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# 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:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# MAE: https://github.com/facebookresearch/mae
# MAE-ST: https://github.com/facebookresearch/mae_st
# --------------------------------------------------------
from functools import partial
import torch
import torch.nn as nn
from einops import rearrange
import copy
from mae_utils import video_vit
class MaskedAutoencoderViT(nn.Module):
"""Masked Autoencoder with VisionTransformer backbone"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=1024,
depth=24,
num_heads=16,
decoder_embed_dim=512,
decoder_depth=8,
decoder_num_heads=16,
mlp_ratio=4.0,
norm_layer=nn.LayerNorm,
norm_pix_loss=False,
num_frames=16,
t_patch_size=2,
patch_embed=video_vit.PatchEmbed,
no_qkv_bias=False,
sep_pos_embed=True,
trunc_init=False,
cls_embed=True,
pred_t_dim=8,
img_mask=None,
nsd_mask=None,
hcp_mask=None,
pct_masks_to_decode=1,
use_source_embeds=False,
**kwargs,
):
super().__init__()
self.trunc_init = trunc_init
self.sep_pos_embed = sep_pos_embed
self.cls_embed = cls_embed
self.pred_t_dim = pred_t_dim
self.t_pred_patch_size = t_patch_size * pred_t_dim // num_frames
self.embed_dim = embed_dim
self.use_source_embeds = use_source_embeds
self.pct_masks_to_decode = pct_masks_to_decode
self.patch_embed = patch_embed(
img_size,
patch_size,
in_chans,
embed_dim,
num_frames,
t_patch_size,
)
num_patches = self.patch_embed.num_patches
input_size = self.patch_embed.input_size
self.input_size = input_size
if self.cls_embed:
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.decoder_cls_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
if self.use_source_embeds:
self.source_embeds = nn.Embedding(3, embed_dim)
if sep_pos_embed:
self.pos_embed_spatial = nn.Parameter(
torch.zeros(1, input_size[1] * input_size[2], embed_dim)
)
self.pos_embed_temporal = nn.Parameter(
torch.zeros(1, input_size[0], embed_dim)
)
if self.cls_embed:
self.pos_embed_class = nn.Parameter(torch.zeros(1, 1, embed_dim))
else:
if self.cls_embed:
_num_patches = num_patches + 1
else:
_num_patches = num_patches
self.pos_embed = nn.Parameter(
torch.zeros(1, _num_patches, embed_dim),
)
self.blocks = nn.ModuleList(
[
video_vit.Block(
embed_dim,
num_heads,
mlp_ratio,
qkv_bias=not no_qkv_bias,
qk_scale=None,
norm_layer=norm_layer,
)
for i in range(depth)
]
)
self.norm = norm_layer(embed_dim)
self.decoder_embed = nn.Linear(embed_dim, decoder_embed_dim, bias=True)
self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
if sep_pos_embed:
self.decoder_pos_embed_spatial = nn.Parameter(
torch.zeros(1, input_size[1] * input_size[2], decoder_embed_dim)
)
self.decoder_pos_embed_temporal = nn.Parameter(
torch.zeros(1, input_size[0], decoder_embed_dim)
)
if self.cls_embed:
self.decoder_pos_embed_class = nn.Parameter(
torch.zeros(1, 1, decoder_embed_dim)
)
else:
if self.cls_embed:
_num_patches = num_patches + 1
else:
_num_patches = num_patches
self.decoder_pos_embed = nn.Parameter(
torch.zeros(1, _num_patches, decoder_embed_dim),
)
self.decoder_blocks = nn.ModuleList(
[
video_vit.Block(
decoder_embed_dim,
decoder_num_heads,
mlp_ratio,
qkv_bias=not no_qkv_bias,
qk_scale=None,
norm_layer=norm_layer,
)
for i in range(decoder_depth)
]
)
self.decoder_norm = norm_layer(decoder_embed_dim)
self.decoder_pred = nn.Linear(
decoder_embed_dim,
self.t_pred_patch_size * patch_size**2 * in_chans,
bias=True,
)
self.norm_pix_loss = norm_pix_loss
if img_mask is not None:
self.initialize_mask(img_mask)
else:
self.initialize_mask(nsd_mask)
