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hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/models/vision_transformer_relpos.py | """ Relative Position Vision Transformer (ViT) in PyTorch
NOTE: these models are experimental / WIP, expect changes
Hacked together by / Copyright 2022, Ross Wightman
"""
import logging
import math
from functools import partial
from typing import Optional, Tuple
import torch
import torch.nn as nn
from torch.jit import Final
from torch.utils.checkpoint import checkpoint
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.layers import PatchEmbed, Mlp, DropPath, RelPosMlp, RelPosBias, use_fused_attn
from ._builder import build_model_with_cfg
from ._registry import generate_default_cfgs, register_model
__all__ = ['VisionTransformerRelPos'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
class RelPosAttention(nn.Module):
fused_attn: Final[bool]
def __init__(
self,
dim,
num_heads=8,
qkv_bias=False,
qk_norm=False,
rel_pos_cls=None,
attn_drop=0.,
proj_drop=0.,
norm_layer=nn.LayerNorm,
):
super().__init__()
assert dim % num_heads == 0, 'dim should be divisible by num_heads'
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.scale = self.head_dim ** -0.5
self.fused_attn = use_fused_attn()
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.rel_pos = rel_pos_cls(num_heads=num_heads) if rel_pos_cls else None
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0)
q = self.q_norm(q)
k = self.k_norm(k)
if self.fused_attn:
if self.rel_pos is not None:
attn_bias = self.rel_pos.get_bias()
elif shared_rel_pos is not None:
attn_bias = shared_rel_pos
else:
attn_bias = None
x = torch.nn.functional.scaled_dot_product_attention(
q, k, v,
attn_mask=attn_bias,
dropout_p=self.attn_drop.p,
)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
if self.rel_pos is not None:
attn = self.rel_pos(attn, shared_rel_pos=shared_rel_pos)
elif shared_rel_pos is not None:
attn = attn + shared_rel_pos
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class LayerScale(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
return x.mul_(self.gamma) if self.inplace else x * self.gamma
class RelPosBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_norm=False,
rel_pos_cls=None,
init_values=None,
proj_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 = RelPosAttention(
dim,
num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
rel_pos_cls=rel_pos_cls,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
x = x + self.drop_path1(self.ls1(self.attn(self.norm1(x), shared_rel_pos=shared_rel_pos)))
x = x + self.drop_path2(self.ls2(self.mlp(self.norm2(x))))
return x
class ResPostRelPosBlock(nn.Module):
def __init__(
self,
dim,
num_heads,
mlp_ratio=4.,
qkv_bias=False,
qk_norm=False,
rel_pos_cls=None,
init_values=None,
proj_drop=0.,
attn_drop=0.,
drop_path=0.,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.init_values = init_values
self.attn = RelPosAttention(
dim,
num_heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
rel_pos_cls=rel_pos_cls,
attn_drop=attn_drop,
proj_drop=proj_drop,
)
self.norm1 = norm_layer(dim)
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
)
self.norm2 = norm_layer(dim)
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.init_weights()
def init_weights(self):
# NOTE this init overrides that base model init with specific changes for the block type
if self.init_values is not None:
nn.init.constant_(self.norm1.weight, self.init_values)
nn.init.constant_(self.norm2.weight, self.init_values)
def forward(self, x, shared_rel_pos: Optional[torch.Tensor] = None):
x = x + self.drop_path1(self.norm1(self.attn(x, shared_rel_pos=shared_rel_pos)))
x = x + self.drop_path2(self.norm2(self.mlp(x)))
return x
class VisionTransformerRelPos(nn.Module):
""" Vision Transformer w/ Relative Position Bias
Differing from classic vit, this impl
* uses relative position index (swin v1 / beit) or relative log coord + mlp (swin v2) pos embed
* defaults to no class token (can be enabled)
* defaults to global avg pool for head (can be changed)
* layer-scale (residual branch gain) enabled
"""
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,
qk_norm=False,
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.,
proj_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
patch_size (int, tuple): patch size
in_chans (int): number of input channels
num_classes (int): number of classes for classification head
global_pool (str): type of global pooling for final sequence (default: 'avg')
embed_dim (int): embedding dimension
depth (int): depth of transformer
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
qk_norm (bool): Enable normalization of query and key in attention
init_values: (float): layer-scale init values
class_token (bool): use class token (default: False)
fc_norm (bool): use pre classifier norm instead of pre-pool
rel_pos_ty pe (str): type of relative position
shared_rel_pos (bool): share relative pos across all blocks
drop_rate (float): dropout rate
proj_drop_rate (float): projection dropout rate
attn_drop_rate (float): attention dropout rate
drop_path_rate (float): stochastic depth rate
weight_init (str): weight init scheme
embed_layer (nn.Module): patch embedding layer
norm_layer: (nn.Module): normalization layer
act_layer: (nn.Module): MLP activation layer
"""
super().__init__()
assert global_pool in ('', 'avg', 'token')
assert class_token or global_pool != 'token'
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
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
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, prefix_tokens=self.num_prefix_tokens)
if rel_pos_type.startswith('mlp'):
if rel_pos_dim:
rel_pos_args['hidden_dim'] = rel_pos_dim
if 'swin' in rel_pos_type:
rel_pos_args['mode'] = 'swin'
rel_pos_cls = partial(RelPosMlp, **rel_pos_args)
else:
rel_pos_cls = partial(RelPosBias, **rel_pos_args)
self.shared_rel_pos = None
if shared_rel_pos:
self.shared_rel_pos = rel_pos_cls(num_heads=num_heads)
# 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_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([
block_fn(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
rel_pos_cls=rel_pos_cls,
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,
)
for i in range(depth)])
self.norm = norm_layer(embed_dim) if not fc_norm else nn.Identity()
# Classifier Head
self.fc_norm = norm_layer(embed_dim) if fc_norm else nn.Identity()
self.head_drop = nn.Dropout(drop_rate)
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
if weight_init != 'skip':
self.init_weights(weight_init)
def init_weights(self, mode=''):
assert mode in ('jax', 'moco', '')
if self.cls_token is not None:
nn.init.normal_(self.cls_token, std=1e-6)
# FIXME weight init scheme using PyTorch defaults curently
#named_apply(get_init_weights_vit(mode, head_bias), self)
@torch.jit.ignore
def no_weight_decay(self):
return {'cls_token'}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^cls_token|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: int, global_pool=None):
self.num_classes = num_classes
if global_pool is not None:
assert global_pool in ('', 'avg', 'token')
self.global_pool = global_pool
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
if self.cls_token is not None:
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
shared_rel_pos = self.shared_rel_pos.get_bias() if self.shared_rel_pos is not None else None
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(blk, x, shared_rel_pos=shared_rel_pos)
else:
x = blk(x, shared_rel_pos=shared_rel_pos)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool:
x = x[:, self.num_prefix_tokens:].mean(dim=1) if self.global_pool == 'avg' else x[:, 0]
x = self.fc_norm(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _create_vision_transformer_relpos(variant, pretrained=False, **kwargs):
if kwargs.get('features_only', None):
raise RuntimeError('features_only not implemented for Vision Transformer models.')
model = build_model_with_cfg(VisionTransformerRelPos, variant, pretrained, **kwargs)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), '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({
'vit_relpos_base_patch32_plus_rpn_256.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth',
hf_hub_id='timm/',
input_size=(3, 256, 256)),
'vit_relpos_base_patch16_plus_240.untrained': _cfg(url='', input_size=(3, 240, 240)),
'vit_relpos_small_patch16_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_small_patch16_224-sw-ec2778b4.pth',
hf_hub_id='timm/'),
'vit_relpos_medium_patch16_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_224-sw-11c174af.pth',
hf_hub_id='timm/'),
'vit_relpos_base_patch16_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth',
hf_hub_id='timm/'),
'vit_srelpos_small_patch16_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth',
hf_hub_id='timm/'),
'vit_srelpos_medium_patch16_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth',
hf_hub_id='timm/'),
'vit_relpos_medium_patch16_cls_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth',
hf_hub_id='timm/'),
'vit_relpos_base_patch16_cls_224.untrained': _cfg(),
'vit_relpos_base_patch16_clsgap_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth',
hf_hub_id='timm/'),
'vit_relpos_small_patch16_rpn_224.untrained': _cfg(),
'vit_relpos_medium_patch16_rpn_224.sw_in1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_rpn_224-sw-5d2befd8.pth',
hf_hub_id='timm/'),
'vit_relpos_base_patch16_rpn_224.untrained': _cfg(),
})
@register_model
def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token
"""
model_args = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, block_fn=ResPostRelPosBlock)
model = _create_vision_transformer_relpos(
'vit_relpos_base_patch32_plus_rpn_256', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_base_patch16_plus_240(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16+) w/ relative log-coord position, no class token
"""
model_args = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14)
model = _create_vision_transformer_relpos(
'vit_relpos_base_patch16_plus_240', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_small_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
"""
model_args = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, fc_norm=True)
model = _create_vision_transformer_relpos(
'vit_relpos_small_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_medium_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
"""
model_args = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, fc_norm=True)
model = _create_vision_transformer_relpos(
'vit_relpos_medium_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_base_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ relative log-coord position, no class token
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True)
model = _create_vision_transformer_relpos(
'vit_relpos_base_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_srelpos_small_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
"""
model_args = 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)
model = _create_vision_transformer_relpos(
'vit_srelpos_small_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_srelpos_medium_patch16_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ shared relative log-coord position, no class token
"""
model_args = 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)
model = _create_vision_transformer_relpos(
'vit_srelpos_medium_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_medium_patch16_cls_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-M/16) w/ relative log-coord position, class token present
"""
model_args = 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')
model = _create_vision_transformer_relpos(
'vit_relpos_medium_patch16_cls_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_base_patch16_cls_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ relative log-coord position, class token present
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, class_token=True, global_pool='token')
model = _create_vision_transformer_relpos(
'vit_relpos_base_patch16_cls_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_base_patch16_clsgap_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" 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_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, fc_norm=True, class_token=True)
model = _create_vision_transformer_relpos(
'vit_relpos_base_patch16_clsgap_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_small_patch16_rpn_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
"""
model_args = dict(
patch_size=16, embed_dim=384, depth=12, num_heads=6, qkv_bias=False, block_fn=ResPostRelPosBlock)
model = _create_vision_transformer_relpos(
'vit_relpos_small_patch16_rpn_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_medium_patch16_rpn_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
"""
model_args = dict(
patch_size=16, embed_dim=512, depth=12, num_heads=8, qkv_bias=False, block_fn=ResPostRelPosBlock)
model = _create_vision_transformer_relpos(
'vit_relpos_medium_patch16_rpn_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def vit_relpos_base_patch16_rpn_224(pretrained=False, **kwargs) -> VisionTransformerRelPos:
""" ViT-Base (ViT-B/16) w/ relative log-coord position and residual post-norm, no class token
"""
model_args = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, block_fn=ResPostRelPosBlock)
model = _create_vision_transformer_relpos(
'vit_relpos_base_patch16_rpn_224', pretrained=pretrained, **dict(model_args, **kwargs))
return model
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/models/nest.py | """ 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 timm.layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_, _assert
from timm.layers import create_conv2d, create_pool2d, to_ntuple, use_fused_attn, LayerNorm
from ._builder import build_model_with_cfg
from ._features_fx import register_notrace_function
from ._manipulate import checkpoint_seq, named_apply
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['Nest'] # model_registry will add each entrypoint fn to this
_logger = logging.getLogger(__name__)
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
"""
fused_attn: torch.jit.Final[bool]
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.fused_attn = use_fused_attn()
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.unbind(0) # make torchscript happy (cannot use tensor as tuple)
if self.fused_attn:
x = F.scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1) # (B, H, T, N, N)
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = attn @ v
# (B, H, T, N, C'), permute -> (B, T, N, C', H)
x = x.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,
proj_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=proj_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=proj_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)
@register_notrace_function # reason: int receives Proxy
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,
proj_drop=0.,
attn_drop=0.,
drop_path=[],
norm_layer=None,
act_layer=None,
pad_type='',
):
super().__init__()
self.block_size = block_size
self.grad_checkpointing = False
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):
assert len(drop_path) == 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,
proj_drop=proj_drop,
attn_drop=attn_drop,
drop_path=drop_path[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
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.transformer_encoder, x)
else:
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.,
proj_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 LayerNorm
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=mlp_ratio,
qkv_bias=qkv_bias,
proj_drop=proj_drop_rate,
attn_drop=attn_drop_rate,
drop_path=dp_rates[i],
norm_layer=norm_layer,
act_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
global_pool, head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
self.global_pool = global_pool
self.head_drop = nn.Dropout(drop_rate)
self.head = head
self.init_weights(weight_init)
@torch.jit.ignore
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))}
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^patch_embed', # stem and embed
blocks=[
(r'^levels\.(\d+)' if coarse else r'^levels\.(\d+)\.transformer_encoder\.(\d+)', None),
(r'^levels\.(\d+)\.(?:pool|pos_embed)', (0,)),
(r'^norm', (99999,))
]
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for l in self.levels:
l.grad_checkpointing = enable
@torch.jit.ignore
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 = 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_head(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return 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)
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='bicubic', align_corners=False)
# 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, **kwargs):
model = build_model_with_cfg(
Nest,
variant,
pretrained,
feature_cfg=dict(out_indices=(0, 1, 2), flatten_sequential=True),
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs,
)
return model
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 = generate_default_cfgs({
'nest_base.untrained': _cfg(),
'nest_small.untrained': _cfg(),
'nest_tiny.untrained': _cfg(),
# (weights from official Google JAX impl, require 'SAME' padding)
'nest_base_jx.goog_in1k': _cfg(hf_hub_id='timm/'),
'nest_small_jx.goog_in1k': _cfg(hf_hub_id='timm/'),
'nest_tiny_jx.goog_in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def nest_base(pretrained=False, **kwargs) -> Nest:
""" 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:
""" 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:
""" 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 nest_base_jx(pretrained=False, **kwargs) -> Nest:
""" Nest-B @ 224x224
"""
kwargs.setdefault('pad_type', 'same')
model_kwargs = dict(
embed_dims=(128, 256, 512), num_heads=(4, 8, 16), depths=(2, 2, 20), **kwargs)
model = _create_nest('nest_base_jx', pretrained=pretrained, **model_kwargs)
return model
@register_model
def nest_small_jx(pretrained=False, **kwargs) -> Nest:
""" Nest-S @ 224x224
"""
kwargs.setdefault('pad_type', 'same')
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 20), **kwargs)
model = _create_nest('nest_small_jx', pretrained=pretrained, **model_kwargs)
return model
@register_model
def nest_tiny_jx(pretrained=False, **kwargs) -> Nest:
""" Nest-T @ 224x224
"""
kwargs.setdefault('pad_type', 'same')
model_kwargs = dict(embed_dims=(96, 192, 384), num_heads=(3, 6, 12), depths=(2, 2, 8), **kwargs)
model = _create_nest('nest_tiny_jx', pretrained=pretrained, **model_kwargs)
return model
register_model_deprecations(__name__, {
'jx_nest_base': 'nest_base_jx',
'jx_nest_small': 'nest_small_jx',
'jx_nest_tiny': 'nest_tiny_jx',
}) | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/models/focalnet.py | """ FocalNet
As described in `Focal Modulation Networks` - https://arxiv.org/abs/2203.11926
Significant modifications and refactoring from the original impl at https://github.com/microsoft/FocalNet
This impl is/has:
* fully convolutional, NCHW tensor layout throughout, seemed to have minimal performance impact but more flexible
* re-ordered downsample / layer so that striding always at beginning of layer (stage)
* no input size constraints or input resolution/H/W tracking through the model
* torchscript fixed and a number of quirks cleaned up
* feature extraction support via `features_only=True`
"""
# --------------------------------------------------------
# FocalNets -- Focal Modulation Networks
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Jianwei Yang (jianwyan@microsoft.com)
# --------------------------------------------------------
from functools import partial
from typing import Callable, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as checkpoint
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import Mlp, DropPath, LayerNorm2d, trunc_normal_, ClassifierHead, NormMlpClassifierHead
from ._builder import build_model_with_cfg
from ._manipulate import named_apply
from ._registry import generate_default_cfgs, register_model
__all__ = ['FocalNet']
class FocalModulation(nn.Module):
def __init__(
self,
dim: int,
focal_window,
focal_level: int,
focal_factor: int = 2,
bias: bool = True,
use_post_norm: bool = False,
normalize_modulator: bool = False,
proj_drop: float = 0.,
norm_layer: Callable = LayerNorm2d,
):
super().__init__()
self.dim = dim
self.focal_window = focal_window
self.focal_level = focal_level
self.focal_factor = focal_factor
self.use_post_norm = use_post_norm
self.normalize_modulator = normalize_modulator
self.input_split = [dim, dim, self.focal_level + 1]
self.f = nn.Conv2d(dim, 2 * dim + (self.focal_level + 1), kernel_size=1, bias=bias)
self.h = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
self.act = nn.GELU()
self.proj = nn.Conv2d(dim, dim, kernel_size=1)
self.proj_drop = nn.Dropout(proj_drop)
self.focal_layers = nn.ModuleList()
self.kernel_sizes = []
for k in range(self.focal_level):
kernel_size = self.focal_factor * k + self.focal_window
self.focal_layers.append(nn.Sequential(
nn.Conv2d(dim, dim, kernel_size=kernel_size, groups=dim, padding=kernel_size // 2, bias=False),
nn.GELU(),
))
self.kernel_sizes.append(kernel_size)
self.norm = norm_layer(dim) if self.use_post_norm else nn.Identity()
def forward(self, x):
# pre linear projection
x = self.f(x)
q, ctx, gates = torch.split(x, self.input_split, 1)
# context aggreation
ctx_all = 0
for l, focal_layer in enumerate(self.focal_layers):
ctx = focal_layer(ctx)
ctx_all = ctx_all + ctx * gates[:, l:l + 1]
ctx_global = self.act(ctx.mean((2, 3), keepdim=True))
ctx_all = ctx_all + ctx_global * gates[:, self.focal_level:]
# normalize context
if self.normalize_modulator:
ctx_all = ctx_all / (self.focal_level + 1)
# focal modulation
x_out = q * self.h(ctx_all)
x_out = self.norm(x_out)
# post linear projection
x_out = self.proj(x_out)
x_out = self.proj_drop(x_out)
return x_out
class LayerScale2d(nn.Module):
def __init__(self, dim, init_values=1e-5, inplace=False):
super().__init__()
self.inplace = inplace
self.gamma = nn.Parameter(init_values * torch.ones(dim))
def forward(self, x):
gamma = self.gamma.view(1, -1, 1, 1)
return x.mul_(gamma) if self.inplace else x * gamma
class FocalNetBlock(nn.Module):
""" Focal Modulation Network Block.
"""
def __init__(
self,
dim: int,
mlp_ratio: float = 4.,
focal_level: int = 1,
focal_window: int = 3,
use_post_norm: bool = False,
use_post_norm_in_modulation: bool = False,
normalize_modulator: bool = False,
layerscale_value: float = 1e-4,
proj_drop: float = 0.,
drop_path: float = 0.,
act_layer: Callable = nn.GELU,
norm_layer: Callable = LayerNorm2d,
):
"""
Args:
dim: Number of input channels.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
focal_level: Number of focal levels.
focal_window: Focal window size at first focal level.
use_post_norm: Whether to use layer norm after modulation.
use_post_norm_in_modulation: Whether to use layer norm in modulation.
layerscale_value: Initial layerscale value.
proj_drop: Dropout rate.
drop_path: Stochastic depth rate.
act_layer: Activation layer.
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.mlp_ratio = mlp_ratio
self.focal_window = focal_window
self.focal_level = focal_level
self.use_post_norm = use_post_norm
self.norm1 = norm_layer(dim) if not use_post_norm else nn.Identity()
self.modulation = FocalModulation(
dim,
focal_window=focal_window,
focal_level=self.focal_level,
use_post_norm=use_post_norm_in_modulation,
normalize_modulator=normalize_modulator,
proj_drop=proj_drop,
norm_layer=norm_layer,
)
self.norm1_post = norm_layer(dim) if use_post_norm else nn.Identity()
self.ls1 = LayerScale2d(dim, layerscale_value) if layerscale_value is not None else nn.Identity()
self.drop_path1 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim) if not use_post_norm else nn.Identity()
self.mlp = Mlp(
in_features=dim,
hidden_features=int(dim * mlp_ratio),
act_layer=act_layer,
drop=proj_drop,
use_conv=True,
)
self.norm2_post = norm_layer(dim) if use_post_norm else nn.Identity()
self.ls2 = LayerScale2d(dim, layerscale_value) if layerscale_value is not None else nn.Identity()
self.drop_path2 = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def forward(self, x):
shortcut = x
# Focal Modulation
x = self.norm1(x)
x = self.modulation(x)
x = self.norm1_post(x)
x = shortcut + self.drop_path1(self.ls1(x))
# FFN
x = x + self.drop_path2(self.ls2(self.norm2_post(self.mlp(self.norm2(x)))))
return x
class FocalNetStage(nn.Module):
""" A basic Focal Transformer layer for one stage.
"""
def __init__(
self,
dim: int,
out_dim: int,
depth: int,
mlp_ratio: float = 4.,
downsample: bool = True,
focal_level: int = 1,
focal_window: int = 1,
use_overlap_down: bool = False,
use_post_norm: bool = False,
use_post_norm_in_modulation: bool = False,
normalize_modulator: bool = False,
layerscale_value: float = 1e-4,
proj_drop: float = 0.,
drop_path: float = 0.,
norm_layer: Callable = LayerNorm2d,
):
"""
Args:
dim: Number of input channels.
out_dim: Number of output channels.
depth: Number of blocks.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
downsample: Downsample layer at start of the layer.
focal_level: Number of focal levels
focal_window: Focal window size at first focal level
use_overlap_down: User overlapped convolution in downsample layer.
use_post_norm: Whether to use layer norm after modulation.
use_post_norm_in_modulation: Whether to use layer norm in modulation.
layerscale_value: Initial layerscale value
proj_drop: Dropout rate for projections.
drop_path: Stochastic depth rate.
norm_layer: Normalization layer.
"""
super().__init__()
self.dim = dim
self.depth = depth
self.grad_checkpointing = False
if downsample:
self.downsample = Downsample(
in_chs=dim,
out_chs=out_dim,
stride=2,
overlap=use_overlap_down,
norm_layer=norm_layer,
)
else:
self.downsample = nn.Identity()
# build blocks
self.blocks = nn.ModuleList([
FocalNetBlock(
dim=out_dim,
mlp_ratio=mlp_ratio,
focal_level=focal_level,
focal_window=focal_window,
use_post_norm=use_post_norm,
use_post_norm_in_modulation=use_post_norm_in_modulation,
normalize_modulator=normalize_modulator,
layerscale_value=layerscale_value,
proj_drop=proj_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer,
)
for i in range(depth)])
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
def forward(self, x):
x = self.downsample(x)
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
return x
class Downsample(nn.Module):
def __init__(
self,
in_chs: int,
out_chs: int,
stride: int = 4,
overlap: bool = False,
norm_layer: Optional[Callable] = None,
):
"""
Args:
in_chs: Number of input image channels.
out_chs: Number of linear projection output channels.
stride: Downsample stride.
overlap: Use overlapping convolutions if True.
norm_layer: Normalization layer.
"""
super().__init__()
self.stride = stride
padding = 0
kernel_size = stride
if overlap:
assert stride in (2, 4)
if stride == 4:
kernel_size, padding = 7, 2
elif stride == 2:
kernel_size, padding = 3, 1
self.proj = nn.Conv2d(in_chs, out_chs, kernel_size=kernel_size, stride=stride, padding=padding)
self.norm = norm_layer(out_chs) if norm_layer is not None else nn.Identity()
def forward(self, x):
x = self.proj(x)
x = self.norm(x)
return x
class FocalNet(nn.Module):
"""" Focal Modulation Networks (FocalNets)
"""
def __init__(
self,
in_chans: int = 3,
num_classes: int = 1000,
global_pool: str = 'avg',
embed_dim: int = 96,
depths: Tuple[int, ...] = (2, 2, 6, 2),
mlp_ratio: float = 4.,
focal_levels: Tuple[int, ...] = (2, 2, 2, 2),
focal_windows: Tuple[int, ...] = (3, 3, 3, 3),
use_overlap_down: bool = False,
use_post_norm: bool = False,
use_post_norm_in_modulation: bool = False,
normalize_modulator: bool = False,
head_hidden_size: Optional[int] = None,
head_init_scale: float = 1.0,
layerscale_value: Optional[float] = None,
drop_rate: bool = 0.,
proj_drop_rate: bool = 0.,
drop_path_rate: bool = 0.1,
norm_layer: Callable = partial(LayerNorm2d, eps=1e-5),
):
"""
Args:
in_chans: Number of input image channels.
num_classes: Number of classes for classification head.
embed_dim: Patch embedding dimension.
depths: Depth of each Focal Transformer layer.
mlp_ratio: Ratio of mlp hidden dim to embedding dim.
focal_levels: How many focal levels at all stages. Note that this excludes the finest-grain level.
focal_windows: The focal window size at all stages.
use_overlap_down: Whether to use convolutional embedding.
use_post_norm: Whether to use layernorm after modulation (it helps stablize training of large models)
layerscale_value: Value for layer scale.
drop_rate: Dropout rate.
drop_path_rate: Stochastic depth rate.
norm_layer: Normalization layer.
"""
super().__init__()
self.num_layers = len(depths)
embed_dim = [embed_dim * (2 ** i) for i in range(self.num_layers)]
self.num_classes = num_classes
self.embed_dim = embed_dim
self.num_features = embed_dim[-1]
self.feature_info = []
self.stem = Downsample(
in_chs=in_chans,
out_chs=embed_dim[0],
overlap=use_overlap_down,
norm_layer=norm_layer,
)
in_dim = embed_dim[0]
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
layers = []
for i_layer in range(self.num_layers):
out_dim = embed_dim[i_layer]
layer = FocalNetStage(
dim=in_dim,
out_dim=out_dim,
depth=depths[i_layer],
mlp_ratio=mlp_ratio,
downsample=i_layer > 0,
focal_level=focal_levels[i_layer],
focal_window=focal_windows[i_layer],
use_overlap_down=use_overlap_down,
use_post_norm=use_post_norm,
use_post_norm_in_modulation=use_post_norm_in_modulation,
normalize_modulator=normalize_modulator,
layerscale_value=layerscale_value,
proj_drop=proj_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
)
in_dim = out_dim
layers += [layer]
self.feature_info += [dict(num_chs=out_dim, reduction=4 * 2 ** i_layer, module=f'layers.{i_layer}')]
self.layers = nn.Sequential(*layers)
if head_hidden_size:
self.norm = nn.Identity()
self.head = NormMlpClassifierHead(
self.num_features,
num_classes,
hidden_size=head_hidden_size,
pool_type=global_pool,
drop_rate=drop_rate,
norm_layer=norm_layer,
)
else:
self.norm = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate
)
named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
@torch.jit.ignore
def no_weight_decay(self):
return {''}
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=[
(r'^layers\.(\d+)', None),
(r'^norm', (99999,))
] if coarse else [
(r'^layers\.(\d+).downsample', (0,)),
(r'^layers\.(\d+)\.\w+\.(\d+)', None),
(r'^norm', (99999,)),
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
for l in self.layers:
l.set_grad_checkpointing(enable=enable)
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.layers(x)
x = self.norm(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return self.head(x, pre_logits=pre_logits)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
def _init_weights(module, name=None, head_init_scale=1.0):
if isinstance(module, nn.Conv2d):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Linear):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
if name and 'head.fc' in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': .9, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.proj', 'classifier': 'head.fc',
'license': 'mit', **kwargs
}
default_cfgs = generate_default_cfgs({
"focalnet_tiny_srf.ms_in1k": _cfg(
hf_hub_id='timm/'),
"focalnet_small_srf.ms_in1k": _cfg(
hf_hub_id='timm/'),
"focalnet_base_srf.ms_in1k": _cfg(
hf_hub_id='timm/'),
"focalnet_tiny_lrf.ms_in1k": _cfg(
hf_hub_id='timm/'),
"focalnet_small_lrf.ms_in1k": _cfg(
hf_hub_id='timm/'),
"focalnet_base_lrf.ms_in1k": _cfg(
hf_hub_id='timm/'),
"focalnet_large_fl3.ms_in22k": _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21842),
"focalnet_large_fl4.ms_in22k": _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21842),
"focalnet_xlarge_fl3.ms_in22k": _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21842),
"focalnet_xlarge_fl4.ms_in22k": _cfg(
hf_hub_id='timm/',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, num_classes=21842),
"focalnet_huge_fl3.ms_in22k": _cfg(
hf_hub_id='timm/',
num_classes=21842),
"focalnet_huge_fl4.ms_in22k": _cfg(
hf_hub_id='timm/',
num_classes=0),
})
def checkpoint_filter_fn(state_dict, model: FocalNet):
state_dict = state_dict.get('model', state_dict)
if 'stem.proj.weight' in state_dict:
return state_dict
import re
out_dict = {}
dest_dict = model.state_dict()
for k, v in state_dict.items():
k = re.sub(r'gamma_([0-9])', r'ls\1.gamma', k)
k = k.replace('patch_embed', 'stem')
k = re.sub(r'layers.(\d+).downsample', lambda x: f'layers.{int(x.group(1)) + 1}.downsample', k)
if 'norm' in k and k not in dest_dict:
k = re.sub(r'norm([0-9])', r'norm\1_post', k)
k = k.replace('ln.', 'norm.')
k = k.replace('head', 'head.fc')
if k in dest_dict and dest_dict[k].numel() == v.numel() and dest_dict[k].shape != v.shape:
v = v.reshape(dest_dict[k].shape)
out_dict[k] = v
return out_dict
def _create_focalnet(variant, pretrained=False, **kwargs):
default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1))))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
FocalNet, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs)
return model
@register_model
def focalnet_tiny_srf(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(depths=[2, 2, 6, 2], embed_dim=96, **kwargs)
return _create_focalnet('focalnet_tiny_srf', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_small_srf(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=96, **kwargs)
return _create_focalnet('focalnet_small_srf', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_base_srf(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=128, **kwargs)
return _create_focalnet('focalnet_base_srf', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_tiny_lrf(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(depths=[2, 2, 6, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
return _create_focalnet('focalnet_tiny_lrf', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_small_lrf(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=96, focal_levels=[3, 3, 3, 3], **kwargs)
return _create_focalnet('focalnet_small_lrf', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_base_lrf(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(depths=[2, 2, 18, 2], embed_dim=128, focal_levels=[3, 3, 3, 3], **kwargs)
return _create_focalnet('focalnet_base_lrf', pretrained=pretrained, **model_kwargs)
# FocalNet large+ models
@register_model
def focalnet_large_fl3(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(
depths=[2, 2, 18, 2], embed_dim=192, focal_levels=[3, 3, 3, 3], focal_windows=[5] * 4,
use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
return _create_focalnet('focalnet_large_fl3', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_large_fl4(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(
depths=[2, 2, 18, 2], embed_dim=192, focal_levels=[4, 4, 4, 4],
use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
return _create_focalnet('focalnet_large_fl4', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_xlarge_fl3(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(
depths=[2, 2, 18, 2], embed_dim=256, focal_levels=[3, 3, 3, 3], focal_windows=[5] * 4,
use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
return _create_focalnet('focalnet_xlarge_fl3', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_xlarge_fl4(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(
depths=[2, 2, 18, 2], embed_dim=256, focal_levels=[4, 4, 4, 4],
use_post_norm=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
return _create_focalnet('focalnet_xlarge_fl4', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_huge_fl3(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(
depths=[2, 2, 18, 2], embed_dim=352, focal_levels=[3, 3, 3, 3], focal_windows=[3] * 4,
use_post_norm=True, use_post_norm_in_modulation=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
return _create_focalnet('focalnet_huge_fl3', pretrained=pretrained, **model_kwargs)
@register_model
def focalnet_huge_fl4(pretrained=False, **kwargs) -> FocalNet:
model_kwargs = dict(
depths=[2, 2, 18, 2], embed_dim=352, focal_levels=[4, 4, 4, 4],
use_post_norm=True, use_post_norm_in_modulation=True, use_overlap_down=True, layerscale_value=1e-4, **kwargs)
return _create_focalnet('focalnet_huge_fl4', pretrained=pretrained, **model_kwargs)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/models/repvit.py | """ RepViT
Paper: `RepViT: Revisiting Mobile CNN From ViT Perspective`
- https://arxiv.org/abs/2307.09283
@misc{wang2023repvit,
title={RepViT: Revisiting Mobile CNN From ViT Perspective},
author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
year={2023},
eprint={2307.09283},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Adapted from official impl at https://github.com/jameslahm/RepViT
"""
__all__ = ['RepViT']
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from ._registry import register_model, generate_default_cfgs
from ._builder import build_model_with_cfg
from timm.layers import SqueezeExcite, trunc_normal_, to_ntuple, to_2tuple
from ._manipulate import checkpoint_seq
import torch
class ConvNorm(nn.Sequential):
def __init__(self, in_dim, out_dim, ks=1, stride=1, pad=0, dilation=1, groups=1, bn_weight_init=1):
super().__init__()
self.add_module('c', nn.Conv2d(in_dim, out_dim, ks, stride, pad, dilation, groups, bias=False))
self.add_module('bn', nn.BatchNorm2d(out_dim))
nn.init.constant_(self.bn.weight, bn_weight_init)
nn.init.constant_(self.bn.bias, 0)
@torch.no_grad()
def fuse(self):
c, bn = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
w = c.weight * w[:, None, None, None]
b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
m = nn.Conv2d(
w.size(1) * self.c.groups,
w.size(0),
w.shape[2:],
stride=self.c.stride,
padding=self.c.padding,
dilation=self.c.dilation,
groups=self.c.groups,
device=c.weight.device,
)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class NormLinear(nn.Sequential):
def __init__(self, in_dim, out_dim, bias=True, std=0.02):
super().__init__()
self.add_module('bn', nn.BatchNorm1d(in_dim))
self.add_module('l', nn.Linear(in_dim, out_dim, bias=bias))
trunc_normal_(self.l.weight, std=std)
if bias:
nn.init.constant_(self.l.bias, 0)
@torch.no_grad()
def fuse(self):
bn, l = self._modules.values()
w = bn.weight / (bn.running_var + bn.eps) ** 0.5
b = bn.bias - self.bn.running_mean * self.bn.weight / (bn.running_var + bn.eps) ** 0.5
w = l.weight * w[None, :]
if l.bias is None:
b = b @ self.l.weight.T
else:
b = (l.weight @ b[:, None]).view(-1) + self.l.bias
m = nn.Linear(w.size(1), w.size(0), device=l.weight.device)
m.weight.data.copy_(w)
m.bias.data.copy_(b)
return m
class RepVGGDW(nn.Module):
def __init__(self, ed, kernel_size):
super().__init__()
self.conv = ConvNorm(ed, ed, kernel_size, 1, (kernel_size - 1) // 2, groups=ed)
self.conv1 = ConvNorm(ed, ed, 1, 1, 0, groups=ed)
self.dim = ed
def forward(self, x):
return self.conv(x) + self.conv1(x) + x
@torch.no_grad()
def fuse(self):
conv = self.conv.fuse()
conv1 = self.conv1.fuse()
conv_w = conv.weight
conv_b = conv.bias
conv1_w = conv1.weight
conv1_b = conv1.bias
conv1_w = nn.functional.pad(conv1_w, [1, 1, 1, 1])
identity = nn.functional.pad(
torch.ones(conv1_w.shape[0], conv1_w.shape[1], 1, 1, device=conv1_w.device), [1, 1, 1, 1]
)
final_conv_w = conv_w + conv1_w + identity
final_conv_b = conv_b + conv1_b
conv.weight.data.copy_(final_conv_w)
conv.bias.data.copy_(final_conv_b)
return conv
class RepViTMlp(nn.Module):
def __init__(self, in_dim, hidden_dim, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_dim, hidden_dim, 1, 1, 0)
self.act = act_layer()
self.conv2 = ConvNorm(hidden_dim, in_dim, 1, 1, 0, bn_weight_init=0)
def forward(self, x):
return self.conv2(self.act(self.conv1(x)))
class RepViTBlock(nn.Module):
def __init__(self, in_dim, mlp_ratio, kernel_size, use_se, act_layer):
super(RepViTBlock, self).__init__()
self.token_mixer = RepVGGDW(in_dim, kernel_size)
self.se = SqueezeExcite(in_dim, 0.25) if use_se else nn.Identity()
self.channel_mixer = RepViTMlp(in_dim, in_dim * mlp_ratio, act_layer)
def forward(self, x):
x = self.token_mixer(x)
x = self.se(x)
identity = x
x = self.channel_mixer(x)
return identity + x
class RepViTStem(nn.Module):
def __init__(self, in_chs, out_chs, act_layer):
super().__init__()
self.conv1 = ConvNorm(in_chs, out_chs // 2, 3, 2, 1)
self.act1 = act_layer()
self.conv2 = ConvNorm(out_chs // 2, out_chs, 3, 2, 1)
self.stride = 4
def forward(self, x):
return self.conv2(self.act1(self.conv1(x)))
class RepViTDownsample(nn.Module):
def __init__(self, in_dim, mlp_ratio, out_dim, kernel_size, act_layer):
super().__init__()
self.pre_block = RepViTBlock(in_dim, mlp_ratio, kernel_size, use_se=False, act_layer=act_layer)
self.spatial_downsample = ConvNorm(in_dim, in_dim, kernel_size, 2, (kernel_size - 1) // 2, groups=in_dim)
self.channel_downsample = ConvNorm(in_dim, out_dim, 1, 1)
self.ffn = RepViTMlp(out_dim, out_dim * mlp_ratio, act_layer)
def forward(self, x):
x = self.pre_block(x)
x = self.spatial_downsample(x)
x = self.channel_downsample(x)
identity = x
x = self.ffn(x)
return x + identity
class RepViTClassifier(nn.Module):
def __init__(self, dim, num_classes, distillation=False):
super().__init__()
self.head = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
self.distillation = distillation
if distillation:
self.head_dist = NormLinear(dim, num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x):
if self.distillation:
x1, x2 = self.head(x), self.head_dist(x)
if (not self.training) or torch.jit.is_scripting():
return (x1 + x2) / 2
else:
return x1, x2
else:
x = self.head(x)
return x
@torch.no_grad()
def fuse(self):
if not self.num_classes > 0:
return nn.Identity()
head = self.head.fuse()
if self.distillation:
head_dist = self.head_dist.fuse()
head.weight += head_dist.weight
head.bias += head_dist.bias
head.weight /= 2
head.bias /= 2
return head
else:
return head
class RepViTStage(nn.Module):
def __init__(self, in_dim, out_dim, depth, mlp_ratio, act_layer, kernel_size=3, downsample=True):
super().__init__()
if downsample:
self.downsample = RepViTDownsample(in_dim, mlp_ratio, out_dim, kernel_size, act_layer)
else:
assert in_dim == out_dim
self.downsample = nn.Identity()
blocks = []
use_se = True
for _ in range(depth):
blocks.append(RepViTBlock(out_dim, mlp_ratio, kernel_size, use_se, act_layer))
use_se = not use_se
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
x = self.downsample(x)
x = self.blocks(x)
return x
class RepViT(nn.Module):
def __init__(
self,
in_chans=3,
img_size=224,
embed_dim=(48,),
depth=(2,),
mlp_ratio=2,
global_pool='avg',
kernel_size=3,
num_classes=1000,
act_layer=nn.GELU,
distillation=True,
):
super(RepViT, self).__init__()
self.grad_checkpointing = False
self.global_pool = global_pool
self.embed_dim = embed_dim
self.num_classes = num_classes
in_dim = embed_dim[0]
self.stem = RepViTStem(in_chans, in_dim, act_layer)
stride = self.stem.stride
resolution = tuple([i // p for i, p in zip(to_2tuple(img_size), to_2tuple(stride))])
num_stages = len(embed_dim)
mlp_ratios = to_ntuple(num_stages)(mlp_ratio)
self.feature_info = []
stages = []
for i in range(num_stages):
downsample = True if i != 0 else False
stages.append(
RepViTStage(
in_dim,
embed_dim[i],
depth[i],
mlp_ratio=mlp_ratios[i],
act_layer=act_layer,
kernel_size=kernel_size,
downsample=downsample,
)
)
stage_stride = 2 if downsample else 1
stride *= stage_stride
resolution = tuple([(r - 1) // stage_stride + 1 for r in resolution])
self.feature_info += [dict(num_chs=embed_dim[i], reduction=stride, module=f'stages.{i}')]
in_dim = embed_dim[i]
self.stages = nn.Sequential(*stages)
self.num_features = embed_dim[-1]
self.head = RepViTClassifier(embed_dim[-1], num_classes, distillation)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^stem', # stem and embed
blocks=[(r'^blocks\.(\d+)', None), (r'^norm', (99999,))]
)
return matcher
@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, global_pool=None, distillation=False):
self.num_classes = num_classes
if global_pool is not None:
self.global_pool = global_pool
self.head = (
RepViTClassifier(self.embed_dim[-1], num_classes, distillation) if num_classes > 0 else nn.Identity()
)
def forward_features(self, x):
x = self.stem(x)
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint_seq(self.stages, x)
else:
x = self.stages(x)
return x
def forward_head(self, x, pre_logits: bool = False):
if self.global_pool == 'avg':
x = nn.functional.adaptive_avg_pool2d(x, 1).flatten(1)
return x if pre_logits else self.head(x)
def forward(self, x):
x = self.forward_features(x)
x = self.forward_head(x)
return x
@torch.no_grad()
def fuse(self):
def fuse_children(net):
for child_name, child in net.named_children():
if hasattr(child, 'fuse'):
fused = child.fuse()
setattr(net, child_name, fused)
fuse_children(fused)
else:
fuse_children(child)
fuse_children(self)
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000,
'input_size': (3, 224, 224),
'pool_size': (7, 7),
'crop_pct': 0.95,
'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN,
'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.conv1.c',
'classifier': ('head.head.l', 'head.head_dist.l'),
**kwargs,
}
default_cfgs = generate_default_cfgs(
{
'repvit_m1.dist_in1k': _cfg(
url='https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m1_distill_300_timm.pth'
),
'repvit_m2.dist_in1k': _cfg(
url='https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m2_distill_300_timm.pth'
),
'repvit_m3.dist_in1k': _cfg(
url='https://github.com/THU-MIG/RepViT/releases/download/v1.0/repvit_m3_distill_300_timm.pth'
),
}
)
def _create_repvit(variant, pretrained=False, **kwargs):
out_indices = kwargs.pop('out_indices', (0, 1, 2, 3))
model = build_model_with_cfg(
RepViT, variant, pretrained, feature_cfg=dict(flatten_sequential=True, out_indices=out_indices), **kwargs
)
return model
@register_model
def repvit_m1(pretrained=False, **kwargs):
"""
Constructs a RepViT-M1 model
"""
model_args = dict(embed_dim=(48, 96, 192, 384), depth=(2, 2, 14, 2))
return _create_repvit('repvit_m1', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m2(pretrained=False, **kwargs):
"""
Constructs a RepViT-M2 model
"""
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(2, 2, 12, 2))
return _create_repvit('repvit_m2', pretrained=pretrained, **dict(model_args, **kwargs))
@register_model
def repvit_m3(pretrained=False, **kwargs):
"""
Constructs a RepViT-M3 model
"""
model_args = dict(embed_dim=(64, 128, 256, 512), depth=(4, 4, 18, 2))
return _create_repvit('repvit_m3', pretrained=pretrained, **dict(model_args, **kwargs))
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/models/hrnet.py | """ HRNet
Copied from https://github.com/HRNet/HRNet-Image-Classification
Original header:
Copyright (c) Microsoft
Licensed under the MIT License.
