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from functools import partial
import numpy as np
from tqdm import tqdm
import scipy.stats as stats
import math
import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from timm.models.vision_transformer import Block
from .diffloss import DiffLoss
def mask_by_order(mask_len, order, bsz, seq_len):
masking = torch.zeros(bsz, seq_len).cuda()
masking = torch.scatter(masking, dim=-1, index=order[:, :mask_len.long()], src=torch.ones(bsz, seq_len).cuda()).bool()
return masking
class MARBert(nn.Module):
""" Masked Autoencoder with VisionTransformer backbone
"""
def __init__(self, img_size=256, vae_stride=16, patch_size=1,
encoder_embed_dim=1024, encoder_depth=16, encoder_num_heads=16,
decoder_embed_dim=1024, decoder_depth=16, decoder_num_heads=16,
mlp_ratio=4., norm_layer=nn.LayerNorm,
vae_embed_dim=16,
mask_ratio_min=0.7,
label_drop_prob=0.1,
class_num=1000,
attn_dropout=0.1,
proj_dropout=0.1,
buffer_size=64,
diffloss_d=3,
diffloss_w=1024,
num_sampling_steps='100',
diffusion_batch_mul=4,
grad_checkpointing=False,
):
super().__init__()
# --------------------------------------------------------------------------
# VAE and patchify specifics
self.vae_embed_dim = vae_embed_dim
self.img_size = img_size
self.vae_stride = vae_stride
self.patch_size = patch_size
self.seq_h = self.seq_w = img_size // vae_stride // patch_size
self.seq_len = self.seq_h * self.seq_w
self.token_embed_dim = vae_embed_dim * patch_size**2
self.grad_checkpointing = grad_checkpointing
# --------------------------------------------------------------------------
# Class Embedding
self.num_classes = class_num
self.class_emb = nn.Embedding(1000, encoder_embed_dim)
self.label_drop_prob = label_drop_prob
# Fake class embedding for CFG's unconditional generation
self.fake_latent = nn.Parameter(torch.zeros(1, encoder_embed_dim))
# --------------------------------------------------------------------------
# MAR variant masking ratio, a left-half truncated Gaussian centered at 100% masking ratio with std 0.25
self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - 1.0) / 0.25, 0, loc=1.0, scale=0.25)
# --------------------------------------------------------------------------
# MAR encoder specifics
self.z_proj = nn.Linear(self.token_embed_dim, encoder_embed_dim, bias=True)
self.z_proj_ln = nn.LayerNorm(encoder_embed_dim, eps=1e-6)
self.buffer_size = buffer_size
self.encoder_pos_embed_learned = nn.Parameter(torch.zeros(1, self.seq_len + self.buffer_size, encoder_embed_dim))
self.encoder_blocks = nn.ModuleList([
Block(encoder_embed_dim, encoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer,
proj_drop=proj_dropout, attn_drop=attn_dropout) for _ in range(encoder_depth)])
self.encoder_norm = norm_layer(encoder_embed_dim)
# --------------------------------------------------------------------------
# MAR decoder specifics
self.decoder_embed = nn.Linear(encoder_embed_dim, decoder_embed_dim, bias=True)
self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, self.seq_len + self.buffer_size, decoder_embed_dim))
self.decoder_blocks = nn.ModuleList([
Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer,
proj_drop=proj_dropout, attn_drop=attn_dropout) for _ in range(decoder_depth)])
self.decoder_norm = norm_layer(decoder_embed_dim)
self.diffusion_pos_embed_learned = nn.Parameter(torch.zeros(1, self.seq_len, decoder_embed_dim))
self.initialize_weights()
# --------------------------------------------------------------------------
# Diffusion Loss
self.diffloss = DiffLoss(
target_channels=self.token_embed_dim,
