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Functions1,193 in github.com/apple/ml-4m

Method__getattr__
(self, attr)
fourm/utils/logger.py:110
Method__getitem__
(self, index)
save_vq_tokens.py:120
Method__getitem__
(self, index)
fourm/utils/generation_datasets/image_caption_dataset.py:85
Method__getitem__
(self, index)
fourm/utils/generation_datasets/empty_dataset.py:23
Method__getitem__
(self, index)
fourm/utils/generation_datasets/parti_prompts_dataset.py:48
Method__getitem__
Args: index (int): Index Returns: tuple: (sample, target) where target is class_index of the target class.
fourm/data/multimodal_dataset_folder.py:173
Method__getitem__
Args: index (int): Index Returns: tuple: (sample, target) where target is class_index of the target class.
fourm/data/multimodal_dataset_folder.py:330
Method__getitem__
Retrieve the item at the given index. Args: index (int): The index of the item to be retrieved.
fourm/data/dataset_utils.py:30
Method__getitem__
Retrieve the item at the given index. Args: index (int): The index of the item to be retrieved.
fourm/data/dataset_utils.py:67
Method__init__
(self, root: str, tokens_dir: str, crop_settings_dir: st
save_vq_tokens.py:48
Method__init__
(self, fm: str = 'EPFL-VILAB/4M-21_XL_CC12M', fm_sr: Optional[str] = 'EPFL
fourm/demo_4M_sampler.py:226
Method__init__
(self, enabled=True)
fourm/utils/native_scaler.py:24
Method__init__
(self, window_size=20, fmt=None)
fourm/utils/logger.py:39
Method__init__
(self, delimiter="\t")
fourm/utils/logger.py:97
Method__init__
(self, args)
fourm/utils/logger.py:186
Method__init__
(self, values, is_beit3=False)
fourm/utils/optim_factory.py:83
Method__init__
(self, values, num_enc_layers, num_dec_layers, last_layer_mod_emb=False)
fourm/utils/optim_factory.py:97
Method__init__
(self, bpe_path: str = default_bpe())
fourm/utils/clip/simple_tokenizer.py:68
Method__init__
(self, inplanes, planes, stride=1)
fourm/utils/clip/model.py:18
Method__init__
(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None)
fourm/utils/clip/model.py:62
Method__init__
(self, layers, output_dim, heads, input_resolution=224, width=64)
fourm/utils/clip/model.py:108
Method__init__
(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None)
fourm/utils/clip/model.py:186
Method__init__
(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None)
fourm/utils/clip/model.py:210
Method__init__
(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int)
fourm/utils/clip/model.py:228
Method__init__
(self, name, prob=0.5, magnitude=10, hparams=None)
fourm/utils/timm/auto_augment.py:321
Method__init__
(self, policy)
fourm/utils/timm/auto_augment.py:508
Method__init__
(self, ops, num_layers=2, choice_weights=None)
fourm/utils/timm/auto_augment.py:625
Method__init__
(self, ops, alpha=1., width=3, depth=-1, blended=False)
fourm/utils/timm/auto_augment.py:728
Method__init__
(self, dtype=torch.float32)
fourm/utils/timm/transforms.py:28
Method__init__
(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation='bilinear')
fourm/utils/timm/transforms.py:79
Method__init__
(self, smoothing=0.1)
fourm/utils/timm/cross_entropy.py:20
Method__init__
(self, model, decay=0.9999, device=None, resume='')
fourm/utils/timm/model_ema.py:108
Method__init__
(self)
fourm/utils/timm/metrics.py:14
Method__init__
(self, mixup_alpha=1., cutmix_alpha=0., cutmix_minmax=None, prob=1.0, switch_prob=0.5, mode='
fourm/utils/timm/mixup.py:109
Method__init__
(self, model, hook_fn_locs, hook_fns)
fourm/utils/timm/model.py:64
Method__init__
( self, probability=0.5, min_area=0.02, max_area=1 / 3, min_aspect=0.3, max_aspect=Non
fourm/utils/timm/random_erasing.py:50
Method__init__
(self, root: str, augmenter: Optional[Callable] = None, m
fourm/utils/generation_datasets/image_caption_dataset.py:26
Method__init__
(self, dataset_size: int)
fourm/utils/generation_datasets/empty_dataset.py:20
Method__init__
(self, text_tokenizer, max_length=128, parti_prompts_csv='fourm/utils/generation_datasets/PartiPrompts.tsv', p
fourm/utils/generation_datasets/parti_prompts_dataset.py:29
Method__init__
Wrapper around the pyrender renderer to render SMPL meshes. Args: cfg (CfgNode): Model config file. faces (np
fourm/utils/hmr2_utils/hmr2/utils/renderer.py:146
Method__init__
(self, cfg, faces=None)
fourm/utils/hmr2_utils/hmr2/utils/mesh_renderer.py:51
Method__init__
Object used to render 3D keypoints. Faster for use during training. Args: cfg (CfgNode): Model config file.
