189 lines
7.7 KiB
Python
189 lines
7.7 KiB
Python
from functools import partial
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from .roi_head_template import RoIHeadTemplate
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from ...utils import common_utils, loss_utils
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class SECONDHead(RoIHeadTemplate):
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def __init__(self, input_channels, model_cfg, num_class=1, **kwargs):
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super().__init__(num_class=num_class, model_cfg=model_cfg)
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self.model_cfg = model_cfg
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GRID_SIZE = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
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pre_channel = self.model_cfg.ROI_GRID_POOL.IN_CHANNEL * GRID_SIZE * GRID_SIZE
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shared_fc_list = []
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for k in range(0, self.model_cfg.SHARED_FC.__len__()):
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shared_fc_list.extend([
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nn.Conv1d(pre_channel, self.model_cfg.SHARED_FC[k], kernel_size=1, bias=False),
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nn.BatchNorm1d(self.model_cfg.SHARED_FC[k]),
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nn.ReLU()
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])
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pre_channel = self.model_cfg.SHARED_FC[k]
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if k != self.model_cfg.SHARED_FC.__len__() - 1 and self.model_cfg.DP_RATIO > 0:
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shared_fc_list.append(nn.Dropout(self.model_cfg.DP_RATIO))
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self.shared_fc_layer = nn.Sequential(*shared_fc_list)
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self.iou_layers = self.make_fc_layers(
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input_channels=pre_channel, output_channels=1, fc_list=self.model_cfg.IOU_FC
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)
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self.init_weights(weight_init='xavier')
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if torch.__version__ >= '1.3':
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self.affine_grid = partial(F.affine_grid, align_corners=True)
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self.grid_sample = partial(F.grid_sample, align_corners=True)
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else:
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self.affine_grid = F.affine_grid
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self.grid_sample = F.grid_sample
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def init_weights(self, weight_init='xavier'):
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if weight_init == 'kaiming':
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init_func = nn.init.kaiming_normal_
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elif weight_init == 'xavier':
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init_func = nn.init.xavier_normal_
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elif weight_init == 'normal':
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init_func = nn.init.normal_
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else:
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raise NotImplementedError
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Conv1d):
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if weight_init == 'normal':
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init_func(m.weight, mean=0, std=0.001)
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else:
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init_func(m.weight)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def roi_grid_pool(self, batch_dict):
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"""
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Args:
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batch_dict:
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batch_size:
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rois: (B, num_rois, 7 + C)
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spatial_features_2d: (B, C, H, W)
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Returns:
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"""
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batch_size = batch_dict['batch_size']
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rois = batch_dict['rois'].detach()
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spatial_features_2d = batch_dict['spatial_features_2d'].detach()
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height, width = spatial_features_2d.size(2), spatial_features_2d.size(3)
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dataset_cfg = batch_dict['dataset_cfg']
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min_x = dataset_cfg.POINT_CLOUD_RANGE[0]
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min_y = dataset_cfg.POINT_CLOUD_RANGE[1]
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voxel_size_x = dataset_cfg.DATA_PROCESSOR[-1].VOXEL_SIZE[0]
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voxel_size_y = dataset_cfg.DATA_PROCESSOR[-1].VOXEL_SIZE[1]
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down_sample_ratio = self.model_cfg.ROI_GRID_POOL.DOWNSAMPLE_RATIO
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pooled_features_list = []
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torch.backends.cudnn.enabled = False
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for b_id in range(batch_size):
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# Map global boxes coordinates to feature map coordinates
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x1 = (rois[b_id, :, 0] - rois[b_id, :, 3] / 2 - min_x) / (voxel_size_x * down_sample_ratio)
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x2 = (rois[b_id, :, 0] + rois[b_id, :, 3] / 2 - min_x) / (voxel_size_x * down_sample_ratio)
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y1 = (rois[b_id, :, 1] - rois[b_id, :, 4] / 2 - min_y) / (voxel_size_y * down_sample_ratio)
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y2 = (rois[b_id, :, 1] + rois[b_id, :, 4] / 2 - min_y) / (voxel_size_y * down_sample_ratio)
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angle, _ = common_utils.check_numpy_to_torch(rois[b_id, :, 6])
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cosa = torch.cos(angle)
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sina = torch.sin(angle)
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theta = torch.stack((
