mirror of https://github.com/alibaba/EasyCV.git
329 lines
11 KiB
Python
329 lines
11 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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"""
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Mostly copy-paste from timm library.
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https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
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dynamic Input support borrow from
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https://github.com/microsoft/esvit/blob/main/models/vision_transformer.py
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"""
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import math
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from functools import partial
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import torch
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import torch.nn as nn
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from easycv.models.backbones.vision_transformer import Block, VisionTransformer
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class DynamicVisionTransformer(VisionTransformer):
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"""Dynamic Vision Transformer
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Args:
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use_dense_prediction (bool): If use_dense_prediction is True, the global
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pool and norm will before head will be removed.(if any) Default: False
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"""
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def __init__(self, use_dense_prediction=False, **kwargs):
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super(DynamicVisionTransformer, self).__init__(**kwargs)
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num_patches = self.patch_embed.num_patches
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self.pos_embed = nn.Parameter(
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torch.zeros(1, num_patches + 1, self.embed_dim))
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if self.hydra_attention:
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hy = [
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x >= (self.depth - self.hydra_attention_layers)
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for x in range(self.depth)
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]
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head = [
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self.embed_dim if x >=
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(self.depth - self.hydra_attention_layers) else self.num_heads
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for x in range(self.depth)
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]
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else:
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hy = [False for x in range(self.depth)]
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head = [self.num_heads for x in range(self.depth)]
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self.blocks = nn.ModuleList([
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Block(
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dim=self.embed_dim,
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num_heads=head[i],
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mlp_ratio=self.mlp_ratio,
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qkv_bias=self.qkv_bias,
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qk_scale=self.qk_scale,
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drop=self.drop_rate,
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attn_drop=self.attn_drop_rate,
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drop_path=self.dpr[i],
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norm_layer=self.norm_layer,
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use_layer_scale=self.use_layer_scale,
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init_values=self.init_scale,
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hydra_attention=hy[i]) for i in range(self.depth)
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])
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# Dense prediction head
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self.use_dense_prediction = use_dense_prediction
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if self.use_dense_prediction:
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self.head_dense = None
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def forward(self, x):
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# convert to list
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if not isinstance(x, list):
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x = [x]
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# Perform forward pass separately on each resolution input.
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# The inputs corresponding to a single resolution are clubbed and single
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# forward is run on the same resolution inputs. Hence we do several
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# forward passes = number of different resolutions used. We then
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# concatenate all the output features.
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idx_crops = torch.cumsum(
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torch.unique_consecutive(
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torch.tensor([inp.shape[-1] for inp in x]),
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return_counts=True,
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)[1], 0)
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if self.use_dense_prediction:
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start_idx = 0
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for end_idx in idx_crops:
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_out_cls, _out_fea = self.forward_features(
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torch.cat(x[start_idx:end_idx]))
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B, N, C = _out_fea.shape
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if start_idx == 0:
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output_cls = _out_cls
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output_fea = _out_fea.reshape(B * N, C)
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npatch = [N]
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else:
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output_cls = torch.cat((output_cls, _out_cls))
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output_fea = torch.cat(
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(output_fea, _out_fea.reshape(B * N, C)))
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npatch.append(N)
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start_idx = end_idx
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return [
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self.head(output_cls),
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self.head_dense(output_fea), output_fea, npatch
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]
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else:
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start_idx = 0
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for end_idx in idx_crops:
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_out = self.forward_features(torch.cat(x[start_idx:end_idx]))
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# _out = self.forward_return_n_last_blocks(torch.cat(x[start_idx: end_idx]), 4, True)
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if start_idx == 0:
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output = _out
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else:
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output = torch.cat((output, _out))
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start_idx = end_idx
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# print(f'output[0] {output[0].shape}')
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# Run the head forward on the concatenated features.