fourm/utils/hmr2_utils/hmr2/utils/skeleton_renderer.py:18
Method__init__
Extension of the official SMPL implementation to support more joints. Args: Same as SMPLLayer. joint_regresso
fourm/utils/hmr2_utils/hmr2/models/smpl_wrapper.py:17
Method__init__
Setup HMR2 model Args: cfg (CfgNode): Config file as a yacs CfgNode
fourm/utils/hmr2_utils/hmr2/models/hmr2.py:19
Method__init__
(self, data_dim: int, norm_cond_dim: int)
fourm/utils/hmr2_utils/hmr2/models/components/t_cond_mlp.py:13
Method__init__
( self, input_dim: int, hidden_dim: int, num_hidden_layers: int, outpu
fourm/utils/hmr2_utils/hmr2/models/components/t_cond_mlp.py:110
Method__init__
(self, num_frequencies, max_freq_log2)
fourm/utils/hmr2_utils/hmr2/models/components/t_cond_mlp.py:186
Method__init__
(self, dim, hidden_dim, dropout=0.0)
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:46
Method__init__
(self, dim, heads=8, dim_head=64, dropout=0.0)
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:61
Method__init__
(self, dim, context_dim=None, heads=8, dim_head=64, dropout=0.0)
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:95
Method__init__
( self, dim: int, depth: int, heads: int, dim_head: int, mlp_d
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:133
Method__init__
( self, dim: int, depth: int, heads: int, dim_head: int, mlp_d
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:166
Method__init__
(self, p: float = 0.1)
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:210
Method__init__
(self, p: float = 0.1)
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:229
Method__init__
( self, num_tokens: int, token_dim: int, dim: int, depth: int,
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:247
Method__init__
( self, num_tokens: int, token_dim: int, dim: int, depth: int,
fourm/utils/hmr2_utils/hmr2/models/components/pose_transformer.py:307
Method__init__
(self, drop_prob=None)
fourm/utils/hmr2_utils/hmr2/models/backbones/vit.py:67
Method__init__
( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., a
fourm/utils/hmr2_utils/hmr2/models/backbones/vit.py:95
Method__init__
(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., d
fourm/utils/hmr2_utils/hmr2/models/backbones/vit.py:135
Method__init__
(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, ratio=1)
fourm/utils/hmr2_utils/hmr2/models/backbones/vit.py:162
Method__init__
(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768)
fourm/utils/hmr2_utils/hmr2/models/backbones/vit.py:188
Method__init__
(self, img_size=224, patch_size=16, in_chans=3, num_classes=80, embed_dim=768, depth=12,
fourm/utils/hmr2_utils/hmr2/models/backbones/vit.py:218
Method__init__
(self, cfg)
fourm/utils/hmr2_utils/hmr2/models/heads/smpl_head.py:26
Method__init__
(self, data_iters, weights, modality_info)
fourm/data/unified_datasets.py:525
Method__init__
( self, root: str, modalities: List[str], modality_paths: Dict
fourm/data/multimodal_dataset_folder.py:238
Method__init__
Performs masking on a dict of modalities (both image based and sequence based modalities) Args: modality_info: Dict with the moda
fourm/data/masking.py:132
Method__init__
Performs masking for transfer on a dict of modalities (both image based and sequence based modalities), by specifying which modalities are inp
fourm/data/masking.py:568
Method__init__
Unified data augmentation for FourM Args: transforms_dict (dict): Dict of transforms for each modality image_augmente
fourm/data/modality_transforms.py:68
Method__init__
(self, imagenet_default_mean_and_std=True, color_jitter=False, color_jitter_strength=0.5)