Written by Bin Xiao (Bin.Xiao@microsoft.com)
Modified by Ke Sun (sunk@mail.ustc.edu.cn)
"""
import logging
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import create_classifier
from ._builder import build_model_with_cfg, pretrained_cfg_for_features
from ._features import FeatureInfo
from ._registry import register_model, generate_default_cfgs
from .resnet import BasicBlock, Bottleneck # leveraging ResNet block_types w/ additional features like SE
__all__ = ['HighResolutionNet', 'HighResolutionNetFeatures'] # model_registry will add each entrypoint fn to this
_BN_MOMENTUM = 0.1
_logger = logging.getLogger(__name__)
cfg_cls = dict(
hrnet_w18_small=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(1,),
num_channels=(32,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(2, 2),
num_channels=(16, 32),
fuse_method='SUM'
),
stage3=dict(
num_modules=1,
num_branches=3,
block_type='BASIC',
num_blocks=(2, 2, 2),
num_channels=(16, 32, 64),
fuse_method='SUM'
),
stage4=dict(
num_modules=1,
num_branches=4,
block_type='BASIC',
num_blocks=(2, 2, 2, 2),
num_channels=(16, 32, 64, 128),
fuse_method='SUM',
),
),
hrnet_w18_small_v2=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(2,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(2, 2),
num_channels=(18, 36),
fuse_method='SUM'
),
stage3=dict(
num_modules=3,
num_branches=3,
block_type='BASIC',
num_blocks=(2, 2, 2),
num_channels=(18, 36, 72),
fuse_method='SUM'
),
stage4=dict(
num_modules=2,
num_branches=4,
block_type='BASIC',
num_blocks=(2, 2, 2, 2),
num_channels=(18, 36, 72, 144),
fuse_method='SUM',
),
),
hrnet_w18=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(4,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(4, 4),
num_channels=(18, 36),
fuse_method='SUM'
),
stage3=dict(
num_modules=4,
num_branches=3,
block_type='BASIC',
num_blocks=(4, 4, 4),
num_channels=(18, 36, 72),
fuse_method='SUM'
),
stage4=dict(
num_modules=3,
num_branches=4,
block_type='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(18, 36, 72, 144),
fuse_method='SUM',
),
),
hrnet_w30=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(4,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(4, 4),
num_channels=(30, 60),
fuse_method='SUM'
),
stage3=dict(
num_modules=4,
num_branches=3,
block_type='BASIC',
num_blocks=(4, 4, 4),
num_channels=(30, 60, 120),
fuse_method='SUM'
),
stage4=dict(
num_modules=3,
num_branches=4,
block_type='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(30, 60, 120, 240),
fuse_method='SUM',
),
),
hrnet_w32=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(4,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64),
fuse_method='SUM'
),
stage3=dict(
num_modules=4,
num_branches=3,
block_type='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128),
fuse_method='SUM'
),
stage4=dict(
num_modules=3,
num_branches=4,
block_type='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256),
fuse_method='SUM',
),
),
hrnet_w40=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(4,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(4, 4),
num_channels=(40, 80),
fuse_method='SUM'
),
stage3=dict(
num_modules=4,
num_branches=3,
block_type='BASIC',
num_blocks=(4, 4, 4),
num_channels=(40, 80, 160),
fuse_method='SUM'
),
stage4=dict(
num_modules=3,
num_branches=4,
block_type='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(40, 80, 160, 320),
fuse_method='SUM',
),
),
hrnet_w44=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(4,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(4, 4),
num_channels=(44, 88),
fuse_method='SUM'
),
stage3=dict(
num_modules=4,
num_branches=3,
block_type='BASIC',
num_blocks=(4, 4, 4),
num_channels=(44, 88, 176),
fuse_method='SUM'
),
stage4=dict(
num_modules=3,
num_branches=4,
block_type='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(44, 88, 176, 352),
fuse_method='SUM',
),
),
hrnet_w48=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(4,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(4, 4),
num_channels=(48, 96),
fuse_method='SUM'
),
stage3=dict(
num_modules=4,
num_branches=3,
block_type='BASIC',
num_blocks=(4, 4, 4),
num_channels=(48, 96, 192),
fuse_method='SUM'
),
stage4=dict(
num_modules=3,
num_branches=4,
block_type='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(48, 96, 192, 384),
fuse_method='SUM',
),
),
hrnet_w64=dict(
stem_width=64,
stage1=dict(
num_modules=1,
num_branches=1,
block_type='BOTTLENECK',
num_blocks=(4,),
num_channels=(64,),
fuse_method='SUM',
),
stage2=dict(
num_modules=1,
num_branches=2,
block_type='BASIC',
num_blocks=(4, 4),
num_channels=(64, 128),
fuse_method='SUM'
),
stage3=dict(
num_modules=4,
num_branches=3,
block_type='BASIC',
num_blocks=(4, 4, 4),
num_channels=(64, 128, 256),
fuse_method='SUM'
),
stage4=dict(
num_modules=3,
num_branches=4,
block_type='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(64, 128, 256, 512),
fuse_method='SUM',
),
)
)
class HighResolutionModule(nn.Module):
def __init__(
self,
num_branches,
block_types,
num_blocks,
num_in_chs,
num_channels,
fuse_method,
multi_scale_output=True,
):
super(HighResolutionModule, self).__init__()
self._check_branches(
num_branches,
block_types,
num_blocks,
num_in_chs,
num_channels,
)
self.num_in_chs = num_in_chs
self.fuse_method = fuse_method
self.num_branches = num_branches
self.multi_scale_output = multi_scale_output
self.branches = self._make_branches(
num_branches,
block_types,
num_blocks,
num_channels,
)
self.fuse_layers = self._make_fuse_layers()
self.fuse_act = nn.ReLU(False)
def _check_branches(self, num_branches, block_types, num_blocks, num_in_chs, num_channels):
error_msg = ''
if num_branches != len(num_blocks):
error_msg = 'num_branches({}) <> num_blocks({})'.format(num_branches, len(num_blocks))
elif num_branches != len(num_channels):
error_msg = 'num_branches({}) <> num_channels({})'.format(num_branches, len(num_channels))
elif num_branches != len(num_in_chs):
error_msg = 'num_branches({}) <> num_in_chs({})'.format(num_branches, len(num_in_chs))
if error_msg:
_logger.error(error_msg)
raise ValueError(error_msg)
def _make_one_branch(self, branch_index, block_type, num_blocks, num_channels, stride=1):
downsample = None
if stride != 1 or self.num_in_chs[branch_index] != num_channels[branch_index] * block_type.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.num_in_chs[branch_index], num_channels[branch_index] * block_type.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(num_channels[branch_index] * block_type.expansion, momentum=_BN_MOMENTUM),
)
layers = [block_type(self.num_in_chs[branch_index], num_channels[branch_index], stride, downsample)]
self.num_in_chs[branch_index] = num_channels[branch_index] * block_type.expansion
for i in range(1, num_blocks[branch_index]):
layers.append(block_type(self.num_in_chs[branch_index], num_channels[branch_index]))
return nn.Sequential(*layers)
def _make_branches(self, num_branches, block_type, num_blocks, num_channels):
branches = []
for i in range(num_branches):
branches.append(self._make_one_branch(i, block_type, num_blocks, num_channels))
return nn.ModuleList(branches)
def _make_fuse_layers(self):
if self.num_branches == 1:
return nn.Identity()
num_branches = self.num_branches
num_in_chs = self.num_in_chs
fuse_layers = []
for i in range(num_branches if self.multi_scale_output else 1):
fuse_layer = []
for j in range(num_branches):
if j > i:
fuse_layer.append(nn.Sequential(
nn.Conv2d(num_in_chs[j], num_in_chs[i], 1, 1, 0, bias=False),
nn.BatchNorm2d(num_in_chs[i], momentum=_BN_MOMENTUM),
nn.Upsample(scale_factor=2 ** (j - i), mode='nearest')))
elif j == i:
fuse_layer.append(nn.Identity())
else:
conv3x3s = []
for k in range(i - j):
if k == i - j - 1:
num_out_chs_conv3x3 = num_in_chs[i]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_in_chs[j], num_out_chs_conv3x3, 3, 2, 1, bias=False),
nn.BatchNorm2d(num_out_chs_conv3x3, momentum=_BN_MOMENTUM)
))
else:
num_out_chs_conv3x3 = num_in_chs[j]
conv3x3s.append(nn.Sequential(
nn.Conv2d(num_in_chs[j], num_out_chs_conv3x3, 3, 2, 1, bias=False),
nn.BatchNorm2d(num_out_chs_conv3x3, momentum=_BN_MOMENTUM),
nn.ReLU(False)
))
fuse_layer.append(nn.Sequential(*conv3x3s))
fuse_layers.append(nn.ModuleList(fuse_layer))
return nn.ModuleList(fuse_layers)
def get_num_in_chs(self):
return self.num_in_chs
def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
if self.num_branches == 1:
return [self.branches[0](x[0])]
for i, branch in enumerate(self.branches):
x[i] = branch(x[i])
x_fuse = []
for i, fuse_outer in enumerate(self.fuse_layers):
y = None
for j, f in enumerate(fuse_outer):
if y is None:
y = f(x[j])
else:
y = y + f(x[j])
x_fuse.append(self.fuse_act(y))
return x_fuse
class SequentialList(nn.Sequential):
def __init__(self, *args):
super(SequentialList, self).__init__(*args)
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (List[torch.Tensor]) -> (List[torch.Tensor])
pass
@torch.jit._overload_method # noqa: F811
def forward(self, x):
# type: (torch.Tensor) -> (List[torch.Tensor])
pass
def forward(self, x) -> List[torch.Tensor]:
for module in self:
x = module(x)
return x
@torch.jit.interface
class ModuleInterface(torch.nn.Module):
def forward(self, input: torch.Tensor) -> torch.Tensor: # `input` has a same name in Sequential forward
pass
block_types_dict = {
'BASIC': BasicBlock,
'BOTTLENECK': Bottleneck
}
class HighResolutionNet(nn.Module):
def __init__(
self,
cfg,
in_chans=3,
num_classes=1000,
output_stride=32,
global_pool='avg',
drop_rate=0.0,
head='classification',
**kwargs,
):
super(HighResolutionNet, self).__init__()
self.num_classes = num_classes
assert output_stride == 32 # FIXME support dilation
cfg.update(**kwargs)
stem_width = cfg['stem_width']
self.conv1 = nn.Conv2d(in_chans, stem_width, kernel_size=3, stride=2, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(stem_width, momentum=_BN_MOMENTUM)
self.act1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(stem_width, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(64, momentum=_BN_MOMENTUM)
self.act2 = nn.ReLU(inplace=True)
self.stage1_cfg = cfg['stage1']
num_channels = self.stage1_cfg['num_channels'][0]
block_type = block_types_dict[self.stage1_cfg['block_type']]
num_blocks = self.stage1_cfg['num_blocks'][0]
self.layer1 = self._make_layer(block_type, 64, num_channels, num_blocks)
stage1_out_channel = block_type.expansion * num_channels
self.stage2_cfg = cfg['stage2']
num_channels = self.stage2_cfg['num_channels']
block_type = block_types_dict[self.stage2_cfg['block_type']]
num_channels = [num_channels[i] * block_type.expansion for i in range(len(num_channels))]
self.transition1 = self._make_transition_layer([stage1_out_channel], num_channels)
self.stage2, pre_stage_channels = self._make_stage(self.stage2_cfg, num_channels)
self.stage3_cfg = cfg['stage3']
num_channels = self.stage3_cfg['num_channels']
block_type = block_types_dict[self.stage3_cfg['block_type']]
num_channels = [num_channels[i] * block_type.expansion for i in range(len(num_channels))]
self.transition2 = self._make_transition_layer(pre_stage_channels, num_channels)
self.stage3, pre_stage_channels = self._make_stage(self.stage3_cfg, num_channels)
self.stage4_cfg = cfg['stage4']
num_channels = self.stage4_cfg['num_channels']
block_type = block_types_dict[self.stage4_cfg['block_type']]
num_channels = [num_channels[i] * block_type.expansion for i in range(len(num_channels))]
self.transition3 = self._make_transition_layer(pre_stage_channels, num_channels)
self.stage4, pre_stage_channels = self._make_stage(self.stage4_cfg, num_channels, multi_scale_output=True)
self.head = head
self.head_channels = None # set if _make_head called
head_conv_bias = cfg.pop('head_conv_bias', True)
if head == 'classification':
# Classification Head
self.num_features = 2048
self.incre_modules, self.downsamp_modules, self.final_layer = self._make_head(
pre_stage_channels,
conv_bias=head_conv_bias,
)
self.global_pool, self.head_drop, self.classifier = create_classifier(
self.num_features,
self.num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
)
else:
if head == 'incre':
self.num_features = 2048
self.incre_modules, _, _ = self._make_head(pre_stage_channels, incre_only=True)
else:
self.num_features = 256
self.incre_modules = None
self.global_pool = nn.Identity()
self.head_drop = nn.Identity()
self.classifier = nn.Identity()
curr_stride = 2
# module names aren't actually valid here, hook or FeatureNet based extraction would not work
self.feature_info = [dict(num_chs=64, reduction=curr_stride, module='stem')]
for i, c in enumerate(self.head_channels if self.head_channels else num_channels):
curr_stride *= 2
c = c * 4 if self.head_channels else c # head block_type expansion factor of 4
self.feature_info += [dict(num_chs=c, reduction=curr_stride, module=f'stage{i + 1}')]
self.init_weights()
def _make_head(self, pre_stage_channels, incre_only=False, conv_bias=True):
head_block_type = Bottleneck
self.head_channels = [32, 64, 128, 256]
# Increasing the #channels on each resolution
# from C, 2C, 4C, 8C to 128, 256, 512, 1024
incre_modules = []
for i, channels in enumerate(pre_stage_channels):
incre_modules.append(self._make_layer(head_block_type, channels, self.head_channels[i], 1, stride=1))
incre_modules = nn.ModuleList(incre_modules)
if incre_only:
return incre_modules, None, None
# downsampling modules
downsamp_modules = []
for i in range(len(pre_stage_channels) - 1):
in_channels = self.head_channels[i] * head_block_type.expansion
out_channels = self.head_channels[i + 1] * head_block_type.expansion
downsamp_module = nn.Sequential(
nn.Conv2d(
in_channels=in_channels, out_channels=out_channels,
kernel_size=3, stride=2, padding=1, bias=conv_bias),
nn.BatchNorm2d(out_channels, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True)
)
downsamp_modules.append(downsamp_module)
downsamp_modules = nn.ModuleList(downsamp_modules)
final_layer = nn.Sequential(
nn.Conv2d(
in_channels=self.head_channels[3] * head_block_type.expansion, out_channels=self.num_features,
kernel_size=1, stride=1, padding=0, bias=conv_bias),
nn.BatchNorm2d(self.num_features, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True)
)
return incre_modules, downsamp_modules, final_layer
def _make_transition_layer(self, num_channels_pre_layer, num_channels_cur_layer):
num_branches_cur = len(num_channels_cur_layer)
num_branches_pre = len(num_channels_pre_layer)
transition_layers = []
for i in range(num_branches_cur):
if i < num_branches_pre:
if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
transition_layers.append(nn.Sequential(
nn.Conv2d(num_channels_pre_layer[i], num_channels_cur_layer[i], 3, 1, 1, bias=False),
nn.BatchNorm2d(num_channels_cur_layer[i], momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True)))
else:
transition_layers.append(nn.Identity())
else:
conv3x3s = []
for j in range(i + 1 - num_branches_pre):
_in_chs = num_channels_pre_layer[-1]
_out_chs = num_channels_cur_layer[i] if j == i - num_branches_pre else _in_chs
conv3x3s.append(nn.Sequential(
nn.Conv2d(_in_chs, _out_chs, 3, 2, 1, bias=False),
nn.BatchNorm2d(_out_chs, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True)))
transition_layers.append(nn.Sequential(*conv3x3s))
return nn.ModuleList(transition_layers)
def _make_layer(self, block_type, inplanes, planes, block_types, stride=1):
downsample = None
if stride != 1 or inplanes != planes * block_type.expansion:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes * block_type.expansion, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block_type.expansion, momentum=_BN_MOMENTUM),
)
layers = [block_type(inplanes, planes, stride, downsample)]
inplanes = planes * block_type.expansion
for i in range(1, block_types):
layers.append(block_type(inplanes, planes))
return nn.Sequential(*layers)
def _make_stage(self, layer_config, num_in_chs, multi_scale_output=True):
num_modules = layer_config['num_modules']
num_branches = layer_config['num_branches']
num_blocks = layer_config['num_blocks']
num_channels = layer_config['num_channels']
block_type = block_types_dict[layer_config['block_type']]
fuse_method = layer_config['fuse_method']
modules = []
for i in range(num_modules):
# multi_scale_output is only used last module
reset_multi_scale_output = multi_scale_output or i < num_modules - 1
modules.append(HighResolutionModule(
num_branches, block_type, num_blocks, num_in_chs, num_channels, fuse_method, reset_multi_scale_output)
)
num_in_chs = modules[-1].get_num_in_chs()
return SequentialList(*modules), num_in_chs
@torch.jit.ignore
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(
stem=r'^conv[12]|bn[12]',
block_types=r'^(?:layer|stage|transition)(\d+)' if coarse else [
(r'^layer(\d+)\.(\d+)', None),
(r'^stage(\d+)\.(\d+)', None),
(r'^transition(\d+)', (99999,)),
],
)
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, "gradient checkpointing not supported"
@torch.jit.ignore
def get_classifier(self):
return self.classifier
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
def stages(self, x) -> List[torch.Tensor]:
x = self.layer1(x)
xl = [t(x) for i, t in enumerate(self.transition1)]
yl = self.stage2(xl)
xl = [t(yl[-1]) if not isinstance(t, nn.Identity) else yl[i] for i, t in enumerate(self.transition2)]
yl = self.stage3(xl)
xl = [t(yl[-1]) if not isinstance(t, nn.Identity) else yl[i] for i, t in enumerate(self.transition3)]
yl = self.stage4(xl)
return yl
def forward_features(self, x):
# Stem
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
# Stages
yl = self.stages(x)
if self.incre_modules is None or self.downsamp_modules is None:
return yl
y = None
for i, incre in enumerate(self.incre_modules):
if y is None:
y = incre(yl[i])
else:
down: ModuleInterface = self.downsamp_modules[i - 1] # needed for torchscript module indexing
y = incre(yl[i]) + down.forward(y)
y = self.final_layer(y)
return y
def forward_head(self, x, pre_logits: bool = False):
# Classification Head
x = self.global_pool(x)
x = self.head_drop(x)
return x if pre_logits else self.classifier(x)
def forward(self, x):
y = self.forward_features(x)
x = self.forward_head(y)
return x
class HighResolutionNetFeatures(HighResolutionNet):
"""HighResolutionNet feature extraction
The design of HRNet makes it easy to grab feature maps, this class provides a simple wrapper to do so.
It would be more complicated to use the FeatureNet helpers.
The `feature_location=incre` allows grabbing increased channel count features using part of the
classification head. If `feature_location=''` the default HRNet features are returned. First stem
conv is used for stride 2 features.
"""
def __init__(
self,
cfg,
in_chans=3,
num_classes=1000,
output_stride=32,
global_pool='avg',
drop_rate=0.0,
feature_location='incre',
out_indices=(0, 1, 2, 3, 4),
**kwargs,
):
assert feature_location in ('incre', '')
super(HighResolutionNetFeatures, self).__init__(
cfg,
in_chans=in_chans,
num_classes=num_classes,
output_stride=output_stride,
global_pool=global_pool,
drop_rate=drop_rate,
head=feature_location,
**kwargs,
)
self.feature_info = FeatureInfo(self.feature_info, out_indices)
self._out_idx = {f['index'] for f in self.feature_info.get_dicts()}
def forward_features(self, x):
assert False, 'Not supported'
def forward(self, x) -> List[torch.tensor]:
out = []
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
if 0 in self._out_idx:
out.append(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
x = self.stages(x)
if self.incre_modules is not None:
x = [incre(f) for f, incre in zip(x, self.incre_modules)]
for i, f in enumerate(x):
if i + 1 in self._out_idx:
out.append(f)
return out
def _create_hrnet(variant, pretrained=False, cfg_variant=None, **model_kwargs):
model_cls = HighResolutionNet
features_only = False
kwargs_filter = None
if model_kwargs.pop('features_only', False):
model_cls = HighResolutionNetFeatures
kwargs_filter = ('num_classes', 'global_pool')
features_only = True
cfg_variant = cfg_variant or variant
model = build_model_with_cfg(
model_cls,
variant,
pretrained,
model_cfg=cfg_cls[cfg_variant],
pretrained_strict=not features_only,
kwargs_filter=kwargs_filter,
**model_kwargs,
)
if features_only:
model.pretrained_cfg = pretrained_cfg_for_features(model.default_cfg)
model.default_cfg = model.pretrained_cfg # backwards compat
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'conv1', 'classifier': 'classifier',
**kwargs
}
default_cfgs = generate_default_cfgs({
'hrnet_w18_small.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w18_small_v2.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w18.ms_aug_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95,
),
'hrnet_w18.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w30.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w32.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w40.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w44.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w48.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w64.ms_in1k': _cfg(hf_hub_id='timm/'),
'hrnet_w18_ssld.paddle_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_crop_pct=1.0, test_input_size=(3, 288, 288)
),
'hrnet_w48_ssld.paddle_in1k': _cfg(
hf_hub_id='timm/',
crop_pct=0.95, test_crop_pct=1.0, test_input_size=(3, 288, 288)
),
})
@register_model
def hrnet_w18_small(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w18_small', pretrained, **kwargs)
@register_model
def hrnet_w18_small_v2(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w18_small_v2', pretrained, **kwargs)
@register_model
def hrnet_w18(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w18', pretrained, **kwargs)
@register_model
def hrnet_w30(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w30', pretrained, **kwargs)
@register_model
def hrnet_w32(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w32', pretrained, **kwargs)
@register_model
def hrnet_w40(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w40', pretrained, **kwargs)
@register_model
def hrnet_w44(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w44', pretrained, **kwargs)
@register_model
def hrnet_w48(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w48', pretrained, **kwargs)
@register_model
def hrnet_w64(pretrained=False, **kwargs) -> HighResolutionNet:
return _create_hrnet('hrnet_w64', pretrained, **kwargs)
@register_model
def hrnet_w18_ssld(pretrained=False, **kwargs) -> HighResolutionNet:
kwargs.setdefault('head_conv_bias', False)
return _create_hrnet('hrnet_w18_ssld', cfg_variant='hrnet_w18', pretrained=pretrained, **kwargs)
@register_model
def hrnet_w48_ssld(pretrained=False, **kwargs) -> HighResolutionNet:
kwargs.setdefault('head_conv_bias', False)
return _create_hrnet('hrnet_w48_ssld', cfg_variant='hrnet_w48', pretrained=pretrained, **kwargs)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/models/sequencer.py | """ Sequencer
Paper: `Sequencer: Deep LSTM for Image Classification` - https://arxiv.org/pdf/2205.01972.pdf
"""
# Copyright (c) 2022. Yuki Tatsunami
# Licensed under the Apache License, Version 2.0 (the "License");
import math
from functools import partial
from itertools import accumulate
from typing import Tuple
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
from timm.layers import lecun_normal_, DropPath, Mlp, PatchEmbed, ClassifierHead
from ._builder import build_model_with_cfg
from ._manipulate import named_apply
from ._registry import register_model, generate_default_cfgs
__all__ = ['Sequencer2d'] # model_registry will add each entrypoint fn to this
def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):
if isinstance(module, nn.Linear):
if name.startswith('head'):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
else:
if flax:
# Flax defaults
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
else:
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
if 'mlp' in name:
nn.init.normal_(module.bias, std=1e-6)
else:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.RNN, nn.GRU, nn.LSTM)):
stdv = 1.0 / math.sqrt(module.hidden_size)
for weight in module.parameters():
nn.init.uniform_(weight, -stdv, stdv)
elif hasattr(module, 'init_weights'):
module.init_weights()
class RNNIdentity(nn.Module):
def __init__(self, *args, **kwargs):
super(RNNIdentity, self).__init__()
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, None]:
return x, None
class RNN2dBase(nn.Module):
def __init__(
self,
input_size: int,
hidden_size: int,
num_layers: int = 1,
bias: bool = True,
bidirectional: bool = True,
union="cat",
with_fc=True,
):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = 2 * hidden_size if bidirectional else hidden_size
self.union = union
self.with_vertical = True
self.with_horizontal = True
self.with_fc = with_fc
self.fc = None
if with_fc:
if union == "cat":
self.fc = nn.Linear(2 * self.output_size, input_size)
elif union == "add":
self.fc = nn.Linear(self.output_size, input_size)
elif union == "vertical":
self.fc = nn.Linear(self.output_size, input_size)
self.with_horizontal = False
elif union == "horizontal":
self.fc = nn.Linear(self.output_size, input_size)
self.with_vertical = False
else:
raise ValueError("Unrecognized union: " + union)
elif union == "cat":
pass
if 2 * self.output_size != input_size:
raise ValueError(f"The output channel {2 * self.output_size} is different from the input channel {input_size}.")
elif union == "add":
pass
if self.output_size != input_size:
raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
elif union == "vertical":
if self.output_size != input_size:
raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
self.with_horizontal = False
elif union == "horizontal":
if self.output_size != input_size:
raise ValueError(f"The output channel {self.output_size} is different from the input channel {input_size}.")
self.with_vertical = False
else:
raise ValueError("Unrecognized union: " + union)
self.rnn_v = RNNIdentity()
self.rnn_h = RNNIdentity()
def forward(self, x):
B, H, W, C = x.shape
if self.with_vertical:
v = x.permute(0, 2, 1, 3)
v = v.reshape(-1, H, C)
v, _ = self.rnn_v(v)
v = v.reshape(B, W, H, -1)
v = v.permute(0, 2, 1, 3)
else:
v = None
if self.with_horizontal:
h = x.reshape(-1, W, C)
h, _ = self.rnn_h(h)
h = h.reshape(B, H, W, -1)
else:
h = None
if v is not None and h is not None:
if self.union == "cat":
x = torch.cat([v, h], dim=-1)
else:
x = v + h
elif v is not None:
x = v
elif h is not None:
x = h
if self.fc is not None:
x = self.fc(x)
return x
class LSTM2d(RNN2dBase):
def __init__(
self,
input_size: int,
hidden_size: int,
num_layers: int = 1,
bias: bool = True,
bidirectional: bool = True,
union="cat",
with_fc=True,
):
super().__init__(input_size, hidden_size, num_layers, bias, bidirectional, union, with_fc)
if self.with_vertical:
self.rnn_v = nn.LSTM(
input_size,
hidden_size,
num_layers,
batch_first=True,
bias=bias,
bidirectional=bidirectional,
)
if self.with_horizontal:
self.rnn_h = nn.LSTM(
input_size,
hidden_size,
num_layers,
batch_first=True,
bias=bias,
bidirectional=bidirectional,
)
class Sequencer2dBlock(nn.Module):
def __init__(
self,
dim,
hidden_size,
mlp_ratio=3.0,
rnn_layer=LSTM2d,
mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
num_layers=1,
bidirectional=True,
union="cat",
with_fc=True,
drop=0.,
drop_path=0.,
):
super().__init__()
channels_dim = int(mlp_ratio * dim)
self.norm1 = norm_layer(dim)
self.rnn_tokens = rnn_layer(
dim,
hidden_size,
num_layers=num_layers,
bidirectional=bidirectional,
union=union,
with_fc=with_fc,
)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
self.mlp_channels = mlp_layer(dim, channels_dim, act_layer=act_layer, drop=drop)
def forward(self, x):
x = x + self.drop_path(self.rnn_tokens(self.norm1(x)))
x = x + self.drop_path(self.mlp_channels(self.norm2(x)))
return x
class Shuffle(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
if self.training:
B, H, W, C = x.shape
r = torch.randperm(H * W)
x = x.reshape(B, -1, C)
x = x[:, r, :].reshape(B, H, W, -1)
return x
class Downsample2d(nn.Module):
def __init__(self, input_dim, output_dim, patch_size):
super().__init__()
self.down = nn.Conv2d(input_dim, output_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
x = x.permute(0, 3, 1, 2)
x = self.down(x)
x = x.permute(0, 2, 3, 1)
return x
class Sequencer2dStage(nn.Module):
def __init__(
self,
dim,
dim_out,
depth,
patch_size,
hidden_size,
mlp_ratio,
downsample=False,
block_layer=Sequencer2dBlock,
rnn_layer=LSTM2d,
mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
num_layers=1,
bidirectional=True,
union="cat",
with_fc=True,
drop=0.,
drop_path=0.,
):
super().__init__()
if downsample:
self.downsample = Downsample2d(dim, dim_out, patch_size)
else:
assert dim == dim_out
self.downsample = nn.Identity()
blocks = []
for block_idx in range(depth):
blocks.append(block_layer(
dim_out,
hidden_size,
mlp_ratio=mlp_ratio,
rnn_layer=rnn_layer,
mlp_layer=mlp_layer,
norm_layer=norm_layer,
act_layer=act_layer,
num_layers=num_layers,
bidirectional=bidirectional,
union=union,
with_fc=with_fc,
drop=drop,
drop_path=drop_path[block_idx] if isinstance(drop_path, (list, tuple)) else drop_path,
))
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
x = self.downsample(x)
x = self.blocks(x)
return x
class Sequencer2d(nn.Module):
def __init__(
self,
num_classes=1000,
img_size=224,
in_chans=3,
global_pool='avg',
layers=(4, 3, 8, 3),
patch_sizes=(7, 2, 2, 1),
embed_dims=(192, 384, 384, 384),
hidden_sizes=(48, 96, 96, 96),
mlp_ratios=(3.0, 3.0, 3.0, 3.0),
block_layer=Sequencer2dBlock,
rnn_layer=LSTM2d,
mlp_layer=Mlp,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
act_layer=nn.GELU,
num_rnn_layers=1,
bidirectional=True,
union="cat",
with_fc=True,
drop_rate=0.,
drop_path_rate=0.,
nlhb=False,
stem_norm=False,
):
super().__init__()
assert global_pool in ('', 'avg')
self.num_classes = num_classes
self.global_pool = global_pool
self.num_features = embed_dims[-1] # num_features for consistency with other models
self.feature_dim = -1 # channel dim index for feature outputs (rank 4, NHWC)
self.output_fmt = 'NHWC'
self.feature_info = []
self.stem = PatchEmbed(
img_size=None,
patch_size=patch_sizes[0],
in_chans=in_chans,
embed_dim=embed_dims[0],
norm_layer=norm_layer if stem_norm else None,
flatten=False,
output_fmt='NHWC',
)
assert len(layers) == len(patch_sizes) == len(embed_dims) == len(hidden_sizes) == len(mlp_ratios)
reductions = list(accumulate(patch_sizes, lambda x, y: x * y))
stages = []
prev_dim = embed_dims[0]
for i, _ in enumerate(embed_dims):
stages += [Sequencer2dStage(
prev_dim,
embed_dims[i],
depth=layers[i],
downsample=i > 0,
patch_size=patch_sizes[i],
hidden_size=hidden_sizes[i],
mlp_ratio=mlp_ratios[i],
block_layer=block_layer,
rnn_layer=rnn_layer,
mlp_layer=mlp_layer,
norm_layer=norm_layer,
act_layer=act_layer,
num_layers=num_rnn_layers,
bidirectional=bidirectional,
union=union,
with_fc=with_fc,
drop=drop_rate,
drop_path=drop_path_rate,
)]
prev_dim = embed_dims[i]
self.feature_info += [dict(num_chs=prev_dim, reduction=reductions[i], module=f'stages.{i}')]
self.stages = nn.Sequential(*stages)
self.norm = norm_layer(embed_dims[-1])
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=drop_rate,
input_fmt=self.output_fmt,
)
self.init_weights(nlhb=nlhb)
def init_weights(self, nlhb=False):
head_bias = -math.log(self.num_classes) if nlhb else 0.
named_apply(partial(_init_weights, head_bias=head_bias), module=self) # depth-first
@torch.jit.ignore
def group_matcher(self, coarse=False):
return dict(
stem=r'^stem',
blocks=[
(r'^stages\.(\d+)', None),
(r'^norm', (99999,))
] if coarse else [
(r'^stages\.(\d+)\.blocks\.(\d+)', None),
(r'^stages\.(\d+)\.downsample', (0,)),
(r'^norm', (99999,))
]
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
assert not enable, 'gradient checkpointing not supported'
@torch.jit.ignore
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=None):
self.num_classes = num_classes
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.stages(x)
x = self.norm(x)
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 original checkpoints -> timm """
if 'stages.0.blocks.0.norm1.weight' in state_dict:
return state_dict # already translated checkpoint
if 'model' in state_dict:
state_dict = state_dict['model']
import re
out_dict = {}
for k, v in state_dict.items():
k = re.sub(r'blocks.([0-9]+).([0-9]+).down', lambda x: f'stages.{int(x.group(1)) + 1}.downsample.down', k)
k = re.sub(r'blocks.([0-9]+).([0-9]+)', r'stages.\1.blocks.\2', k)
k = k.replace('head.', 'head.fc.')
out_dict[k] = v
return out_dict
def _create_sequencer2d(variant, pretrained=False, **kwargs):
default_out_indices = tuple(range(3))
out_indices = kwargs.pop('out_indices', default_out_indices)
model = build_model_with_cfg(
Sequencer2d,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(flatten_sequential=True, out_indices=out_indices),
**kwargs,
)
return model
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': DEFAULT_CROP_PCT, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'stem.proj', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'sequencer2d_s.in1k': _cfg(hf_hub_id='timm/'),
'sequencer2d_m.in1k': _cfg(hf_hub_id='timm/'),
'sequencer2d_l.in1k': _cfg(hf_hub_id='timm/'),
})
@register_model
def sequencer2d_s(pretrained=False, **kwargs) -> Sequencer2d:
model_args = dict(
layers=[4, 3, 8, 3],
patch_sizes=[7, 2, 1, 1],
embed_dims=[192, 384, 384, 384],
hidden_sizes=[48, 96, 96, 96],
mlp_ratios=[3.0, 3.0, 3.0, 3.0],
rnn_layer=LSTM2d,
bidirectional=True,
union="cat",
with_fc=True,
)
model = _create_sequencer2d('sequencer2d_s', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def sequencer2d_m(pretrained=False, **kwargs) -> Sequencer2d:
model_args = dict(
layers=[4, 3, 14, 3],
patch_sizes=[7, 2, 1, 1],
embed_dims=[192, 384, 384, 384],
hidden_sizes=[48, 96, 96, 96],
mlp_ratios=[3.0, 3.0, 3.0, 3.0],
rnn_layer=LSTM2d,
bidirectional=True,
union="cat",
with_fc=True,
**kwargs)
model = _create_sequencer2d('sequencer2d_m', pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def sequencer2d_l(pretrained=False, **kwargs) -> Sequencer2d:
model_args = dict(
layers=[8, 8, 16, 4],
patch_sizes=[7, 2, 1, 1],
embed_dims=[192, 384, 384, 384],
hidden_sizes=[48, 96, 96, 96],
mlp_ratios=[3.0, 3.0, 3.0, 3.0],
rnn_layer=LSTM2d,
bidirectional=True,
union="cat",
with_fc=True,
**kwargs)
model = _create_sequencer2d('sequencer2d_l', pretrained=pretrained, **dict(model_args, **kwargs))
return model
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/models/tresnet.py | """
TResNet: High Performance GPU-Dedicated Architecture
https://arxiv.org/pdf/2003.13630.pdf
Original model: https://github.com/mrT23/TResNet
"""
from collections import OrderedDict
from functools import partial
import torch
import torch.nn as nn
from timm.layers import SpaceToDepth, BlurPool2d, ClassifierHead, SEModule,\
ConvNormActAa, ConvNormAct, DropPath
from ._builder import build_model_with_cfg
from ._manipulate import checkpoint_seq
from ._registry import register_model, generate_default_cfgs, register_model_deprecations
__all__ = ['TResNet'] # model_registry will add each entrypoint fn to this
class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
aa_layer=None,
drop_path_rate=0.
