""" 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 """ import warnings import math import torch from functools import partial 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_ 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 # 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, 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) # trunc_normal_(self.relative_position_bias_table, std=.0) 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, training_window_size=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: if training_window_size == self.window_size: 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) else: training_window_size = tuple(training_window_size.tolist()) new_num_relative_distance = (2 * training_window_size[0] - 1) * (2 * training_window_size[1] - 1) + 3 # new_num_relative_dis 为 所有可能的相对位置选项，包含cls-cls，tok-cls，与cls-tok new_relative_position_bias_table = F.interpolate( self.relative_position_bias_table[:-3, :].permute(1, 0).view(1, self.num_heads, 2 * self.window_size[0] - 1, 2 * self.window_size[1] - 1), size=(2 * training_window_size[0] - 1, 2 * training_window_size[1] - 1), mode='bicubic', align_corners=False) new_relative_position_bias_table = new_relative_position_bias_table.view(self.num_heads, new_num_relative_distance - 3).permute( 1, 0) new_relative_position_bias_table = torch.cat( [new_relative_position_bias_table, self.relative_position_bias_table[-3::]], dim=0) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(training_window_size[0]) coords_w = torch.arange(training_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] += training_window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += training_window_size[1] - 1 relative_coords[:, :, 0] *= 2 * training_window_size[1] - 1 relative_position_index = \ torch.zeros(size=(training_window_size[0] * training_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:] = new_num_relative_distance - 3 relative_position_index[0:, 0] = new_num_relative_distance - 2 relative_position_index[0, 0] = new_num_relative_distance - 1 relative_position_bias = \ new_relative_position_bias_table[relative_position_index.view(-1)].view( training_window_size[0] * training_window_size[1] + 1, training_window_size[0] * training_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: 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, training_window_size=None): if self.gamma_1 is None: x = x + self.drop_path( self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias, training_window_size=training_window_size)) 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, training_window_size=training_window_size)) 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, 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.num_patches_w = self.patch_shape[0] self.num_patches_h = self.patch_shape[1] # the so-called patch_shape is the patch shape during pre-training 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, position_embedding=None, **kwargs): # 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) Hp, Wp = x.shape[2], x.shape[3] if position_embedding is not None: # interpolate the position embedding to the corresponding size position_embedding = position_embedding.view(1, self.patch_shape[0], self.patch_shape[1], -1).permute(0, 3, 1, 2) position_embedding = F.interpolate(position_embedding, size=(Hp, Wp), mode='bicubic') x = x + position_embedding x = x.flatten(2).transpose(1, 2) return x, (Hp, Wp) 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, 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]))[-1] feature_size = o.shape[-2:] feature_dim = o.shape[1] backbone.train(training) else: feature_size = to_2tuple(feature_size) feature_dim = self.backbone.feature_info.channels()[-1] self.num_patches = feature_size[0] * feature_size[1] self.proj = nn.Linear(feature_dim, embed_dim) def forward(self, x): x = self.backbone(x)[-1] x = x.flatten(2).transpose(1, 2) x = self.proj(x) return x class RelativePositionBias(nn.Module): def __init__(self, window_size, num_heads): super().__init__() self.window_size = window_size self.num_heads = num_heads 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, training_window_size): if training_window_size == self.window_size: 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 else: training_window_size = tuple(training_window_size.tolist()) new_num_relative_distance = (2 * training_window_size[0] - 1) * (2 * training_window_size[1] - 1) + 3 # new_num_relative_dis 为 所有可能的相对位置选项，包含cls-cls，tok-cls，与cls-tok new_relative_position_bias_table = F.interpolate( self.relative_position_bias_table[:-3, :].permute(1, 0).view(1, self.num_heads, 2 * self.window_size[0] - 1, 2 * self.window_size[1] - 1), size=(2 * training_window_size[0] - 1, 2 * training_window_size[1] - 1), mode='bicubic', align_corners=False) new_relative_position_bias_table = new_relative_position_bias_table.view(self.num_heads, new_num_relative_distance - 3).permute( 1, 0) new_relative_position_bias_table = torch.cat( [new_relative_position_bias_table, self.relative_position_bias_table[-3::]], dim=0) # get pair-wise relative position index for each token inside the window coords_h = torch.arange(training_window_size[0]) coords_w = torch.arange(training_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] += training_window_size[0] - 1 # shift to start from 0 relative_coords[:, :, 1] += training_window_size[1] - 1 relative_coords[:, :, 0] *= 2 * training_window_size[1] - 1 relative_position_index = \ torch.zeros(size=(training_window_size[0] * training_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:] = new_num_relative_distance - 3 