z_channels=decoder_embed_dim,
width=diffloss_w,
depth=diffloss_d,
num_sampling_steps=num_sampling_steps,
grad_checkpointing=grad_checkpointing
)
self.diffusion_batch_mul = diffusion_batch_mul
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
print("test")
def initialize_weights(self):
# parameters
torch.nn.init.normal_(self.class_emb.weight, std=.02)
torch.nn.init.normal_(self.fake_latent, std=.02)
torch.nn.init.normal_(self.mask_token, std=.02)
torch.nn.init.normal_(self.encoder_pos_embed_learned, std=.02)
torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02)
torch.nn.init.normal_(self.diffusion_pos_embed_learned, std=.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:
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):
if m.bias is not None:
nn.init.constant_(m.bias, 0)
if m.weight is not None:
nn.init.constant_(m.weight, 1.0)
def patchify(self, x):
bsz, c, h, w = x.shape
p = self.patch_size
h_, w_ = h // p, w // p
x = x.reshape(bsz, c, h_, p, w_, p)
x = torch.einsum('nchpwq->nhwcpq', x)
x = x.reshape(bsz, h_ * w_, c * p ** 2)
return x # [n, l, d]
def unpatchify(self, x):
bsz = x.shape[0]
p = self.patch_size
c = self.vae_embed_dim
h_, w_ = self.seq_h, self.seq_w
x = x.reshape(bsz, h_, w_, c, p, p)
x = torch.einsum('nhwcpq->nchpwq', x)
x = x.reshape(bsz, c, h_ * p, w_ * p)
return x # [n, c, h, w]
def sample_orders(self, bsz):
# generate a batch of random generation orders
orders = []
for _ in range(bsz):
order = np.array(list(range(self.seq_len)))
np.random.shuffle(order)
orders.append(order)
orders = torch.Tensor(np.array(orders)).cuda().long()
return orders
def random_masking(self, x, orders):
# generate token mask
bsz, seq_len, embed_dim = x.shape
mask_rate = self.mask_ratio_generator.rvs(1)[0]
num_masked_tokens = int(np.ceil(seq_len * mask_rate))
mask = torch.zeros(bsz, seq_len, device=x.device)
mask = torch.scatter(mask, dim=-1, index=orders[:, :num_masked_tokens],
src=torch.ones(bsz, seq_len, device=x.device))
return mask
def forward_mae_encoder(self, x, mask, class_embedding):
x = self.z_proj(x)
bsz, seq_len, embed_dim = x.shape
# concat buffer
x = torch.cat([torch.zeros(bsz, self.buffer_size, embed_dim, device=x.device), x], dim=1)
mask_with_buffer = torch.cat([torch.zeros(x.size(0), self.buffer_size, device=x.device), mask], dim=1)
# random drop class embedding during training
if self.training:
drop_latent_mask = torch.rand(bsz) < self.label_drop_prob
drop_latent_mask = drop_latent_mask.unsqueeze(-1).cuda().to(x.dtype)
class_embedding = drop_latent_mask * self.fake_latent + (1 - drop_latent_mask) * class_embedding
x[:, :self.buffer_size] = class_embedding.unsqueeze(1)
# encoder position embedding
x = x + self.encoder_pos_embed_learned
x = self.z_proj_ln(x)
# dropping
x = x[(1-mask_with_buffer).nonzero(as_tuple=True)].reshape(bsz, -1, embed_dim)
# apply Transformer blocks
if self.grad_checkpointing and not torch.jit.is_scripting():
for block in self.encoder_blocks:
x = checkpoint(block, x)
else:
for block in self.encoder_blocks:
x = block(x)
x = self.encoder_norm(x)
return x
def forward_mae_decoder(self, x, mask):
x = self.decoder_embed(x)
mask_with_buffer = torch.cat([torch.zeros(x.size(0), self.buffer_size, device=x.device), mask], dim=1)
# pad mask tokens
mask_tokens = self.mask_token.repeat(mask_with_buffer.shape[0], mask_with_buffer.shape[1], 1).to(x.dtype)
x_after_pad = mask_tokens.clone()
x_after_pad[(1 - mask_with_buffer).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2])
# decoder position embedding
x = x_after_pad + self.decoder_pos_embed_learned
# apply Transformer blocks
if self.grad_checkpointing and not torch.jit.is_scripting():