self.initialize_mask2(hcp_mask)
self.initialize_weights()
print("model initialized")
def initialize_weights(self):
if self.cls_embed:
torch.nn.init.trunc_normal_(self.cls_token, std=0.02)
if self.sep_pos_embed:
torch.nn.init.trunc_normal_(self.pos_embed_spatial, std=0.02)
torch.nn.init.trunc_normal_(self.pos_embed_temporal, std=0.02)
torch.nn.init.trunc_normal_(self.decoder_pos_embed_spatial, std=0.02)
torch.nn.init.trunc_normal_(self.decoder_pos_embed_temporal, std=0.02)
if self.cls_embed:
torch.nn.init.trunc_normal_(self.pos_embed_class, std=0.02)
torch.nn.init.trunc_normal_(self.decoder_pos_embed_class, std=0.02)
else:
torch.nn.init.trunc_normal_(self.pos_embed, std=0.02)
torch.nn.init.trunc_normal_(self.decoder_pos_embed, std=0.02)
w = self.patch_embed.proj.weight.data
if self.trunc_init:
torch.nn.init.trunc_normal_(w)
torch.nn.init.trunc_normal_(self.mask_token, std=0.02)
else:
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
torch.nn.init.normal_(self.mask_token, std=0.02)
# initialize nn.Linear and nn.LayerNorm
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
# we use xavier_uniform following official JAX ViT:
if self.trunc_init:
nn.init.trunc_normal_(m.weight, std=0.02)
else:
torch.nn.init.xavier_uniform_(m.weight)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def initialize_mask(self, img_mask):
if img_mask is not None:
img_mask = torch.as_tensor(img_mask > 0).float()
H, W = img_mask.shape
img_mask_patches = self.patchify(
img_mask
.view(1, 1, 1, H, W)
.repeat(1, self.patch_embed.in_chans, self.pred_t_dim, 1, 1)
)
patch_mask = rearrange(
img_mask,
"(h ph) (w pw) -> (h w) (ph pw)",
ph=self.patch_embed.patch_size[0],
pw=self.patch_embed.patch_size[1],
).any(dim=1).float()
patch_mask_indices, = patch_mask.nonzero(as_tuple=True)
self.register_buffer("img_mask", img_mask)
self.register_buffer("img_mask_patches", img_mask_patches)
self.register_buffer("patch_mask", patch_mask)
self.register_buffer("patch_mask_indices", patch_mask_indices)
self.n_mask_patches = int(len(patch_mask_indices) * self.pct_masks_to_decode)
else:
self.register_buffer("img_mask", None)
self.register_buffer("img_mask_patches", None)
self.register_buffer("patch_mask", None)
self.register_buffer("patch_mask_indices", None)
self.n_mask_patches = None
def initialize_mask2(self, img_mask):
if img_mask is not None:
img_mask = torch.as_tensor(img_mask > 0).float()
H, W = img_mask.shape
img_mask_patches = self.patchify(
img_mask
.view(1, 1, 1, H, W)
.repeat(1, self.patch_embed.in_chans, self.pred_t_dim, 1, 1)
)
patch_mask = rearrange(
img_mask,
"(h ph) (w pw) -> (h w) (ph pw)",
ph=self.patch_embed.patch_size[0],
pw=self.patch_embed.patch_size[1],
).any(dim=1).float()
patch_mask_indices, = patch_mask.nonzero(as_tuple=True)
self.register_buffer("img_mask2", img_mask)
self.register_buffer("img_mask_patches2", img_mask_patches)
self.register_buffer("patch_mask2", patch_mask)
self.register_buffer("patch_mask_indices2", patch_mask_indices)
self.n_mask_patches = int(len(patch_mask_indices) * self.pct_masks_to_decode)
else:
self.register_buffer("img_mask2", None)
self.register_buffer("img_mask_patches2", None)
self.register_buffer("patch_mask2", None)
self.register_buffer("patch_mask_indices2", None)
self.n_mask_patches2 = None
def patchify(self, imgs):
"""
imgs: (N, C, T, H, W)
x: (N, L, patch_size**2 *C)
"""
N, C, T, H, W = imgs.shape
ph, pw = self.patch_embed.patch_size
u = self.t_pred_patch_size
assert H % ph == 0 and W % pw == 0 and T % u == 0
h = H // ph
w = W // pw
t = T // u