relative_position_index[0:, 0] = new_num_relative_distance - 2 relative_position_index[0, 0] = new_num_relative_distance - 1 relative_position_bias = \ new_relative_position_bias_table[relative_position_index.view(-1)].view( training_window_size[0] * training_window_size[1] + 1, training_window_size[0] * training_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 return relative_position_bias class BEiT(nn.Module): """ Vision Transformer with support for patch or hybrid CNN input stage """ def __init__(self, img_size=[224, 224], patch_size=16, in_chans=3, num_classes=80, 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., hybrid_backbone=None, norm_layer=None, init_values=None, use_abs_pos_emb=False, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, use_checkpoint=True, pretrained=None, out_features=None, ): super(BEiT, self).__init__() norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) self.num_classes = num_classes self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models self.use_checkpoint = use_checkpoint 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( img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) num_patches = self.patch_embed.num_patches self.out_features = out_features self.out_indices = [int(name[5:]) for name in out_features] self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) # self.mask_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) self.use_shared_rel_pos_bias = use_shared_rel_pos_bias 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 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)]) # trunc_normal_(self.mask_token, std=.02) if patch_size == 16: self.fpn1 = nn.Sequential( nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2), # nn.SyncBatchNorm(embed_dim), nn.BatchNorm2d(embed_dim), nn.GELU(), nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2), ) self.fpn2 = nn.Sequential( nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2), ) self.fpn3 = nn.Identity() self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2) elif patch_size == 8: self.fpn1 = nn.Sequential( nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2), ) self.fpn2 = nn.Identity() self.fpn3 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2), ) self.fpn4 = nn.Sequential( nn.MaxPool2d(kernel_size=4, stride=4), ) if self.pos_embed is not None: trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) self.fix_init_weight() 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 init_weights(self): """Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to None. """ logger = get_root_logger() if self.pos_embed is not None: trunc_normal_(self.pos_embed, std=.02) trunc_normal_(self.cls_token, std=.02) self.apply(self._init_weights) self.fix_init_weight() if self.init_cfg is None: logger.warn(f'No pre-trained weights for ' f'{self.__class__.__name__}, ' f'training start from scratch') else: assert 'checkpoint' in self.init_cfg, f'Only support ' \ f'specify `Pretrained` in ' \ f'`init_cfg` in ' \ f'{self.__class__.__name__} ' logger.info(f"Will load ckpt from {self.init_cfg['checkpoint']}") load_checkpoint(self, filename=self.init_cfg['checkpoint'], strict=False, logger=logger, beit_spec_expand_rel_pos = self.use_rel_pos_bias, ) ''' def get_num_layers(self): return len(self.blocks) @torch.jit.ignore def no_weight_decay(self): return {'pos_embed', 'cls_token'} def forward_features(self, x): B, C, H, W = x.shape x, (Hp, Wp) = self.patch_embed(x, self.pos_embed[:, 1:, :] if self.pos_embed is not None else None) # Hp, Wp are HW for patches batch_size, seq_len, _ = x.size() cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks if self.pos_embed is not None: cls_tokens = cls_tokens + self.pos_embed[:, :1, :] x = torch.cat((cls_tokens, x), dim=1) x = self.pos_drop(x) features = [] training_window_size = torch.tensor([Hp, Wp]) rel_pos_bias = self.rel_pos_bias(training_window_size) if self.rel_pos_bias is not None else None for i, blk in enumerate(self.blocks): if self.use_checkpoint: x = checkpoint.checkpoint(blk, x, rel_pos_bias, training_window_size) else: x = blk(x, rel_pos_bias=rel_pos_bias, training_window_size=training_window_size) if i in self.out_indices: xp = x[:, 1:, :].permute(0, 2, 1).reshape(B, -1, Hp, Wp) features.append(xp.contiguous()) ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4] for i in range(len(features)): features[i] = ops[i](features[i]) feat_out = {} for name, value in zip(self.out_features, features): feat_out[name] = value return feat_out def forward(self, x): x = self.forward_features(x) return x def beit_base_patch16(pretrained=False, **kwargs): model = BEiT( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=None, **kwargs) model.default_cfg = _cfg() return model def beit_large_patch16(pretrained=False, **kwargs): model = BEiT( patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=None, **kwargs) model.default_cfg = _cfg() return model def dit_base_patch16(pretrained=False, **kwargs): model = BEiT( patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=0.1, **kwargs) model.default_cfg = _cfg() return model def dit_large_patch16(pretrained=False, **kwargs): model = BEiT( patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), init_values=1e-5, **kwargs) model.default_cfg = _cfg() return model if __name__ == '__main__': model = BEiT(use_checkpoint=True, use_shared_rel_pos_bias=True) model = model.to("cuda:0") input1 = torch.rand(2, 3, 512, 762).to("cuda:0") input2 = torch.rand(2, 3, 800, 1200).to("cuda:0") input3 = torch.rand(2, 3, 720, 1000).to("cuda:0") output1 = model(input1) output2 = model(input2) output3 = model(input3) print("all done")