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return [self.head(output)]
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def forward_features(self, x):
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B = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
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x = x + pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks:
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x = blk(x)
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if self.norm is not None:
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x = self.norm(x)
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if self.use_dense_prediction:
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return x[:, 0], x[:, 1:]
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else:
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if self.global_pool:
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x = x[:, 1:, :].mean(dim=1)
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return self.fc_norm(x)
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else:
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return x[:, 0]
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def forward_feature_maps(self, x):
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B = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
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x = x + pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks:
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x = blk(x)
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if self.norm is not None:
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x = self.norm(x)
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return x
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def interpolate_pos_encoding(self, x, pos_embed):
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npatch = x.shape[1] - 1
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N = pos_embed.shape[1] - 1
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if npatch == N:
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return pos_embed
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class_emb = pos_embed[:, 0]
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pos_embed = pos_embed[:, 1:]
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dim = x.shape[-1]
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pos_embed = nn.functional.interpolate(
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pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)),
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dim).permute(0, 3, 1, 2),
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scale_factor=math.sqrt(npatch / N),
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mode='bicubic',
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)
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pos_embed = pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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return torch.cat((class_emb.unsqueeze(0), pos_embed), dim=1)
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def forward_selfattention(self, x, n=1):
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# n=1 return the last layer attn map; otherwise return attn maps in all layers
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B, nc, w, h = x.shape
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N = self.pos_embed.shape[1] - 1
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x = self.patch_embed(x)
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# interpolate patch embeddings
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dim = x.shape[-1]
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w0 = w // self.patch_embed.patch_size
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h0 = h // self.patch_embed.patch_size
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class_pos_embed = self.pos_embed[:, 0]
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patch_pos_embed = self.pos_embed[:, 1:]
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)),
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dim).permute(0, 3, 1, 2),
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scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
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mode='bicubic',
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)
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if w0 != patch_pos_embed.shape[-2]:
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helper = torch.zeros(h0)[None, None, None, :].repeat(
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1, dim, w0 - patch_pos_embed.shape[-2], 1).to(x.device)
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patch_pos_embed = torch.cat((patch_pos_embed, helper), dim=-2)
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if h0 != patch_pos_embed.shape[-1]:
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helper = torch.zeros(w0)[None, None, :, None].repeat(
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1, dim, 1, h0 - patch_pos_embed.shape[-1]).to(x.device)
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pos_embed = torch.cat((patch_pos_embed, helper), dim=-1)
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patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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pos_embed = torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed),
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dim=1)
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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x = x + pos_embed
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x = self.pos_drop(x)
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if n == 1:
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return self.forward_last_selfattention(x)
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else:
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return self.forward_all_selfattention(x)
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def forward_last_selfattention(self, x):
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for i, blk in enumerate(self.blocks):
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if i < len(self.blocks) - 1:
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x = blk(x)
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else:
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return blk(x, return_attention=True)
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def forward_all_selfattention(self, x):
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attn_out = []
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for i, blk in enumerate(self.blocks):
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x, attn = blk.forward_fea_and_attn(x)
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attn_out.append(attn)
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return attn_out
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def forward_return_n_last_blocks(self,
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x,
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n=1,
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return_patch_avgpool=False,
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depths=[]):
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B = x.shape[0]
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x = self.patch_embed(x)
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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pos_embed = self.interpolate_pos_encoding(x, self.pos_embed)
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x = x + pos_embed
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x = self.pos_drop(x)
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# we will return the [CLS] tokens from the `n` last blocks
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output = []
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for i, blk in enumerate(self.blocks):
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x = blk(x)
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if len(self.blocks) - i <= n:
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output.append(self.norm(x)[:, 0])
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if return_patch_avgpool:
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x = self.norm(x)
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# In addition to the [CLS] tokens from the `n` last blocks, we also return
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# the patch tokens from the last block. This is useful for linear eval.
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output.append(torch.mean(x[:, 1:], dim=1))
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return torch.cat(output, dim=-1)
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def dynamic_deit_tiny_p16(patch_size=16, **kwargs):
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model = DynamicVisionTransformer(
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patch_size=patch_size,
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embed_dim=192,
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depth=12,
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num_heads=3,
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mlp_ratio=4,
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qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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**kwargs)
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return model
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def dynamic_deit_small_p16(patch_size=16, **kwargs):
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model = DynamicVisionTransformer(
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patch_size=patch_size,
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embed_dim=384,
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depth=12,
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num_heads=6,
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mlp_ratio=4,
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qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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**kwargs)
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return model
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def dynamic_vit_base_p16(patch_size=16, **kwargs):
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model = DynamicVisionTransformer(
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patch_size=patch_size,
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embed_dim=768,
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depth=12,
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num_heads=12,
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mlp_ratio=4,
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qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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**kwargs)
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return model
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def dynamic_vit_large_p16(patch_size=16, **kwargs):
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model = DynamicVisionTransformer(
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patch_size=patch_size,
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embed_dim=1024,
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depth=24,
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num_heads=16,
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mlp_ratio=4,
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qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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**kwargs)
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return model
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def dynamic_vit_huge_p14(patch_size=14, **kwargs):
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model = DynamicVisionTransformer(
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patch_size=patch_size,
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embed_dim=1280,
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depth=32,
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num_heads=16,
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mlp_ratio=4,
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qkv_bias=True,
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norm_layer=partial(nn.LayerNorm, eps=1e-6),
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**kwargs)
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return model
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