Functions — github.com/NVlabs/prismer

Method__getitem__

(self, index)

experts/segmentation/generate_dataset.py:29

Method__getitem__

(self, index)

experts/normal/generate_dataset.py:27

Method__getitem__

(self, index)

experts/obj_detection/generate_dataset.py:29

Method__init__

(self, config, train=True)

dataset/caption_dataset.py:16

Method__init__

(self, resize_resolution=384, scale_size=[0.5, 1.0], train=False)

dataset/utils.py:24

Method__init__

(self, alphastd, eigval, eigvec)

dataset/randaugment.py:210

Method__init__

(self, length)

dataset/randaugment.py:232

Method__init__

(self, n, m)

dataset/randaugment.py:254

Method__init__

(self, config)

dataset/pretrain_dataset.py:14

Method__init__

(self, config, train=True)

dataset/vqa_dataset.py:12

Method__init__

(self, config, train)

dataset/classification_dataset.py:13

Method__init__

(self, config)

model/prismer.py:16

Method__init__

(self, d_model: int, n_head: int)

model/modules/vit.py:38

Method__init__

(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, experts: dict)

model/modules/vit.py:79

Method__init__

(self, embed_dim: int, norm_late=False)

model/modules/utils.py:49

Method__init__

(self, config)

model/modules/roberta.py:49

Method__init__

(self, config, is_cross_attention=False)

model/modules/roberta.py:80

Method__init__

(self, config)

model/modules/roberta.py:130

Method__init__

(self, config, is_cross_attention=False)

model/modules/roberta.py:144

Method__init__

(self, config)

model/modules/roberta.py:161

Method__init__

(self, config)

model/modules/roberta.py:173

Method__init__

(self, config)

model/modules/roberta.py:202

Method__init__

(self, config)

model/modules/roberta.py:279

Method__init__

(self, config)

model/modules/roberta.py:341

Method__init__

(self, config)

model/modules/roberta.py:412

Method__init__

(self, d_model: int, n_heads: int)

model/modules/resampler.py:16

Method__init__

(self, data_path, transform)

experts/ocr_detection/generate_dataset.py:17

Method__init__

(self, in_channels, bottleneck_channels, dilation=1)

experts/ocr_detection/charnet/modeling/model.py:43

Method__init__

(self, in_channels, bottleneck_channels, curved_text_on=False)

experts/ocr_detection/charnet/modeling/model.py:71

Method__init__

(self, in_channels, bottleneck_channels, num_classes)

experts/ocr_detection/charnet/modeling/model.py:97

Method__init__

( self, word_min_score, word_stride, word_nms_iou_thresh, char_stride, cha

experts/ocr_detection/charnet/modeling/postprocessing.py:48

Method__init__

(self, in_channels_list, out_channels)

experts/ocr_detection/charnet/modeling/backbone/decoder.py:14

Method__init__

( self, block_module, stages, num_groups=1, width_per_group=64,

experts/ocr_detection/charnet/modeling/backbone/resnet.py:147

Method__init__

( self, in_channels, bottleneck_channels, out_channels, num_groups,

experts/ocr_detection/charnet/modeling/backbone/resnet.py:225

Method__init__

(self, out_channels, norm_func)

experts/ocr_detection/charnet/modeling/backbone/resnet.py:315

Method__init__

( self, in_channels, bottleneck_channels, out_channels, num_groups=1,

experts/ocr_detection/charnet/modeling/backbone/resnet.py:335

Method__init__

(self, out_channels)

experts/ocr_detection/charnet/modeling/backbone/resnet.py:358

Method__init__

(self, n, channels, blocks)

experts/ocr_detection/charnet/modeling/backbone/hourglass.py:63

Method__init__

(self, n, channels, blocks)

experts/ocr_detection/charnet/modeling/backbone/hourglass.py:83

Method__init__

(self, init_value=1.0)

experts/ocr_detection/charnet/modeling/layers/scale.py:13

Method__init__

(self, in_ch, out_ch)

experts/edge/model.py:31

Method__init__

(self, input_features, out_features)

experts/edge/model.py:54

Method__init__

(self, num_layers, input_features, out_features)

experts/edge/model.py:77

Method__init__

(self, in_features, up_scale)

experts/edge/model.py:86

Method__init__

(self, in_features, out_features, stride, use_bs=True )

experts/edge/model.py:117

Method__init__

(self, in_features, mid_features, out_features=None, stride=1,

experts/edge/model.py:134

Method__init__

(self, data_path, transform)

experts/edge/generate_dataset.py:17

Method__init__

(self, start_index=1)

experts/depth/vit.py:67

Method__init__

(self, in_features, start_index=1)

experts/depth/vit.py:80

Method__init__

(self, dim0, dim1)

experts/depth/vit.py:94

Method__init__

Init. Args: features (int): number of features

experts/depth/blocks.py:178

Method__init__

Init. Args: features (int): number of features

experts/depth/blocks.py:216

Method__init__

Init. Args: features (int): number of features

experts/depth/blocks.py:250

Method__init__

Init. Args: features (int): number of features

experts/depth/blocks.py:321

Method__init__

( self, path=None, non_negative=True, scale=1.0, shift=0.0, invert=False, **kwargs )

