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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
Based on timm code base
https://github.com/rwightman/pytorch-image-models/tree/master/timm
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import _cfg, PatchEmbed
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_, DropPath
from timm.models.helpers import named_apply, adapt_input_conv
from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
from lavis.models.base_model import BaseEncoder
class Mlp(nn.Module):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.0,
):
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=0.0,
proj_drop=0.0,
):
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.attn_gradients = None
self.attention_map = None
def save_attn_gradients(self, attn_gradients):
self.attn_gradients = attn_gradients
def get_attn_gradients(self):
return self.attn_gradients
def save_attention_map(self, attention_map):
self.attention_map = attention_map
def get_attention_map(self):
return self.attention_map
def forward(self, x, register_hook=False):
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],
) # make torchscript happy (cannot use tensor as tuple)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
if register_hook:
self.save_attention_map(attn)
attn.register_hook(self.save_attn_gradients)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.0,
qkv_bias=False,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
use_grad_checkpointing=False,
):
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,
)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0.0 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,
)
if use_grad_checkpointing:
self.attn = checkpoint_wrapper(self.attn)
self.mlp = checkpoint_wrapper(self.mlp)
def forward(self, x, register_hook=False):
x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class VisionTransformer(nn.Module):
"""Vision Transformer
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
https://arxiv.org/abs/2010.11929
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
representation_size=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.0,
norm_layer=None,
use_grad_checkpointing=False,
ckpt_layer=0,
):
"""
Args:
img_size (int, tuple): input image size
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
drop_rate (float): dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
norm_layer: (nn.Module): normalization layer
"""
super().__init__()
self.num_features = (
self.embed_dim
) = embed_dim # num_features for consistency with other models
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
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,
use_grad_checkpointing=(
use_grad_checkpointing and i >= depth - ckpt_layer
),
)
for i in range(depth)
]
)
self.norm = norm_layer(embed_dim)
trunc_normal_(self.pos_embed, std=0.02)
trunc_normal_(self.cls_token, std=0.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
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)
@torch.jit.ignore
def no_weight_decay(self):
return {"pos_embed", "cls_token"}
def forward(self, x, register_blk=-1):
B = x.shape[0]
x = self.patch_embed(x)
cls_tokens = self.cls_token.expand(
B, -1, -1
) # stole cls_tokens impl from Phil Wang, thanks
x = torch.cat((cls_tokens, x), dim=1)
x = x + self.pos_embed[:, : x.size(1), :]
x = self.pos_drop(x)
for i, blk in enumerate(self.blocks):
x = blk(x, register_blk == i)
x = self.norm(x)
return x
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path, prefix=""):
_load_weights(self, checkpoint_path, prefix)
@torch.no_grad()
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ""):
"""Load weights from .npz checkpoints for official Google Brain Flax implementation"""
import numpy as np
def _n2p(w, t=True):
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
w = w.flatten()
if t:
if w.ndim == 4:
w = w.transpose([3, 2, 0, 1])
elif w.ndim == 3:
w = w.transpose([2, 0, 1])
elif w.ndim == 2:
w = w.transpose([1, 0])
return torch.from_numpy(w)
w = np.load(checkpoint_path)
if not prefix and "opt/target/embedding/kernel" in w:
prefix = "opt/target/"
if hasattr(model.patch_embed, "backbone"):
# hybrid
backbone = model.patch_embed.backbone
stem_only = not hasattr(backbone, "stem")
stem = backbone if stem_only else backbone.stem
stem.conv.weight.copy_(
adapt_input_conv(
stem.conv.weight.shape[1], _n2p(w[f"{prefix}conv_root/kernel"])
)
)
stem.norm.weight.copy_(_n2p(w[f"{prefix}gn_root/scale"]))
stem.norm.bias.copy_(_n2p(w[f"{prefix}gn_root/bias"]))
if not stem_only:
for i, stage in enumerate(backbone.stages):
for j, block in enumerate(stage.blocks):
bp = f"{prefix}block{i + 1}/unit{j + 1}/"
for r in range(3):
getattr(block, f"conv{r + 1}").weight.copy_(
_n2p(w[f"{bp}conv{r + 1}/kernel"])
)
getattr(block, f"norm{r + 1}").weight.copy_(
_n2p(w[f"{bp}gn{r + 1}/scale"])
)
getattr(block, f"norm{r + 1}").bias.copy_(
_n2p(w[f"{bp}gn{r + 1}/bias"])
)
if block.downsample is not None:
block.downsample.conv.weight.copy_(
_n2p(w[f"{bp}conv_proj/kernel"])
)
block.downsample.norm.weight.copy_(
_n2p(w[f"{bp}gn_proj/scale"])
)
block.downsample.norm.bias.copy_(_n2p(w[f"{bp}gn_proj/bias"]))
embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"])
else:
embed_conv_w = adapt_input_conv(
model.patch_embed.proj.weight.shape[1], _n2p(w[f"{prefix}embedding/kernel"])
)
model.patch_embed.proj.weight.copy_(embed_conv_w)
model.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"]))
