""" 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