rename GaussianHeatMap to GaussianHeatmap (open-mmlab#745)

* rename GaussianHeatMap to GaussianHeatmap
* allow .lower() case match
* add unittest and .copy()

configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w32_coco_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=17,
     dataset_joints=17,

configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w32_coco_384x288_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=17,
     dataset_joints=17,

configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=17,
     dataset_joints=17,

configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_w48_coco_384x288_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=17,
     dataset_joints=17,

configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/hrnet_w32_mpii_256x256_udp.py

@@ -24,7 +24,7 @@
         dict(type='TextLoggerHook'),
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=16,
     dataset_joints=16,

configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/hrnet_w48_mpii_256x256_udp.py

@@ -24,7 +24,7 @@
         dict(type='TextLoggerHook'),
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=16,
     dataset_joints=16,

configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/hrnet_w32_deepfashion_full_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=8,
     dataset_joints=8,

configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/hrnet_w32_deepfashion_lower_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=4,
     dataset_joints=4,

configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/hrnet_w32_deepfashion_upper_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=6,
     dataset_joints=6,

configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/hrnet_w48_deepfashion_full_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=8,
     dataset_joints=8,

configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/hrnet_w48_deepfashion_lower_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=4,
     dataset_joints=4,

configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/hrnet_w48_deepfashion_upper_256x192_udp.py

@@ -26,7 +26,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=6,
     dataset_joints=6,

configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/hrnetv2_w18_onehand10k_256x256_udp.py

@@ -27,7 +27,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=21,
     dataset_joints=21,

configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/panoptic2d/hrnetv2_w18_panoptic2d_256x256_udp.py

@@ -27,7 +27,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=21,
     dataset_joints=21,

configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/rhd2d/hrnetv2_w18_rhd2d_256x256_udp.py

@@ -27,7 +27,7 @@
         # dict(type='TensorboardLoggerHook')
     ])
 
-target_type = 'GaussianHeatMap'
+target_type = 'GaussianHeatmap'
 channel_cfg = dict(
     num_output_channels=21,
     dataset_joints=21,

mmpose/core/evaluation/top_down_eval.py

@@ -475,7 +475,7 @@ def keypoints_from_heatmaps(heatmaps,
                             kernel=11,
                             valid_radius_factor=0.0546875,
                             use_udp=False,
-                            target_type='GaussianHeatMap'):
+                            target_type='GaussianHeatmap'):
     """Get final keypoint predictions from heatmaps and transform them back to
     the image.
 
@@ -505,8 +505,8 @@ def keypoints_from_heatmaps(heatmaps,
         valid_radius_factor (float): The radius factor of the positive area
             in classification heatmap for UDP.
         use_udp (bool): Use unbiased data processing.
-        target_type (str): 'GaussianHeatMap' or 'CombinedTarget'.
-            GaussianHeatMap: Classification target with gaussian distribution.
+        target_type (str): 'GaussianHeatmap' or 'CombinedTarget'.
+            GaussianHeatmap: Classification target with gaussian distribution.
             CombinedTarget: The combination of classification target
             (response map) and regression target (offset map).
             Paper ref: Huang et al. The Devil is in the Details: Delving into
@@ -518,6 +518,9 @@ def keypoints_from_heatmaps(heatmaps,
         - preds (np.ndarray[N, K, 2]): Predicted keypoint location in images.
         - maxvals (np.ndarray[N, K, 1]): Scores (confidence) of the keypoints.
     """
+    # Avoid being affected
+    heatmaps = heatmaps.copy()
+
     # detect conflicts
     if unbiased:
         assert post_process not in [False, None, 'megvii']
@@ -558,13 +561,10 @@ def keypoints_from_heatmaps(heatmaps,
 
     N, K, H, W = heatmaps.shape
     if use_udp:
-        assert target_type.lower() in [
-            'GaussianHeatMap'.lower(), 'CombinedTarget'.lower()
-        ]
-        if target_type == 'GaussianHeatMap':
+        if target_type.lower() == 'GaussianHeatMap'.lower():
             preds, maxvals = _get_max_preds(heatmaps)
             preds = post_dark_udp(preds, heatmaps, kernel=kernel)
-        elif target_type == 'CombinedTarget':
+        elif target_type.lower() == 'CombinedTarget'.lower():
             for person_heatmaps in heatmaps:
                 for i, heatmap in enumerate(person_heatmaps):
                     kt = 2 * kernel + 1 if i % 3 == 0 else kernel
@@ -579,6 +579,9 @@ def keypoints_from_heatmaps(heatmaps,
             index += W * H * np.arange(0, N * K / 3)
             index = index.astype(np.int).reshape(N, K // 3, 1)
             preds += np.concatenate((offset_x[index], offset_y[index]), axis=2)
+        else:
+            raise ValueError('target_type should be either '
+                             "'GaussianHeatmap' or 'CombinedTarget'")
     else:
         preds, maxvals = _get_max_preds(heatmaps)
         if post_process == 'unbiased':  # alleviate biased coordinate

mmpose/core/post_processing/post_transforms.py

@@ -105,7 +105,7 @@ def fliplr_regression(regression,
     return regression_flipped
 
 
-def flip_back(output_flipped, flip_pairs, target_type='GaussianHeatMap'):
+def flip_back(output_flipped, flip_pairs, target_type='GaussianHeatmap'):
     """Flip the flipped heatmaps back to the original form.
 
