update models

Jeff-sjtu · Nov 25, 2022 · ec86a3b · ec86a3b
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diff --git a/README.md b/README.md
@@ -152,6 +152,20 @@ Download the pretrained model ([ResNet-34](https://drive.google.com/file/d/16Y_M
 
 </center>
 
+## MODEL ZOO
+
+| Backbone | Training Data |     PA-MPJPE (3DPW)     | MPJPE (3DPW) | PA-MPJPE (Human3.6M) | MPJPE (Human3.6M) |  Download | Config |  
+|----------|----------|------------|------------|-------|-----------|--------|--------------|
+| ResNet-34           | w/o 3DPW | | | | | [model]() | [cfg]()    |
+| ResNet-34          | w/ 3DPW | 44.5 | 72.4 | 33.8 | 55.5 | [model](https://drive.google.com/file/d/19ktHbERz0Un5EzJYZBdzdzTrFyd9gLCx/view?usp=share_link) | [cfg](./configs/256x192_adam_lr1e-3-res34_smpl_3d_cam_2x_mix_w_pw3d.yaml)    |
+| HRNet-W48           | w/o 3DPW | 48.6 | 88.0 | 29.5 | 50.4 | [model](https://drive.google.com/file/d/1o3z99bebm2XImElc3XEUzTNVhQboGJE9/view?usp=share_link) | [cfg](./configs/256x192_adam_lr1e-3-hrw48_cam_2x_wo_pw3d.yaml)    |
+| HRNet-W48          | w/ 3DPW | 41.8 | 71.3 | 29.8 | 47.1 | [model](https://drive.google.com/file/d/1gp3549vIEKfbc8SDQ-YF3Idi1aoR3DkW/view?usp=share_link) | [cfg](configs/256x192_adam_lr1e-3-hrw48_cam_2x_w_pw3d_3dhp.yaml)    |
+
+
+#### Notes
+- All models assume a fixed focal length and predict camera parameters.
+- Flip test is used by default.
+
 
 ## Citing
 If our code helps your research, please consider citing the following paper:

diff --git a/configs/256x192_adam_lr1e-3-hrw48_cam_2x_w_pw3d_3dhp.yaml b/configs/256x192_adam_lr1e-3-hrw48_cam_2x_w_pw3d_3dhp.yaml
@@ -21,7 +21,7 @@ DATASET:
 MODEL:
   TYPE: 'HRNetSMPLCam'
   HR_PRETRAINED: './pose_hrnet_w48_256x192.pth'
-  PRETRAINED: './exp/hrnetw48_ckpt13.pth'
+  PRETRAINED: ''
   # TRY_LOAD: ''
   TRY_LOAD: ''
   RESUME: ''

diff --git a/configs/256x192_adam_lr1e-3-hrw48_cam_2x_wo_pw3d.yaml b/configs/256x192_adam_lr1e-3-hrw48_cam_2x_wo_pw3d.yaml
@@ -0,0 +1,79 @@
+DATASET:
+  DATASET: 'mix_smpl_cam'
+  SET_LIST:
+    - ROOT: './data/h36m/'
+      TEST_SET: 'Sample_20_test_Human36M_smpl'
+      TRAIN_SET: 'Sample_trainmin_train_Human36M_smpl_leaf_twist'
+    - ROOT: './data/coco/'
+      TRAIN_SET: 'train2017'
+    - ROOT: './data/3dhp/'
+      TRAIN_SET: 'train_v2'
+  PROTOCOL: 2
+  FLIP: True
+  ROT_FACTOR: 30
+  SCALE_FACTOR: 0.3
+  NUM_JOINTS_HALF_BODY: 8
+  PROB_HALF_BODY: -1
+  COLOR_FACTOR: 0.2
+  OCCLUSION: True
+MODEL:
+  TYPE: 'HRNetSMPLCam'
+  HR_PRETRAINED: './pose_hrnet_w48_256x192.pth'
+  PRETRAINED: ''
+  # TRY_LOAD: ''
+  TRY_LOAD: ''
+  RESUME: ''
+  FOCAL_LENGTH: 1000
+  IMAGE_SIZE:
+  - 256
+  - 256
+  HEATMAP_SIZE:
+  - 64
+  - 64
+  NUM_JOINTS: 29
+  HRNET_TYPE: 48
+  EXTRA:
+    SIGMA: 2
+    BACKBONE: 'resnet'
+    CROP: 'padding'
+    AUGMENT: 'none'
+    PRESET: 'simple_smpl_3d_cam'
+    DEPTH_DIM: 64
+  POST:
+    NORM_TYPE: 'softmax'
+  BBOX_3D_SHAPE:
+    - 2200
+    - 2200
+    - 2200
+LOSS:
+  TYPE: 'LaplaceLossDimSMPLCam'
+  ELEMENTS:
+    BETA_WEIGHT: 1
+    BETA_REG_WEIGHT: 0
+    PHI_REG_WEIGHT: 0.0001
+    LEAF_REG_WEIGHT: 0
+    TWIST_WEIGHT: 1
+    THETA_WEIGHT: 0.01
+    UVD24_WEIGHT: 1
+    XYZ24_WEIGHT: 0
+    XYZ_SMPL24_WEIGHT: 0
+    XYZ_SMPL17_WEIGHT: 0
+    VERTICE_WEIGHT: 0
+    USE_LAPLACE: True
+TEST:
+  HEATMAP2COORD: 'coord'
+TRAIN:
+  WORLD_SIZE: 4
+  BATCH_SIZE: 36
+  BEGIN_EPOCH: 0
+  END_EPOCH: 200
+  OPTIMIZER: 'adam'
+  LR: 0.001
+  LR_FACTOR: 0.1
+  LR_STEP:
+  - 60
+  - 80
+  DPG_MILESTONE: 140
+  DPG_STEP:
+  - 160
+  - 190
diff --git a/hybrik/utils/presets/simple_transform_3d_smpl_cam.py b/hybrik/utils/presets/simple_transform_3d_smpl_cam.py
@@ -185,12 +185,12 @@ def _integral_target_generator(self, joints_3d, num_joints, patch_height, patch_
         target[:, 1] = joints_3d[:, 1, 0] / patch_height - 0.5
         target[:, 2] = joints_3d[:, 2, 0] / self.bbox_3d_shape[0]
 
