Spaces:
Sleeping
Sleeping
| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # All rights reserved. | |
| # This source code is licensed under the license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| # -------------------------------------------------------- | |
| # References: | |
| # GLIDE: https://github.com/openai/glide-text2im | |
| # MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py | |
| # -------------------------------------------------------- | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| import math | |
| from timm.models.vision_transformer import PatchEmbed, Mlp | |
| from timm.models.layers import trunc_normal_ | |
| import math | |
| def modulate(x, shift, scale): | |
| return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) | |
| class Attention(nn.Module): | |
| def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., num_patches=None): | |
| super().__init__() | |
| self.num_heads = num_heads | |
| head_dim = dim // num_heads | |
| # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights | |
| self.scale = qk_scale or head_dim ** -0.5 | |
| self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) | |
| self.attn_drop = nn.Dropout(attn_drop) | |
| self.proj = nn.Linear(dim, dim) | |
| self.proj_drop = nn.Dropout(proj_drop) | |
| self.rel_pos_bias = RelativePositionBias( | |
| window_size=[int(num_patches**0.5), int(num_patches**0.5)], num_heads=num_heads) | |
| def get_masked_rel_bias(self, B, ids_keep): | |
| # get masked rel_pos_bias | |
| rel_pos_bias = self.rel_pos_bias() | |
| rel_pos_bias = rel_pos_bias.unsqueeze(dim=0).repeat(B, 1, 1, 1) | |
| rel_pos_bias_masked = torch.gather( | |
| rel_pos_bias, dim=2, index=ids_keep.unsqueeze(dim=1).unsqueeze(dim=-1).repeat(1, rel_pos_bias.shape[1], 1, rel_pos_bias.shape[-1])) | |
| rel_pos_bias_masked = torch.gather( | |
| rel_pos_bias_masked, dim=3, index=ids_keep.unsqueeze(dim=1).unsqueeze(dim=2).repeat(1, rel_pos_bias.shape[1], ids_keep.shape[1], 1)) | |
| return rel_pos_bias_masked | |
| def forward(self, x, ids_keep=None): | |
| B, N, C = x.shape | |
| qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // | |
| self.num_heads).permute(2, 0, 3, 1, 4) | |
| # make torchscript happy (cannot use tensor as tuple) | |
| q, k, v = qkv[0], qkv[1], qkv[2] | |
| attn = (q @ k.transpose(-2, -1)) * self.scale | |
| if ids_keep is not None: | |
| rp_bias = self.get_masked_rel_bias(B, ids_keep) | |
| else: | |
| rp_bias = self.rel_pos_bias() | |
| attn += rp_bias | |
| attn = attn.softmax(dim=-1) | |
| attn = self.attn_drop(attn) | |
| x = (attn @ v).transpose(1, 2).reshape(B, N, C) | |
| x = self.proj(x) | |
| x = self.proj_drop(x) | |
| return x | |
| class RelativePositionBias(nn.Module): | |
| # https://github.com/microsoft/unilm/blob/master/beit/modeling_finetune.py | |
| def __init__(self, window_size, num_heads): | |
| super().__init__() | |
| self.window_size = window_size | |
| self.num_relative_distance = ( | |
| 2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 | |
| self.relative_position_bias_table = nn.Parameter( | |
| torch.zeros(self.num_relative_distance, num_heads)) | |
| # get pair-wise relative position index for each token inside the window | |
| coords_h = torch.arange(window_size[0]) | |
| coords_w = torch.arange(window_size[1]) | |
| coords = torch.stack(torch.meshgrid([coords_h, coords_w])) | |
| coords_flatten = torch.flatten(coords, 1) | |
| relative_coords = coords_flatten[:, :, None] - \ | |
| coords_flatten[:, None, :] | |
| relative_coords = relative_coords.permute( | |
| 1, 2, 0).contiguous() | |
| relative_coords[:, :, 0] += window_size[0] - 1 | |
| relative_coords[:, :, 1] += window_size[1] - 1 | |
| relative_coords[:, :, 0] *= 2 * window_size[1] - 1 | |
| relative_position_index = \ | |
| torch.zeros( | |
| size=(window_size[0] * window_size[1],) * 2, dtype=relative_coords.dtype) | |
| relative_position_index = relative_coords.sum(-1) | |
| self.register_buffer("relative_position_index", | |
| relative_position_index) | |
| trunc_normal_(self.relative_position_bias_table, std=.02) | |
| def forward(self): | |
| relative_position_bias = \ | |
| self.relative_position_bias_table[self.relative_position_index.view(-1)].view( | |
| self.window_size[0] * self.window_size[1], | |
