# Copyright (c) 2020 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. import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle.nn.initializer import KaimingNormal from ppdet.core.workspace import register, create from ppdet.modeling.layers import ConvNormLayer from .roi_extractor import RoIAlign from ..cls_utils import _get_class_default_kwargs @register class MaskFeat(nn.Layer): """ Feature extraction in Mask head Args: in_channel (int): Input channels out_channel (int): Output channels num_convs (int): The number of conv layers, default 4 norm_type (string | None): Norm type, bn, gn, sync_bn are available, default None """ def __init__(self, in_channel=256, out_channel=256, num_convs=4, norm_type=None): super(MaskFeat, self).__init__() self.num_convs = num_convs self.in_channel = in_channel self.out_channel = out_channel self.norm_type = norm_type fan_conv = out_channel * 3 * 3 fan_deconv = out_channel * 2 * 2 mask_conv = nn.Sequential() if norm_type == 'gn': for i in range(self.num_convs): conv_name = 'mask_inter_feat_{}'.format(i + 1) mask_conv.add_sublayer( conv_name, ConvNormLayer( ch_in=in_channel if i == 0 else out_channel, ch_out=out_channel, filter_size=3, stride=1, norm_type=self.norm_type, initializer=KaimingNormal(fan_in=fan_conv), skip_quant=True)) mask_conv.add_sublayer(conv_name + 'act', nn.ReLU()) else: for i in range(self.num_convs): conv_name = 'mask_inter_feat_{}'.format(i + 1) conv = nn.Conv2D( in_channels=in_channel if i == 0 else out_channel, out_channels=out_channel, kernel_size=3, padding=1, weight_attr=paddle.ParamAttr( initializer=KaimingNormal(fan_in=fan_conv))) conv.skip_quant = True mask_conv.add_sublayer(conv_name, conv) mask_conv.add_sublayer(conv_name + 'act', nn.ReLU()) mask_conv.add_sublayer( 'conv5_mask', nn.Conv2DTranspose( in_channels=self.in_channel, out_channels=self.out_channel, kernel_size=2, stride=2, weight_attr=paddle.ParamAttr( initializer=KaimingNormal(fan_in=fan_deconv)))) mask_conv.add_sublayer('conv5_mask' + 'act', nn.ReLU()) self.upsample = mask_conv @classmethod def from_config(cls, cfg, input_shape): if isinstance(input_shape, (list, tuple)): input_shape = input_shape[0] return {'in_channel': input_shape.channels, } def out_channels(self): return self.out_channel def forward(self, feats): return self.upsample(feats) @register class MaskHead(nn.Layer): __shared__ = ['num_classes', 'export_onnx'] __inject__ = ['mask_assigner'] """ RCNN mask head Args: head (nn.Layer): Extract feature in mask head roi_extractor (object): The module of RoI Extractor mask_assigner (object): The module of Mask Assigner, label and sample the mask num_classes (int): The number of classes share_bbox_feat (bool): Whether to share the feature from bbox head, default false """ def __init__(self, head, roi_extractor=_get_class_default_kwargs(RoIAlign), mask_assigner='MaskAssigner', num_classes=80, share_bbox_feat=False, export_onnx=False): super(MaskHead, self).__init__() self.num_classes = num_classes self.export_onnx = export_onnx self.roi_extractor = roi_extractor if isinstance(roi_extractor, dict): self.roi_extractor = RoIAlign(**roi_extractor) self.head = head self.in_channels = head.out_channels() self.mask_assigner = mask_assigner self.share_bbox_feat = share_bbox_feat self.bbox_head = None self.mask_fcn_logits = nn.Conv2D( in_channels=self.in_channels, out_channels=self.num_classes, kernel_size=1, weight_attr=paddle.ParamAttr(initializer=KaimingNormal( fan_in=self.num_classes))) self.mask_fcn_logits.skip_quant = True @classmethod def from_config(cls, cfg, input_shape): roi_pooler = cfg['roi_extractor'] assert isinstance(roi_pooler, dict) kwargs = RoIAlign.from_config(cfg, input_shape) roi_pooler.update(kwargs) kwargs = {'input_shape': input_shape} head = create(cfg['head'], **kwargs) return { 'roi_extractor': roi_pooler, 'head': head, } def get_loss(self, mask_logits, mask_label, mask_target, mask_weight): mask_label = F.one_hot(mask_label, self.num_classes).unsqueeze([2, 3]) mask_label = paddle.expand_as(mask_label, mask_logits) mask_label.stop_gradient = True mask_pred = paddle.gather_nd(mask_logits, paddle.nonzero(mask_label)) shape = mask_logits.shape mask_pred = paddle.reshape(mask_pred, [shape[0], shape[2], shape[3]]) mask_target = mask_target.cast('float32') mask_weight = mask_weight.unsqueeze([1, 2]) loss_mask = F.binary_cross_entropy_with_logits( mask_pred, mask_target, weight=mask_weight, reduction="mean") return loss_mask def forward_train(self, body_feats, rois, rois_num, inputs, targets, bbox_feat): """ body_feats (list[Tensor]): Multi-level backbone features rois (list[Tensor]): Proposals for each batch with shape [N, 4] rois_num (Tensor): The number of proposals for each batch inputs (dict): ground truth info """ tgt_labels, _, tgt_gt_inds = targets rois, rois_num, tgt_classes, tgt_masks, mask_index, tgt_weights = self.mask_assigner( rois, tgt_labels, tgt_gt_inds, inputs) if self.share_bbox_feat: rois_feat = paddle.gather(bbox_feat, mask_index) else: rois_feat = self.roi_extractor(body_feats, rois, rois_num) mask_feat = self.head(rois_feat) mask_logits = self.mask_fcn_logits(mask_feat) loss_mask = self.get_loss(mask_logits, tgt_classes, tgt_masks, tgt_weights) return {'loss_mask': loss_mask} def forward_test(self, body_feats, rois, rois_num, scale_factor, feat_func=None): """ body_feats (list[Tensor]): Multi-level backbone features rois (Tensor): Prediction from bbox head with shape [N, 6] rois_num (Tensor): The number of prediction for each batch scale_factor (Tensor): The scale factor from origin size to input size """ if not self.export_onnx and rois.shape[0] == 0: mask_out = paddle.full([1, 1, 1], -1) else: bbox = [rois[:, 2:]] labels = rois[:, 0].cast('int32') rois_feat = self.roi_extractor(body_feats, bbox, rois_num) if self.share_bbox_feat: assert feat_func is not None rois_feat = feat_func(rois_feat) mask_feat = self.head(rois_feat) mask_logit = self.mask_fcn_logits(mask_feat) if self.num_classes == 1: mask_out = F.sigmoid(mask_logit)[:, 0, :, :] else: num_masks = paddle.shape(mask_logit)[0] index = paddle.arange(num_masks).cast('int32') mask_out = mask_logit[index, labels] mask_out_shape = paddle.shape(mask_out) mask_out = paddle.reshape(mask_out, [ paddle.shape(index), mask_out_shape[-2], mask_out_shape[-1] ]) mask_out = F.sigmoid(mask_out) return mask_out def forward(self, body_feats, rois, rois_num, inputs, targets=None, bbox_feat=None, feat_func=None): if self.training: return self.forward_train(body_feats, rois, rois_num, inputs, targets, bbox_feat) else: im_scale = inputs['scale_factor'] return self.forward_test(body_feats, rois, rois_num, im_scale, feat_func)