x = imgs.reshape(shape=(N, C, t, u, h, ph, w, pw))
x = torch.einsum("nctuhpwq->nthwupqc", x)
x = x.reshape(shape=(N, t * h * w, u * ph * pw * C))
self.patch_info = (N, C, T, H, W, ph, pw, u, t, h, w)
return x
def unpatchify(self, x):
"""
x: (N, L, patch_size**2 *C)
imgs: (N, C, H, W)
"""
N, C, T, H, W, ph, pw, u, t, h, w = self.patch_info
x = x.reshape(shape=(N, t, h, w, u, ph, pw, C))
x = torch.einsum("nthwupqc->nctuhpwq", x)
imgs = x.reshape(shape=(N, C, T, H, W))
return imgs
def random_masking(self, x, mask_ratio, use_contrastive_loss=False):
"""
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
T = self.patch_embed.t_grid_size
H, W = self.patch_embed.grid_size
assert L == T * H * W
# adjust number to keep relative to image mask
if self.img_mask is not None:
len_keep = int(T * self.n_mask_patches * (1 - mask_ratio))
else:
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
# shift missing patches to not be selected
if self.img_mask is not None:
noise = noise.view(N, T, H * W)
noise = noise + (1.0 - self.patch_mask)
noise = noise.view(N, L)
# sort noise for each sample
ids_shuffle = torch.argsort(
noise, dim=1
) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
if not use_contrastive_loss:
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
else:
x_masked1 = torch.gather(x, dim=1, index=ids_keep[:,:len_keep//2].unsqueeze(-1).repeat(1, 1, D))
x_masked2 = torch.gather(x, dim=1, index=ids_keep[:,len_keep//2:len_keep].unsqueeze(-1).repeat(1, 1, D))
if not use_contrastive_loss:
# 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)
else:
# generate the binary mask: 0 is keep, 1 is remove
mask1 = torch.ones([N, L], device=x.device)
mask2 = torch.ones([N, L], device=x.device)
mask1[:, :len_keep//2] = 0
mask2[:, len_keep//2:len_keep] = 0
# unshuffle to get the binary mask
mask1 = torch.gather(mask1, dim=1, index=ids_restore)
mask2 = torch.gather(mask2, dim=1, index=ids_restore)
if not use_contrastive_loss:
return x_masked, mask, ids_restore, ids_keep
else:
return [x_masked1,x_masked2], [mask1,mask2], ids_restore, ids_keep
def forward_encoder(self, x, mask_ratio, use_contrastive_loss=False, source_ids=None):
x = self.patch_embed(x)
N, T, L, C = x.shape
x = x.reshape(N, T * L, C)
# masking: length -> length * mask_ratio
if not use_contrastive_loss:
x, mask, ids_restore, ids_keep = self.random_masking(x, mask_ratio)
x = x.view(N, -1, C)
else:
[x1,x2], [mask1,mask2], ids_restore, ids_keep = self.random_masking(x, mask_ratio, use_contrastive_loss=use_contrastive_loss)
x1 = x1.view(len(x1), -1, C)
x2 = x2.view(len(x2), -1, C)
# append cls token
if self.cls_embed:
cls_token = self.cls_token
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
if not use_contrastive_loss:
x = torch.cat((cls_tokens, x), dim=1)
else:
x1 = torch.cat((cls_tokens, x1), dim=1)
x2 = torch.cat((cls_tokens, x2), dim=1)
# add pos embed w/o cls token
if self.sep_pos_embed:
pos_embed = self.pos_embed_spatial.repeat(
1, self.input_size[0], 1
) + torch.repeat_interleave(
self.pos_embed_temporal,
self.input_size[1] * self.input_size[2],
dim=1,
)
pos_embed = pos_embed.expand(x.shape[0], -1, -1)
pos_embed = torch.gather(
pos_embed,
dim=1,
index=ids_keep.unsqueeze(-1).repeat(1, 1, pos_embed.shape[2]),
)
if self.cls_embed:
pos_embed = torch.cat(
[
self.pos_embed_class.expand(pos_embed.shape[0], -1, -1),
pos_embed,
],
1,
)
else:
if self.cls_embed:
cls_ind = 1
else:
cls_ind = 0
pos_embed = self.pos_embed[:, cls_ind:, :].expand(x.shape[0], -1, -1)
pos_embed = torch.gather(
pos_embed,
dim=1,
index=ids_keep.unsqueeze(-1).repeat(1, 1, pos_embed.shape[2]),
