You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
464 lines
19 KiB
464 lines
19 KiB
""" Nested Transformer (NesT) in PyTorch
|
|
|
|
A PyTorch implement of Aggregating Nested Transformers as described in:
|
|
|
|
'Aggregating Nested Transformers'
|
|
- https://arxiv.org/abs/2105.12723
|
|
|
|
The official Jax code is released and available at https://github.com/google-research/nested-transformer. The weights
|
|
have been converted with convert/convert_nest_flax.py
|
|
|
|
Acknowledgments:
|
|
* The paper authors for sharing their research, code, and model weights
|
|
* Ross Wightman's existing code off which I based this
|
|
|
|
Copyright 2021 Alexander Soare
|
|
"""
|
|
|
|
import collections.abc
|
|
import logging
|
|
import math
|
|
from functools import partial
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from torch import nn
|
|
|
|
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
|
|
from .helpers import build_model_with_cfg, named_apply
|
|
from .layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_
|
|
from .layers import create_conv2d, create_pool2d, to_ntuple
|
|
from .registry import register_model
|
|
|
|
_logger = logging.getLogger(__name__)
|
|
|
|
|
|
def _cfg(url='', **kwargs):
|
|
return {
|
|
'url': url,
|
|
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': [14, 14],
|
|
'crop_pct': .875, 'interpolation': 'bicubic', 'fixed_input_size': True,
|
|
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
|
|
'first_conv': 'patch_embed.proj', 'classifier': 'head',
|
|
**kwargs
|
|
}
|
|
|
|
|
|
default_cfgs = {
|
|
# (weights from official Google JAX impl)
|
|
'nest_base': _cfg(),
|
|
'nest_small': _cfg(),
|
|
'nest_tiny': _cfg(),
|
|
'jx_nest_base': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/jx_nest_base-8bc41011.pth'),
|
|
'jx_nest_small': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/jx_nest_small-422eaded.pth'),
|
|
'jx_nest_tiny': _cfg(
|
|
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/jx_nest_tiny-e3428fb9.pth'),
|
|
}
|
|
|
|
|
|
class Attention(nn.Module):
|
|
"""
|
|
This is much like `.vision_transformer.Attention` but uses *localised* self attention by accepting an input with
|
|
an extra "image block" dim
|
|
"""
|
|
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
|
|
super().__init__()
|
|
self.num_heads = num_heads
|
|
head_dim = dim // num_heads
|
|
self.scale = head_dim ** -0.5
|
|
|
|
self.qkv = nn.Linear(dim, 3*dim, bias=qkv_bias)
|
|
self.attn_drop = nn.Dropout(attn_drop)
|
|
self.proj = nn.Linear(dim, dim)
|
|
self.proj_drop = nn.Dropout(proj_drop)
|
|
|
|
def forward(self, x):
|
|
"""
|
|
x is shape: B (batch_size), T (image blocks), N (seq length per image block), C (embed dim)
|
|
"""
|
|
B, T, N, C = x.shape
|
|
# result of next line is (qkv, B, num (H)eads, T, N, (C')hannels per head)
|
|
qkv = self.qkv(x).reshape(B, T, N, 3, self.num_heads, C // self.num_heads).permute(3, 0, 4, 1, 2, 5)
|
|
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
|
|
|
attn = (q @ k.transpose(-2, -1)) * self.scale # (B, H, T, N, N)
|
|
attn = attn.softmax(dim=-1)
|
|
attn = self.attn_drop(attn)
|
|
|
|
# (B, H, T, N, C'), permute -> (B, T, N, C', H)
|
|
x = (attn @ v).permute(0, 2, 3, 4, 1).reshape(B, T, N, C)
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
return x # (B, T, N, C)
|
|
|
|
|
|
class TransformerLayer(nn.Module):
|
|
"""
|
|
This is much like `.vision_transformer.Block` but:
|
|
- Called TransformerLayer here to allow for "block" as defined in the paper ("non-overlapping image blocks")
|
|