):
super(BasicBlock, self).__init__()
self.downsample = downsample
self.stride = stride
act_layer = partial(nn.LeakyReLU, negative_slope=1e-3)
if stride == 1:
self.conv1 = ConvNormAct(inplanes, planes, kernel_size=3, stride=1, act_layer=act_layer)
else:
self.conv1 = ConvNormActAa(
inplanes, planes, kernel_size=3, stride=2, act_layer=act_layer, aa_layer=aa_layer)
self.conv2 = ConvNormAct(planes, planes, kernel_size=3, stride=1, apply_act=False, act_layer=None)
self.act = nn.ReLU(inplace=True)
rd_chs = max(planes * self.expansion // 4, 64)
self.se = SEModule(planes * self.expansion, rd_channels=rd_chs) if use_se else None
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def forward(self, x):
if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.drop_path(out) + shortcut
out = self.act(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes,
planes,
stride=1,
downsample=None,
use_se=True,
act_layer=None,
aa_layer=None,
drop_path_rate=0.,
):
super(Bottleneck, self).__init__()
self.downsample = downsample
self.stride = stride
act_layer = act_layer or partial(nn.LeakyReLU, negative_slope=1e-3)
self.conv1 = ConvNormAct(
inplanes, planes, kernel_size=1, stride=1, act_layer=act_layer)
if stride == 1:
self.conv2 = ConvNormAct(
planes, planes, kernel_size=3, stride=1, act_layer=act_layer)
else:
self.conv2 = ConvNormActAa(
planes, planes, kernel_size=3, stride=2, act_layer=act_layer, aa_layer=aa_layer)
reduction_chs = max(planes * self.expansion // 8, 64)
self.se = SEModule(planes, rd_channels=reduction_chs) if use_se else None
self.conv3 = ConvNormAct(
planes, planes * self.expansion, kernel_size=1, stride=1, apply_act=False, act_layer=None)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
self.act = nn.ReLU(inplace=True)
def forward(self, x):
if self.downsample is not None:
shortcut = self.downsample(x)
else:
shortcut = x
out = self.conv1(x)
out = self.conv2(out)
if self.se is not None:
out = self.se(out)
out = self.conv3(out)
out = self.drop_path(out) + shortcut
out = self.act(out)
return out
class TResNet(nn.Module):
def __init__(
self,
layers,
in_chans=3,
num_classes=1000,
width_factor=1.0,
v2=False,
global_pool='fast',
drop_rate=0.,
drop_path_rate=0.,
):
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
super(TResNet, self).__init__()
aa_layer = BlurPool2d
act_layer = nn.LeakyReLU
# TResnet stages
self.inplanes = int(64 * width_factor)
self.planes = int(64 * width_factor)
if v2:
self.inplanes = self.inplanes // 8 * 8
self.planes = self.planes // 8 * 8
dpr = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(layers)).split(layers)]
conv1 = ConvNormAct(in_chans * 16, self.planes, stride=1, kernel_size=3, act_layer=act_layer)
layer1 = self._make_layer(
Bottleneck if v2 else BasicBlock,
self.planes, layers[0], stride=1, use_se=True, aa_layer=aa_layer, drop_path_rate=dpr[0])
layer2 = self._make_layer(
Bottleneck if v2 else BasicBlock,
self.planes * 2, layers[1], stride=2, use_se=True, aa_layer=aa_layer, drop_path_rate=dpr[1])
layer3 = self._make_layer(
Bottleneck,
self.planes * 4, layers[2], stride=2, use_se=True, aa_layer=aa_layer, drop_path_rate=dpr[2])
layer4 = self._make_layer(
Bottleneck,
self.planes * 8, layers[3], stride=2, use_se=False, aa_layer=aa_layer, drop_path_rate=dpr[3])
# body
self.body = nn.Sequential(OrderedDict([
('s2d', SpaceToDepth()),
('conv1', conv1),
('layer1', layer1),
('layer2', layer2),
('layer3', layer3),
('layer4', layer4),
]))
self.feature_info = [
dict(num_chs=self.planes, reduction=2, module=''), # Not with S2D?
dict(num_chs=self.planes * (Bottleneck.expansion if v2 else 1), reduction=4, module='body.layer1'),
dict(num_chs=self.planes * 2 * (Bottleneck.expansion if v2 else 1), reduction=8, module='body.layer2'),
dict(num_chs=self.planes * 4 * Bottleneck.expansion, reduction=16, module='body.layer3'),
dict(num_chs=self.planes * 8 * Bottleneck.expansion, reduction=32, module='body.layer4'),
]
# head
self.num_features = (self.planes * 8) * Bottleneck.expansion
self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate)
# model initialization
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
if isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
# residual connections special initialization
for m in self.modules():
if isinstance(m, BasicBlock):
nn.init.zeros_(m.conv2.bn.weight)
if isinstance(m, Bottleneck):
nn.init.zeros_(m.conv3.bn.weight)
def _make_layer(self, block, planes, blocks, stride=1, use_se=True, aa_layer=None, drop_path_rate=0.):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
layers = []
if stride == 2:
# avg pooling before 1x1 conv
layers.append(nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True, count_include_pad=False))
layers += [ConvNormAct(
self.inplanes, planes * block.expansion, kernel_size=1, stride=1, apply_act=False, act_layer=None)]
downsample = nn.Sequential(*layers)
layers = []
for i in range(blocks):
layers.append(block(
self.inplanes,
planes,
stride=stride if i == 0 else 1,
downsample=downsample if i == 0 else None,
use_se=use_se,
aa_layer=aa_layer,
drop_path_rate=drop_path_rate[i] if isinstance(drop_path_rate, list) else drop_path_rate,
))
self.inplanes = planes * block.expansion
return nn.Sequential(*layers)
@torch.jit.ignore
def group_matcher(self, coarse=False):
matcher = dict(stem=r'^body\.conv1', blocks=r'^body\.layer(\d+)' if coarse else r'^body\.layer(\d+)\.(\d+)')
return matcher
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
self.grad_checkpointing = enable
@torch.jit.ignore
def get_classifier(self):
return self.head.fc
def reset_classifier(self, num_classes, global_pool=None):
self.head.reset(num_classes, pool_type=global_pool)
def forward_features(self, x):
if self.grad_checkpointing and not torch.jit.is_scripting():
x = self.body.s2d(x)
x = self.body.conv1(x)
x = checkpoint_seq([
self.body.layer1,
self.body.layer2,
self.body.layer3,
self.body.layer4],
x, flatten=True)
else:
x = self.body(x)
return x
def forward_head(self, x, pre_logits: bool = False):
return x 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):
if 'body.conv1.conv.weight' in state_dict:
return state_dict
import re
state_dict = state_dict.get('model', state_dict)
state_dict = state_dict.get('state_dict', state_dict)
out_dict = {}
for k, v in state_dict.items():
k = re.sub(r'conv(\d+)\.0.0', lambda x: f'conv{int(x.group(1))}.conv', k)
k = re.sub(r'conv(\d+)\.0.1', lambda x: f'conv{int(x.group(1))}.bn', k)
k = re.sub(r'conv(\d+)\.0', lambda x: f'conv{int(x.group(1))}.conv', k)
k = re.sub(r'conv(\d+)\.1', lambda x: f'conv{int(x.group(1))}.bn', k)
k = re.sub(r'downsample\.(\d+)\.0', lambda x: f'downsample.{int(x.group(1))}.conv', k)
k = re.sub(r'downsample\.(\d+)\.1', lambda x: f'downsample.{int(x.group(1))}.bn', k)
if k.endswith('bn.weight'):
# convert weight from inplace_abn to batchnorm
v = v.abs().add(1e-5)
out_dict[k] = v
return out_dict
def _create_tresnet(variant, pretrained=False, **kwargs):
return build_model_with_cfg(
TResNet,
variant,
pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(1, 2, 3, 4), flatten_sequential=True),
**kwargs,
)
def _cfg(url='', **kwargs):
return {
'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': (0., 0., 0.), 'std': (1., 1., 1.),
'first_conv': 'body.conv1.conv', 'classifier': 'head.fc',
**kwargs
}
default_cfgs = generate_default_cfgs({
'tresnet_m.miil_in21k_ft_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_m.miil_in21k': _cfg(hf_hub_id='timm/', num_classes=11221),
'tresnet_m.miil_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_l.miil_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_xl.miil_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_m.miil_in1k_448': _cfg(
input_size=(3, 448, 448), pool_size=(14, 14),
hf_hub_id='timm/'),
'tresnet_l.miil_in1k_448': _cfg(
input_size=(3, 448, 448), pool_size=(14, 14),
hf_hub_id='timm/'),
'tresnet_xl.miil_in1k_448': _cfg(
input_size=(3, 448, 448), pool_size=(14, 14),
hf_hub_id='timm/'),
'tresnet_v2_l.miil_in21k_ft_in1k': _cfg(hf_hub_id='timm/'),
'tresnet_v2_l.miil_in21k': _cfg(hf_hub_id='timm/', num_classes=11221),
})
@register_model
def tresnet_m(pretrained=False, **kwargs) -> TResNet:
model_kwargs = dict(layers=[3, 4, 11, 3], **kwargs)
return _create_tresnet('tresnet_m', pretrained=pretrained, **model_kwargs)
@register_model
def tresnet_l(pretrained=False, **kwargs) -> TResNet:
model_kwargs = dict(layers=[4, 5, 18, 3], width_factor=1.2, **kwargs)
return _create_tresnet('tresnet_l', pretrained=pretrained, **model_kwargs)
@register_model
def tresnet_xl(pretrained=False, **kwargs) -> TResNet:
model_kwargs = dict(layers=[4, 5, 24, 3], width_factor=1.3, **kwargs)
return _create_tresnet('tresnet_xl', pretrained=pretrained, **model_kwargs)
@register_model
def tresnet_v2_l(pretrained=False, **kwargs) -> TResNet:
model_kwargs = dict(layers=[3, 4, 23, 3], width_factor=1.0, v2=True, **kwargs)
return _create_tresnet('tresnet_v2_l', pretrained=pretrained, **model_kwargs)
register_model_deprecations(__name__, {
'tresnet_m_miil_in21k': 'tresnet_m.miil_in21k',
'tresnet_m_448': 'tresnet_m.miil_in1k_448',
'tresnet_l_448': 'tresnet_l.miil_in1k_448',
'tresnet_xl_448': 'tresnet_xl.miil_in1k_448',
}) | 0 |
hf_public_repos/pytorch-image-models/timm/models | hf_public_repos/pytorch-image-models/timm/models/layers/__init__.py | # NOTE timm.models.layers is DEPRECATED, please use timm.layers, this is here to reduce breakages in transition
from timm.layers.activations import *
from timm.layers.adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from timm.layers.attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding
from timm.layers.blur_pool import BlurPool2d
from timm.layers.classifier import ClassifierHead, create_classifier
from timm.layers.cond_conv2d import CondConv2d, get_condconv_initializer
from timm.layers.config import is_exportable, is_scriptable, is_no_jit, set_exportable, set_scriptable, set_no_jit,\
set_layer_config
from timm.layers.conv2d_same import Conv2dSame, conv2d_same
from timm.layers.conv_bn_act import ConvNormAct, ConvNormActAa, ConvBnAct
from timm.layers.create_act import create_act_layer, get_act_layer, get_act_fn
from timm.layers.create_attn import get_attn, create_attn
from timm.layers.create_conv2d import create_conv2d
from timm.layers.create_norm import get_norm_layer, create_norm_layer
from timm.layers.create_norm_act import get_norm_act_layer, create_norm_act_layer, get_norm_act_layer
from timm.layers.drop import DropBlock2d, DropPath, drop_block_2d, drop_path
from timm.layers.eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn
from timm.layers.evo_norm import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2,\
EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
from timm.layers.fast_norm import is_fast_norm, set_fast_norm, fast_group_norm, fast_layer_norm
from timm.layers.filter_response_norm import FilterResponseNormTlu2d, FilterResponseNormAct2d
from timm.layers.gather_excite import GatherExcite
from timm.layers.global_context import GlobalContext
from timm.layers.helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, extend_tuple
from timm.layers.inplace_abn import InplaceAbn
from timm.layers.linear import Linear
from timm.layers.mixed_conv2d import MixedConv2d
from timm.layers.mlp import Mlp, GluMlp, GatedMlp, ConvMlp
from timm.layers.non_local_attn import NonLocalAttn, BatNonLocalAttn
from timm.layers.norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d
from timm.layers.norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm
from timm.layers.padding import get_padding, get_same_padding, pad_same
from timm.layers.patch_embed import PatchEmbed
from timm.layers.pool2d_same import AvgPool2dSame, create_pool2d
from timm.layers.squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite
from timm.layers.selective_kernel import SelectiveKernel
from timm.layers.separable_conv import SeparableConv2d, SeparableConvNormAct
from timm.layers.space_to_depth import SpaceToDepthModule
from timm.layers.split_attn import SplitAttn
from timm.layers.split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
from timm.layers.std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
from timm.layers.test_time_pool import TestTimePoolHead, apply_test_time_pool
from timm.layers.trace_utils import _assert, _float_to_int
from timm.layers.weight_init import trunc_normal_, trunc_normal_tf_, variance_scaling_, lecun_normal_
import warnings
warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", DeprecationWarning)
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3]***blocks.6.1.bn2.weight:[2301]***blocks.6.1.bn2.bias:[2301]***blocks.6.1.bn2.running_mean:[2301]***blocks.6.1.bn2.running_var:[2301]***blocks.6.1.bn2.num_batches_tracked:[]***blocks.6.1.se.conv_reduce.weight:[96, 2301, 1, 1]***blocks.6.1.se.conv_reduce.bias:[96]***blocks.6.1.se.conv_expand.weight:[2301, 96, 1, 1]***blocks.6.1.se.conv_expand.bias:[2301]***blocks.6.1.conv_pwl.weight:[384, 2301, 1, 1]***blocks.6.1.bn3.weight:[384]***blocks.6.1.bn3.bias:[384]***blocks.6.1.bn3.running_mean:[384]***blocks.6.1.bn3.running_var:[384]***blocks.6.1.bn3.num_batches_tracked:[]***conv_head.weight:[1536, 384, 1, 1]***bn2.weight:[1536]***bn2.bias:[1536]***bn2.running_mean:[1536]***bn2.running_var:[1536]***bn2.num_batches_tracked:[]***classifier.weight:[1000, 1536]***classifier.bias:[1000] | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/squeeze_excite.py | """ Squeeze-and-Excitation Channel Attention
An SE implementation originally based on PyTorch SE-Net impl.
Has since evolved with additional functionality / configuration.
Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
Also included is Effective Squeeze-Excitation (ESE).
Paper: `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
from .create_act import create_act_layer
from .helpers import make_divisible
class SEModule(nn.Module):
""" SE Module as defined in original SE-Nets with a few additions
Additions include:
* divisor can be specified to keep channels % div == 0 (default: 8)
* reduction channels can be specified directly by arg (if rd_channels is set)
* reduction channels can be specified by float rd_ratio (default: 1/16)
* global max pooling can be added to the squeeze aggregation
* customizable activation, normalization, and gate layer
"""
def __init__(
self, channels, rd_ratio=1. / 16, rd_channels=None, rd_divisor=8, add_maxpool=False,
bias=True, act_layer=nn.ReLU, norm_layer=None, gate_layer='sigmoid'):
super(SEModule, self).__init__()
self.add_maxpool = add_maxpool
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.fc1 = nn.Conv2d(channels, rd_channels, kernel_size=1, bias=bias)
self.bn = norm_layer(rd_channels) if norm_layer else nn.Identity()
self.act = create_act_layer(act_layer, inplace=True)
self.fc2 = nn.Conv2d(rd_channels, channels, kernel_size=1, bias=bias)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_se = x.mean((2, 3), keepdim=True)
if self.add_maxpool:
# experimental codepath, may remove or change
x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
x_se = self.fc1(x_se)
x_se = self.act(self.bn(x_se))
x_se = self.fc2(x_se)
return x * self.gate(x_se)
SqueezeExcite = SEModule # alias
class EffectiveSEModule(nn.Module):
""" 'Effective Squeeze-Excitation
From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
"""
def __init__(self, channels, add_maxpool=False, gate_layer='hard_sigmoid', **_):
super(EffectiveSEModule, self).__init__()
self.add_maxpool = add_maxpool
self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_se = x.mean((2, 3), keepdim=True)
if self.add_maxpool:
# experimental codepath, may remove or change
x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
x_se = self.fc(x_se)
return x * self.gate(x_se)
EffectiveSqueezeExcite = EffectiveSEModule # alias
class SqueezeExciteCl(nn.Module):
""" SE Module as defined in original SE-Nets with a few additions
Additions include:
* divisor can be specified to keep channels % div == 0 (default: 8)
* reduction channels can be specified directly by arg (if rd_channels is set)
* reduction channels can be specified by float rd_ratio (default: 1/16)
* global max pooling can be added to the squeeze aggregation
* customizable activation, normalization, and gate layer
"""
def __init__(
self, channels, rd_ratio=1. / 16, rd_channels=None, rd_divisor=8,
bias=True, act_layer=nn.ReLU, gate_layer='sigmoid'):
super().__init__()
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.fc1 = nn.Linear(channels, rd_channels, bias=bias)
self.act = create_act_layer(act_layer, inplace=True)
self.fc2 = nn.Linear(rd_channels, channels, bias=bias)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_se = x.mean((1, 2), keepdims=True) # FIXME avg dim [1:n-1], don't assume 2D NHWC
x_se = self.fc1(x_se)
x_se = self.act(x_se)
x_se = self.fc2(x_se)
return x * self.gate(x_se) | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/__init__.py | from .activations import *
from .adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from .attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding
from .blur_pool import BlurPool2d
from .classifier import ClassifierHead, create_classifier, NormMlpClassifierHead
from .cond_conv2d import CondConv2d, get_condconv_initializer
from .config import is_exportable, is_scriptable, is_no_jit, use_fused_attn, \
set_exportable, set_scriptable, set_no_jit, set_layer_config, set_fused_attn
from .conv2d_same import Conv2dSame, conv2d_same
from .conv_bn_act import ConvNormAct, ConvNormActAa, ConvBnAct
from .create_act import create_act_layer, get_act_layer, get_act_fn
from .create_attn import get_attn, create_attn
from .create_conv2d import create_conv2d
from .create_norm import get_norm_layer, create_norm_layer
from .create_norm_act import get_norm_act_layer, create_norm_act_layer, get_norm_act_layer
from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn
from .evo_norm import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2,\
EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
from .fast_norm import is_fast_norm, set_fast_norm, fast_group_norm, fast_layer_norm
from .filter_response_norm import FilterResponseNormTlu2d, FilterResponseNormAct2d
from .format import Format, get_channel_dim, get_spatial_dim, nchw_to, nhwc_to
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, extend_tuple
from .inplace_abn import InplaceAbn
from .linear import Linear
from .mixed_conv2d import MixedConv2d
from .mlp import Mlp, GluMlp, GatedMlp, SwiGLU, SwiGLUPacked, ConvMlp, GlobalResponseNormMlp
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from .norm_act import BatchNormAct2d, GroupNormAct, GroupNorm1Act, LayerNormAct, LayerNormAct2d,\
SyncBatchNormAct, convert_sync_batchnorm, FrozenBatchNormAct2d, freeze_batch_norm_2d, unfreeze_batch_norm_2d
from .padding import get_padding, get_same_padding, pad_same
from .patch_dropout import PatchDropout
from .patch_embed import PatchEmbed, PatchEmbedWithSize, resample_patch_embed
from .pool2d_same import AvgPool2dSame, create_pool2d
from .pos_embed import resample_abs_pos_embed, resample_abs_pos_embed_nhwc
from .pos_embed_rel import RelPosMlp, RelPosBias, RelPosBiasTf, gen_relative_position_index, gen_relative_log_coords
from .pos_embed_sincos import pixel_freq_bands, freq_bands, build_sincos2d_pos_embed, build_fourier_pos_embed, \
build_rotary_pos_embed, apply_rot_embed, apply_rot_embed_cat, apply_rot_embed_list, apply_keep_indices_nlc, \
FourierEmbed, RotaryEmbedding, RotaryEmbeddingCat
from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite
from .selective_kernel import SelectiveKernel
from .separable_conv import SeparableConv2d, SeparableConvNormAct
from .space_to_depth import SpaceToDepthModule, SpaceToDepth, DepthToSpace
from .split_attn import SplitAttn
from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
from .test_time_pool import TestTimePoolHead, apply_test_time_pool
from .trace_utils import _assert, _float_to_int
from .weight_init import trunc_normal_, trunc_normal_tf_, variance_scaling_, lecun_normal_
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/blur_pool.py | """
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .padding import get_padding
class BlurPool2d(nn.Module):
r"""Creates a module that computes blurs and downsample a given feature map.
See :cite:`zhang2019shiftinvar` for more details.
Corresponds to the Downsample class, which does blurring and subsampling
Args:
channels = Number of input channels
filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5.
stride (int): downsampling filter stride
Returns:
torch.Tensor: the transformed tensor.
"""
def __init__(self, channels, filt_size=3, stride=2) -> None:
super(BlurPool2d, self).__init__()
assert filt_size > 1
self.channels = channels
self.filt_size = filt_size
self.stride = stride
self.padding = [get_padding(filt_size, stride, dilation=1)] * 4
coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs.astype(np.float32))
blur_filter = (coeffs[:, None] * coeffs[None, :])[None, None, :, :].repeat(self.channels, 1, 1, 1)
self.register_buffer('filt', blur_filter, persistent=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = F.pad(x, self.padding, 'reflect')
return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/config.py | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to have layers with no jit optimization (includes activations)
_NO_JIT = False
# Set to True if prefer to have activation layers with no jit optimization
# NOTE not currently used as no difference between no_jit and no_activation jit as only layers obeying
# the jit flags so far are activations. This will change as more layers are updated and/or added.
_NO_ACTIVATION_JIT = False
# Set to True if exporting a model with Same padding via ONNX
_EXPORTABLE = False
# Set to True if wanting to use torch.jit.script on a model
_SCRIPTABLE = False
# use torch.scaled_dot_product_attention where possible
_HAS_FUSED_ATTN = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if 'TIMM_FUSED_ATTN' in os.environ:
_USE_FUSED_ATTN = int(os.environ['TIMM_FUSED_ATTN'])
else:
_USE_FUSED_ATTN = 1 # 0 == off, 1 == on (for tested use), 2 == on (for experimental use)
def is_no_jit():
return _NO_JIT
class set_no_jit:
def __init__(self, mode: bool) -> None:
global _NO_JIT
self.prev = _NO_JIT
_NO_JIT = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _NO_JIT
_NO_JIT = self.prev
return False
def is_exportable():
return _EXPORTABLE
class set_exportable:
def __init__(self, mode: bool) -> None:
global _EXPORTABLE
self.prev = _EXPORTABLE
_EXPORTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _EXPORTABLE
_EXPORTABLE = self.prev
return False
def is_scriptable():
return _SCRIPTABLE
class set_scriptable:
def __init__(self, mode: bool) -> None:
global _SCRIPTABLE
self.prev = _SCRIPTABLE
_SCRIPTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
_SCRIPTABLE = self.prev
return False
class set_layer_config:
""" Layer config context manager that allows setting all layer config flags at once.
If a flag arg is None, it will not change the current value.
"""
def __init__(
self,
scriptable: Optional[bool] = None,
exportable: Optional[bool] = None,
no_jit: Optional[bool] = None,
no_activation_jit: Optional[bool] = None):
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
self.prev = _SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT
if scriptable is not None:
_SCRIPTABLE = scriptable
if exportable is not None:
_EXPORTABLE = exportable
if no_jit is not None:
_NO_JIT = no_jit
if no_activation_jit is not None:
_NO_ACTIVATION_JIT = no_activation_jit
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
_SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT = self.prev
return False
def use_fused_attn(experimental: bool = False) -> bool:
# NOTE: ONNX export cannot handle F.scaled_dot_product_attention as of pytorch 2.0
if not _HAS_FUSED_ATTN or _EXPORTABLE:
return False
if experimental:
return _USE_FUSED_ATTN > 1
return _USE_FUSED_ATTN > 0
def set_fused_attn(enable: bool = True, experimental: bool = False):
global _USE_FUSED_ATTN
if not _HAS_FUSED_ATTN:
warnings.warn('This version of pytorch does not have F.scaled_dot_product_attention, fused_attn flag ignored.')
return
if experimental and enable:
_USE_FUSED_ATTN = 2
elif enable:
_USE_FUSED_ATTN = 1
else:
_USE_FUSED_ATTN = 0
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/linear.py | """ Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting
weight & bias to input.dtype to work around an issue w/ torch.addmm in this use case.
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
if torch.jit.is_scripting():
bias = self.bias.to(dtype=input.dtype) if self.bias is not None else None
return F.linear(input, self.weight.to(dtype=input.dtype), bias=bias)
else:
return F.linear(input, self.weight, self.bias)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_attn.py | """ Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
def get_attn(attn_type):
if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network architecture blocks in addition to existing convolutions.
if attn_type == 'se':
module_cls = SEModule
elif attn_type == 'ese':
module_cls = EffectiveSEModule
elif attn_type == 'eca':
module_cls = EcaModule
elif attn_type == 'ecam':
module_cls = partial(EcaModule, use_mlp=True)
elif attn_type == 'ceca':
module_cls = CecaModule
elif attn_type == 'ge':
module_cls = GatherExcite
elif attn_type == 'gc':
module_cls = GlobalContext
elif attn_type == 'gca':
module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
elif attn_type == 'cbam':
module_cls = CbamModule
elif attn_type == 'lcbam':
module_cls = LightCbamModule
# Attention / attention-like modules w/ significant params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'sk':
module_cls = SelectiveKernel
elif attn_type == 'splat':
module_cls = SplitAttn
# Self-attention / attention-like modules w/ significant compute and/or params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'lambda':
return LambdaLayer
elif attn_type == 'bottleneck':
return BottleneckAttn
elif attn_type == 'halo':
return HaloAttn
elif attn_type == 'nl':
module_cls = NonLocalAttn
elif attn_type == 'bat':
module_cls = BatNonLocalAttn
# Woops!
else:
assert False, "Invalid attn module (%s)" % attn_type
elif isinstance(attn_type, bool):
if attn_type:
module_cls = SEModule
else:
module_cls = attn_type
return module_cls
def create_attn(attn_type, channels, **kwargs):
module_cls = get_attn(attn_type)
if module_cls is not None:
# NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
return module_cls(channels, **kwargs)
return None
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pos_embed_sincos.py | """ Sin-cos, fourier, rotary position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import List, Tuple, Optional, Union
import torch
from torch import nn as nn
from .trace_utils import _assert
def pixel_freq_bands(
num_bands: int,
max_freq: float = 224.,
linear_bands: bool = True,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
):
if linear_bands:
bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=dtype, device=device)
else:
bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=dtype, device=device)
return bands * torch.pi
def freq_bands(
num_bands: int,
temperature: float = 10000.,
step: int = 2,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
) -> torch.Tensor:
bands = 1. / (temperature ** (torch.arange(0, num_bands, step, dtype=dtype, device=device) / num_bands))
return bands
def build_sincos2d_pos_embed(
feat_shape: List[int],
dim: int = 64,
temperature: float = 10000.,
reverse_coord: bool = False,
interleave_sin_cos: bool = False,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None
) -> torch.Tensor:
"""
Args:
feat_shape:
dim:
temperature:
reverse_coord: stack grid order W, H instead of H, W
interleave_sin_cos: sin, cos, sin, cos stack instead of sin, sin, cos, cos
dtype:
device:
Returns:
"""
assert dim % 4 == 0, 'Embed dimension must be divisible by 4 for sin-cos 2D position embedding'
pos_dim = dim // 4
bands = freq_bands(pos_dim, temperature=temperature, step=1, dtype=dtype, device=device)
if reverse_coord:
feat_shape = feat_shape[::-1] # stack W, H instead of H, W
grid = torch.stack(torch.meshgrid(
[torch.arange(s, device=device, dtype=dtype) for s in feat_shape])).flatten(1).transpose(0, 1)
pos2 = grid.unsqueeze(-1) * bands.unsqueeze(0)
# FIXME add support for unflattened spatial dim?
stack_dim = 2 if interleave_sin_cos else 1 # stack sin, cos, sin, cos instead of sin sin cos cos
pos_emb = torch.stack([torch.sin(pos2), torch.cos(pos2)], dim=stack_dim).flatten(1)
return pos_emb
def build_fourier_pos_embed(
feat_shape: List[int],
bands: Optional[torch.Tensor] = None,
num_bands: int = 64,
max_res: int = 224,
temperature: float = 10000.,
linear_bands: bool = False,
include_grid: bool = False,
in_pixels: bool = True,
ref_feat_shape: Optional[List[int]] = None,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
) -> List[torch.Tensor]:
"""
Args:
feat_shape: Feature shape for embedding.
bands: Pre-calculated frequency bands.
num_bands: Number of frequency bands (determines output dim).
max_res: Maximum resolution for pixel based freq.
temperature: Temperature for non-pixel freq.
linear_bands: Linear band spacing for pixel based freq.
include_grid: Include the spatial grid in output.
in_pixels: Output in pixel freq.
ref_feat_shape: Reference feature shape for resize / fine-tune.
dtype: Output dtype.
device: Output device.
Returns:
"""
if bands is None:
if in_pixels:
bands = pixel_freq_bands(
num_bands,
float(max_res),
linear_bands=linear_bands,
dtype=dtype,
device=device,
)
else:
bands = freq_bands(
num_bands,
temperature=temperature,
step=1,
dtype=dtype,
device=device,
)
else:
if device is None:
device = bands.device
if dtype is None:
dtype = bands.dtype
if in_pixels:
t = [torch.linspace(-1., 1., steps=s, device=device, dtype=dtype) for s in feat_shape]
else:
t = [torch.arange(s, device=device, dtype=dtype) for s in feat_shape]
if ref_feat_shape is not None:
# eva's scheme for resizing rope embeddings (ref shape = pretrain)
t = [x / f * r for x, f, r in zip(t, feat_shape, ref_feat_shape)]
grid = torch.stack(torch.meshgrid(t), dim=-1)
grid = grid.unsqueeze(-1)
pos = grid * bands
pos_sin, pos_cos = pos.sin(), pos.cos()
out = [grid, pos_sin, pos_cos] if include_grid else [pos_sin, pos_cos]
return out
class FourierEmbed(nn.Module):
def __init__(
self,
max_res: int = 224,
num_bands: int = 64,
concat_grid=True,
keep_spatial=False,
):
super().__init__()
self.max_res = max_res
self.num_bands = num_bands
self.concat_grid = concat_grid
self.keep_spatial = keep_spatial
self.register_buffer(
'bands',
pixel_freq_bands(max_res, num_bands),
persistent=False,
)
def forward(self, x):
B, C = x.shape[:2]
feat_shape = x.shape[2:]
emb = build_fourier_pos_embed(
feat_shape,
self.bands,
include_grid=self.concat_grid,
dtype=x.dtype,
device=x.device,
)
emb = torch.cat(emb, dim=-1)
emb = emb.transpose(-1, -2).flatten(len(feat_shape))
batch_expand = (B,) + (-1,) * (x.ndim - 1)
# FIXME support nD
if self.keep_spatial:
x = torch.cat([x, emb.unsqueeze(0).expand(batch_expand).permute(0, 3, 1, 2)], dim=1)
else:
x = torch.cat([x.permute(0, 2, 3, 1), emb.unsqueeze(0).expand(batch_expand)], dim=-1)
x = x.reshape(B, feat_shape.numel(), -1)
return x
def rot(x):
return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape)
def apply_rot_embed(x: torch.Tensor, sin_emb, cos_emb):
if sin_emb.ndim == 3:
return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
return x * cos_emb + rot(x) * sin_emb
def apply_rot_embed_list(x: List[torch.Tensor], sin_emb, cos_emb):
if isinstance(x, torch.Tensor):
x = [x]
return [t * cos_emb + rot(t) * sin_emb for t in x]
def apply_rot_embed_cat(x: torch.Tensor, emb):
sin_emb, cos_emb = emb.tensor_split(2, -1)
if sin_emb.ndim == 3:
return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
return x * cos_emb + rot(x) * sin_emb
def apply_keep_indices_nlc(x, pos_embed, keep_indices):
pos_embed = pos_embed.unsqueeze(0).expand(x.shape[0], -1, -1)
pos_embed = pos_embed.gather(1, keep_indices.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1]))
return pos_embed
def build_rotary_pos_embed(
feat_shape: List[int],
bands: Optional[torch.Tensor] = None,
dim: int = 64,
max_res: int = 224,
temperature: float = 10000.,
linear_bands: bool = False,
in_pixels: bool = True,
ref_feat_shape: Optional[List[int]] = None,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
):
"""
Args:
feat_shape: Spatial shape of the target tensor for embedding.
bands: Optional pre-generated frequency bands
dim: Output dimension of embedding tensor.
max_res: Maximum resolution for pixel mode.
temperature: Temperature (inv freq) for non-pixel mode
linear_bands: Linearly (instead of log) spaced bands for pixel mode
in_pixels: Pixel vs language (inv freq) mode.
dtype: Output dtype.
device: Output device.
Returns:
"""
sin_emb, cos_emb = build_fourier_pos_embed(
feat_shape,
bands=bands,
num_bands=dim // 4,
max_res=max_res,
temperature=temperature,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=ref_feat_shape,
device=device,
dtype=dtype,
)
num_spatial_dim = 1
# this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks
for x in feat_shape:
num_spatial_dim *= x
sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
return sin_emb, cos_emb
class RotaryEmbedding(nn.Module):
""" Rotary position embedding
NOTE: This is my initial attempt at impl rotary embedding for spatial use, it has not
been well tested, and will likely change. It will be moved to its own file.
The following impl/resources were referenced for this impl:
* https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
* https://blog.eleuther.ai/rotary-embeddings/
"""
def __init__(
self,
dim,
max_res=224,
temperature=10000,
in_pixels=True,
linear_bands: bool = False,
feat_shape: Optional[List[int]] = None,
ref_feat_shape: Optional[List[int]] = None,
):
super().__init__()
self.dim = dim
self.max_res = max_res
self.temperature = temperature
self.in_pixels = in_pixels
self.feat_shape = feat_shape
self.ref_feat_shape = ref_feat_shape
if feat_shape is None:
# only cache bands
if in_pixels:
bands = pixel_freq_bands(
dim // 4,
float(max_res),
linear_bands=linear_bands,
)
else:
bands = freq_bands(
dim // 4,
temperature=temperature,
step=1,
)
print(bands)
self.register_buffer(
'bands',
bands,
persistent=False,
)
self.pos_embed_sin = None
self.pos_embed_cos = None
else:
# cache full sin/cos embeddings if shape provided up front
emb_sin, emb_cos = build_rotary_pos_embed(
feat_shape=feat_shape,
dim=dim,
max_res=max_res,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
self.bands = None
self.register_buffer(
'pos_embed_sin',
emb_sin,
persistent=False,
)
self.register_buffer(
'pos_embed_cos',
emb_cos,
persistent=False,
)
def get_embed(self, shape: Optional[List[int]] = None):
if self.bands is not None:
# rebuild embeddings every call, use if target shape changes
assert shape is not None
return build_rotary_pos_embed(
shape,
self.bands,
in_pixels=self.in_pixels,
)
else:
return self.pos_embed_sin, self.pos_embed_cos
def forward(self, x):
# assuming channel-first tensor where spatial dim are >= 2
sin_emb, cos_emb = self.get_embed(x.shape[2:])
return apply_rot_embed(x, sin_emb, cos_emb)
class RotaryEmbeddingCat(nn.Module):
""" Rotary position embedding w/ concatenatd sin & cos
The following impl/resources were referenced for this impl:
* https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
* https://blog.eleuther.ai/rotary-embeddings/
"""
def __init__(
self,
dim,
max_res=224,
temperature=10000,
in_pixels=True,
linear_bands: bool = False,
feat_shape: Optional[List[int]] = None,
ref_feat_shape: Optional[List[int]] = None,
):
super().__init__()
self.dim = dim
self.max_res = max_res
self.temperature = temperature
self.in_pixels = in_pixels
self.feat_shape = feat_shape
self.ref_feat_shape = ref_feat_shape
if feat_shape is None:
# only cache bands
if in_pixels:
bands = pixel_freq_bands(
dim // 4,
float(max_res),
linear_bands=linear_bands,
)
else:
bands = freq_bands(
dim // 4,
temperature=temperature,
step=1,
)
print(bands)
self.register_buffer(
'bands',
bands,
persistent=False,
)
self.embed = None
else:
# cache full sin/cos embeddings if shape provided up front
embeds = build_rotary_pos_embed(
feat_shape=feat_shape,
dim=dim,
max_res=max_res,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
self.bands = None
self.register_buffer(
'pos_embed',
torch.cat(embeds, -1),
persistent=False,
)
def get_embed(self, shape: Optional[List[int]] = None):
if self.bands is not None:
# rebuild embeddings every call, use if target shape changes
_assert(shape is not None, 'valid shape needed')
embeds = build_rotary_pos_embed(
shape,
self.bands,
in_pixels=self.in_pixels,
)
return torch.cat(embeds, -1)
else:
return self.pos_embed
def forward(self, x):
# assuming channel-first tensor where spatial dim are >= 2
pos_embed = self.get_embed(x.shape[2:])
return apply_rot_embed_cat(x, pos_embed)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_act.py | """ Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Union, Callable, Type
from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit
# PyTorch has an optimized, native 'silu' (aka 'swish') operator as of PyTorch 1.7.
# Also hardsigmoid, hardswish, and soon mish. This code will use native version if present.
# Eventually, the custom SiLU, Mish, Hard*, layers will be removed and only native variants will be used.
_has_silu = 'silu' in dir(torch.nn.functional)
_has_hardswish = 'hardswish' in dir(torch.nn.functional)
_has_hardsigmoid = 'hardsigmoid' in dir(torch.nn.functional)
_has_mish = 'mish' in dir(torch.nn.functional)
_ACT_FN_DEFAULT = dict(
silu=F.silu if _has_silu else swish,
swish=F.silu if _has_silu else swish,
mish=F.mish if _has_mish else mish,
relu=F.relu,
relu6=F.relu6,
leaky_relu=F.leaky_relu,
elu=F.elu,
celu=F.celu,
selu=F.selu,
gelu=gelu,
gelu_tanh=gelu_tanh,
sigmoid=sigmoid,
tanh=tanh,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid,
hard_swish=F.hardswish if _has_hardswish else hard_swish,
hard_mish=hard_mish,
)
_ACT_FN_JIT = dict(
silu=F.silu if _has_silu else swish_jit,
swish=F.silu if _has_silu else swish_jit,
mish=F.mish if _has_mish else mish_jit,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_jit,
hard_swish=F.hardswish if _has_hardswish else hard_swish_jit,
hard_mish=hard_mish_jit
)
_ACT_FN_ME = dict(
silu=F.silu if _has_silu else swish_me,
swish=F.silu if _has_silu else swish_me,
mish=F.mish if _has_mish else mish_me,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_me,
hard_swish=F.hardswish if _has_hardswish else hard_swish_me,
hard_mish=hard_mish_me,
)
_ACT_FNS = (_ACT_FN_ME, _ACT_FN_JIT, _ACT_FN_DEFAULT)
for a in _ACT_FNS:
a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
a.setdefault('hardswish', a.get('hard_swish'))
_ACT_LAYER_DEFAULT = dict(
silu=nn.SiLU if _has_silu else Swish,
swish=nn.SiLU if _has_silu else Swish,
mish=nn.Mish if _has_mish else Mish,
relu=nn.ReLU,
relu6=nn.ReLU6,
leaky_relu=nn.LeakyReLU,
elu=nn.ELU,
prelu=PReLU,
celu=nn.CELU,
selu=nn.SELU,
gelu=GELU,
gelu_tanh=GELUTanh,
sigmoid=Sigmoid,
tanh=Tanh,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoid,
hard_swish=nn.Hardswish if _has_hardswish else HardSwish,
hard_mish=HardMish,
identity=nn.Identity,
)
_ACT_LAYER_JIT = dict(
silu=nn.SiLU if _has_silu else SwishJit,
swish=nn.SiLU if _has_silu else SwishJit,
mish=nn.Mish if _has_mish else MishJit,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidJit,
hard_swish=nn.Hardswish if _has_hardswish else HardSwishJit,
hard_mish=HardMishJit
)
_ACT_LAYER_ME = dict(
silu=nn.SiLU if _has_silu else SwishMe,
swish=nn.SiLU if _has_silu else SwishMe,
mish=nn.Mish if _has_mish else MishMe,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidMe,
hard_swish=nn.Hardswish if _has_hardswish else HardSwishMe,
hard_mish=HardMishMe,
)
_ACT_LAYERS = (_ACT_LAYER_ME, _ACT_LAYER_JIT, _ACT_LAYER_DEFAULT)
for a in _ACT_LAYERS:
a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
a.setdefault('hardswish', a.get('hard_swish'))
def get_act_fn(name: Union[Callable, str] = 'relu'):
""" Activation Function Factory
Fetching activation fns by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if not name:
return None
if isinstance(name, Callable):
return name
if not (is_no_jit() or is_exportable() or is_scriptable()):
# If not exporting or scripting the model, first look for a memory-efficient version with
# custom autograd, then fallback
if name in _ACT_FN_ME:
return _ACT_FN_ME[name]
if not (is_no_jit() or is_exportable()):
if name in _ACT_FN_JIT:
return _ACT_FN_JIT[name]
return _ACT_FN_DEFAULT[name]
def get_act_layer(name: Union[Type[nn.Module], str] = 'relu'):
""" Activation Layer Factory
Fetching activation layers by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if not name:
return None
if not isinstance(name, str):
# callable, module, etc
return name
if not (is_no_jit() or is_exportable() or is_scriptable()):
if name in _ACT_LAYER_ME:
return _ACT_LAYER_ME[name]
if not (is_no_jit() or is_exportable()):
if name in _ACT_LAYER_JIT:
return _ACT_LAYER_JIT[name]
return _ACT_LAYER_DEFAULT[name]
def create_act_layer(name: Union[nn.Module, str], inplace=None, **kwargs):
act_layer = get_act_layer(name)
if act_layer is None:
return None
if inplace is None:
return act_layer(**kwargs)
try:
return act_layer(inplace=inplace, **kwargs)
except TypeError:
# recover if act layer doesn't have inplace arg
return act_layer(**kwargs)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/split_attn.py | """ Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
import torch
import torch.nn.functional as F
from torch import nn
from .helpers import make_divisible
class RadixSoftmax(nn.Module):
def __init__(self, radix, cardinality):
super(RadixSoftmax, self).__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
class SplitAttn(nn.Module):
"""Split-Attention (aka Splat)
"""
def __init__(self, in_channels, out_channels=None, kernel_size=3, stride=1, padding=None,
dilation=1, groups=1, bias=False, radix=2, rd_ratio=0.25, rd_channels=None, rd_divisor=8,
act_layer=nn.ReLU, norm_layer=None, drop_layer=None, **kwargs):
super(SplitAttn, self).__init__()
out_channels = out_channels or in_channels
self.radix = radix
mid_chs = out_channels * radix
if rd_channels is None:
attn_chs = make_divisible(in_channels * radix * rd_ratio, min_value=32, divisor=rd_divisor)
else:
attn_chs = rd_channels * radix
padding = kernel_size // 2 if padding is None else padding
self.conv = nn.Conv2d(
in_channels, mid_chs, kernel_size, stride, padding, dilation,
groups=groups * radix, bias=bias, **kwargs)
self.bn0 = norm_layer(mid_chs) if norm_layer else nn.Identity()
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act0 = act_layer(inplace=True)
self.fc1 = nn.Conv2d(out_channels, attn_chs, 1, groups=groups)
self.bn1 = norm_layer(attn_chs) if norm_layer else nn.Identity()
self.act1 = act_layer(inplace=True)
self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups)
self.rsoftmax = RadixSoftmax(radix, groups)
def forward(self, x):
x = self.conv(x)
x = self.bn0(x)
x = self.drop(x)
x = self.act0(x)
B, RC, H, W = x.shape
if self.radix > 1:
x = x.reshape((B, self.radix, RC // self.radix, H, W))
x_gap = x.sum(dim=1)
else:
x_gap = x
x_gap = x_gap.mean((2, 3), keepdim=True)
x_gap = self.fc1(x_gap)
x_gap = self.bn1(x_gap)
x_gap = self.act1(x_gap)
x_attn = self.fc2(x_gap)
x_attn = self.rsoftmax(x_attn).view(B, -1, 1, 1)
if self.radix > 1:
out = (x * x_attn.reshape((B, self.radix, RC // self.radix, 1, 1))).sum(dim=1)
else:
out = x * x_attn
return out.contiguous()
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/weight_init.py | import torch
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2)
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
tensor.clamp_(min=a, max=b)
return tensor
def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
applied while sampling the normal with mean/std applied, therefore a, b args
should be adjusted to match the range of mean, std args.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
with torch.no_grad():
return _trunc_normal_(tensor, mean, std, a, b)
def trunc_normal_tf_(tensor, mean=0., std=1., a=-2., b=2.):
# type: (Tensor, float, float, float, float) -> Tensor
r"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \leq \text{mean} \leq b`.
NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
and the result is subsquently scaled and shifted by the mean and std args.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
Examples:
>>> w = torch.empty(3, 5)
>>> nn.init.trunc_normal_(w)
"""
with torch.no_grad():
_trunc_normal_(tensor, 0, 1.0, a, b)
tensor.mul_(std).add_(mean)
return tensor
def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'):
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
if mode == 'fan_in':
denom = fan_in
elif mode == 'fan_out':
denom = fan_out
elif mode == 'fan_avg':
denom = (fan_in + fan_out) / 2
variance = scale / denom
if distribution == "truncated_normal":
# constant is stddev of standard normal truncated to (-2, 2)
trunc_normal_tf_(tensor, std=math.sqrt(variance) / .87962566103423978)
elif distribution == "normal":
with torch.no_grad():
tensor.normal_(std=math.sqrt(variance))
elif distribution == "uniform":
bound = math.sqrt(3 * variance)
with torch.no_grad():
tensor.uniform_(-bound, bound)
else:
raise ValueError(f"invalid distribution {distribution}")
def lecun_normal_(tensor):
variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal')
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/separable_conv.py | """ Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class SeparableConvNormAct(nn.Module):
""" Separable Conv w/ trailing Norm and Activation
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False,
channel_multiplier=1.0, pw_kernel_size=1, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU,
apply_act=True, drop_layer=None):
super(SeparableConvNormAct, self).__init__()
self.conv_dw = create_conv2d(
in_channels, int(in_channels * channel_multiplier), kernel_size,
stride=stride, dilation=dilation, padding=padding, depthwise=True)
self.conv_pw = create_conv2d(
int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
@property
def in_channels(self):
return self.conv_dw.in_channels
@property
def out_channels(self):
return self.conv_pw.out_channels
def forward(self, x):
x = self.conv_dw(x)
x = self.conv_pw(x)
x = self.bn(x)
return x
SeparableConvBnAct = SeparableConvNormAct
class SeparableConv2d(nn.Module):
""" Separable Conv
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False,
channel_multiplier=1.0, pw_kernel_size=1):
super(SeparableConv2d, self).__init__()
self.conv_dw = create_conv2d(
in_channels, int(in_channels * channel_multiplier), kernel_size,
stride=stride, dilation=dilation, padding=padding, depthwise=True)
self.conv_pw = create_conv2d(
int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
@property
def in_channels(self):
return self.conv_dw.in_channels
@property
def out_channels(self):
return self.conv_pw.out_channels
def forward(self, x):
x = self.conv_dw(x)
x = self.conv_pw(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/evo_norm.py | """ EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {13539--13550},
publisher = {Curran Associates, Inc.},
title = {Evolving Normalization-Activation Layers},
url = {https://proceedings.neurips.cc/paper/2020/file/9d4c03631b8b0c85ae08bf05eda37d0f-Paper.pdf},
volume = {33},
year = {2020}
}
An attempt at getting decent performing EvoNorms running in PyTorch.
While faster than other PyTorch impl, still quite a ways off the built-in BatchNorm
in terms of memory usage and throughput on GPUs.
I'm testing these modules on TPU w/ PyTorch XLA. Promising start but
currently working around some issues with builtin torch/tensor.var/std. Unlike
GPU, similar train speeds for EvoNormS variants and BatchNorm.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Sequence, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer
from .trace_utils import _assert
def instance_std(x, eps: float = 1e-5):
std = x.float().var(dim=(2, 3), unbiased=False, keepdim=True).add(eps).sqrt().to(x.dtype)
return std.expand(x.shape)
def instance_std_tpu(x, eps: float = 1e-5):
std = manual_var(x, dim=(2, 3)).add(eps).sqrt()
return std.expand(x.shape)
# instance_std = instance_std_tpu
def instance_rms(x, eps: float = 1e-5):
rms = x.float().square().mean(dim=(2, 3), keepdim=True).add(eps).sqrt().to(x.dtype)
return rms.expand(x.shape)
def manual_var(x, dim: Union[int, Sequence[int]], diff_sqm: bool = False):
xm = x.mean(dim=dim, keepdim=True)
if diff_sqm:
# difference of squared mean and mean squared, faster on TPU can be less stable
var = ((x * x).mean(dim=dim, keepdim=True) - (xm * xm)).clamp(0)
else:
var = ((x - xm) * (x - xm)).mean(dim=dim, keepdim=True)
return var
def group_std(x, groups: int = 32, eps: float = 1e-5, flatten: bool = False):
B, C, H, W = x.shape
x_dtype = x.dtype
_assert(C % groups == 0, '')
if flatten:
x = x.reshape(B, groups, -1) # FIXME simpler shape causing TPU / XLA issues
std = x.float().var(dim=2, unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
else:
x = x.reshape(B, groups, C // groups, H, W)
std = x.float().var(dim=(2, 3, 4), unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
return std.expand(x.shape).reshape(B, C, H, W)
def group_std_tpu(x, groups: int = 32, eps: float = 1e-5, diff_sqm: bool = False, flatten: bool = False):
# This is a workaround for some stability / odd behaviour of .var and .std
# running on PyTorch XLA w/ TPUs. These manual var impl are producing much better results
B, C, H, W = x.shape
_assert(C % groups == 0, '')
if flatten:
x = x.reshape(B, groups, -1) # FIXME simpler shape causing TPU / XLA issues
var = manual_var(x, dim=-1, diff_sqm=diff_sqm)
else:
x = x.reshape(B, groups, C // groups, H, W)
var = manual_var(x, dim=(2, 3, 4), diff_sqm=diff_sqm)
return var.add(eps).sqrt().expand(x.shape).reshape(B, C, H, W)
#group_std = group_std_tpu # FIXME TPU temporary
def group_rms(x, groups: int = 32, eps: float = 1e-5):
B, C, H, W = x.shape
_assert(C % groups == 0, '')
x_dtype = x.dtype
x = x.reshape(B, groups, C // groups, H, W)
rms = x.float().square().mean(dim=(2, 3, 4), keepdim=True).add(eps).sqrt_().to(x_dtype)
return rms.expand(x.shape).reshape(B, C, H, W)
class EvoNorm2dB0(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-3, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.v is not None:
nn.init.ones_(self.v)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.v is not None:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
# var = manual_var(x, dim=(0, 2, 3)).squeeze()
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach() * self.momentum * (n / (n - 1)))
else:
var = self.running_var
left = var.add(self.eps).sqrt_().to(x_dtype).view(v_shape).expand_as(x)
v = self.v.to(x_dtype).view(v_shape)
right = x * v + instance_std(x, self.eps)
x = x / left.max(right)
return x * self.weight.to(x_dtype).view(v_shape) + self.bias.to(x_dtype).view(v_shape)
class EvoNorm2dB1(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
else:
var = self.running_var
var = var.to(x_dtype).view(v_shape)
left = var.add(self.eps).sqrt_()
right = (x + 1) * instance_rms(x, self.eps)
x = x / left.max(right)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dB2(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
else:
var = self.running_var
var = var.to(x_dtype).view(v_shape)
left = var.add(self.eps).sqrt_()
right = instance_rms(x, self.eps) - x
x = x / left.max(right)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS0(nn.Module):
def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.v is not None:
nn.init.ones_(self.v)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.v is not None:
v = self.v.view(v_shape).to(x_dtype)
x = x * (x * v).sigmoid() / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS0a(EvoNorm2dS0):
def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
d = group_std(x, self.groups, self.eps)
if self.v is not None:
v = self.v.view(v_shape).to(x_dtype)
x = x * (x * v).sigmoid()
x = x / d
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS1(nn.Module):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-5, **_):
super().__init__()
act_layer = act_layer or nn.SiLU
self.apply_act = apply_act # apply activation (non-linearity)
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer)
else:
self.act = nn.Identity()
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.pre_act_norm = False
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
x = self.act(x) / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS1a(EvoNorm2dS1):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = self.act(x) / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS2(nn.Module):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-5, **_):
super().__init__()
act_layer = act_layer or nn.SiLU
self.apply_act = apply_act # apply activation (non-linearity)
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer)
else:
self.act = nn.Identity()
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
x = self.act(x) / group_rms(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS2a(EvoNorm2dS2):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = self.act(x) / group_rms(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/global_context.py | """ Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer, get_act_layer
from .helpers import make_divisible
from .mlp import ConvMlp
from .norm import LayerNorm2d
class GlobalContext(nn.Module):
def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False,
rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'):
super(GlobalContext, self).__init__()
act_layer = get_act_layer(act_layer)
self.conv_attn = nn.Conv2d(channels, 1, kernel_size=1, bias=True) if use_attn else None
if rd_channels is None:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
if fuse_add:
self.mlp_add = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
else:
self.mlp_add = None
if fuse_scale:
self.mlp_scale = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
else:
self.mlp_scale = None
self.gate = create_act_layer(gate_layer)
self.init_last_zero = init_last_zero
self.reset_parameters()
def reset_parameters(self):
if self.conv_attn is not None:
nn.init.kaiming_normal_(self.conv_attn.weight, mode='fan_in', nonlinearity='relu')
if self.mlp_add is not None:
nn.init.zeros_(self.mlp_add.fc2.weight)
def forward(self, x):
B, C, H, W = x.shape
if self.conv_attn is not None:
attn = self.conv_attn(x).reshape(B, 1, H * W) # (B, 1, H * W)
attn = F.softmax(attn, dim=-1).unsqueeze(3) # (B, 1, H * W, 1)
context = x.reshape(B, C, H * W).unsqueeze(1) @ attn
context = context.view(B, C, 1, 1)
else:
context = x.mean(dim=(2, 3), keepdim=True)
if self.mlp_scale is not None:
mlp_x = self.mlp_scale(context)
x = x * self.gate(mlp_x)
if self.mlp_add is not None:
mlp_x = self.mlp_add(context)
x = x + mlp_x
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/test_time_pool.py | """ Test Time Pooling (Average-Max Pool)
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from torch import nn
import torch.nn.functional as F
from .adaptive_avgmax_pool import adaptive_avgmax_pool2d
_logger = logging.getLogger(__name__)
class TestTimePoolHead(nn.Module):
def __init__(self, base, original_pool=7):
super(TestTimePoolHead, self).__init__()
self.base = base
self.original_pool = original_pool
base_fc = self.base.get_classifier()
if isinstance(base_fc, nn.Conv2d):
self.fc = base_fc
else:
self.fc = nn.Conv2d(
self.base.num_features, self.base.num_classes, kernel_size=1, bias=True)
self.fc.weight.data.copy_(base_fc.weight.data.view(self.fc.weight.size()))
self.fc.bias.data.copy_(base_fc.bias.data.view(self.fc.bias.size()))
self.base.reset_classifier(0) # delete original fc layer
def forward(self, x):
x = self.base.forward_features(x)
x = F.avg_pool2d(x, kernel_size=self.original_pool, stride=1)
x = self.fc(x)
x = adaptive_avgmax_pool2d(x, 1)
return x.view(x.size(0), -1)
def apply_test_time_pool(model, config, use_test_size=False):
test_time_pool = False
if not hasattr(model, 'default_cfg') or not model.default_cfg:
return model, False
if use_test_size and 'test_input_size' in model.default_cfg:
df_input_size = model.default_cfg['test_input_size']
else:
df_input_size = model.default_cfg['input_size']
if config['input_size'][-1] > df_input_size[-1] and config['input_size'][-2] > df_input_size[-2]:
_logger.info('Target input size %s > pretrained default %s, using test time pooling' %
(str(config['input_size'][-2:]), str(df_input_size[-2:])))
model = TestTimePoolHead(model, original_pool=model.default_cfg['pool_size'])
test_time_pool = True
return model, test_time_pool
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/activations_jit.py | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op boundaries, thus performance is equal to or less than the non-scripted
versions if they contain in-place ops.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
@torch.jit.script
def swish_jit(x, inplace: bool = False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul(x.sigmoid())
@torch.jit.script
def mish_jit(x, _inplace: bool = False):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
"""
return x.mul(F.softplus(x).tanh())
class SwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishJit, self).__init__()
def forward(self, x):
return swish_jit(x)
class MishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(MishJit, self).__init__()
def forward(self, x):
return mish_jit(x)
@torch.jit.script
def hard_sigmoid_jit(x, inplace: bool = False):
# return F.relu6(x + 3.) / 6.
return (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
class HardSigmoidJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidJit, self).__init__()
def forward(self, x):
return hard_sigmoid_jit(x)
@torch.jit.script
def hard_swish_jit(x, inplace: bool = False):
# return x * (F.relu6(x + 3.) / 6)
return x * (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
class HardSwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishJit, self).__init__()
def forward(self, x):
return hard_swish_jit(x)
@torch.jit.script
def hard_mish_jit(x, inplace: bool = False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishJit, self).__init__()
def forward(self, x):
return hard_mish_jit(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/mlp.py | """ MLP module w/ dropout and configurable activation layer
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
from torch import nn as nn
from .grn import GlobalResponseNorm
from .helpers import to_2tuple
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class GluMlp(nn.Module):
""" MLP w/ GLU style gating
See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.Sigmoid,
norm_layer=None,
bias=True,
drop=0.,
use_conv=False,
gate_last=True,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
assert hidden_features % 2 == 0
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.chunk_dim = 1 if use_conv else -1
self.gate_last = gate_last # use second half of width for gate
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features // 2) if norm_layer is not None else nn.Identity()
self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def init_weights(self):
# override init of fc1 w/ gate portion set to weight near zero, bias=1
fc1_mid = self.fc1.bias.shape[0] // 2
nn.init.ones_(self.fc1.bias[fc1_mid:])
nn.init.normal_(self.fc1.weight[fc1_mid:], std=1e-6)
def forward(self, x):
x = self.fc1(x)
x1, x2 = x.chunk(2, dim=self.chunk_dim)
x = x1 * self.act(x2) if self.gate_last else self.act(x1) * x2
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
SwiGLUPacked = partial(GluMlp, act_layer=nn.SiLU, gate_last=False)
class SwiGLU(nn.Module):
""" SwiGLU
NOTE: GluMLP above can implement SwiGLU, but this impl has split fc1 and
better matches some other common impl which makes mapping checkpoints simpler.
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.SiLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1_g = nn.Linear(in_features, hidden_features, bias=bias[0])
self.fc1_x = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
self.drop = nn.Dropout(drop)
def init_weights(self):
# override init of fc1 w/ gate portion set to weight near zero, bias=1
nn.init.ones_(self.fc1_g.bias)
nn.init.normal_(self.fc1_g.weight, std=1e-6)
def forward(self, x):
x_gate = self.fc1_g(x)
x = self.fc1_x(x)
x = self.act(x_gate) * x
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class GatedMlp(nn.Module):
""" MLP as used in gMLP
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
gate_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
if gate_layer is not None:
assert hidden_features % 2 == 0
self.gate = gate_layer(hidden_features)
hidden_features = hidden_features // 2 # FIXME base reduction on gate property?
else:
self.gate = nn.Identity()
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.gate(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.ReLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
self.act = act_layer()
self.drop = nn.Dropout(drop)
self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])
def forward(self, x):
x = self.fc1(x)
x = self.norm(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
return x
class GlobalResponseNormMlp(nn.Module):
""" MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv)
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.grn(x)
x = self.fc2(x)
x = self.drop2(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/mixed_conv2d.py | """ PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(num_chan, num_groups):
split = [num_chan // num_groups for _ in range(num_groups)]
split[0] += num_chan - sum(split)
return split
class MixedConv2d(nn.ModuleDict):
""" Mixed Grouped Convolution
Based on MDConv and GroupedConv in MixNet impl:
https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
"""
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilation=1, depthwise=False, **kwargs):
super(MixedConv2d, self).__init__()
kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
num_groups = len(kernel_size)
in_splits = _split_channels(in_channels, num_groups)
out_splits = _split_channels(out_channels, num_groups)
self.in_channels = sum(in_splits)
self.out_channels = sum(out_splits)
for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
conv_groups = in_ch if depthwise else 1
# use add_module to keep key space clean
self.add_module(
str(idx),
create_conv2d_pad(
in_ch, out_ch, k, stride=stride,
padding=padding, dilation=dilation, groups=conv_groups, **kwargs)
)
self.splits = in_splits
def forward(self, x):
x_split = torch.split(x, self.splits, 1)
x_out = [c(x_split[i]) for i, c in enumerate(self.values())]
x = torch.cat(x_out, 1)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/cbam.py | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
import torch.nn.functional as F
from .conv_bn_act import ConvNormAct
from .create_act import create_act_layer, get_act_layer
from .helpers import make_divisible
class ChannelAttn(nn.Module):
""" Original CBAM channel attention module, currently avg + max pool variant only.
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(ChannelAttn, self).__init__()
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.fc1 = nn.Conv2d(channels, rd_channels, 1, bias=mlp_bias)
self.act = act_layer(inplace=True)
self.fc2 = nn.Conv2d(rd_channels, channels, 1, bias=mlp_bias)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_avg = self.fc2(self.act(self.fc1(x.mean((2, 3), keepdim=True))))
x_max = self.fc2(self.act(self.fc1(x.amax((2, 3), keepdim=True))))
return x * self.gate(x_avg + x_max)
class LightChannelAttn(ChannelAttn):
"""An experimental 'lightweight' that sums avg + max pool first
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightChannelAttn, self).__init__(
channels, rd_ratio, rd_channels, rd_divisor, act_layer, gate_layer, mlp_bias)
def forward(self, x):
x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * x.amax((2, 3), keepdim=True)
x_attn = self.fc2(self.act(self.fc1(x_pool)))
return x * F.sigmoid(x_attn)
class SpatialAttn(nn.Module):
""" Original CBAM spatial attention module
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(SpatialAttn, self).__init__()
self.conv = ConvNormAct(2, 1, kernel_size, apply_act=False)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_attn = torch.cat([x.mean(dim=1, keepdim=True), x.amax(dim=1, keepdim=True)], dim=1)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
class LightSpatialAttn(nn.Module):
"""An experimental 'lightweight' variant that sums avg_pool and max_pool results.
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(LightSpatialAttn, self).__init__()
self.conv = ConvNormAct(1, 1, kernel_size, apply_act=False)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_attn = 0.5 * x.mean(dim=1, keepdim=True) + 0.5 * x.amax(dim=1, keepdim=True)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
class CbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(CbamModule, self).__init__()
self.channel = ChannelAttn(
channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
self.spatial = SpatialAttn(spatial_kernel_size, gate_layer=gate_layer)
def forward(self, x):
x = self.channel(x)
x = self.spatial(x)
return x
class LightCbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightCbamModule, self).__init__()
self.channel = LightChannelAttn(
channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
self.spatial = LightSpatialAttn(spatial_kernel_size)
def forward(self, x):
x = self.channel(x)
x = self.spatial(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/patch_embed.py | """ Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on code in:
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision/tree/main/big_vision
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch import nn as nn
import torch.nn.functional as F
from .format import Format, nchw_to
from .helpers import to_2tuple
from .trace_utils import _assert
_logger = logging.getLogger(__name__)
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding
"""
output_fmt: Format
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
output_fmt: Optional[str] = None,
bias: bool = True,
strict_img_size: bool = True,
):
super().__init__()
self.patch_size = to_2tuple(patch_size)
if img_size is not None:
self.img_size = to_2tuple(img_size)
self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
self.num_patches = self.grid_size[0] * self.grid_size[1]
else:
self.img_size = None
self.grid_size = None
self.num_patches = None
if output_fmt is not None:
self.flatten = False
self.output_fmt = Format(output_fmt)
else:
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.output_fmt = Format.NCHW
self.strict_img_size = strict_img_size
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
B, C, H, W = x.shape
if self.img_size is not None:
if self.strict_img_size:
_assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
_assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
else:
_assert(
H % self.patch_size[0] == 0,
f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
)
_assert(
W % self.patch_size[1] == 0,
f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
)
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
x = self.norm(x)
return x
class PatchEmbedWithSize(PatchEmbed):
""" 2D Image to Patch Embedding
"""
output_fmt: Format
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
output_fmt: Optional[str] = None,
bias: bool = True,
):
super().__init__(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer,
flatten=flatten,
output_fmt=output_fmt,
bias=bias,
)
def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
B, C, H, W = x.shape
if self.img_size is not None:
_assert(H % self.patch_size[0] == 0, f"Input image height ({H}) must be divisible by patch size ({self.patch_size[0]}).")
_assert(W % self.patch_size[1] == 0, f"Input image width ({W}) must be divisible by patch size ({self.patch_size[1]}).")
x = self.proj(x)
grid_size = x.shape[-2:]
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
x = self.norm(x)
return x, grid_size
def resample_patch_embed(
patch_embed,
new_size: List[int],
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
"""Resample the weights of the patch embedding kernel to target resolution.
We resample the patch embedding kernel by approximately inverting the effect
of patch resizing.
Code based on:
https://github.com/google-research/big_vision/blob/b00544b81f8694488d5f36295aeb7972f3755ffe/big_vision/models/proj/flexi/vit.py
With this resizing, we can for example load a B/8 filter into a B/16 model
and, on 2x larger input image, the result will match.
Args:
patch_embed: original parameter to be resized.
new_size (tuple(int, int): target shape (height, width)-only.
interpolation (str): interpolation for resize
antialias (bool): use anti-aliasing filter in resize
verbose (bool): log operation
Returns:
Resized patch embedding kernel.
"""
import numpy as np
try:
import functorch
vmap = functorch.vmap
except ImportError:
if hasattr(torch, 'vmap'):
vmap = torch.vmap
else:
assert False, "functorch or a version of torch with vmap is required for FlexiViT resizing."
assert len(patch_embed.shape) == 4, "Four dimensions expected"
assert len(new_size) == 2, "New shape should only be hw"
old_size = patch_embed.shape[-2:]
if tuple(old_size) == tuple(new_size):
return patch_embed
if verbose:
_logger.info(f"Resize patch embedding {patch_embed.shape} to {new_size}, w/ {interpolation} interpolation.")
def resize(x_np, _new_size):
x_tf = torch.Tensor(x_np)[None, None, ...]
x_upsampled = F.interpolate(
x_tf, size=_new_size, mode=interpolation, antialias=antialias)[0, 0, ...].numpy()
return x_upsampled
def get_resize_mat(_old_size, _new_size):
mat = []
for i in range(np.prod(_old_size)):
basis_vec = np.zeros(_old_size)
basis_vec[np.unravel_index(i, _old_size)] = 1.
mat.append(resize(basis_vec, _new_size).reshape(-1))
return np.stack(mat).T
resize_mat = get_resize_mat(old_size, new_size)
resize_mat_pinv = torch.Tensor(np.linalg.pinv(resize_mat.T))
def resample_kernel(kernel):
resampled_kernel = resize_mat_pinv @ kernel.reshape(-1)
return resampled_kernel.reshape(new_size)
v_resample_kernel = vmap(vmap(resample_kernel, 0, 0), 1, 1)
return v_resample_kernel(patch_embed)
# def divs(n, m=None):
# m = m or n // 2
# if m == 1:
# return [1]
# if n % m == 0:
# return [m] + divs(n, m - 1)
# return divs(n, m - 1)
#
#
# class FlexiPatchEmbed(nn.Module):
# """ 2D Image to Patch Embedding w/ Flexible Patch sizes (FlexiViT)
# FIXME WIP
# """
# def __init__(
# self,
# img_size=240,
# patch_size=16,
# in_chans=3,
# embed_dim=768,
# base_img_size=240,
# base_patch_size=32,
# norm_layer=None,
# flatten=True,
# bias=True,
# ):
# super().__init__()
# self.img_size = to_2tuple(img_size)
# self.patch_size = to_2tuple(patch_size)
# self.num_patches = 0
#
# # full range for 240 = (5, 6, 8, 10, 12, 14, 15, 16, 20, 24, 30, 40, 48)
# self.seqhw = (6, 8, 10, 12, 14, 15, 16, 20, 24, 30)
#
# self.base_img_size = to_2tuple(base_img_size)
# self.base_patch_size = to_2tuple(base_patch_size)
# self.base_grid_size = tuple([i // p for i, p in zip(self.base_img_size, self.base_patch_size)])
# self.base_num_patches = self.base_grid_size[0] * self.base_grid_size[1]
#
# self.flatten = flatten
# self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=bias)
# self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
#
# def forward(self, x):
# B, C, H, W = x.shape
#
# if self.patch_size == self.base_patch_size:
# weight = self.proj.weight
# else:
# weight = resample_patch_embed(self.proj.weight, self.patch_size)
# patch_size = self.patch_size
# x = F.conv2d(x, weight, bias=self.proj.bias, stride=patch_size)
# if self.flatten:
# x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
# x = self.norm(x)
# return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_conv2d.py | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
""" Select a 2d convolution implementation based on arguments
Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.
Used extensively by EfficientNet, MobileNetv3 and related networks.
"""
if isinstance(kernel_size, list):
assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently
if 'groups' in kwargs:
groups = kwargs.pop('groups')
if groups == in_channels:
kwargs['depthwise'] = True
else:
assert groups == 1
# We're going to use only lists for defining the MixedConv2d kernel groups,
# ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs)
else:
depthwise = kwargs.pop('depthwise', False)
# for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0
groups = in_channels if depthwise else kwargs.pop('groups', 1)
if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
else:
m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
return m
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/cond_conv2d.py | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torch import nn as nn
from torch.nn import functional as F
from .helpers import to_2tuple
from .conv2d_same import conv2d_same
from .padding import get_padding_value
def get_condconv_initializer(initializer, num_experts, expert_shape):
def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.prod(expert_shape)
if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
weight.shape[1] != num_params):
raise (ValueError(
'CondConv variables must have shape [num_experts, num_params]'))
for i in range(num_experts):
initializer(weight[i].view(expert_shape))
return condconv_initializer
class CondConv2d(nn.Module):
""" Conditionally Parameterized Convolution
Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
https://github.com/pytorch/pytorch/issues/17983
"""
__constants__ = ['in_channels', 'out_channels', 'dynamic_padding']
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
super(CondConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = to_2tuple(kernel_size)
self.stride = to_2tuple(stride)
padding_val, is_padding_dynamic = get_padding_value(
padding, kernel_size, stride=stride, dilation=dilation)
self.dynamic_padding = is_padding_dynamic # if in forward to work with torchscript
self.padding = to_2tuple(padding_val)
self.dilation = to_2tuple(dilation)
self.groups = groups
self.num_experts = num_experts
self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight_num_param = 1
for wd in self.weight_shape:
weight_num_param *= wd
self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))
if bias:
self.bias_shape = (self.out_channels,)
self.bias = torch.nn.Parameter(torch.Tensor(self.num_experts, self.out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
init_weight = get_condconv_initializer(
partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
init_weight(self.weight)
if self.bias is not None:
fan_in = np.prod(self.weight_shape[1:])
bound = 1 / math.sqrt(fan_in)
init_bias = get_condconv_initializer(
partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
init_bias(self.bias)
def forward(self, x, routing_weights):
B, C, H, W = x.shape
weight = torch.matmul(routing_weights, self.weight)
new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight = weight.view(new_weight_shape)
bias = None
if self.bias is not None:
bias = torch.matmul(routing_weights, self.bias)
bias = bias.view(B * self.out_channels)
# move batch elements with channels so each batch element can be efficiently convolved with separate kernel
# reshape instead of view to work with channels_last input
x = x.reshape(1, B * C, H, W)
if self.dynamic_padding:
out = conv2d_same(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
else:
out = F.conv2d(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])
# Literal port (from TF definition)
# x = torch.split(x, 1, 0)
# weight = torch.split(weight, 1, 0)
# if self.bias is not None:
# bias = torch.matmul(routing_weights, self.bias)
# bias = torch.split(bias, 1, 0)
# else:
# bias = [None] * B
# out = []
# for xi, wi, bi in zip(x, weight, bias):
# wi = wi.view(*self.weight_shape)
# if bi is not None:
# bi = bi.view(*self.bias_shape)
# out.append(self.conv_fn(
# xi, wi, bi, stride=self.stride, padding=self.padding,
# dilation=self.dilation, groups=self.groups))
# out = torch.cat(out, 0)
return out
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/space_to_depth.py | import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
bs: torch.jit.Final[int]
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs) # (N, C, H//bs, bs, W//bs, bs)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs)
x = x.view(N, C * self.bs * self.bs, H // self.bs, W // self.bs) # (N, C*bs^2, H//bs, W//bs)
return x
@torch.jit.script
class SpaceToDepthJit:
def __call__(self, x: torch.Tensor):
# assuming hard-coded that block_size==4 for acceleration
N, C, H, W = x.size()
x = x.view(N, C, H // 4, 4, W // 4, 4) # (N, C, H//bs, bs, W//bs, bs)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs)
x = x.view(N, C * 16, H // 4, W // 4) # (N, C*bs^2, H//bs, W//bs)
return x
class SpaceToDepthModule(nn.Module):
def __init__(self, no_jit=False):
super().__init__()
if not no_jit:
self.op = SpaceToDepthJit()
else:
self.op = SpaceToDepth()
def forward(self, x):
return self.op(x)
class DepthToSpace(nn.Module):
def __init__(self, block_size):
super().__init__()
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, self.bs, self.bs, C // (self.bs ** 2), H, W) # (N, bs, bs, C//bs^2, H, W)
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # (N, C//bs^2, H, bs, W, bs)
x = x.view(N, C // (self.bs ** 2), H * self.bs, W * self.bs) # (N, C//bs^2, H * bs, W * bs)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/norm_act.py | """ Normalization + Activation Layers
Provides Norm+Act fns for standard PyTorch norm layers such as
* BatchNorm
* GroupNorm
* LayerNorm
This allows swapping with alternative layers that are natively both norm + act such as
* EvoNorm (evo_norm.py)
* FilterResponseNorm (filter_response_norm.py)
* InplaceABN (inplace_abn.py)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import Union, List, Optional, Any
import torch
from torch import nn as nn
from torch.nn import functional as F
from torchvision.ops.misc import FrozenBatchNorm2d
from .create_act import get_act_layer
from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm
from .trace_utils import _assert
def _create_act(act_layer, act_kwargs=None, inplace=False, apply_act=True):
act_layer = get_act_layer(act_layer) # string -> nn.Module
act_kwargs = act_kwargs or {}
if act_layer is not None and apply_act:
if inplace:
act_kwargs['inplace'] = inplace
act = act_layer(**act_kwargs)
else:
act = nn.Identity()
return act
class BatchNormAct2d(nn.BatchNorm2d):
"""BatchNorm + Activation
This module performs BatchNorm + Activation in a manner that will remain backwards
compatible with weights trained with separate bn, act. This is why we inherit from BN
instead of composing it as a .bn member.
"""
def __init__(
self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
device=None,
dtype=None,
):
try:
factory_kwargs = {'device': device, 'dtype': dtype}
super(BatchNormAct2d, self).__init__(
num_features,
eps=eps,
momentum=momentum,
affine=affine,
track_running_stats=track_running_stats,
**factory_kwargs,
)
except TypeError:
# NOTE for backwards compat with old PyTorch w/o factory device/dtype support
super(BatchNormAct2d, self).__init__(
num_features,
eps=eps,
momentum=momentum,
affine=affine,
track_running_stats=track_running_stats,
)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
def forward(self, x):
# cut & paste of torch.nn.BatchNorm2d.forward impl to avoid issues with torchscript and tracing
_assert(x.ndim == 4, f'expected 4D input (got {x.ndim}D input)')
# exponential_average_factor is set to self.momentum
# (when it is available) only so that it gets updated
# in ONNX graph when this node is exported to ONNX.
if self.momentum is None:
exponential_average_factor = 0.0
else:
exponential_average_factor = self.momentum
if self.training and self.track_running_stats:
# TODO: if statement only here to tell the jit to skip emitting this when it is None
if self.num_batches_tracked is not None: # type: ignore[has-type]
self.num_batches_tracked.add_(1) # type: ignore[has-type]
if self.momentum is None: # use cumulative moving average
exponential_average_factor = 1.0 / float(self.num_batches_tracked)
else: # use exponential moving average
exponential_average_factor = self.momentum
r"""
Decide whether the mini-batch stats should be used for normalization rather than the buffers.
Mini-batch stats are used in training mode, and in eval mode when buffers are None.
"""
if self.training:
bn_training = True
else:
bn_training = (self.running_mean is None) and (self.running_var is None)
r"""
Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
used for normalization (i.e. in eval mode when buffers are not None).
"""
x = F.batch_norm(
x,
# If buffers are not to be tracked, ensure that they won't be updated
self.running_mean if not self.training or self.track_running_stats else None,
self.running_var if not self.training or self.track_running_stats else None,
self.weight,
self.bias,
bn_training,
exponential_average_factor,
self.eps,
)
x = self.drop(x)
x = self.act(x)
return x
class SyncBatchNormAct(nn.SyncBatchNorm):
# Thanks to Selim Seferbekov (https://github.com/rwightman/pytorch-image-models/issues/1254)
# This is a quick workaround to support SyncBatchNorm for timm BatchNormAct2d layers
# but ONLY when used in conjunction with the timm conversion function below.
# Do not create this module directly or use the PyTorch conversion function.
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = super().forward(x) # SyncBN doesn't work with torchscript anyways, so this is fine
if hasattr(self, "drop"):
x = self.drop(x)
if hasattr(self, "act"):
x = self.act(x)
return x
def convert_sync_batchnorm(module, process_group=None):
# convert both BatchNorm and BatchNormAct layers to Synchronized variants
module_output = module
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
if isinstance(module, BatchNormAct2d):
# convert timm norm + act layer
module_output = SyncBatchNormAct(
module.num_features,
module.eps,
module.momentum,
module.affine,
module.track_running_stats,
process_group=process_group,
)
# set act and drop attr from the original module
module_output.act = module.act
module_output.drop = module.drop
else:
# convert standard BatchNorm layers
module_output = torch.nn.SyncBatchNorm(
module.num_features,
module.eps,
module.momentum,
module.affine,
module.track_running_stats,
process_group,
)
if module.affine:
with torch.no_grad():
module_output.weight = module.weight
module_output.bias = module.bias
module_output.running_mean = module.running_mean
module_output.running_var = module.running_var
module_output.num_batches_tracked = module.num_batches_tracked
if hasattr(module, "qconfig"):
module_output.qconfig = module.qconfig
for name, child in module.named_children():
module_output.add_module(name, convert_sync_batchnorm(child, process_group))
del module
return module_output
class FrozenBatchNormAct2d(torch.nn.Module):
"""
BatchNormAct2d where the batch statistics and the affine parameters are fixed
Args:
num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)``
eps (float): a value added to the denominator for numerical stability. Default: 1e-5
"""
def __init__(
self,
num_features: int,
eps: float = 1e-5,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super().__init__()
self.eps = eps
self.register_buffer("weight", torch.ones(num_features))
self.register_buffer("bias", torch.zeros(num_features))
self.register_buffer("running_mean", torch.zeros(num_features))
self.register_buffer("running_var", torch.ones(num_features))
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
def _load_from_state_dict(
self,
state_dict: dict,
prefix: str,
local_metadata: dict,
strict: bool,
missing_keys: List[str],
unexpected_keys: List[str],
error_msgs: List[str],
):
num_batches_tracked_key = prefix + "num_batches_tracked"
if num_batches_tracked_key in state_dict:
del state_dict[num_batches_tracked_key]
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# move reshapes to the beginning
# to make it fuser-friendly
w = self.weight.reshape(1, -1, 1, 1)
b = self.bias.reshape(1, -1, 1, 1)
rv = self.running_var.reshape(1, -1, 1, 1)
rm = self.running_mean.reshape(1, -1, 1, 1)
scale = w * (rv + self.eps).rsqrt()
bias = b - rm * scale
x = x * scale + bias
x = self.act(self.drop(x))
return x
def __repr__(self) -> str:
return f"{self.__class__.__name__}({self.weight.shape[0]}, eps={self.eps}, act={self.act})"
def freeze_batch_norm_2d(module):
"""
Converts all `BatchNorm2d` and `SyncBatchNorm` or `BatchNormAct2d` and `SyncBatchNormAct2d` layers
of provided module into `FrozenBatchNorm2d` or `FrozenBatchNormAct2d` respectively.
Args:
module (torch.nn.Module): Any PyTorch module.
Returns:
torch.nn.Module: Resulting module
Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
"""
res = module
if isinstance(module, (BatchNormAct2d, SyncBatchNormAct)):
res = FrozenBatchNormAct2d(module.num_features)
res.num_features = module.num_features
res.affine = module.affine
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
res.drop = module.drop
res.act = module.act
elif isinstance(module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
res = FrozenBatchNorm2d(module.num_features)
res.num_features = module.num_features
res.affine = module.affine
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
else:
for name, child in module.named_children():
new_child = freeze_batch_norm_2d(child)
if new_child is not child:
res.add_module(name, new_child)
return res
def unfreeze_batch_norm_2d(module):
"""
Converts all `FrozenBatchNorm2d` layers of provided module into `BatchNorm2d`. If `module` is itself and instance
of `FrozenBatchNorm2d`, it is converted into `BatchNorm2d` and returned. Otherwise, the module is walked
recursively and submodules are converted in place.
Args:
module (torch.nn.Module): Any PyTorch module.
Returns:
torch.nn.Module: Resulting module
Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
"""
res = module
if isinstance(module, FrozenBatchNormAct2d):
res = BatchNormAct2d(module.num_features)
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
res.drop = module.drop
res.act = module.act
elif isinstance(module, FrozenBatchNorm2d):
res = torch.nn.BatchNorm2d(module.num_features)
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
else:
for name, child in module.named_children():
new_child = unfreeze_batch_norm_2d(child)
if new_child is not child:
res.add_module(name, new_child)
return res
def _num_groups(num_channels, num_groups, group_size):
if group_size:
assert num_channels % group_size == 0
return num_channels // group_size
return num_groups
class GroupNormAct(nn.GroupNorm):
# NOTE num_channel and num_groups order flipped for easier layer swaps / binding of fixed args
def __init__(
self,
num_channels,
num_groups=32,
eps=1e-5,
affine=True,
group_size=None,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(GroupNormAct, self).__init__(
_num_groups(num_channels, num_groups, group_size),
num_channels,
eps=eps,
affine=affine,
)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
if self._fast_norm:
x = fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
x = self.drop(x)
x = self.act(x)
return x
class GroupNorm1Act(nn.GroupNorm):
def __init__(
self,
num_channels,
eps=1e-5,
affine=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(GroupNorm1Act, self).__init__(1, num_channels, eps=eps, affine=affine)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
if self._fast_norm:
x = fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
x = self.drop(x)
x = self.act(x)
return x
class LayerNormAct(nn.LayerNorm):
def __init__(
self,
normalization_shape: Union[int, List[int], torch.Size],
eps=1e-5,
affine=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(LayerNormAct, self).__init__(normalization_shape, eps=eps, elementwise_affine=affine)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
act_layer = get_act_layer(act_layer) # string -> nn.Module
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = self.drop(x)
x = self.act(x)
return x
class LayerNormAct2d(nn.LayerNorm):
def __init__(
self,
num_channels,
eps=1e-5,
affine=True,
apply_act=True,
act_layer=nn.ReLU,
act_kwargs=None,
inplace=True,
drop_layer=None,
):
super(LayerNormAct2d, self).__init__(num_channels, eps=eps, elementwise_affine=affine)
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act = _create_act(act_layer, act_kwargs=act_kwargs, inplace=inplace, apply_act=apply_act)
self._fast_norm = is_fast_norm()
def forward(self, x):
x = x.permute(0, 2, 3, 1)
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = x.permute(0, 3, 1, 2)
x = self.drop(x)
x = self.act(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pos_embed_rel.py | """ Relative position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .mlp import Mlp
from .weight_init import trunc_normal_
def gen_relative_position_index(
q_size: Tuple[int, int],
k_size: Optional[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
if k_size is None:
coords = torch.stack(
torch.meshgrid([
torch.arange(q_size[0]),
torch.arange(q_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) # Qh*Qw, Kh*Kw, 2
num_relative_distance = (2 * q_size[0] - 1) * (2 * q_size[1] - 1) + 3
else:
# FIXME different q vs k sizes is a WIP, need to better offset the two grids?
q_coords = torch.stack(
torch.meshgrid([
torch.arange(q_size[0]),
torch.arange(q_size[1])
])
).flatten(1) # 2, Wh, Ww
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) # Qh*Qw, Kh*Kw, 2
# relative_coords[:, :, 0] += max(q_size[0], k_size[0]) - 1 # shift to start from 0
# relative_coords[:, :, 1] += max(q_size[1], k_size[1]) - 1
# relative_coords[:, :, 0] *= k_size[1] + q_size[1] - 1
# relative_position_index = relative_coords.sum(-1) # Qh*Qw, Kh*Kw
num_relative_distance = (q_size[0] + k_size[0] - 1) * (q_size[1] + q_size[1] - 1) + 3
_, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0)
if class_token:
# 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
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
return relative_position_index.contiguous()
class RelPosBias(nn.Module):
""" Relative Position Bias
Adapted from Swin-V1 relative position bias impl, modularized.