for block in self.decoder_blocks:
x = checkpoint(block, x)
else:
for block in self.decoder_blocks:
x = block(x)
x = self.decoder_norm(x)
x = x[:, self.buffer_size:]
x = x + self.diffusion_pos_embed_learned
return x
def forward_loss(self, z, target, mask):
bsz, seq_len, _ = target.shape
target = target.reshape(bsz * seq_len, -1).repeat(self.diffusion_batch_mul, 1)
z = z.reshape(bsz*seq_len, -1).repeat(self.diffusion_batch_mul, 1)
mask = mask.reshape(bsz*seq_len).repeat(self.diffusion_batch_mul)
loss = self.diffloss(z=z, target=target, mask=mask)
return loss
def forward(self, imgs, labels):
# class embed
class_embedding = self.class_emb(labels)
# patchify and mask (drop) tokens
x = self.patchify(imgs)
gt_latents = x.clone().detach()
orders = self.sample_orders(bsz=x.size(0))
mask = self.random_masking(x, orders)
# mae encoder
x = self.forward_mae_encoder(x, mask, class_embedding)
# mae decoder
z = self.forward_mae_decoder(x, mask)
# diffloss
loss = self.forward_loss(z=z, target=gt_latents, mask=mask)
return loss
def sample_tokens(self, bsz, num_iter=64, cfg=1.0, cfg_schedule="linear", labels=None, temperature=1.0, progress=False):
# init and sample generation orders
mask = torch.ones(bsz, self.seq_len).cuda()
tokens = torch.zeros(bsz, self.seq_len, self.token_embed_dim).cuda()
orders = self.sample_orders(bsz)
indices = list(range(num_iter))
if progress:
indices = tqdm(indices)
# generate latents
for step in indices:
cur_tokens = tokens.clone()
print("test")
# class embedding and CFG
if labels is not None:
class_embedding = self.class_emb(labels)
else:
class_embedding = self.fake_latent.repeat(bsz, 1)
if not cfg == 1.0:
tokens = torch.cat([tokens, tokens], dim=0)
class_embedding = torch.cat([class_embedding, self.fake_latent.repeat(bsz, 1)], dim=0)
mask = torch.cat([mask, mask], dim=0)
# mae encoder
x = self.forward_mae_encoder(tokens, mask, class_embedding)
# mae decoder
z = self.forward_mae_decoder(x, mask)
# mask ratio for the next round, following MaskGIT and MAGE.
mask_ratio = np.cos(math.pi / 2. * (step + 1) / num_iter)
mask_len = torch.Tensor([np.floor(self.seq_len * mask_ratio)]).cuda()
# masks out at least one for the next iteration
mask_len = torch.maximum(torch.Tensor([1]).cuda(),
torch.minimum(torch.sum(mask, dim=-1, keepdims=True) - 1, mask_len))
# get masking for next iteration and locations to be predicted in this iteration
mask_next = mask_by_order(mask_len[0], orders, bsz, self.seq_len)
if step >= num_iter - 1:
mask_to_pred = mask[:bsz].bool()
else:
mask_to_pred = torch.logical_xor(mask[:bsz].bool(), mask_next.bool())
mask = mask_next
if not cfg == 1.0:
mask_to_pred = torch.cat([mask_to_pred, mask_to_pred], dim=0)
# sample token latents for this step
print(z.shape)
print("-----------")
z = z[mask_to_pred.nonzero(as_tuple=True)]
print(z.shape)
# cfg schedule follow Muse
if cfg_schedule == "linear":
cfg_iter = 1 + (cfg - 1) * (self.seq_len - mask_len[0]) / self.seq_len
elif cfg_schedule == "constant":
cfg_iter = cfg
else:
raise NotImplementedError
sampled_token_latent = self.diffloss.sample(z, temperature, cfg_iter)
if not cfg == 1.0:
sampled_token_latent, _ = sampled_token_latent.chunk(2, dim=0) # Remove null class samples
mask_to_pred, _ = mask_to_pred.chunk(2, dim=0)
cur_tokens[mask_to_pred.nonzero(as_tuple=True)] = sampled_token_latent
tokens = cur_tokens.clone()
# unpatchify
tokens = self.unpatchify(tokens)
return tokens
class MAR(nn.Module):
""" Masked Autoencoder with VisionTransformer backbone
"""
def __init__(self, img_size=256, vae_stride=16, patch_size=1,