-        target_weight[target[:, 0] > 0.5] = 0
-        target_weight[target[:, 0] < -0.5] = 0
-        target_weight[target[:, 1] > 0.5] = 0
-        target_weight[target[:, 1] < -0.5] = 0
-        target_weight[target[:, 2] > 0.5] = 0
-        target_weight[target[:, 2] < -0.5] = 0
+        # target_weight[target[:, 0] > 0.5] = 0
+        # target_weight[target[:, 0] < -0.5] = 0
+        # target_weight[target[:, 1] > 0.5] = 0
+        # target_weight[target[:, 1] < -0.5] = 0
+        # target_weight[target[:, 2] > 0.5] = 0
+        # target_weight[target[:, 2] < -0.5] = 0
 
         target = target.reshape((-1))
         target_weight = target_weight.reshape((-1))
@@ -208,12 +208,12 @@ def _integral_uvd_target_generator(self, joints_3d, num_joints, patch_height, pa
         target[:, 1] = joints_3d[:, 1, 0] / patch_height - 0.5
         target[:, 2] = joints_3d[:, 2, 0] / self.bbox_3d_shape[2]
 
-        target_weight[target[:, 0] > 0.5] = 0
-        target_weight[target[:, 0] < -0.5] = 0
-        target_weight[target[:, 1] > 0.5] = 0
-        target_weight[target[:, 1] < -0.5] = 0
-        target_weight[target[:, 2] > 0.5] = 0
-        target_weight[target[:, 2] < -0.5] = 0
+        # target_weight[target[:, 0] > 0.5] = 0
+        # target_weight[target[:, 0] < -0.5] = 0
+        # target_weight[target[:, 1] > 0.5] = 0
+        # target_weight[target[:, 1] < -0.5] = 0
+        # target_weight[target[:, 2] > 0.5] = 0
+        # target_weight[target[:, 2] < -0.5] = 0
 
         target = target.reshape((-1))
         target_weight = target_weight.reshape((-1))
@@ -401,8 +401,13 @@ def __call__(self, src, label):
 
             # half body transform
             if self._train and (np.sum(joints_vis_17[:, 0]) > self.num_joints_half_body and np.random.rand() < self.prob_half_body):
+                # self.num_joints = 17
+                # c_half_body, s_half_body = self.half_body_transform(
+                #     gt_joints_17[:, :, 0], joints_vis_17
+                # )
+                self.num_joints = 24
                 c_half_body, s_half_body = self.half_body_transform(
-                    gt_joints_17[:, :, 0], joints_vis_17
+                    gt_joints_29[:, :, 0], joints_vis_29
                 )
 
                 if c_half_body is not None and s_half_body is not None:
@@ -513,9 +518,9 @@ def __call__(self, src, label):
 
             if self.focal_length > 0:
                 cam_scale, cam_trans, cam_valid, cam_error, new_uvd = self.calc_cam_scale_trans2(
-                                                                target_xyz_24.reshape(-1, 3).copy(), 
-                                                                tmp_uvd_24.copy(),
-                                                                tmp_uvd_24_weight.copy())
+                    target_xyz_24.reshape(-1, 3).copy(),
+                    tmp_uvd_24.copy(),
+                    tmp_uvd_24_weight.copy())
 
                 target_uvd_29 = (target_uvd_29 * target_weight_29).reshape(-1, 3)
             else: