353 lines
14 KiB
Python
353 lines
14 KiB
Python
import math
|
|
from itertools import repeat
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
import collections.abc
|
|
|
|
|
|
# From PyTorch internals
|
|
def _ntuple(n):
|
|
def parse(x):
|
|
if isinstance(x, collections.abc.Iterable):
|
|
return x
|
|
return tuple(repeat(x, n))
|
|
return parse
|
|
|
|
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
|
|
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
|
|
to_2tuple = _ntuple(2)
|
|
|
|
def drop_path(x, drop_prob: float = 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. 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
|
|
# 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., 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)
|
|
# 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)
|
|
|
|
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 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. / 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"""
|
|
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., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., camera=0,
|
|
drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, cam_lambda =3.0):
|
|
super().__init__()
|
|
self.num_classes = num_classes
|
|
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
|
|
self.cam_num = camera
|
|
self.cam_lambda = cam_lambda
|
|
|
|
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 = nn.Parameter(torch.zeros(camera, 1, embed_dim))
|
|
|
|
trunc_normal_(self.Cam, std=.02)
|
|
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=.02)
|
|
trunc_normal_(self.pos_embed, std=.02)
|
|
|
|
self.apply(self._init_weights)
|
|
|
|
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)
|
|
|
|
@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):
|
|
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 + self.cam_lambda * self.Cam[camera_id]
|
|
x = self.pos_drop(x)
|
|
|
|
for blk in self.blocks[:-1]:
|
|
x = blk(x)
|
|
return x
|
|
|
|
def forward(self, x, cam_label=None):
|
|
x = self.forward_features(x, cam_label)
|
|
return x
|
|
|
|
def load_param(self, model_path, load=False):
|
|
print("Run load_param")
|
|
if not load:
|
|
param_dict = torch.load(model_path, map_location='cpu', weights_only=False)
|
|
else:
|
|
param_dict = model_path
|
|
|
|
if 'model' in param_dict:
|
|
param_dict = param_dict['model']
|
|
if 'state_dict' in param_dict:
|
|
param_dict = param_dict['state_dict']
|
|
|
|
model_dict = self.state_dict()
|
|
new_param_dict = {}
|
|
|
|
for k, v in param_dict.items():
|
|
if 'head' in k or 'dist' in k:
|
|
continue
|
|
|
|
if k in ['Cam', 'base.Cam'] and k in model_dict:
|
|
expected_shape = model_dict[k].shape
|
|
if v.shape[0] > expected_shape[0]:
|
|
print(f"⚠️ Resizing '{k}' from {v.shape} to {expected_shape}")
|
|
v = v[:expected_shape[0], :, :]
|
|
elif v.shape[0] < expected_shape[0]:
|
|
print(f"⚠️ Expanding '{k}' from {v.shape} to {expected_shape}")
|
|
new_v = torch.randn(expected_shape)
|
|
new_v[:v.shape[0], :, :] = v
|
|
v = new_v
|
|
new_param_dict[k] = v
|
|
continue
|
|
|
|
if k in model_dict and model_dict[k].shape == v.shape:
|
|
new_param_dict[k] = v
|
|
|
|
model_dict.update(new_param_dict)
|
|
self.load_state_dict(model_dict, strict=False)
|
|
print("✅ Checkpoint loaded successfully.")
|
|
|
|
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
|
|
|
|
gs_old = 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, gs_old, gs_old, -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 _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. + math.erf(x / math.sqrt(2.))) / 2.
|
|
|
|
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.))
|
|
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., std=1., a=-2., b=2.):
|
|
# 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)
|