encoder_embed_dim=1024, encoder_depth=16, encoder_num_heads=16,
decoder_embed_dim=1024, decoder_depth=16, decoder_num_heads=16,
mlp_ratio=4., norm_layer=nn.LayerNorm,
vae_embed_dim=16,
mask_ratio_min=0.7,
label_drop_prob=0.1,
class_num=1000,
attn_dropout=0.1,
proj_dropout=0.1,
buffer_size=64,
diffloss_d=3,
diffloss_w=1024,
num_sampling_steps='100',
diffusion_batch_mul=4,
grad_checkpointing=False,
):
super().__init__()
# --------------------------------------------------------------------------
# VAE and patchify specifics
self.vae_embed_dim = vae_embed_dim
self.img_size = img_size
self.vae_stride = vae_stride
self.patch_size = patch_size
self.seq_h = self.seq_w = img_size // vae_stride // patch_size
self.seq_len = self.seq_h * self.seq_w
self.token_embed_dim = vae_embed_dim * patch_size**2
self.grad_checkpointing = grad_checkpointing
# --------------------------------------------------------------------------
# Class Embedding
self.num_classes = class_num
self.class_emb = nn.Embedding(1000, encoder_embed_dim)
self.label_drop_prob = label_drop_prob
# Fake class embedding for CFG's unconditional generation
self.fake_latent = nn.Parameter(torch.zeros(1, encoder_embed_dim))
# --------------------------------------------------------------------------
# MAR variant masking ratio, a left-half truncated Gaussian centered at 100% masking ratio with std 0.25
self.mask_ratio_generator = stats.truncnorm((mask_ratio_min - 1.0) / 0.25, 0, loc=1.0, scale=0.25)
# --------------------------------------------------------------------------
# MAR encoder specifics
self.z_proj = nn.Linear(self.token_embed_dim, encoder_embed_dim, bias=True)
self.z_proj_ln = nn.LayerNorm(encoder_embed_dim, eps=1e-6)
self.buffer_size = buffer_size
self.encoder_pos_embed_learned = nn.Parameter(torch.zeros(1, self.seq_len + self.buffer_size, encoder_embed_dim))
self.encoder_blocks = nn.ModuleList([
Block(encoder_embed_dim, encoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer,
proj_drop=proj_dropout, attn_drop=attn_dropout) for _ in range(encoder_depth)])
self.encoder_norm = norm_layer(encoder_embed_dim)
# --------------------------------------------------------------------------
# MAR decoder specifics
self.decoder_embed = nn.Linear(encoder_embed_dim, decoder_embed_dim, bias=True)
self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim))
self.decoder_pos_embed_learned = nn.Parameter(torch.zeros(1, self.seq_len + self.buffer_size, decoder_embed_dim))
self.decoder_blocks = nn.ModuleList([
Block(decoder_embed_dim, decoder_num_heads, mlp_ratio, qkv_bias=True, norm_layer=norm_layer,
proj_drop=proj_dropout, attn_drop=attn_dropout) for _ in range(decoder_depth)])
self.decoder_norm = norm_layer(decoder_embed_dim)
self.diffusion_pos_embed_learned = nn.Parameter(torch.zeros(1, self.seq_len, decoder_embed_dim))
self.initialize_weights()
# --------------------------------------------------------------------------
# Diffusion Loss
self.diffloss = DiffLoss(
target_channels=self.token_embed_dim,
z_channels=decoder_embed_dim,
width=diffloss_w,
depth=diffloss_d,
num_sampling_steps=num_sampling_steps,
grad_checkpointing=grad_checkpointing
)
self.diffusion_batch_mul = diffusion_batch_mul
def initialize_weights(self):
# parameters
torch.nn.init.normal_(self.class_emb.weight, std=.02)
torch.nn.init.normal_(self.fake_latent, std=.02)
torch.nn.init.normal_(self.mask_token, std=.02)
torch.nn.init.normal_(self.encoder_pos_embed_learned, std=.02)
torch.nn.init.normal_(self.decoder_pos_embed_learned, std=.02)
torch.nn.init.normal_(self.diffusion_pos_embed_learned, std=.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:
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):
if m.bias is not None:
nn.init.constant_(m.bias, 0)
if m.weight is not None:
nn.init.constant_(m.weight, 1.0)
def patchify(self, x):
bsz, c, h, w = x.shape
p = self.patch_size
h_, w_ = h // p, w // p
x = x.reshape(bsz, c, h_, p, w_, p)
x = torch.einsum('nchpwq->nhwcpq', x)
x = x.reshape(bsz, h_ * w_, c * p ** 2)
return x # [n, l, d]
def unpatchify(self, x):
bsz = x.shape[0]
p = self.patch_size
c = self.vae_embed_dim
h_, w_ = self.seq_h, self.seq_w
x = x.reshape(bsz, h_, w_, c, p, p)
x = torch.einsum('nhwcpq->nchpwq', x)
x = x.reshape(bsz, c, h_ * p, w_ * p)
return x # [n, c, h, w]
def sample_orders(self, bsz):
# generate a batch of random generation orders
orders = []
for _ in range(bsz):
order = np.array(list(range(self.seq_len)))
np.random.shuffle(order)
orders.append(order)
orders = torch.Tensor(np.array(orders)).cuda().long()
return orders
def random_masking(self, x, orders):
# generate token mask
bsz, seq_len, embed_dim = x.shape
mask_rate = self.mask_ratio_generator.rvs(1)[0]
num_masked_tokens = int(np.ceil(seq_len * mask_rate))
mask = torch.zeros(bsz, seq_len, device=x.device)
mask = torch.scatter(mask, dim=-1, index=orders[:, :num_masked_tokens],
src=torch.ones(bsz, seq_len, device=x.device))
return mask
def forward_mae_encoder(self, x, mask, class_embedding):
x = self.z_proj(x)
bsz, seq_len, embed_dim = x.shape
# concat buffer
x = torch.cat([torch.zeros(bsz, self.buffer_size, embed_dim, device=x.device), x], dim=1)
mask_with_buffer = torch.cat([torch.zeros(x.size(0), self.buffer_size, device=x.device), mask], dim=1)
# random drop class embedding during training
if self.training:
drop_latent_mask = torch.rand(bsz) < self.label_drop_prob
drop_latent_mask = drop_latent_mask.unsqueeze(-1).cuda().to(x.dtype)
class_embedding = drop_latent_mask * self.fake_latent + (1 - drop_latent_mask) * class_embedding
x[:, :self.buffer_size] = class_embedding.unsqueeze(1)
# encoder position embedding
x = x + self.encoder_pos_embed_learned
x = self.z_proj_ln(x)
# dropping
x = x[(1-mask_with_buffer).nonzero(as_tuple=True)].reshape(bsz, -1, embed_dim)
# apply Transformer blocks
if self.grad_checkpointing and not torch.jit.is_scripting():
for block in self.encoder_blocks:
x = checkpoint(block, x)
else:
for block in self.encoder_blocks:
x = block(x)
x = self.encoder_norm(x)
return x
def forward_mae_decoder(self, x, mask):
x = self.decoder_embed(x)
mask_with_buffer = torch.cat([torch.zeros(x.size(0), self.buffer_size, device=x.device), mask], dim=1)
# pad mask tokens
mask_tokens = self.mask_token.repeat(mask_with_buffer.shape[0], mask_with_buffer.shape[1], 1).to(x.dtype)
x_after_pad = mask_tokens.clone()
x_after_pad[(1 - mask_with_buffer).nonzero(as_tuple=True)] = x.reshape(x.shape[0] * x.shape[1], x.shape[2])
# decoder position embedding
x = x_after_pad + self.decoder_pos_embed_learned
# apply Transformer blocks
if self.grad_checkpointing and not torch.jit.is_scripting():
for block in self.decoder_blocks:
x = checkpoint(block, x)
else:
for block in self.decoder_blocks:
x = block(x)
x = self.decoder_norm(x)
x = x[:, self.buffer_size:]
x = x + self.diffusion_pos_embed_learned
return x
def forward_loss(self, z, target, mask):
bsz, seq_len, _ = target.shape
target = target.reshape(bsz * seq_len, -1).repeat(self.diffusion_batch_mul, 1)
z = z.reshape(bsz*seq_len, -1).repeat(self.diffusion_batch_mul, 1)
mask = mask.reshape(bsz*seq_len).repeat(self.diffusion_batch_mul)
loss = self.diffloss(z=z, target=target, mask=mask)
return loss
def forward(self, imgs, labels):
