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"""
RDNet
Copyright (c) 2024-present NAVER Cloud Corp.
Apache-2.0
"""
from functools import partial
from typing import List, Optional, Tuple, Union, Callable
import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.layers import DropPath, NormMlpClassifierHead, ClassifierHead, EffectiveSEModule, \
make_divisible, get_act_layer, get_norm_layer
from ._builder import build_model_with_cfg
from ._features import feature_take_indices
from ._manipulate import named_apply
from ._registry import register_model, generate_default_cfgs
__all__ = ["RDNet"]
class Block(nn.Module):
def __init__(self, in_chs, inter_chs, out_chs, norm_layer, act_layer):
super().__init__()
self.layers = nn.Sequential(
nn.Conv2d(in_chs, in_chs, groups=in_chs, kernel_size=7, stride=1, padding=3),
norm_layer(in_chs),
nn.Conv2d(in_chs, inter_chs, kernel_size=1, stride=1, padding=0),
act_layer(),
nn.Conv2d(inter_chs, out_chs, kernel_size=1, stride=1, padding=0),
)
def forward(self, x):
return self.layers(x)
class BlockESE(nn.Module):
def __init__(self, in_chs, inter_chs, out_chs, norm_layer, act_layer):
super().__init__()
self.layers = nn.Sequential(
nn.Conv2d(in_chs, in_chs, groups=in_chs, kernel_size=7, stride=1, padding=3),
norm_layer(in_chs),
nn.Conv2d(in_chs, inter_chs, kernel_size=1, stride=1, padding=0),
act_layer(),
nn.Conv2d(inter_chs, out_chs, kernel_size=1, stride=1, padding=0),
EffectiveSEModule(out_chs),
)
def forward(self, x):
return self.layers(x)
def _get_block_type(block: str):
block = block.lower().strip()
if block == "block":
return Block
elif block == "blockese":
return BlockESE
else:
assert False, f"Unknown block type ({block})."
class DenseBlock(nn.Module):
def __init__(
self,
num_input_features: int = 64,
growth_rate: int = 64,
bottleneck_width_ratio: float = 4.0,
drop_path_rate: float = 0.0,
drop_rate: float = 0.0,
rand_gather_step_prob: float = 0.0,
block_idx: int = 0,
block_type: str = "Block",
ls_init_value: float = 1e-6,
norm_layer: str = "layernorm2d",
act_layer: str = "gelu",
):
super().__init__()
self.drop_rate = drop_rate
self.drop_path_rate = drop_path_rate
self.rand_gather_step_prob = rand_gather_step_prob
self.block_idx = block_idx
self.growth_rate = growth_rate
self.gamma = nn.Parameter(ls_init_value * torch.ones(growth_rate)) if ls_init_value > 0 else None
growth_rate = int(growth_rate)
inter_chs = int(num_input_features * bottleneck_width_ratio / 8) * 8
self.drop_path = DropPath(drop_path_rate)
self.layers = _get_block_type(block_type)(
in_chs=num_input_features,
inter_chs=inter_chs,
out_chs=growth_rate,
norm_layer=norm_layer,
act_layer=act_layer,
)
def forward(self, x: List[torch.Tensor]) -> torch.Tensor:
x = torch.cat(x, 1)
x = self.layers(x)
if self.gamma is not None:
x = x.mul(self.gamma.reshape(1, -1, 1, 1))
x = self.drop_path(x)
return x
class DenseStage(nn.Sequential):
def __init__(self, num_block, num_input_features, drop_path_rates, growth_rate, **kwargs):
super().__init__()
for i in range(num_block):
layer = DenseBlock(
num_input_features=num_input_features,
growth_rate=growth_rate,
drop_path_rate=drop_path_rates[i],
block_idx=i,
**kwargs,
)
num_input_features += growth_rate
self.add_module(f"dense_block{i}", layer)
self.num_out_features = num_input_features
def forward(self, init_feature: torch.Tensor) -> torch.Tensor:
features = [init_feature]
for module in self:
new_feature = module(features)
features.append(new_feature)
return torch.cat(features, 1)
class RDNet(nn.Module):
def __init__(
self,
in_chans: int = 3, # timm option [--in-chans]
num_classes: int = 1000, # timm option [--num-classes]
global_pool: str = 'avg', # timm option [--gp]
growth_rates: Union[List[int], Tuple[int]] = (64, 104, 128, 128, 128, 128, 224),
num_blocks_list: Union[List[int], Tuple[int]] = (3, 3, 3, 3, 3, 3, 3),
block_type: Union[List[int], Tuple[int]] = ("Block",) * 2 + ("BlockESE",) * 5,
is_downsample_block: Union[List[bool], Tuple[bool]] = (None, True, True, False, False, False, True),
bottleneck_width_ratio: float = 4.0,
transition_compression_ratio: float = 0.5,
ls_init_value: float = 1e-6,
stem_type: str = 'patch',
patch_size: int = 4,
num_init_features: int = 64,
head_init_scale: float = 1.,
head_norm_first: bool = False,
conv_bias: bool = True,
act_layer: Union[str, Callable] = 'gelu',
norm_layer: str = "layernorm2d",
norm_eps: Optional[float] = None,
drop_rate: float = 0.0, # timm option [--drop: dropout ratio]
drop_path_rate: float = 0.0, # timm option [--drop-path: drop-path ratio]
):
"""
Args:
in_chans: Number of input image channels.
