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Update vit_relpos w/ some additional weights, some cleanup to match recent vit updates, more MLP log coord experiments.

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pull/1327/head
Ross Wightman 2 years ago
parent 58621723bd
commit ce65a7b29f
1 changed files with 145 additions and 49 deletions
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timm/models/vision_transformer_relpos.py
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@ -8,6 +8,7 @@ import math
import logging
from functools import partial
from collections import OrderedDict
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
@ -16,7 +17,7 @@ import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .helpers import build_model_with_cfg, named_apply
from .helpers import build_model_with_cfg, resolve_pretrained_cfg, named_apply
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, to_2tuple
from .registry import register_model
@ -47,9 +48,16 @@ default_cfgs = {
'vit_relpos_base_patch16_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth'),
'vit_srelpos_small_patch16_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth'),
'vit_srelpos_medium_patch16_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth'),
'vit_relpos_medium_patch16_cls_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth'),
'vit_relpos_base_patch16_cls_224': _cfg(
url=''),
'vit_relpos_base_patch16_gapcls_224': _cfg(
'vit_relpos_base_patch16_clsgap_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth'),
'vit_relpos_small_patch16_rpn_224': _cfg(url=''),
@ -59,35 +67,43 @@ default_cfgs = {
}
def gen_relative_position_index(win_size: Tuple[int, int], class_token: int = 0) -> torch.Tensor:
# cut and paste w/ modifications from swin / beit codebase
# cls to token & token 2 cls & cls to cls
def gen_relative_position_index(
q_size: Tuple[int, int],
k_size: Tuple[int, int] = None,
class_token: bool = False) -> torch.Tensor:
# Adapted with significant modifications from Swin / BeiT codebases
# get pair-wise relative position index for each token inside the window
window_area = win_size[0] * win_size[1]
coords = torch.stack(torch.meshgrid([torch.arange(win_size[0]), torch.arange(win_size[1])])).flatten(1) # 2, Wh, Ww
relative_coords = coords[:, :, None] - coords[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += win_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += win_size[1] - 1
relative_coords[:, :, 0] *= 2 * win_size[1] - 1
q_coords = torch.stack(torch.meshgrid([torch.arange(q_size[0]), torch.arange(q_size[1])])).flatten(1) # 2, Wh, Ww
if k_size is None:
k_coords = q_coords
k_size = q_size
else:
# different q vs k sizes is a WIP
k_coords = torch.stack(torch.meshgrid([torch.arange(k_size[0]), torch.arange(k_size[1])])).flatten(1)
relative_coords = q_coords[:, :, None] - k_coords[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0) # Wh*Ww, Wh*Ww, 2
_, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0)
if class_token:
num_relative_distance = (2 * win_size[0] - 1) * (2 * win_size[1] - 1) + 3
relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
# handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias
# NOTE not intended or tested with MLP log-coords
max_size = (max(q_size[0], k_size[0]), max(q_size[1], k_size[1]))
num_relative_distance = (2 * max_size[0] - 1) * (2 * max_size[1] - 1) + 3
relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0])
relative_position_index[0, 0:] = num_relative_distance - 3
relative_position_index[0:, 0] = num_relative_distance - 2
relative_position_index[0, 0] = num_relative_distance - 1
else:
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
return relative_position_index
return relative_position_index.contiguous()
def gen_relative_log_coords(
win_size: Tuple[int, int],
pretrained_win_size: Tuple[int, int] = (0, 0),
mode='swin'
mode='swin',
):
# as per official swin-v2 impl, supporting timm swin-v2-cr coords as well
assert mode in ('swin', 'cr', 'rw')
# as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well
relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32)
relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32)
relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
@ -100,12 +116,22 @@ def gen_relative_log_coords(
relative_coords_table[:, :, 0] /= (win_size[0] - 1)
relative_coords_table[:, :, 1] /= (win_size[1] - 1)
relative_coords_table *= 8 # normalize to -8, 8
scale = math.log2(8)
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
1.0 + relative_coords_table.abs()) / math.log2(8)
else:
# FIXME we should support a form of normalization (to -1/1) for this mode?
scale = math.log2(math.e)
if mode == 'rw':
# cr w/ window size normalization -> [-1,1] log coords
relative_coords_table[:, :, 0] /= (win_size[0] - 1)
relative_coords_table[:, :, 1] /= (win_size[1] - 1)
relative_coords_table *= 8 # scale to -8, 8
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
1.0 + relative_coords_table.abs()) / scale
1.0 + relative_coords_table.abs())
relative_coords_table /= math.log2(9) # -> [-1, 1]
else:
# mode == 'cr'
relative_coords_table = torch.sign(relative_coords_table) * torch.log(
1.0 + relative_coords_table.abs())
return relative_coords_table
@ -115,19 +141,29 @@ class RelPosMlp(nn.Module):
window_size,
num_heads=8,
hidden_dim=128,
class_token=False,
prefix_tokens=0,
mode='cr',
pretrained_window_size=(0, 0)
):
super().__init__()
self.window_size = window_size
self.window_area = self.window_size[0] * self.window_size[1]
self.class_token = 1 if class_token else 0
self.prefix_tokens = prefix_tokens
self.num_heads = num_heads
self.bias_shape = (self.window_area,) * 2 + (num_heads,)
self.apply_sigmoid = mode == 'swin'
if mode == 'swin':
self.bias_act = nn.Sigmoid()
self.bias_gain = 16
mlp_bias = (True, False)
elif mode == 'rw':
self.bias_act = nn.Tanh()
self.bias_gain = 4
mlp_bias = True
else:
self.bias_act = nn.Identity()
self.bias_gain = None
mlp_bias = True
mlp_bias = (True, False) if mode == 'swin' else True
self.mlp = Mlp(
2, # x, y
hidden_features=hidden_dim,
@ -155,10 +191,11 @@ class RelPosMlp(nn.Module):
self.relative_position_index.view(-1)] # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.view(self.bias_shape)
relative_position_bias = relative_position_bias.permute(2, 0, 1)
if self.apply_sigmoid:
relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
if self.class_token:
relative_position_bias = F.pad(relative_position_bias, [self.class_token, 0, self.class_token, 0])
relative_position_bias = self.bias_act(relative_position_bias)
if self.bias_gain is not None:
relative_position_bias = self.bias_gain * relative_position_bias
if self.prefix_tokens:
relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0])
return relative_position_bias.unsqueeze(0).contiguous()
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
@ -167,18 +204,18 @@ class RelPosMlp(nn.Module):
class RelPosBias(nn.Module):
def __init__(self, window_size, num_heads, class_token=False):
def __init__(self, window_size, num_heads, prefix_tokens=0):
super().__init__()
assert prefix_tokens <= 1
self.window_size = window_size
self.window_area = window_size[0] * window_size[1]
self.class_token = 1 if class_token else 0
self.bias_shape = (self.window_area + self.class_token,) * 2 + (num_heads,)
self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,)
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * self.class_token
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens
self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads))
self.register_buffer(
"relative_position_index",
gen_relative_position_index(self.window_size, class_token=self.class_token),
gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0),
persistent=False,
)
@ -306,11 +343,32 @@ class VisionTransformerRelPos(nn.Module):
"""
def __init__(
self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, global_pool='avg',
embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True, init_values=1e-6,
class_token=False, fc_norm=False, rel_pos_type='mlp', shared_rel_pos=False, rel_pos_dim=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., weight_init='skip',
embed_layer=PatchEmbed, norm_layer=None, act_layer=None, block_fn=RelPosBlock):
self,
img_size=224,
patch_size=16,
in_chans=3,
num_classes=1000,
global_pool='avg',
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.,
qkv_bias=True,
init_values=1e-6,
class_token=False,
fc_norm=False,
rel_pos_type='mlp',
rel_pos_dim=None,
shared_rel_pos=False,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.,
weight_init='skip',
embed_layer=PatchEmbed,
norm_layer=None,
act_layer=None,
block_fn=RelPosBlock
):
"""
Args:
img_size (int, tuple): input image size
@ -345,19 +403,22 @@ class VisionTransformerRelPos(nn.Module):
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_tokens = 1 if class_token else 0
self.num_prefix_tokens = 1 if class_token else 0
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)
feat_size = self.patch_embed.grid_size
rel_pos_args = dict(window_size=feat_size, class_token=class_token)
rel_pos_args = dict(window_size=feat_size, prefix_tokens=self.num_prefix_tokens)
if rel_pos_type.startswith('mlp'):
if rel_pos_dim:
rel_pos_args['hidden_dim'] = rel_pos_dim
# FIXME experimenting with different relpos log coord configs
if 'swin' in rel_pos_type:
rel_pos_args['mode'] = 'swin'
elif 'rw' in rel_pos_type:
rel_pos_args['mode'] = 'rw'
rel_pos_cls = partial(RelPosMlp, **rel_pos_args)
else:
rel_pos_cls = partial(RelPosBias, **rel_pos_args)
@ -367,7 +428,7 @@ class VisionTransformerRelPos(nn.Module):
# NOTE shared rel pos currently mutually exclusive w/ per-block, but could support both...
rel_pos_cls = None
self.cls_token = nn.Parameter(torch.zeros(1, self.num_tokens, embed_dim)) if self.num_tokens else None
self.cls_token = nn.Parameter(torch.zeros(1, self.num_prefix_tokens, embed_dim)) if class_token else None
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
@ -434,7 +495,7 @@ class VisionTransformerRelPos(nn.Module):
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, self.num_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.fc_norm(x)
return x if pre_logits else self.head(x)
@ -502,6 +563,41 @@ def vit_relpos_base_patch16_224(pretrained=False, **kwargs):
return model
@register_model
def vit_srelpos_small_patch16_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
"""
model_kwargs = dict(
patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=False,
rel_pos_dim=384, shared_rel_pos=True, **kwargs)
model = _create_vision_transformer_relpos('vit_srelpos_small_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_srelpos_medium_patch16_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
"""
model_kwargs = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False,
rel_pos_dim=512, shared_rel_pos=True, **kwargs)
model = _create_vision_transformer_relpos(
'vit_srelpos_medium_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_relpos_medium_patch16_cls_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-M/16) w/ relative log-coord position, class token present
"""
model_kwargs = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=False,
rel_pos_dim=256, class_token=True, global_pool='token', **kwargs)
model = _create_vision_transformer_relpos(
'vit_relpos_medium_patch16_cls_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
@ -514,14 +610,14 @@ def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs):
@register_model
def vit_relpos_base_patch16_gapcls_224(pretrained=False, **kwargs):
def vit_relpos_base_patch16_clsgap_224(pretrained=False, **kwargs):
""" ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
NOTE this config is a bit of a mistake, class token was enabled but global avg-pool w/ fc-norm was not disabled
Leaving here for comparisons w/ a future re-train as it performs quite well.
"""
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, class_token=True, **kwargs)
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_gapcls_224', pretrained=pretrained, **model_kwargs)
model = _create_vision_transformer_relpos('vit_relpos_base_patch16_clsgap_224', pretrained=pretrained, **model_kwargs)
return model

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