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lora-scripts/sd-scripts/finetune/blip/vit.py ADDED
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+ '''
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+ * Copyright (c) 2022, salesforce.com, inc.
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+ * All rights reserved.
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+ * SPDX-License-Identifier: BSD-3-Clause
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+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
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+ * By Junnan Li
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+ * Based on timm code base
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+ * https://github.com/rwightman/pytorch-image-models/tree/master/timm
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+ '''
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+
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+ import torch
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+ import torch.nn as nn
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+ import torch.nn.functional as F
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+ from functools import partial
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+
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+ from timm.models.vision_transformer import _cfg, PatchEmbed
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+ from timm.models.registry import register_model
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+ from timm.models.layers import trunc_normal_, DropPath
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+ from timm.models.helpers import named_apply, adapt_input_conv
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+
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+ from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
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+
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+ class Mlp(nn.Module):
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+ """ MLP as used in Vision Transformer, MLP-Mixer and related networks
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+ """
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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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+
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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.drop(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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+
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+
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+ class Attention(nn.Module):
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+ def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
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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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+ # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
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+ self.scale = qk_scale or head_dim ** -0.5
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+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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+ self.attn_drop = nn.Dropout(attn_drop)
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+ self.proj = nn.Linear(dim, dim)
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+ self.proj_drop = nn.Dropout(proj_drop)
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+ self.attn_gradients = None
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+ self.attention_map = None
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+
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+ def save_attn_gradients(self, attn_gradients):
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+ self.attn_gradients = attn_gradients
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+
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+ def get_attn_gradients(self):
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+ return self.attn_gradients
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+
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+ def save_attention_map(self, attention_map):
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+ self.attention_map = attention_map
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+
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+ def get_attention_map(self):
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+ return self.attention_map
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+
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+ def forward(self, x, register_hook=False):
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+ B, N, C = x.shape
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+ qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
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+
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+ attn = (q @ k.transpose(-2, -1)) * self.scale
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+ attn = attn.softmax(dim=-1)
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+ attn = self.attn_drop(attn)
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+
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+ if register_hook:
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+ self.save_attention_map(attn)
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+ attn.register_hook(self.save_attn_gradients)
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+
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+ x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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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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+
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+
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+ class Block(nn.Module):
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+
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+ def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
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+ drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False):
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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, attn_drop=attn_drop, proj_drop=drop)
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+ # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
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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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+
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+ if use_grad_checkpointing:
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+ self.attn = checkpoint_wrapper(self.attn)
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+ self.mlp = checkpoint_wrapper(self.mlp)
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+
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+ def forward(self, x, register_hook=False):
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+ x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
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+ x = x + self.drop_path(self.mlp(self.norm2(x)))
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+ return x
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+
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+
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+ class VisionTransformer(nn.Module):
114
+ """ Vision Transformer
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+ A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` -
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+ https://arxiv.org/abs/2010.11929
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+ """
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+ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
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+ num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None,
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+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None,
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+ use_grad_checkpointing=False, ckpt_layer=0):
122
+ """
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+ Args:
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+ img_size (int, tuple): input image size
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+ patch_size (int, tuple): patch size
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+ in_chans (int): number of input channels
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+ num_classes (int): number of classes for classification head
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+ embed_dim (int): embedding dimension
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+ depth (int): depth of transformer
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+ num_heads (int): number of attention heads
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+ mlp_ratio (int): ratio of mlp hidden dim to embedding dim
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+ qkv_bias (bool): enable bias for qkv if True
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+ qk_scale (float): override default qk scale of head_dim ** -0.5 if set
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+ representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
135
+ drop_rate (float): dropout rate
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+ attn_drop_rate (float): attention dropout rate
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+ drop_path_rate (float): stochastic depth rate
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+ norm_layer: (nn.Module): normalization layer
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+ """
