[WIP] Merge MambaFlow into codebase

src/lossfuncs.py

@@ -10,6 +10,7 @@
 # Description: Define the loss function for training.
 """
 import torch
+import numpy as np
 from assets.cuda.chamfer3D import nnChamferDis
 MyCUDAChamferDis = nnChamferDis()
 from src.utils.av2_eval import CATEGORY_TO_INDEX, BUCKETED_METACATAGORIES
@@ -18,7 +19,7 @@
 # If your scenario is different, may need adjust this TRUNCATED to 80-120km/h vel.
 TRUNCATED_DIST = 4
 
-
+# ---------------------- Self-Supervised Flow Loss without GT ----------------------
 def seflowLoss(res_dict, timer=None):
     pc0_label = res_dict['pc0_labels']
     pc1_label = res_dict['pc1_labels']
@@ -99,6 +100,52 @@ def seflowLoss(res_dict, timer=None):
     }
     return res_loss
 
+# ---------------------- Supervised Flow Loss with GT ----------------------
+# designed from MambaFlow: https://github.com/SCNU-RISLAB/MambaFlow
+def mambaflowLoss(res_dict):
+    pred = res_dict['est_flow']
+    gt = res_dict['gt_flow']
+    mask_no_nan = (~gt.isnan() & ~pred.isnan() & ~gt.isinf() & ~pred.isinf())
+
+    pred = pred[mask_no_nan].reshape(-1, 3)
+    gt = gt[mask_no_nan].reshape(-1, 3)
+
+    speed = gt.norm(dim=1, p=2) / 0.1
+    # pts_loss = torch.norm(pred - gt, dim=1, p=2)
+    pts_loss = torch.linalg.vector_norm(pred - gt, dim=-1)
+
+    velocities = speed.cpu().numpy()
+
+    # 计算直方图，返回每个区间的计数和区间边界
+    counts, bin_edges = np.histogram(velocities, bins=100, density=False)
+
+    # 计算每个区间的点数占总点数的比例
+    total_points = len(velocities)
+    proportions = counts / total_points
+
+    # 计算每个区间的中心位置，用于绘图
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+    # 设置占比阈值
+    proportion_threshold = 0.01  # 可以根据需要调整这个值
+
+    # 找出第一个占比小于阈值的柱子
+    first_below_threshold = next((i for i, prop in enumerate(proportions) if prop < proportion_threshold), None)
+    turning_speed = bin_centers[first_below_threshold]
+
+    weight_loss = 0.0
+    speed_mid = 2
+    speed_0 = pts_loss[speed < turning_speed].mean()
+    speed_1 = pts_loss[(speed >= turning_speed) & (speed <= speed_mid)].mean()
+    speed_2 = pts_loss[speed > speed_mid].mean()
+    if ~speed_1.isnan():
+        weight_loss += speed_1
+    if ~speed_0.isnan():
+        weight_loss += speed_0
+    if ~speed_2.isnan():
+        weight_loss += speed_2
+    return {'loss': weight_loss}
+
 def deflowLoss(res_dict):
     pred = res_dict['est_flow']
     gt = res_dict['gt_flow']