"""
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.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,)
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=prefix_tokens > 0).view(-1),
persistent=False,
)
self.init_weights()
def init_weights(self):
trunc_normal_(self.relative_position_bias_table, std=.02)
def get_bias(self) -> torch.Tensor:
relative_position_bias = self.relative_position_bias_table[self.relative_position_index]
# win_h * win_w, win_h * win_w, num_heads
relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1)
return relative_position_bias.unsqueeze(0).contiguous()
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
return attn + self.get_bias()
def gen_relative_log_coords(
win_size: Tuple[int, int],
pretrained_win_size: Tuple[int, int] = (0, 0),
mode='swin',
):
assert mode in ('swin', 'cr')
# as per official swin-v2 impl, supporting timm specific 'cr' 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]))
relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous() # 2*Wh-1, 2*Ww-1, 2
if mode == 'swin':
if pretrained_win_size[0] > 0:
relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1)
relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1)
else:
relative_coords_table[:, :, 0] /= (win_size[0] - 1)
relative_coords_table[:, :, 1] /= (win_size[1] - 1)
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
1.0 + relative_coords_table.abs()) / math.log2(8)
else:
# mode == 'cr'
relative_coords_table = torch.sign(relative_coords_table) * torch.log(
1.0 + relative_coords_table.abs())
return relative_coords_table
class RelPosMlp(nn.Module):
""" Log-Coordinate Relative Position MLP
Based on ideas presented in Swin-V2 paper (https://arxiv.org/abs/2111.09883)
This impl covers the 'swin' implementation as well as two timm specific modes ('cr', and 'rw')
"""
def __init__(
self,
window_size,
num_heads=8,
hidden_dim=128,
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.prefix_tokens = prefix_tokens
self.num_heads = num_heads
self.bias_shape = (self.window_area,) * 2 + (num_heads,)
if mode == 'swin':
self.bias_act = nn.Sigmoid()
self.bias_gain = 16
mlp_bias = (True, False)
else:
self.bias_act = nn.Identity()
self.bias_gain = None
mlp_bias = True
self.mlp = Mlp(
2, # x, y
hidden_features=hidden_dim,
out_features=num_heads,
act_layer=nn.ReLU,
bias=mlp_bias,
drop=(0.125, 0.)
)
self.register_buffer(
"relative_position_index",
gen_relative_position_index(window_size).view(-1),
persistent=False)
# get relative_coords_table
self.register_buffer(
"rel_coords_log",
gen_relative_log_coords(window_size, pretrained_window_size, mode=mode),
persistent=False)
def get_bias(self) -> torch.Tensor:
relative_position_bias = self.mlp(self.rel_coords_log)
if self.relative_position_index is not None:
relative_position_bias = relative_position_bias.view(-1, self.num_heads)[self.relative_position_index]
relative_position_bias = relative_position_bias.view(self.bias_shape)
relative_position_bias = relative_position_bias.permute(2, 0, 1)
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):
return attn + self.get_bias()
def generate_lookup_tensor(
length: int,
max_relative_position: Optional[int] = None,
):
"""Generate a one_hot lookup tensor to reindex embeddings along one dimension.
Args:
length: the length to reindex to.
max_relative_position: the maximum relative position to consider.
Relative position embeddings for distances above this threshold
are zeroed out.
Returns:
a lookup Tensor of size [length, length, vocab_size] that satisfies
ret[n,m,v] = 1{m - n + max_relative_position = v}.
"""
if max_relative_position is None:
max_relative_position = length - 1
# Return the cached lookup tensor, otherwise compute it and cache it.
vocab_size = 2 * max_relative_position + 1
ret = torch.zeros(length, length, vocab_size)
for i in range(length):
for x in range(length):
v = x - i + max_relative_position
if abs(x - i) > max_relative_position:
continue
ret[i, x, v] = 1
return ret
def reindex_2d_einsum_lookup(
relative_position_tensor,
height: int,
width: int,
height_lookup: torch.Tensor,
width_lookup: torch.Tensor,
) -> torch.Tensor:
"""Reindex 2d relative position bias with 2 independent einsum lookups.
Adapted from:
https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py
Args:
relative_position_tensor: tensor of shape
[..., vocab_height, vocab_width, ...].
height: height to reindex to.
width: width to reindex to.
height_lookup: one-hot height lookup
width_lookup: one-hot width lookup
Returns:
reindexed_tensor: a Tensor of shape
[..., height * width, height * width, ...]
"""
reindexed_tensor = torch.einsum('nhw,ixh->nixw', relative_position_tensor, height_lookup)
reindexed_tensor = torch.einsum('nixw,jyw->nijxy', reindexed_tensor, width_lookup)
area = height * width
return reindexed_tensor.reshape(relative_position_tensor.shape[0], area, area)
class RelPosBiasTf(nn.Module):
""" Relative Position Bias Impl (Compatible with Tensorflow MaxViT models)
Adapted from:
https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py
"""
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.num_heads = num_heads
vocab_height = 2 * window_size[0] - 1
vocab_width = 2 * window_size[1] - 1
self.bias_shape = (self.num_heads, vocab_height, vocab_width)
self.relative_position_bias_table = nn.Parameter(torch.zeros(self.bias_shape))
self.register_buffer('height_lookup', generate_lookup_tensor(window_size[0]), persistent=False)
self.register_buffer('width_lookup', generate_lookup_tensor(window_size[1]), persistent=False)
self.init_weights()
def init_weights(self):
nn.init.normal_(self.relative_position_bias_table, std=.02)
def get_bias(self) -> torch.Tensor:
# FIXME change to not use one-hot/einsum?
return reindex_2d_einsum_lookup(
self.relative_position_bias_table,
self.window_size[0],
self.window_size[1],
self.height_lookup,
self.width_lookup
)
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
return attn + self.get_bias()
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/drop.py | """ DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl inspired by two Tensorflow impl that I liked:
- https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
- https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def drop_block_2d(
x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0,
with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
runs with success, but needs further validation and possibly optimization for lower runtime impact.
"""
B, C, H, W = x.shape
total_size = W * H
clipped_block_size = min(block_size, min(W, H))
# seed_drop_rate, the gamma parameter
gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
(W - block_size + 1) * (H - block_size + 1))
# Forces the block to be inside the feature map.
w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
if batchwise:
# one mask for whole batch, quite a bit faster
uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
else:
uniform_noise = torch.rand_like(x)
block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
block_mask = -F.max_pool2d(
-block_mask,
kernel_size=clipped_block_size, # block_size,
stride=1,
padding=clipped_block_size // 2)
if with_noise:
normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
if inplace:
x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
else:
x = x * block_mask + normal_noise * (1 - block_mask)
else:
normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
if inplace:
x.mul_(block_mask * normalize_scale)
else:
x = x * block_mask * normalize_scale
return x
def drop_block_fast_2d(
x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,
gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
block mask at edges.
"""
B, C, H, W = x.shape
total_size = W * H
clipped_block_size = min(block_size, min(W, H))
gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
(W - block_size + 1) * (H - block_size + 1))
block_mask = torch.empty_like(x).bernoulli_(gamma)
block_mask = F.max_pool2d(
block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
if with_noise:
normal_noise = torch.empty_like(x).normal_()
if inplace:
x.mul_(1. - block_mask).add_(normal_noise * block_mask)
else:
x = x * (1. - block_mask) + normal_noise * block_mask
else:
block_mask = 1 - block_mask
normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6)).to(dtype=x.dtype)
if inplace:
x.mul_(block_mask * normalize_scale)
else:
x = x * block_mask * normalize_scale
return x
class DropBlock2d(nn.Module):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
"""
def __init__(
self,
drop_prob: float = 0.1,
block_size: int = 7,
gamma_scale: float = 1.0,
with_noise: bool = False,
inplace: bool = False,
batchwise: bool = False,
fast: bool = True):
super(DropBlock2d, self).__init__()
self.drop_prob = drop_prob
self.gamma_scale = gamma_scale
self.block_size = block_size
self.with_noise = with_noise
self.inplace = inplace
self.batchwise = batchwise
self.fast = fast # FIXME finish comparisons of fast vs not
def forward(self, x):
if not self.training or not self.drop_prob:
return x
if self.fast:
return drop_block_fast_2d(
x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace)
else:
return drop_block_2d(
x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def extra_repr(self):
return f'drop_prob={round(self.drop_prob,3):0.3f}'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/helpers.py | """ Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
min_value = min_value or divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < round_limit * v:
new_v += divisor
return new_v
def extend_tuple(x, n):
# pdas a tuple to specified n by padding with last value
if not isinstance(x, (tuple, list)):
x = (x,)
else:
x = tuple(x)
pad_n = n - len(x)
if pad_n <= 0:
return x[:n]
return x + (x[-1],) * pad_n
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim
Both a functional and a nn.Module version of the pooling is provided.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from .format import get_spatial_dim, get_channel_dim
_int_tuple_2_t = Union[int, Tuple[int, int]]
def adaptive_pool_feat_mult(pool_type='avg'):
if pool_type.endswith('catavgmax'):
return 2
else:
return 1
def adaptive_avgmax_pool2d(x, output_size: _int_tuple_2_t = 1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return 0.5 * (x_avg + x_max)
def adaptive_catavgmax_pool2d(x, output_size: _int_tuple_2_t = 1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return torch.cat((x_avg, x_max), 1)
def select_adaptive_pool2d(x, pool_type='avg', output_size: _int_tuple_2_t = 1):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avg':
x = F.adaptive_avg_pool2d(x, output_size)
elif pool_type == 'avgmax':
x = adaptive_avgmax_pool2d(x, output_size)
elif pool_type == 'catavgmax':
x = adaptive_catavgmax_pool2d(x, output_size)
elif pool_type == 'max':
x = F.adaptive_max_pool2d(x, output_size)
else:
assert False, 'Invalid pool type: %s' % pool_type
return x
class FastAdaptiveAvgPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: F = 'NCHW'):
super(FastAdaptiveAvgPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
return x.mean(self.dim, keepdim=not self.flatten)
class FastAdaptiveMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
return x.amax(self.dim, keepdim=not self.flatten)
class FastAdaptiveAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
x_avg = x.mean(self.dim, keepdim=not self.flatten)
x_max = x.amax(self.dim, keepdim=not self.flatten)
return 0.5 * x_avg + 0.5 * x_max
class FastAdaptiveCatAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveCatAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim_reduce = get_spatial_dim(input_fmt)
if flatten:
self.dim_cat = 1
else:
self.dim_cat = get_channel_dim(input_fmt)
def forward(self, x):
x_avg = x.mean(self.dim_reduce, keepdim=not self.flatten)
x_max = x.amax(self.dim_reduce, keepdim=not self.flatten)
return torch.cat((x_avg, x_max), self.dim_cat)
class AdaptiveAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_avgmax_pool2d(x, self.output_size)
class AdaptiveCatAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveCatAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_catavgmax_pool2d(x, self.output_size)
class SelectAdaptivePool2d(nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(
self,
output_size: _int_tuple_2_t = 1,
pool_type: str = 'fast',
flatten: bool = False,
input_fmt: str = 'NCHW',
):
super(SelectAdaptivePool2d, self).__init__()
assert input_fmt in ('NCHW', 'NHWC')
self.pool_type = pool_type or '' # convert other falsy values to empty string for consistent TS typing
if not pool_type:
self.pool = nn.Identity() # pass through
self.flatten = nn.Flatten(1) if flatten else nn.Identity()
elif pool_type.startswith('fast') or input_fmt != 'NCHW':
assert output_size == 1, 'Fast pooling and non NCHW input formats require output_size == 1.'
if pool_type.endswith('avgmax'):
self.pool = FastAdaptiveAvgMaxPool(flatten, input_fmt=input_fmt)
elif pool_type.endswith('catavgmax'):
self.pool = FastAdaptiveCatAvgMaxPool(flatten, input_fmt=input_fmt)
elif pool_type.endswith('max'):
self.pool = FastAdaptiveMaxPool(flatten, input_fmt=input_fmt)
else:
self.pool = FastAdaptiveAvgPool(flatten, input_fmt=input_fmt)
self.flatten = nn.Identity()
else:
assert input_fmt == 'NCHW'
if pool_type == 'avgmax':
self.pool = AdaptiveAvgMaxPool2d(output_size)
elif pool_type == 'catavgmax':
self.pool = AdaptiveCatAvgMaxPool2d(output_size)
elif pool_type == 'max':
self.pool = nn.AdaptiveMaxPool2d(output_size)
else:
self.pool = nn.AdaptiveAvgPool2d(output_size)
self.flatten = nn.Flatten(1) if flatten else nn.Identity()
def is_identity(self):
return not self.pool_type
def forward(self, x):
x = self.pool(x)
x = self.flatten(x)
return x
def feat_mult(self):
return adaptive_pool_feat_mult(self.pool_type)
def __repr__(self):
return self.__class__.__name__ + ' (' \
+ 'pool_type=' + self.pool_type \
+ ', flatten=' + str(self.flatten) + ')'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/filter_response_norm.py | """ Filter Response Norm in PyTorch
Based on `Filter Response Normalization Layer` - https://arxiv.org/abs/1911.09737
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
from .create_act import create_act_layer
from .trace_utils import _assert
def inv_instance_rms(x, eps: float = 1e-5):
rms = x.square().float().mean(dim=(2, 3), keepdim=True).add(eps).rsqrt().to(x.dtype)
return rms.expand(x.shape)
class FilterResponseNormTlu2d(nn.Module):
def __init__(self, num_features, apply_act=True, eps=1e-5, rms=True, **_):
super(FilterResponseNormTlu2d, self).__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.rms = rms
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.tau = nn.Parameter(torch.zeros(num_features)) if apply_act else None
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.tau is not None:
nn.init.zeros_(self.tau)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = x * inv_instance_rms(x, self.eps)
x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
return torch.maximum(x, self.tau.reshape(v_shape).to(dtype=x_dtype)) if self.tau is not None else x
class FilterResponseNormAct2d(nn.Module):
def __init__(self, num_features, apply_act=True, act_layer=nn.ReLU, inplace=None, rms=True, eps=1e-5, **_):
super(FilterResponseNormAct2d, self).__init__()
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer, inplace=inplace)
else:
self.act = nn.Identity()
self.rms = rms
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = x * inv_instance_rms(x, self.eps)
x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
return self.act(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/lambda_layer.py | """ Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
@misc{2102.08602,
Author = {Irwan Bello},
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
Year = {2021},
}
Status:
This impl is a WIP. Code snippets in the paper were used as reference but
good chance some details are missing/wrong.
I've only implemented local lambda conv based pos embeddings.
For a PyTorch impl that includes other embedding options checkout
https://github.com/lucidrains/lambda-networks
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from torch import nn
import torch.nn.functional as F
from .helpers import to_2tuple, make_divisible
from .weight_init import trunc_normal_
def rel_pos_indices(size):
size = to_2tuple(size)
pos = torch.stack(torch.meshgrid(torch.arange(size[0]), torch.arange(size[1]))).flatten(1)
rel_pos = pos[:, None, :] - pos[:, :, None]
rel_pos[0] += size[0] - 1
rel_pos[1] += size[1] - 1
return rel_pos # 2, H * W, H * W
class LambdaLayer(nn.Module):
"""Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
NOTE: intra-depth parameter 'u' is fixed at 1. It did not appear worth the complexity to add.
The internal dimensions of the lambda module are controlled via the interaction of several arguments.
* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
* the query (q) and key (k) dimension are determined by
* dim_head = (dim_out * attn_ratio // num_heads) if dim_head is None
* q = num_heads * dim_head, k = dim_head
* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not set
Args:
dim (int): input dimension to the module
dim_out (int): output dimension of the module, same as dim if not set
feat_size (Tuple[int, int]): size of input feature_map for relative pos variant H, W
stride (int): output stride of the module, avg pool used if stride == 2
num_heads (int): parallel attention heads.
dim_head (int): dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
r (int): local lambda convolution radius. Use lambda conv if set, else relative pos if not. (default: 9)
qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
qkv_bias (bool): add bias to q, k, and v projections
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=16, r=9,
qk_ratio=1.0, qkv_bias=False):
super().__init__()
dim_out = dim_out or dim
assert dim_out % num_heads == 0, ' should be divided by num_heads'
self.dim_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
self.num_heads = num_heads
self.dim_v = dim_out // num_heads
self.qkv = nn.Conv2d(
dim,
num_heads * self.dim_qk + self.dim_qk + self.dim_v,
kernel_size=1, bias=qkv_bias)
self.norm_q = nn.BatchNorm2d(num_heads * self.dim_qk)
self.norm_v = nn.BatchNorm2d(self.dim_v)
if r is not None:
# local lambda convolution for pos
self.conv_lambda = nn.Conv3d(1, self.dim_qk, (r, r, 1), padding=(r // 2, r // 2, 0))
self.pos_emb = None
self.rel_pos_indices = None
else:
# relative pos embedding
assert feat_size is not None
feat_size = to_2tuple(feat_size)
rel_size = [2 * s - 1 for s in feat_size]
self.conv_lambda = None
self.pos_emb = nn.Parameter(torch.zeros(rel_size[0], rel_size[1], self.dim_qk))
self.register_buffer('rel_pos_indices', rel_pos_indices(feat_size), persistent=False)
self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
self.reset_parameters()
def reset_parameters(self):
trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) # fan-in
if self.conv_lambda is not None:
trunc_normal_(self.conv_lambda.weight, std=self.dim_qk ** -0.5)
if self.pos_emb is not None:
trunc_normal_(self.pos_emb, std=.02)
def forward(self, x):
B, C, H, W = x.shape
M = H * W
qkv = self.qkv(x)
q, k, v = torch.split(qkv, [
self.num_heads * self.dim_qk, self.dim_qk, self.dim_v], dim=1)
q = self.norm_q(q).reshape(B, self.num_heads, self.dim_qk, M).transpose(-1, -2) # B, num_heads, M, K
v = self.norm_v(v).reshape(B, self.dim_v, M).transpose(-1, -2) # B, M, V
k = F.softmax(k.reshape(B, self.dim_qk, M), dim=-1) # B, K, M
content_lam = k @ v # B, K, V
content_out = q @ content_lam.unsqueeze(1) # B, num_heads, M, V
if self.pos_emb is None:
position_lam = self.conv_lambda(v.reshape(B, 1, H, W, self.dim_v)) # B, H, W, V, K
position_lam = position_lam.reshape(B, 1, self.dim_qk, H * W, self.dim_v).transpose(2, 3) # B, 1, M, K, V
else:
# FIXME relative pos embedding path not fully verified
pos_emb = self.pos_emb[self.rel_pos_indices[0], self.rel_pos_indices[1]].expand(B, -1, -1, -1)
position_lam = (pos_emb.transpose(-1, -2) @ v.unsqueeze(1)).unsqueeze(1) # B, 1, M, K, V
position_out = (q.unsqueeze(-2) @ position_lam).squeeze(-2) # B, num_heads, M, V
out = (content_out + position_out).transpose(-1, -2).reshape(B, C, H, W) # B, C (num_heads * V), H, W
out = self.pool(out)
return out
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/selective_kernel.py | """ Selective Kernel Convolution/Attention
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv_bn_act import ConvNormActAa
from .helpers import make_divisible
from .trace_utils import _assert
def _kernel_valid(k):
if isinstance(k, (list, tuple)):
for ki in k:
return _kernel_valid(ki)
assert k >= 3 and k % 2
class SelectiveKernelAttn(nn.Module):
def __init__(self, channels, num_paths=2, attn_channels=32, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
""" Selective Kernel Attention Module
Selective Kernel attention mechanism factored out into its own module.
"""
super(SelectiveKernelAttn, self).__init__()
self.num_paths = num_paths
self.fc_reduce = nn.Conv2d(channels, attn_channels, kernel_size=1, bias=False)
self.bn = norm_layer(attn_channels)
self.act = act_layer(inplace=True)
self.fc_select = nn.Conv2d(attn_channels, channels * num_paths, kernel_size=1, bias=False)
def forward(self, x):
_assert(x.shape[1] == self.num_paths, '')
x = x.sum(1).mean((2, 3), keepdim=True)
x = self.fc_reduce(x)
x = self.bn(x)
x = self.act(x)
x = self.fc_select(x)
B, C, H, W = x.shape
x = x.view(B, self.num_paths, C // self.num_paths, H, W)
x = torch.softmax(x, dim=1)
return x
class SelectiveKernel(nn.Module):
def __init__(self, in_channels, out_channels=None, kernel_size=None, stride=1, dilation=1, groups=1,
rd_ratio=1./16, rd_channels=None, rd_divisor=8, keep_3x3=True, split_input=True,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_layer=None):
""" Selective Kernel Convolution Module
As described in Selective Kernel Networks (https://arxiv.org/abs/1903.06586) with some modifications.
Largest change is the input split, which divides the input channels across each convolution path, this can
be viewed as a grouping of sorts, but the output channel counts expand to the module level value. This keeps
the parameter count from ballooning when the convolutions themselves don't have groups, but still provides
a noteworthy increase in performance over similar param count models without this attention layer. -Ross W
Args:
in_channels (int): module input (feature) channel count
out_channels (int): module output (feature) channel count
kernel_size (int, list): kernel size for each convolution branch
stride (int): stride for convolutions
dilation (int): dilation for module as a whole, impacts dilation of each branch
groups (int): number of groups for each branch
rd_ratio (int, float): reduction factor for attention features
keep_3x3 (bool): keep all branch convolution kernels as 3x3, changing larger kernels for dilations
split_input (bool): split input channels evenly across each convolution branch, keeps param count lower,
can be viewed as grouping by path, output expands to module out_channels count
act_layer (nn.Module): activation layer to use
norm_layer (nn.Module): batchnorm/norm layer to use
aa_layer (nn.Module): anti-aliasing module
drop_layer (nn.Module): spatial drop module in convs (drop block, etc)
"""
super(SelectiveKernel, self).__init__()
out_channels = out_channels or in_channels
kernel_size = kernel_size or [3, 5] # default to one 3x3 and one 5x5 branch. 5x5 -> 3x3 + dilation
_kernel_valid(kernel_size)
if not isinstance(kernel_size, list):
kernel_size = [kernel_size] * 2
if keep_3x3:
dilation = [dilation * (k - 1) // 2 for k in kernel_size]
kernel_size = [3] * len(kernel_size)
else:
dilation = [dilation] * len(kernel_size)
self.num_paths = len(kernel_size)
self.in_channels = in_channels
self.out_channels = out_channels
self.split_input = split_input
if self.split_input:
assert in_channels % self.num_paths == 0
in_channels = in_channels // self.num_paths
groups = min(out_channels, groups)
conv_kwargs = dict(
stride=stride, groups=groups, act_layer=act_layer, norm_layer=norm_layer,
aa_layer=aa_layer, drop_layer=drop_layer)
self.paths = nn.ModuleList([
ConvNormActAa(in_channels, out_channels, kernel_size=k, dilation=d, **conv_kwargs)
for k, d in zip(kernel_size, dilation)])
attn_channels = rd_channels or make_divisible(out_channels * rd_ratio, divisor=rd_divisor)
self.attn = SelectiveKernelAttn(out_channels, self.num_paths, attn_channels)
def forward(self, x):
if self.split_input:
x_split = torch.split(x, self.in_channels // self.num_paths, 1)
x_paths = [op(x_split[i]) for i, op in enumerate(self.paths)]
else:
x_paths = [op(x) for op in self.paths]
x = torch.stack(x_paths, dim=1)
x_attn = self.attn(x)
x = x * x_attn
x = torch.sum(x, dim=1)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/patch_dropout.py | from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
num_prefix_tokens: int = 1,
ordered: bool = False,
return_indices: bool = False,
):
super().__init__()
assert 0 <= prob < 1.
self.prob = prob
self.num_prefix_tokens = num_prefix_tokens # exclude CLS token (or other prefix tokens)
self.ordered = ordered
self.return_indices = return_indices
def forward(self, x) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
if not self.training or self.prob == 0.:
if self.return_indices:
return x, None
return x
if self.num_prefix_tokens:
prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.num_prefix_tokens:]
else:
prefix_tokens = None
B = x.shape[0]
L = x.shape[1]
num_keep = max(1, int(L * (1. - self.prob)))
keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1)[:, :num_keep]
if self.ordered:
# NOTE does not need to maintain patch order in typical transformer use,
# but possibly useful for debug / visualization
keep_indices = keep_indices.sort(dim=-1)[0]
x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.shape[2:]))
if prefix_tokens is not None:
x = torch.cat((prefix_tokens, x), dim=1)
if self.return_indices:
return x, keep_indices
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/bottleneck_attn.py | """ Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers for Visual Recognition},
Year = {2021},
}
Based on ref gist at: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
This impl is a WIP but given that it is based on the ref gist likely not too far off.
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, make_divisible
from .weight_init import trunc_normal_
from .trace_utils import _assert
def rel_logits_1d(q, rel_k, permute_mask: List[int]):
""" Compute relative logits along one dimension
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
Args:
q: (batch, heads, height, width, dim)
rel_k: (2 * width - 1, dim)
permute_mask: permute output dim according to this
"""
B, H, W, dim = q.shape
x = (q @ rel_k.transpose(-1, -2))
x = x.reshape(-1, W, 2 * W -1)
# pad to shift from relative to absolute indexing
x_pad = F.pad(x, [0, 1]).flatten(1)
x_pad = F.pad(x_pad, [0, W - 1])
# reshape and slice out the padded elements
x_pad = x_pad.reshape(-1, W + 1, 2 * W - 1)
x = x_pad[:, :W, W - 1:]
# reshape and tile
x = x.reshape(B, H, 1, W, W).expand(-1, -1, H, -1, -1)
return x.permute(permute_mask)
class PosEmbedRel(nn.Module):
""" Relative Position Embedding
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
"""
def __init__(self, feat_size, dim_head, scale):
super().__init__()
self.height, self.width = to_2tuple(feat_size)
self.dim_head = dim_head
self.height_rel = nn.Parameter(torch.randn(self.height * 2 - 1, dim_head) * scale)
self.width_rel = nn.Parameter(torch.randn(self.width * 2 - 1, dim_head) * scale)
def forward(self, q):
B, HW, _ = q.shape
# relative logits in width dimension.
q = q.reshape(B, self.height, self.width, -1)
rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
# relative logits in height dimension.
q = q.transpose(1, 2)
rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
rel_logits = rel_logits_h + rel_logits_w
rel_logits = rel_logits.reshape(B, HW, HW)
return rel_logits
class BottleneckAttn(nn.Module):
""" Bottleneck Attention
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
The internal dimensions of the attention module are controlled by the interaction of several arguments.
* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
* the query and key (qk) dimensions are determined by
* num_heads * dim_head if dim_head is not None
* num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
Args:
dim (int): input dimension to the module
dim_out (int): output dimension of the module, same as dim if not set
stride (int): output stride of the module, avg pool used if stride == 2 (default: 1).
num_heads (int): parallel attention heads (default: 4)
dim_head (int): dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
qkv_bias (bool): add bias to q, k, and v projections
scale_pos_embed (bool): scale the position embedding as well as Q @ K
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=None,
qk_ratio=1.0, qkv_bias=False, scale_pos_embed=False):
super().__init__()
assert feat_size is not None, 'A concrete feature size matching expected input (H, W) is required'
dim_out = dim_out or dim
assert dim_out % num_heads == 0
self.num_heads = num_heads
self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
self.dim_head_v = dim_out // self.num_heads
self.dim_out_qk = num_heads * self.dim_head_qk
self.dim_out_v = num_heads * self.dim_head_v
self.scale = self.dim_head_qk ** -0.5
self.scale_pos_embed = scale_pos_embed
self.qkv = nn.Conv2d(dim, self.dim_out_qk * 2 + self.dim_out_v, 1, bias=qkv_bias)
# NOTE I'm only supporting relative pos embedding for now
self.pos_embed = PosEmbedRel(feat_size, dim_head=self.dim_head_qk, scale=self.scale)
self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
self.reset_parameters()
def reset_parameters(self):
trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) # fan-in
trunc_normal_(self.pos_embed.height_rel, std=self.scale)
trunc_normal_(self.pos_embed.width_rel, std=self.scale)
def forward(self, x):
B, C, H, W = x.shape
_assert(H == self.pos_embed.height, '')
_assert(W == self.pos_embed.width, '')
x = self.qkv(x) # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W
# NOTE head vs channel split ordering in qkv projection was decided before I allowed qk to differ from v
# So, this is more verbose than if heads were before qkv splits, but throughput is not impacted.
q, k, v = torch.split(x, [self.dim_out_qk, self.dim_out_qk, self.dim_out_v], dim=1)
q = q.reshape(B * self.num_heads, self.dim_head_qk, -1).transpose(-1, -2)
k = k.reshape(B * self.num_heads, self.dim_head_qk, -1) # no transpose, for q @ k
v = v.reshape(B * self.num_heads, self.dim_head_v, -1).transpose(-1, -2)
if self.scale_pos_embed:
attn = (q @ k + self.pos_embed(q)) * self.scale # B * num_heads, H * W, H * W
else:
attn = (q @ k) * self.scale + self.pos_embed(q)
attn = attn.softmax(dim=-1)
out = (attn @ v).transpose(-1, -2).reshape(B, self.dim_out_v, H, W) # B, dim_out, H, W
out = self.pool(out)
return out
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/split_batchnorm.py | """ Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
This allows easily removing the auxiliary BN layers after training to efficiently
achieve the 'Auxiliary BatchNorm' as described in the AdvProp Paper, section 4.2,
'Disentangled Learning via An Auxiliary BN'
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
class SplitBatchNorm2d(torch.nn.BatchNorm2d):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
track_running_stats=True, num_splits=2):
super().__init__(num_features, eps, momentum, affine, track_running_stats)
assert num_splits > 1, 'Should have at least one aux BN layer (num_splits at least 2)'
self.num_splits = num_splits
self.aux_bn = nn.ModuleList([
nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) for _ in range(num_splits - 1)])
def forward(self, input: torch.Tensor):
if self.training: # aux BN only relevant while training
split_size = input.shape[0] // self.num_splits
assert input.shape[0] == split_size * self.num_splits, "batch size must be evenly divisible by num_splits"
split_input = input.split(split_size)
x = [super().forward(split_input[0])]
for i, a in enumerate(self.aux_bn):
x.append(a(split_input[i + 1]))
return torch.cat(x, dim=0)
else:
return super().forward(input)
def convert_splitbn_model(module, num_splits=2):
"""
Recursively traverse module and its children to replace all instances of
``torch.nn.modules.batchnorm._BatchNorm`` with `SplitBatchnorm2d`.
Args:
module (torch.nn.Module): input module
num_splits: number of separate batchnorm layers to split input across
Example::
>>> # model is an instance of torch.nn.Module
>>> model = timm.models.convert_splitbn_model(model, num_splits=2)
"""
mod = module
if isinstance(module, torch.nn.modules.instancenorm._InstanceNorm):
return module
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
mod = SplitBatchNorm2d(
module.num_features, module.eps, module.momentum, module.affine,
module.track_running_stats, num_splits=num_splits)
mod.running_mean = module.running_mean
mod.running_var = module.running_var
mod.num_batches_tracked = module.num_batches_tracked
if module.affine:
mod.weight.data = module.weight.data.clone().detach()
mod.bias.data = module.bias.data.clone().detach()
for aux in mod.aux_bn:
aux.running_mean = module.running_mean.clone()
aux.running_var = module.running_var.clone()
aux.num_batches_tracked = module.num_batches_tracked.clone()
if module.affine:
aux.weight.data = module.weight.data.clone().detach()
aux.bias.data = module.bias.data.clone().detach()
for name, child in module.named_children():
mod.add_module(name, convert_splitbn_model(child, num_splits=num_splits))
del module
return mod
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/conv2d_same.py | """ Conv2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
from .config import is_exportable, is_scriptable
from .padding import pad_same, pad_same_arg, get_padding_value
_USE_EXPORT_CONV = False
def conv2d_same(
x,
weight: torch.Tensor,
bias: Optional[torch.Tensor] = None,
stride: Tuple[int, int] = (1, 1),
padding: Tuple[int, int] = (0, 0),
dilation: Tuple[int, int] = (1, 1),
groups: int = 1,
):
x = pad_same(x, weight.shape[-2:], stride, dilation)
return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
class Conv2dSame(nn.Conv2d):
""" Tensorflow like 'SAME' convolution wrapper for 2D convolutions
"""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
):
super(Conv2dSame, self).__init__(
in_channels, out_channels, kernel_size,
stride, 0, dilation, groups, bias,
)
def forward(self, x):
return conv2d_same(
x, self.weight, self.bias,
self.stride, self.padding, self.dilation, self.groups,
)
class Conv2dSameExport(nn.Conv2d):
""" ONNX export friendly Tensorflow like 'SAME' convolution wrapper for 2D convolutions
NOTE: This does not currently work with torch.jit.script
"""
# pylint: disable=unused-argument
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
):
super(Conv2dSameExport, self).__init__(
in_channels, out_channels, kernel_size,
stride, 0, dilation, groups, bias,
)
self.pad = None
self.pad_input_size = (0, 0)
def forward(self, x):
input_size = x.size()[-2:]
if self.pad is None:
pad_arg = pad_same_arg(input_size, self.weight.size()[-2:], self.stride, self.dilation)
self.pad = nn.ZeroPad2d(pad_arg)
self.pad_input_size = input_size
x = self.pad(x)
return F.conv2d(
x, self.weight, self.bias,
self.stride, self.padding, self.dilation, self.groups,
)
def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
padding = kwargs.pop('padding', '')
kwargs.setdefault('bias', False)
padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
if is_dynamic:
if _USE_EXPORT_CONV and is_exportable():
# older PyTorch ver needed this to export same padding reasonably
assert not is_scriptable() # Conv2DSameExport does not work with jit
return Conv2dSameExport(in_chs, out_chs, kernel_size, **kwargs)
else:
return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
else:
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/grn.py | """ Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching existing torch norm layers
* slightly improves eager mode performance via fused addcmul
Hacked together by / Copyright 2023 Ross Wightman
"""
import torch
from torch import nn as nn
class GlobalResponseNorm(nn.Module):
""" Global Response Normalization layer
"""
def __init__(self, dim, eps=1e-6, channels_last=True):
super().__init__()
self.eps = eps
if channels_last:
self.spatial_dim = (1, 2)
self.channel_dim = -1
self.wb_shape = (1, 1, 1, -1)
else:
self.spatial_dim = (2, 3)
self.channel_dim = 1
self.wb_shape = (1, -1, 1, 1)
self.weight = nn.Parameter(torch.zeros(dim))
self.bias = nn.Parameter(torch.zeros(dim))
def forward(self, x):
x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True)
x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps)
return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/inplace_abn.py | import torch
from torch import nn as nn
try:
from inplace_abn.functions import inplace_abn, inplace_abn_sync
has_iabn = True
except ImportError:
has_iabn = False
def inplace_abn(x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation="leaky_relu", activation_param=0.01):
raise ImportError(
"Please install InplaceABN:'pip install git+https://github.com/mapillary/inplace_abn.git@v1.0.12'")
def inplace_abn_sync(**kwargs):
inplace_abn(**kwargs)
class InplaceAbn(nn.Module):
"""Activated Batch Normalization
This gathers a BatchNorm and an activation function in a single module
Parameters
----------
num_features : int
Number of feature channels in the input and output.
eps : float
Small constant to prevent numerical issues.
momentum : float
Momentum factor applied to compute running statistics.
affine : bool
If `True` apply learned scale and shift transformation after normalization.
act_layer : str or nn.Module type
Name or type of the activation functions, one of: `leaky_relu`, `elu`
act_param : float
Negative slope for the `leaky_relu` activation.
"""
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, apply_act=True,
act_layer="leaky_relu", act_param=0.01, drop_layer=None):
super(InplaceAbn, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
self.momentum = momentum
if apply_act:
if isinstance(act_layer, str):
assert act_layer in ('leaky_relu', 'elu', 'identity', '')
self.act_name = act_layer if act_layer else 'identity'
else:
# convert act layer passed as type to string
if act_layer == nn.ELU:
self.act_name = 'elu'
elif act_layer == nn.LeakyReLU:
self.act_name = 'leaky_relu'
elif act_layer is None or act_layer == nn.Identity:
self.act_name = 'identity'
else:
assert False, f'Invalid act layer {act_layer.__name__} for IABN'
else:
self.act_name = 'identity'
self.act_param = act_param
if self.affine:
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.constant_(self.running_mean, 0)
nn.init.constant_(self.running_var, 1)
if self.affine:
nn.init.constant_(self.weight, 1)
nn.init.constant_(self.bias, 0)
def forward(self, x):
output = inplace_abn(
x, self.weight, self.bias, self.running_mean, self.running_var,
self.training, self.momentum, self.eps, self.act_name, self.act_param)
if isinstance(output, tuple):
output = output[0]
return output
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_norm_act.py | """ NormAct (Normalizaiton + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
isntances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN or EvoNorms.
Hacked together by / Copyright 2020 Ross Wightman
"""
import types
import functools
from .evo_norm import *
from .filter_response_norm import FilterResponseNormAct2d, FilterResponseNormTlu2d
from .norm_act import BatchNormAct2d, GroupNormAct, LayerNormAct, LayerNormAct2d
from .inplace_abn import InplaceAbn
_NORM_ACT_MAP = dict(
batchnorm=BatchNormAct2d,
batchnorm2d=BatchNormAct2d,
groupnorm=GroupNormAct,
groupnorm1=functools.partial(GroupNormAct, num_groups=1),
layernorm=LayerNormAct,
layernorm2d=LayerNormAct2d,
evonormb0=EvoNorm2dB0,
evonormb1=EvoNorm2dB1,
evonormb2=EvoNorm2dB2,
evonorms0=EvoNorm2dS0,
evonorms0a=EvoNorm2dS0a,
evonorms1=EvoNorm2dS1,
evonorms1a=EvoNorm2dS1a,
evonorms2=EvoNorm2dS2,
evonorms2a=EvoNorm2dS2a,
frn=FilterResponseNormAct2d,
frntlu=FilterResponseNormTlu2d,
inplaceabn=InplaceAbn,
iabn=InplaceAbn,
)
_NORM_ACT_TYPES = {m for n, m in _NORM_ACT_MAP.items()}
# has act_layer arg to define act type
_NORM_ACT_REQUIRES_ARG = {
BatchNormAct2d, GroupNormAct, LayerNormAct, LayerNormAct2d, FilterResponseNormAct2d, InplaceAbn}
def create_norm_act_layer(layer_name, num_features, act_layer=None, apply_act=True, jit=False, **kwargs):
layer = get_norm_act_layer(layer_name, act_layer=act_layer)
layer_instance = layer(num_features, apply_act=apply_act, **kwargs)
if jit:
layer_instance = torch.jit.script(layer_instance)
return layer_instance
def get_norm_act_layer(norm_layer, act_layer=None):
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
assert act_layer is None or isinstance(act_layer, (type, str, types.FunctionType, functools.partial))
norm_act_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_act_kwargs.update(norm_layer.keywords)
norm_layer = norm_layer.func
if isinstance(norm_layer, str):
layer_name = norm_layer.replace('_', '').lower().split('-')[0]
norm_act_layer = _NORM_ACT_MAP.get(layer_name, None)
elif norm_layer in _NORM_ACT_TYPES:
norm_act_layer = norm_layer
elif isinstance(norm_layer, types.FunctionType):
# if function type, must be a lambda/fn that creates a norm_act layer
norm_act_layer = norm_layer
else:
type_name = norm_layer.__name__.lower()
if type_name.startswith('batchnorm'):
norm_act_layer = BatchNormAct2d
elif type_name.startswith('groupnorm'):
norm_act_layer = GroupNormAct
elif type_name.startswith('groupnorm1'):
norm_act_layer = functools.partial(GroupNormAct, num_groups=1)
elif type_name.startswith('layernorm2d'):
norm_act_layer = LayerNormAct2d
elif type_name.startswith('layernorm'):
norm_act_layer = LayerNormAct
else:
assert False, f"No equivalent norm_act layer for {type_name}"
if norm_act_layer in _NORM_ACT_REQUIRES_ARG:
# pass `act_layer` through for backwards compat where `act_layer=None` implies no activation.
# In the future, may force use of `apply_act` with `act_layer` arg bound to relevant NormAct types
norm_act_kwargs.setdefault('act_layer', act_layer)
if norm_act_kwargs:
norm_act_layer = functools.partial(norm_act_layer, **norm_act_kwargs) # bind/rebind args
return norm_act_layer
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/format.py | from enum import Enum
from typing import Union
import torch
class Format(str, Enum):
NCHW = 'NCHW'
NHWC = 'NHWC'
NCL = 'NCL'
NLC = 'NLC'
FormatT = Union[str, Format]
def get_spatial_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NLC:
dim = (1,)
elif fmt is Format.NCL:
dim = (2,)
elif fmt is Format.NHWC:
dim = (1, 2)
else:
dim = (2, 3)
return dim
def get_channel_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NHWC:
dim = 3
elif fmt is Format.NLC:
dim = 2
else:
dim = 1
return dim
def nchw_to(x: torch.Tensor, fmt: Format):
if fmt == Format.NHWC:
x = x.permute(0, 2, 3, 1)
elif fmt == Format.NLC:
x = x.flatten(2).transpose(1, 2)
elif fmt == Format.NCL:
x = x.flatten(2)
return x
def nhwc_to(x: torch.Tensor, fmt: Format):
if fmt == Format.NCHW:
x = x.permute(0, 3, 1, 2)
elif fmt == Format.NLC:
x = x.flatten(1, 2)
elif fmt == Format.NCL:
x = x.flatten(1, 2).transpose(1, 2)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/halo_attn.py | """ Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon Shlens},
Title = {Scaling Local Self-Attention for Parameter Efficient Visual Backbones},
Year = {2021},
}
Status:
This impl is a WIP, there is no official ref impl and some details in paper weren't clear to me.
