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# Based on EVA, BEIT, timm and DeiT code bases | |
# https://github.com/baaivision/EVA | |
# https://github.com/rwightman/pytorch-image-models/tree/master/timm | |
# https://github.com/microsoft/unilm/tree/master/beit | |
# https://github.com/facebookresearch/deit/ | |
# https://github.com/facebookresearch/dino | |
# --------------------------------------------------------' | |
import math | |
from functools import partial | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.utils.checkpoint as checkpoint | |
from timm.models.layers import drop_path, to_2tuple, trunc_normal_ | |
from timm.models.registry import register_model | |
from minigpt4.common.dist_utils import download_cached_file | |
def _cfg(url='', **kwargs): | |
return { | |
'url': url, | |
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, | |
'crop_pct': .9, 'interpolation': 'bicubic', | |
'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), | |
**kwargs | |
} | |
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) | |
def extra_repr(self) -> str: | |
return 'p={}'.format(self.drop_prob) | |
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) | |
# commit this for the orignal BERT implement | |
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., window_size=None, attn_head_dim=None): | |
super().__init__() | |
self.num_heads = num_heads | |
head_dim = dim // num_heads | |
if attn_head_dim is not None: | |
head_dim = attn_head_dim | |
all_head_dim = head_dim * self.num_heads | |
self.scale = qk_scale or head_dim ** -0.5 | |
self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) | |
if qkv_bias: | |
self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) | |
self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) | |
else: | |
self.q_bias = None | |
self.v_bias = None | |
if window_size: | |
self.window_size = window_size | |
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 | |
self.relative_position_bias_table = nn.Parameter( | |
torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH | |
# cls to token & token 2 cls & cls to cls | |
# get pair-wise relative position index for each token inside the window | |
coords_h = torch.arange(window_size[0]) | |
coords_w = torch.arange(window_size[1]) | |
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww | |
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 | |
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 | |
relative_coords[:, :, 1] += window_size[1] - 1 | |
relative_coords[:, :, 0] *= 2 * window_size[1] - 1 | |
relative_position_index = \ | |
torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype) | |
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww | |
relative_position_index[0, 0:] = self.num_relative_distance - 3 | |
relative_position_index[0:, 0] = self.num_relative_distance - 2 | |
relative_position_index[0, 0] = self.num_relative_distance - 1 | |
self.register_buffer("relative_position_index", relative_position_index) | |
else: | |
self.window_size = None | |
self.relative_position_bias_table = None | |
self.relative_position_index = None | |
self.attn_drop = nn.Dropout(attn_drop) | |
self.proj = nn.Linear(all_head_dim, dim) | |
self.proj_drop = nn.Dropout(proj_drop) | |
def forward(self, x, rel_pos_bias=None): | |
B, N, C = x.shape | |
qkv_bias = None | |
if self.q_bias is not None: | |
qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) | |
# qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) | |
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) | |
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) | |
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) | |
q = q * self.scale | |
attn = (q @ k.transpose(-2, -1)) | |
if self.relative_position_bias_table is not None: | |
relative_position_bias = \ | |
self.relative_position_bias_table[self.relative_position_index.view(-1)].view( | |
self.window_size[0] * self.window_size[1] + 1, | |
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH | |
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww | |
attn = attn + relative_position_bias.unsqueeze(0) | |
if rel_pos_bias is not None: | |
attn = attn + rel_pos_bias | |
attn = attn.softmax(dim=-1) | |
attn = self.attn_drop(attn) | |
x = (attn @ v).transpose(1, 2).reshape(B, N, -1) | |
x = self.proj(x) | |
x = self.proj_drop(x) | |
return x | |
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., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm, | |
window_size=None, attn_head_dim=None): | |
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, window_size=window_size, attn_head_dim=attn_head_dim) | |
# 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. 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 init_values is not None and init_values > 0: | |
self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) | |
self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True) | |
else: | |
self.gamma_1, self.gamma_2 = None, None | |
def forward(self, x, rel_pos_bias=None): | |
if self.gamma_1 is None: | |
x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)) | |
x = x + self.drop_path(self.mlp(self.norm2(x))) | |
else: | |
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)) | |
x = x + self.drop_path(self.gamma_2 * 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__() | |
img_size = to_2tuple(img_size) | |
patch_size = to_2tuple(patch_size) | |
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) | |
self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) | |
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, **kwargs): | |
B, C, H, W = x.shape | |
# FIXME look at relaxing size constraints | |
assert H == self.img_size[0] and W == self.img_size[1], \ | |
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." | |
x = self.proj(x).flatten(2).transpose(1, 2) | |
return x | |
class RelativePositionBias(nn.Module): | |
def __init__(self, window_size, num_heads): | |
super().__init__() | |
self.window_size = window_size | |
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 | |
self.relative_position_bias_table = nn.Parameter( | |
torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH | |
# cls to token & token 2 cls & cls to cls | |
# get pair-wise relative position index for each token inside the window | |
coords_h = torch.arange(window_size[0]) | |
coords_w = torch.arange(window_size[1]) | |
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww | |
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 | |
