DiT_Extractor/dit_object_detection/ditod/deit.py

"""
Mostly copy-paste from DINO and timm library:
https://github.com/facebookresearch/dino
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
import warnings

import math
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.models.layers import trunc_normal_, drop_path, to_2tuple
from functools import partial

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)
 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
 q, k, v = self.qkv(x).reshape(B, N, 3, self.num_heads,
 C // self.num_heads).permute(2, 0, 3, 1, 4)

 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)

 self.window_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])

 self.num_patches_w, self.num_patches_h = self.window_size

 self.num_patches = self.window_size[0] * self.window_size[1]
 self.img_size = img_size
 self.patch_size = patch_size

 self.proj = nn.Conv2d(in_chans, embed_dim,
 kernel_size=patch_size, stride=patch_size)

 def forward(self, x):
 x = self.proj(x)
 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]))[-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 ViT(nn.Module):
 """ Vision Transformer with support for patch or hybrid CNN input stage
 """

 def __init__(self,
 model_name='vit_base_patch16_224',
 img_size=384,
 patch_size=16,
 in_chans=3,
 embed_dim=1024,
 depth=24,
 num_heads=16,
 num_classes=19,
 mlp_ratio=4.,
 qkv_bias=True,
 qk_scale=None,
 drop_rate=0.1,
 attn_drop_rate=0.,
 drop_path_rate=0.,
 hybrid_backbone=None,
 norm_layer=partial(nn.LayerNorm, eps=1e-6),
 norm_cfg=None,
 pos_embed_interp=False,
 random_init=False,
 align_corners=False,
 use_checkpoint=False,
 num_extra_tokens=1,
 out_features=None,
 **kwargs,
 ):

 super(ViT, self).__init__()
 self.model_name = model_name
 self.img_size = img_size
 self.patch_size = patch_size
 self.in_chans = in_chans
 self.embed_dim = embed_dim
 self.depth = depth
 self.num_heads = num_heads
 self.num_classes = num_classes
 self.mlp_ratio = mlp_ratio
 self.qkv_bias = qkv_bias
 self.qk_scale = qk_scale
 self.drop_rate = drop_rate
 self.attn_drop_rate = attn_drop_rate
 self.drop_path_rate = drop_path_rate
 self.hybrid_backbone = hybrid_backbone
 self.norm_layer = norm_layer
 self.norm_cfg = norm_cfg
 self.pos_embed_interp = pos_embed_interp
 self.random_init = random_init
 self.align_corners = align_corners
 self.use_checkpoint = use_checkpoint
 self.num_extra_tokens = num_extra_tokens
 self.out_features = out_features
 self.out_indices = [int(name[5:]) for name in out_features]

 # self.num_stages = self.depth
 # self.out_indices = tuple(range(self.num_stages))

 if self.hybrid_backbone is not None:
 self.patch_embed = HybridEmbed(
 self.hybrid_backbone, img_size=self.img_size, in_chans=self.in_chans, embed_dim=self.embed_dim)
 else:
 self.patch_embed = PatchEmbed(
 img_size=self.img_size, patch_size=self.patch_size, in_chans=self.in_chans, embed_dim=self.embed_dim)
 self.num_patches = self.patch_embed.num_patches

 self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))

 if self.num_extra_tokens == 2:
 self.dist_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))

 self.pos_embed = nn.Parameter(torch.zeros(
 1, self.num_patches + self.num_extra_tokens, self.embed_dim))
 self.pos_drop = nn.Dropout(p=self.drop_rate)

 # self.num_extra_tokens = self.pos_embed.shape[-2] - self.num_patches
 dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate,
 self.depth)] # stochastic depth decay rule
 self.blocks = nn.ModuleList([
 Block(
 dim=self.embed_dim, num_heads=self.num_heads, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias,
 qk_scale=self.qk_scale,
 drop=self.drop_rate, attn_drop=self.attn_drop_rate, drop_path=dpr[i], norm_layer=self.norm_layer)
 for i in range(self.depth)])