The attention mechanism works but it's slow as implemented.
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List
import torch
from torch import nn
import torch.nn.functional as F
from .helpers import make_divisible
from .weight_init import trunc_normal_
from .trace_utils import _assert
def rel_logits_1d(q, rel_k, permute_mask: List[int]):
""" Compute relative logits along one dimension
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
Args:
q: (batch, height, width, dim)
rel_k: (2 * window - 1, dim)
permute_mask: permute output dim according to this
"""
B, H, W, dim = q.shape
rel_size = rel_k.shape[0]
win_size = (rel_size + 1) // 2
x = (q @ rel_k.transpose(-1, -2))
x = x.reshape(-1, W, rel_size)
# pad to shift from relative to absolute indexing
x_pad = F.pad(x, [0, 1]).flatten(1)
x_pad = F.pad(x_pad, [0, rel_size - W])
# reshape and slice out the padded elements
x_pad = x_pad.reshape(-1, W + 1, rel_size)
x = x_pad[:, :W, win_size - 1:]
# reshape and tile
x = x.reshape(B, H, 1, W, win_size).expand(-1, -1, win_size, -1, -1)
return x.permute(permute_mask)
class PosEmbedRel(nn.Module):
""" Relative Position Embedding
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
"""
def __init__(self, block_size, win_size, dim_head, scale):
"""
Args:
block_size (int): block size
win_size (int): neighbourhood window size
dim_head (int): attention head dim
scale (float): scale factor (for init)
"""
super().__init__()
self.block_size = block_size
self.dim_head = dim_head
self.height_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
self.width_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
def forward(self, q):
B, BB, HW, _ = q.shape
# relative logits in width dimension.
q = q.reshape(-1, self.block_size, self.block_size, self.dim_head)
rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
# relative logits in height dimension.
q = q.transpose(1, 2)
rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
rel_logits = rel_logits_h + rel_logits_w
rel_logits = rel_logits.reshape(B, BB, HW, -1)
return rel_logits
class HaloAttn(nn.Module):
""" Halo Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
The internal dimensions of the attention module are controlled by the interaction of several arguments.
* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
* the query and key (qk) dimensions are determined by
* num_heads * dim_head if dim_head is not None
* num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
Args:
dim (int): input dimension to the module
dim_out (int): output dimension of the module, same as dim if not set
feat_size (Tuple[int, int]): size of input feature_map (not used, for arg compat with bottle/lambda)
stride: output stride of the module, query downscaled if > 1 (default: 1).
num_heads: parallel attention heads (default: 8).
dim_head: dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
block_size (int): size of blocks. (default: 8)
halo_size (int): size of halo overlap. (default: 3)
qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
qkv_bias (bool) : add bias to q, k, and v projections
avg_down (bool): use average pool downsample instead of strided query blocks
scale_pos_embed (bool): scale the position embedding as well as Q @ K
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, num_heads=8, dim_head=None, block_size=8, halo_size=3,
qk_ratio=1.0, qkv_bias=False, avg_down=False, scale_pos_embed=False):
super().__init__()
dim_out = dim_out or dim
assert dim_out % num_heads == 0
assert stride in (1, 2)
self.num_heads = num_heads
self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
self.dim_head_v = dim_out // self.num_heads
self.dim_out_qk = num_heads * self.dim_head_qk
self.dim_out_v = num_heads * self.dim_head_v
self.scale = self.dim_head_qk ** -0.5
self.scale_pos_embed = scale_pos_embed
self.block_size = self.block_size_ds = block_size
self.halo_size = halo_size
self.win_size = block_size + halo_size * 2 # neighbourhood window size
self.block_stride = 1
use_avg_pool = False
if stride > 1:
use_avg_pool = avg_down or block_size % stride != 0
self.block_stride = 1 if use_avg_pool else stride
self.block_size_ds = self.block_size // self.block_stride
# FIXME not clear if this stride behaviour is what the paper intended
# Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving
# data in unfolded block form. I haven't wrapped my head around how that'd look.
self.q = nn.Conv2d(dim, self.dim_out_qk, 1, stride=self.block_stride, bias=qkv_bias)
self.kv = nn.Conv2d(dim, self.dim_out_qk + self.dim_out_v, 1, bias=qkv_bias)
self.pos_embed = PosEmbedRel(
block_size=self.block_size_ds, win_size=self.win_size, dim_head=self.dim_head_qk, scale=self.scale)
self.pool = nn.AvgPool2d(2, 2) if use_avg_pool else nn.Identity()
self.reset_parameters()
def reset_parameters(self):
std = self.q.weight.shape[1] ** -0.5 # fan-in
trunc_normal_(self.q.weight, std=std)
trunc_normal_(self.kv.weight, std=std)
trunc_normal_(self.pos_embed.height_rel, std=self.scale)
trunc_normal_(self.pos_embed.width_rel, std=self.scale)
def forward(self, x):
B, C, H, W = x.shape
_assert(H % self.block_size == 0, '')
_assert(W % self.block_size == 0, '')
num_h_blocks = H // self.block_size
num_w_blocks = W // self.block_size
num_blocks = num_h_blocks * num_w_blocks
q = self.q(x)
# unfold
q = q.reshape(
-1, self.dim_head_qk,
num_h_blocks, self.block_size_ds, num_w_blocks, self.block_size_ds).permute(0, 1, 3, 5, 2, 4)
# B, num_heads * dim_head * block_size ** 2, num_blocks
q = q.reshape(B * self.num_heads, self.dim_head_qk, -1, num_blocks).transpose(1, 3)
# B * num_heads, num_blocks, block_size ** 2, dim_head
kv = self.kv(x)
# Generate overlapping windows for kv. This approach is good for GPU and CPU. However, unfold() is not
# lowered for PyTorch XLA so it will be very slow. See code at bottom of file for XLA friendly approach.
# FIXME figure out how to switch impl between this and conv2d if XLA being used.
kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size])
kv = kv.unfold(2, self.win_size, self.block_size).unfold(3, self.win_size, self.block_size).reshape(
B * self.num_heads, self.dim_head_qk + self.dim_head_v, num_blocks, -1).permute(0, 2, 3, 1)
k, v = torch.split(kv, [self.dim_head_qk, self.dim_head_v], dim=-1)
# B * num_heads, num_blocks, win_size ** 2, dim_head_qk or dim_head_v
if self.scale_pos_embed:
attn = (q @ k.transpose(-1, -2) + self.pos_embed(q)) * self.scale
else:
attn = (q @ k.transpose(-1, -2)) * self.scale + self.pos_embed(q)
# B * num_heads, num_blocks, block_size ** 2, win_size ** 2
attn = attn.softmax(dim=-1)
out = (attn @ v).transpose(1, 3) # B * num_heads, dim_head_v, block_size ** 2, num_blocks
# fold
out = out.reshape(-1, self.block_size_ds, self.block_size_ds, num_h_blocks, num_w_blocks)
out = out.permute(0, 3, 1, 4, 2).contiguous().view(
B, self.dim_out_v, H // self.block_stride, W // self.block_stride)
# B, dim_out, H // block_stride, W // block_stride
out = self.pool(out)
return out
""" Three alternatives for overlapping windows.
`.unfold().unfold()` is same speed as stride tricks with similar clarity as F.unfold()
if is_xla:
# This code achieves haloing on PyTorch XLA with reasonable runtime trade-off, it is
# EXTREMELY slow for backward on a GPU though so I need a way of selecting based on environment.
WW = self.win_size ** 2
pw = torch.eye(WW, dtype=x.dtype, device=x.device).reshape(WW, 1, self.win_size, self.win_size)
kv = F.conv2d(kv.reshape(-1, 1, H, W), pw, stride=self.block_size, padding=self.halo_size)
elif self.stride_tricks:
kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous()
kv = kv.as_strided((
B, self.dim_out_qk + self.dim_out_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks),
stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size))
else:
kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
kv = kv.reshape(
B * self.num_heads, self.dim_head_qk + self.dim_head_v, -1, num_blocks).transpose(1, 3)
"""
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/non_local_attn.py | """ Bilinear-Attention-Transform and Non-Local Attention
Paper: `Non-Local Neural Networks With Grouped Bilinear Attentional Transforms`
- https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.html
Adapted from original code: https://github.com/BA-Transform/BAT-Image-Classification
"""
import torch
from torch import nn
from torch.nn import functional as F
from .conv_bn_act import ConvNormAct
from .helpers import make_divisible
from .trace_utils import _assert
class NonLocalAttn(nn.Module):
"""Spatial NL block for image classification.
This was adapted from https://github.com/BA-Transform/BAT-Image-Classification
Their NonLocal impl inspired by https://github.com/facebookresearch/video-nonlocal-net.
"""
def __init__(self, in_channels, use_scale=True, rd_ratio=1/8, rd_channels=None, rd_divisor=8, **kwargs):
super(NonLocalAttn, self).__init__()
if rd_channels is None:
rd_channels = make_divisible(in_channels * rd_ratio, divisor=rd_divisor)
self.scale = in_channels ** -0.5 if use_scale else 1.0
self.t = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True)
self.p = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True)
self.g = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True)
self.z = nn.Conv2d(rd_channels, in_channels, kernel_size=1, stride=1, bias=True)
self.norm = nn.BatchNorm2d(in_channels)
self.reset_parameters()
def forward(self, x):
shortcut = x
t = self.t(x)
p = self.p(x)
g = self.g(x)
B, C, H, W = t.size()
t = t.view(B, C, -1).permute(0, 2, 1)
p = p.view(B, C, -1)
g = g.view(B, C, -1).permute(0, 2, 1)
att = torch.bmm(t, p) * self.scale
att = F.softmax(att, dim=2)
x = torch.bmm(att, g)
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.z(x)
x = self.norm(x) + shortcut
return x
def reset_parameters(self):
for name, m in self.named_modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
if len(list(m.parameters())) > 1:
nn.init.constant_(m.bias, 0.0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 0)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.GroupNorm):
nn.init.constant_(m.weight, 0)
nn.init.constant_(m.bias, 0)
class BilinearAttnTransform(nn.Module):
def __init__(self, in_channels, block_size, groups, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
super(BilinearAttnTransform, self).__init__()
self.conv1 = ConvNormAct(in_channels, groups, 1, act_layer=act_layer, norm_layer=norm_layer)
self.conv_p = nn.Conv2d(groups, block_size * block_size * groups, kernel_size=(block_size, 1))
self.conv_q = nn.Conv2d(groups, block_size * block_size * groups, kernel_size=(1, block_size))
self.conv2 = ConvNormAct(in_channels, in_channels, 1, act_layer=act_layer, norm_layer=norm_layer)
self.block_size = block_size
self.groups = groups
self.in_channels = in_channels
def resize_mat(self, x, t: int):
B, C, block_size, block_size1 = x.shape
_assert(block_size == block_size1, '')
if t <= 1:
return x
x = x.view(B * C, -1, 1, 1)
x = x * torch.eye(t, t, dtype=x.dtype, device=x.device)
x = x.view(B * C, block_size, block_size, t, t)
x = torch.cat(torch.split(x, 1, dim=1), dim=3)
x = torch.cat(torch.split(x, 1, dim=2), dim=4)
x = x.view(B, C, block_size * t, block_size * t)
return x
def forward(self, x):
_assert(x.shape[-1] % self.block_size == 0, '')
_assert(x.shape[-2] % self.block_size == 0, '')
B, C, H, W = x.shape
out = self.conv1(x)
rp = F.adaptive_max_pool2d(out, (self.block_size, 1))
cp = F.adaptive_max_pool2d(out, (1, self.block_size))
p = self.conv_p(rp).view(B, self.groups, self.block_size, self.block_size).sigmoid()
q = self.conv_q(cp).view(B, self.groups, self.block_size, self.block_size).sigmoid()
p = p / p.sum(dim=3, keepdim=True)
q = q / q.sum(dim=2, keepdim=True)
p = p.view(B, self.groups, 1, self.block_size, self.block_size).expand(x.size(
0), self.groups, C // self.groups, self.block_size, self.block_size).contiguous()
p = p.view(B, C, self.block_size, self.block_size)
q = q.view(B, self.groups, 1, self.block_size, self.block_size).expand(x.size(
0), self.groups, C // self.groups, self.block_size, self.block_size).contiguous()
q = q.view(B, C, self.block_size, self.block_size)
p = self.resize_mat(p, H // self.block_size)
q = self.resize_mat(q, W // self.block_size)
y = p.matmul(x)
y = y.matmul(q)
y = self.conv2(y)
return y
class BatNonLocalAttn(nn.Module):
""" BAT
Adapted from: https://github.com/BA-Transform/BAT-Image-Classification
"""
def __init__(
self, in_channels, block_size=7, groups=2, rd_ratio=0.25, rd_channels=None, rd_divisor=8,
drop_rate=0.2, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, **_):
super().__init__()
if rd_channels is None:
rd_channels = make_divisible(in_channels * rd_ratio, divisor=rd_divisor)
self.conv1 = ConvNormAct(in_channels, rd_channels, 1, act_layer=act_layer, norm_layer=norm_layer)
self.ba = BilinearAttnTransform(rd_channels, block_size, groups, act_layer=act_layer, norm_layer=norm_layer)
self.conv2 = ConvNormAct(rd_channels, in_channels, 1, act_layer=act_layer, norm_layer=norm_layer)
self.dropout = nn.Dropout2d(p=drop_rate)
def forward(self, x):
xl = self.conv1(x)
y = self.ba(xl)
y = self.conv2(y)
y = self.dropout(y)
return y + x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/attention_pool2d.py | """ Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Union, Tuple
import torch
import torch.nn as nn
from .helpers import to_2tuple
from .pos_embed_sincos import apply_rot_embed, RotaryEmbedding
from .weight_init import trunc_normal_
class RotAttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ rotary (relative) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
Adapted from the AttentionPool2d in CLIP w/ rotary embedding instead of learned embed.
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
NOTE: While this impl does not require a fixed feature size, performance at differeing resolutions from
train varies widely and falls off dramatically. I'm not sure if there is a way around this... -RW
"""
def __init__(
self,
in_features: int,
out_features: int = None,
embed_dim: int = None,
num_heads: int = 4,
qkv_bias: bool = True,
):
super().__init__()
embed_dim = embed_dim or in_features
out_features = out_features or in_features
self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias)
self.proj = nn.Linear(embed_dim, out_features)
self.num_heads = num_heads
assert embed_dim % num_heads == 0
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim ** -0.5
self.pos_embed = RotaryEmbedding(self.head_dim)
trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
nn.init.zeros_(self.qkv.bias)
def forward(self, x):
B, _, H, W = x.shape
N = H * W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
qc, q = q[:, :, :1], q[:, :, 1:]
sin_emb, cos_emb = self.pos_embed.get_embed((H, W))
q = apply_rot_embed(q, sin_emb, cos_emb)
q = torch.cat([qc, q], dim=2)
kc, k = k[:, :, :1], k[:, :, 1:]
k = apply_rot_embed(k, sin_emb, cos_emb)
k = torch.cat([kc, k], dim=2)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N + 1, -1)
x = self.proj(x)
return x[:, 0]
class AttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ learned (absolute) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
It was based on impl in CLIP by OpenAI
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
NOTE: This requires feature size upon construction and well prevent adaptive sizing of the network.
"""
def __init__(
self,
in_features: int,
feat_size: Union[int, Tuple[int, int]],
out_features: int = None,
embed_dim: int = None,
num_heads: int = 4,
qkv_bias: bool = True,
):
super().__init__()
embed_dim = embed_dim or in_features
out_features = out_features or in_features
assert embed_dim % num_heads == 0
self.feat_size = to_2tuple(feat_size)
self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias)
self.proj = nn.Linear(embed_dim, out_features)
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim ** -0.5
spatial_dim = self.feat_size[0] * self.feat_size[1]
self.pos_embed = nn.Parameter(torch.zeros(spatial_dim + 1, in_features))
trunc_normal_(self.pos_embed, std=in_features ** -0.5)
trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
nn.init.zeros_(self.qkv.bias)
def forward(self, x):
B, _, H, W = x.shape
N = H * W
assert self.feat_size[0] == H
assert self.feat_size[1] == W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N + 1, -1)
x = self.proj(x)
return x[:, 0]
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pos_embed.py | """ Position Embedding Utilities
Hacked together by / Copyright 2022 Ross Wightman
"""
import logging
import math
from typing import List, Tuple, Optional, Union
import torch
import torch.nn.functional as F
from .helpers import to_2tuple
_logger = logging.getLogger(__name__)
def resample_abs_pos_embed(
posemb,
new_size: List[int],
old_size: Optional[List[int]] = None,
num_prefix_tokens: int = 1,
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
# sort out sizes, assume square if old size not provided
num_pos_tokens = posemb.shape[1]
num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens
if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:
return posemb
if not old_size:
hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens))
old_size = hw, hw
if num_prefix_tokens:
posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:]
else:
posemb_prefix, posemb = None, posemb
# do the interpolation
embed_dim = posemb.shape[-1]
posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)
posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)
# add back extra (class, etc) prefix tokens
if posemb_prefix is not None:
posemb = torch.cat([posemb_prefix, posemb], dim=1)
if not torch.jit.is_scripting() and verbose:
_logger.info(f'Resized position embedding: {old_size} to {new_size}.')
return posemb
def resample_abs_pos_embed_nhwc(
posemb,
new_size: List[int],
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
if new_size[0] == posemb.shape[-3] and new_size[1] == posemb.shape[-2]:
return posemb
# do the interpolation
posemb = posemb.reshape(1, posemb.shape[-3], posemb.shape[-2], posemb.shape[-1]).permute(0, 3, 1, 2)
posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
posemb = posemb.permute(0, 2, 3, 1)
if not torch.jit.is_scripting() and verbose:
_logger.info(f'Resized position embedding: {posemb.shape[-3:-1]} to {new_size}.')
return posemb | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/eca.py | """
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by Chris Ha https://github.com/VRandme
Original License:
MIT License
Copyright (c) 2019 BangguWu, Qilong Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import math
from torch import nn
import torch.nn.functional as F
from .create_act import create_act_layer
from .helpers import make_divisible
class EcaModule(nn.Module):
"""Constructs an ECA module.
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual calculations according to channel.
gamma, beta: when channel is given parameters of mapping function
refer to original paper https://arxiv.org/pdf/1910.03151.pdf
(default=None. if channel size not given, use k_size given for kernel size.)
kernel_size: Adaptive selection of kernel size (default=3)
gamm: used in kernel_size calc, see above
beta: used in kernel_size calc, see above
act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
gate_layer: gating non-linearity to use
"""
def __init__(
self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid',
rd_ratio=1/8, rd_channels=None, rd_divisor=8, use_mlp=False):
super(EcaModule, self).__init__()
if channels is not None:
t = int(abs(math.log(channels, 2) + beta) / gamma)
kernel_size = max(t if t % 2 else t + 1, 3)
assert kernel_size % 2 == 1
padding = (kernel_size - 1) // 2
if use_mlp:
# NOTE 'mlp' mode is a timm experiment, not in paper
assert channels is not None
if rd_channels is None:
rd_channels = make_divisible(channels * rd_ratio, divisor=rd_divisor)
act_layer = act_layer or nn.ReLU
self.conv = nn.Conv1d(1, rd_channels, kernel_size=1, padding=0, bias=True)
self.act = create_act_layer(act_layer)
self.conv2 = nn.Conv1d(rd_channels, 1, kernel_size=kernel_size, padding=padding, bias=True)
else:
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)
self.act = None
self.conv2 = None
self.gate = create_act_layer(gate_layer)
def forward(self, x):
y = x.mean((2, 3)).view(x.shape[0], 1, -1) # view for 1d conv
y = self.conv(y)
if self.conv2 is not None:
y = self.act(y)
y = self.conv2(y)
y = self.gate(y).view(x.shape[0], -1, 1, 1)
return x * y.expand_as(x)
EfficientChannelAttn = EcaModule # alias
class CecaModule(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circular padding rather than zero padding.
Unlike the spatial dimension, the channels do not have inherent ordering nor
locality. Although this module in essence, applies such an assumption, it is unnecessary
to limit the channels on either "edge" from being circularly adapted to each other.
This will fundamentally increase connectivity and possibly increase performance metrics
(accuracy, robustness), without significantly impacting resource metrics
(parameter size, throughput,latency, etc)
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual calculations according to channel.
gamma, beta: when channel is given parameters of mapping function
refer to original paper https://arxiv.org/pdf/1910.03151.pdf
(default=None. if channel size not given, use k_size given for kernel size.)
kernel_size: Adaptive selection of kernel size (default=3)
gamm: used in kernel_size calc, see above
beta: used in kernel_size calc, see above
act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
gate_layer: gating non-linearity to use
"""
def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid'):
super(CecaModule, self).__init__()
if channels is not None:
t = int(abs(math.log(channels, 2) + beta) / gamma)
kernel_size = max(t if t % 2 else t + 1, 3)
has_act = act_layer is not None
assert kernel_size % 2 == 1
# PyTorch circular padding mode is buggy as of pytorch 1.4
# see https://github.com/pytorch/pytorch/pull/17240
# implement manual circular padding
self.padding = (kernel_size - 1) // 2
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=0, bias=has_act)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
y = x.mean((2, 3)).view(x.shape[0], 1, -1)
# Manually implement circular padding, F.pad does not seemed to be bugged
y = F.pad(y, (self.padding, self.padding), mode='circular')
y = self.conv(y)
y = self.gate(y).view(x.shape[0], -1, 1, 1)
return x * y.expand_as(x)
CircularEfficientChannelAttn = CecaModule
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/activations_me.py | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or basic versions of the activations.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
@torch.jit.script
def swish_jit_fwd(x):
return x.mul(torch.sigmoid(x))
@torch.jit.script
def swish_jit_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
return grad_output * (x_sigmoid * (1 + x * (1 - x_sigmoid)))
class SwishJitAutoFn(torch.autograd.Function):
""" torch.jit.script optimised Swish w/ memory-efficient checkpoint
Inspired by conversation btw Jeremy Howard & Adam Pazske
https://twitter.com/jeremyphoward/status/1188251041835315200
"""
@staticmethod
def symbolic(g, x):
return g.op("Mul", x, g.op("Sigmoid", x))
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return swish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return swish_jit_bwd(x, grad_output)
def swish_me(x, inplace=False):
return SwishJitAutoFn.apply(x)
class SwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishMe, self).__init__()
def forward(self, x):
return SwishJitAutoFn.apply(x)
@torch.jit.script
def mish_jit_fwd(x):
return x.mul(torch.tanh(F.softplus(x)))
@torch.jit.script
def mish_jit_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
x_tanh_sp = F.softplus(x).tanh()
return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))
class MishJitAutoFn(torch.autograd.Function):
""" Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
A memory efficient, jit scripted variant of Mish
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return mish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return mish_jit_bwd(x, grad_output)
def mish_me(x, inplace=False):
return MishJitAutoFn.apply(x)
class MishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(MishMe, self).__init__()
def forward(self, x):
return MishJitAutoFn.apply(x)
@torch.jit.script
def hard_sigmoid_jit_fwd(x, inplace: bool = False):
return (x + 3).clamp(min=0, max=6).div(6.)
@torch.jit.script
def hard_sigmoid_jit_bwd(x, grad_output):
m = torch.ones_like(x) * ((x >= -3.) & (x <= 3.)) / 6.
return grad_output * m
class HardSigmoidJitAutoFn(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_sigmoid_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_sigmoid_jit_bwd(x, grad_output)
def hard_sigmoid_me(x, inplace: bool = False):
return HardSigmoidJitAutoFn.apply(x)
class HardSigmoidMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidMe, self).__init__()
def forward(self, x):
return HardSigmoidJitAutoFn.apply(x)
@torch.jit.script
def hard_swish_jit_fwd(x):
return x * (x + 3).clamp(min=0, max=6).div(6.)
@torch.jit.script
def hard_swish_jit_bwd(x, grad_output):
m = torch.ones_like(x) * (x >= 3.)
m = torch.where((x >= -3.) & (x <= 3.), x / 3. + .5, m)
return grad_output * m
class HardSwishJitAutoFn(torch.autograd.Function):
"""A memory efficient, jit-scripted HardSwish activation"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_swish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_swish_jit_bwd(x, grad_output)
@staticmethod
def symbolic(g, self):
input = g.op("Add", self, g.op('Constant', value_t=torch.tensor(3, dtype=torch.float)))
hardtanh_ = g.op("Clip", input, g.op('Constant', value_t=torch.tensor(0, dtype=torch.float)), g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
hardtanh_ = g.op("Div", hardtanh_, g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
return g.op("Mul", self, hardtanh_)
def hard_swish_me(x, inplace=False):
return HardSwishJitAutoFn.apply(x)
class HardSwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishMe, self).__init__()
def forward(self, x):
return HardSwishJitAutoFn.apply(x)
@torch.jit.script
def hard_mish_jit_fwd(x):
return 0.5 * x * (x + 2).clamp(min=0, max=2)
@torch.jit.script
def hard_mish_jit_bwd(x, grad_output):
m = torch.ones_like(x) * (x >= -2.)
m = torch.where((x >= -2.) & (x <= 0.), x + 1., m)
return grad_output * m
class HardMishJitAutoFn(torch.autograd.Function):
""" A memory efficient, jit scripted variant of Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_mish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_mish_jit_bwd(x, grad_output)
def hard_mish_me(x, inplace: bool = False):
return HardMishJitAutoFn.apply(x)
class HardMishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishMe, self).__init__()
def forward(self, x):
return HardMishJitAutoFn.apply(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/norm.py | """ Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm, fast_rms_norm
class GroupNorm(nn.GroupNorm):
def __init__(self, num_channels, num_groups=32, eps=1e-5, affine=True):
# NOTE num_channels is swapped to first arg for consistency in swapping norm layers with BN
super().__init__(num_groups, num_channels, eps=eps, affine=affine)
self.fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x):
if self.fast_norm:
return fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
class GroupNorm1(nn.GroupNorm):
""" Group Normalization with 1 group.
Input: tensor in shape [B, C, *]
"""
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs)
self.fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.fast_norm:
return fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
class LayerNorm(nn.LayerNorm):
""" LayerNorm w/ fast norm option
"""
def __init__(self, num_channels, eps=1e-6, affine=True):
super().__init__(num_channels, eps=eps, elementwise_affine=affine)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
return x
class LayerNorm2d(nn.LayerNorm):
""" LayerNorm for channels of '2D' spatial NCHW tensors """
def __init__(self, num_channels, eps=1e-6, affine=True):
super().__init__(num_channels, eps=eps, elementwise_affine=affine)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.permute(0, 2, 3, 1)
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = x.permute(0, 3, 1, 2)
return x
def _is_contiguous(tensor: torch.Tensor) -> bool:
# jit is oh so lovely :/
if torch.jit.is_scripting():
return tensor.is_contiguous()
else:
return tensor.is_contiguous(memory_format=torch.contiguous_format)
@torch.jit.script
def _layer_norm_cf(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
s, u = torch.var_mean(x, dim=1, unbiased=False, keepdim=True)
x = (x - u) * torch.rsqrt(s + eps)
x = x * weight[:, None, None] + bias[:, None, None]
return x
def _layer_norm_cf_sqm(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
u = x.mean(dim=1, keepdim=True)
s = ((x * x).mean(dim=1, keepdim=True) - (u * u)).clamp(0)
x = (x - u) * torch.rsqrt(s + eps)
x = x * weight.view(1, -1, 1, 1) + bias.view(1, -1, 1, 1)
return x
class LayerNormExp2d(nn.LayerNorm):
""" LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
Experimental implementation w/ manual norm for tensors non-contiguous tensors.
This improves throughput in some scenarios (tested on Ampere GPU), esp w/ channels_last
layout. However, benefits are not always clear and can perform worse on other GPUs.
"""
def __init__(self, num_channels, eps=1e-6):
super().__init__(num_channels, eps=eps)
def forward(self, x) -> torch.Tensor:
if _is_contiguous(x):
x = F.layer_norm(
x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
else:
x = _layer_norm_cf(x, self.weight, self.bias, self.eps)
return x
class RmsNorm(nn.Module):
""" RmsNorm w/ fast (apex) norm if available
"""
__constants__ = ['normalized_shape', 'eps', 'elementwise_affine']
normalized_shape: Tuple[int, ...]
eps: float
elementwise_affine: bool
def __init__(self, channels, eps=1e-6, affine=True, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
normalized_shape = channels
if isinstance(normalized_shape, numbers.Integral):
# mypy error: incompatible types in assignment
normalized_shape = (normalized_shape,) # type: ignore[assignment]
self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type]
self.eps = eps
self.elementwise_affine = affine
if self.elementwise_affine:
self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self) -> None:
if self.elementwise_affine:
nn.init.ones_(self.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# NOTE fast norm fallback needs our rms norm impl, so both paths through here.
# Since there is no built-in PyTorch impl, always use APEX RmsNorm if is installed.
x = fast_rms_norm(x, self.normalized_shape, self.weight, self.eps)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/trace_utils.py | try:
from torch import _assert
except ImportError:
def _assert(condition: bool, message: str):
assert condition, message
def _float_to_int(x: float) -> int:
"""
Symbolic tracing helper to substitute for inbuilt `int`.
Hint: Inbuilt `int` can't accept an argument of type `Proxy`
"""
return int(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/fast_norm.py | """ 'Fast' Normalization Functions
For GroupNorm and LayerNorm these functions bypass typical AMP upcast to float32.
Additionally, for LayerNorm, the APEX fused LN is used if available (which also does not upcast)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import List, Optional
import torch
from torch.nn import functional as F
try:
from apex.normalization.fused_layer_norm import fused_layer_norm_affine
has_apex = True
except ImportError:
has_apex = False
try:
from apex.normalization.fused_layer_norm import fused_rms_norm_affine, fused_rms_norm
has_apex_rmsnorm = True
except ImportError:
has_apex_rmsnorm = False
# fast (ie lower precision LN) can be disabled with this flag if issues crop up
_USE_FAST_NORM = False # defaulting to False for now
def is_fast_norm():
return _USE_FAST_NORM
def set_fast_norm(enable=True):
global _USE_FAST_NORM
_USE_FAST_NORM = enable
def fast_group_norm(
x: torch.Tensor,
num_groups: int,
weight: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
eps: float = 1e-5
) -> torch.Tensor:
if torch.jit.is_scripting():
# currently cannot use is_autocast_enabled within torchscript
return F.group_norm(x, num_groups, weight, bias, eps)
if torch.is_autocast_enabled():
# normally native AMP casts GN inputs to float32
# here we use the low precision autocast dtype
# FIXME what to do re CPU autocast?
dt = torch.get_autocast_gpu_dtype()
x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
with torch.cuda.amp.autocast(enabled=False):
return F.group_norm(x, num_groups, weight, bias, eps)
def fast_layer_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
eps: float = 1e-5
) -> torch.Tensor:
if torch.jit.is_scripting():
# currently cannot use is_autocast_enabled within torchscript
return F.layer_norm(x, normalized_shape, weight, bias, eps)
if has_apex:
return fused_layer_norm_affine(x, weight, bias, normalized_shape, eps)
if torch.is_autocast_enabled():
# normally native AMP casts LN inputs to float32
# apex LN does not, this is behaving like Apex
dt = torch.get_autocast_gpu_dtype()
# FIXME what to do re CPU autocast?
x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
with torch.cuda.amp.autocast(enabled=False):
return F.layer_norm(x, normalized_shape, weight, bias, eps)
def rms_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
eps: float = 1e-5,
):
norm_ndim = len(normalized_shape)
if torch.jit.is_scripting():
# ndim = len(x.shape)
# dims = list(range(ndim - norm_ndim, ndim)) # this doesn't work on pytorch <= 1.13.x
# NOTE -ve dims cause torchscript to crash in some cases, out of options to work around
assert norm_ndim == 1
v = torch.var(x, dim=-1).unsqueeze(-1) # ts crashes with -ve dim + keepdim=True
else:
dims = tuple(range(-1, -norm_ndim - 1, -1))
v = torch.var(x, dim=dims, keepdim=True)
x = x * torch.rsqrt(v + eps)
if weight is not None:
x = x * weight
return x
def fast_rms_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
eps: float = 1e-5,
) -> torch.Tensor:
if torch.jit.is_scripting():
# this must be by itself, cannot merge with has_apex_rmsnorm
return rms_norm(x, normalized_shape, weight, eps)
if has_apex_rmsnorm:
if weight is None:
return fused_rms_norm(x, normalized_shape, eps)
else:
return fused_rms_norm_affine(x, weight, normalized_shape, eps)
# fallback
return rms_norm(x, normalized_shape, weight, eps)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/classifier.py | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from .create_act import get_act_layer
from .create_norm import get_norm_layer
def _create_pool(
num_features: int,
num_classes: int,
pool_type: str = 'avg',
use_conv: bool = False,
input_fmt: Optional[str] = None,
):
flatten_in_pool = not use_conv # flatten when we use a Linear layer after pooling
if not pool_type:
assert num_classes == 0 or use_conv,\
'Pooling can only be disabled if classifier is also removed or conv classifier is used'
flatten_in_pool = False # disable flattening if pooling is pass-through (no pooling)
global_pool = SelectAdaptivePool2d(
pool_type=pool_type,
flatten=flatten_in_pool,
input_fmt=input_fmt,
)
num_pooled_features = num_features * global_pool.feat_mult()
return global_pool, num_pooled_features
def _create_fc(num_features, num_classes, use_conv=False):
if num_classes <= 0:
fc = nn.Identity() # pass-through (no classifier)
elif use_conv:
fc = nn.Conv2d(num_features, num_classes, 1, bias=True)
else:
fc = nn.Linear(num_features, num_classes, bias=True)
return fc
def create_classifier(
num_features: int,
num_classes: int,
pool_type: str = 'avg',
use_conv: bool = False,
input_fmt: str = 'NCHW',
drop_rate: Optional[float] = None,
):
global_pool, num_pooled_features = _create_pool(
num_features,
num_classes,
pool_type,
use_conv=use_conv,
input_fmt=input_fmt,
)
fc = _create_fc(
num_pooled_features,
num_classes,
use_conv=use_conv,
)
if drop_rate is not None:
dropout = nn.Dropout(drop_rate)
return global_pool, dropout, fc
return global_pool, fc
class ClassifierHead(nn.Module):
"""Classifier head w/ configurable global pooling and dropout."""
def __init__(
self,
in_features: int,
num_classes: int,
pool_type: str = 'avg',
drop_rate: float = 0.,
use_conv: bool = False,
input_fmt: str = 'NCHW',
):
"""
Args:
in_features: The number of input features.
num_classes: The number of classes for the final classifier layer (output).
pool_type: Global pooling type, pooling disabled if empty string ('').
drop_rate: Pre-classifier dropout rate.
"""
super(ClassifierHead, self).__init__()
self.in_features = in_features
self.use_conv = use_conv
self.input_fmt = input_fmt
global_pool, fc = create_classifier(
in_features,
num_classes,
pool_type,
use_conv=use_conv,
input_fmt=input_fmt,
)
self.global_pool = global_pool
self.drop = nn.Dropout(drop_rate)
self.fc = fc
self.flatten = nn.Flatten(1) if use_conv and pool_type else nn.Identity()
def reset(self, num_classes, pool_type=None):
if pool_type is not None and pool_type != self.global_pool.pool_type:
self.global_pool, self.fc = create_classifier(
self.in_features,
num_classes,
pool_type=pool_type,
use_conv=self.use_conv,
input_fmt=self.input_fmt,
)
self.flatten = nn.Flatten(1) if self.use_conv and pool_type else nn.Identity()
else:
num_pooled_features = self.in_features * self.global_pool.feat_mult()
self.fc = _create_fc(
num_pooled_features,
num_classes,
use_conv=self.use_conv,
)
def forward(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.drop(x)
if pre_logits:
return self.flatten(x)
x = self.fc(x)
return self.flatten(x)
class NormMlpClassifierHead(nn.Module):
def __init__(
self,
in_features: int,
num_classes: int,
hidden_size: Optional[int] = None,
pool_type: str = 'avg',
drop_rate: float = 0.,
norm_layer: Union[str, Callable] = 'layernorm2d',
act_layer: Union[str, Callable] = 'tanh',
):
"""
Args:
in_features: The number of input features.
num_classes: The number of classes for the final classifier layer (output).
hidden_size: The hidden size of the MLP (pre-logits FC layer) if not None.
pool_type: Global pooling type, pooling disabled if empty string ('').
drop_rate: Pre-classifier dropout rate.
norm_layer: Normalization layer type.
act_layer: MLP activation layer type (only used if hidden_size is not None).
"""
super().__init__()
self.in_features = in_features
self.hidden_size = hidden_size
self.num_features = in_features
self.use_conv = not pool_type
norm_layer = get_norm_layer(norm_layer)
act_layer = get_act_layer(act_layer)
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
self.norm = norm_layer(in_features)
self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
if hidden_size:
self.pre_logits = nn.Sequential(OrderedDict([
('fc', linear_layer(in_features, hidden_size)),
('act', act_layer()),
]))
self.num_features = hidden_size
else:
self.pre_logits = nn.Identity()
self.drop = nn.Dropout(drop_rate)
self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def reset(self, num_classes, global_pool=None):
if global_pool is not None:
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
self.use_conv = self.global_pool.is_identity()
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
if self.hidden_size:
if ((isinstance(self.pre_logits.fc, nn.Conv2d) and not self.use_conv) or
(isinstance(self.pre_logits.fc, nn.Linear) and self.use_conv)):
with torch.no_grad():
new_fc = linear_layer(self.in_features, self.hidden_size)
new_fc.weight.copy_(self.pre_logits.fc.weight.reshape(new_fc.weight.shape))
new_fc.bias.copy_(self.pre_logits.fc.bias)
self.pre_logits.fc = new_fc
self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.norm(x)
x = self.flatten(x)
x = self.pre_logits(x)
x = self.drop(x)
if pre_logits:
return x
x = self.fc(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pool2d_same.py | """ AvgPool2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
from .helpers import to_2tuple
from .padding import pad_same, get_padding_value
def avg_pool2d_same(x, kernel_size: List[int], stride: List[int], padding: List[int] = (0, 0),
ceil_mode: bool = False, count_include_pad: bool = True):
# FIXME how to deal with count_include_pad vs not for external padding?
x = pad_same(x, kernel_size, stride)
return F.avg_pool2d(x, kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
class AvgPool2dSame(nn.AvgPool2d):
""" Tensorflow like 'SAME' wrapper for 2D average pooling
"""
def __init__(self, kernel_size: int, stride=None, padding=0, ceil_mode=False, count_include_pad=True):
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
super(AvgPool2dSame, self).__init__(kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
def forward(self, x):
x = pad_same(x, self.kernel_size, self.stride)
return F.avg_pool2d(
x, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad)
def max_pool2d_same(
x, kernel_size: List[int], stride: List[int], padding: List[int] = (0, 0),
dilation: List[int] = (1, 1), ceil_mode: bool = False):
x = pad_same(x, kernel_size, stride, value=-float('inf'))
return F.max_pool2d(x, kernel_size, stride, (0, 0), dilation, ceil_mode)
class MaxPool2dSame(nn.MaxPool2d):
""" Tensorflow like 'SAME' wrapper for 2D max pooling
"""
def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False):
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
dilation = to_2tuple(dilation)
super(MaxPool2dSame, self).__init__(kernel_size, stride, (0, 0), dilation, ceil_mode)
def forward(self, x):
x = pad_same(x, self.kernel_size, self.stride, value=-float('inf'))
return F.max_pool2d(x, self.kernel_size, self.stride, (0, 0), self.dilation, self.ceil_mode)
def create_pool2d(pool_type, kernel_size, stride=None, **kwargs):
stride = stride or kernel_size
padding = kwargs.pop('padding', '')
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, **kwargs)
if is_dynamic:
if pool_type == 'avg':
return AvgPool2dSame(kernel_size, stride=stride, **kwargs)
elif pool_type == 'max':
return MaxPool2dSame(kernel_size, stride=stride, **kwargs)
else:
assert False, f'Unsupported pool type {pool_type}'
else:
if pool_type == 'avg':
return nn.AvgPool2d(kernel_size, stride=stride, padding=padding, **kwargs)
elif pool_type == 'max':
return nn.MaxPool2d(kernel_size, stride=stride, padding=padding, **kwargs)
else:
assert False, f'Unsupported pool type {pool_type}'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/std_conv.py | """ Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
Code: https://github.com/joe-siyuan-qiao/WeightStandardization
ScaledStdConv:
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets
Hacked together by / copyright Ross Wightman, 2021.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .padding import get_padding, get_padding_value, pad_same
class StdConv2d(nn.Conv2d):
"""Conv2d with Weight Standardization. Used for BiT ResNet-V2 models.
Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=False, eps=1e-6):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
self.eps = eps
def forward(self, x):
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
class StdConv2dSame(nn.Conv2d):
"""Conv2d with Weight Standardization. TF compatible SAME padding. Used for ViT Hybrid model.
Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=False, eps=1e-6):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.same_pad = is_dynamic
self.eps = eps
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
class ScaledStdConv2d(nn.Conv2d):
"""Conv2d layer with Scaled Weight Standardization.
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
https://arxiv.org/abs/2101.08692
NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5 # gamma * 1 / sqrt(fan-in)
self.eps = eps
def forward(self, x):
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class ScaledStdConv2dSame(nn.Conv2d):
"""Conv2d layer with Scaled Weight Standardization and Tensorflow-like SAME padding support
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
https://arxiv.org/abs/2101.08692
NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5
self.same_pad = is_dynamic
self.eps = eps
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/activations.py | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace: bool = False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())
class Swish(nn.Module):
def __init__(self, inplace: bool = False):
super(Swish, self).__init__()
self.inplace = inplace
def forward(self, x):
return swish(x, self.inplace)
def mish(x, inplace: bool = False):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
NOTE: I don't have a working inplace variant
"""
return x.mul(F.softplus(x).tanh())
class Mish(nn.Module):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
"""
def __init__(self, inplace: bool = False):
super(Mish, self).__init__()
def forward(self, x):
return mish(x)
def sigmoid(x, inplace: bool = False):
return x.sigmoid_() if inplace else x.sigmoid()
# PyTorch has this, but not with a consistent inplace argmument interface
class Sigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(Sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.sigmoid_() if self.inplace else x.sigmoid()
def tanh(x, inplace: bool = False):
return x.tanh_() if inplace else x.tanh()
# PyTorch has this, but not with a consistent inplace argmument interface
class Tanh(nn.Module):
def __init__(self, inplace: bool = False):
super(Tanh, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.tanh_() if self.inplace else x.tanh()
def hard_swish(x, inplace: bool = False):
inner = F.relu6(x + 3.).div_(6.)
return x.mul_(inner) if inplace else x.mul(inner)
class HardSwish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwish, self).__init__()
self.inplace = inplace
def forward(self, x):
return hard_swish(x, self.inplace)
def hard_sigmoid(x, inplace: bool = False):
if inplace:
return x.add_(3.).clamp_(0., 6.).div_(6.)
else:
return F.relu6(x + 3.) / 6.
class HardSigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return hard_sigmoid(x, self.inplace)
def hard_mish(x, inplace: bool = False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMish, self).__init__()
self.inplace = inplace
def forward(self, x):
return hard_mish(x, self.inplace)
class PReLU(nn.PReLU):
"""Applies PReLU (w/ dummy inplace arg)
"""
def __init__(self, num_parameters: int = 1, init: float = 0.25, inplace: bool = False) -> None:
super(PReLU, self).__init__(num_parameters=num_parameters, init=init)
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.prelu(input, self.weight)
def gelu(x: torch.Tensor, inplace: bool = False) -> torch.Tensor:
return F.gelu(x)
class GELU(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELU, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.gelu(input)
def gelu_tanh(x: torch.Tensor, inplace: bool = False) -> torch.Tensor:
return F.gelu(x, approximate='tanh')
class GELUTanh(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELUTanh, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.gelu(input, approximate='tanh')
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/conv_bn_act.py | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.BatchNorm2d,
norm_kwargs=None,
act_layer=nn.ReLU,
act_kwargs=None,
drop_layer=None,
):
super(ConvNormAct, self).__init__()
norm_kwargs = norm_kwargs or {}
act_kwargs = act_kwargs or {}
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
# NOTE for backwards compatibility with models that use separate norm and act layer definitions
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
if drop_layer:
norm_kwargs['drop_layer'] = drop_layer
self.bn = norm_act_layer(
out_channels,
apply_act=apply_act,
act_kwargs=act_kwargs,
**norm_kwargs,
)
@property
def in_channels(self):
return self.conv.in_channels
@property
def out_channels(self):
return self.conv.out_channels
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
ConvBnAct = ConvNormAct
def create_aa(aa_layer, channels, stride=2, enable=True):
if not aa_layer or not enable:
return nn.Identity()
if isinstance(aa_layer, functools.partial):
if issubclass(aa_layer.func, nn.AvgPool2d):
return aa_layer()
else:
return aa_layer(channels)
elif issubclass(aa_layer, nn.AvgPool2d):
return aa_layer(stride)
else:
return aa_layer(channels=channels, stride=stride)
class ConvNormActAa(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.BatchNorm2d,
norm_kwargs=None,
act_layer=nn.ReLU,
act_kwargs=None,
aa_layer=None,
drop_layer=None,
):
super(ConvNormActAa, self).__init__()
use_aa = aa_layer is not None and stride == 2
norm_kwargs = norm_kwargs or {}
act_kwargs = act_kwargs or {}
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
# NOTE for backwards compatibility with models that use separate norm and act layer definitions
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
if drop_layer:
norm_kwargs['drop_layer'] = drop_layer
self.bn = norm_act_layer(out_channels, apply_act=apply_act, act_kwargs=act_kwargs, **norm_kwargs)
self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
@property
def in_channels(self):
return self.conv.in_channels
@property
def out_channels(self):
return self.conv.out_channels
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.aa(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/padding.py | """ Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple
import torch
import torch.nn.functional as F
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution
def get_same_padding(x: int, kernel_size: int, stride: int, dilation: int):
if isinstance(x, torch.Tensor):
return torch.clamp(((x / stride).ceil() - 1) * stride + (kernel_size - 1) * dilation + 1 - x, min=0)
else:
return max((math.ceil(x / stride) - 1) * stride + (kernel_size - 1) * dilation + 1 - x, 0)
# Can SAME padding for given args be done statically?
def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_):
return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
def pad_same_arg(
input_size: List[int],
kernel_size: List[int],
stride: List[int],
dilation: List[int] = (1, 1),
) -> List[int]:
ih, iw = input_size
kh, kw = kernel_size
pad_h = get_same_padding(ih, kh, stride[0], dilation[0])
pad_w = get_same_padding(iw, kw, stride[1], dilation[1])
return [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
# Dynamically pad input x with 'SAME' padding for conv with specified args
def pad_same(
x,
kernel_size: List[int],
stride: List[int],
dilation: List[int] = (1, 1),
value: float = 0,
):
ih, iw = x.size()[-2:]
pad_h = get_same_padding(ih, kernel_size[0], stride[0], dilation[0])
pad_w = get_same_padding(iw, kernel_size[1], stride[1], dilation[1])
x = F.pad(x, (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2), value=value)
return x
def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]:
dynamic = False
if isinstance(padding, str):
# for any string padding, the padding will be calculated for you, one of three ways
padding = padding.lower()
if padding == 'same':
# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
if is_static_pad(kernel_size, **kwargs):
# static case, no extra overhead
padding = get_padding(kernel_size, **kwargs)
else:
# dynamic 'SAME' padding, has runtime/GPU memory overhead
padding = 0
dynamic = True
elif padding == 'valid':
# 'VALID' padding, same as padding=0
padding = 0
else:
# Default to PyTorch style 'same'-ish symmetric padding
padding = get_padding(kernel_size, **kwargs)
return padding, dynamic
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/median_pool.py | """ Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int or 2-tuple
stride: pool stride, int or 2-tuple
padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad
same: override padding and enforce same padding, boolean
"""
def __init__(self, kernel_size=3, stride=1, padding=0, same=False):
super(MedianPool2d, self).__init__()
self.k = to_2tuple(kernel_size)
self.stride = to_2tuple(stride)
self.padding = to_4tuple(padding) # convert to l, r, t, b
self.same = same
def _padding(self, x):
if self.same:
ih, iw = x.size()[2:]
if ih % self.stride[0] == 0:
ph = max(self.k[0] - self.stride[0], 0)
else:
ph = max(self.k[0] - (ih % self.stride[0]), 0)
if iw % self.stride[1] == 0:
pw = max(self.k[1] - self.stride[1], 0)
else:
pw = max(self.k[1] - (iw % self.stride[1]), 0)
pl = pw // 2
pr = pw - pl
pt = ph // 2
pb = ph - pt
padding = (pl, pr, pt, pb)
else:
padding = self.padding
return padding
def forward(self, x):
x = F.pad(x, self._padding(x), mode='reflect')
x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1])
x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0]
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/gather_excite.py | """ Gather-Excite Attention Block
Paper: `Gather-Excite: Exploiting Feature Context in CNNs` - https://arxiv.org/abs/1810.12348
Official code here, but it's only partial impl in Caffe: https://github.com/hujie-frank/GENet
I've tried to support all of the extent both w/ and w/o params. I don't believe I've seen another
impl that covers all of the cases.
NOTE: extent=0 + extra_params=False is equivalent to Squeeze-and-Excitation
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
from torch import nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer, get_act_layer
from .create_conv2d import create_conv2d
from .helpers import make_divisible
from .mlp import ConvMlp
class GatherExcite(nn.Module):
""" Gather-Excite Attention Module
"""
def __init__(
self, channels, feat_size=None, extra_params=False, extent=0, use_mlp=True,
rd_ratio=1./16, rd_channels=None, rd_divisor=1, add_maxpool=False,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, gate_layer='sigmoid'):
super(GatherExcite, self).__init__()
self.add_maxpool = add_maxpool
act_layer = get_act_layer(act_layer)
self.extent = extent
if extra_params:
self.gather = nn.Sequential()
if extent == 0:
assert feat_size is not None, 'spatial feature size must be specified for global extent w/ params'
self.gather.add_module(
'conv1', create_conv2d(channels, channels, kernel_size=feat_size, stride=1, depthwise=True))
if norm_layer:
self.gather.add_module(f'norm1', nn.BatchNorm2d(channels))
else:
assert extent % 2 == 0
num_conv = int(math.log2(extent))
for i in range(num_conv):
self.gather.add_module(
f'conv{i + 1}',
create_conv2d(channels, channels, kernel_size=3, stride=2, depthwise=True))
if norm_layer:
self.gather.add_module(f'norm{i + 1}', nn.BatchNorm2d(channels))
if i != num_conv - 1:
self.gather.add_module(f'act{i + 1}', act_layer(inplace=True))
else:
self.gather = None
if self.extent == 0:
self.gk = 0
self.gs = 0
else:
assert extent % 2 == 0
self.gk = self.extent * 2 - 1
self.gs = self.extent
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.mlp = ConvMlp(channels, rd_channels, act_layer=act_layer) if use_mlp else nn.Identity()
self.gate = create_act_layer(gate_layer)
def forward(self, x):
size = x.shape[-2:]
if self.gather is not None:
x_ge = self.gather(x)
else:
if self.extent == 0:
# global extent
x_ge = x.mean(dim=(2, 3), keepdims=True)
if self.add_maxpool:
# experimental codepath, may remove or change
x_ge = 0.5 * x_ge + 0.5 * x.amax((2, 3), keepdim=True)
else:
x_ge = F.avg_pool2d(
x, kernel_size=self.gk, stride=self.gs, padding=self.gk // 2, count_include_pad=False)
if self.add_maxpool:
# experimental codepath, may remove or change
x_ge = 0.5 * x_ge + 0.5 * F.max_pool2d(x, kernel_size=self.gk, stride=self.gs, padding=self.gk // 2)
x_ge = self.mlp(x_ge)
if x_ge.shape[-1] != 1 or x_ge.shape[-2] != 1:
x_ge = F.interpolate(x_ge, size=size)
return x * self.gate(x_ge)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_norm.py | """ Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import types
import functools
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d
_NORM_MAP = dict(
batchnorm=nn.BatchNorm2d,
batchnorm2d=nn.BatchNorm2d,
batchnorm1d=nn.BatchNorm1d,
groupnorm=GroupNorm,
groupnorm1=GroupNorm1,
layernorm=LayerNorm,
layernorm2d=LayerNorm2d,
)
_NORM_TYPES = {m for n, m in _NORM_MAP.items()}
def create_norm_layer(layer_name, num_features, **kwargs):
layer = get_norm_layer(layer_name)
layer_instance = layer(num_features, **kwargs)
return layer_instance
def get_norm_layer(norm_layer):
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
norm_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_kwargs.update(norm_layer.keywords)
norm_layer = norm_layer.func
if isinstance(norm_layer, str):
layer_name = norm_layer.replace('_', '')
norm_layer = _NORM_MAP.get(layer_name, None)
elif norm_layer in _NORM_TYPES:
norm_layer = norm_layer
elif isinstance(norm_layer, types.FunctionType):
# if function type, assume it is a lambda/fn that creates a norm layer
norm_layer = norm_layer
else:
type_name = norm_layer.__name__.lower().replace('_', '')
norm_layer = _NORM_MAP.get(type_name, None)
assert norm_layer is not None, f"No equivalent norm layer for {type_name}"
if norm_kwargs:
norm_layer = functools.partial(norm_layer, **norm_kwargs) # bind/rebind args
return norm_layer
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/ml_decoder.py | from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identity()
del model.fc
num_classes = model.num_classes
num_features = model.num_features
model.fc = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
elif hasattr(model, 'global_pool') and hasattr(model, 'classifier'): # EfficientNet
model.global_pool = nn.Identity()
del model.classifier
num_classes = model.num_classes
num_features = model.num_features
model.classifier = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
elif 'RegNet' in model._get_name() or 'TResNet' in model._get_name(): # hasattr(model, 'head')
del model.head
num_classes = model.num_classes
num_features = model.num_features
model.head = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
else:
print("Model code-writing is not aligned currently with ml-decoder")
exit(-1)
if hasattr(model, 'drop_rate'): # Ml-Decoder has inner dropout
model.drop_rate = 0
return model
class TransformerDecoderLayerOptimal(nn.Module):
def __init__(self, d_model, nhead=8, dim_feedforward=2048, dropout=0.1, activation="relu",
layer_norm_eps=1e-5) -> None:
super(TransformerDecoderLayerOptimal, self).__init__()
self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.dropout = nn.Dropout(dropout)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.activation = _get_activation_fn(activation)
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = torch.nn.functional.relu
super(TransformerDecoderLayerOptimal, self).__setstate__(state)
def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
tgt = tgt + self.dropout1(tgt)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(tgt, memory, memory)[0]
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
# @torch.jit.script
# class ExtrapClasses(object):
# def __init__(self, num_queries: int, group_size: int):
# self.num_queries = num_queries
# self.group_size = group_size
#
# def __call__(self, h: torch.Tensor, class_embed_w: torch.Tensor, class_embed_b: torch.Tensor, out_extrap:
# torch.Tensor):
# # h = h.unsqueeze(-1).expand(-1, -1, -1, self.group_size)
# h = h[..., None].repeat(1, 1, 1, self.group_size) # torch.Size([bs, 5, 768, groups])
# w = class_embed_w.view((self.num_queries, h.shape[2], self.group_size))
# out = (h * w).sum(dim=2) + class_embed_b
# out = out.view((h.shape[0], self.group_size * self.num_queries))
# return out
@torch.jit.script
class GroupFC(object):
def __init__(self, embed_len_decoder: int):
self.embed_len_decoder = embed_len_decoder
def __call__(self, h: torch.Tensor, duplicate_pooling: torch.Tensor, out_extrap: torch.Tensor):
for i in range(self.embed_len_decoder):
h_i = h[:, i, :]
w_i = duplicate_pooling[i, :, :]
out_extrap[:, i, :] = torch.matmul(h_i, w_i)
class MLDecoder(nn.Module):
def __init__(self, num_classes, num_of_groups=-1, decoder_embedding=768, initial_num_features=2048):
super(MLDecoder, self).__init__()
embed_len_decoder = 100 if num_of_groups < 0 else num_of_groups
if embed_len_decoder > num_classes:
embed_len_decoder = num_classes
# switching to 768 initial embeddings
decoder_embedding = 768 if decoder_embedding < 0 else decoder_embedding
self.embed_standart = nn.Linear(initial_num_features, decoder_embedding)
# decoder
decoder_dropout = 0.1
num_layers_decoder = 1
dim_feedforward = 2048
layer_decode = TransformerDecoderLayerOptimal(d_model=decoder_embedding,
dim_feedforward=dim_feedforward, dropout=decoder_dropout)
self.decoder = nn.TransformerDecoder(layer_decode, num_layers=num_layers_decoder)
# non-learnable queries
self.query_embed = nn.Embedding(embed_len_decoder, decoder_embedding)
self.query_embed.requires_grad_(False)
# group fully-connected
self.num_classes = num_classes
self.duplicate_factor = int(num_classes / embed_len_decoder + 0.999)
self.duplicate_pooling = torch.nn.Parameter(
torch.Tensor(embed_len_decoder, decoder_embedding, self.duplicate_factor))
self.duplicate_pooling_bias = torch.nn.Parameter(torch.Tensor(num_classes))
torch.nn.init.xavier_normal_(self.duplicate_pooling)
torch.nn.init.constant_(self.duplicate_pooling_bias, 0)
self.group_fc = GroupFC(embed_len_decoder)
def forward(self, x):
if len(x.shape) == 4: # [bs,2048, 7,7]
embedding_spatial = x.flatten(2).transpose(1, 2)
else: # [bs, 197,468]
embedding_spatial = x
embedding_spatial_786 = self.embed_standart(embedding_spatial)
embedding_spatial_786 = torch.nn.functional.relu(embedding_spatial_786, inplace=True)
bs = embedding_spatial_786.shape[0]
query_embed = self.query_embed.weight
# tgt = query_embed.unsqueeze(1).repeat(1, bs, 1)
tgt = query_embed.unsqueeze(1).expand(-1, bs, -1) # no allocation of memory with expand
h = self.decoder(tgt, embedding_spatial_786.transpose(0, 1)) # [embed_len_decoder, batch, 768]
h = h.transpose(0, 1)
out_extrap = torch.zeros(h.shape[0], h.shape[1], self.duplicate_factor, device=h.device, dtype=h.dtype)
self.group_fc(h, self.duplicate_pooling, out_extrap)
h_out = out_extrap.flatten(1)[:, :self.num_classes]
h_out += self.duplicate_pooling_bias
logits = h_out
return logits
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/loss/asymmetric_loss.py | import torch
import torch.nn as nn
class AsymmetricLossMultiLabel(nn.Module):
def __init__(self, gamma_neg=4, gamma_pos=1, clip=0.05, eps=1e-8, disable_torch_grad_focal_loss=False):
super(AsymmetricLossMultiLabel, self).__init__()
self.gamma_neg = gamma_neg
self.gamma_pos = gamma_pos
self.clip = clip
self.disable_torch_grad_focal_loss = disable_torch_grad_focal_loss
self.eps = eps
def forward(self, x, y):
""""
Parameters
----------
x: input logits
y: targets (multi-label binarized vector)
"""
# Calculating Probabilities
x_sigmoid = torch.sigmoid(x)
xs_pos = x_sigmoid
xs_neg = 1 - x_sigmoid
# Asymmetric Clipping
if self.clip is not None and self.clip > 0:
xs_neg = (xs_neg + self.clip).clamp(max=1)
# Basic CE calculation
los_pos = y * torch.log(xs_pos.clamp(min=self.eps))
los_neg = (1 - y) * torch.log(xs_neg.clamp(min=self.eps))
loss = los_pos + los_neg
# Asymmetric Focusing
if self.gamma_neg > 0 or self.gamma_pos > 0:
if self.disable_torch_grad_focal_loss:
torch._C.set_grad_enabled(False)
pt0 = xs_pos * y
pt1 = xs_neg * (1 - y) # pt = p if t > 0 else 1-p
pt = pt0 + pt1
one_sided_gamma = self.gamma_pos * y + self.gamma_neg * (1 - y)
one_sided_w = torch.pow(1 - pt, one_sided_gamma)
if self.disable_torch_grad_focal_loss:
torch._C.set_grad_enabled(True)
loss *= one_sided_w
return -loss.sum()
class AsymmetricLossSingleLabel(nn.Module):
def __init__(self, gamma_pos=1, gamma_neg=4, eps: float = 0.1, reduction='mean'):
super(AsymmetricLossSingleLabel, self).__init__()
self.eps = eps
self.logsoftmax = nn.LogSoftmax(dim=-1)
self.targets_classes = [] # prevent gpu repeated memory allocation
self.gamma_pos = gamma_pos
self.gamma_neg = gamma_neg
self.reduction = reduction
def forward(self, inputs, target, reduction=None):
""""
Parameters
----------
x: input logits
y: targets (1-hot vector)
"""
num_classes = inputs.size()[-1]
log_preds = self.logsoftmax(inputs)
self.targets_classes = torch.zeros_like(inputs).scatter_(1, target.long().unsqueeze(1), 1)
# ASL weights
targets = self.targets_classes
anti_targets = 1 - targets
xs_pos = torch.exp(log_preds)
xs_neg = 1 - xs_pos
xs_pos = xs_pos * targets
xs_neg = xs_neg * anti_targets
asymmetric_w = torch.pow(1 - xs_pos - xs_neg,
self.gamma_pos * targets + self.gamma_neg * anti_targets)
log_preds = log_preds * asymmetric_w
if self.eps > 0: # label smoothing
self.targets_classes = self.targets_classes.mul(1 - self.eps).add(self.eps / num_classes)
# loss calculation
loss = - self.targets_classes.mul(log_preds)
loss = loss.sum(dim=-1)
if self.reduction == 'mean':
loss = loss.mean()
return loss
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/loss/__init__.py | from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
from .binary_cross_entropy import BinaryCrossEntropy
from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from .jsd import JsdCrossEntropy
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/loss/binary_cross_entropy.py | """ Binary Cross Entropy w/ a few extras
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
class BinaryCrossEntropy(nn.Module):
""" BCE with optional one-hot from dense targets, label smoothing, thresholding
NOTE for experiments comparing CE to BCE /w label smoothing, may remove
"""
def __init__(
self, smoothing=0.1, target_threshold: Optional[float] = None, weight: Optional[torch.Tensor] = None,
reduction: str = 'mean', pos_weight: Optional[torch.Tensor] = None):
super(BinaryCrossEntropy, self).__init__()
assert 0. <= smoothing < 1.0
self.smoothing = smoothing
self.target_threshold = target_threshold
self.reduction = reduction
self.register_buffer('weight', weight)
self.register_buffer('pos_weight', pos_weight)
def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
assert x.shape[0] == target.shape[0]
if target.shape != x.shape:
# NOTE currently assume smoothing or other label softening is applied upstream if targets are already sparse
num_classes = x.shape[-1]
# FIXME should off/on be different for smoothing w/ BCE? Other impl out there differ
off_value = self.smoothing / num_classes
on_value = 1. - self.smoothing + off_value
target = target.long().view(-1, 1)
target = torch.full(
(target.size()[0], num_classes),
off_value,
device=x.device, dtype=x.dtype).scatter_(1, target, on_value)
if self.target_threshold is not None:
# Make target 0, or 1 if threshold set
target = target.gt(self.target_threshold).to(dtype=target.dtype)
return F.binary_cross_entropy_with_logits(
x, target,
self.weight,
pos_weight=self.pos_weight,
reduction=self.reduction)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/loss/jsd.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .cross_entropy import LabelSmoothingCrossEntropy
class JsdCrossEntropy(nn.Module):
""" Jensen-Shannon Divergence + Cross-Entropy Loss
Based on impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
https://arxiv.org/abs/1912.02781
Hacked together by / Copyright 2020 Ross Wightman
"""
def __init__(self, num_splits=3, alpha=12, smoothing=0.1):
super().__init__()
self.num_splits = num_splits
self.alpha = alpha
if smoothing is not None and smoothing > 0:
self.cross_entropy_loss = LabelSmoothingCrossEntropy(smoothing)
else:
self.cross_entropy_loss = torch.nn.CrossEntropyLoss()
def __call__(self, output, target):
split_size = output.shape[0] // self.num_splits
assert split_size * self.num_splits == output.shape[0]
logits_split = torch.split(output, split_size)
# Cross-entropy is only computed on clean images
loss = self.cross_entropy_loss(logits_split[0], target[:split_size])
probs = [F.softmax(logits, dim=1) for logits in logits_split]
# Clamp mixture distribution to avoid exploding KL divergence
logp_mixture = torch.clamp(torch.stack(probs).mean(axis=0), 1e-7, 1).log()
loss += self.alpha * sum([F.kl_div(
logp_mixture, p_split, reduction='batchmean') for p_split in probs]) / len(probs)
return loss
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/loss/cross_entropy.py | """ Cross Entropy w/ smoothing or soft targets
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothingCrossEntropy(nn.Module):
""" NLL loss with label smoothing.
"""
def __init__(self, smoothing=0.1):
super(LabelSmoothingCrossEntropy, self).__init__()
assert smoothing < 1.0
self.smoothing = smoothing
self.confidence = 1. - smoothing
def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
logprobs = F.log_softmax(x, dim=-1)
nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
nll_loss = nll_loss.squeeze(1)
smooth_loss = -logprobs.mean(dim=-1)
loss = self.confidence * nll_loss + self.smoothing * smooth_loss
return loss.mean()
class SoftTargetCrossEntropy(nn.Module):
def __init__(self):
super(SoftTargetCrossEntropy, self).__init__()
def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
loss = torch.sum(-target * F.log_softmax(x, dim=-1), dim=-1)
return loss.mean()
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/distributed.py | """ Distributed training/validation utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
from torch import distributed as dist
try:
import horovod.torch as hvd
except ImportError:
hvd = None
from .model import unwrap_model
def reduce_tensor(tensor, n):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.ReduceOp.SUM)
rt /= n
return rt
def distribute_bn(model, world_size, reduce=False):
# ensure every node has the same running bn stats
for bn_name, bn_buf in unwrap_model(model).named_buffers(recurse=True):
if ('running_mean' in bn_name) or ('running_var' in bn_name):
if reduce:
# average bn stats across whole group
torch.distributed.all_reduce(bn_buf, op=dist.ReduceOp.SUM)
bn_buf /= float(world_size)
else:
# broadcast bn stats from rank 0 to whole group
torch.distributed.broadcast(bn_buf, 0)
def is_global_primary(args):
return args.rank == 0
def is_local_primary(args):
return args.local_rank == 0
def is_primary(args, local=False):
return is_local_primary(args) if local else is_global_primary(args)
def is_distributed_env():
if 'WORLD_SIZE' in os.environ:
return int(os.environ['WORLD_SIZE']) > 1
if 'SLURM_NTASKS' in os.environ:
return int(os.environ['SLURM_NTASKS']) > 1
return False
def world_info_from_env():
local_rank = 0
for v in ('LOCAL_RANK', 'MPI_LOCALRANKID', 'SLURM_LOCALID', 'OMPI_COMM_WORLD_LOCAL_RANK'):
if v in os.environ:
local_rank = int(os.environ[v])
break
global_rank = 0
for v in ('RANK', 'PMI_RANK', 'SLURM_PROCID', 'OMPI_COMM_WORLD_RANK'):
if v in os.environ:
global_rank = int(os.environ[v])
break
world_size = 1
for v in ('WORLD_SIZE', 'PMI_SIZE', 'SLURM_NTASKS', 'OMPI_COMM_WORLD_SIZE'):
if v in os.environ:
world_size = int(os.environ[v])
break
return local_rank, global_rank, world_size
def init_distributed_device(args):
# Distributed training = training on more than one GPU.
# Works in both single and multi-node scenarios.
args.distributed = False
args.world_size = 1
args.rank = 0 # global rank
args.local_rank = 0
# TBD, support horovod?
# if args.horovod:
# assert hvd is not None, "Horovod is not installed"
# hvd.init()
# args.local_rank = int(hvd.local_rank())
# args.rank = hvd.rank()
# args.world_size = hvd.size()
# args.distributed = True
# os.environ['LOCAL_RANK'] = str(args.local_rank)
# os.environ['RANK'] = str(args.rank)
# os.environ['WORLD_SIZE'] = str(args.world_size)
dist_backend = getattr(args, 'dist_backend', 'nccl')
dist_url = getattr(args, 'dist_url', 'env://')
if is_distributed_env():
if 'SLURM_PROCID' in os.environ:
# DDP via SLURM
args.local_rank, args.rank, args.world_size = world_info_from_env()
# SLURM var -> torch.distributed vars in case needed
os.environ['LOCAL_RANK'] = str(args.local_rank)
os.environ['RANK'] = str(args.rank)
os.environ['WORLD_SIZE'] = str(args.world_size)
torch.distributed.init_process_group(
backend=dist_backend,
init_method=dist_url,
world_size=args.world_size,
rank=args.rank,
)
else:
# DDP via torchrun, torch.distributed.launch
args.local_rank, _, _ = world_info_from_env()
torch.distributed.init_process_group(
backend=dist_backend,
init_method=dist_url,
)
args.world_size = torch.distributed.get_world_size()
args.rank = torch.distributed.get_rank()
args.distributed = True
if torch.cuda.is_available():
if args.distributed:
device = 'cuda:%d' % args.local_rank
else:
device = 'cuda:0'
torch.cuda.set_device(device)
else:
device = 'cpu'
args.device = device
device = torch.device(device)
return device
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/__init__.py | from .agc import adaptive_clip_grad
from .checkpoint_saver import CheckpointSaver
from .clip_grad import dispatch_clip_grad
from .cuda import ApexScaler, NativeScaler
from .decay_batch import decay_batch_step, check_batch_size_retry
from .distributed import distribute_bn, reduce_tensor, init_distributed_device,\
world_info_from_env, is_distributed_env, is_primary
from .jit import set_jit_legacy, set_jit_fuser
from .log import setup_default_logging, FormatterNoInfo
from .metrics import AverageMeter, accuracy
from .misc import natural_key, add_bool_arg, ParseKwargs
from .model import unwrap_model, get_state_dict, freeze, unfreeze
from .model_ema import ModelEma, ModelEmaV2
from .random import random_seed
from .summary import update_summary, get_outdir
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/model.py | """ Model / state_dict utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import fnmatch
import torch
from torchvision.ops.misc import FrozenBatchNorm2d
from timm.layers import BatchNormAct2d, SyncBatchNormAct, FrozenBatchNormAct2d,\
freeze_batch_norm_2d, unfreeze_batch_norm_2d
from .model_ema import ModelEma
def unwrap_model(model):
if isinstance(model, ModelEma):
return unwrap_model(model.ema)
else:
return model.module if hasattr(model, 'module') else model
def get_state_dict(model, unwrap_fn=unwrap_model):
return unwrap_fn(model).state_dict()
def avg_sq_ch_mean(model, input, output):
""" calculate average channel square mean of output activations
"""
return torch.mean(output.mean(axis=[0, 2, 3]) ** 2).item()
def avg_ch_var(model, input, output):
""" calculate average channel variance of output activations
"""
return torch.mean(output.var(axis=[0, 2, 3])).item()
def avg_ch_var_residual(model, input, output):
""" calculate average channel variance of output activations
"""
return torch.mean(output.var(axis=[0, 2, 3])).item()
class ActivationStatsHook:
"""Iterates through each of `model`'s modules and matches modules using unix pattern
matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is
a match.
Arguments:
model (nn.Module): model from which we will extract the activation stats
hook_fn_locs (List[str]): List of `hook_fn` locations based on Unix type string
matching with the name of model's modules.
hook_fns (List[Callable]): List of hook functions to be registered at every
module in `layer_names`.
Inspiration from https://docs.fast.ai/callback.hook.html.
Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example
on how to plot Signal Propogation Plots using `ActivationStatsHook`.
"""
def __init__(self, model, hook_fn_locs, hook_fns):
self.model = model
self.hook_fn_locs = hook_fn_locs
self.hook_fns = hook_fns
if len(hook_fn_locs) != len(hook_fns):
raise ValueError("Please provide `hook_fns` for each `hook_fn_locs`, \
their lengths are different.")
self.stats = dict((hook_fn.__name__, []) for hook_fn in hook_fns)
for hook_fn_loc, hook_fn in zip(hook_fn_locs, hook_fns):
self.register_hook(hook_fn_loc, hook_fn)
def _create_hook(self, hook_fn):
def append_activation_stats(module, input, output):
out = hook_fn(module, input, output)
self.stats[hook_fn.__name__].append(out)
return append_activation_stats
def register_hook(self, hook_fn_loc, hook_fn):
for name, module in self.model.named_modules():
if not fnmatch.fnmatch(name, hook_fn_loc):
continue
module.register_forward_hook(self._create_hook(hook_fn))
def extract_spp_stats(
model,
hook_fn_locs,
hook_fns,
input_shape=[8, 3, 224, 224]):
"""Extract average square channel mean and variance of activations during
forward pass to plot Signal Propogation Plots (SPP).
Paper: https://arxiv.org/abs/2101.08692
Example Usage: https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950
"""
x = torch.normal(0., 1., input_shape)
hook = ActivationStatsHook(model, hook_fn_locs=hook_fn_locs, hook_fns=hook_fns)
_ = model(x)
return hook.stats
def _freeze_unfreeze(root_module, submodules=[], include_bn_running_stats=True, mode='freeze'):
"""
Freeze or unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is
done in place.
Args:
root_module (nn.Module, optional): Root module relative to which the `submodules` are referenced.
submodules (list[str]): List of modules for which the parameters will be (un)frozen. They are to be provided as
named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list
means that the whole root module will be (un)frozen. Defaults to []
include_bn_running_stats (bool): Whether to also (un)freeze the running statistics of batch norm 2d layers.
Defaults to `True`.
mode (bool): Whether to freeze ("freeze") or unfreeze ("unfreeze"). Defaults to `"freeze"`.
"""
assert mode in ["freeze", "unfreeze"], '`mode` must be one of "freeze" or "unfreeze"'
if isinstance(root_module, (
torch.nn.modules.batchnorm.BatchNorm2d,
torch.nn.modules.batchnorm.SyncBatchNorm,
BatchNormAct2d,
SyncBatchNormAct,
)):
# Raise assertion here because we can't convert it in place
raise AssertionError(
"You have provided a batch norm layer as the `root module`. Please use "
"`timm.utils.model.freeze_batch_norm_2d` or `timm.utils.model.unfreeze_batch_norm_2d` instead.")
if isinstance(submodules, str):
submodules = [submodules]
named_modules = submodules
submodules = [root_module.get_submodule(m) for m in submodules]
if not len(submodules):
named_modules, submodules = list(zip(*root_module.named_children()))
for n, m in zip(named_modules, submodules):
# (Un)freeze parameters
for p in m.parameters():
p.requires_grad = False if mode == 'freeze' else True
if include_bn_running_stats:
# Helper to add submodule specified as a named_module
def _add_submodule(module, name, submodule):
split = name.rsplit('.', 1)
if len(split) > 1:
module.get_submodule(split[0]).add_module(split[1], submodule)
else:
module.add_module(name, submodule)
# Freeze batch norm
if mode == 'freeze':
res = freeze_batch_norm_2d(m)
# It's possible that `m` is a type of BatchNorm in itself, in which case `unfreeze_batch_norm_2d` won't
# convert it in place, but will return the converted result. In this case `res` holds the converted
# result and we may try to re-assign the named module
if isinstance(m, (
torch.nn.modules.batchnorm.BatchNorm2d,
torch.nn.modules.batchnorm.SyncBatchNorm,
BatchNormAct2d,
SyncBatchNormAct,
)):
_add_submodule(root_module, n, res)
# Unfreeze batch norm
else:
res = unfreeze_batch_norm_2d(m)
# Ditto. See note above in mode == 'freeze' branch
if isinstance(m, (FrozenBatchNorm2d, FrozenBatchNormAct2d)):
_add_submodule(root_module, n, res)
def freeze(root_module, submodules=[], include_bn_running_stats=True):
"""
Freeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
Args:
root_module (nn.Module): Root module relative to which `submodules` are referenced.
submodules (list[str]): List of modules for which the parameters will be frozen. They are to be provided as
named modules relative to the root module (accessible via `root_module.named_modules()`). An empty list
means that the whole root module will be frozen. Defaults to `[]`.
include_bn_running_stats (bool): Whether to also freeze the running statistics of `BatchNorm2d` and
`SyncBatchNorm` layers. These will be converted to `FrozenBatchNorm2d` in place. Hint: During fine tuning,
it's good practice to freeze batch norm stats. And note that these are different to the affine parameters
which are just normal PyTorch parameters. Defaults to `True`.
Hint: If you want to freeze batch norm ONLY, use `timm.utils.model.freeze_batch_norm_2d`.
Examples::
>>> model = timm.create_model('resnet18')
>>> # Freeze up to and including layer2
>>> submodules = [n for n, _ in model.named_children()]
>>> print(submodules)
['conv1', 'bn1', 'act1', 'maxpool', 'layer1', 'layer2', 'layer3', 'layer4', 'global_pool', 'fc']
>>> freeze(model, submodules[:submodules.index('layer2') + 1])
>>> # Check for yourself that it works as expected
>>> print(model.layer2[0].conv1.weight.requires_grad)
False
>>> print(model.layer3[0].conv1.weight.requires_grad)
True
>>> # Unfreeze
>>> unfreeze(model)
"""
_freeze_unfreeze(root_module, submodules, include_bn_running_stats=include_bn_running_stats, mode="freeze")
def unfreeze(root_module, submodules=[], include_bn_running_stats=True):
"""
Unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is done in place.
Args:
root_module (nn.Module): Root module relative to which `submodules` are referenced.
submodules (list[str]): List of submodules for which the parameters will be (un)frozen. They are to be provided
as named modules relative to the root module (accessible via `root_module.named_modules()`). An empty
list means that the whole root module will be unfrozen. Defaults to `[]`.
include_bn_running_stats (bool): Whether to also unfreeze the running statistics of `FrozenBatchNorm2d` layers.
These will be converted to `BatchNorm2d` in place. Defaults to `True`.
See example in docstring for `freeze`.
"""
_freeze_unfreeze(root_module, submodules, include_bn_running_stats=include_bn_running_stats, mode="unfreeze")
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/jit.py | """ JIT scripting/tracing utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import os
import torch
def set_jit_legacy():
""" Set JIT executor to legacy w/ support for op fusion
This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
in the JIT exectutor. These API are not supported so could change.
"""
#
assert hasattr(torch._C, '_jit_set_profiling_executor'), "Old JIT behavior doesn't exist!"
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._jit_override_can_fuse_on_gpu(True)
#torch._C._jit_set_texpr_fuser_enabled(True)
def set_jit_fuser(fuser):
if fuser == "te":
# default fuser should be == 'te'
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(True)
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(True)
try:
torch._C._jit_set_nvfuser_enabled(False)
except Exception:
pass
elif fuser == "old" or fuser == "legacy":
torch._C._jit_set_profiling_executor(False)
torch._C._jit_set_profiling_mode(False)
torch._C._jit_override_can_fuse_on_gpu(True)
torch._C._jit_set_texpr_fuser_enabled(False)
try:
torch._C._jit_set_nvfuser_enabled(False)
except Exception:
pass
elif fuser == "nvfuser" or fuser == "nvf":
os.environ['PYTORCH_NVFUSER_DISABLE_FALLBACK'] = '1'
#os.environ['PYTORCH_NVFUSER_DISABLE_FMA'] = '1'
#os.environ['PYTORCH_NVFUSER_JIT_OPT_LEVEL'] = '0'
torch._C._jit_set_texpr_fuser_enabled(False)
torch._C._jit_set_profiling_executor(True)
torch._C._jit_set_profiling_mode(True)
torch._C._jit_can_fuse_on_cpu()
torch._C._jit_can_fuse_on_gpu()
torch._C._jit_override_can_fuse_on_cpu(False)
torch._C._jit_override_can_fuse_on_gpu(False)
torch._C._jit_set_nvfuser_guard_mode(True)
torch._C._jit_set_nvfuser_enabled(True)
else:
assert False, f"Invalid jit fuser ({fuser})"
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/metrics.py | """ Eval metrics and related
Hacked together by / Copyright 2020 Ross Wightman
"""
class AverageMeter:
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
maxk = min(max(topk), output.size()[1])
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.reshape(1, -1).expand_as(pred))
return [correct[:min(k, maxk)].reshape(-1).float().sum(0) * 100. / batch_size for k in topk]
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/clip_grad.py | import torch
from timm.utils.agc import adaptive_clip_grad
def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
""" Dispatch to gradient clipping method
Args:
parameters (Iterable): model parameters to clip
value (float): clipping value/factor/norm, mode dependant
mode (str): clipping mode, one of 'norm', 'value', 'agc'
norm_type (float): p-norm, default 2.0
"""
if mode == 'norm':
torch.nn.utils.clip_grad_norm_(parameters, value, norm_type=norm_type)
elif mode == 'value':
torch.nn.utils.clip_grad_value_(parameters, value)
elif mode == 'agc':
adaptive_clip_grad(parameters, value, norm_type=norm_type)
else:
assert False, f"Unknown clip mode ({mode})."