- Uses modified Attention layer that handles the "block" dimension
|
|
"""
|
|
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.,
|
|
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
|
super().__init__()
|
|
self.norm1 = norm_layer(dim)
|
|
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
|
|
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
|
self.norm2 = norm_layer(dim)
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
|
|
|
def forward(self, x):
|
|
y = self.norm1(x)
|
|
x = x + self.drop_path(self.attn(y))
|
|
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
|
return x
|
|
|
|
|
|
class ConvPool(nn.Module):
|
|
def __init__(self, in_channels, out_channels, norm_layer, pad_type=''):
|
|
super().__init__()
|
|
self.conv = create_conv2d(in_channels, out_channels, kernel_size=3, padding=pad_type, bias=True)
|
|
self.norm = norm_layer(out_channels)
|
|
self.pool = create_pool2d('max', kernel_size=3, stride=2, padding=pad_type)
|
|
|
|
def forward(self, x):
|
|
"""
|
|
x is expected to have shape (B, C, H, W)
|
|
"""
|
|
assert x.shape[-2] % 2 == 0, 'BlockAggregation requires even input spatial dims'
|
|
assert x.shape[-1] % 2 == 0, 'BlockAggregation requires even input spatial dims'
|
|
x = self.conv(x)
|
|
# Layer norm done over channel dim only
|
|
x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
|
|
x = self.pool(x)
|
|
return x # (B, C, H//2, W//2)
|
|
|
|
|
|
def blockify(x, block_size: int):
|
|
"""image to blocks
|
|
Args:
|
|
x (Tensor): with shape (B, H, W, C)
|
|
block_size (int): edge length of a single square block in units of H, W
|
|
"""
|
|
B, H, W, C = x.shape
|
|
assert H % block_size == 0, '`block_size` must divide input height evenly'
|
|
assert W % block_size == 0, '`block_size` must divide input width evenly'
|
|
grid_height = H // block_size
|
|
grid_width = W // block_size
|
|
x = x.reshape(B, grid_height, block_size, grid_width, block_size, C)
|
|
x = x.transpose(2, 3).reshape(B, grid_height * grid_width, -1, C)
|
|
return x # (B, T, N, C)
|
|
|
|
|
|
def deblockify(x, block_size: int):
|
|
"""blocks to image
|
|
Args:
|
|
x (Tensor): with shape (B, T, N, C) where T is number of blocks and N is sequence size per block
|
|
block_size (int): edge length of a single square block in units of desired H, W
|
|
"""
|
|
B, T, _, C = x.shape
|
|
grid_size = int(math.sqrt(T))
|
|
height = width = grid_size * block_size
|
|
x = x.reshape(B, grid_size, grid_size, block_size, block_size, C)
|
|
x = x.transpose(2, 3).reshape(B, height, width, C)
|
|
return x # (B, H, W, C)
|
|
|
|
|
|
class NestLevel(nn.Module):
|
|
""" Single hierarchical level of a Nested Transformer
|
|
"""
|
|
def __init__(
|
|
self, num_blocks, block_size, seq_length, num_heads, depth, embed_dim, prev_embed_dim=None,
|
|
mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rates=[],
|
|
norm_layer=None, act_layer=None, pad_type=''):
|
|
super().__init__()
|
|
self.block_size = block_size
|
|
self.pos_embed = nn.Parameter(torch.zeros(1, num_blocks, seq_length, embed_dim))
|
|
|
|
if prev_embed_dim is not None:
|
|
self.pool = ConvPool(prev_embed_dim, embed_dim, norm_layer=norm_layer, pad_type=pad_type)
|
|
else:
|
|
self.pool = nn.Identity()
|
|
|
|
# Transformer encoder
|
|
if len(drop_path_rates):
|
|
assert len(drop_path_rates) == depth, 'Must provide as many drop path rates as there are transformer layers'
|
|
self.transformer_encoder = nn.Sequential(*[
|
|
TransformerLayer(
|
|
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
|
|
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=drop_path_rates[i],