140
+ super().__init__()
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+ self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
142
+ norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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+
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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)
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+
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+ num_patches = self.patch_embed.num_patches
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+
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+ self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
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+ self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
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+ self.pos_drop = nn.Dropout(p=drop_rate)
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+
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+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
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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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+ use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer)
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+ )
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+ for i in range(depth)])
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+ self.norm = norm_layer(embed_dim)
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+
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+ trunc_normal_(self.pos_embed, std=.02)
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+ trunc_normal_(self.cls_token, std=.02)
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+ self.apply(self._init_weights)
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+
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+ def _init_weights(self, m):
168
+ 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:
171
+ 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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+
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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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+
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+ def forward(self, x, register_blk=-1):
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+ B = x.shape[0]
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+ x = self.patch_embed(x)
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+
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+ cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
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+ x = torch.cat((cls_tokens, x), dim=1)
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+
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+ x = x + self.pos_embed[:,:x.size(1),:]
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+ x = self.pos_drop(x)
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+
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+ for i,blk in enumerate(self.blocks):
191
+ x = blk(x, register_blk==i)
192
+ x = self.norm(x)
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+
194
+ return x
195
+
196
+ @torch.jit.ignore()
197
+ def load_pretrained(self, checkpoint_path, prefix=''):
198
+ _load_weights(self, checkpoint_path, prefix)
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+
200
+
201
+ @torch.no_grad()
202
+ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
203
+ """ Load weights from .npz checkpoints for official Google Brain Flax implementation
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+ """
205
+ import numpy as np
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+
207
+ def _n2p(w, t=True):
208
+ if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
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+ w = w.flatten()
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+ if t:
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+ if w.ndim == 4:
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+ w = w.transpose([3, 2, 0, 1])
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+ elif w.ndim == 3:
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+ w = w.transpose([2, 0, 1])
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+ elif w.ndim == 2:
216
+ w = w.transpose([1, 0])
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+ return torch.from_numpy(w)
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+
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+ w = np.load(checkpoint_path)
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+ if not prefix and 'opt/target/embedding/kernel' in w:
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+ prefix = 'opt/target/'
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+
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+ if hasattr(model.patch_embed, 'backbone'):
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+ # hybrid
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+ backbone = model.patch_embed.backbone
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+ stem_only = not hasattr(backbone, 'stem')
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+ stem = backbone if stem_only else backbone.stem
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+ stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
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+ stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
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+ stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
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+ if not stem_only:
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+ for i, stage in enumerate(backbone.stages):
233
+ for j, block in enumerate(stage.blocks):
234
+ bp = f'{prefix}block{i + 1}/unit{j + 1}/'
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+ for r in range(3):
236
+ getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
237
+ getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
238
+ getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
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+ if block.downsample is not None:
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+ block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
241
+ block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
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+ block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
243
+ embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
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+ else:
245
+ embed_conv_w = adapt_input_conv(
246
+ model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
247
+ model.patch_embed.proj.weight.copy_(embed_conv_w)
248
+ model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
249
+ model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
250
+ pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False)
251
+ if pos_embed_w.shape != model.pos_embed.shape:
252
+ pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
253
+ pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
254
+ model.pos_embed.copy_(pos_embed_w)
255
+ model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
256
+ model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
257
+ # if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
258
+ # model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
259
+ # model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
260
+ # if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
261
+ # model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
262
+ # model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
263
+ for i, block in enumerate(model.blocks.children()):
264
+ block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
265
+ mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
266
+ block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
267
+ block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
268
+ block.attn.qkv.weight.copy_(torch.cat([
269
+ _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
270
+ block.attn.qkv.bias.copy_(torch.cat([
271
+ _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
272
+ block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
273
+ block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
274
+ for r in range(2):
275
+ getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel']))
276
+ getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias']))
277
+ block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale']))
278
+ block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias']))
279
+
280
+
281
+ def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
282
+ # interpolate position embedding
283
+ embedding_size = pos_embed_checkpoint.shape[-1]
284
+ num_patches = visual_encoder.patch_embed.num_patches
285
+ num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
286
+ # height (== width) for the checkpoint position embedding
287
+ orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
288
+ # height (== width) for the new position embedding
289
+ new_size = int(num_patches ** 0.5)
290
+
291
+ if orig_size!=new_size:
292
+ # class_token and dist_token are kept unchanged
293
+ extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
294
+ # only the position tokens are interpolated
295
+ pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
296
+ pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
297
+ pos_tokens = torch.nn.functional.interpolate(
298
+ pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
299
+ pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
300
+ new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
301
+ print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2))
302
+
303
+ return new_pos_embed
304
+ else:
305
+ return pos_embed_checkpoint