     Note:
@@ -119,18 +119,16 @@ def flip_back(output_flipped, flip_pairs, target_type='GaussianHeatMap'):
             from the flipped images.
         flip_pairs (list[tuple()): Pairs of keypoints which are mirrored
             (for example, left ear -- right ear).
-        target_type (str): GaussianHeatMap or CombinedTarget
+        target_type (str): GaussianHeatmap or CombinedTarget
 
     Returns:
         np.ndarray: heatmaps that flipped back to the original image
     """
     assert output_flipped.ndim == 4, \
         'output_flipped should be [batch_size, num_keypoints, height, width]'
-    assert target_type.lower() in ('GaussianHeatMap'.lower(),
-                                   'CombinedTarget'.lower())
     shape_ori = output_flipped.shape
     channels = 1
-    if target_type == 'CombinedTarget':
+    if target_type.lower() == 'CombinedTarget'.lower():
         channels = 3
         output_flipped[:, 1::3, ...] = -output_flipped[:, 1::3, ...]
     output_flipped = output_flipped.reshape(shape_ori[0], -1, channels,

mmpose/datasets/pipelines/top_down_transform.py

@@ -241,8 +241,8 @@ class TopDownGenerateTarget:
         keypoint_pose_distance: Keypoint pose distance for UDP.
             Paper ref: Huang et al. The Devil is in the Details: Delving into
             Unbiased Data Processing for Human Pose Estimation (CVPR 2020).
-        target_type (str): supported targets: 'GaussianHeatMap',
-            'CombinedTarget'. Default:'GaussianHeatMap'
+        target_type (str): supported targets: 'GaussianHeatmap',
+            'CombinedTarget'. Default:'GaussianHeatmap'
             CombinedTarget: The combination of classification target
             (response map) and regression target (offset map).
             Paper ref: Huang et al. The Devil is in the Details: Delving into
@@ -253,7 +253,7 @@ def __init__(self,
                  sigma=2,
                  kernel=(11, 11),
                  valid_radius_factor=0.0546875,
-                 target_type='GaussianHeatMap',
+                 target_type='GaussianHeatmap',
                  encoding='MSRA',
                  unbiased_encoding=False):
         self.sigma = sigma
@@ -405,17 +405,17 @@ def _udp_generate_target(self, cfg, joints_3d, joints_3d_visible, factor,
             heatmap height: H
             heatmap width: W
             num target channels: C
-            C = K if target_type=='GaussianHeatMap'
+            C = K if target_type=='GaussianHeatmap'
             C = 3*K if target_type=='CombinedTarget'
 
         Args:
             cfg (dict): data config
             joints_3d (np.ndarray[K, 3]): Annotated keypoints.
             joints_3d_visible (np.ndarray[K, 3]): Visibility of keypoints.
-            factor (float): kernel factor for GaussianHeatMap target or
+            factor (float): kernel factor for GaussianHeatmap target or
                 valid radius factor for CombinedTarget.
-            target_type (str): 'GaussianHeatMap' or 'CombinedTarget'.
-                GaussianHeatMap: Heatmap target with gaussian distribution.
+            target_type (str): 'GaussianHeatmap' or 'CombinedTarget'.
+                GaussianHeatmap: Heatmap target with gaussian distribution.
                 CombinedTarget: The combination of classification target
                 (response map) and regression target (offset map).
 
@@ -434,11 +434,7 @@ def _udp_generate_target(self, cfg, joints_3d, joints_3d_visible, factor,
         target_weight = np.ones((num_joints, 1), dtype=np.float32)
         target_weight[:, 0] = joints_3d_visible[:, 0]
 
-        assert target_type.lower() in [
-            'GaussianHeatMap'.lower(), 'CombinedTarget'.lower()
-        ]
-
-        if target_type == 'GaussianHeatMap':
+        if target_type.lower() == 'GaussianHeatmap'.lower():
             target = np.zeros((num_joints, heatmap_size[1], heatmap_size[0]),
                               dtype=np.float32)
 
