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import math |
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import torch |
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from functools import partial |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.utils.checkpoint as checkpoint |
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from timm.models.layers import drop_path, to_2tuple, trunc_normal_ |
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from ..builder import BACKBONES |
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from .base_backbone import BaseBackbone |
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from einops import repeat |
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class DropPath(nn.Module): |
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). |
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""" |
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def __init__(self, drop_prob=None): |
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super(DropPath, self).__init__() |
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self.drop_prob = drop_prob |
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def forward(self, x): |
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return drop_path(x, self.drop_prob, self.training) |
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def extra_repr(self): |
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return 'p={}'.format(self.drop_prob) |
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class Mlp(nn.Module): |
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def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): |
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super().__init__() |
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out_features = out_features or in_features |
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hidden_features = hidden_features or in_features |
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self.fc1 = nn.Linear(in_features, hidden_features) |
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self.act = act_layer() |
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self.fc2 = nn.Linear(hidden_features, out_features) |
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self.drop = nn.Dropout(drop) |
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def forward(self, x): |
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x = self.fc1(x) |
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x = self.act(x) |
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x = self.fc2(x) |
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x = self.drop(x) |
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return x |
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class Attention(nn.Module): |
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def __init__( |
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self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., |
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proj_drop=0., attn_head_dim=None, ): |
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super().__init__() |
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self.num_heads = num_heads |
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head_dim = dim // num_heads |
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self.dim = dim |
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if attn_head_dim is not None: |
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head_dim = attn_head_dim |
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all_head_dim = head_dim * self.num_heads |
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self.scale = qk_scale or head_dim ** -0.5 |
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self.qkv = nn.Linear(dim, all_head_dim * 3, bias=qkv_bias) |
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self.attn_drop = nn.Dropout(attn_drop) |
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self.proj = nn.Linear(all_head_dim, dim) |
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self.proj_drop = nn.Dropout(proj_drop) |
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def forward(self, x): |
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B, N, C = x.shape |
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qkv = self.qkv(x) |
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qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) |
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q, k, v = qkv[0], qkv[1], qkv[2] |
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q = q * self.scale |
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attn = (q @ k.transpose(-2, -1)) |
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attn = attn.softmax(dim=-1) |
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attn = self.attn_drop(attn) |
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x = (attn @ v).transpose(1, 2).reshape(B, N, -1) |
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x = self.proj(x) |
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x = self.proj_drop(x) |
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return x |
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class Block(nn.Module): |
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def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, |
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drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, |
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norm_layer=nn.LayerNorm, attn_head_dim=None |
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): |
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super().__init__() |
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self.norm1 = norm_layer(dim) |
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self.attn = Attention( |
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dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, |
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attn_drop=attn_drop, proj_drop=drop, attn_head_dim=attn_head_dim |
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) |
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() |
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self.norm2 = norm_layer(dim) |
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mlp_hidden_dim = int(dim * mlp_ratio) |
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self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) |
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def forward(self, x): |
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x = x + self.drop_path(self.attn(self.norm1(x))) |
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x = x + self.drop_path(self.mlp(self.norm2(x))) |
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return x |
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class PatchEmbed(nn.Module): |
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""" Image to Patch Embedding |
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""" |
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def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, ratio=1): |
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super().__init__() |
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img_size = to_2tuple(img_size) |
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patch_size = to_2tuple(patch_size) |
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num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (ratio ** 2) |
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self.patch_shape = (int(img_size[0] // patch_size[0] * ratio), int(img_size[1] // patch_size[1] * ratio)) |
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self.origin_patch_shape = (int(img_size[0] // patch_size[0]), int(img_size[1] // patch_size[1])) |
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self.img_size = img_size |
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self.patch_size = patch_size |
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self.num_patches = num_patches |
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=(patch_size[0] // ratio), |
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padding=4 + 2 * (ratio // 2 - 1)) |
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def forward(self, x, **kwargs): |
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B, C, H, W = x.shape |
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x = self.proj(x) |
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Hp, Wp = x.shape[2], x.shape[3] |
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x = x.flatten(2).transpose(1, 2) |
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return x, (Hp, Wp) |
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class HybridEmbed(nn.Module): |
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""" CNN Feature Map Embedding |
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Extract feature map from CNN, flatten, project to embedding dim. |
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""" |
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def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768): |
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super().__init__() |
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assert isinstance(backbone, nn.Module) |
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img_size = to_2tuple(img_size) |
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self.img_size = img_size |
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self.backbone = backbone |
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if feature_size is None: |
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with torch.no_grad(): |
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training = backbone.training |
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if training: |
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backbone.eval() |
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o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1] |
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feature_size = o.shape[-2:] |
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feature_dim = o.shape[1] |
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backbone.train(training) |
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else: |
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feature_size = to_2tuple(feature_size) |
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feature_dim = self.backbone.feature_info.channels()[-1] |
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self.num_patches = feature_size[0] * feature_size[1] |
