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""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
Status/TODO:
* Models updated to be compatible with official impl. Args added to support backward compat for old PyTorch weights.
* Weights ported from official jax impl for 384x384 base and small models, 16x16 and 32x32 patches.
* Trained (supervised on ImageNet-1k) my custom 'small' patch model to 77.9, 'base' to 79.4 top-1 with this code.
* Hopefully find time and GPUs for SSL or unsupervised pretraining on OpenImages w/ ImageNet fine-tune in future.
Acknowledgments:
* The paper authors for releasing code and weights, thanks!
* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out
for some einops/einsum fun
* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT
* Bert reference code checks against Huggingface Transformers and Tensorflow Bert
Hacked together by / Copyright 2020 Ross Wightman
"""
from transformers import (
PreTrainedModel,
PretrainedConfig,
AutoConfig,
AutoModel,
AutoModelForImageClassification,
)
import math
from functools import partial
from itertools import repeat
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
TORCH_MAJOR = int(torch.__version__.split(".")[0])
TORCH_MINOR = int(torch.__version__.split(".")[1])
if TORCH_MAJOR == 1 and TORCH_MINOR < 8:
from torch._six import container_abcs, int_classes
else:
import collections.abc as container_abcs
int_classes = int
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, container_abcs.Iterable):
return x
return tuple(repeat(x, n))
return parse
to_2tuple = _ntuple(2)
def drop_path(x, drop_prob: float = 0.0, training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0.0 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)
def _cfg(url="", **kwargs):
return {
"url": url,
"num_classes": 1000,
"input_size": (3, 224, 224),
"pool_size": None,
"crop_pct": 0.9,
"interpolation": "bicubic",
"mean": IMAGENET_DEFAULT_MEAN,
"std": IMAGENET_DEFAULT_STD,
"first_conv": "patch_embed.proj",
"classifier": "head",
**kwargs,
}
default_cfgs = {
# patch models
"vit_small_patch16_224": _cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth",
),
"vit_base_patch16_224": _cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth",
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
),
"vit_base_patch16_384": _cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth",
input_size=(3, 384, 384),
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
crop_pct=1.0,
),
"vit_base_patch32_384": _cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth",
input_size=(3, 384, 384),
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
crop_pct=1.0,
),
"vit_large_patch16_224": _cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth",
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
),
"vit_large_patch16_384": _cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth",
input_size=(3, 384, 384),
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
crop_pct=1.0,
),
"vit_large_patch32_384": _cfg(
url="https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth",
input_size=(3, 384, 384),
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
crop_pct=1.0,
),
"vit_huge_patch16_224": _cfg(),
"vit_huge_patch32_384": _cfg(input_size=(3, 384, 384)),
# hybrid models
"vit_small_resnet26d_224": _cfg(),
"vit_small_resnet50d_s3_224": _cfg(),
"vit_base_resnet26d_224": _cfg(),
"vit_base_resnet50d_224": _cfg(),
}
class Mlp(nn.Module):
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)
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],
) # 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)
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,
):
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,
)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
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__()
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.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
# 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 HybridEmbed(nn.Module):
"""CNN Feature Map Embedding
Extract feature map from CNN, flatten, project to embedding dim.
"""
def __init__(
self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768
):
super().__init__()
assert isinstance(backbone, nn.Module)
img_size = to_2tuple(img_size)
self.img_size = img_size
self.backbone = backbone
if feature_size is None:
with torch.no_grad():
