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lingbot_map/heads/head_act.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import torch
+import torch.nn.functional as F
+
+
+def activate_pose(pred_pose_enc, trans_act="linear", quat_act="linear", fl_act="linear"):
+    """
+    Activate pose parameters with specified activation functions.
+
+    Args:
+        pred_pose_enc: Tensor containing encoded pose parameters [translation, quaternion, focal length]
+        trans_act: Activation type for translation component
+        quat_act: Activation type for quaternion component
+        fl_act: Activation type for focal length component
+
+    Returns:
+        Activated pose parameters tensor
+    """
+    T = pred_pose_enc[..., :3]
+    quat = pred_pose_enc[..., 3:7]
+    fl = pred_pose_enc[..., 7:]  # or fov
+
+    T = base_pose_act(T, trans_act)
+    quat = base_pose_act(quat, quat_act)
+    fl = base_pose_act(fl, fl_act)  # or fov
+
+    pred_pose_enc = torch.cat([T, quat, fl], dim=-1)
+
+    return pred_pose_enc
+
+
+def base_pose_act(pose_enc, act_type="linear"):
+    """
+    Apply basic activation function to pose parameters.
+
+    Args:
+        pose_enc: Tensor containing encoded pose parameters
+        act_type: Activation type ("linear", "inv_log", "exp", "relu")
+
+    Returns:
+        Activated pose parameters
+    """
+    if act_type == "linear":
+        return pose_enc
+    elif act_type == "inv_log":
+        return inverse_log_transform(pose_enc)
+    elif act_type == "exp":
+        return torch.exp(pose_enc)
+    elif act_type == "relu":
+        return F.relu(pose_enc)
+    else:
+        raise ValueError(f"Unknown act_type: {act_type}")
+
+
+def activate_head(out, activation="norm_exp", conf_activation="expp1"):
+    """
+    Process network output to extract 3D points and confidence values.
+
+    Args:
+        out: Network output tensor (B, C, H, W)
+        activation: Activation type for 3D points
+        conf_activation: Activation type for confidence values
+
+    Returns:
+        Tuple of (3D points tensor, confidence tensor)
+    """
+    # Move channels from last dim to the 4th dimension => (B, H, W, C)
+    fmap = out.permute(0, 2, 3, 1)  # B,H,W,C expected
+
+    # Split into xyz (first C-1 channels) and confidence (last channel)
+    xyz = fmap[:, :, :, :-1]
+    conf = fmap[:, :, :, -1]
+
+    if activation == "norm_exp":
+        d = xyz.norm(dim=-1, keepdim=True).clamp(min=1e-8)
+        xyz_normed = xyz / d
+        pts3d = xyz_normed * torch.expm1(d)
+    elif activation == "norm":
+        pts3d = xyz / xyz.norm(dim=-1, keepdim=True)
+    elif activation == "exp":
+        pts3d = torch.exp(xyz)
+    elif activation == "relu":
+        pts3d = F.relu(xyz)
+    elif activation == "inv_log":
+        pts3d = inverse_log_transform(xyz)
+    elif activation == "xy_inv_log":
+        xy, z = xyz.split([2, 1], dim=-1)
+        z = inverse_log_transform(z)
+        pts3d = torch.cat([xy * z, z], dim=-1)
+    elif activation == "sigmoid":
+        pts3d = torch.sigmoid(xyz)
+    elif activation == "linear":
+        pts3d = xyz
+    else:
+        raise ValueError(f"Unknown activation: {activation}")
+
+    if conf_activation == "expp1":
+        conf_out = 1 + conf.exp()
+    elif conf_activation == "expp0":
+        conf_out = conf.exp()
+    elif conf_activation == "sigmoid":
+        conf_out = torch.sigmoid(conf)
+    else:
+        raise ValueError(f"Unknown conf_activation: {conf_activation}")
+
+    return pts3d, conf_out
+
+
+def inverse_log_transform(y):
+    """
+    Apply inverse log transform: sign(y) * (exp(|y|) - 1)
+
+    Args:
+        y: Input tensor
+
+    Returns:
+        Transformed tensor
+    """
+    return torch.sign(y) * (torch.expm1(torch.abs(y)))