code/pointcept_framework/codex_tmp/pointcept/models/default.py

import os
import warnings

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_scatter
import torch_cluster

from pointcept.models.losses import build_criteria
from pointcept.models.utils.structure import Point
from pointcept.models.utils import offset2batch
from .builder import MODELS, build_model


def _segmentor_debug_enabled():
    return os.environ.get("POINTCEPT_DEBUG_SEGMENTOR", "").strip().lower() in (
        "1",
        "true",
        "yes",
    )


def superpoint_semantic_mixed_per_point(superpoint_ids, segment):
    """Per-point mask: True iff the point's superpoint id spans multiple GT labels."""
    sp = superpoint_ids.long().view(-1)
    seg = segment.long().view(-1)
    unique_ids, inverse, counts = torch.unique(
        sp, sorted=True, return_inverse=True, return_counts=True
    )
    _, order = torch.sort(inverse)
    idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
    seg_ord = seg[order].float()
    smin = torch_scatter.segment_csr(seg_ord, idx_ptr, reduce="min")
    smax = torch_scatter.segment_csr(seg_ord, idx_ptr, reduce="max")
    mixed = smin != smax
    return mixed[inverse]


def _get_superpoint_ids(input_dict, num_points):
    for key in ("superpoint", "spt"):
        value = input_dict.get(key)
        if value is not None and value.numel() == num_points:
            return value.long().view(-1)
    return None


def _get_point_batch_ids(input_dict, num_points, device):
    offset = input_dict.get("offset")
    if offset is None:
        return torch.zeros(num_points, device=device, dtype=torch.long)
    batch = offset2batch(offset)
    if batch.numel() != num_points:
        return torch.zeros(num_points, device=device, dtype=torch.long)
    return batch.to(device=device, dtype=torch.long)


@MODELS.register_module()
class DefaultSegmentor(nn.Module):
    def __init__(self, backbone=None, criteria=None):
        super().__init__()
        self.backbone = build_model(backbone)
        self.criteria = build_criteria(criteria)

    def forward(self, input_dict):
        if "condition" in input_dict.keys():
            # PPT (https://arxiv.org/abs/2308.09718)
            # currently, only support one batch one condition
            input_dict["condition"] = input_dict["condition"][0]
        seg_logits = self.backbone(input_dict)
        # train
        if self.training:
            if _segmentor_debug_enabled():
                print(f"[DEBUG DefaultSegmentor] Batch size (from target): {input_dict['segment'].shape[0]}")
                print(f"[DEBUG DefaultSegmentor] seg_logits shape: {seg_logits.shape}")
                print(f"[DEBUG DefaultSegmentor] target (input_dict['segment']) shape: {input_dict['segment'].shape}")
            loss = self.criteria(seg_logits, input_dict["segment"])
            return dict(loss=loss)
        # eval
        elif "segment" in input_dict.keys():
            if _segmentor_debug_enabled():
                print(f"[DEBUG DefaultSegmentor] Batch size (from target): {input_dict['segment'].shape[0]}")
                print(f"[DEBUG DefaultSegmentor] seg_logits shape: {seg_logits.shape}")
                print(f"[DEBUG DefaultSegmentor] target (input_dict['segment']) shape: {input_dict['segment'].shape}")
            loss = self.criteria(seg_logits, input_dict["segment"])
            return dict(loss=loss, seg_logits=seg_logits)
        # test
        else:
            return dict(seg_logits=seg_logits)


