760 lines
28 KiB
Python
760 lines
28 KiB
Python
""" 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, List, Optional, Tuple, Union
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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 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, RotaryEmbeddingCat, apply_rot_embed_cat, to_2tuple, use_fused_attn
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from torch.jit import Final
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from ._builder import build_model_with_cfg
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from ._features import feature_take_indices
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from ._features_fx import register_notrace_function
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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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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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register_notrace_function(get_rel_pos)
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def get_decomposed_rel_pos_bias(
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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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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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bias (Tensor): attention bias to add to attention map
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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_bias = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
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return attn_bias.reshape(-1, q_h * q_w, k_h * k_w)
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class Attention(nn.Module):
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fused_attn: Final[bool]
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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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input_size: Optional[Tuple[int, int]] = None,
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rope: Optional[nn.Module] = 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.fused_attn = use_fused_attn()
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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 rope is None
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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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self.rope = rope
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def forward(self, x):
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B, H, W, _ = x.shape
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N = H * W
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x = x.reshape(B, N, -1)
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qkv = self.qkv(x).view(B, N, 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, N, -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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if self.use_rel_pos:
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attn_bias = get_decomposed_rel_pos_bias(q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
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else:
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attn_bias = None
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if self.rope is not None:
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rope = self.rope.get_embed()
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q = apply_rot_embed_cat(q, rope).type_as(v)
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k = apply_rot_embed_cat(k, rope).type_as(v)
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if self.fused_attn:
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x = torch.nn.functional.scaled_dot_product_attention(
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q, k, v,
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attn_mask=attn_bias,
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dropout_p=self.attn_drop.p if self.training else 0.,
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)
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else:
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q = q * self.scale
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attn = q @ k.transpose(-2, -1)
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if attn_bias is not None:
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attn = attn + attn_bias
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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
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x = x.view(B, self.num_heads, N, -1).transpose(1, 2).reshape(B, N, -1)
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x = self.proj(x)
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x = self.proj_drop(x)
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x = x.view(B, H, W, -1)
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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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rope=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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rope=rope,
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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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B, H, W, _ = x.shape
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shortcut = x
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x = self.norm1(x)
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# Window partition
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pad_hw: Optional[Tuple[int, int]] = None
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if self.window_size > 0:
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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, (H, W), pad_hw)
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x = shortcut + x
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x = x.reshape(B, H * W, -1) # MLP is faster for N, L, C tensor
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x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
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x = x.reshape(B, H, W, -1)
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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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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, hw: Tuple[int, int], pad_hw: Optional[Tuple[int, int]] = None,
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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 if pad_hw is not None else 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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x = x[:, :H, :W, :].contiguous()
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return x
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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,
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qk_norm: bool = False,
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init_values: Optional[float] = None,
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pre_norm: bool = False,
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drop_rate: float = 0.,
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pos_drop_rate: float = 0.,
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patch_drop_rate: float = 0.,
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proj_drop_rate: float = 0.,
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attn_drop_rate: float = 0.,
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drop_path_rate: float = 0.,
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weight_init: str = '',
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embed_layer: Callable = partial(PatchEmbed, output_fmt=Format.NHWC, strict_img_size=False),
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norm_layer: Optional[Callable] = nn.LayerNorm,
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act_layer: Optional[Callable] = nn.GELU,
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block_fn: Callable = Block,
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mlp_layer: Callable = Mlp,
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use_abs_pos: bool = True,
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use_rel_pos: bool = False,
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use_rope: bool = False,
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window_size: int = 14,
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global_attn_indexes: Tuple[int, ...] = (),
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neck_chans: int = 256,
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global_pool: str = 'avg',
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head_hidden_size: Optional[int] = None,
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ref_feat_shape: Optional[Tuple[Tuple[int, int], Tuple[int, int]]] = None
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):
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"""
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Args:
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img_size: Input image size.
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patch_size: Patch size.
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in_chans: Number of image input channels.
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num_classes: Number of classes for classification head.