# class embed
class_embedding = self.class_emb(labels)
# patchify and mask (drop) tokens
x = self.patchify(imgs)
gt_latents = x.clone().detach()
orders = self.sample_orders(bsz=x.size(0))
mask = self.random_masking(x, orders)
# mae encoder
x = self.forward_mae_encoder(x, mask, class_embedding)
# mae decoder
z = self.forward_mae_decoder(x, mask)
# diffloss
loss = self.forward_loss(z=z, target=gt_latents, mask=mask)
return loss
def sample_tokens(self, bsz, num_iter=64, cfg=1.0, cfg_schedule="linear", labels=None, temperature=1.0, progress=False):
# init and sample generation orders
mask = torch.ones(bsz, self.seq_len).cuda()
tokens = torch.zeros(bsz, self.seq_len, self.token_embed_dim).cuda()
orders = self.sample_orders(bsz)
indices = list(range(num_iter))
if progress:
indices = tqdm(indices)
# generate latents
for step in indices:
cur_tokens = tokens.clone()
print(cur_tokens.shape)
print("+++++++++++++++++++++++")
# class embedding and CFG
if labels is not None:
class_embedding = self.class_emb(labels)
else:
class_embedding = self.fake_latent.repeat(bsz, 1)
if not cfg == 1.0:
tokens = torch.cat([tokens, tokens], dim=0)
class_embedding = torch.cat([class_embedding, self.fake_latent.repeat(bsz, 1)], dim=0)
mask = torch.cat([mask, mask], dim=0)
# mae encoder
x = self.forward_mae_encoder(tokens, mask, class_embedding)
# mae decoder
z = self.forward_mae_decoder(x, mask)
# mask ratio for the next round, following MaskGIT and MAGE.
mask_ratio = np.cos(math.pi / 2. * (step + 1) / num_iter)
mask_len = torch.Tensor([np.floor(self.seq_len * mask_ratio)]).cuda()
# masks out at least one for the next iteration
mask_len = torch.maximum(torch.Tensor([1]).cuda(),
torch.minimum(torch.sum(mask, dim=-1, keepdims=True) - 1, mask_len))
# get masking for next iteration and locations to be predicted in this iteration
mask_next = mask_by_order(mask_len[0], orders, bsz, self.seq_len)
if step >= num_iter - 1:
mask_to_pred = mask[:bsz].bool()
else:
mask_to_pred = torch.logical_xor(mask[:bsz].bool(), mask_next.bool())
mask = mask_next
if not cfg == 1.0:
mask_to_pred = torch.cat([mask_to_pred, mask_to_pred], dim=0)
# sample token latents for this step
print(z.shape)
z = z[mask_to_pred.nonzero(as_tuple=True)]
print(z.shape)
print("==============================")
# cfg schedule follow Muse
if cfg_schedule == "linear":
cfg_iter = 1 + (cfg - 1) * (self.seq_len - mask_len[0]) / self.seq_len
elif cfg_schedule == "constant":
cfg_iter = cfg
else:
raise NotImplementedError
sampled_token_latent = self.diffloss.sample(z, temperature, cfg_iter)
if not cfg == 1.0:
sampled_token_latent, _ = sampled_token_latent.chunk(2, dim=0) # Remove null class samples
mask_to_pred, _ = mask_to_pred.chunk(2, dim=0)
cur_tokens[mask_to_pred.nonzero(as_tuple=True)] = sampled_token_latent
tokens = cur_tokens.clone()
# unpatchify
print(tokens.shape)
tokens = self.unpatchify(tokens)
print(tokens.shape)
return tokens
def mar_base(**kwargs):
model = MAR(
encoder_embed_dim=768, encoder_depth=12, encoder_num_heads=12,
decoder_embed_dim=768, decoder_depth=12, decoder_num_heads=12,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def mar_large(**kwargs):
model = MAR(
encoder_embed_dim=1024, encoder_depth=16, encoder_num_heads=16,
decoder_embed_dim=1024, decoder_depth=16, decoder_num_heads=16,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def mar_huge(**kwargs):
model = MAR(
encoder_embed_dim=1280, encoder_depth=20, encoder_num_heads=16,
decoder_embed_dim=1280, decoder_depth=20, decoder_num_heads=16,
mlp_ratio=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model |