 # NOTE as per official impl, we could have a pre-logits representation dense layer + tanh here
 # self.repr = nn.Linear(embed_dim, representation_size)
 # self.repr_act = nn.Tanh()

 if patch_size == 16:
 self.fpn1 = nn.Sequential(
 nn.ConvTranspose2d(embed_dim, embed_dim, kernel_size=2, stride=2),
 nn.SyncBatchNorm(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),
 )

 trunc_normal_(self.pos_embed, std=.02)
 trunc_normal_(self.cls_token, std=.02)
 if self.num_extra_tokens==2:
 trunc_normal_(self.dist_token, std=0.2)
 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):
 logger = get_root_logger()

 trunc_normal_(self.pos_embed, std=.02)
 trunc_normal_(self.cls_token, std=.02)
 self.apply(self._init_weights)

 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)
 '''

 def get_num_layers(self):
 return len(self.blocks)

 @torch.jit.ignore
 def no_weight_decay(self):
 return {'pos_embed', 'cls_token'}

 def _conv_filter(self, state_dict, patch_size=16):
 """ convert patch embedding weight from manual patchify + linear proj to conv"""
 out_dict = {}
 for k, v in state_dict.items():
 if 'patch_embed.proj.weight' in k:
 v = v.reshape((v.shape[0], 3, patch_size, patch_size))
 out_dict[k] = v
 return out_dict

 def to_2D(self, x):
 n, hw, c = x.shape
 h = w = int(math.sqrt(hw))
 x = x.transpose(1, 2).reshape(n, c, h, w)
 return x

 def to_1D(self, x):
 n, c, h, w = x.shape
 x = x.reshape(n, c, -1).transpose(1, 2)
 return x

 def interpolate_pos_encoding(self, x, w, h):
 npatch = x.shape[1] - self.num_extra_tokens
 N = self.pos_embed.shape[1] - self.num_extra_tokens
 if npatch == N and w == h:
 return self.pos_embed

 class_ORdist_pos_embed = self.pos_embed[:, 0:self.num_extra_tokens]

 patch_pos_embed = self.pos_embed[:, self.num_extra_tokens:]

 dim = x.shape[-1]
 w0 = w // self.patch_embed.patch_size[0]
 h0 = h // self.patch_embed.patch_size[1]
 # we add a small number to avoid floating point error in the interpolation
 # see discussion at https://github.com/facebookresearch/dino/issues/8
 w0, h0 = w0 + 0.1, h0 + 0.1
 patch_pos_embed = nn.functional.interpolate(
 patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
 scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
 mode='bicubic',
 )
 assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
 patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)

 return torch.cat((class_ORdist_pos_embed, patch_pos_embed), dim=1)

 def prepare_tokens(self, x, mask=None):
 B, nc, w, h = x.shape
 # patch linear embedding
 x = self.patch_embed(x)

 # mask image modeling
 if mask is not None:
 x = self.mask_model(x, mask)
 x = x.flatten(2).transpose(1, 2)

 # add the [CLS] token to the embed patch tokens
 all_tokens = [self.cls_token.expand(B, -1, -1)]

 if self.num_extra_tokens == 2:
 dist_tokens = self.dist_token.expand(B, -1, -1)
 all_tokens.append(dist_tokens)
 all_tokens.append(x)

 x = torch.cat(all_tokens, dim=1)

 # add positional encoding to each token
 x = x + self.interpolate_pos_encoding(x, w, h)

 return self.pos_drop(x)

 def forward_features(self, x):
 # print(f"==========shape of x is {x.shape}==========")
 B, _, H, W = x.shape
 Hp, Wp = H // self.patch_size, W // self.patch_size
 x = self.prepare_tokens(x)

 features = []
 for i, blk in enumerate(self.blocks):
 if self.use_checkpoint:
 x = checkpoint.checkpoint(blk, x)
 else:
 x = blk(x)
 if i in self.out_indices:
 xp = x[:, self.num_extra_tokens:, :].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 deit_base_patch16(pretrained=False, **kwargs):
 model = ViT(
 patch_size=16,
 drop_rate=0.,
 embed_dim=768,
 depth=12,
 num_heads=12,
 num_classes=1000,
 mlp_ratio=4.,
 qkv_bias=True,
 use_checkpoint=True,
 num_extra_tokens=2,
 **kwargs)
 model.default_cfg = _cfg()
 return model

def mae_base_patch16(pretrained=False, **kwargs):
 model = ViT(
 patch_size=16,
 drop_rate=0.,
 embed_dim=768,
 depth=12,
 num_heads=12,
 num_classes=1000,
 mlp_ratio=4.,
 qkv_bias=True,
 use_checkpoint=True,
 num_extra_tokens=1,
 **kwargs)
 model.default_cfg = _cfg()
 return model