)
if self.cls_embed:
pos_embed = torch.cat(
[
self.pos_embed[:, :1, :].expand(x.shape[0], -1, -1),
pos_embed,
],
1,
)
if not use_contrastive_loss:
x = x.view([N, -1, C]) + pos_embed
else:
x1 = x1.view([len(x1), -1, C]) + pos_embed[:,:x1.shape[1]]
x2 = x2.view([len(x2), -1, C]) + torch.cat((pos_embed[:,:1], pos_embed[:,x1.shape[1]:]),dim=1)
if source_ids is not None:
assert source_ids.ndim == 1, "source_ids should be a 1D tensor of source indices"
assert torch.all((source_ids >= 0) & (source_ids <= 2)), "All values in source_ids must be integers between 0 and 2"
source_embeds = self.source_embeds(source_ids) # bs, embed_dim
# Use token order: [cls, source, patch]
if not use_contrastive_loss:
if self.cls_embed:
x = torch.cat((x[:, :1], source_embeds[:, None], x[:, 1:]), dim=1)
else:
x = torch.cat((source_embeds[:, None], x), dim=1)
else:
if self.cls_embed:
x1 = torch.cat((x1[:, :1], source_embeds[:, None], x1[:, 1:]), dim=1)
x2 = torch.cat((x2[:, :1], source_embeds[:, None], x2[:, 1:]), dim=1)
else:
x1 = torch.cat((source_embeds[:, None], x1), dim=1)
x2 = torch.cat((source_embeds[:, None], x2), dim=1)
if not use_contrastive_loss:
# apply Transformer blocks
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
else:
# apply Transformer blocks
for blk in self.blocks:
x1 = blk(x1)
x2 = blk(x2)
x1 = self.norm(x1)
x2 = self.norm(x2)
if not use_contrastive_loss:
if self.cls_embed:
# remove cls token
x = x[:, 1:, :]
if source_ids is not None:
# remove source token
x = x[:, 1:, :]
return x, mask, ids_restore
else:
if self.cls_embed:
# remove cls token
x1 = x1[:, 1:, :]
x2 = x2[:, 1:, :]
if source_ids is not None:
# remove source token
x1 = x1[:, 1:, :]
x2 = x2[:, 1:, :]
return [x1,x2], [mask1,mask2], ids_restore
def forward_encoder_with_mask(self, x, ids_keep):
# embed patches
x = self.patch_embed(x)
N, T, L, C = x.shape
x = x.reshape(N, T * L, C)
# mask out tokens
x = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, C))
# append cls token
if self.cls_embed:
cls_token = self.cls_token
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
# add pos embed w/o cls token
if self.sep_pos_embed:
pos_embed = self.pos_embed_spatial.repeat(
1, self.input_size[0], 1
) + torch.repeat_interleave(
self.pos_embed_temporal,
self.input_size[1] * self.input_size[2],
dim=1,
)
pos_embed = pos_embed.expand(x.shape[0], -1, -1)
pos_embed = torch.gather(
pos_embed,
dim=1,
index=ids_keep.unsqueeze(-1).repeat(1, 1, pos_embed.shape[2]),
)
if self.cls_embed:
pos_embed = torch.cat(
[
self.pos_embed_class.expand(pos_embed.shape[0], -1, -1),
pos_embed,
],
1,
)
else:
if self.cls_embed:
cls_ind = 1
else:
cls_ind = 0
pos_embed = self.pos_embed[:, cls_ind:, :].expand(x.shape[0], -1, -1)
pos_embed = torch.gather(
pos_embed,
dim=1,
index=ids_keep.unsqueeze(-1).repeat(1, 1, pos_embed.shape[2]),
)
if self.cls_embed:
pos_embed = torch.cat(
[
self.pos_embed[:, :1, :].expand(x.shape[0], -1, -1),
pos_embed,
],
1,
)
x = x.view([N, -1, C]) + pos_embed
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
def forward_decoder(self, x, ids_restore, use_contrastive_loss=False):
N = x.shape[0]
T = self.patch_embed.t_grid_size
H, W = self.patch_embed.grid_size
# embed tokens
x = self.decoder_embed(x)
C = x.shape[-1]
# append mask tokens to sequence
mask_tokens = self.mask_token.repeat(N, T * H * W + 0 - x.shape[1], 1)
x_ = torch.cat([x[:, :, :], mask_tokens], dim=1) # no cls token
x_ = x_.view([N, T * H * W, C])
x_ = torch.gather(
x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x_.shape[2])
) # unshuffle
x = x_.view([N, T * H * W, C])