| self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH | |
| # nH, Wh*Ww, Wh*Ww | |
| return relative_position_bias.permute(2, 0, 1).contiguous() | |
| ################################################################################# | |
| # Embedding Layers for Timesteps and Class Labels # | |
| ################################################################################# | |
| class TimestepEmbedder(nn.Module): | |
| """ | |
| Embeds scalar timesteps into vector representations. | |
| """ | |
| def __init__(self, hidden_size, frequency_embedding_size=256): | |
| super().__init__() | |
| self.mlp = nn.Sequential( | |
| nn.Linear(frequency_embedding_size, hidden_size, bias=True), | |
| nn.SiLU(), | |
| nn.Linear(hidden_size, hidden_size, bias=True), | |
| ) | |
| self.frequency_embedding_size = frequency_embedding_size | |
| def timestep_embedding(t, dim, max_period=10000): | |
| """ | |
| Create sinusoidal timestep embeddings. | |
| :param t: a 1-D Tensor of N indices, one per batch element. | |
| These may be fractional. | |
| :param dim: the dimension of the output. | |
| :param max_period: controls the minimum frequency of the embeddings. | |
| :return: an (N, D) Tensor of positional embeddings. | |
| """ | |
| # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py | |
| half = dim // 2 | |
| freqs = torch.exp( | |
| -math.log(max_period) * torch.arange(start=0, | |
| end=half, dtype=torch.float32) / half | |
| ).to(device=t.device) | |
| args = t[:, None].float() * freqs[None] | |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) | |
| if dim % 2: | |
| embedding = torch.cat( | |
| [embedding, torch.zeros_like(embedding[:, :1])], dim=-1) | |
| return embedding | |
| def forward(self, t): | |
| t_freq = self.timestep_embedding(t, self.frequency_embedding_size) | |
| t_emb = self.mlp(t_freq) | |
| return t_emb | |
| class LabelEmbedder(nn.Module): | |
| """ | |
| Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. | |
| """ | |
| def __init__(self, num_classes, hidden_size, dropout_prob): | |
| super().__init__() | |
| use_cfg_embedding = dropout_prob > 0 | |
| self.embedding_table = nn.Embedding( | |
| num_classes + use_cfg_embedding, hidden_size) | |
| self.num_classes = num_classes | |
| self.dropout_prob = dropout_prob | |
| def token_drop(self, labels, force_drop_ids=None): | |
| """ | |
| Drops labels to enable classifier-free guidance. | |
| """ | |
| if force_drop_ids is None: | |
| drop_ids = torch.rand(labels.shape[0]) < self.dropout_prob | |
| else: | |
| drop_ids = force_drop_ids == 1 | |
| labels = torch.where(drop_ids.to(labels.device), | |
| self.num_classes, labels) | |
| return labels | |
| def forward(self, labels, train, force_drop_ids=None): | |
| use_dropout = self.dropout_prob > 0 | |
| if (train and use_dropout) or (force_drop_ids is not None): | |
| labels = self.token_drop(labels, force_drop_ids) | |
| embeddings = self.embedding_table(labels) | |
| return embeddings | |
| ################################################################################# | |
| # Core MDT Model # | |
| ################################################################################# | |
| class MDTBlock(nn.Module): | |
| """ | |
| A MDT block with adaptive layer norm zero (adaLN-Zero) conMDTioning. | |
| """ | |
| def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs): | |
| super().__init__() | |
| self.norm1 = nn.LayerNorm( | |
| hidden_size, elementwise_affine=False, eps=1e-6) | |
| self.attn = Attention( | |
| hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) | |
| self.norm2 = nn.LayerNorm( | |
| hidden_size, elementwise_affine=False, eps=1e-6) | |
| mlp_hidden_dim = int(hidden_size * mlp_ratio) | |
| def approx_gelu(): return nn.GELU(approximate="tanh") | |
| self.mlp = Mlp(in_features=hidden_size, | |
| hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) | |
| self.adaLN_modulation = nn.Sequential( | |
| nn.SiLU(), | |
| nn.Linear(hidden_size, 6 * hidden_size, bias=True) | |
| ) | |
| def forward(self, x, c, ids_keep=None): | |
| shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation( | |
| c).chunk(6, dim=1) | |
| x = x + gate_msa.unsqueeze(1) * self.attn( | |
| modulate(self.norm1(x), shift_msa, scale_msa), ids_keep=ids_keep) | |
| x = x + \ | |
| gate_mlp.unsqueeze( | |
| 1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) | |
| return x | |
| class FinalLayer(nn.Module): | |
| """ | |
| The final layer of MDT. | |
| """ | |
| def __init__(self, hidden_size, patch_size, out_channels): | |
| super().__init__() | |
| self.norm_final = nn.LayerNorm( | |
| hidden_size, elementwise_affine=False, eps=1e-6) | |
| self.linear = nn.Linear( | |
| hidden_size, patch_size * patch_size * out_channels, bias=True) | |
| self.adaLN_modulation = nn.Sequential( | |
| nn.SiLU(), | |
| nn.Linear(hidden_size, 2 * hidden_size, bias=True) | |
| ) | |
| def forward(self, x, c): | |
| shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) | |
| x = modulate(self.norm_final(x), shift, scale) | |
| x = self.linear(x) | |
| return x | |
| class MDT(nn.Module): | |
| """ | |
| Diffusion model with a Transformer backbone. | |
| """ | |
| def __init__( | |
| self, | |
| input_size=32, | |
| patch_size=2, | |
| in_channels=4, | |
| hidden_size=1152, | |
| depth=28, | |
| num_heads=16, | |
| mlp_ratio=4.0, | |
| class_dropout_prob=0.1, | |
| num_classes=1000, | |
| learn_sigma=True, | |
| mask_ratio=None, | |
| decode_layer=None, | |
| ): | |
| super().__init__() | |
| self.learn_sigma = learn_sigma | |
| self.in_channels = in_channels | |
| self.out_channels = in_channels * 2 if learn_sigma else in_channels | |
| self.patch_size = patch_size | |
| self.num_heads = num_heads | |
| self.x_embedder = PatchEmbed( | |
| input_size, patch_size, in_channels, hidden_size, bias=True) | |
| self.t_embedder = TimestepEmbedder(hidden_size) | |
| self.y_embedder = LabelEmbedder( | |
| num_classes, hidden_size, class_dropout_prob) | |
| num_patches = self.x_embedder.num_patches | |
| # Will use learnbale sin-cos embedding: | |
| self.pos_embed = nn.Parameter(torch.zeros( | |
| 1, num_patches, hidden_size), requires_grad=True) | |
| self.blocks = nn.ModuleList([ | |
| MDTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, num_patches=num_patches) for _ in range(depth) | |
| ]) | |
| self.sideblocks = nn.ModuleList([ | |
| MDTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, num_patches=num_patches) for _ in range(1) | |
| ]) | |
| self.final_layer = FinalLayer( | |
| hidden_size, patch_size, self.out_channels) | |
| self.decoder_pos_embed = nn.Parameter(torch.zeros( | |
| 1, num_patches, hidden_size), requires_grad=True) | |
| if mask_ratio is not None: | |
| self.mask_token = nn.Parameter(torch.zeros(1, 1, hidden_size)) | |
| self.mask_ratio = float(mask_ratio) | |
| self.decode_layer = int(decode_layer) | |
| else: | |
| self.mask_token = nn.Parameter(torch.zeros( | |
| 1, 1, hidden_size), requires_grad=False) | |
| self.mask_ratio = None | |
| self.decode_layer = int(decode_layer) | |
| print("mask ratio:", self.mask_ratio, | |
| "decode_layer:", self.decode_layer) | |
| self.initialize_weights() | |
| def initialize_weights(self): | |
| # Initialize transformer layers: | |
| def _basic_init(module): | |
| if isinstance(module, nn.Linear): | |
| torch.nn.init.xavier_uniform_(module.weight) | |
| if module.bias is not None: | |
| nn.init.constant_(module.bias, 0) | |
| self.apply(_basic_init) | |
| # Initialize pos_embed by sin-cos embedding: | |
| pos_embed = get_2d_sincos_pos_embed( | |
| self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5)) | |
| self.pos_embed.data.copy_( | |
| torch.from_numpy(pos_embed).float().unsqueeze(0)) | |
| decoder_pos_embed = get_2d_sincos_pos_embed( | |
| self.decoder_pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5)) | |
| self.decoder_pos_embed.data.copy_( | |
| torch.from_numpy(decoder_pos_embed).float().unsqueeze(0)) | |
| # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): | |
| w = self.x_embedder.proj.weight.data | |
| nn.init.xavier_uniform_(w.view([w.shape[0], -1])) | |
| nn.init.constant_(self.x_embedder.proj.bias, 0) | |
| # Initialize label embedding table: | |
| nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02) | |
| # Initialize timestep embedding MLP: | |
| nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) | |
| nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) | |
| # Zero-out adaLN modulation layers in MDT blocks: | |
| for block in self.blocks: | |
| nn.init.constant_(block.adaLN_modulation[-1].weight, 0) | |