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global_pool: Type of global pooling for final sequence (default: 'token').
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embed_dim: Transformer embedding dimension.
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depth: Depth of transformer.
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num_heads: Number of attention heads.
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mlp_ratio: Ratio of mlp hidden dim to embedding dim.
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qkv_bias: Enable bias for qkv projections if True.
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init_values: Layer-scale init values (layer-scale enabled if not None).
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drop_rate: Head dropout rate.
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pos_drop_rate: Position embedding dropout rate.
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attn_drop_rate: Attention dropout rate.
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drop_path_rate: Stochastic depth rate.
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weight_init: Weight initialization scheme.
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embed_layer: Patch embedding layer.
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norm_layer: Normalization layer.
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act_layer: MLP activation layer.
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block_fn: Transformer block layer.
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use_abs_pos: If True, use absolute positional embeddings.
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use_rel_pos: If True, add relative positional embeddings to the attention map.
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use_rope: If True, add rotary position embeddings to q/k in attention block.
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window_size: Window size for window attention blocks. If 0, not use window attention.
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global_attn_indexes: Indexes for blocks using global attention. Used when window_size > 0.
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global_pool: Global pooling type.
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head_hidden_size: If set, use NormMlpHead
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ref_feat_shape: Tuple of reference feature shapes for ROPE, (global, local)
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"""
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super().__init__()
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norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
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act_layer = act_layer or nn.GELU
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self.num_classes = num_classes
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self.global_pool = global_pool
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self.num_features = self.head_hidden_size = self.embed_dim = embed_dim # for consistency with other models
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self.grad_checkpointing = False
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self.patch_embed = embed_layer(
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img_size=img_size,
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patch_size=patch_size,
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in_chans=in_chans,
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embed_dim=embed_dim,
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bias=not pre_norm, # disable bias if pre-norm is used
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)
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grid_size = self.patch_embed.grid_size
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r = self.patch_embed.feat_ratio() if hasattr(self.patch_embed, 'feat_ratio') else patch_size
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if use_abs_pos:
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# Initialize absolute positional embedding with pretrain image size.
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self.pos_embed = nn.Parameter(torch.zeros(1, grid_size[0], grid_size[1], embed_dim))
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else:
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self.pos_embed = None
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self.pos_drop = nn.Dropout(p=pos_drop_rate)
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if patch_drop_rate > 0:
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self.patch_drop = PatchDropout(
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patch_drop_rate,
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num_prefix_tokens=0,
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)
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else:
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self.patch_drop = nn.Identity()
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self.norm_pre = norm_layer(embed_dim) if pre_norm else nn.Identity()
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if use_rope:
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assert not use_rel_pos, "ROPE and relative pos embeddings should not be enabled at same time"
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if ref_feat_shape is not None:
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assert len(ref_feat_shape) == 2
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ref_feat_shape_global = to_2tuple(ref_feat_shape[0])
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ref_feat_shape_window = to_2tuple(ref_feat_shape[1])
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else:
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ref_feat_shape_global = ref_feat_shape_window = None
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self.rope_global = RotaryEmbeddingCat(
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embed_dim // num_heads,
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in_pixels=False,
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feat_shape=grid_size,
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ref_feat_shape=ref_feat_shape_global,
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)
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self.rope_window = RotaryEmbeddingCat(
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embed_dim // num_heads,
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in_pixels=False,
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|
feat_shape=to_2tuple(window_size),
|
|
ref_feat_shape=ref_feat_shape_window,
|
|
)
|
|
else:
|
|
self.rope_global = None
|
|
self.rope_window = None
|
|
|
|
# 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,
|
|
rope=self.rope_window if i not in global_attn_indexes else self.rope_global,
|
|
)
|
|
for i in range(depth)])
|
|
self.feature_info = [
|
|
dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) 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),
|
|
)
|
|
self.num_features = neck_chans
|
|
else:
|
|
if head_hidden_size:
|
|
self.neck = nn.Identity()
|
|
else:
|
|
# should have a final norm with standard ClassifierHead
|
|
self.neck = LayerNorm2d(embed_dim)
|
|
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) -> nn.Module:
|
|
return self.head
|
|
|
|
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
|
|
self.num_classes = num_classes
|
|
self.head.reset(num_classes, global_pool)
|
|
|
|
def forward_intermediates(
|
|
self,
|
|
x: torch.Tensor,
|
|
indices: Optional[Union[int, List[int]]] = None,
|
|
norm: bool = False,
|
|
stop_early: bool = False,
|
|
output_fmt: str = 'NCHW',
|
|
intermediates_only: bool = False,
|
|
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
|
|
""" Forward features that returns intermediates.