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self.proj = nn.Linear(feature_dim, embed_dim) |
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def forward(self, x): |
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x = self.backbone(x)[-1] |
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x = x.flatten(2).transpose(1, 2) |
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x = self.proj(x) |
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return x |
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@BACKBONES.register_module() |
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class ViT(BaseBackbone): |
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def __init__(self, |
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img_size=224, patch_size=16, in_chans=3, num_classes=80, embed_dim=768, depth=12, |
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num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., |
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drop_path_rate=0., hybrid_backbone=None, norm_layer=None, use_checkpoint=False, |
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frozen_stages=-1, ratio=1, last_norm=True, |
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patch_padding='pad', freeze_attn=False, freeze_ffn=False, task_tokens_num=1+1+2+2+25 |
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): |
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super(ViT, self).__init__() |
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) |
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self.num_classes = num_classes |
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self.num_features = self.embed_dim = embed_dim |
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self.frozen_stages = frozen_stages |
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self.use_checkpoint = use_checkpoint |
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self.patch_padding = patch_padding |
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self.freeze_attn = freeze_attn |
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self.freeze_ffn = freeze_ffn |
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self.depth = depth |
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self.task_tokens_num = task_tokens_num |
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if hybrid_backbone is not None: |
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self.patch_embed = HybridEmbed( |
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hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) |
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else: |
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self.patch_embed = PatchEmbed( |
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img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, ratio=ratio) |
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num_patches = self.patch_embed.num_patches |
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self.task_tokens = nn.Parameter(torch.zeros(1, task_tokens_num, embed_dim)) |
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trunc_normal_(self.task_tokens, std=.02) |
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) |
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dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] |
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self.blocks = nn.ModuleList([ |
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Block( |
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dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, |
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drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, |
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) |
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for i in range(depth)]) |
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self.last_norm = norm_layer(embed_dim) if last_norm else nn.Identity() |
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if self.pos_embed is not None: |
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trunc_normal_(self.pos_embed, std=.02) |
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self._freeze_stages() |
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def _freeze_stages(self): |
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"""Freeze parameters.""" |
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if self.frozen_stages >= 0: |
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self.patch_embed.eval() |
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for param in self.patch_embed.parameters(): |
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param.requires_grad = False |
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for i in range(1, self.frozen_stages + 1): |
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m = self.blocks[i] |
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m.eval() |
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for param in m.parameters(): |
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param.requires_grad = False |
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if self.freeze_attn: |
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for i in range(0, self.depth): |
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m = self.blocks[i] |
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m.attn.eval() |
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m.norm1.eval() |
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for param in m.attn.parameters(): |
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param.requires_grad = False |
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for param in m.norm1.parameters(): |
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param.requires_grad = False |
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if self.freeze_ffn: |
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self.pos_embed.requires_grad = False |
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self.patch_embed.eval() |
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for param in self.patch_embed.parameters(): |
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param.requires_grad = False |
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for i in range(0, self.depth): |
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m = self.blocks[i] |
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m.mlp.eval() |
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m.norm2.eval() |
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for param in m.mlp.parameters(): |
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param.requires_grad = False |
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for param in m.norm2.parameters(): |
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param.requires_grad = False |
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def init_weights(self, pretrained=None): |
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"""Initialize the weights in backbone. |
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Args: |
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pretrained (str, optional): Path to pre-trained weights. |
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Defaults to None. |
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""" |
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super().init_weights(pretrained, patch_padding=self.patch_padding) |
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if pretrained is None: |
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def _init_weights(m): |
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if isinstance(m, nn.Linear): |
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trunc_normal_(m.weight, std=.02) |
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if isinstance(m, nn.Linear) and m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.LayerNorm): |
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nn.init.constant_(m.bias, 0) |
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nn.init.constant_(m.weight, 1.0) |
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self.apply(_init_weights) |
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def get_num_layers(self): |
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return len(self.blocks) |
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@torch.jit.ignore |
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def no_weight_decay(self): |
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return {'pos_embed', 'cls_token'} |
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def forward_features(self, x): |
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B, C, H, W = x.shape |
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x, (Hp, Wp) = self.patch_embed(x) |
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task_tokens = repeat(self.task_tokens, '() n d -> b n d', b=B) |
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if self.pos_embed is not None: |
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x = x + self.pos_embed[:, 1:] + self.pos_embed[:, :1] |
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x = torch.cat((task_tokens, x), dim=1) |
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for blk in self.blocks: |
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if self.use_checkpoint: |
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x = checkpoint.checkpoint(blk, x) |
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else: |
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x = blk(x) |
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x = self.last_norm(x) |
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task_tokens = x[:, :self.task_tokens_num] |
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xp = x[:, self.task_tokens_num:] |
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xp = xp.permute(0, 2, 1).reshape(B, -1, Hp, Wp).contiguous() |
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return xp, task_tokens |
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def forward(self, x): |
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x = self.forward_features(x) |
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return x |
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def train(self, mode=True): |
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"""Convert the model into training mode.""" |
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super().train(mode) |
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self._freeze_stages() |