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Mido Assran
2023-06-13 13:03:30 +00:00
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# 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.
#
import math
from functools import partial
import numpy as np
import torch
import torch.nn as nn
from src.utils.tensors import (
trunc_normal_,
repeat_interleave_batch
)
from src.masks.utils import apply_masks
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
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=float)
grid_w = np.arange(grid_size, dtype=float)
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:
pos_embed = np.concatenate([np.zeros([1, 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(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid length
return:
pos_embed: [grid_size, embed_dim] or [1+grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid = np.arange(grid_size, dtype=float)
pos_embed = get_1d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed
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=float)
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
def drop_path(x, drop_prob: float = 0., training: bool = False):
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
class MLP(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
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)
def forward(self, x):
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)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
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, attn
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
def forward(self, x, return_attention=False):
y, attn = self.attn(self.norm1(x))
if return_attention:
return attn
x = x + self.drop_path(y)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
num_patches = (img_size // patch_size) * (img_size // patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class ConvEmbed(nn.Module):
"""
3x3 Convolution stems for ViT following ViTC models
"""
def __init__(self, channels, strides, img_size=224, in_chans=3, batch_norm=True):
super().__init__()
# Build the stems
stem = []
channels = [in_chans] + channels
for i in range(len(channels) - 2):
stem += [nn.Conv2d(channels[i], channels[i+1], kernel_size=3,
stride=strides[i], padding=1, bias=(not batch_norm))]
if batch_norm:
stem += [nn.BatchNorm2d(channels[i+1])]
stem += [nn.ReLU(inplace=True)]
stem += [nn.Conv2d(channels[-2], channels[-1], kernel_size=1, stride=strides[-1])]
self.stem = nn.Sequential(*stem)
# Comptute the number of patches
stride_prod = int(np.prod(strides))
self.num_patches = (img_size[0] // stride_prod)**2
def forward(self, x):
p = self.stem(x)
return p.flatten(2).transpose(1, 2)
class VisionTransformerPredictor(nn.Module):
""" Vision Transformer """
def __init__(
self,
num_patches,
embed_dim=768,
predictor_embed_dim=384,
depth=6,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
init_std=0.02,
**kwargs
):
super().__init__()
self.predictor_embed = nn.Linear(embed_dim, predictor_embed_dim, bias=True)
self.mask_token = nn.Parameter(torch.zeros(1, 1, predictor_embed_dim))
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
# --
self.predictor_pos_embed = nn.Parameter(torch.zeros(1, num_patches, predictor_embed_dim),
requires_grad=False)
predictor_pos_embed = get_2d_sincos_pos_embed(self.predictor_pos_embed.shape[-1],
int(num_patches**.5),
cls_token=False)
self.predictor_pos_embed.data.copy_(torch.from_numpy(predictor_pos_embed).float().unsqueeze(0))
# --
self.predictor_blocks = nn.ModuleList([
Block(
dim=predictor_embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.predictor_norm = norm_layer(predictor_embed_dim)
self.predictor_proj = nn.Linear(predictor_embed_dim, embed_dim, bias=True)
# ------
self.init_std = init_std
trunc_normal_(self.mask_token, std=self.init_std)
self.apply(self._init_weights)
self.fix_init_weight()
def fix_init_weight(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.predictor_blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=self.init_std)
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):
trunc_normal_(m.weight, std=self.init_std)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x, masks_x, masks):
assert (masks is not None) and (masks_x is not None), 'Cannot run predictor without mask indices'
if not isinstance(masks_x, list):
masks_x = [masks_x]
if not isinstance(masks, list):
masks = [masks]
# -- Batch Size
B = len(x) // len(masks_x)
# -- map from encoder-dim to pedictor-dim
x = self.predictor_embed(x)
# -- add positional embedding to x tokens
x_pos_embed = self.predictor_pos_embed.repeat(B, 1, 1)
x += apply_masks(x_pos_embed, masks_x)
_, N_ctxt, D = x.shape
# -- concat mask tokens to x
pos_embs = self.predictor_pos_embed.repeat(B, 1, 1)
pos_embs = apply_masks(pos_embs, masks)
pos_embs = repeat_interleave_batch(pos_embs, B, repeat=len(masks_x))
# --
pred_tokens = self.mask_token.repeat(pos_embs.size(0), pos_embs.size(1), 1)
# --
pred_tokens += pos_embs
x = x.repeat(len(masks), 1, 1)
x = torch.cat([x, pred_tokens], dim=1)
# -- fwd prop
for blk in self.predictor_blocks:
x = blk(x)
x = self.predictor_norm(x)
# -- return preds for mask tokens
x = x[:, N_ctxt:]
x = self.predictor_proj(x)
return x
class VisionTransformer(nn.Module):
""" Vision Transformer """
def __init__(
self,
img_size=[224],
patch_size=16,
in_chans=3,
embed_dim=768,
predictor_embed_dim=384,
depth=12,
predictor_depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
init_std=0.02,
**kwargs
):
super().__init__()
self.num_features = self.embed_dim = embed_dim
self.num_heads = num_heads
# --
self.patch_embed = PatchEmbed(
img_size=img_size[0],
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
# --
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim), requires_grad=False)
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))
# --
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
# ------
self.init_std = init_std
self.apply(self._init_weights)
self.fix_init_weight()
def fix_init_weight(self):
def rescale(param, layer_id):
param.div_(math.sqrt(2.0 * layer_id))
for layer_id, layer in enumerate(self.blocks):
rescale(layer.attn.proj.weight.data, layer_id + 1)
rescale(layer.mlp.fc2.weight.data, layer_id + 1)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=self.init_std)
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):
trunc_normal_(m.weight, std=self.init_std)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def forward(self, x, masks=None):
if masks is not None:
if not isinstance(masks, list):
masks = [masks]
# -- patchify x
x = self.patch_embed(x)
B, N, D = x.shape
# -- add positional embedding to x
pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
x = x + pos_embed
# -- mask x
if masks is not None:
x = apply_masks(x, masks)
# -- fwd prop
for i, blk in enumerate(self.blocks):
x = blk(x)
if self.norm is not None:
x = self.norm(x)
return x
def interpolate_pos_encoding(self, x, pos_embed):
npatch = x.shape[1] - 1
N = pos_embed.shape[1] - 1
if npatch == N:
return pos_embed
class_emb = pos_embed[:, 0]
pos_embed = pos_embed[:, 1:]
dim = x.shape[-1]
pos_embed = nn.functional.interpolate(
pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
scale_factor=math.sqrt(npatch / N),
mode='bicubic',
)
pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_emb.unsqueeze(0), pos_embed), dim=1)
def vit_predictor(**kwargs):
model = VisionTransformerPredictor(
mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs)
return model
def vit_tiny(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def vit_small(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def vit_base(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def vit_large(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def vit_huge(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size, embed_dim=1280, depth=32, num_heads=16, mlp_ratio=4,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
def vit_giant(patch_size=16, **kwargs):
model = VisionTransformer(
patch_size=patch_size, embed_dim=1408, depth=40, num_heads=16, mlp_ratio=48/11,
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
return model
VIT_EMBED_DIMS = {
'vit_tiny': 192,
'vit_small': 384,
'vit_base': 768,
'vit_large': 1024,
'vit_huge': 1280,
'vit_giant': 1408,
}