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(x2 - x1) / (width - 1) * cosa, (x2 - x1) / (width - 1) * (-sina), (x1 + x2 - width + 1) / (width - 1),
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(y2 - y1) / (height - 1) * sina, (y2 - y1) / (height - 1) * cosa, (y1 + y2 - height + 1) / (height - 1)
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), dim=1).view(-1, 2, 3).float()
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grid_size = self.model_cfg.ROI_GRID_POOL.GRID_SIZE
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grid = self.affine_grid(
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theta,
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torch.Size((rois.size(1), spatial_features_2d.size(1), grid_size, grid_size))
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)
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pooled_features = self.grid_sample(
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spatial_features_2d[b_id].unsqueeze(0).expand(rois.size(1), spatial_features_2d.size(1), height, width),
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grid
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)
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pooled_features_list.append(pooled_features)
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torch.backends.cudnn.enabled = True
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pooled_features = torch.cat(pooled_features_list, dim=0)
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return pooled_features
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def forward(self, batch_dict):
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"""
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:param input_data: input dict
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:return:
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"""
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targets_dict = self.proposal_layer(
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batch_dict, nms_config=self.model_cfg.NMS_CONFIG['TRAIN' if self.training else 'TEST']
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)
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if self.training:
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targets_dict = self.assign_targets(batch_dict)
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batch_dict['rois'] = targets_dict['rois']
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batch_dict['roi_labels'] = targets_dict['roi_labels']
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# RoI aware pooling
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pooled_features = self.roi_grid_pool(batch_dict) # (BxN, C, 7, 7)
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batch_size_rcnn = pooled_features.shape[0]
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shared_features = self.shared_fc_layer(pooled_features.view(batch_size_rcnn, -1, 1))
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rcnn_iou = self.iou_layers(shared_features).transpose(1, 2).contiguous().squeeze(dim=1) # (B*N, 1)
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if not self.training:
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batch_dict['batch_cls_preds'] = rcnn_iou.view(batch_dict['batch_size'], -1, rcnn_iou.shape[-1])
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batch_dict['batch_box_preds'] = batch_dict['rois']
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batch_dict['cls_preds_normalized'] = False
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else:
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targets_dict['rcnn_iou'] = rcnn_iou
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self.forward_ret_dict = targets_dict
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return batch_dict
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def get_loss(self, tb_dict=None):
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tb_dict = {} if tb_dict is None else tb_dict
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rcnn_loss = 0
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rcnn_loss_cls, cls_tb_dict = self.get_box_iou_layer_loss(self.forward_ret_dict)
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rcnn_loss += rcnn_loss_cls
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tb_dict.update(cls_tb_dict)
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tb_dict['rcnn_loss'] = rcnn_loss.item()
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return rcnn_loss, tb_dict
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def get_box_iou_layer_loss(self, forward_ret_dict):
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loss_cfgs = self.model_cfg.LOSS_CONFIG
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rcnn_iou = forward_ret_dict['rcnn_iou']
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rcnn_iou_labels = forward_ret_dict['rcnn_cls_labels'].view(-1)
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rcnn_iou_flat = rcnn_iou.view(-1)
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if loss_cfgs.IOU_LOSS == 'BinaryCrossEntropy':
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batch_loss_iou = nn.functional.binary_cross_entropy_with_logits(
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rcnn_iou_flat,
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rcnn_iou_labels.float(), reduction='none'
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)
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elif loss_cfgs.IOU_LOSS == 'L2':
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batch_loss_iou = nn.functional.mse_loss(rcnn_iou_flat, rcnn_iou_labels, reduction='none')
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elif loss_cfgs.IOU_LOSS == 'smoothL1':
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diff = rcnn_iou_flat - rcnn_iou_labels
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batch_loss_iou = loss_utils.WeightedSmoothL1Loss.smooth_l1_loss(diff, 1.0 / 9.0)
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elif loss_cfgs.IOU_LOSS == 'focalbce':
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batch_loss_iou = loss_utils.sigmoid_focal_cls_loss(rcnn_iou_flat, rcnn_iou_labels)
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else:
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raise NotImplementedError
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iou_valid_mask = (rcnn_iou_labels >= 0).float()
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rcnn_loss_iou = (batch_loss_iou * iou_valid_mask).sum() / torch.clamp(iou_valid_mask.sum(), min=1.0)
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rcnn_loss_iou = rcnn_loss_iou * loss_cfgs.LOSS_WEIGHTS['rcnn_iou_weight']
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tb_dict = {'rcnn_loss_iou': rcnn_loss_iou.item()}
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return rcnn_loss_iou, tb_dict
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