| nn.init.constant_(block.adaLN_modulation[-1].bias, 0) | |
| for block in self.sideblocks: | |
| nn.init.constant_(block.adaLN_modulation[-1].weight, 0) | |
| nn.init.constant_(block.adaLN_modulation[-1].bias, 0) | |
| # Zero-out output layers: | |
| nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) | |
| nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) | |
| nn.init.constant_(self.final_layer.linear.weight, 0) | |
| nn.init.constant_(self.final_layer.linear.bias, 0) | |
| if self.mask_ratio is not None: | |
| torch.nn.init.normal_(self.mask_token, std=.02) | |
| def unpatchify(self, x): | |
| """ | |
| x: (N, T, patch_size**2 * C) | |
| imgs: (N, H, W, C) | |
| """ | |
| c = self.out_channels | |
| p = self.x_embedder.patch_size[0] | |
| h = w = int(x.shape[1] ** 0.5) | |
| assert h * w == x.shape[1] | |
| x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) | |
| x = torch.einsum('nhwpqc->nchpwq', x) | |
| imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p)) | |
| return imgs | |
| def random_masking(self, x, mask_ratio): | |
| """ | |
| Perform per-sample random masking by per-sample shuffling. | |
| Per-sample shuffling is done by argsort random noise. | |
| x: [N, L, D], sequence | |
| """ | |
| N, L, D = x.shape # batch, length, dim | |
| len_keep = int(L * (1 - mask_ratio)) | |
| noise = torch.rand(N, L, device=x.device) # noise in [0, 1] | |
| # sort noise for each sample | |
| # ascend: small is keep, large is remove | |
| ids_shuffle = torch.argsort(noise, dim=1) | |
| ids_restore = torch.argsort(ids_shuffle, dim=1) | |
| # keep the first subset | |
| ids_keep = ids_shuffle[:, :len_keep] | |
| x_masked = torch.gather( | |
| x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D)) | |
| # generate the binary mask: 0 is keep, 1 is remove | |
| mask = torch.ones([N, L], device=x.device) | |
| mask[:, :len_keep] = 0 | |
| # unshuffle to get the binary mask | |
| mask = torch.gather(mask, dim=1, index=ids_restore) | |
| return x_masked, mask, ids_restore, ids_keep | |
| def forward_side_interpolater(self, x, c, mask, ids_restore): | |
| # append mask tokens to sequence | |
| mask_tokens = self.mask_token.repeat( | |
| x.shape[0], ids_restore.shape[1] - x.shape[1], 1) | |
| x_ = torch.cat([x, mask_tokens], dim=1) | |
| x = torch.gather( | |
| x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle | |
| # add pos embed | |
| x = x + self.decoder_pos_embed | |
| # pass to the basic block | |
| x_before = x | |
| for sideblock in self.sideblocks: | |
| x = sideblock(x, c, ids_keep=None) | |
| # masked shortcut | |
| mask = mask.unsqueeze(dim=-1) | |
| x = x*mask + (1-mask)*x_before | |
| return x | |
| def forward(self, x, t, y, enable_mask=False): | |
| """ | |
| Forward pass of MDT. | |
| x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) | |
| t: (N,) tensor of diffusion timesteps | |
| y: (N,) tensor of class labels | |
| enable_mask: Use mask latent modeling | |
| """ | |
| x = self.x_embedder( | |
| x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2 | |
| t = self.t_embedder(t) # (N, D) | |
| y = self.y_embedder(y, self.training) # (N, D) | |
| c = t + y # (N, D) | |
| masked_stage = False | |
| # masking op for training | |
| if self.mask_ratio is not None and enable_mask: | |
| # masking: length -> length * mask_ratio | |
| x, mask, ids_restore, ids_keep = self.random_masking( | |
| x, self.mask_ratio) | |
| masked_stage = True | |
| for i in range(len(self.blocks)): | |
| if i == (len(self.blocks) - self.decode_layer): | |
| if self.mask_ratio is not None and enable_mask: | |
| x = self.forward_side_interpolater(x, c, mask, ids_restore) | |
| masked_stage = False | |
| else: | |
| # add pos embed | |
| x = x + self.decoder_pos_embed | |
| block = self.blocks[i] | |
| if masked_stage: | |
| x = block(x, c, ids_keep=ids_keep) | |
| else: | |
| x = block(x, c, ids_keep=None) | |
| # (N, T, patch_size ** 2 * out_channels) | |
| x = self.final_layer(x, c) | |
| x = self.unpatchify(x) # (N, out_channels, H, W) | |
| return x | |
| def forward_with_cfg(self, x, t, y, cfg_scale=None, diffusion_steps=1000, scale_pow=4.0): | |
| """ | |
| Forward pass of MDT, but also batches the unconditional forward pass for classifier-free guidance. | |
| """ | |
| # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb | |
| if cfg_scale is not None: | |
| half = x[: len(x) // 2] | |
| combined = torch.cat([half, half], dim=0) | |
| model_out = self.forward(combined, t, y) | |
| eps, rest = model_out[:, :3], model_out[:, 3:] | |
| cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) | |
| scale_step = ( | |
| 1-torch.cos(((1-t/diffusion_steps)**scale_pow)*math.pi))*1/2 # power-cos scaling | |
| real_cfg_scale = (cfg_scale-1)*scale_step + 1 | |
| real_cfg_scale = real_cfg_scale[: len(x) // 2].view(-1, 1, 1, 1) | |
| half_eps = uncond_eps + real_cfg_scale * (cond_eps - uncond_eps) | |
| eps = torch.cat([half_eps, half_eps], dim=0) | |
| return torch.cat([eps, rest], dim=1) | |
| else: | |
| model_out = self.forward(x, t, y) | |
| eps, rest = model_out[:, :3], model_out[:, 3:] | |
| return torch.cat([eps, rest], dim=1) | |
| ################################################################################# | |
| # Sine/Cosine Positional Embedding Functions # | |
| ################################################################################# | |
| # https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py | |
| def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0): | |
| """ | |
| grid_size: int of the grid height and width | |
| return: | |
| pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) | |
| """ | |
| grid_h = np.arange(grid_size, dtype=np.float32) | |
| grid_w = np.arange(grid_size, dtype=np.float32) | |
| grid = np.meshgrid(grid_w, grid_h) # here w goes first | |
| grid = np.stack(grid, axis=0) | |
| grid = grid.reshape([2, 1, grid_size, grid_size]) | |
| pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) | |
| if cls_token and extra_tokens > 0: | |
| pos_embed = np.concatenate( | |
| [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) | |
| return pos_embed | |
| def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): | |
| assert embed_dim % 2 == 0 | |
| # use half of dimensions to encode grid_h | |
| emb_h = get_1d_sincos_pos_embed_from_grid( | |
| embed_dim // 2, grid[0]) # (H*W, D/2) | |
| emb_w = get_1d_sincos_pos_embed_from_grid( | |
| embed_dim // 2, grid[1]) # (H*W, D/2) | |
| emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) | |
| return emb | |
| def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): | |
| """ | |
| embed_dim: output dimension for each position | |
| pos: a list of positions to be encoded: size (M,) | |
| out: (M, D) | |
| """ | |
| assert embed_dim % 2 == 0 | |
| omega = np.arange(embed_dim // 2, dtype=np.float64) | |
| omega /= embed_dim / 2. | |
| omega = 1. / 10000**omega # (D/2,) | |
| pos = pos.reshape(-1) # (M,) | |
| out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product | |
| emb_sin = np.sin(out) # (M, D/2) | |
| emb_cos = np.cos(out) # (M, D/2) | |
| emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) | |
| return emb | |
| ################################################################################# | |
| # MDT Configs # | |
| ################################################################################# | |
| def MDT_XL_2(**kwargs): | |
| return MDT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs) | |
| def MDT_XL_4(**kwargs): | |
| return MDT(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs) | |
| def MDT_XL_8(**kwargs): | |
| return MDT(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs) | |
| def MDT_L_2(**kwargs): | |
| return MDT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs) | |
| def MDT_L_4(**kwargs): | |
| return MDT(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs) | |
| def MDT_L_8(**kwargs): | |
| return MDT(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs) | |
| def MDT_B_2(**kwargs): | |
| return MDT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs) | |
| def MDT_B_4(**kwargs): | |
| return MDT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs) | |
| def MDT_B_8(**kwargs): | |
| return MDT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs) | |
| def MDT_S_2(**kwargs): | |
| return MDT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs) | |
| def MDT_S_4(**kwargs): | |
| return MDT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs) | |
| def MDT_S_8(**kwargs): | |
| return MDT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs) | |