116.28,103.53],std=[58.395,57.12,57.375],to_rgb=True)# 裁剪大小crop_size=(512,512)train_pipeline=[dict(type='LoadImageFromFile'),dict(type='LoadAnnotations',reduce_zero_label=True),dict(type='Resize',img_scale
reduce_zero_label=False),dict(type='Resize',img_scale=scale,ratio_range=(0.5,2.0)),dict(type='RandomCrop',crop_size=crop_size,cat_max_ratio=0.75),dict(type='RandomFlip',prob=0.5),dict(type='PhotoMetricDistortion
/home/lyk/lyk/mmsegmentation-1.0.0rc6/mmseg/datasets/transforms/loading.py:78: UserWarning:reduce_zero_labelwill be deprecated, if you would like to ignore the zero label, please setreduce_zero_label=Truewhen dataset initialized warnings.warn('reduce_zero_labelwill be deprecated, ' Traceback (mo...
seg_map_suffix='.png', reduce_zero_label=False, **kwargs) -> None: super().__init__( img_suffix=img_suffix, seg_map_suffix=seg_map_suffix, reduce_zero_label=reduce_zero_label, **kwargs) assert fileio.exists( self.data_prefix['img_path'], backend_args=self.backend_args) mmsegme...
for logit, label in zip(logits, labels) ] loss = weight_reduce_loss( torch.stack(loss), None, reduction, avg_factor) else: loss = lovasz_hinge_flat( *flatten_binary_logits(logits, labels, ignore_index)) return loss def lovasz_softmax_flat(probs, labels, classes='present'...
reduce_zero_label 参数 其他问题 openmmlab教程3-MMSeg 使用 3. MMSeg 使用 需要基础,首先得跑过一个完整的语义分割的网络模型,这样子学起来比较轻松 准备数据集 dataset 数据增强 准备网络模型model 准备训练train 设置优化器sgd,adawm 设置学习策略poly,step,cosine 设置损失函数CrossEntropy 设置评价指标miou 3.1...
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/__init__.py中添加声明 ...
dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict( type='Norm...
label_map (dict): Mapping old labels to new labels. Default: dict(). reduce_zero_label (bool): Whether ignore zero label. Default: False. Returns: ndarray: The intersection of prediction and ground truth histogram on all classes. ndarray: The union of prediction and ground truth histogram ...
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