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models_simmim_pvt.py
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models_simmim_pvt.py
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# 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
# SimMIM: https://github.com/microsoft/SimMIM
# --------------------------------------------------------
from functools import partial
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.vision_transformer import Block
from timm.models.layers import DropPath, to_2tuple
from util.pos_embed import get_2d_sincos_pos_embed
from einops import rearrange
from models_mae_pvt import PatchEmbed, PatchMerge, PVTBlock
class SimMIMPVT(nn.Module):
""" SimMIM with Pyramid Vision Transformer backbone
"""
def __init__(self, img_size=224, patch_size=4, in_chans=3, stride=16,
embed_dims=[64, 128, 320, 512], depths=[3, 4, 6, 3], num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4], sr_ratios=[8, 4, 2, 1],
norm_layer=nn.LayerNorm, norm_pix_loss=False):
super().__init__()
self.embed_dims = embed_dims
self.stride = stride
self.kernel_stride = stride // patch_size
self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dims[0]))
# --------------------------------------------------------------------------
# MAE encoder specifics
self.patch_embed = PatchEmbed(img_size, patch_size, in_chans, embed_dims[0])
num_patches = self.patch_embed.num_patches
self.patch_size = patch_size
self.embed_h = self.embed_w = int(self.patch_embed.num_patches ** 0.5)
self.patches_resolution = self.patch_embed.patches_resolution
self.num_layers = len(depths)
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dims[0]), requires_grad=False) # fixed sin-cos embedding
self.kernel = torch.ones(embed_dims[0], 1, 2, 2)
self.blocks = nn.ModuleList()
for i_layer in range(self.num_layers):
for dep in range(depths[i_layer]):
downsample_flag = (i_layer > 0) and (dep == 0)
layer = PVTBlock(dim=embed_dims[i_layer],
num_heads=num_heads[i_layer],
sr_ratio=sr_ratios[i_layer],
mlp_ratio=mlp_ratios[i_layer],
qkv_bias=True, qk_scale=None,
drop_path=0.,
downsample=PatchMerge(
patch_size=2,
in_chans=embed_dims[i_layer - 1],
embed_dim=embed_dims[i_layer]
) if downsample_flag else None
)
self.blocks.append(layer)
self.norm = norm_layer(embed_dims[-1])
# --------------------------------------------------------------------------
# --------------------------------------------------------------------------
# MAE decoder specifics
self.decoder_pred = nn.Linear(embed_dims[-1], 4 * stride**2 * in_chans, bias=True)
self.decoder_shuffle = nn.PixelShuffle(2)
# --------------------------------------------------------------------------
self.norm_pix_loss = norm_pix_loss
self.initialize_weights()
def initialize_weights(self):
# initialization
# initialize (and freeze) pos_embed by sin-cos embedding
pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.patch_embed.num_patches**.5), cls_token=False)
self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
# timm's trunc_normal_(std=.02) is effectively normal_(std=0.02) as cutoff is too big (2.)
torch.nn.init.normal_(self.mask_token, std=.02)
# initialize nn.Linear and nn.LayerNorm
self.apply(self._init_weights)
# initialize patch_embed like nn.Linear (instead of nn.Conv2d)
w = self.patch_embed.proj.weight.data
torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def unpatchify(self, x, stride=16):
"""
x: (N, L, patch_size**2 *3)
imgs: (N, 3, H, W)
"""
p = stride
h = w = int(x.shape[1]**.5)
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, 3))
x = torch.einsum('nhwpqc->nchpwq', x)
imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p))
return imgs
def patchify(self, imgs, stride=16):
"""
imgs: (N, 3, H, W)
x: (N, L, patch_size**2 *3)
"""
p = stride
assert imgs.shape[2] == imgs.shape[3] and imgs.shape[2] % p == 0
h = w = imgs.shape[2] // p
x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p))
x = torch.einsum('nchpwq->nhwpqc', x)
x = x.reshape(shape=(imgs.shape[0], h * w, p**2 * 3))
return x
def forward_encoder(self, x, mask):
N, _, H, W = x.size()
# embed patches
x = self.patch_embed(x)
H, W = H//self.patch_size, W//self.patch_size
L = mask.size(1)
M = int(L**0.5)
scale = self.embed_h // M
mask = mask.reshape(N, M, M)
mask = mask.repeat_interleave(scale, 1).repeat_interleave(scale, 2)
N, L, _ = x.size()
mask_tokens = self.mask_token.expand(N, L, -1)
w = mask.flatten(1).unsqueeze(-1).type_as(mask_tokens)
x = x * (1. - w) + mask_tokens * w
# add position embedding
x = x + self.pos_embed
# apply Transformer blocks
for blk in self.blocks:
x, (H, W) = blk(x, H, W)
x = self.norm(x)
return x
def forward_decoder(self, x):
x = self.decoder_pred(x) # 4 * p^2*3
N, L, D = x.shape
M = int(L**0.5)
x = self.decoder_shuffle(x.permute(0, 2, 1).reshape(N, D, M, M)).flatten(2)
x = x.permute(0, 2, 1)
return x
def forward_loss(self, imgs, pred, mask):
"""
imgs: [N, 3, H, W]
pred: [N, mask, p*p*3]
mask: [N, L], 0 is keep, 1 is remove,
"""
target = self.patchify(imgs, self.stride)
N, _, D = target.shape
target = target[mask].reshape(N, -1, D)
if self.norm_pix_loss:
mean = target.mean(dim=-1, keepdim=True)
var = target.var(dim=-1, keepdim=True)
target = (target - mean) / (var + 1.e-6)**.5 # (N, L, p*p*3)
# add new type
pred = pred[mask].reshape(N, -1, D)
#loss = (pred - target) ** 2
loss = (pred - target).abs()
loss = loss.mean()
return loss
def forward(self, imgs, mask):
latent = self.forward_encoder(imgs, mask) # returned mask may change
# pred, mask_num = self.forward_decoder(latent, mask) # [N, L, p*p*3]
pred = self.forward_decoder(latent)
loss = self.forward_loss(imgs, pred, mask)
return loss, pred, mask
def simmim_pvt_small_256(**kwargs):
model = SimMIMPVT(
img_size=256, patch_size=4, in_chans=3, stride=16,
embed_dims=[64, 128, 320, 512], depths=[3, 4, 6, 3], num_heads=[1, 2, 5, 8],
mlp_ratios=[8, 8, 4, 4], sr_ratios=[8, 4, 2, 1], # [8, 4, 2, 1] for finetune
norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model