_size)
 
             imgs.append(_img)
 
+            # print('#' * 20)
+            # print('w,h: ', img_w, img_h, 'center: ', center, 'scale: ',
+            # scale,
+            #       'actual_input_size: ', actual_input_size,
+            #       'padded_input_size: ', padded_input_size)
+            # print(warp_mat)
+            # print('#' * 20)
+
             # Store the transform information w.r.t. the main input size
             if i == 0:
                 results['img_shape'] = padded_input_size[::-1]