(1, 2, 3, 6), channels=512, dropout_ratio=0.1, # 使用的是 pascal voc 数据集,分类数目为21 num_classes=21, norm_cfg=norm_cfg, align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), # model training and testing settings train_cfg=dict...
根据自定义数据集的特点,可能需要调整模型的某些参数,如输出类别数(num_classes)。 在模型的配置文件中,修改decode_head和auxiliary_head(如果存在)中的num_classes参数,使其与自定义数据集中的类别数一致。 使用MMSegmentation进行训练: 运行训练脚本(tools/train.py),并指定自定义的模型配置文件和数据集配置文件。
norm_cfg = dict(type='BN', requires_grad=True) # 模型是预训练 好的 model = dict( type='EncoderDecoder', pretrained='open-mmlab://resnet50_v1c', backbone=dict( type='ResNetV1c', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), dilations=(1, 1, 2, 4), strides=(1, 2...
seg_map_suffix='.png', # 默认标注的后缀为‘.png’,根据数据修改 reduce_zero_label=False, # 管理0值标签,例:标签值为[0,1,2],当False时,num_classes=3,当True,num_classes=2 **kwargs) assert osp.exists(self.img_dir) # assert self.file_client.exists(self.img_dir) 在./mmseg/datasets/...
onehot_labels=seg_label.new_zeros((batch_size,num_classes)) foriinrange(batch_size): hist=seg_label[i].float().histc( bins=num_classes,min=0,max=num_classes-1) onehot_labels[i]=hist>0 returnonehot_labels defloss_by_feat(self,seg_logit:Tuple[Tensor], ...
#include <string> #include <vector> #include <chrono> #include <iostream> #include "mmdeploy/segmentor.h" using namespace std; vector<cv::Vec3b> gen_palette(int num_classes) { std::mt19937 gen; std::uniform_int_distribution<ushort> uniform_dist(0, 255); vector<cv::Vec3b> palette;...
size(1) probs = probs.view(batch_size, num_classes, -1) feats = feats.view(batch_size, channels, -1) # [batch_size, height*width, num_classes] feats = feats.permute(0, 2, 1) # [batch_size, channels, height*width] probs = F.softmax(self.scale * probs, dim=2) ...
num_classes=6, pool_scales=( 1, 2, 3, 6, ), type='PSPHead'), pretrained='open-mmlab://resnet50_v1c', test_cfg=dict(mode='whole'), train_cfg=dict(), type='EncoderDecoder') norm_cfg = dict(requires_grad=True, type='BN') ...
(config_file,checkpoint_file,device='cuda:0')img=mmcv.imread(img,'color')result=inference_segmentor(model,img)gt_semantic_seg=mmcv.imread(gt_semantic_seg,'unchanged')gt_semantic_seg=gt_semantic_seg-1iou=mean_iou(result[0],gt_semantic_seg,num_classes,ignore_index)mIoU=np.nanmean(iou['IoU...
num_classes=19,# 分割前景的种类数目。 通常情况下,cityscapes 为19,VOC为21,ADE20k 为150。norm_cfg=dict(type='SyncBN',requires_grad=True),# 归一化层的配置项。align_corners=False,# 解码里调整大小(resize)的 align_corners 参数。loss_decode=dict(# 解码头(decode_head)里的损失函数的配置项。