Source code for models.dualprompt_utils.vision_transformer
import math
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint
from timm.models import named_apply
from timm.layers import trunc_normal_
from backbone.vit import LoRAAttention, create_vision_transformer, VisionTransformer as MammothVP, get_init_weights_vit
from models.dualprompt_utils.prompt import EPrompt
from models.dualprompt_utils.attention import PreT_Attention
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class VisionTransformer(MammothVP):
def __init__(
self, prompt_length=None, embedding_key='cls', prompt_init='uniform', prompt_pool=False, prompt_key=False, pool_size=None,
top_k=None, batchwise_prompt=False, prompt_key_init='uniform', head_type='token', use_prompt_mask=False,
use_g_prompt=False, g_prompt_length=None, g_prompt_layer_idx=None, use_prefix_tune_for_g_prompt=False,
use_e_prompt=False, e_prompt_layer_idx=None, use_prefix_tune_for_e_prompt=False, same_key_value=False, args=None, **kwargs):
if not (use_g_prompt or use_e_prompt):
attn_layer = LoRAAttention
elif not (use_prefix_tune_for_g_prompt or use_prefix_tune_for_e_prompt):
# Prompt tunning
attn_layer = LoRAAttention
else:
# Prefix tunning
attn_layer = PreT_Attention
super().__init__(args=args, attn_layer=attn_layer, **kwargs)
self.prompt_pool = prompt_pool
self.head_type = head_type
self.use_prompt_mask = use_prompt_mask
self.use_g_prompt = use_g_prompt
self.g_prompt_layer_idx = g_prompt_layer_idx
# num_g_prompt : The actual number of layers to which g-prompt is attached.
# In official code, create as many layers as the total number of layers and select them based on the index
num_g_prompt = len(self.g_prompt_layer_idx) if self.g_prompt_layer_idx is not None else 0
self.use_prefix_tune_for_g_prompt = use_prefix_tune_for_g_prompt
self.use_e_prompt = use_e_prompt
self.e_prompt_layer_idx = e_prompt_layer_idx
num_e_prompt = len(self.e_prompt_layer_idx) if self.e_prompt_layer_idx is not None else 0
self.use_prefix_tune_for_e_prompt = use_prefix_tune_for_e_prompt
if not self.use_prefix_tune_for_g_prompt and not self.use_prefix_tune_for_g_prompt:
self.use_g_prompt = False
self.g_prompt_layer_idx = []
if use_g_prompt and g_prompt_length is not None and len(g_prompt_layer_idx) != 0:
if not use_prefix_tune_for_g_prompt:
g_prompt_shape = (num_g_prompt, g_prompt_length, self.embed_dim)
if prompt_init == 'zero':
self.g_prompt = nn.Parameter(torch.zeros(g_prompt_shape))
elif prompt_init == 'uniform':
self.g_prompt = nn.Parameter(torch.randn(g_prompt_shape))
nn.init.uniform_(self.g_prompt, -1, 1)
else:
if same_key_value:
g_prompt_shape = (num_g_prompt, 1, g_prompt_length, self.num_heads, self.embed_dim // self.num_heads)
if prompt_init == 'zero':
self.g_prompt = nn.Parameter(torch.zeros(g_prompt_shape))
elif prompt_init == 'uniform':
self.g_prompt = nn.Parameter(torch.randn(g_prompt_shape))
nn.init.uniform_(self.g_prompt, -1, 1)
self.g_prompt = self.g_prompt.repeat(1, 2, 1, 1, 1)
else:
g_prompt_shape = (num_g_prompt, 2, g_prompt_length, self.num_heads, self.embed_dim // self.num_heads)
if prompt_init == 'zero':
self.g_prompt = nn.Parameter(torch.zeros(g_prompt_shape))
elif prompt_init == 'uniform':
self.g_prompt = nn.Parameter(torch.randn(g_prompt_shape))
nn.init.uniform_(self.g_prompt, -1, 1)
else:
self.g_prompt = None
if use_e_prompt and e_prompt_layer_idx is not None:
self.e_prompt = EPrompt(length=prompt_length, embed_dim=self.embed_dim, embedding_key=embedding_key, prompt_init=prompt_init,
prompt_pool=prompt_pool, prompt_key=prompt_key, pool_size=pool_size, top_k=top_k, batchwise_prompt=batchwise_prompt,
prompt_key_init=prompt_key_init, num_layers=num_e_prompt, use_prefix_tune_for_e_prompt=use_prefix_tune_for_e_prompt,
num_heads=self.num_heads, same_key_value=same_key_value)
self.total_prompt_len = 0
if self.prompt_pool:
if not self.use_prefix_tune_for_g_prompt:
self.total_prompt_len += g_prompt_length * len(self.g_prompt_layer_idx)
if not self.use_prefix_tune_for_e_prompt:
self.total_prompt_len += prompt_length * top_k * len(self.e_prompt_layer_idx)
self.head = nn.Linear(self.embed_dim, self.num_classes) if self.num_classes > 0 else nn.Identity()
if self.weight_init != 'skip':
self.init_weights(self.weight_init)
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def init_weights(self, mode=''):
assert mode in ('jax', 'jax_nlhb', 'moco', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
trunc_normal_(self.pos_embed, std=.02)
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
named_apply(get_init_weights_vit(mode, head_bias), self)
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def forward_features(self, x, task_id=-1, cls_features=None, train=False):
x = self.patch_embed(x)
if self.cls_token is not None:
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
x = self.pos_drop(x + self.pos_embed)