|
|
|
|
Args:
|
|
x: Input image tensor
|
|
indices: Take last n blocks if int, all if None, select matching indices if sequence
|
|
norm: Apply norm layer to all intermediates
|
|
stop_early: Stop iterating over blocks when last desired intermediate hit
|
|
output_fmt: Shape of intermediate feature outputs
|
|
intermediates_only: Only return intermediate features
|
|
Returns:
|
|
|
|
"""
|
|
assert output_fmt == 'NCHW', 'Output shape for ViT-SAM must be NCHW.'
|
|
intermediates = []
|
|
take_indices, max_index = feature_take_indices(len(self.blocks), indices)
|
|
|
|
# forward pass, collect intermediates
|
|
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 torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
|
|
blocks = self.blocks
|
|
else:
|
|
blocks = self.blocks[:max_index + 1]
|
|
for i, blk in enumerate(blocks):
|
|
x = blk(x)
|
|
if i in take_indices:
|
|
# make output BCHW
|
|
if norm:
|
|
# norm is intertwined with neck convs so apply both, changes the dim
|
|
# FIXME only apply to final? Need experiments
|
|
intermediates.append(self.neck(x.permute(0, 3, 1, 2)))
|
|
else:
|
|
intermediates.append(x.permute(0, 3, 1, 2))
|
|
|
|
if intermediates_only:
|
|
return intermediates
|
|
|
|
x = self.neck(x.permute(0, 3, 1, 2))
|
|
|
|
return x, intermediates
|
|
|
|
def prune_intermediate_layers(
|
|
self,
|
|
indices: Optional[Union[int, List[int]]] = None,
|
|
prune_norm: bool = False,
|
|
prune_head: bool = True,
|
|
):
|
|
""" Prune layers not required for specified intermediates.
|
|
"""
|
|
take_indices, max_index = feature_take_indices(len(self.blocks), indices)
|
|
self.blocks = self.blocks[:max_index + 1] # truncate blocks
|
|
if prune_norm:
|
|
# neck is being treated as equivalent to final norm here
|
|
self.neck = nn.Identity()
|
|
if prune_head:
|
|
self.reset_classifier(0, '')
|
|
return take_indices
|
|
|
|
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.fc',
|
|
**kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = generate_default_cfgs({
|
|
|
|
# Segment-Anything 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),
|
|
|
|
'samvit_base_patch16_224': _cfg(
|
|
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=1000,
|
|
input_size=(3, 224, 224), crop_pct=0.9),
|
|
})
|
|
|
|
|
|
def _create_vision_transformer(variant, pretrained=False, **kwargs):
|
|
out_indices = kwargs.pop('out_indices', 3)
|
|
return build_model_with_cfg(
|
|
VisionTransformerSAM,
|
|
variant,
|
|
pretrained,
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
feature_cfg=dict(out_indices=out_indices, feature_cls='getter'),
|
|
**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
|
|
|
|
|
|
@register_model
|
|
def samvit_base_patch16_224(pretrained=False, **kwargs) -> VisionTransformerSAM:
|
|
""" ViT-B/16 based on samvit arch
|
|
"""
|
|
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, use_abs_pos=False, img_size=224, neck_chans=None,
|
|
)
|
|
model = _create_vision_transformer(
|
|
'samvit_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
|
|
return model
|
|
|