(self, dataloader, interval=1, gpu_collect=False,

(self, split, **kwargs)

(self, **kwargs)

(self, pipeline, img_dir, img_suffix='.jpg',

(self, **kwargs)

(self, datasets)

(self, dataset, times)

(self, split, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, transforms, img_scale, img_ratios=None,

(self, img_scale=None, multiscale_mode='range', ratio_range

(self, img_scale=None, multiscale_mode='range', ratio_range

(self, prob=None, direction='horizontal')

(self, size=None, size_divisor=None, pad_val=0,

(self, mean, std, to_rgb=True)

(self, min_value=0, max_value=255)

(self, clip_limit=40.0, tile_grid_size=(8, 8))

(self, crop_size, cat_max_ratio=1., ignore_index=255)

(self, crop_size, ignore_index=255)

(self, prob, degree, pad_val=0, seg_pad_va

(self, out_channels=None, weights=(0.299, 0.587, 0.114))

(self, gamma=1.0)

(self)

(self, scale_factor=1)

(self, brightness_delta=32, contrast_range=(0.5, 1.5), satu

(self, to_float32=False, color_type='color', file_client_ar

(self, reduce_zero_label=False, file_client_args=dict(backend='disk'),

(self, transforms)

(self, keys)

(self, keys)

(self, keys, order)

(self, fields=(dict(key='img', stack=True), dict(key='gt_semant

(self, keys, meta_keys=('filename', 'ori_filename', 'ori_shape',

(self)

(self, backbone, decode_head, neck=None, a

(self, num_stages, backbone, decode_head,

(self, in_channels, out_channels, num_outs,

(self, radix, groups)

(self, in_channels, channels, kernel_size,

(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1)

(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., dr

(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768)

(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],

(self, dim=768)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, **kwargs)

(self, in_channels, out_channels, num_convs=2,

(self, in_channels, out_channels, with_cp=False,

(self, in_channels, out_channels, with_cp=False,

(self, channel, reduction=16, with_cp=False)

(self, in_channels, out_channels, dilation=2,

(self, num_downsampling)

(self, arch='small', conv_cfg=None, norm_cfg=dict(type='BN'

(self, inplanes, planes, groups=1, base_wi

(self, num_branches, blocks, num_blocks, i

(self, in_channels, dw_channels, out_channels,

(self, in_channels=64, block_channels=(64, 96, 128), out_ch

(self, higher_in_channels, lower_in_channels, out_channels,

(self, widen_factor=1., strides=(1, 2, 2, 2, 1, 2, 1), dila

(self, inplanes, planes, stride=1, dilatio

(self, inplanes, planes, stride=1, dilatio

(self, **kwargs)

(self, **kwargs)

(self, drop_prob=0.1, block_size=7, gamma_scale=1.0,

(self, key_in_channels, query_in_channels, channels, out_channels, share_key_query, query_dow

(self, channels, ratio=16, conv_cfg=None,

(self, conv_block, in_channels, skip_channels,

(self, block, inplanes, planes, num_blocks

(self, in_channels, out_channels, mid_channels,

(self, in_channels, channels, *, num_class

(self, **kwargs)

(self, pool_scales=(1, 2, 3, 6), **kwargs)

(self, feature_strides, **kwargs)

(self, ratio=1 / 4., pooling_type='att', fusion_types=('cha

(self, **kwargs)

(self, feature_strides, **kwargs)

(self, dilations, in_channels, channels, conv_cfg, norm_cfg, act_cfg)

(self, recurrence=2, **kwargs)

(self, *arg, temperature, **kwargs)

(self, pool_scales=(1, 2, 3, 6), **kwargs)

(self, num_fcs=3, coarse_pred_each_layer=True, conv_cfg=dic

(self, in_channels, num_codes, conv_cfg, norm_cfg, act_cfg)

(self, scale)

(self, in_channels, channels, scale, conv_cfg, norm_cfg, act_cfg)

(self, num_convs=2, kernel_size=3, concat_input=True,

(self, *args, **kwargs)

(self, pool_scales=(1, 3, 6, 8))

(self, low_in_channels, high_in_channels, channels, out_channels, share_key_query, query_scal

(self, in_channels, channels, out_channels, query_scales, key_pool_scales, conv_cfg, norm_cfg

(self, project_channels, query_scales=(1, ), key_pool_scale