@MODELS.register_module()
class DefaultSegmentorV2(nn.Module):
    def __init__(
        self,
        num_classes,
        backbone_out_channels,
        backbone=None,
        criteria=None,
        freeze_backbone=False,
        superpoint_edge_aux_weight=0.0,
        superpoint_edge_boost=2.0,
        superpoint_contrastive_weight=0.0,
        superpoint_contrastive_temperature=0.1,
        superpoint_contrastive_max_samples=2048,
        superpoint_contrastive_max_points_per_superpoint=4,
        superpoint_contrastive_min_points_per_superpoint=2,
    ):
        super().__init__()
        self.seg_head = (
            nn.Linear(backbone_out_channels, num_classes)
            if num_classes > 0
            else nn.Identity()
        )
        self.backbone = build_model(backbone)
        self.criteria = build_criteria(criteria)
        self.freeze_backbone = freeze_backbone
        self.superpoint_edge_aux_weight = superpoint_edge_aux_weight
        self.superpoint_edge_boost = superpoint_edge_boost
        self.superpoint_contrastive_weight = superpoint_contrastive_weight
        self.superpoint_contrastive_temperature = superpoint_contrastive_temperature
        self.superpoint_contrastive_max_samples = int(superpoint_contrastive_max_samples)
        self.superpoint_contrastive_max_points_per_superpoint = int(
            superpoint_contrastive_max_points_per_superpoint
        )
        self.superpoint_contrastive_min_points_per_superpoint = int(
            superpoint_contrastive_min_points_per_superpoint
        )
        if self.freeze_backbone:
            for p in self.backbone.parameters():
                p.requires_grad = False

    def _maybe_superpoint_edge_aux_loss(self, seg_logits, target, input_dict):
        if self.superpoint_edge_aux_weight <= 0:
            return seg_logits.new_tensor(0.0)
        sp = input_dict.get("superpoint")
        if sp is None:
            sp = input_dict.get("spt")
        if sp is None or sp.numel() != seg_logits.shape[0]:
            return seg_logits.new_tensor(0.0)
        mixed = superpoint_semantic_mixed_per_point(sp, target)
        ce = F.cross_entropy(seg_logits, target, reduction="none", ignore_index=-1)
        valid = target != -1
        w = 1.0 + (self.superpoint_edge_boost - 1.0) * mixed.float()
        denom = valid.float().sum().clamp_min(1.0)
        return (ce * (w - 1.0) * valid.float()).sum() / denom

    def _maybe_superpoint_contrastive_loss(self, feat, target, input_dict):
        if self.superpoint_contrastive_weight <= 0:
            return feat.new_tensor(0.0)
        if self.superpoint_contrastive_max_samples < 2:
            return feat.new_tensor(0.0)
        if self.superpoint_contrastive_max_points_per_superpoint < 2:
            return feat.new_tensor(0.0)

        num_points = feat.shape[0]
        sp = _get_superpoint_ids(input_dict, num_points)
        if sp is None:
            return feat.new_tensor(0.0)

        batch = _get_point_batch_ids(input_dict, num_points, feat.device)
        valid_idx = torch.nonzero(target != -1, as_tuple=False).flatten()
        if valid_idx.numel() < self.superpoint_contrastive_min_points_per_superpoint * 2:
            return feat.new_tensor(0.0)

        valid_batch = batch[valid_idx]
        valid_sp = sp[valid_idx]
        group_keys = torch.stack([valid_batch, valid_sp], dim=1)
        _, inverse, counts = torch.unique(
            group_keys, dim=0, sorted=False, return_inverse=True, return_counts=True
        )
        candidate_groups = torch.nonzero(
            counts >= self.superpoint_contrastive_min_points_per_superpoint,
            as_tuple=False,
        ).flatten()
        if candidate_groups.numel() < 2:
            return feat.new_tensor(0.0)

        order = torch.argsort(inverse)
        idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
        ordered_idx = valid_idx[order]
        if self.training:
            candidate_groups = candidate_groups[
                torch.randperm(candidate_groups.numel(), device=feat.device)
            ]

        selected = []
        budget = 0
        for group_id in candidate_groups.tolist():
            start = idx_ptr[group_id].item()
            end = idx_ptr[group_id + 1].item()
            group_point_idx = ordered_idx[start:end]
            remaining = self.superpoint_contrastive_max_samples - budget
            if remaining < self.superpoint_contrastive_min_points_per_superpoint:
                break
            take = min(
                group_point_idx.numel(),
                self.superpoint_contrastive_max_points_per_superpoint,
                remaining,
            )
            if take < self.superpoint_contrastive_min_points_per_superpoint:
                continue
            if self.training and group_point_idx.numel() > take:
                choice = torch.randperm(group_point_idx.numel(), device=feat.device)[:take]
                group_point_idx = group_point_idx[choice]
            else:
                group_point_idx = group_point_idx[:take]
            selected.append(group_point_idx)
            budget += group_point_idx.numel()
            if budget >= self.superpoint_contrastive_max_samples:
                break