model.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False))
pos_embed_w = _n2p(w[f"{prefix}Transformer/posembed_input/pos_embedding"], t=False)
if pos_embed_w.shape != model.pos_embed.shape:
pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
pos_embed_w,
model.pos_embed,
getattr(model, "num_tokens", 1),
model.patch_embed.grid_size,
)
model.pos_embed.copy_(pos_embed_w)
model.norm.weight.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/scale"]))
model.norm.bias.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/bias"]))
# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
for i, block in enumerate(model.blocks.children()):
block_prefix = f"{prefix}Transformer/encoderblock_{i}/"
mha_prefix = block_prefix + "MultiHeadDotProductAttention_1/"
block.norm1.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/scale"]))
block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"]))
block.attn.qkv.weight.copy_(
torch.cat(
[
_n2p(w[f"{mha_prefix}{n}/kernel"], t=False).flatten(1).T
for n in ("query", "key", "value")
]
)
)
block.attn.qkv.bias.copy_(
torch.cat(
[
_n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1)
for n in ("query", "key", "value")
]
)
)
block.attn.proj.weight.copy_(_n2p(w[f"{mha_prefix}out/kernel"]).flatten(1))
block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"]))
for r in range(2):
getattr(block.mlp, f"fc{r + 1}").weight.copy_(
_n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/kernel"])
)
getattr(block.mlp, f"fc{r + 1}").bias.copy_(
_n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/bias"])
)
block.norm2.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/scale"]))
block.norm2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/bias"]))
def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
print("Resized position embedding: %s to %s", posemb.shape, posemb_new.shape)
ntok_new = posemb_new.shape[1]
if num_tokens:
posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
ntok_new -= num_tokens
else:
posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
gs_old = int(math.sqrt(len(posemb_grid)))
if not len(gs_new): # backwards compatibility
gs_new = [int(math.sqrt(ntok_new))] * 2
assert len(gs_new) >= 2
print("Position embedding grid-size from %s to %s", [gs_old, gs_old], gs_new)
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(
posemb_grid, size=gs_new, mode="bicubic", align_corners=False
)
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
return
def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
# interpolate position embedding
embedding_size = pos_embed_checkpoint.shape[-1]
num_patches = visual_encoder.patch_embed.num_patches
num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
# height (== width) for the checkpoint position embedding
orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
# height (== width) for the new position embedding
new_size = int(num_patches**0.5)
if orig_size != new_size:
# class_token and dist_token are kept unchanged
extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
# only the position tokens are interpolated
pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
pos_tokens = pos_tokens.reshape(
-1, orig_size, orig_size, embedding_size
).permute(0, 3, 1, 2)
pos_tokens = torch.nn.functional.interpolate(
pos_tokens, size=(new_size, new_size), mode="bicubic", align_corners=False
)
pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
print(
"reshape position embedding from %d to %d" % (orig_size**2, new_size**2)
)
return new_pos_embed
else:
return pos_embed_checkpoint
class VisionTransformerEncoder(VisionTransformer, BaseEncoder):
@classmethod
def from_config(cls, cfg, from_pretrained=False):
vit_type = cfg.get("vit_type", "base")
image_size = cfg.get("image_size", 384)
ckpt_layer = cfg.get("vit_ckpt_layer", 0)
drop_path_rate = cfg.get("vit_drop_path_rate", 0)
norm_layer_eps = cfg.get("vit_layer_norm_epsilon", -1)
use_grad_checkpointing = cfg.get("vit_grad_ckpt", False)
if norm_layer_eps == -1:
norm_layer = None
else:
norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
# norm_layer=partial(nn.LayerNorm, eps=1e-6),
assert vit_type in ["base", "large"], "vit parameter must be base or large"
if vit_type == "base":
vision_width = 768
visual_encoder = cls(
img_size=image_size,
patch_size=16,
embed_dim=vision_width,
depth=12,
num_heads=12,
use_grad_checkpointing=use_grad_checkpointing,
ckpt_layer=ckpt_layer,
drop_path_rate=0 or drop_path_rate,
norm_layer=norm_layer,
)
if from_pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
map_location="cpu",
check_hash=True,
)
state_dict = checkpoint["model"]
state_dict["pos_embed"] = interpolate_pos_embed(
state_dict["pos_embed"], visual_encoder
)
msg = visual_encoder.load_state_dict(state_dict, strict=False)
elif vit_type == "large":
vision_width = 1024
visual_encoder = cls(
img_size=image_size,
patch_size=16,
embed_dim=vision_width,
depth=24,
num_heads=16,
use_grad_checkpointing=use_grad_checkpointing,
ckpt_layer=ckpt_layer,
drop_path_rate=0.1 or drop_path_rate,
norm_layer=norm_layer,
)
if from_pretrained:
from timm.models.helpers import load_custom_pretrained
from timm.models.vision_transformer import default_cfgs
load_custom_pretrained(
visual_encoder, default_cfgs["vit_large_patch16_224_in21k"]
)
visual_encoder.vision_width = vision_width
return visual_encoder
def forward_features(self, x, register_blk=-1):
return super().forward(x, register_blk)