# FIXME this is hacky, but most reliable way of determining the exact dim of the output feature
# map for all networks, the feature metadata has reliable channel and stride info, but using
# stride to calc feature dim requires info about padding of each stage that isn't captured.
training = backbone.training
if training:
backbone.eval()
o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
if isinstance(o, (list, tuple)):
o = o[-1] # last feature if backbone outputs list/tuple of features
feature_size = o.shape[-2:]
feature_dim = o.shape[1]
backbone.train(training)
else:
feature_size = to_2tuple(feature_size)
if hasattr(self.backbone, "feature_info"):
feature_dim = self.backbone.feature_info.channels()[-1]
else:
feature_dim = self.backbone.num_features
self.num_patches = feature_size[0] * feature_size[1]
self.proj = nn.Conv2d(feature_dim, embed_dim, 1)
def forward(self, x):
x = self.backbone(x)
if isinstance(x, (list, tuple)):
x = x[-1] # last feature if backbone outputs list/tuple of features
x = self.proj(x).flatten(2).transpose(1, 2)
return x
class PatchEmbed_overlap(nn.Module):
"""Image to Patch Embedding with overlapping patches"""
def __init__(
self, img_size=224, patch_size=16, stride_size=20, in_chans=3, embed_dim=768
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
stride_size_tuple = to_2tuple(stride_size)
self.num_x = (img_size[1] - patch_size[1]) // stride_size_tuple[1] + 1
self.num_y = (img_size[0] - patch_size[0]) // stride_size_tuple[0] + 1
print(
"using stride: {}, and patch number is num_y{} * num_x{}".format(
stride_size, self.num_y, self.num_x
)
)
num_patches = self.num_x * self.num_y
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=stride_size
)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2.0 / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.InstanceNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def forward(self, x):
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)
x = x.flatten(2).transpose(1, 2) # [64, 8, 768]
return x
class TransReID(nn.Module):
"""Transformer-based Object Re-Identification"""
@classmethod
def from_config(cls, config):
return cls(
img_size=config.get("img_size", [384, 128]),
patch_size=config.get("patch_size", 16),
stride_size=config.get("stride_size", 16),
in_chans=config.get("in_chans", 3),
num_classes=config.get("num_classes", 1000),
embed_dim=config.get("embed_dim", 768),
depth=config.get("depth", 12),
num_heads=config.get("num_heads", 12),
mlp_ratio=config.get("mlp_ratio", 4.0),
qkv_bias=config.get("qkv_bias", False),
qk_scale=config.get("qk_scale", None),
drop_rate=config.get("drop_rate", 0.0),
attn_drop_rate=config.get("attn_drop_rate", 0.0),
drop_path_rate=config.get("drop_path_rate", 0.0),
camera=config.get("camera", 0),
view=config.get("view", 0),
local_feature=config.get("local_feature", False),
sie_xishu=config.get("sie_xishu", 1.0),
)
def __init__(
self,
img_size=224,
patch_size=16,
stride_size=16,
in_chans=3,
num_classes=1000,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=False,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
camera=0,
view=0,
drop_path_rate=0.0,
hybrid_backbone=None,
norm_layer=nn.LayerNorm,
local_feature=False,
sie_xishu=1.0,
):
nn.Module.__init__(self)
self.num_classes = num_classes
self.num_features = self.embed_dim = (
embed_dim # num_features for consistency with other models
)
self.local_feature = local_feature
if hybrid_backbone is not None:
self.patch_embed = HybridEmbed(
hybrid_backbone,
img_size=img_size,
in_chans=in_chans,
embed_dim=embed_dim,
)
else:
self.patch_embed = PatchEmbed_overlap(
img_size=img_size,
patch_size=patch_size,
stride_size=stride_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.cam_num = camera
self.view_num = view
self.sie_xishu = sie_xishu
# Initialize SIE Embedding
if camera > 1 and view > 1:
self.sie_embed = nn.Parameter(torch.zeros(camera * view, 1, embed_dim))
trunc_normal_(self.sie_embed, std=0.02)
print(
"camera number is : {} and viewpoint number is : {}".format(
camera, view
)
)
print("using SIE_Lambda is : {}".format(sie_xishu))
elif camera > 1:
self.sie_embed = nn.Parameter(torch.zeros(camera, 1, embed_dim))
trunc_normal_(self.sie_embed, std=0.02)