|
|
norm_layer=norm_layer, act_layer=act_layer)
|
|
for i in range(depth)])
|
|
|
|
def forward(self, x):
|
|
"""
|
|
expects x as (B, C, H, W)
|
|
"""
|
|
x = self.pool(x)
|
|
x = x.permute(0, 2, 3, 1) # (B, H', W', C), switch to channels last for transformer
|
|
x = blockify(x, self.block_size) # (B, T, N, C')
|
|
x = x + self.pos_embed
|
|
x = self.transformer_encoder(x) # (B, T, N, C')
|
|
x = deblockify(x, self.block_size) # (B, H', W', C')
|
|
# Channel-first for block aggregation, and generally to replicate convnet feature map at each stage
|
|
return x.permute(0, 3, 1, 2) # (B, C, H', W')
|
|
|
|
|
|
class Nest(nn.Module):
|
|
""" Nested Transformer (NesT)
|
|
|
|
A PyTorch impl of : `Aggregating Nested Transformers`
|
|
- https://arxiv.org/abs/2105.12723
|
|
"""
|
|
|
|
def __init__(self, img_size=224, in_chans=3, patch_size=4, num_levels=3, embed_dims=(128, 256, 512),
|
|
num_heads=(4, 8, 16), depths=(2, 2, 20), num_classes=1000, mlp_ratio=4., qkv_bias=True,
|
|
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.5, norm_layer=None, act_layer=None,
|
|
pad_type='', weight_init='', global_pool='avg'):
|
|
"""
|
|
Args:
|
|
img_size (int, tuple): input image size
|
|
in_chans (int): number of input channels
|
|
patch_size (int): patch size
|
|
num_levels (int): number of block hierarchies (T_d in the paper)
|
|
embed_dims (int, tuple): embedding dimensions of each level
|
|
num_heads (int, tuple): number of attention heads for each level
|
|
depths (int, tuple): number of transformer layers for each level
|
|
num_classes (int): number of classes for classification head
|
|
mlp_ratio (int): ratio of mlp hidden dim to embedding dim for MLP of transformer layers
|
|
qkv_bias (bool): enable bias for qkv if True
|
|
drop_rate (float): dropout rate for MLP of transformer layers, MSA final projection layer, and classifier
|
|
attn_drop_rate (float): attention dropout rate
|
|
drop_path_rate (float): stochastic depth rate
|
|
norm_layer: (nn.Module): normalization layer for transformer layers
|
|
act_layer: (nn.Module): activation layer in MLP of transformer layers
|
|
pad_type: str: Type of padding to use '' for PyTorch symmetric, 'same' for TF SAME
|
|
weight_init: (str): weight init scheme
|
|
global_pool: (str): type of pooling operation to apply to final feature map
|
|
|
|
Notes:
|
|
- Default values follow NesT-B from the original Jax code.
|
|
- `embed_dims`, `num_heads`, `depths` should be ints or tuples with length `num_levels`.
|
|
- For those following the paper, Table A1 may have errors!
|
|
- https://github.com/google-research/nested-transformer/issues/2
|
|
"""
|
|
super().__init__()
|
|
|
|
for param_name in ['embed_dims', 'num_heads', 'depths']:
|
|
param_value = locals()[param_name]
|
|
if isinstance(param_value, collections.abc.Sequence):
|
|
assert len(param_value) == num_levels, f'Require `len({param_name}) == num_levels`'
|
|
|
|
embed_dims = to_ntuple(num_levels)(embed_dims)
|
|
num_heads = to_ntuple(num_levels)(num_heads)
|
|
depths = to_ntuple(num_levels)(depths)
|
|
self.num_classes = num_classes
|
|
self.num_features = embed_dims[-1]
|
|
self.feature_info = []
|
|
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
|
|
act_layer = act_layer or nn.GELU
|
|
self.drop_rate = drop_rate
|
|
self.num_levels = num_levels
|
|
if isinstance(img_size, collections.abc.Sequence):
|
|
assert img_size[0] == img_size[1], 'Model only handles square inputs'
|
|
img_size = img_size[0]
|
|
assert img_size % patch_size == 0, '`patch_size` must divide `img_size` evenly'
|
|