        if len(selected) < 2:
            return feat.new_tensor(0.0)

        sample_idx = torch.cat(selected, dim=0)
        sample_batch = batch[sample_idx]
        sample_sp = sp[sample_idx]
        sample_group = torch.unique(
            torch.stack([sample_batch, sample_sp], dim=1),
            dim=0,
            sorted=False,
            return_inverse=True,
        )[1]

        z = F.normalize(feat[sample_idx], p=2, dim=1)
        logits = torch.matmul(z, z.transpose(0, 1))
        logits = logits / max(self.superpoint_contrastive_temperature, 1e-6)
        logits = logits - logits.max(dim=1, keepdim=True).values.detach()

        self_mask = torch.eye(sample_idx.numel(), device=feat.device, dtype=torch.bool)
        same_scene = sample_batch[:, None].eq(sample_batch[None, :])
        positive_mask = sample_group[:, None].eq(sample_group[None, :]) & (~self_mask)
        valid_pair_mask = same_scene & (~self_mask)
        positive_count = positive_mask.sum(dim=1)
        anchor_mask = positive_count > 0
        if not anchor_mask.any():
            return feat.new_tensor(0.0)

        exp_logits = torch.exp(logits) * valid_pair_mask.float()
        denom = exp_logits.sum(dim=1, keepdim=True).clamp_min(1e-12)
        valid_anchor_mask = anchor_mask & (valid_pair_mask.sum(dim=1) > 0)
        if not valid_anchor_mask.any():
            return feat.new_tensor(0.0)

        log_prob = logits - torch.log(denom)
        mean_log_prob_pos = (log_prob * positive_mask.float()).sum(dim=1) / positive_count.clamp_min(1)
        return -mean_log_prob_pos[valid_anchor_mask].mean()

    def forward(self, input_dict, return_point=False):
        point = Point(input_dict)

        if _segmentor_debug_enabled():
            if hasattr(point, "offset") and point.offset is not None:
                batch_size = len(point.offset)
                print(f"[DEBUG DefaultSegmentorV2] Batch Size (from offset): {batch_size}")
                for i in range(batch_size):
                    start_idx = 0 if i == 0 else point.offset[i - 1].item()
                    end_idx = point.offset[i].item()
                    num_points = end_idx - start_idx
                    print(f"[DEBUG DefaultSegmentorV2] Sample {i}: Number of points = {num_points}")
                    if num_points <= 0:
                        print(
                            f"[ERROR] Sample {i} has {num_points} points! This will cause arange error."
                        )
            else:
                print(f"[DEBUG DefaultSegmentorV2] Point keys: {point.keys()}")
                if "coord" in point.keys():
                    print(f"[DEBUG DefaultSegmentorV2] Point cloud shape: {point.coord.shape}")