fourm/data/modality_transforms.py:200
Method__init__
(self, standardize_depth=True)
fourm/data/modality_transforms.py:247
Method__init__
(self, standardize_surface_normals=False)
fourm/data/modality_transforms.py:291
Method__init__
(self, scale_factor=1.0, shift_idx_by_one=False, id_mapping: Optional[Dict] = None, select_channel=None)
fourm/data/modality_transforms.py:329
Method__init__
(self, mask_size=64, max_instance_n=20, bbox_area_threshold=0.0005)
fourm/data/modality_transforms.py:386
Method__init__
(self, mask_pool_size=1)
fourm/data/modality_transforms.py:572
Method__init__
(self)
fourm/data/modality_transforms.py:604
Method__init__
(self, det_threshold=0.6, det_max_instances=None, bbox_order='dist_to_orig', coord_bins=1000, min_visibility=0
fourm/data/modality_transforms.py:627
Method__init__
(self, aligned_captions=True, no_aug=False)
fourm/data/modality_transforms.py:757
Method__init__
(self, aligned_captions=True, no_aug=False)
fourm/data/modality_transforms.py:798
Method__init__
Metadata transform that takes in a metadata dictionary and converts it into a string, or list of strings (for chunked span masking).
fourm/data/modality_transforms.py:845
Method__init__
(self, coord_bins=1000, only_pose=False, return_raw=False)
fourm/data/modality_transforms.py:1011
Method__init__
(self, coord_bins=1000, return_raw=False)
fourm/data/modality_transforms.py:1176
Method__init__
(self, image_size=224, points_per_side=7, point_order='random')
fourm/data/modality_transforms.py:1233
Method__init__
Dataset wrapper that repeats the original dataset n times. Args: original_dataset (torch.utils.data.Dataset): The origin
fourm/data/dataset_utils.py:19
Method__init__
Dataset wrapper that randomly subsamples the original dataset. Args: original_dataset (torch.utils.data.Dataset): The or
fourm/data/dataset_utils.py:51
Method__init__
(self, target_size=224, hflip=0.5, crop_scale=(0.2, 1.0), crop_ratio=(0.75, 1.3333), main_domain='rgb')
fourm/data/image_augmenter.py:34
Method__init__
(self, no_aug=True, main_domain='human_poses')
fourm/data/image_augmenter.py:63
Method__init__
(self, target_size, no_aug=False, main_domain='rgb')
fourm/data/image_augmenter.py:80
Method__init__
(self, target_size, hflip=0.0, main_domain='rgb')
fourm/data/image_augmenter.py:102
Method__init__
(self, target_size, hflip=0.0, main_domain='rgb')
fourm/data/image_augmenter.py:129
Method__init__
(self, target_size, hflip=0.0, scale=(0.1, 2.0), main_domain='rgb')
fourm/data/image_augmenter.py:149
Method__init__
(self)
fourm/data/image_augmenter.py:182
Method__init__
(self, dec_type: str = 'vit_b_dec', out_conv: bool = False,
fourm/vq/vqvae.py:412
Method__init__
(self, dec_type: str = 'unet_patched', num_train_timesteps: int = 1000,
fourm/vq/vqvae.py:539
Method__init__
(self, sd_path: str = "runwayml/stable-diffusion-v1-5", image_size_sd: Opt
fourm/vq/vqvae.py:790
Method__init__
( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end:
fourm/vq/scheduling/scheduling_pndm.py:118
Method__init__
( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end:
fourm/vq/scheduling/scheduling_ddpm.py:96
Method__init__
(self, model: torch.nn.Module, scheduler: SchedulerMixin)
fourm/vq/scheduling/diffusion_pipeline.py:47
Method__init__
( self, num_train_timesteps: int = 1000, beta_start: float = 0.0001, beta_end:
fourm/vq/scheduling/scheduling_ddim.py:103
Method__init__
(self, model_id: str, feature_ids: Union[str, List[str]],
fourm/vq/percept_losses/timm_perceptual_loss.py:41
Method__init__
(self)
fourm/vq/percept_losses/lpips.py:113
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