mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Merge pull request #1812 from seefun/master
add ViT for Segment-Anything Model
This commit is contained in:
Ross Wightman
GitHub
commit
9fcc01930a
@ -41,7 +41,7 @@ NON_STD_FILTERS = [
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'vit_*', 'tnt_*', 'pit_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
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'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*',
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'poolformer_*', 'volo_*', 'sequencer2d_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*',
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'eva_*', 'flexivit*', 'eva02*'
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'eva_*', 'flexivit*', 'eva02*', 'samvit_*'
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]
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NUM_NON_STD = len(NON_STD_FILTERS)
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@ -36,7 +36,7 @@ from .padding import get_padding, get_same_padding, pad_same
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from .patch_dropout import PatchDropout
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from .patch_embed import PatchEmbed, PatchEmbedWithSize, resample_patch_embed
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from .pool2d_same import AvgPool2dSame, create_pool2d
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from .pos_embed import resample_abs_pos_embed
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from .pos_embed import resample_abs_pos_embed, resample_abs_pos_embed_nhwc
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from .pos_embed_rel import RelPosMlp, RelPosBias, RelPosBiasTf, gen_relative_position_index, gen_relative_log_coords
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from .pos_embed_sincos import pixel_freq_bands, freq_bands, build_sincos2d_pos_embed, build_fourier_pos_embed, \
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build_rotary_pos_embed, apply_rot_embed, apply_rot_embed_cat, apply_rot_embed_list, apply_keep_indices_nlc, \
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@ -37,6 +37,7 @@ class PatchEmbed(nn.Module):
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flatten: bool = True,
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output_fmt: Optional[str] = None,
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bias: bool = True,
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strict_img_size: bool = True,
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):
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super().__init__()
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self.patch_size = to_2tuple(patch_size)
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@ -56,6 +57,7 @@ class PatchEmbed(nn.Module):
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# flatten spatial dim and transpose to channels last, kept for bwd compat
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self.flatten = flatten
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self.output_fmt = Format.NCHW
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self.strict_img_size = strict_img_size
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self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
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self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
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@ -63,8 +65,18 @@ class PatchEmbed(nn.Module):
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def forward(self, x):
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B, C, H, W = x.shape
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if self.img_size is not None:
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_assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).")
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_assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).")
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if self.strict_img_size:
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_assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
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_assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
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else:
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_assert(
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H % self.patch_size[0] == 0,
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f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
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)
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_assert(
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W % self.patch_size[1] == 0,
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f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
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)
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x = self.proj(x)
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if self.flatten:
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@ -52,3 +52,24 @@ def resample_abs_pos_embed(
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_logger.info(f'Resized position embedding: {old_size} to {new_size}.')
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return posemb
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def resample_abs_pos_embed_nhwc(
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posemb,
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new_size: List[int],
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interpolation: str = 'bicubic',
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antialias: bool = True,
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verbose: bool = False,
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):
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if new_size[0] == posemb.shape[-3] and new_size[1] == posemb.shape[-2]:
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return posemb
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# do the interpolation
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posemb = posemb.reshape(1, posemb.shape[-3], posemb.shape[-2], posemb.shape[-1]).permute(0, 3, 1, 2)
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posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
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posemb = posemb.permute(0, 2, 3, 1)
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if not torch.jit.is_scripting() and verbose:
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_logger.info(f'Resized position embedding: {posemb.shape[-3:-1]} to {new_size}.')
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return posemb
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@ -60,6 +60,7 @@ from .visformer import *
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from .vision_transformer import *
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from .vision_transformer_hybrid import *
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from .vision_transformer_relpos import *
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from .vision_transformer_sam import *
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from .volo import *
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from .vovnet import *
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from .xception import *
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610
timm/models/vision_transformer_sam.py
Normal file
610
timm/models/vision_transformer_sam.py
Normal file
@ -0,0 +1,610 @@
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""" Vision Transformer (ViT) in PyTorch
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A PyTorch implement of Vision Transformers as described in:
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'Exploring Plain Vision Transformer Backbones for Object Detection'
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- https://arxiv.org/abs/2203.16527
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'Segment Anything Model (SAM)'
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- https://github.com/facebookresearch/segment-anything/
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"""
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import logging
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from functools import partial
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from typing import Callable, Optional, Tuple
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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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import torch.utils.checkpoint
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.layers import PatchEmbed, Mlp, DropPath, PatchDropout, LayerNorm2d, ClassifierHead, NormMlpClassifierHead,\
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Format, resample_abs_pos_embed_nhwc
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from ._builder import build_model_with_cfg
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from ._manipulate import checkpoint_seq
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from ._registry import generate_default_cfgs, register_model
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# model_registry will add each entrypoint fn to this
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__all__ = ['VisionTransformerSAM']
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_logger = logging.getLogger(__name__)
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class Attention(nn.Module):
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def __init__(
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self,
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dim,
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num_heads=8,
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qkv_bias=True,
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qk_norm=False,
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attn_drop=0.,
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proj_drop=0.,
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norm_layer=nn.LayerNorm,
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use_rel_pos: bool = False,
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rel_pos_zero_init: bool = True,
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input_size: Optional[Tuple[int, int]] = None,
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):
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super().__init__()
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assert dim % num_heads == 0, 'dim should be divisible by num_heads'
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.scale = self.head_dim ** -0.5
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
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self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
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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.use_rel_pos = use_rel_pos
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if self.use_rel_pos:
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assert (
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input_size is not None
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), "Input size must be provided if using relative positional encoding."
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# initialize relative positional embeddings
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self.rel_pos_h = nn.Parameter(torch.zeros(
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2 * input_size[0] - 1, self.head_dim))
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self.rel_pos_w = nn.Parameter(torch.zeros(
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2 * input_size[1] - 1, self.head_dim))
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def forward(self, x):
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B, H, W, _ = x.shape
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qkv = self.qkv(x).reshape(
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B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
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# qkv with shape (3, B, nHead, H * W, C)
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q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
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# q, k, v with shape (B * nHead, H * W, C)
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q, k = self.q_norm(q), self.k_norm(k)
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q = q * self.scale
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attn = q @ k.transpose(-2, -1)
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if self.use_rel_pos:
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attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
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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).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
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x = self.proj(x)
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return x
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class LayerScale(nn.Module):
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def __init__(self, dim, init_values=1e-5, inplace=False):
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super().__init__()
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self.inplace = inplace
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self.gamma = nn.Parameter(init_values * torch.ones(dim))
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def forward(self, x):
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return x.mul_(self.gamma) if self.inplace else x * self.gamma
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class Block(nn.Module):
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def __init__(
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self,
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dim,
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num_heads,
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mlp_ratio=4.,
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qkv_bias=True,
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qk_norm=False,
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proj_drop=0.,
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attn_drop=0.,
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init_values=None,
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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mlp_layer=Mlp,
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use_rel_pos=False,
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window_size=0,
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input_size=None,
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):
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super().__init__()
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self.window_size = window_size
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self.norm1 = norm_layer(dim)
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self.attn = Attention(
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dim,
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num_heads=num_heads,
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qkv_bias=qkv_bias,
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qk_norm=qk_norm,
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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norm_layer=norm_layer,
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use_rel_pos=use_rel_pos,
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input_size=input_size if window_size == 0 else (window_size, window_size),
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)
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self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm2 = norm_layer(dim)
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self.mlp = mlp_layer(
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in_features=dim,
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hidden_features=int(dim * mlp_ratio),
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act_layer=act_layer,
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drop=proj_drop,
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)
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self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
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self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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def forward(self, x):
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shortcut = x
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x = self.norm1(x)
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# Window partition
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if self.window_size > 0:
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H, W = x.shape[1], x.shape[2]
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x, pad_hw = window_partition(x, self.window_size)
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x = self.drop_path1(self.ls1(self.attn(x)))
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# Reverse window partition
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if self.window_size > 0:
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x = window_unpartition(x, self.window_size, pad_hw, (H, W))
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x = shortcut + x
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x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
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return x
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def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
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"""
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Partition into non-overlapping windows with padding if needed.