        point = self.backbone(point)
        # Backbone added after v1.5.0 return Point instead of feat and use DefaultSegmentorV2
        # TODO: remove this part after make all backbone return Point only.
        if isinstance(point, Point):
            while "pooling_parent" in point.keys():
                assert "pooling_inverse" in point.keys()
                parent = point.pop("pooling_parent")
                inverse = point.pop("pooling_inverse")
                parent.feat = torch.cat([parent.feat, point.feat[inverse]], dim=-1)
                point = parent
            feat = point.feat
        else:
            feat = point
        seg_logits = self.seg_head(feat)
        return_dict = dict()
        if return_point:
            # PCA evaluator parse feat and coord in point
            return_dict["point"] = point
        # train
        if self.training:
            if _segmentor_debug_enabled():
                print(f"[DEBUG DefaultSegmentorV2] Batch size (from target): {input_dict['segment'].shape[0]}")
                print(f"[DEBUG DefaultSegmentorV2] seg_logits shape: {seg_logits.shape}")
                print(f"[DEBUG DefaultSegmentorV2] target (input_dict['segment']) shape: {input_dict['segment'].shape}")
            valid_mask = input_dict["segment"] != -1
            num_valid_points = valid_mask.sum().item()
            if _segmentor_debug_enabled():
                print(f"[DEBUG] Number of valid points (not ignored): {num_valid_points}")
            if num_valid_points == 0:
                warnings.warn(
                    "DefaultSegmentorV2: batch has zero valid labels (all ignore_index).",
                    UserWarning,
                    stacklevel=2,
                )
            loss_seg = self.criteria(seg_logits, input_dict["segment"])
            loss_sp_edge = self.superpoint_edge_aux_weight * self._maybe_superpoint_edge_aux_loss(
                seg_logits, input_dict["segment"], input_dict
            )
            loss_sp_contrast = self.superpoint_contrastive_weight * self._maybe_superpoint_contrastive_loss(
                feat, input_dict["segment"], input_dict
            )
            loss = loss_seg + loss_sp_edge + loss_sp_contrast
            return_dict["loss"] = loss
            return_dict["loss_seg"] = loss_seg
            return_dict["loss_superpoint_edge"] = loss_sp_edge
            return_dict["loss_superpoint_contrast"] = loss_sp_contrast
        # eval
        elif "segment" in input_dict.keys():
            if _segmentor_debug_enabled():
                print(f"[DEBUG DefaultSegmentorV2] Batch size (from target): {input_dict['segment'].shape[0]}")
                print(f"[DEBUG DefaultSegmentorV2] seg_logits shape: {seg_logits.shape}")
                print(f"[DEBUG DefaultSegmentorV2] target (input_dict['segment']) shape: {input_dict['segment'].shape}")
            valid_mask = input_dict["segment"] != -1
            num_valid_points = valid_mask.sum().item()
            if _segmentor_debug_enabled():
                print(f"[DEBUG] Number of valid points (not ignored): {num_valid_points}")
            if num_valid_points == 0:
                warnings.warn(
                    "DefaultSegmentorV2: batch has zero valid labels (all ignore_index).",
                    UserWarning,
                    stacklevel=2,
                )
            loss_seg = self.criteria(seg_logits, input_dict["segment"])
            loss_sp_edge = self.superpoint_edge_aux_weight * self._maybe_superpoint_edge_aux_loss(
                seg_logits, input_dict["segment"], input_dict
            )
            loss_sp_contrast = self.superpoint_contrastive_weight * self._maybe_superpoint_contrastive_loss(
                feat, input_dict["segment"], input_dict
            )
            loss = loss_seg + loss_sp_edge + loss_sp_contrast
            return_dict["loss"] = loss
            return_dict["loss_seg"] = loss_seg
            return_dict["loss_superpoint_edge"] = loss_sp_edge
            return_dict["loss_superpoint_contrast"] = loss_sp_contrast
            return_dict["seg_logits"] = seg_logits
        # test
        else:
            return_dict["seg_logits"] = seg_logits
        return return_dict


@MODELS.register_module()
class DINOEnhancedSegmentor(nn.Module):
    def __init__(
        self,
        num_classes,
        backbone_out_channels,
        backbone=None,
        criteria=None,
        freeze_backbone=False,
    ):
        super().__init__()
        self.seg_head = (
            nn.Linear(backbone_out_channels, num_classes)
            if num_classes > 0
            else nn.Identity()
        )
        self.backbone = build_model(backbone) if backbone is not None else None
        self.criteria = build_criteria(criteria)
        self.freeze_backbone = freeze_backbone
        if self.backbone is not None and self.freeze_backbone:
            for p in self.backbone.parameters():
                p.requires_grad = False