if self.use_g_prompt or self.use_e_prompt:
if self.use_prompt_mask and train:
start = task_id * self.e_prompt.top_k
end = (task_id + 1) * self.e_prompt.top_k
single_prompt_mask = torch.arange(start, end).to(x.device)
prompt_mask = single_prompt_mask.unsqueeze(0).expand(x.shape[0], -1)
if end > self.e_prompt.pool_size:
prompt_mask = None
else:
prompt_mask = None
g_prompt_counter = -1
e_prompt_counter = -1
res = self.e_prompt(x, prompt_mask=prompt_mask, cls_features=cls_features)
e_prompt = res['batched_prompt']
for i, block in enumerate(self.blocks):
if i in self.g_prompt_layer_idx:
if self.use_prefix_tune_for_g_prompt:
g_prompt_counter += 1
# Prefix tunning, [B, 2, g_prompt_length, num_heads, embed_dim // num_heads]
idx = torch.tensor([g_prompt_counter] * x.shape[0]).to(x.device)
g_prompt = self.g_prompt[idx]
else:
g_prompt = None
x = block(x, prompt=g_prompt)
elif i in self.e_prompt_layer_idx:
e_prompt_counter += 1
if self.use_prefix_tune_for_e_prompt:
# Prefix tunning, [B, 2, top_k * e_prompt_length, num_heads, embed_dim // num_heads]
x = block(x, prompt=e_prompt[e_prompt_counter])
else:
# Pommpt tunning, [B, top_k * e_prompt_length, embed_dim]
prompt = e_prompt[e_prompt_counter]
x = torch.cat([prompt, x], dim=1)
x = block(x)
else:
x = block(x)
else:
x = self.blocks(x)
res = dict()
x = self.norm(x)
res['x'] = x
return res
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def forward_head(self, res, pre_logits: bool = False):
x = res['x']
if self.class_token and self.head_type == 'token':
if self.prompt_pool:
x = x[:, self.total_prompt_len]
else:
x = x[:, 0]
elif self.head_type == 'gap' and self.global_pool == 'avg':
x = x.mean(dim=1)
elif self.head_type == 'prompt' and self.prompt_pool:
x = x[:, 1:(1 + self.total_prompt_len)] if self.class_token else x[:, 0:self.total_prompt_len]
x = x.mean(dim=1)
elif self.head_type == 'token+prompt' and self.prompt_pool and self.class_token:
x = x[:, 0:self.total_prompt_len + 1]
x = x.mean(dim=1)
else:
raise ValueError(f'Invalid classifier={self.classifier}')
res['pre_logits'] = x
x = self.fc_norm(x)
res['logits'] = self.head(x)
return res
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def forward(self, x, task_id=-1, cls_features=None, train=False):
res = self.forward_features(x, task_id=task_id, cls_features=cls_features, train=train)
res = self.forward_head(res)
return res
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def resize_pos_embed(posemb, posemb_new, num_prefix_tokens=1, gs_new=()):
# 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
# modify
logging.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
ntok_new = posemb_new.shape[1]
if num_prefix_tokens:
posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:]
# ntok_new -= num_prefix_tokens
else:
posemb_prefix, posemb_grid = posemb[:, :0], posemb[0]
gs_old = int(math.sqrt(len(posemb_grid)))
if ntok_new > gs_old ** 2:
ntok_new -= gs_old ** 2
# expand cls's pos embedding for prompt tokens
posemb_prefix = posemb_prefix.expand(-1, ntok_new, -1)
if not len(gs_new): # backwards compatibility
gs_new = [int(math.sqrt(ntok_new))] * 2
assert len(gs_new) >= 2
logging.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new)
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bicubic', align_corners=False)
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
posemb = torch.cat([posemb_prefix, posemb_grid], dim=1)
return posemb
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def checkpoint_filter_fn(state_dict, model, adapt_layer_scale=False):
""" convert patch embedding weight from manual patchify + linear proj to conv"""
import re
out_dict = {}
if 'model' in state_dict:
# For deit models
state_dict = state_dict['model']
for k, v in state_dict.items():
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 = model.patch_embed.proj.weight.shape
v = v.reshape(O, -1, H, W)
elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]:
# To resize pos embedding when using model at different size from pretrained weights
v = resize_pos_embed(
v,
model.pos_embed,
0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1),
model.patch_embed.grid_size
)
elif adapt_layer_scale and 'gamma_' in k:
# remap layer-scale gamma into sub-module (deit3 models)
k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
elif 'pre_logits' in k:
# NOTE representation layer removed as not used in latest 21k/1k pretrained weights
continue
out_dict[k] = v
return out_dict
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def vit_base_patch16_224_dualprompt(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer.
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = create_vision_transformer('vit_base_patch16_224', base_class=VisionTransformer, filter_fn=checkpoint_filter_fn, pretrained=pretrained, **model_kwargs)
return model