zhanghang-cv

我设置的预训练权重确实是新版的init_cfg=dict(type='Pretrained',checkpoint='/root/factory/pre_trained/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth') 训练的时候可以正常创建检测器，但是测试就报错了，我的配置文件如下： ``` model = dict( type='SoftTeacher', model=dict( type='FasterRCNN', backbone=dict( type='SwinTransformer', embed_dims=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7, mlp_ratio=4, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.2, patch_norm=True, out_indices=(0, 1,...

> 我现在更换为原始的config，还是出现相同的错误 TypeError: SoftTeacher: __init__() got an unexpected keyword argument 'pretrained' ``` model = dict( type='SoftTeacher', model=dict( type='FasterRCNN', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=False), norm_eval=True,...


class DALILoader(): def __init__(self, batch_size, file_list, sequence_length, step, stride, crop_size, device_id, mode): if mode == 'train': self.pipeline = self._create_video_reader_pipeline_train(batch_size=batch_size, device_id=device_id, num_threads=8, file_list=file_list, sequence_length=sequence_length, step=step, stride=stride, crop_size=crop_size) else: self.pipeline = self._create_video_reader_pipeline_infer(batch_size=batch_size,...

Thank you for your reply. What was not stated before is that this task uses 8 pipelines for data loading, and the large memory usage is understandable. Below is an...

This situation is also possible. I want to make sure that after loading each batch of data, dali will save the data to memory for the next call?