self.patch_size = patch_size
|
|
|
|
# Number of blocks at each level
|
|
self.num_blocks = (4 ** torch.arange(num_levels)).flip(0).tolist()
|
|
assert (img_size // patch_size) % math.sqrt(self.num_blocks[0]) == 0, \
|
|
'First level blocks don\'t fit evenly. Check `img_size`, `patch_size`, and `num_levels`'
|
|
|
|
# Block edge size in units of patches
|
|
# Hint: (img_size // patch_size) gives number of patches along edge of image. sqrt(self.num_blocks[0]) is the
|
|
# number of blocks along edge of image
|
|
self.block_size = int((img_size // patch_size) // math.sqrt(self.num_blocks[0]))
|
|
|
|
# Patch embedding
|
|
self.patch_embed = PatchEmbed(
|
|
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dims[0], flatten=False)
|
|
self.num_patches = self.patch_embed.num_patches
|
|
self.seq_length = self.num_patches // self.num_blocks[0]
|
|
|
|
# Build up each hierarchical level
|
|
levels = []
|
|
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(depths)).split(depths)]
|
|
prev_dim = None
|
|
curr_stride = 4
|
|
for i in range(len(self.num_blocks)):
|
|
dim = embed_dims[i]
|
|
levels.append(NestLevel(
|
|
self.num_blocks[i], self.block_size, self.seq_length, num_heads[i], depths[i], dim, prev_dim,
|
|
mlp_ratio, qkv_bias, drop_rate, attn_drop_rate, dp_rates[i], norm_layer, act_layer, pad_type=pad_type))
|
|
self.feature_info += [dict(num_chs=dim, reduction=curr_stride, module=f'levels.{i}')]
|
|
prev_dim = dim
|
|
curr_stride *= 2
|
|
self.levels = nn.Sequential(*levels)
|
|
|
|
# Final normalization layer
|
|
self.norm = norm_layer(embed_dims[-1])
|
|
|
|
# Classifier
|
|
self.global_pool, self.head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
|
|
|
|
self.init_weights(weight_init)
|
|
|
|
def init_weights(self, mode=''):
|
|
assert mode in ('nlhb', '')
|
|
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
|
|
for level in self.levels:
|
|
trunc_normal_(level.pos_embed, std=.02, a=-2, b=2)
|
|
named_apply(partial(_init_nest_weights, head_bias=head_bias), self)
|
|
|
|
@torch.jit.ignore
|
|
def no_weight_decay(self):
|
|
return {f'level.{i}.pos_embed' for i in range(len(self.levels))}
|
|
|
|
def get_classifier(self):
|
|
return self.head
|
|
|
|
def reset_classifier(self, num_classes, global_pool='avg'):
|
|
self.num_classes = num_classes
|
|
self.global_pool, self.head = create_classifier(
|
|
self.num_features, self.num_classes, pool_type=global_pool)
|
|
|
|
def forward_features(self, x):
|
|
""" x shape (B, C, H, W)
|
|
"""
|
|
B, _, H, W = x.shape
|
|
x = self.patch_embed(x)
|
|
x = self.levels(x)
|
|
# Layer norm done over channel dim only (to NHWC and back)
|
|
x = self.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
|
|
return x
|
|
|
|
def forward(self, x):
|
|
""" x shape (B, C, H, W)
|
|
"""
|
|
x = self.forward_features(x)
|
|
x = self.global_pool(x)
|
|
if self.drop_rate > 0.:
|
|
x = F.dropout(x, p=self.drop_rate, training=self.training)
|
|
return self.head(x)
|
|
|
|
|
|
def _init_nest_weights(module: nn.Module, name: str = '', head_bias: float = 0.):
|
|
""" NesT weight initialization
|
|
Can replicate Jax implementation. Otherwise follows vision_transformer.py
|
|
"""
|
|
if isinstance(module, nn.Linear):
|
|
if name.startswith('head'):
|
|
trunc_normal_(module.weight, std=.02, a=-2, b=2)
|
|
nn.init.constant_(module.bias, head_bias)
|
|
else:
|
|
trunc_normal_(module.weight, std=.02, a=-2, b=2)
|
|
if module.bias is not None:
|
|
nn.init.zeros_(module.bias)
|
|
elif isinstance(module, nn.Conv2d):
|
|
trunc_normal_(module.weight, std=.02, a=-2, b=2)
|
|