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Args:
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x (tensor): input tokens with [B, H, W, C].
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window_size (int): window size.
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Returns:
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windows: windows after partition with [B * num_windows, window_size, window_size, C].
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(Hp, Wp): padded height and width before partition
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"""
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B, H, W, C = x.shape
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pad_h = (window_size - H % window_size) % window_size
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pad_w = (window_size - W % window_size) % window_size
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if pad_h > 0 or pad_w > 0:
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x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
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Hp, Wp = H + pad_h, W + pad_w
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x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
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windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
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return windows, (Hp, Wp)
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def window_unpartition(
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windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
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) -> torch.Tensor:
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"""
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Window unpartition into original sequences and removing padding.
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Args:
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windows (tensor): input tokens with [B * num_windows, window_size, window_size, C].
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window_size (int): window size.
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pad_hw (Tuple): padded height and width (Hp, Wp).
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hw (Tuple): original height and width (H, W) before padding.
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Returns:
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x: unpartitioned sequences with [B, H, W, C].
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"""
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Hp, Wp = pad_hw
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H, W = hw
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B = windows.shape[0] // (Hp * Wp // window_size // window_size)
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x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
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x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
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if Hp > H or Wp > W:
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x = x[:, :H, :W, :].contiguous()
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return x
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def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
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"""
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Get relative positional embeddings according to the relative positions of
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query and key sizes.
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Args:
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q_size (int): size of query q.
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k_size (int): size of key k.
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rel_pos (Tensor): relative position embeddings (L, C).
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Returns:
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Extracted positional embeddings according to relative positions.
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"""
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max_rel_dist = int(2 * max(q_size, k_size) - 1)
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# Interpolate rel pos if needed.
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if rel_pos.shape[0] != max_rel_dist:
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# Interpolate rel pos.
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rel_pos_resized = F.interpolate(
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rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
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size=max_rel_dist,
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mode="linear",
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)
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rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
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else:
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rel_pos_resized = rel_pos
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# Scale the coords with short length if shapes for q and k are different.
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q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
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k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
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relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
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return rel_pos_resized[relative_coords.long()]
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def add_decomposed_rel_pos(
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attn: torch.Tensor,
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q: torch.Tensor,
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rel_pos_h: torch.Tensor,
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rel_pos_w: torch.Tensor,
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q_size: Tuple[int, int],
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k_size: Tuple[int, int],
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) -> torch.Tensor:
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"""
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Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
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https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py
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Args:
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attn (Tensor): attention map.
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q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
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rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
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rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
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q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
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k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
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Returns:
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attn (Tensor): attention map with added relative positional embeddings.
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"""
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q_h, q_w = q_size
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k_h, k_w = k_size
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Rh = get_rel_pos(q_h, k_h, rel_pos_h)
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Rw = get_rel_pos(q_w, k_w, rel_pos_w)
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B, _, dim = q.shape
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r_q = q.reshape(B, q_h, q_w, dim)
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rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
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rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
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attn = (
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attn.view(B, q_h, q_w, k_h, k_w) +
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rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
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).view(B, q_h * q_w, k_h * k_w)
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return attn
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class VisionTransformerSAM(nn.Module):
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""" Vision Transformer for Segment-Anything Model(SAM)
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A PyTorch impl of : `Exploring Plain Vision Transformer Backbones for Object Detection` or `Segment Anything Model (SAM)`
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- https://arxiv.org/abs/2010.11929
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"""
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def __init__(
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self,
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img_size: int = 1024,
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patch_size: int = 16,
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in_chans: int = 3,
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num_classes: int = 768,
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embed_dim: int = 768,
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depth: int = 12,
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num_heads: int = 12,
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mlp_ratio: float = 4.,
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qkv_bias: bool = True,
|
||||
qk_norm: bool = False,
|
||||
init_values: Optional[float] = None,
|
||||
pre_norm: bool = False,
|
||||
drop_rate: float = 0.,
|
||||
pos_drop_rate: float = 0.,
|
||||
patch_drop_rate: float = 0.,
|
||||
proj_drop_rate: float = 0.,
|
||||
attn_drop_rate: float = 0.,
|
||||
drop_path_rate: float = 0.,
|
||||
weight_init: str = '',
|
||||
embed_layer: Callable = partial(
|
||||
PatchEmbed, output_fmt=Format.NHWC, strict_img_size=False),
|
||||
norm_layer: Optional[Callable] = nn.LayerNorm,
|
||||
act_layer: Optional[Callable] = nn.GELU,
|
||||
block_fn: Callable = Block,
|
||||
mlp_layer: Callable = Mlp,
|
||||
use_abs_pos: bool = True,
|
||||
use_rel_pos: bool = False,
|
||||
window_size: int = 14,
|
||||
global_attn_indexes: Tuple[int, ...] = (),
|
||||
neck_chans: int = 256,
|
||||
global_pool: str = 'avg',
|
||||
head_hidden_size: Optional[int] = None
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
img_size: Input image size.