@@ -481,7 +477,8 @@ def _udp_generate_target(self, cfg, joints_3d, joints_3d_visible, factor,
                 if v > 0.5:
                     target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \
                         g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
-        elif target_type == 'CombinedTarget':
+
+        elif target_type.lower() == 'CombinedTarget'.lower():
             target = np.zeros(
                 (num_joints, 3, heatmap_size[1] * heatmap_size[0]),
                 dtype=np.float32)
@@ -510,6 +507,9 @@ def _udp_generate_target(self, cfg, joints_3d, joints_3d_visible, factor,
                     target[joint_id, 2, keep_pos] = y_offset[keep_pos]
             target = target.reshape(num_joints * 3, heatmap_size[1],
                                     heatmap_size[0])
+        else:
+            raise ValueError('target_type should be either '
+                             "'GaussianHeatmap' or 'CombinedTarget'")
 
         if use_different_joint_weights:
             target_weight = np.multiply(target_weight, joint_weights)
@@ -544,6 +544,7 @@ def __call__(self, results):
                 target, target_weight = self._msra_generate_target(
                     results['ann_info'], joints_3d, joints_3d_visible,
                     self.sigma)
+
         elif self.encoding == 'Megvii':
             if isinstance(self.kernel, list):
                 num_kernels = len(self.kernel)
@@ -563,13 +564,17 @@ def __call__(self, results):
                 target, target_weight = self._megvii_generate_target(
                     results['ann_info'], joints_3d, joints_3d_visible,
                     self.kernel)
+
         elif self.encoding == 'UDP':
-            if self.target_type == 'CombinedTarget':
+            if self.target_type.lower() == 'CombinedTarget'.lower():
                 factors = self.valid_radius_factor
                 channel_factor = 3
-            elif self.target_type == 'GaussianHeatMap':
+            elif self.target_type.lower() == 'GaussianHeatmap'.lower():
                 factors = self.sigma
                 channel_factor = 1
+            else:
+                raise ValueError('target_type should be either '
+                                 "'GaussianHeatmap' or 'CombinedTarget'")
             if isinstance(factors, list):
                 num_factors = len(factors)
                 cfg = results['ann_info']

mmpose/models/heads/interhand_3d_head.py

@@ -321,7 +321,7 @@ def __init__(self,
         self.hand_type_loss = build_loss(loss_hand_type)
         self.train_cfg = {} if train_cfg is None else train_cfg
         self.test_cfg = {} if test_cfg is None else test_cfg
-        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatMap')
+        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatmap')
 
     def init_weights(self):
         self.left_hand_head.init_weights()

mmpose/models/heads/topdown_heatmap_base_head.py

@@ -80,7 +80,7 @@ def decode(self, img_metas, output, **kwargs):
             valid_radius_factor=self.test_cfg.get('valid_radius_factor',
                                                   0.0546875),
             use_udp=self.test_cfg.get('use_udp', False),
-            target_type=self.test_cfg.get('target_type', 'GaussianHeatMap'))
+            target_type=self.test_cfg.get('target_type', 'GaussianHeatmap'))
 
         all_preds = np.zeros((batch_size, preds.shape[1], 3), dtype=np.float32)
         all_boxes = np.zeros((batch_size, 6), dtype=np.float32)

mmpose/models/heads/topdown_heatmap_multi_stage_head.py

@@ -53,7 +53,7 @@ def __init__(self,
 
         self.train_cfg = {} if train_cfg is None else train_cfg
         self.test_cfg = {} if test_cfg is None else test_cfg
-        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatMap')
+        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatmap')
 
         if extra is not None and not isinstance(extra, dict):
             raise TypeError('extra should be dict or None.')
@@ -164,7 +164,7 @@ def get_accuracy(self, output, target, target_weight):
 
         accuracy = dict()
 
-        if self.target_type == 'GaussianHeatMap':
+        if self.target_type == 'GaussianHeatmap':
             _, avg_acc, _ = pose_pck_accuracy(
                 output[-1].detach().cpu().numpy(),
                 target.detach().cpu().numpy(),
@@ -420,7 +420,7 @@ def __init__(self,
 
         self.train_cfg = {} if train_cfg is None else train_cfg
         self.test_cfg = {} if test_cfg is None else test_cfg
-        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatMap')
+        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatmap')
 
         self.out_shape = out_shape
         self.unit_channels = unit_channels
@@ -501,7 +501,7 @@ def get_accuracy(self, output, target, target_weight):
 
         accuracy = dict()
 
-        if self.target_type == 'GaussianHeatMap':
+        if self.target_type == 'GaussianHeatmap':
             assert isinstance(output, list)
             assert target.dim() == 5 and target_weight.dim() == 4
             _, avg_acc, _ = pose_pck_accuracy(

mmpose/models/heads/topdown_heatmap_simple_head.py

@@ -63,7 +63,7 @@ def __init__(self,
 
         self.train_cfg = {} if train_cfg is None else train_cfg
         self.test_cfg = {} if test_cfg is None else test_cfg
-        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatMap')
+        self.target_type = self.test_cfg.get('target_type', 'GaussianHeatmap')
 
         self._init_inputs(in_channels, in_index, input_transform)
         self.in_index = in_index
@@ -180,7 +180,7 @@ def get_accuracy(self, output, target, target_weight):
 
         accuracy = dict()
 
-        if self.target_type == 'GaussianHeatMap':
+        if self.target_type == 'GaussianHeatmap':
             _, avg_acc, _ = pose_pck_accuracy(
                 output.detach().cpu().numpy(),
                 target.detach().cpu().numpy(),