reverted sigma scaling w.r.t. scale factor in JointsDataset.py

Author: helmchoi

experiments/mpii/fastvit/w32_256x256_adam_lr1e-3_Unreal.yaml

@@ -12,22 +12,22 @@ PRINT_FREQ: 100
 
 DATASET:
   COLOR_RGB: false
-  DATASET: unrealS
+  DATASET: unreal
   DATA_FORMAT: png
   FLIP: true
   NUM_JOINTS_HALF_BODY: 8
   PROB_HALF_BODY: -1.0
-  ROOT: '/data/local/HL/HRNet_data/240604-1007_target_unreal/dSigma_1.0_sigma_2.0_thresh_2.0/'
+  ROOT: '/data/local/HL/HRNet_data/240604-1007_black_bright_unreal/'
   ROT_FACTOR: 45
-  SCALE_FACTOR: 1.5
+  SCALE_FACTOR: 3.5
   SHIFT_FACTOR: 0.5
   TEST_SET: valid
   TRAIN_SET: train
 MODEL:
   INIT_WEIGHTS: true
   NAME: pose_fastvit
   NUM_JOINTS: 16
-  PRETRAINED: 'output/unrealS/pose_fastvit/unreal_pretrained_model_best.pth'
+  PRETRAINED: 'output/unreal/pose_fastvit/unreal_pretrained_model_best.pth'
   TARGET_TYPE: gaussian
   IMAGE_SIZE:
   - 256
@@ -46,19 +46,19 @@ TRAIN:
   BEGIN_EPOCH: 0
   END_EPOCH: 500
   OPTIMIZER: adamw
-  LR: 0.005
+  LR: 0.0005
   LR_FACTOR: 0.5
   LR_STEP:
-  - 150
-  - 300
+  - 170
+  - 210
   WD: 0.0005
   GAMMA1: 0.99
   GAMMA2: 0.0
   MOMENTUM: 0.9
   NESTEROV: false
 TEST:
   BATCH_SIZE_PER_GPU: 32
-  MODEL_FILE: 'output/unrealS/pose_fastvit/unreal_pretrained_model_best.pth'
+  MODEL_FILE: 'output/unreal/pose_fastvit/unreal_pretrained_model_best.pth'
   FLIP_TEST: true
   POST_PROCESS: true
   SHIFT_HEATMAP: true

lib/dataset/JointsDataset.py

@@ -251,7 +251,8 @@ def generate_target(self, joints, joints_vis, sc = 1.0):
         # target_weight[:, 0] = np.maximum(joints_vis[:, 0], 0.4)
 
         # [24.10.28] GT variance ~ scale factor
-        sigma_ = self.sigma / sc
+        # sigma_ = self.sigma / sc
+        sigma_ = self.sigma
 
         assert self.target_type == 'gaussian', \
             'Only support gaussian map now!'