print("camera number is : {}".format(camera))
print("using SIE_Lambda is : {}".format(sie_xishu))
elif view > 1:
self.sie_embed = nn.Parameter(torch.zeros(view, 1, embed_dim))
trunc_normal_(self.sie_embed, std=0.02)
print("viewpoint number is : {}".format(view))
print("using SIE_Lambda is : {}".format(sie_xishu))
print("using drop_out rate is : {}".format(drop_rate))
print("using attn_drop_out rate is : {}".format(attn_drop_rate))
print("using drop_path rate is : {}".format(drop_path_rate))
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,
)
for i in range(depth)
]
)
self.norm = norm_layer(embed_dim)
# # Classifier head
self.fc = (
nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
)
trunc_normal_(self.cls_token, std=0.02)
trunc_normal_(self.pos_embed, 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 get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=""):
self.num_classes = num_classes
self.fc = (
nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
)
def forward_features(self, x, camera_id, view_id):
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)
if self.cam_num > 0 and self.view_num > 0:
x = (
x
+ self.pos_embed
+ self.sie_xishu * self.sie_embed[camera_id * self.view_num + view_id]
)
elif self.cam_num > 0:
x = x + self.pos_embed + self.sie_xishu * self.sie_embed[camera_id]
elif self.view_num > 0:
x = x + self.pos_embed + self.sie_xishu * self.sie_embed[view_id]
else:
x = x + self.pos_embed
x = self.pos_drop(x)
if self.local_feature:
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x
else:
for blk in self.blocks:
x = blk(x)
x = self.norm(x)
return x[:, 0]
def forward(self, x, cam_label=None, view_label=None):
x = self.forward_features(x, cam_label, view_label)
return x
def load_param(self, model_path):
param_dict = torch.load(model_path, map_location="cpu")
if "model" in param_dict:
param_dict = param_dict["model"]
if "state_dict" in param_dict:
param_dict = param_dict["state_dict"]
for k, v in param_dict.items():
# print(k)
if "head" in k or "dist" in k:
continue
if "patch_embed.proj.weight" in k and len(v.shape) < 4:
# For old models that I trained prior to conv based patchification
O, I, H, W = self.patch_embed.proj.weight.shape
v = v.reshape(O, -1, H, W)
elif k == "pos_embed" and v.shape != self.pos_embed.shape:
# To resize pos embedding when using model at different size from pretrained weights
if "distilled" in model_path:
print("distill need to choose right cls token in the pth")
v = torch.cat([v[:, 0:1], v[:, 2:]], dim=1)
v = resize_pos_embed(
v, self.pos_embed, self.patch_embed.num_y, self.patch_embed.num_x
)
try:
self.state_dict()[k].copy_(v)
except:
# print("===========================ERROR=========================")
# print(k)
# print('shape do not match in k :{}: param_dict{} vs self.state_dict(){}'.format(k, v.shape, self.state_dict()[k].shape))
pass
def resize_pos_embed(posemb, posemb_new, hight, width):
# 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
ntok_new = posemb_new.shape[1]
posemb_token, posemb_grid = posemb[:, :1], posemb[0, 1:]
ntok_new -= 1
int(math.sqrt(len(posemb_grid)))
print(
"Resized position embedding from size:{} to size: {} with height:{} width: {}".format(
posemb.shape, posemb_new.shape, hight, width
)
)
posemb_grid = posemb_grid.reshape(1, 16, 8, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=(hight, width), mode="bilinear")
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, hight * width, -1)
posemb = torch.cat([posemb_token, posemb_grid], dim=1)
return posemb
def vit_base_patch16_224_TransReID(
img_size=(256, 128),
stride_size=16,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
camera=0,
view=0,
local_feature=False,
sie_xishu=1.5,
**kwargs,
):
model = TransReID(
img_size=img_size,
patch_size=16,
stride_size=stride_size,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=True,
camera=camera,
view=view,
drop_path_rate=drop_path_rate,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
sie_xishu=sie_xishu,
local_feature=local_feature,
**kwargs,
)
return model
def vit_small_patch16_224_TransReID(
img_size=(256, 128),
stride_size=16,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
camera=0,