experts/depth/models.py:90

Method__init__

(self, data_path, transform)

experts/depth/generate_dataset.py:17

Method__init__

(self, data_path, transform)

experts/segmentation/generate_dataset.py:19

Method__init__

Args: cfg (CfgNode): model (SemanticSegmentor): a SemanticSegmentor to apply TTA on. tta_mapper (callable

experts/segmentation/mask2former/test_time_augmentation.py:27

Method__init__

Args: backbone: a backbone module, must follow detectron2's backbone interface sem_seg_head: a module that predicts s

experts/segmentation/mask2former/maskformer_model.py:27

Method__init__

Creates the matcher Params: cost_class: This is the relative weight of the classification error in the matching cost

experts/segmentation/mask2former/modeling/matcher.py:78

Method__init__

Create the criterion. Parameters: num_classes: number of object categories, omitting the special no-object category ma

experts/segmentation/mask2former/modeling/criterion.py:97

Method__init__

(self, encoder_layer, num_layers, norm=None)

experts/segmentation/mask2former/modeling/transformer_decoder/transformer.py:79

Method__init__

(self, decoder_layer, num_layers, norm=None, return_intermediate=False)

experts/segmentation/mask2former/modeling/transformer_decoder/transformer.py:106

Method__init__

( self, d_model, nhead, dim_feedforward=2048, dropout=0.1, act

experts/segmentation/mask2former/modeling/transformer_decoder/transformer.py:155

Method__init__

( self, d_model, nhead, dim_feedforward=2048, dropout=0.1, act

experts/segmentation/mask2former/modeling/transformer_decoder/transformer.py:231

Method__init__

NOTE: this interface is experimental. Args: in_channels: channels of the input features mask_classification:

experts/segmentation/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py:33

Method__init__

(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None)

experts/segmentation/mask2former/modeling/transformer_decoder/position_encoding.py:18

Method__init__

(self, d_model, nhead, dropout=0.0, activation="relu", normalize_before=False)

experts/segmentation/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py:19

Method__init__

(self, d_model, nhead, dropout=0.0, activation="relu", normalize_before=False)

experts/segmentation/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py:77

Method__init__

(self, d_model, dim_feedforward=2048, dropout=0.0, activation="relu", normalize_before=False)

experts/segmentation/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py:140

Method__init__

NOTE: this interface is experimental. Args: in_channels: channels of the input features mask_classification:

experts/segmentation/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py:236

Method__init__

( self, dim, window_size, num_heads, qkv_bias=True, qk_scale=N

experts/segmentation/mask2former/modeling/backbone/swin.py:87

Method__init__

( self, dim, num_heads, window_size=7, shift_size=0, mlp_ratio

experts/segmentation/mask2former/modeling/backbone/swin.py:191

Method__init__

(self, dim, norm_layer=nn.LayerNorm)

experts/segmentation/mask2former/modeling/backbone/swin.py:305

Method__init__

( self, dim, depth, num_heads, window_size=7, mlp_ratio=4.0,

experts/segmentation/mask2former/modeling/backbone/swin.py:358

Method__init__

(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None)

experts/segmentation/mask2former/modeling/backbone/swin.py:465

Method__init__

( self, pretrain_img_size=224, patch_size=4, in_chans=3, embed_dim=96,

experts/segmentation/mask2former/modeling/backbone/swin.py:526

Method__init__

(self, cfg, input_shape)

experts/segmentation/mask2former/modeling/backbone/swin.py:688

Method__init__

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features n

experts/segmentation/mask2former/modeling/meta_arch/mask_former_head.py:48

Method__init__

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features t

experts/segmentation/mask2former/modeling/meta_arch/per_pixel_baseline.py:153

Method__init__

( self, d_model=512, nhead=8, num_encoder_layers=6, dim_feedforward=20

experts/segmentation/mask2former/modeling/pixel_decoder/fpn.py:163

Method__init__

NOTE: this interface is experimental. Args: input_shape: shapes (channels and stride) of the input features t

experts/segmentation/mask2former/modeling/pixel_decoder/fpn.py:207

Method__init__

(self, d_model=256, nhead=8, num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,

experts/segmentation/mask2former/modeling/pixel_decoder/msdeformattn.py:24

Method__init__

(self, d_model=256, d_ffn=1024, dropout=0.1, activation="relu",

experts/segmentation/mask2former/modeling/pixel_decoder/msdeformattn.py:93

Method__init__

(self, encoder_layer, num_layers)

experts/segmentation/mask2former/modeling/pixel_decoder/msdeformattn.py:135

Method__init__

Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

experts/segmentation/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py:35

Method__init__

(self, tensors, mask: Optional[Tensor])

experts/segmentation/mask2former/utils/misc.py:26

Method__init__