if module.bias is not None:
|
|
nn.init.zeros_(module.bias)
|
|
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
|
|
nn.init.zeros_(module.bias)
|
|
nn.init.ones_(module.weight)
|
|
|
|
|
|
def resize_pos_embed(posemb, posemb_new):
|
|
"""
|
|
Rescale the grid of position embeddings when loading from state_dict
|
|
Expected shape of position embeddings is (1, T, N, C), and considers only square images
|
|
"""
|
|
_logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape)
|
|
seq_length_old = posemb.shape[2]
|
|
num_blocks_new, seq_length_new = posemb_new.shape[1:3]
|
|
size_new = int(math.sqrt(num_blocks_new*seq_length_new))
|
|
# First change to (1, C, H, W)
|
|
posemb = deblockify(posemb, int(math.sqrt(seq_length_old))).permute(0, 3, 1, 2)
|
|
posemb = F.interpolate(posemb, size=[size_new, size_new], mode='bilinear')
|
|
# Now change to new (1, T, N, C)
|
|
posemb = blockify(posemb.permute(0, 2, 3, 1), int(math.sqrt(seq_length_new)))
|
|
return posemb
|
|
|
|
|
|
def checkpoint_filter_fn(state_dict, model):
|
|
""" resize positional embeddings of pretrained weights """
|
|
pos_embed_keys = [k for k in state_dict.keys() if k.startswith('pos_embed_')]
|
|
for k in pos_embed_keys:
|
|
if state_dict[k].shape != getattr(model, k).shape:
|
|
state_dict[k] = resize_pos_embed(state_dict[k], getattr(model, k))
|
|
return state_dict
|
|
|
|
|
|
def _create_nest(variant, pretrained=False, default_cfg=None, **kwargs):
|
|
default_cfg = default_cfg or default_cfgs[variant]
|
|
model = build_model_with_cfg(
|
|
Nest, variant, pretrained,
|
|
default_cfg=default_cfg,
|
|
feature_cfg=dict(out_indices=(0, 1, 2), flatten_sequential=True),
|
|
pretrained_filter_fn=checkpoint_filter_fn,
|
|
**kwargs)
|
|
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def nest_base(pretrained=False, **kwargs):
|
|
""" Nest-B @ 224x224
|
|
"""
|
|
model_kwargs = dict(
|
|
embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), **kwargs)
|
|
model = _create_nest('nest_base', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def nest_small(pretrained=False, **kwargs):
|
|
""" Nest-S @ 224x224
|
|
"""
|
|
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), **kwargs)
|
|
model = _create_nest('nest_small', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def nest_tiny(pretrained=False, **kwargs):
|
|
""" Nest-T @ 224x224
|
|
"""
|
|
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), **kwargs)
|
|
model = _create_nest('nest_tiny', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def jx_nest_base(pretrained=False, **kwargs):
|
|
""" Nest-B @ 224x224, Pretrained weights converted from official Jax impl.
|
|
"""
|
|
kwargs['pad_type'] = 'same'
|
|
model_kwargs = dict(embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), **kwargs)
|
|
model = _create_nest('jx_nest_base', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def jx_nest_small(pretrained=False, **kwargs):
|
|
""" Nest-S @ 224x224, Pretrained weights converted from official Jax impl.
|
|
"""
|
|
kwargs['pad_type'] = 'same'
|
|
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), **kwargs)
|
|
model = _create_nest('jx_nest_small', pretrained=pretrained, **model_kwargs)
|
|
return model
|
|
|
|
|
|
@register_model
|
|
def jx_nest_tiny(pretrained=False, **kwargs):
|
|
""" Nest-T @ 224x224, Pretrained weights converted from official Jax impl.
|
|
"""
|
|
kwargs['pad_type'] = 'same'
|
|
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), **kwargs)
|
|
model = _create_nest('jx_nest_tiny', pretrained=pretrained, **model_kwargs)
|
|
return model
|