view=0,
local_feature=False,
sie_xishu=1.5,
**kwargs,
):
kwargs.setdefault("qk_scale", 768**-0.5)
model = TransReID(
img_size=img_size,
patch_size=16,
stride_size=stride_size,
embed_dim=768,
depth=8,
num_heads=8,
mlp_ratio=3.0,
qkv_bias=False,
drop_path_rate=drop_path_rate,
camera=camera,
view=view,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
sie_xishu=sie_xishu,
local_feature=local_feature,
**kwargs,
)
return model
def deit_small_patch16_224_TransReID(
img_size=(256, 128),
stride_size=16,
drop_path_rate=0.1,
drop_rate=0.0,
attn_drop_rate=0.0,
camera=0,
view=0,
local_feature=False,
sie_xishu=1.5,
**kwargs,
):
model = TransReID(
img_size=img_size,
patch_size=16,
stride_size=stride_size,
embed_dim=384,
depth=12,
num_heads=6,
mlp_ratio=4,
qkv_bias=True,
drop_path_rate=drop_path_rate,
drop_rate=drop_rate,
attn_drop_rate=attn_drop_rate,
camera=camera,
view=view,
sie_xishu=sie_xishu,
local_feature=local_feature,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
**kwargs,
)
return model
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
if (mean < a - 2 * std) or (mean > b + 2 * std):
print(
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
)
with torch.no_grad():
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.0))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
return _no_grad_trunc_normal_(tensor, mean, std, a, b)
class HAPTransReIDConfig(PretrainedConfig):
model_type = "my-vit-b16"
def __init__(
self,
img_size=[384, 128],
stride_size=[16, 16],
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
camera=0, # not used
view=0, # not used
local_feature=True,
sie_xishu=3.0, # not used
num_classes=-1, # not used
patch_size=16,
in_chans=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=False,
qk_scale=None,
hybrid_backbone=None, # not used
norm_layer_eps=1e-6,
**kwargs,
):
super().__init__(**kwargs)
self.img_size = img_size
self.stride_size = stride_size
self.drop_rate = drop_rate
self.attn_drop_rate = attn_drop_rate
self.drop_path_rate = drop_path_rate
self.camera = camera
self.view = view
self.local_feature = local_feature
self.sie_xishu = sie_xishu
self.num_classes = num_classes
self.patch_size = patch_size
self.in_chans = in_chans
self.embed_dim = embed_dim
self.depth = depth
self.num_heads = num_heads
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.qk_scale = qk_scale
self.hybrid_backbone = hybrid_backbone
self.norm_layer_eps = norm_layer_eps
class HAPTransReID(TransReID, PreTrainedModel):
config_class = HAPTransReIDConfig
def __init__(self, config):
PreTrainedModel.__init__(self, config)
self.config = config
self.model = TransReID(
img_size=config.img_size,
stride_size=config.stride_size,
drop_rate=config.drop_rate,
attn_drop_rate=config.attn_drop_rate,
drop_path_rate=config.drop_path_rate,
camera=config.camera,
view=config.view,
local_feature=config.local_feature,
sie_xishu=config.sie_xishu,
num_classes=config.num_classes,
patch_size=config.patch_size,
in_chans=config.in_chans,
embed_dim=config.embed_dim,
depth=config.depth,
num_heads=config.num_heads,
mlp_ratio=config.mlp_ratio,
qkv_bias=config.qkv_bias,
qk_scale=config.qk_scale,
norm_layer=partial(nn.LayerNorm, eps=config.norm_layer_eps),
)
self.model.hidden_size = self.model.vision_width = config.embed_dim
def forward(self, x):
return self.model(x, cam_label=None, view_label=None)
@classmethod
def from_config(cls, config={}, from_path=None, from_pretrained=None):
'''
vision_width = hidden_size = 768, just for get information
not used in the model
'''
model = vit_base_patch16_224_TransReID(
img_size=config.get("img_size", [384, 128]),
stride_size=config.get("stride_size", [16, 16]),
drop_rate=config.get("drop_rate", 0.0),
attn_drop_rate=config.get("attn_drop_rate", 0.0),
drop_path_rate=config.get("drop_path_rate", 0.1),
camera=config.get("camera", 0),
view=config.get("view", 0),
local_feature=config.get("local_feature", True),
sie_xishu=config.get("sie_xishu", 3.0),
num_classes=config.get("num_classes", -1),
# vision_width=config.get("vision_width", 768),
# hidden_size=config.get("hidden_size", 768),
)
model.vision_width = model.hidden_size = 768
if from_path is not None:
model.load_param(from_path)
return model