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/summary.py | """ Summary utilities
Hacked together by / Copyright 2020 Ross Wightman
"""
import csv
import os
from collections import OrderedDict
try:
import wandb
except ImportError:
pass
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.makedirs(outdir)
elif inc:
count = 1
outdir_inc = outdir + '-' + str(count)
while os.path.exists(outdir_inc):
count = count + 1
outdir_inc = outdir + '-' + str(count)
assert count < 100
outdir = outdir_inc
os.makedirs(outdir)
return outdir
def update_summary(
epoch,
train_metrics,
eval_metrics,
filename,
lr=None,
write_header=False,
log_wandb=False,
):
rowd = OrderedDict(epoch=epoch)
rowd.update([('train_' + k, v) for k, v in train_metrics.items()])
rowd.update([('eval_' + k, v) for k, v in eval_metrics.items()])
if lr is not None:
rowd['lr'] = lr
if log_wandb:
wandb.log(rowd)
with open(filename, mode='a') as cf:
dw = csv.DictWriter(cf, fieldnames=rowd.keys())
if write_header: # first iteration (epoch == 1 can't be used)
dw.writeheader()
dw.writerow(rowd)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/model_ema.py | """ Exponential Moving Average (EMA) of model updates
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
_logger = logging.getLogger(__name__)
class ModelEma:
""" Model Exponential Moving Average (DEPRECATED)
Keep a moving average of everything in the model state_dict (parameters and buffers).
This version is deprecated, it does not work with scripted models. Will be removed eventually.
This is intended to allow functionality like
https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
A smoothed version of the weights is necessary for some training schemes to perform well.
E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
smoothing of weights to match results. Pay attention to the decay constant you are using
relative to your update count per epoch.
To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
disable validation of the EMA weights. Validation will have to be done manually in a separate
process, or after the training stops converging.
This class is sensitive where it is initialized in the sequence of model init,
GPU assignment and distributed training wrappers.
"""
def __init__(self, model, decay=0.9999, device='', resume=''):
# make a copy of the model for accumulating moving average of weights
self.ema = deepcopy(model)
self.ema.eval()
self.decay = decay
self.device = device # perform ema on different device from model if set
if device:
self.ema.to(device=device)
self.ema_has_module = hasattr(self.ema, 'module')
if resume:
self._load_checkpoint(resume)
for p in self.ema.parameters():
p.requires_grad_(False)
def _load_checkpoint(self, checkpoint_path):
checkpoint = torch.load(checkpoint_path, map_location='cpu')
assert isinstance(checkpoint, dict)
if 'state_dict_ema' in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict_ema'].items():
# ema model may have been wrapped by DataParallel, and need module prefix
if self.ema_has_module:
name = 'module.' + k if not k.startswith('module') else k
else:
name = k
new_state_dict[name] = v
self.ema.load_state_dict(new_state_dict)
_logger.info("Loaded state_dict_ema")
else:
_logger.warning("Failed to find state_dict_ema, starting from loaded model weights")
def update(self, model):
# correct a mismatch in state dict keys
needs_module = hasattr(model, 'module') and not self.ema_has_module
with torch.no_grad():
msd = model.state_dict()
for k, ema_v in self.ema.state_dict().items():
if needs_module:
k = 'module.' + k
model_v = msd[k].detach()
if self.device:
model_v = model_v.to(device=self.device)
ema_v.copy_(ema_v * self.decay + (1. - self.decay) * model_v)
class ModelEmaV2(nn.Module):
""" Model Exponential Moving Average V2
Keep a moving average of everything in the model state_dict (parameters and buffers).
V2 of this module is simpler, it does not match params/buffers based on name but simply
iterates in order. It works with torchscript (JIT of full model).
This is intended to allow functionality like
https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
A smoothed version of the weights is necessary for some training schemes to perform well.
E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
smoothing of weights to match results. Pay attention to the decay constant you are using
relative to your update count per epoch.
To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
disable validation of the EMA weights. Validation will have to be done manually in a separate
process, or after the training stops converging.
This class is sensitive where it is initialized in the sequence of model init,
GPU assignment and distributed training wrappers.
"""
def __init__(self, model, decay=0.9999, device=None):
super(ModelEmaV2, self).__init__()
# make a copy of the model for accumulating moving average of weights
self.module = deepcopy(model)
self.module.eval()
self.decay = decay
self.device = device # perform ema on different device from model if set
if self.device is not None:
self.module.to(device=device)
def _update(self, model, update_fn):
with torch.no_grad():
for ema_v, model_v in zip(self.module.state_dict().values(), model.state_dict().values()):
if self.device is not None:
model_v = model_v.to(device=self.device)
ema_v.copy_(update_fn(ema_v, model_v))
def update(self, model):
self._update(model, update_fn=lambda e, m: self.decay * e + (1. - self.decay) * m)
def set(self, model):
self._update(model, update_fn=lambda e, m: m)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/decay_batch.py | """ Batch size decay and retry helpers.
Copyright 2022 Ross Wightman
"""
import math
def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
""" power of two batch-size decay with intra steps
Decay by stepping between powers of 2:
* determine power-of-2 floor of current batch size (base batch size)
* divide above value by num_intra_steps to determine step size
* floor batch_size to nearest multiple of step_size (from base batch size)
Examples:
num_steps == 4 --> 64, 56, 48, 40, 32, 28, 24, 20, 16, 14, 12, 10, 8, 7, 6, 5, 4, 3, 2, 1
num_steps (no_odd=True) == 4 --> 64, 56, 48, 40, 32, 28, 24, 20, 16, 14, 12, 10, 8, 6, 4, 2
num_steps == 2 --> 64, 48, 32, 24, 16, 12, 8, 6, 4, 3, 2, 1
num_steps == 1 --> 64, 32, 16, 8, 4, 2, 1
"""
if batch_size <= 1:
# return 0 for stopping value so easy to use in loop
return 0
base_batch_size = int(2 ** (math.log(batch_size - 1) // math.log(2)))
step_size = max(base_batch_size // num_intra_steps, 1)
batch_size = base_batch_size + ((batch_size - base_batch_size - 1) // step_size) * step_size
if no_odd and batch_size % 2:
batch_size -= 1
return batch_size
def check_batch_size_retry(error_str):
""" check failure error string for conditions where batch decay retry should not be attempted
"""
error_str = error_str.lower()
if 'required rank' in error_str:
# Errors involving phrase 'required rank' typically happen when a conv is used that's
# not compatible with channels_last memory format.
return False
if 'illegal' in error_str:
# 'Illegal memory access' errors in CUDA typically leave process in unusable state
return False
return True
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/agc.py | """ Adaptive Gradient Clipping
An impl of AGC, as per (https://arxiv.org/abs/2102.06171):
@article{brock2021high,
author={Andrew Brock and Soham De and Samuel L. Smith and Karen Simonyan},
title={High-Performance Large-Scale Image Recognition Without Normalization},
journal={arXiv preprint arXiv:},
year={2021}
}
Code references:
* Official JAX impl (paper authors): https://github.com/deepmind/deepmind-research/tree/master/nfnets
* Phil Wang's PyTorch gist: https://gist.github.com/lucidrains/0d6560077edac419ab5d3aa29e674d5c
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
def unitwise_norm(x, norm_type=2.0):
if x.ndim <= 1:
return x.norm(norm_type)
else:
# works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor
# might need special cases for other weights (possibly MHA) where this may not be true
return x.norm(norm_type, dim=tuple(range(1, x.ndim)), keepdim=True)
def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
for p in parameters:
if p.grad is None:
continue
p_data = p.detach()
g_data = p.grad.detach()
max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor)
grad_norm = unitwise_norm(g_data, norm_type=norm_type)
clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6))
new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad)
p.grad.detach().copy_(new_grads)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/onnx.py | from typing import Optional, Tuple, List
import torch
def onnx_forward(onnx_file, example_input):
import onnxruntime
sess_options = onnxruntime.SessionOptions()
session = onnxruntime.InferenceSession(onnx_file, sess_options)
input_name = session.get_inputs()[0].name
output = session.run([], {input_name: example_input.numpy()})
output = output[0]
return output
def onnx_export(
model: torch.nn.Module,
output_file: str,
example_input: Optional[torch.Tensor] = None,
training: bool = False,
verbose: bool = False,
check: bool = True,
check_forward: bool = False,
batch_size: int = 64,
input_size: Tuple[int, int, int] = None,
opset: Optional[int] = None,
dynamic_size: bool = False,
aten_fallback: bool = False,
keep_initializers: Optional[bool] = None,
input_names: List[str] = None,
output_names: List[str] = None,
):
import onnx
if training:
training_mode = torch.onnx.TrainingMode.TRAINING
model.train()
else:
training_mode = torch.onnx.TrainingMode.EVAL
model.eval()
if example_input is None:
if not input_size:
assert hasattr(model, 'default_cfg')
input_size = model.default_cfg.get('input_size')
example_input = torch.randn((batch_size,) + input_size, requires_grad=training)
# Run model once before export trace, sets padding for models with Conv2dSameExport. This means
# that the padding for models with Conv2dSameExport (most models with tf_ prefix) is fixed for
# the input img_size specified in this script.
# Opset >= 11 should allow for dynamic padding, however I cannot get it to work due to
# issues in the tracing of the dynamic padding or errors attempting to export the model after jit
# scripting it (an approach that should work). Perhaps in a future PyTorch or ONNX versions...
original_out = model(example_input)
input_names = input_names or ["input0"]
output_names = output_names or ["output0"]
dynamic_axes = {'input0': {0: 'batch'}, 'output0': {0: 'batch'}}
if dynamic_size:
dynamic_axes['input0'][2] = 'height'
dynamic_axes['input0'][3] = 'width'
if aten_fallback:
export_type = torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK
else:
export_type = torch.onnx.OperatorExportTypes.ONNX
torch_out = torch.onnx._export(
model,
example_input,
output_file,
training=training_mode,
export_params=True,
verbose=verbose,
input_names=input_names,
output_names=output_names,
keep_initializers_as_inputs=keep_initializers,
dynamic_axes=dynamic_axes,
opset_version=opset,
operator_export_type=export_type
)
if check:
onnx_model = onnx.load(output_file)
onnx.checker.check_model(onnx_model, full_check=True) # assuming throw on error
if check_forward and not training:
import numpy as np
onnx_out = onnx_forward(output_file, example_input)
np.testing.assert_almost_equal(torch_out.data.numpy(), onnx_out, decimal=3)
np.testing.assert_almost_equal(original_out.data.numpy(), torch_out.data.numpy(), decimal=5)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/checkpoint_saver.py | """ Checkpoint Saver
Track top-n training checkpoints and maintain recovery checkpoints on specified intervals.
Hacked together by / Copyright 2020 Ross Wightman
"""
import glob
import operator
import os
import logging
import torch
from .model import unwrap_model, get_state_dict
_logger = logging.getLogger(__name__)
class CheckpointSaver:
def __init__(
self,
model,
optimizer,
args=None,
model_ema=None,
amp_scaler=None,
checkpoint_prefix='checkpoint',
recovery_prefix='recovery',
checkpoint_dir='',
recovery_dir='',
decreasing=False,
max_history=10,
unwrap_fn=unwrap_model):
# objects to save state_dicts of
self.model = model
self.optimizer = optimizer
self.args = args
self.model_ema = model_ema
self.amp_scaler = amp_scaler
# state
self.checkpoint_files = [] # (filename, metric) tuples in order of decreasing betterness
self.best_epoch = None
self.best_metric = None
self.curr_recovery_file = ''
self.last_recovery_file = ''
# config
self.checkpoint_dir = checkpoint_dir
self.recovery_dir = recovery_dir
self.save_prefix = checkpoint_prefix
self.recovery_prefix = recovery_prefix
self.extension = '.pth.tar'
self.decreasing = decreasing # a lower metric is better if True
self.cmp = operator.lt if decreasing else operator.gt # True if lhs better than rhs
self.max_history = max_history
self.unwrap_fn = unwrap_fn
assert self.max_history >= 1
def save_checkpoint(self, epoch, metric=None):
assert epoch >= 0
tmp_save_path = os.path.join(self.checkpoint_dir, 'tmp' + self.extension)
last_save_path = os.path.join(self.checkpoint_dir, 'last' + self.extension)
self._save(tmp_save_path, epoch, metric)
if os.path.exists(last_save_path):
os.unlink(last_save_path) # required for Windows support.
os.rename(tmp_save_path, last_save_path)
worst_file = self.checkpoint_files[-1] if self.checkpoint_files else None
if (len(self.checkpoint_files) < self.max_history
or metric is None or self.cmp(metric, worst_file[1])):
if len(self.checkpoint_files) >= self.max_history:
self._cleanup_checkpoints(1)
filename = '-'.join([self.save_prefix, str(epoch)]) + self.extension
save_path = os.path.join(self.checkpoint_dir, filename)
os.link(last_save_path, save_path)
self.checkpoint_files.append((save_path, metric))
self.checkpoint_files = sorted(
self.checkpoint_files, key=lambda x: x[1],
reverse=not self.decreasing) # sort in descending order if a lower metric is not better
checkpoints_str = "Current checkpoints:\n"
for c in self.checkpoint_files:
checkpoints_str += ' {}\n'.format(c)
_logger.info(checkpoints_str)
if metric is not None and (self.best_metric is None or self.cmp(metric, self.best_metric)):
self.best_epoch = epoch
self.best_metric = metric
best_save_path = os.path.join(self.checkpoint_dir, 'model_best' + self.extension)
if os.path.exists(best_save_path):
os.unlink(best_save_path)
os.link(last_save_path, best_save_path)
return (None, None) if self.best_metric is None else (self.best_metric, self.best_epoch)
def _save(self, save_path, epoch, metric=None):
save_state = {
'epoch': epoch,
'arch': type(self.model).__name__.lower(),
'state_dict': get_state_dict(self.model, self.unwrap_fn),
'optimizer': self.optimizer.state_dict(),
'version': 2, # version < 2 increments epoch before save
}
if self.args is not None:
save_state['arch'] = self.args.model
save_state['args'] = self.args
if self.amp_scaler is not None:
save_state[self.amp_scaler.state_dict_key] = self.amp_scaler.state_dict()
if self.model_ema is not None:
save_state['state_dict_ema'] = get_state_dict(self.model_ema, self.unwrap_fn)
if metric is not None:
save_state['metric'] = metric
torch.save(save_state, save_path)
def _cleanup_checkpoints(self, trim=0):
trim = min(len(self.checkpoint_files), trim)
delete_index = self.max_history - trim
if delete_index < 0 or len(self.checkpoint_files) <= delete_index:
return
to_delete = self.checkpoint_files[delete_index:]
for d in to_delete:
try:
_logger.debug("Cleaning checkpoint: {}".format(d))
os.remove(d[0])
except Exception as e:
_logger.error("Exception '{}' while deleting checkpoint".format(e))
self.checkpoint_files = self.checkpoint_files[:delete_index]
def save_recovery(self, epoch, batch_idx=0):
assert epoch >= 0
filename = '-'.join([self.recovery_prefix, str(epoch), str(batch_idx)]) + self.extension
save_path = os.path.join(self.recovery_dir, filename)
self._save(save_path, epoch)
if os.path.exists(self.last_recovery_file):
try:
_logger.debug("Cleaning recovery: {}".format(self.last_recovery_file))
os.remove(self.last_recovery_file)
except Exception as e:
_logger.error("Exception '{}' while removing {}".format(e, self.last_recovery_file))
self.last_recovery_file = self.curr_recovery_file
self.curr_recovery_file = save_path
def find_recovery(self):
recovery_path = os.path.join(self.recovery_dir, self.recovery_prefix)
files = glob.glob(recovery_path + '*' + self.extension)
files = sorted(files)
return files[0] if len(files) else ''
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/random.py | import random
import numpy as np
import torch
def random_seed(seed=42, rank=0):
torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
random.seed(seed + rank)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/cuda.py | """ CUDA / AMP utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
try:
from apex import amp
has_apex = True
except ImportError:
amp = None
has_apex = False
from .clip_grad import dispatch_clip_grad
class ApexScaler:
state_dict_key = "amp"
def __call__(
self,
loss,
optimizer,
clip_grad=None,
clip_mode='norm',
parameters=None,
create_graph=False,
need_update=True,
):
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward(create_graph=create_graph)
if need_update:
if clip_grad is not None:
dispatch_clip_grad(amp.master_params(optimizer), clip_grad, mode=clip_mode)
optimizer.step()
def state_dict(self):
if 'state_dict' in amp.__dict__:
return amp.state_dict()
def load_state_dict(self, state_dict):
if 'load_state_dict' in amp.__dict__:
amp.load_state_dict(state_dict)
class NativeScaler:
state_dict_key = "amp_scaler"
def __init__(self):
self._scaler = torch.cuda.amp.GradScaler()
def __call__(
self,
loss,
optimizer,
clip_grad=None,
clip_mode='norm',
parameters=None,
create_graph=False,
need_update=True,
):
self._scaler.scale(loss).backward(create_graph=create_graph)
if need_update:
if clip_grad is not None:
assert parameters is not None
self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
dispatch_clip_grad(parameters, clip_grad, mode=clip_mode)
self._scaler.step(optimizer)
self._scaler.update()
def state_dict(self):
return self._scaler.state_dict()
def load_state_dict(self, state_dict):
self._scaler.load_state_dict(state_dict)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/misc.py | """ Misc utils
Hacked together by / Copyright 2020 Ross Wightman
"""
import argparse
import ast
import re
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def add_bool_arg(parser, name, default=False, help=''):
dest_name = name.replace('-', '_')
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument('--' + name, dest=dest_name, action='store_true', help=help)
group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help)
parser.set_defaults(**{dest_name: default})
class ParseKwargs(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
kw = {}
for value in values:
key, value = value.split('=')
try:
kw[key] = ast.literal_eval(value)
except ValueError:
kw[key] = str(value) # fallback to string (avoid need to escape on command line)
setattr(namespace, self.dest, kw)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/utils/log.py | """ Logging helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import logging.handlers
class FormatterNoInfo(logging.Formatter):
def __init__(self, fmt='%(levelname)s: %(message)s'):
logging.Formatter.__init__(self, fmt)
def format(self, record):
if record.levelno == logging.INFO:
return str(record.getMessage())
return logging.Formatter.format(self, record)
def setup_default_logging(default_level=logging.INFO, log_path=''):
console_handler = logging.StreamHandler()
console_handler.setFormatter(FormatterNoInfo())
logging.root.addHandler(console_handler)
logging.root.setLevel(default_level)
if log_path:
file_handler = logging.handlers.RotatingFileHandler(log_path, maxBytes=(1024 ** 2 * 2), backupCount=3)
file_formatter = logging.Formatter("%(asctime)s - %(name)20s: [%(levelname)8s] - %(message)s")
file_handler.setFormatter(file_formatter)
logging.root.addHandler(file_handler)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/poly_lr.py | """ Polynomial Scheduler
Polynomial LR schedule with warmup, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class PolyLRScheduler(Scheduler):
""" Polynomial LR Scheduler w/ warmup, noise, and k-decay
k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
t_initial: int,
power: float = 0.5,
lr_min: float = 0.,
cycle_mul: float = 1.,
cycle_decay: float = 1.,
cycle_limit: int = 1,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=False,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
k_decay=1.0,
initialize=True,
) -> None:
super().__init__(
optimizer,
param_group_field="lr",
t_in_epochs=t_in_epochs,
noise_range_t=noise_range_t,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
initialize=initialize
)
assert t_initial > 0
assert lr_min >= 0
if t_initial == 1 and cycle_mul == 1 and cycle_decay == 1:
_logger.warning("Cosine annealing scheduler will have no effect on the learning "
"rate since t_initial = t_mul = eta_mul = 1.")
self.t_initial = t_initial
self.power = power
self.lr_min = lr_min
self.cycle_mul = cycle_mul
self.cycle_decay = cycle_decay
self.cycle_limit = cycle_limit
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.warmup_prefix = warmup_prefix
self.k_decay = k_decay
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul))
t_i = self.cycle_mul ** i * self.t_initial
t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial
else:
i = t // self.t_initial
t_i = self.t_initial
t_curr = t - (self.t_initial * i)
gamma = self.cycle_decay ** i
lr_max_values = [v * gamma for v in self.base_values]
k = self.k_decay
if i < self.cycle_limit:
lrs = [
self.lr_min + (lr_max - self.lr_min) * (1 - t_curr ** k / t_i ** k) ** self.power
for lr_max in lr_max_values
]
else:
lrs = [self.lr_min for _ in self.base_values]
return lrs
def get_cycle_length(self, cycles=0):
cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/__init__.py | from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
from .scheduler_factory import create_scheduler, create_scheduler_v2, scheduler_kwargs
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/plateau_lr.py | """ Plateau Scheduler
Adapts PyTorch plateau scheduler and allows application of noise, warmup.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from .scheduler import Scheduler
class PlateauLRScheduler(Scheduler):
"""Decay the LR by a factor every time the validation loss plateaus."""
def __init__(
self,
optimizer,
decay_rate=0.1,
patience_t=10,
verbose=True,
threshold=1e-4,
cooldown_t=0,
warmup_t=0,
warmup_lr_init=0,
lr_min=0,
mode='max',
noise_range_t=None,
noise_type='normal',
noise_pct=0.67,
noise_std=1.0,
noise_seed=None,
initialize=True,
):
super().__init__(
optimizer,
'lr',
noise_range_t=noise_range_t,
noise_type=noise_type,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
initialize=initialize,
)
self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer,
patience=patience_t,
factor=decay_rate,
verbose=verbose,
threshold=threshold,
cooldown=cooldown_t,
mode=mode,
min_lr=lr_min
)
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
self.restore_lr = None
def state_dict(self):
return {
'best': self.lr_scheduler.best,
'last_epoch': self.lr_scheduler.last_epoch,
}
def load_state_dict(self, state_dict):
self.lr_scheduler.best = state_dict['best']
if 'last_epoch' in state_dict:
self.lr_scheduler.last_epoch = state_dict['last_epoch']
# override the base class step fn completely
def step(self, epoch, metric=None):
if epoch <= self.warmup_t:
lrs = [self.warmup_lr_init + epoch * s for s in self.warmup_steps]
super().update_groups(lrs)
else:
if self.restore_lr is not None:
# restore actual LR from before our last noise perturbation before stepping base
for i, param_group in enumerate(self.optimizer.param_groups):
param_group['lr'] = self.restore_lr[i]
self.restore_lr = None
self.lr_scheduler.step(metric, epoch) # step the base scheduler
if self._is_apply_noise(epoch):
self._apply_noise(epoch)
def step_update(self, num_updates: int, metric: float = None):
return None
def _apply_noise(self, epoch):
noise = self._calculate_noise(epoch)
# apply the noise on top of previous LR, cache the old value so we can restore for normal
# stepping of base scheduler
restore_lr = []
for i, param_group in enumerate(self.optimizer.param_groups):
old_lr = float(param_group['lr'])
restore_lr.append(old_lr)
new_lr = old_lr + old_lr * noise
param_group['lr'] = new_lr
self.restore_lr = restore_lr
def _get_lr(self, t: int) -> float:
assert False, 'should not be called as step is overridden'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/scheduler_factory.py | """ Scheduler Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List, Union
from torch.optim import Optimizer
from .cosine_lr import CosineLRScheduler
from .multistep_lr import MultiStepLRScheduler
from .plateau_lr import PlateauLRScheduler
from .poly_lr import PolyLRScheduler
from .step_lr import StepLRScheduler
from .tanh_lr import TanhLRScheduler
def scheduler_kwargs(cfg):
""" cfg/argparse to kwargs helper
Convert scheduler args in argparse args or cfg (.dot) like object to keyword args.
"""
eval_metric = getattr(cfg, 'eval_metric', 'top1')
plateau_mode = 'min' if 'loss' in eval_metric else 'max'
kwargs = dict(
sched=cfg.sched,
num_epochs=getattr(cfg, 'epochs', 100),
decay_epochs=getattr(cfg, 'decay_epochs', 30),
decay_milestones=getattr(cfg, 'decay_milestones', [30, 60]),
warmup_epochs=getattr(cfg, 'warmup_epochs', 5),
cooldown_epochs=getattr(cfg, 'cooldown_epochs', 0),
patience_epochs=getattr(cfg, 'patience_epochs', 10),
decay_rate=getattr(cfg, 'decay_rate', 0.1),
min_lr=getattr(cfg, 'min_lr', 0.),
warmup_lr=getattr(cfg, 'warmup_lr', 1e-5),
warmup_prefix=getattr(cfg, 'warmup_prefix', False),
noise=getattr(cfg, 'lr_noise', None),
noise_pct=getattr(cfg, 'lr_noise_pct', 0.67),
noise_std=getattr(cfg, 'lr_noise_std', 1.),
noise_seed=getattr(cfg, 'seed', 42),
cycle_mul=getattr(cfg, 'lr_cycle_mul', 1.),
cycle_decay=getattr(cfg, 'lr_cycle_decay', 0.1),
cycle_limit=getattr(cfg, 'lr_cycle_limit', 1),
k_decay=getattr(cfg, 'lr_k_decay', 1.0),
plateau_mode=plateau_mode,
step_on_epochs=not getattr(cfg, 'sched_on_updates', False),
)
return kwargs
def create_scheduler(
args,
optimizer: Optimizer,
updates_per_epoch: int = 0,
):
return create_scheduler_v2(
optimizer=optimizer,
**scheduler_kwargs(args),
updates_per_epoch=updates_per_epoch,
)
def create_scheduler_v2(
optimizer: Optimizer,
sched: str = 'cosine',
num_epochs: int = 300,
decay_epochs: int = 90,
decay_milestones: List[int] = (90, 180, 270),
cooldown_epochs: int = 0,
patience_epochs: int = 10,
decay_rate: float = 0.1,
min_lr: float = 0,
warmup_lr: float = 1e-5,
warmup_epochs: int = 0,
warmup_prefix: bool = False,
noise: Union[float, List[float]] = None,
noise_pct: float = 0.67,
noise_std: float = 1.,
noise_seed: int = 42,
cycle_mul: float = 1.,
cycle_decay: float = 0.1,
cycle_limit: int = 1,
k_decay: float = 1.0,
plateau_mode: str = 'max',
step_on_epochs: bool = True,
updates_per_epoch: int = 0,
):
t_initial = num_epochs
warmup_t = warmup_epochs
decay_t = decay_epochs
cooldown_t = cooldown_epochs
if not step_on_epochs:
assert updates_per_epoch > 0, 'updates_per_epoch must be set to number of dataloader batches'
t_initial = t_initial * updates_per_epoch
warmup_t = warmup_t * updates_per_epoch
decay_t = decay_t * updates_per_epoch
decay_milestones = [d * updates_per_epoch for d in decay_milestones]
cooldown_t = cooldown_t * updates_per_epoch
# warmup args
warmup_args = dict(
warmup_lr_init=warmup_lr,
warmup_t=warmup_t,
warmup_prefix=warmup_prefix,
)
# setup noise args for supporting schedulers
if noise is not None:
if isinstance(noise, (list, tuple)):
noise_range = [n * t_initial for n in noise]
if len(noise_range) == 1:
noise_range = noise_range[0]
else:
noise_range = noise * t_initial
else:
noise_range = None
noise_args = dict(
noise_range_t=noise_range,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
)
# setup cycle args for supporting schedulers
cycle_args = dict(
cycle_mul=cycle_mul,
cycle_decay=cycle_decay,
cycle_limit=cycle_limit,
)
lr_scheduler = None
if sched == 'cosine':
lr_scheduler = CosineLRScheduler(
optimizer,
t_initial=t_initial,
lr_min=min_lr,
t_in_epochs=step_on_epochs,
**cycle_args,
**warmup_args,
**noise_args,
k_decay=k_decay,
)
elif sched == 'tanh':
lr_scheduler = TanhLRScheduler(
optimizer,
t_initial=t_initial,
lr_min=min_lr,
t_in_epochs=step_on_epochs,
**cycle_args,
**warmup_args,
**noise_args,
)
elif sched == 'step':
lr_scheduler = StepLRScheduler(
optimizer,
decay_t=decay_t,
decay_rate=decay_rate,
t_in_epochs=step_on_epochs,
**warmup_args,
**noise_args,
)
elif sched == 'multistep':
lr_scheduler = MultiStepLRScheduler(
optimizer,
decay_t=decay_milestones,
decay_rate=decay_rate,
t_in_epochs=step_on_epochs,
**warmup_args,
**noise_args,
)
elif sched == 'plateau':
assert step_on_epochs, 'Plateau LR only supports step per epoch.'
warmup_args.pop('warmup_prefix', False)
lr_scheduler = PlateauLRScheduler(
optimizer,
decay_rate=decay_rate,
patience_t=patience_epochs,
cooldown_t=0,
**warmup_args,
lr_min=min_lr,
mode=plateau_mode,
**noise_args,
)
elif sched == 'poly':
lr_scheduler = PolyLRScheduler(
optimizer,
power=decay_rate, # overloading 'decay_rate' as polynomial power
t_initial=t_initial,
lr_min=min_lr,
t_in_epochs=step_on_epochs,
k_decay=k_decay,
**cycle_args,
**warmup_args,
**noise_args,
)
if hasattr(lr_scheduler, 'get_cycle_length'):
# for cycle based schedulers (cosine, tanh, poly) recalculate total epochs w/ cycles & cooldown
t_with_cycles_and_cooldown = lr_scheduler.get_cycle_length() + cooldown_t
if step_on_epochs:
num_epochs = t_with_cycles_and_cooldown
else:
num_epochs = t_with_cycles_and_cooldown // updates_per_epoch
return lr_scheduler, num_epochs
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/cosine_lr.py | """ Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise, k-decay.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class CosineLRScheduler(Scheduler):
"""
Cosine decay with restarts.
This is described in the paper https://arxiv.org/abs/1608.03983.
Inspiration from
https://github.com/allenai/allennlp/blob/master/allennlp/training/learning_rate_schedulers/cosine.py
k-decay option based on `k-decay: A New Method For Learning Rate Schedule` - https://arxiv.org/abs/2004.05909
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lr_min: float = 0.,
cycle_mul: float = 1.,
cycle_decay: float = 1.,
cycle_limit: int = 1,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=False,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
k_decay=1.0,
initialize=True,
) -> None:
super().__init__(
optimizer,
param_group_field="lr",
t_in_epochs=t_in_epochs,
noise_range_t=noise_range_t,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
initialize=initialize,
)
assert t_initial > 0
assert lr_min >= 0
if t_initial == 1 and cycle_mul == 1 and cycle_decay == 1:
_logger.warning(
"Cosine annealing scheduler will have no effect on the learning "
"rate since t_initial = t_mul = eta_mul = 1.")
self.t_initial = t_initial
self.lr_min = lr_min
self.cycle_mul = cycle_mul
self.cycle_decay = cycle_decay
self.cycle_limit = cycle_limit
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.warmup_prefix = warmup_prefix
self.k_decay = k_decay
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul))
t_i = self.cycle_mul ** i * self.t_initial
t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial
else:
i = t // self.t_initial
t_i = self.t_initial
t_curr = t - (self.t_initial * i)
gamma = self.cycle_decay ** i
lr_max_values = [v * gamma for v in self.base_values]
k = self.k_decay
if i < self.cycle_limit:
lrs = [
self.lr_min + 0.5 * (lr_max - self.lr_min) * (1 + math.cos(math.pi * t_curr ** k / t_i ** k))
for lr_max in lr_max_values
]
else:
lrs = [self.lr_min for _ in self.base_values]
return lrs
def get_cycle_length(self, cycles=0):
cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/multistep_lr.py | """ MultiStep LR Scheduler
Basic multi step LR schedule with warmup, noise.
"""
import torch
import bisect
from timm.scheduler.scheduler import Scheduler
from typing import List
class MultiStepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
decay_t: List[int],
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=True,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
initialize=True,
) -> None:
super().__init__(
optimizer,
param_group_field="lr",
t_in_epochs=t_in_epochs,
noise_range_t=noise_range_t,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
initialize=initialize,
)
self.decay_t = decay_t
self.decay_rate = decay_rate
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.warmup_prefix = warmup_prefix
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def get_curr_decay_steps(self, t):
# find where in the array t goes,
# assumes self.decay_t is sorted
return bisect.bisect_right(self.decay_t, t + 1)
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
lrs = [v * (self.decay_rate ** self.get_curr_decay_steps(t)) for v in self.base_values]
return lrs
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/scheduler.py | import abc
from abc import ABC
from typing import Any, Dict, Optional
import torch
class Scheduler(ABC):
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently called
* At the END of each epoch, before incrementing the epoch count, to calculate next epoch's value
* At the END of each optimizer update, after incrementing the update count, to calculate next update's value
The schedulers built on this should try to remain as stateless as possible (for simplicity).
This family of schedulers is attempting to avoid the confusion of the meaning of 'last_epoch'
and -1 values for special behaviour. All epoch and update counts must be tracked in the training
code and explicitly passed in to the schedulers on the corresponding step or step_update call.
Based on ideas from:
* https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler
* https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
param_group_field: str,
t_in_epochs: bool = True,
noise_range_t=None,
noise_type='normal',
noise_pct=0.67,
noise_std=1.0,
noise_seed=None,
initialize: bool = True,
) -> None:
self.optimizer = optimizer
self.param_group_field = param_group_field
self._initial_param_group_field = f"initial_{param_group_field}"
if initialize:
for i, group in enumerate(self.optimizer.param_groups):
if param_group_field not in group:
raise KeyError(f"{param_group_field} missing from param_groups[{i}]")
group.setdefault(self._initial_param_group_field, group[param_group_field])
else:
for i, group in enumerate(self.optimizer.param_groups):
if self._initial_param_group_field not in group:
raise KeyError(f"{self._initial_param_group_field} missing from param_groups[{i}]")
self.base_values = [group[self._initial_param_group_field] for group in self.optimizer.param_groups]
self.metric = None # any point to having this for all?
self.t_in_epochs = t_in_epochs
self.noise_range_t = noise_range_t
self.noise_pct = noise_pct
self.noise_type = noise_type
self.noise_std = noise_std
self.noise_seed = noise_seed if noise_seed is not None else 42
self.update_groups(self.base_values)
def state_dict(self) -> Dict[str, Any]:
return {key: value for key, value in self.__dict__.items() if key != 'optimizer'}
def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
self.__dict__.update(state_dict)
@abc.abstractmethod
def _get_lr(self, t: int) -> float:
pass
def _get_values(self, t: int, on_epoch: bool = True) -> Optional[float]:
proceed = (on_epoch and self.t_in_epochs) or (not on_epoch and not self.t_in_epochs)
if not proceed:
return None
return self._get_lr(t)
def step(self, epoch: int, metric: float = None) -> None:
self.metric = metric
values = self._get_values(epoch, on_epoch=True)
if values is not None:
values = self._add_noise(values, epoch)
self.update_groups(values)
def step_update(self, num_updates: int, metric: float = None):
self.metric = metric
values = self._get_values(num_updates, on_epoch=False)
if values is not None:
values = self._add_noise(values, num_updates)
self.update_groups(values)
def update_groups(self, values):
if not isinstance(values, (list, tuple)):
values = [values] * len(self.optimizer.param_groups)
for param_group, value in zip(self.optimizer.param_groups, values):
if 'lr_scale' in param_group:
param_group[self.param_group_field] = value * param_group['lr_scale']
else:
param_group[self.param_group_field] = value
def _add_noise(self, lrs, t):
if self._is_apply_noise(t):
noise = self._calculate_noise(t)
lrs = [v + v * noise for v in lrs]
return lrs
def _is_apply_noise(self, t) -> bool:
"""Return True if scheduler in noise range."""
apply_noise = False
if self.noise_range_t is not None:
if isinstance(self.noise_range_t, (list, tuple)):
apply_noise = self.noise_range_t[0] <= t < self.noise_range_t[1]
else:
apply_noise = t >= self.noise_range_t
return apply_noise
def _calculate_noise(self, t) -> float:
g = torch.Generator()
g.manual_seed(self.noise_seed + t)
if self.noise_type == 'normal':
while True:
# resample if noise out of percent limit, brute force but shouldn't spin much
noise = torch.randn(1, generator=g).item()
if abs(noise) < self.noise_pct:
return noise
else:
noise = 2 * (torch.rand(1, generator=g).item() - 0.5) * self.noise_pct
return noise
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/tanh_lr.py | """ TanH Scheduler
TanH schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2021 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
_logger = logging.getLogger(__name__)
class TanhLRScheduler(Scheduler):
"""
Hyberbolic-Tangent decay with restarts.
This is described in the paper https://arxiv.org/abs/1806.01593
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
t_initial: int,
lb: float = -7.,
ub: float = 3.,
lr_min: float = 0.,
cycle_mul: float = 1.,
cycle_decay: float = 1.,
cycle_limit: int = 1,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=False,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
initialize=True,
) -> None:
super().__init__(
optimizer,
param_group_field="lr",
t_in_epochs=t_in_epochs,
noise_range_t=noise_range_t,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
initialize=initialize,
)
assert t_initial > 0
assert lr_min >= 0
assert lb < ub
assert cycle_limit >= 0
assert warmup_t >= 0
assert warmup_lr_init >= 0
self.lb = lb
self.ub = ub
self.t_initial = t_initial
self.lr_min = lr_min
self.cycle_mul = cycle_mul
self.cycle_decay = cycle_decay
self.cycle_limit = cycle_limit
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.warmup_prefix = warmup_prefix
if self.warmup_t:
t_v = self.base_values if self.warmup_prefix else self._get_lr(self.warmup_t)
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in t_v]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
if self.cycle_mul != 1:
i = math.floor(math.log(1 - t / self.t_initial * (1 - self.cycle_mul), self.cycle_mul))
t_i = self.cycle_mul ** i * self.t_initial
t_curr = t - (1 - self.cycle_mul ** i) / (1 - self.cycle_mul) * self.t_initial
else:
i = t // self.t_initial
t_i = self.t_initial
t_curr = t - (self.t_initial * i)
if i < self.cycle_limit:
gamma = self.cycle_decay ** i
lr_max_values = [v * gamma for v in self.base_values]
tr = t_curr / t_i
lrs = [
self.lr_min + 0.5 * (lr_max - self.lr_min) * (1 - math.tanh(self.lb * (1. - tr) + self.ub * tr))
for lr_max in lr_max_values
]
else:
lrs = [self.lr_min for _ in self.base_values]
return lrs
def get_cycle_length(self, cycles=0):
cycles = max(1, cycles or self.cycle_limit)
if self.cycle_mul == 1.0:
return self.t_initial * cycles
else:
return int(math.floor(-self.t_initial * (self.cycle_mul ** cycles - 1) / (1 - self.cycle_mul)))
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/scheduler/step_lr.py | """ Step Scheduler
Basic step LR schedule with warmup, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
import torch
from .scheduler import Scheduler
class StepLRScheduler(Scheduler):
"""
"""
def __init__(
self,
optimizer: torch.optim.Optimizer,
decay_t: float,
decay_rate: float = 1.,
warmup_t=0,
warmup_lr_init=0,
warmup_prefix=True,
t_in_epochs=True,
noise_range_t=None,
noise_pct=0.67,
noise_std=1.0,
noise_seed=42,
initialize=True,
) -> None:
super().__init__(
optimizer,
param_group_field="lr",
t_in_epochs=t_in_epochs,
noise_range_t=noise_range_t,
noise_pct=noise_pct,
noise_std=noise_std,
noise_seed=noise_seed,
initialize=initialize,
)
self.decay_t = decay_t
self.decay_rate = decay_rate
self.warmup_t = warmup_t
self.warmup_lr_init = warmup_lr_init
self.warmup_prefix = warmup_prefix
if self.warmup_t:
self.warmup_steps = [(v - warmup_lr_init) / self.warmup_t for v in self.base_values]
super().update_groups(self.warmup_lr_init)
else:
self.warmup_steps = [1 for _ in self.base_values]
def _get_lr(self, t):
if t < self.warmup_t:
lrs = [self.warmup_lr_init + t * s for s in self.warmup_steps]
else:
if self.warmup_prefix:
t = t - self.warmup_t
lrs = [v * (self.decay_rate ** (t // self.decay_t)) for v in self.base_values]
return lrs
| 0 |