|
||||
patch_size: Patch size.
|
||||
in_chans: Number of image input channels.
|
||||
num_classes: Mumber of classes for classification head.
|
||||
global_pool: Type of global pooling for final sequence (default: 'token').
|
||||
embed_dim: Transformer embedding dimension.
|
||||
depth: Depth of transformer.
|
||||
num_heads: Number of attention heads.
|
||||
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias: Enable bias for qkv projections if True.
|
||||
init_values: Layer-scale init values (layer-scale enabled if not None).
|
||||
drop_rate: Head dropout rate.
|
||||
pos_drop_rate: Position embedding dropout rate.
|
||||
attn_drop_rate: Attention dropout rate.
|
||||
drop_path_rate: Stochastic depth rate.
|
||||
weight_init: Weight initialization scheme.
|
||||
embed_layer: Patch embedding layer.
|
||||
norm_layer: Normalization layer.
|
||||
act_layer: MLP activation layer.
|
||||
block_fn: Transformer block layer.
|
||||
use_abs_pos: If True, use absolute positional embeddings.
|
||||
use_rel_pos: If True, add relative positional embeddings to the attention map.
|
||||
window_size: Window size for window attention blocks. If 0, not use window attention.
|
||||
global_attn_indexes: Indexes for blocks using global attention. Used when window_size > 0.
|
||||
global_pool: Global pooling type.
|
||||
head_hidden_size: If set, use NormMlpHead
|
||||
"""
|
||||
super().__init__()
|
||||
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
||||
act_layer = act_layer or nn.GELU
|
||||
|
||||
self.num_classes = num_classes
|
||||
self.global_pool = global_pool
|
||||
# num_features for consistency with other models
|
||||
self.num_features = self.embed_dim = embed_dim
|
||||
self.grad_checkpointing = False
|
||||
|
||||
self.patch_embed = embed_layer(
|
||||
img_size=img_size,
|
||||
patch_size=patch_size,
|
||||
in_chans=in_chans,
|
||||
embed_dim=embed_dim,
|
||||
bias=not pre_norm, # disable bias if pre-norm is used
|
||||
)
|
||||
grid_size = self.patch_embed.grid_size
|
||||
if use_abs_pos:
|
||||
# Initialize absolute positional embedding with pretrain image size.
|
||||
self.pos_embed = nn.Parameter(torch.zeros(1, grid_size[0], grid_size[1], embed_dim))
|
||||
else:
|
||||
self.pos_embed = None
|
||||
self.pos_drop = nn.Dropout(p=pos_drop_rate)
|
||||
if patch_drop_rate > 0:
|
||||
self.patch_drop = PatchDropout(
|
||||
patch_drop_rate,
|
||||
num_prefix_tokens=0,
|
||||
)
|
||||
else:
|
||||
self.patch_drop = nn.Identity()
|
||||
self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
|
||||
|
||||
# stochastic depth decay rule
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
|
||||
self.blocks = nn.Sequential(*[
|
||||
block_fn(
|
||||
dim=embed_dim,
|
||||
num_heads=num_heads,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_norm=qk_norm,
|
||||
init_values=init_values,
|
||||
proj_drop=proj_drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[i],
|
||||
norm_layer=norm_layer,
|
||||
act_layer=act_layer,
|
||||
mlp_layer=mlp_layer,
|
||||
use_rel_pos=use_rel_pos,
|
||||
window_size=window_size if i not in global_attn_indexes else 0,
|
||||
input_size=grid_size,
|
||||
)
|
||||
for i in range(depth)])
|
||||
|
||||
if neck_chans:
|
||||
self.neck = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
embed_dim,
|
||||
neck_chans,
|
||||
kernel_size=1,
|
||||
bias=False,
|
||||
),
|
||||
LayerNorm2d(neck_chans),
|
||||
nn.Conv2d(
|
||||
neck_chans,
|
||||
neck_chans,
|
||||
kernel_size=3,
|
||||
padding=1,
|
||||
bias=False,
|
||||
),
|
||||
LayerNorm2d(neck_chans),
|
||||
)
|
||||
else:
|
||||
self.neck = nn.Identity()
|
||||
neck_chans = embed_dim
|
||||
|
||||
# Classifier Head
|
||||
if head_hidden_size:
|
||||
self.head = NormMlpClassifierHead(
|
||||
neck_chans,
|
||||
num_classes,
|
||||
hidden_size=head_hidden_size,
|
||||
pool_type=global_pool,
|
||||
drop_rate=drop_rate,
|
||||
)
|
||||
else:
|
||||
self.head = ClassifierHead(
|
||||
neck_chans,
|
||||
num_classes,
|
||||
pool_type=global_pool,
|
||||
drop_rate=drop_rate,
|
||||
)
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed', 'dist_token'}
|
||||
|
||||
@torch.jit.ignore
|
||||
def group_matcher(self, coarse=False):
|
||||
return dict(
|
||||
stem=r'^pos_embed|patch_embed', # stem and embed
|
||||
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
|
||||
)
|
||||
|
||||
@torch.jit.ignore
|
||||
def set_grad_checkpointing(self, enable=True):
|
||||
self.grad_checkpointing = enable
|
||||
|
||||
@torch.jit.ignore
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes=0, global_pool=None):
|
||||
self.head.reset(num_classes, global_pool)
|
||||
|
||||
def forward_features(self, x):
|
||||
x = self.patch_embed(x)
|
||||
if self.pos_embed is not None:
|
||||
# dynamically resize abs pos embedding if needed
|
||||
x = x + resample_abs_pos_embed_nhwc(self.pos_embed, x.shape[1:3])
|
||||
x = self.pos_drop(x)
|
||||
x = self.patch_drop(x)
|
||||
x = self.norm_pre(x)
|
||||
if self.grad_checkpointing and not torch.jit.is_scripting():
|
||||
x = checkpoint_seq(self.blocks, x)
|
||||
else:
|
||||
x = self.blocks(x)
|
||||
x = self.neck(x.permute(0, 3, 1, 2))
|
||||
return x
|
||||
|
||||
def forward_head(self, x, pre_logits: bool = False):
|
||||
return self.head(x, pre_logits=True) if pre_logits else self.head(x)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
x = self.forward_head(x)
|
||||
return x
|
||||
|
||||
|
||||
def checkpoint_filter_fn(
|
||||
state_dict,
|
||||
model,
|
||||
):
|
||||
""" Remap SAM checkpoints -> timm """
|
||||
sam_checkpoint = 'image_encoder.patch_embed.proj.weight' in state_dict
|
||||
out_dict = {}
|
||||
for k, v in state_dict.items():
|
||||
if k.startswith('image_encoder.'):
|