    def forward(self, input_dict, return_point=False):
        point = Point(input_dict)
        if self.backbone is not None:
            if self.freeze_backbone:
                with torch.no_grad():
                    point = self.backbone(point)
            else:
                point = self.backbone(point)
            point_list = [point]
            while "unpooling_parent" in point_list[-1].keys():
                point_list.append(point_list[-1].pop("unpooling_parent"))
            for i in reversed(range(1, len(point_list))):
                point = point_list[i]
                parent = point_list[i - 1]
                assert "pooling_inverse" in point.keys()
                inverse = point.pooling_inverse
                parent.feat = torch.cat([parent.feat, point.feat[inverse]], dim=-1)
            point = point_list[0]
            while "pooling_parent" in point.keys():
                assert "pooling_inverse" in point.keys()
                parent = point.pop("pooling_parent")
                inverse = point.pooling_inverse
                parent.feat = torch.cat([parent.feat, point.feat[inverse]], dim=-1)
                point = parent
            feat = [point.feat]
        else:
            feat = []
        dino_coord = input_dict["dino_coord"]
        dino_feat = input_dict["dino_feat"]
        dino_offset = input_dict["dino_offset"]
        idx = torch_cluster.knn(
            x=dino_coord,
            y=point.origin_coord,
            batch_x=offset2batch(dino_offset),
            batch_y=offset2batch(point.origin_offset),
            k=1,
        )[1]

        feat.append(dino_feat[idx])
        feat = torch.concatenate(feat, dim=-1)
        seg_logits = self.seg_head(feat)
        return_dict = dict()
        if return_point:
            # PCA evaluator parse feat and coord in point
            return_dict["point"] = point
        # train
        if self.training:
            if _segmentor_debug_enabled():
                print(f"[DEBUG DINOEnhancedSegmentor] Batch size (from target): {input_dict['segment'].shape[0]}")
                print(f"[DEBUG DINOEnhancedSegmentor] seg_logits shape: {seg_logits.shape}")
                print(f"[DEBUG DINOEnhancedSegmentor] target (input_dict['segment']) shape: {input_dict['segment'].shape}")
            loss = self.criteria(seg_logits, input_dict["segment"])
            return_dict["loss"] = loss
        # eval
        elif "segment" in input_dict.keys():
            if _segmentor_debug_enabled():
                print(f"[DEBUG DINOEnhancedSegmentor] Batch size (from target): {input_dict['segment'].shape[0]}")
                print(f"[DEBUG DINOEnhancedSegmentor] seg_logits shape: {seg_logits.shape}")
                print(f"[DEBUG DINOEnhancedSegmentor] target (input_dict['segment']) shape: {input_dict['segment'].shape}")
            loss = self.criteria(seg_logits, input_dict["segment"])
            return_dict["loss"] = loss
            return_dict["seg_logits"] = seg_logits
        # test
        else:
            return_dict["seg_logits"] = seg_logits
        return return_dict


@MODELS.register_module()
class DefaultClassifier(nn.Module):
    def __init__(
        self,
        backbone=None,
        criteria=None,
        num_classes=40,
        backbone_embed_dim=256,
    ):
        super().__init__()
        self.backbone = build_model(backbone)
        self.criteria = build_criteria(criteria)
        self.num_classes = num_classes
        self.backbone_embed_dim = backbone_embed_dim
        self.cls_head = nn.Sequential(
            nn.Linear(backbone_embed_dim, 256),
            nn.BatchNorm1d(256),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(256, 128),
            nn.BatchNorm1d(128),
            nn.ReLU(inplace=True),
            nn.Dropout(p=0.5),
            nn.Linear(128, num_classes),
        )

    def forward(self, input_dict):
        point = Point(input_dict)
        point = self.backbone(point)
        # Backbone added after v1.5.0 return Point instead of feat
        # And after v1.5.0 feature aggregation for classification operated in classifier
        # TODO: remove this part after make all backbone return Point only.
        if isinstance(point, Point):
            point.feat = torch_scatter.segment_csr(
                src=point.feat,
                indptr=nn.functional.pad(point.offset, (1, 0)),
                reduce="mean",
            )
            feat = point.feat
        else:
            feat = point
        cls_logits = self.cls_head(feat)
        if self.training:
            loss = self.criteria(cls_logits, input_dict["category"])
            return dict(loss=loss)
        elif "category" in input_dict.keys():
            loss = self.criteria(cls_logits, input_dict["category"])
            return dict(loss=loss, cls_logits=cls_logits)
        else:
            return dict(cls_logits=cls_logits)