From efbe4b6ac9b5fbf1ddcceaa28e7149ad23378886 Mon Sep 17 00:00:00 2001 From: gouhanke <12219217+gouhanke@user.noreply.gitee.com> Date: Thu, 12 Feb 2026 18:31:56 +0800 Subject: [PATCH] Revert "Merge branch 'dev' of gitlab.com:leeeezd0016-group/gouhanke-vla into dev" This reverts commit acb146747340b7fa3d5a24dadf9424a331c6d14b, reversing changes made to 624b926e336c5cff572d7642dddd9a45eae60770. --- roboimi/vla/agent.py | 66 -------------------------------------------- 1 file changed, 66 deletions(-) diff --git a/roboimi/vla/agent.py b/roboimi/vla/agent.py index a1a1883..34fa47c 100644 --- a/roboimi/vla/agent.py +++ b/roboimi/vla/agent.py @@ -94,51 +94,9 @@ class VLAAgent(nn.Module): B = actions.shape[0] # 归一化 states (qpos) 和 actions - # ======== 归一化测试代码 (调试用) ======== - if not hasattr(self, '_norm_test_done'): - self._norm_test_done = True - print("\n" + "=" * 60) - print("归一化测试 - 第一个batch:") - print("=" * 60) - - # 检查action归一化 - action_orig = batch['action'].clone() - print(f"Action 原始范围: [{action_orig.min():.4f}, {action_orig.max():.4f}]") - print(f"Action 各维度范围 (前5维):") - for i in range(min(5, action_orig.shape[-1])): - print(f" 维度{i}: [{action_orig[..., i].min():.4f}, {action_orig[..., i].max():.4f}]") - - # 检查qpos归一化 - state_orig = states.clone() - print(f"Qpos 原始范围: [{state_orig.min():.4f}, {state_orig.max():.4f}]") - states = self.normalization.normalize_qpos(states) actions = self.normalization.normalize_action(actions) - if hasattr(self, '_norm_test_done'): - print(f"Action 归一化后范围: [{actions.min():.4f}, {actions.max():.4f}]") - print(f"Qpos 归一化后范围: [{states.min():.4f}, {states.max():.4f}]") - - # 检查是否在预期范围内 - if self.normalization.normalization_type == 'min_max': - action_in_range = (actions >= -1.1) & (actions <= 1.1) - state_in_range = (states >= -1.1) & (states <= 1.1) - print(f"Action 在[-1,1]范围内: {action_in_range.all().item()}") - print(f"Qpos 在[-1,1]范围内: {state_in_range.all().item()}") - - if not action_in_range.all(): - print(f"⚠️ Action超出范围的维度:") - for i in range(actions.shape[-1]): - if not action_in_range[..., i].all(): - print(f" 维度{i}: min={actions[..., i].min():.4f}, max={actions[..., i].max():.4f}") - if not state_in_range.all(): - print(f"⚠️ Qpos超出范围的维度:") - for i in range(states.shape[-1]): - if not state_in_range[..., i].all(): - print(f" 维度{i}: min={states[..., i].min():.4f}, max={states[..., i].max():.4f}") - print("=" * 60 + "\n") - # ======== 归一化测试代码结束 ======== - state_features = self.state_encoder(states) # 1. 提取视觉特征 @@ -358,24 +316,9 @@ class VLAAgent(nn.Module): """ B = proprioception.shape[0] - # ======== 推理归一化测试代码 (调试用) ======== - if not hasattr(self, '_infer_norm_test_done'): - self._infer_norm_test_done = True - print("\n" + "=" * 60) - print("推理归一化测试 - 第一个推理batch:") - print("=" * 60) - print(f"Qpos输入范围: [{proprioception.min():.4f}, {proprioception.max():.4f}]") # 归一化 proprioception (qpos) - proprioception_orig = proprioception.clone() proprioception = self.normalization.normalize_qpos(proprioception) - if hasattr(self, '_infer_norm_test_done'): - print(f"Qpos归一化后范围: [{proprioception.min():.4f}, {proprioception.max():.4f}]") - if self.normalization.normalization_type == 'min_max': - in_range = (proprioception >= -1.1) & (proprioception <= 1.1) - print(f"Qpos在[-1,1]范围内: {in_range.all().item()}") - # ======== 推理归一化测试代码结束 ======== - # 1. 提取当前观测特征（只提取一次） visual_features = self.vision_encoder(images) state_features = self.state_encoder(proprioception) @@ -413,17 +356,8 @@ class VLAAgent(nn.Module): ).prev_sample # 4. 反归一化动作序列 - # ======== 反归一化测试代码 (调试用) ======== - if hasattr(self, '_infer_norm_test_done'): - print(f"去噪后action范围 (归一化空间): [{current_actions.min():.4f}, {current_actions.max():.4f}]") - denormalized_actions = self.normalization.denormalize_action(current_actions) - if hasattr(self, '_infer_norm_test_done'): - print(f"反归一化后action范围: [{denormalized_actions.min():.4f}, {denormalized_actions.max():.4f}]") - print("=" * 60 + "\n") - # ======== 反归一化测试代码结束 ======== - return denormalized_actions def get_normalization_stats(self):