# append cls token
if self.cls_embed:
decoder_cls_token = self.decoder_cls_token
decoder_cls_tokens = decoder_cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((decoder_cls_tokens, x), dim=1)
if self.sep_pos_embed:
decoder_pos_embed = self.decoder_pos_embed_spatial.repeat(
1, self.input_size[0], 1
) + torch.repeat_interleave(
self.decoder_pos_embed_temporal,
self.input_size[1] * self.input_size[2],
dim=1,
)
if self.cls_embed:
decoder_pos_embed = torch.cat(
[
self.decoder_pos_embed_class.expand(
decoder_pos_embed.shape[0], -1, -1
),
decoder_pos_embed,
],
1,
)
else:
decoder_pos_embed = self.decoder_pos_embed[:, :, :]
# add pos embed
x = x + decoder_pos_embed
attn = self.decoder_blocks[0].attn
# drop patches outside image mask (and then only keep a subset a la VideoMAE2)
if self.img_mask is not None:
if self.cls_embed:
decoder_cls_tokens, x = x[:, :1, :], x[:, 1:, :]
x = x.view([N, T, H * W, C])
# x = x[:, :, self.patch_mask_indices]
# drop patches randomly to preserve memory (VideoMAE2 approach)
included_patches = self.patch_mask_indices
num_to_select = int(self.pct_masks_to_decode * len(included_patches))
selected_idx = torch.randperm(len(included_patches))[:num_to_select]
included_patches = included_patches[selected_idx]
x = x[:, :, included_patches]
x = x.view([N, T * self.n_mask_patches, C])
if self.cls_embed:
x = torch.cat((decoder_cls_tokens, x), dim=1)
# apply Transformer blocks
for blk in self.decoder_blocks:
x = blk(x)
x = self.decoder_norm(x)
# predictor projection
x = self.decoder_pred(x)
if self.cls_embed:
# remove cls token
x = x[:, 1:, :]
# fill outside mask with zeros
if self.img_mask is not None:
C = x.shape[-1]
x = x.view([N, T, self.n_mask_patches, C])
x_ = torch.zeros([N, T, H * W, C], dtype=x.dtype, device=x.device)
x = x_.scatter(
2, included_patches.view(1, 1, -1, 1).expand(N, T, self.n_mask_patches, C), x,
)
x = x.view([N, T * H * W, C])
return x
def forward_loss(self, imgs, pred, mask):
"""
imgs: [N, C, T, H, W]
pred: [N, t*h*w, u*p*p*C]
mask: [N, t*h*w], 0 is keep, 1 is remove,
"""
_imgs = torch.index_select(
imgs,
2,
torch.linspace(
0,
imgs.shape[2] - 1,
self.pred_t_dim,
)
.long()
.to(imgs.device),
)
target = self.patchify(_imgs)
if self.norm_pix_loss:
mean = target.mean(dim=-1, keepdim=True)
var = target.var(dim=-1, keepdim=True)
target = (target - mean) / (var + 1.0e-6) ** 0.5
loss = (pred - target) ** 2
if self.img_mask is not None:
# exclude missing pixels from loss
mask = mask.unsqueeze(-1) * self.img_mask_patches
else:
loss = loss.mean(dim=-1) # [N, L], mean loss per patch
loss = (loss * mask).sum() / mask.sum() # mean loss on removed patches
return loss
def forward_cyclic_loss(self, pred1, pred2, mask):
"""
mask1 and mask2 encoder outputs should be the same since they are predicting the same held-out true mask
"""
loss = (pred1 - pred2) ** 2
if self.img_mask is not None:
# exclude missing pixels from loss
mask = mask.unsqueeze(-1) * self.img_mask_patches
else:
loss = loss.mean(dim=-1) # [N, L], mean loss per patch
loss = (loss * mask).sum() / mask.sum() # mean loss on removed patches
return loss
def forward(self, imgs, mask_ratio=0.75, use_contrastive_loss=False, forward_features=False, global_pool=True, cls_forward=False, source_ids=None):
if forward_features:
# embed patches
x = self.patch_embed(imgs)
N, T, L, C = x.shape # T: temporal; L: spatial
x = x.view([N, T * L, C])
# append cls token
if self.cls_embed:
cls_token = self.cls_token
cls_tokens = cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
if self.sep_pos_embed:
pos_embed = self.pos_embed_spatial.repeat(
1, self.input_size[0], 1
) + torch.repeat_interleave(
self.pos_embed_temporal,
self.input_size[1] * self.input_size[2],
dim=1,
)
if self.cls_embed:
pos_embed = torch.cat(
[
self.pos_embed_class.expand(pos_embed.shape[0], -1, -1),
pos_embed,
],
1,
)
else:
pos_embed = self.pos_embed[:, :, :]
x = x + pos_embed
# drop patches outside image mask
if self.img_mask is not None:
if self.cls_embed:
cls_tokens, x = x[:, :1, :], x[:, 1:, :]
x = x.view([N, T, L, C])
x = x[:, :, self.patch_mask_indices]
x = x.view([N, T * self.n_mask_patches, C])
if self.cls_embed:
x = torch.cat((cls_tokens, x), dim=1)
if source_ids is not None:
assert source_ids.ndim == 1, "source_ids should be a 1D tensor of source indices"
assert torch.all((source_ids >= 0) & (source_ids <= 2)), "All values in source_ids must be integers between 0 and 2"
source_embeds = self.source_embeds(source_ids) # bs, embed_dim
# Use token order: [cls, source, patch]
if self.cls_embed:
x = torch.cat((x[:, :1], source_embeds[:, None], x[:, 1:]), dim=1)
else:
x = torch.cat((source_embeds[:, None], x), dim=1)
# apply Transformer blocks
for blk in self.blocks:
x = blk(x)
if global_pool:
if self.cls_embed:
# remove cls token
x = x[:, 1:, :]
if source_ids is not None:
# remove source token
x = x[:, 1:, :]
x = x.mean(dim=1)
elif cls_forward:
x = x[:, :1, :]
return x
else:
latent, mask, ids_restore = self.forward_encoder(imgs, mask_ratio, use_contrastive_loss=use_contrastive_loss, source_ids=source_ids)
if not use_contrastive_loss:
pred = self.forward_decoder(latent, ids_restore, use_contrastive_loss=use_contrastive_loss) # [N, L, p*p*C]
loss = self.forward_loss(imgs, pred, mask)
return loss, pred, mask, latent
else:
latent1, latent2 = latent
mask1, mask2 = mask
true_mask = copy.deepcopy(mask1)
true_mask[mask2==0]=0 # dont try to predict the masks that were fed to the other encoder
pred1 = self.forward_decoder(latent1, ids_restore, use_contrastive_loss=use_contrastive_loss) # [N, L, p*p*C]
pred2 = self.forward_decoder(latent2, ids_restore, use_contrastive_loss=use_contrastive_loss) # [N, L, p*p*C]
loss1 = self.forward_loss(imgs, pred1, true_mask)
loss2 = self.forward_loss(imgs, pred2, true_mask)
loss3 = self.forward_cyclic_loss(pred1, pred2, true_mask)
return loss1, loss2, loss3, pred1, pred2, mask1, mask2, true_mask, latent1, latent2
def forward_head(self, x):
# classifier
x = self.norm(x)
# x = self.fc_norm(x)
x = self.dropout(x)
x = self.head(x)
return x
def mask_fill(self, x):
N, L, C = x.shape
T = self.patch_embed.t_grid_size
H, W = self.patch_embed.grid_size
assert L == T * self.n_mask_patches
x = x.view(N, T, -1, C)
x_ = torch.zeros([N, T, H * W, C], dtype=x.dtype, device=x.device)
x = x_.scatter(
2, self.patch_mask_indices.view(1, 1, -1, 1).expand(N, T, -1, C), x,
)
return x
def mae_vit_small_fmri(**kwargs):
model = MaskedAutoencoderViT(
img_size=(144, 320),
in_chans=1,
embed_dim=384,
depth=12,
num_heads=6,
mlp_ratio=4,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
return model
def mae_vit_base_fmri(**kwargs):
model = MaskedAutoencoderViT(
img_size=(144, 320),
in_chans=1,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
return model
def mae_vit_large_fmri(**kwargs):
model = MaskedAutoencoderViT(
img_size=(144, 320),
in_chans=1,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
return model
def mae_vit_huge_fmri(**kwargs):
model = MaskedAutoencoderViT(
img_size=(144, 320),
in_chans=1,
embed_dim=1280,
depth=32,
num_heads=16,
mlp_ratio=4,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
return model |