||||
k = k[14:]
|
||||
k = k.replace('mlp.lin', 'mlp.fc')
|
||||
else:
|
||||
if sam_checkpoint:
|
||||
continue
|
||||
out_dict[k] = v
|
||||
return out_dict
|
||||
|
||||
|
||||
def _cfg(url='', **kwargs):
|
||||
return {
|
||||
'url': url,
|
||||
'num_classes': 1000, 'input_size': (3, 1024, 1024), 'pool_size': None,
|
||||
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
|
||||
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
|
||||
'first_conv': 'patch_embed.proj', 'classifier': 'head',
|
||||
**kwargs
|
||||
}
|
||||
|
||||
|
||||
default_cfgs = generate_default_cfgs({
|
||||
|
||||
# Segment-Anyhing Model (SAM) pretrained - https://github.com/facebookresearch/segment-anything (no classifier head, for fine-tune/features only)
|
||||
'samvit_base_patch16.sa1b': _cfg(
|
||||
url='https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth',
|
||||
hf_hub_id='timm/',
|
||||
license='apache-2.0',
|
||||
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
|
||||
input_size=(3, 1024, 1024), crop_pct=1.0),
|
||||
'samvit_large_patch16.sa1b': _cfg(
|
||||
url='https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth',
|
||||
hf_hub_id='timm/',
|
||||
license='apache-2.0',
|
||||
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
|
||||
input_size=(3, 1024, 1024), crop_pct=1.0),
|
||||
'samvit_huge_patch16.sa1b': _cfg(
|
||||
url='https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth',
|
||||
hf_hub_id='timm/',
|
||||
license='apache-2.0',
|
||||
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0,
|
||||
input_size=(3, 1024, 1024), crop_pct=1.0),
|
||||
})
|
||||
|
||||
|
||||
def _create_vision_transformer(variant, pretrained=False, **kwargs):
|
||||
if kwargs.get('features_only', None):
|
||||
raise RuntimeError(
|
||||
'features_only not implemented for Vision Transformer models.')
|
||||
|
||||
return build_model_with_cfg(
|
||||
VisionTransformerSAM,
|
||||
variant,
|
||||
pretrained,
|
||||
pretrained_filter_fn=checkpoint_filter_fn,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
@register_model
|
||||
def samvit_base_patch16(pretrained=False, **kwargs) -> VisionTransformerSAM:
|
||||
""" ViT-B/16 for Segment-Anything
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, embed_dim=768, depth=12, num_heads=12, global_attn_indexes=[2, 5, 8, 11],
|
||||
window_size=14, use_rel_pos=True, img_size=1024,
|
||||
)
|
||||
model = _create_vision_transformer(
|
||||
'samvit_base_patch16', pretrained=pretrained, **dict(model_args, **kwargs))
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def samvit_large_patch16(pretrained=False, **kwargs) -> VisionTransformerSAM:
|
||||
""" ViT-L/16 for Segment-Anything
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, embed_dim=1024, depth=24, num_heads=16, global_attn_indexes=[5, 11, 17, 23],
|
||||
window_size=14, use_rel_pos=True, img_size=1024,
|
||||
)
|
||||
model = _create_vision_transformer(
|
||||
'samvit_large_patch16', pretrained=pretrained, **dict(model_args, **kwargs))
|
||||
return model
|
||||
|
||||
|
||||
@register_model
|
||||
def samvit_huge_patch16(pretrained=False, **kwargs) -> VisionTransformerSAM:
|
||||
""" ViT-H/16 for Segment-Anything
|
||||
"""
|
||||
model_args = dict(
|
||||
patch_size=16, embed_dim=1280, depth=32, num_heads=16, global_attn_indexes=[7, 15, 23, 31],
|
||||
window_size=14, use_rel_pos=True, img_size=1024,
|
||||
)
|
||||
model = _create_vision_transformer(
|
||||
'samvit_huge_patch16', pretrained=pretrained, **dict(model_args, **kwargs))
|
||||
return model
|
||||
|
||||
# TODO:
|
||||
# support any input size, now only 1024 x 1024 (pretrained)
|
||||
Loading…
x
Reference in New Issue
Block a user