# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # Modified from DETR (https://github.com/facebookresearch/detr) # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from __future__ import absolute_import from __future__ import division from __future__ import print_function import paddle import paddle.nn as nn import paddle.nn.functional as F from scipy.optimize import linear_sum_assignment from ppdet.core.workspace import register, serializable from ..losses.iou_loss import GIoULoss from .utils import bbox_cxcywh_to_xyxy __all__ = ['HungarianMatcher'] @register @serializable class HungarianMatcher(nn.Layer): __shared__ = ['use_focal_loss', 'with_mask', 'num_sample_points'] def __init__(self, matcher_coeff={ 'class': 1, 'bbox': 5, 'giou': 2, 'mask': 1, 'dice': 1 }, use_focal_loss=False, with_mask=False, num_sample_points=12544, alpha=0.25, gamma=2.0): r""" Args: matcher_coeff (dict): The coefficient of hungarian matcher cost. """ super(HungarianMatcher, self).__init__() self.matcher_coeff = matcher_coeff self.use_focal_loss = use_focal_loss self.with_mask = with_mask self.num_sample_points = num_sample_points self.alpha = alpha self.gamma = gamma self.giou_loss = GIoULoss() def forward(self, boxes, logits, gt_bbox, gt_class, masks=None, gt_mask=None): r""" Args: boxes (Tensor): [b, query, 4] logits (Tensor): [b, query, num_classes] gt_bbox (List(Tensor)): list[[n, 4]] gt_class (List(Tensor)): list[[n, 1]] masks (Tensor|None): [b, query, h, w] gt_mask (List(Tensor)): list[[n, H, W]] Returns: A list of size batch_size, containing tuples of (index_i, index_j) where: - index_i is the indices of the selected predictions (in order) - index_j is the indices of the corresponding selected targets (in order) For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes) """ bs, num_queries = boxes.shape[:2] num_gts = [len(a) for a in gt_class] if sum(num_gts) == 0: return [(paddle.to_tensor( [], dtype=paddle.int64), paddle.to_tensor( [], dtype=paddle.int64)) for _ in range(bs)] # We flatten to compute the cost matrices in a batch # [batch_size * num_queries, num_classes] logits = logits.detach() out_prob = F.sigmoid(logits.flatten( 0, 1)) if self.use_focal_loss else F.softmax(logits.flatten(0, 1)) # [batch_size * num_queries, 4] out_bbox = boxes.detach().flatten(0, 1) # Also concat the target labels and boxes tgt_ids = paddle.concat(gt_class).flatten() tgt_bbox = paddle.concat(gt_bbox) # Compute the classification cost out_prob = paddle.gather(out_prob, tgt_ids, axis=1) if self.use_focal_loss: neg_cost_class = (1 - self.alpha) * (out_prob**self.gamma) * (-( 1 - out_prob + 1e-8).log()) pos_cost_class = self.alpha * ( (1 - out_prob)**self.gamma) * (-(out_prob + 1e-8).log()) cost_class = pos_cost_class - neg_cost_class else: cost_class = -out_prob # Compute the L1 cost between boxes cost_bbox = ( out_bbox.unsqueeze(1) - tgt_bbox.unsqueeze(0)).abs().sum(-1) # Compute the giou cost betwen boxes cost_giou = self.giou_loss( bbox_cxcywh_to_xyxy(out_bbox.unsqueeze(1)), bbox_cxcywh_to_xyxy(tgt_bbox.unsqueeze(0))).squeeze(-1) # Final cost matrix C = self.matcher_coeff['class'] * cost_class + \ self.matcher_coeff['bbox'] * cost_bbox + \ self.matcher_coeff['giou'] * cost_giou # Compute the mask cost and dice cost if self.with_mask: assert (masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`') # all masks share the same set of points for efficient matching sample_points = paddle.rand([bs, 1, self.num_sample_points, 2]) sample_points = 2.0 * sample_points - 1.0 out_mask = F.grid_sample( masks.detach(), sample_points, align_corners=False).squeeze(-2) out_mask = out_mask.flatten(0, 1) tgt_mask = paddle.concat(gt_mask).unsqueeze(1) sample_points = paddle.concat([ a.tile([b, 1, 1, 1]) for a, b in zip(sample_points, num_gts) if b > 0 ]) tgt_mask = F.grid_sample( tgt_mask, sample_points, align_corners=False).squeeze([1, 2]) with paddle.amp.auto_cast(enable=False): # binary cross entropy cost pos_cost_mask = F.binary_cross_entropy_with_logits( out_mask, paddle.ones_like(out_mask), reduction='none') neg_cost_mask = F.binary_cross_entropy_with_logits( out_mask, paddle.zeros_like(out_mask), reduction='none') cost_mask = paddle.matmul( pos_cost_mask, tgt_mask, transpose_y=True) + paddle.matmul( neg_cost_mask, 1 - tgt_mask, transpose_y=True) cost_mask /= self.num_sample_points # dice cost out_mask = F.sigmoid(out_mask) numerator = 2 * paddle.matmul( out_mask, tgt_mask, transpose_y=True) denominator = out_mask.sum( -1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0) cost_dice = 1 - (numerator + 1) / (denominator + 1) C = C + self.matcher_coeff['mask'] * cost_mask + \ self.matcher_coeff['dice'] * cost_dice C = C.reshape([bs, num_queries, -1]) C = [a.squeeze(0) for a in C.chunk(bs)] sizes = [a.shape[0] for a in gt_bbox] if hasattr(paddle.Tensor, "contiguous"): indices = [ linear_sum_assignment(c.split(sizes, -1)[i].contiguous().numpy()) for i, c in enumerate(C) ] else: indices = [ linear_sum_assignment(c.split(sizes, -1)[i].numpy()) for i, c in enumerate(C) ] return [(paddle.to_tensor( i, dtype=paddle.int64), paddle.to_tensor( j, dtype=paddle.int64)) for i, j in indices]