222 lines
6.7 KiB
Python
Executable File
222 lines
6.7 KiB
Python
Executable File
import torch
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import torch.nn as nn
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import timm
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import types
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import math
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import torch.nn.functional as F
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from .utils import (activations, forward_adapted_unflatten, get_activation, get_readout_oper,
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make_backbone_default, Transpose)
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def forward_vit(pretrained, x):
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return forward_adapted_unflatten(pretrained, x, "forward_flex")
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def _resize_pos_embed(self, posemb, gs_h, gs_w):
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posemb_tok, posemb_grid = (
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posemb[:, : self.start_index],
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posemb[0, self.start_index:],
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)
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gs_old = int(math.sqrt(len(posemb_grid)))
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posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
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posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
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posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
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posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
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return posemb
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def forward_flex(self, x):
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b, c, h, w = x.shape
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pos_embed = self._resize_pos_embed(
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self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
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)
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B = x.shape[0]
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if hasattr(self.patch_embed, "backbone"):
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x = self.patch_embed.backbone(x)
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if isinstance(x, (list, tuple)):
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x = x[-1] # last feature if backbone outputs list/tuple of features
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x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
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if getattr(self, "dist_token", None) is not None:
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cls_tokens = self.cls_token.expand(
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B, -1, -1
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) # stole cls_tokens impl from Phil Wang, thanks
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dist_token = self.dist_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, dist_token, x), dim=1)
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else:
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if self.no_embed_class:
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x = x + pos_embed
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cls_tokens = self.cls_token.expand(
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B, -1, -1
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) # stole cls_tokens impl from Phil Wang, thanks
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x = torch.cat((cls_tokens, x), dim=1)
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if not self.no_embed_class:
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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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x = self.norm(x)
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return x
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def _make_vit_b16_backbone(
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model,
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features=[96, 192, 384, 768],
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size=[384, 384],
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hooks=[2, 5, 8, 11],
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vit_features=768,
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use_readout="ignore",
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start_index=1,
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start_index_readout=1,
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):
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pretrained = make_backbone_default(model, features, size, hooks, vit_features, use_readout, start_index,
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start_index_readout)
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# We inject this function into the VisionTransformer instances so that
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# we can use it with interpolated position embeddings without modifying the library source.
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pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
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pretrained.model._resize_pos_embed = types.MethodType(
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_resize_pos_embed, pretrained.model
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)
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return pretrained
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def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
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model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
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hooks = [5, 11, 17, 23] if hooks == None else hooks
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return _make_vit_b16_backbone(
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model,
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features=[256, 512, 1024, 1024],
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hooks=hooks,
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vit_features=1024,
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use_readout=use_readout,
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)
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def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
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model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
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hooks = [2, 5, 8, 11] if hooks == None else hooks
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return _make_vit_b16_backbone(
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model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
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)
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def _make_vit_b_rn50_backbone(
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model,
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features=[256, 512, 768, 768],
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size=[384, 384],
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hooks=[0, 1, 8, 11],
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vit_features=768,
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patch_size=[16, 16],
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number_stages=2,
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use_vit_only=False,
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use_readout="ignore",
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start_index=1,
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):
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pretrained = nn.Module()
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pretrained.model = model
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used_number_stages = 0 if use_vit_only else number_stages
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for s in range(used_number_stages):
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pretrained.model.patch_embed.backbone.stages[s].register_forward_hook(
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get_activation(str(s + 1))
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)
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for s in range(used_number_stages, 4):
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pretrained.model.blocks[hooks[s]].register_forward_hook(get_activation(str(s + 1)))
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pretrained.activations = activations
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readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
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for s in range(used_number_stages):
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value = nn.Sequential(nn.Identity(), nn.Identity(), nn.Identity())
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exec(f"pretrained.act_postprocess{s + 1}=value")
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for s in range(used_number_stages, 4):
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if s < number_stages:
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final_layer = nn.ConvTranspose2d(
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in_channels=features[s],
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out_channels=features[s],
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kernel_size=4 // (2 ** s),
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stride=4 // (2 ** s),
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padding=0,
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bias=True,
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dilation=1,
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groups=1,
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)
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elif s > number_stages:
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final_layer = nn.Conv2d(
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in_channels=features[3],
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out_channels=features[3],
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kernel_size=3,
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stride=2,
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padding=1,
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)
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else:
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final_layer = None
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layers = [
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readout_oper[s],
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Transpose(1, 2),
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nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
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nn.Conv2d(
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in_channels=vit_features,
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out_channels=features[s],
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kernel_size=1,
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stride=1,
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padding=0,
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),
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]
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if final_layer is not None:
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layers.append(final_layer)
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value = nn.Sequential(*layers)
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exec(f"pretrained.act_postprocess{s + 1}=value")
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pretrained.model.start_index = start_index
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pretrained.model.patch_size = patch_size
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# We inject this function into the VisionTransformer instances so that
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# we can use it with interpolated position embeddings without modifying the library source.
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pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
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# We inject this function into the VisionTransformer instances so that
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# we can use it with interpolated position embeddings without modifying the library source.
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pretrained.model._resize_pos_embed = types.MethodType(
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_resize_pos_embed, pretrained.model
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)
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return pretrained
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def _make_pretrained_vitb_rn50_384(
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pretrained, use_readout="ignore", hooks=None, use_vit_only=False
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):
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model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
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hooks = [0, 1, 8, 11] if hooks == None else hooks
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return _make_vit_b_rn50_backbone(
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model,
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features=[256, 512, 768, 768],
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size=[384, 384],
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hooks=hooks,
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use_vit_only=use_vit_only,
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use_readout=use_readout,
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)
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