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0 | |
relative_coords[:, :, 1] += window_size[1] - 1 | |
relative_coords[:, :, 0] *= 2 * window_size[1] - 1 | |
relative_position_index = \ | |
torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype) | |
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww | |
relative_position_index[0, 0:] = self.num_relative_distance - 3 | |
relative_position_index[0:, 0] = self.num_relative_distance - 2 | |
relative_position_index[0, 0] = self.num_relative_distance - 1 | |
self.register_buffer("relative_position_index", relative_position_index) | |
# trunc_normal_(self.relative_position_bias_table, std=.02) | |
def forward(self): | |
relative_position_bias = \ | |
self.relative_position_bias_table[self.relative_position_index.view(-1)].view( | |
self.window_size[0] * self.window_size[1] + 1, | |
self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH | |
return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww | |
class VisionTransformer(nn.Module): | |
""" Vision Transformer with support for patch or hybrid CNN input stage | |
""" | |
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., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., | |
drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None, | |
use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, | |
use_mean_pooling=True, init_scale=0.001, use_checkpoint=False): | |
super().__init__() | |
self.image_size = img_size | |
self.num_classes = num_classes | |
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models | |
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)) | |
if use_abs_pos_emb: | |
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) | |
else: | |
self.pos_embed = None | |
self.pos_drop = nn.Dropout(p=drop_rate) | |
if use_shared_rel_pos_bias: | |
self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) | |
else: | |
self.rel_pos_bias = None | |
self.use_checkpoint = use_checkpoint | |
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule | |
self.use_rel_pos_bias = use_rel_pos_bias | |
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, | |
init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None) | |
for i in range(depth)]) | |
# self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) | |
# self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None | |
# self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() | |
if self.pos_embed is not None: | |
trunc_normal_(self.pos_embed, std=.02) | |
trunc_normal_(self.cls_token, std=.02) | |
# trunc_normal_(self.mask_token, std=.02) | |
# if isinstance(self.head, nn.Linear): | |
# trunc_normal_(self.head.weight, std=.02) | |
self.apply(self._init_weights) | |
self.fix_init_weight() | |
# if isinstance(self.head, nn.Linear): | |
# self.head.weight.data.mul_(init_scale) | |
# self.head.bias.data.mul_(init_scale) | |
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=.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) | |
def get_classifier(self): | |
return self.head | |
def reset_classifier(self, num_classes, global_pool=''): | |
self.num_classes = num_classes | |
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() | |
def forward_features(self, x): | |
x = self.patch_embed(x) | |
batch_size, seq_len, _ = x.size() | |
cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks | |
x = torch.cat((cls_tokens, x), dim=1) | |
if self.pos_embed is not None: | |
x = x + self.pos_embed | |
x = self.pos_drop(x) | |
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None | |
for blk in self.blocks: | |
if self.use_checkpoint: | |
x = checkpoint.checkpoint(blk, x, rel_pos_bias) | |
else: | |
x = blk(x, rel_pos_bias) | |
return x | |
# x = self.norm(x) | |
# if self.fc_norm is not None: | |
# t = x[:, 1:, :] | |
# return self.fc_norm(t.mean(1)) | |
# else: | |
# return x[:, 0] | |
def forward(self, x): | |
x = self.forward_features(x) | |
# x = self.head(x) | |
return x | |
def get_intermediate_layers(self, x): | |
x = self.patch_embed(x) | |
batch_size, seq_len, _ = x.size() | |
cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks | |
x = torch.cat((cls_tokens, x), dim=1) | |
if self.pos_embed is not None: | |
x = x + self.pos_embed | |
x = self.pos_drop(x) | |
features = [] | |
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None | |
for blk in self.blocks: | |
x = blk(x, rel_pos_bias) | |
features.append(x) | |
return features | |
def interpolate_pos_embed(model, checkpoint_model): | |
if 'pos_embed' in checkpoint_model: | |
pos_embed_checkpoint = checkpoint_model['pos_embed'].float() | |
embedding_size = pos_embed_checkpoint.shape[-1] | |
num_patches = model.patch_embed.num_patches | |
num_extra_tokens = model.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) | |
# class_token and dist_token are kept unchanged | |
if orig_size != new_size: | |
print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) | |
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) | |
checkpoint_model['pos_embed'] = new_pos_embed | |
def convert_weights_to_fp16(model: nn.Module): | |
"""Convert applicable model parameters to fp16""" | |
def _convert_weights_to_fp16(l): | |
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): | |
l.weight.data = l.weight.data.half() | |
if l.bias is not None: | |
l.bias.data = l.bias.data.half() | |
# if isinstance(l, (nn.MultiheadAttention, Attention)): | |
# for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: | |
# tensor = getattr(l, attr) | |
# if tensor is not None: | |
# tensor.data = tensor.data.half() | |
model.apply(_convert_weights_to_fp16) | |
def create_eva_vit_g(img_size=224,drop_path_rate=0.4,use_checkpoint=False,precision="fp16"): | |
model = VisionTransformer( | |
img_size=img_size, | |
patch_size=14, | |
use_mean_pooling=False, | |
embed_dim=1408, | |
depth=39, | |
# depth = 37, | |
num_heads=1408//88, | |
mlp_ratio=4.3637, | |
qkv_bias=True, | |
drop_path_rate=drop_path_rate, | |
norm_layer=partial(nn.LayerNorm, eps=1e-6), | |
use_checkpoint=use_checkpoint, | |
) | |
url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth" | |
cached_file = download_cached_file( | |
url, check_hash=False, progress=True | |
) | |
state_dict = torch.load(cached_file, map_location="cpu") | |
interpolate_pos_embed(model,state_dict) | |
incompatible_keys = model.load_state_dict(state_dict, strict=False) | |
# print(incompatible_keys) | |
if precision == "fp16": | |
# model.to("cuda") | |
convert_weights_to_fp16(model) | |
return model |