num_classes: Number of classes for classification head.
global_pool: Global pooling type.
growth_rates: Growth rate at each stage.
num_blocks_list: Number of blocks at each stage.
is_downsample_block: Whether to downsample at each stage.
bottleneck_width_ratio: Bottleneck width ratio (similar to mlp expansion ratio).
transition_compression_ratio: Channel compression ratio of transition layers.
ls_init_value: Init value for Layer Scale, disabled if None.
stem_type: Type of stem.
patch_size: Stem patch size for patch stem.
num_init_features: Number of features of stem.
head_init_scale: Init scaling value for classifier weights and biases.
head_norm_first: Apply normalization before global pool + head.
conv_bias: Use bias layers w/ all convolutions.
act_layer: Activation layer type.
norm_layer: Normalization layer type.
norm_eps: Small value to avoid division by zero in normalization.
drop_rate: Head pre-classifier dropout rate.
drop_path_rate: Stochastic depth drop rate.
"""
super().__init__()
assert len(growth_rates) == len(num_blocks_list) == len(is_downsample_block)
act_layer = get_act_layer(act_layer)
norm_layer = get_norm_layer(norm_layer)
if norm_eps is not None:
norm_layer = partial(norm_layer, eps=norm_eps)
self.num_classes = num_classes
self.drop_rate = drop_rate
# stem
assert stem_type in ('patch', 'overlap', 'overlap_tiered')
if stem_type == 'patch':
# NOTE: this stem is a minimal form of ViT PatchEmbed, as used in SwinTransformer w/ patch_size = 4
self.stem = nn.Sequential(
nn.Conv2d(in_chans, num_init_features, kernel_size=patch_size, stride=patch_size, bias=conv_bias),
norm_layer(num_init_features),
)
stem_stride = patch_size
else:
mid_chs = make_divisible(num_init_features // 2) if 'tiered' in stem_type else num_init_features
self.stem = nn.Sequential(
nn.Conv2d(in_chans, mid_chs, kernel_size=3, stride=2, padding=1, bias=conv_bias),
nn.Conv2d(mid_chs, num_init_features, kernel_size=3, stride=2, padding=1, bias=conv_bias),
norm_layer(num_init_features),
)
stem_stride = 4
# features
self.feature_info = []
self.num_stages = len(growth_rates)
curr_stride = stem_stride
num_features = num_init_features
dp_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(num_blocks_list)).split(num_blocks_list)]
dense_stages = []
for i in range(self.num_stages):
dense_stage_layers = []
if i != 0:
compressed_num_features = int(num_features * transition_compression_ratio / 8) * 8
k_size = stride = 1
if is_downsample_block[i]:
curr_stride *= 2
k_size = stride = 2
dense_stage_layers.append(norm_layer(num_features))
dense_stage_layers.append(
nn.Conv2d(num_features, compressed_num_features, kernel_size=k_size, stride=stride, padding=0)
)
num_features = compressed_num_features
stage = DenseStage(
num_block=num_blocks_list[i],
num_input_features=num_features,
growth_rate=growth_rates[i],
bottleneck_width_ratio=bottleneck_width_ratio,
drop_rate=drop_rate,
drop_path_rates=dp_rates[i],
ls_init_value=ls_init_value,
block_type=block_type[i],
norm_layer=norm_layer,
act_layer=act_layer,
)
dense_stage_layers.append(stage)
num_features += num_blocks_list[i] * growth_rates[i]
if i + 1 == self.num_stages or (i + 1 != self.num_stages and is_downsample_block[i + 1]):
self.feature_info += [
dict(
num_chs=num_features,
reduction=curr_stride,
module=f'dense_stages.{i}',
growth_rate=growth_rates[i],
)
]
dense_stages.append(nn.Sequential(*dense_stage_layers))
self.dense_stages = nn.Sequential(*dense_stages)
self.num_features = self.head_hidden_size = num_features
# if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets
# otherwise pool -> norm -> fc, the default RDNet ordering (pretrained NV weights)
if head_norm_first:
self.norm_pre = norm_layer(self.num_features)
self.head = ClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
)
else:
self.norm_pre = nn.Identity()
self.head = NormMlpClassifierHead(
self.num_features,
num_classes,
pool_type=global_pool,
drop_rate=self.drop_rate,
norm_layer=norm_layer,
)
named_apply(partial(_init_weights, head_init_scale=head_init_scale), self)
def forward_intermediates(
self,
x: torch.Tensor,
indices: Optional[Union[int, List[int]]] = None,
norm: bool = False,
stop_early: bool = False,
output_fmt: str = 'NCHW',
intermediates_only: bool = False,
) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
""" Forward features that returns intermediates.
Args:
x: Input image tensor
indices: Take last n blocks if int, all if None, select matching indices if sequence
norm: Apply norm layer to compatible intermediates
stop_early: Stop iterating over blocks when last desired intermediate hit
output_fmt: Shape of intermediate feature outputs
intermediates_only: Only return intermediate features
"""
assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
intermediates = []
take_indices, max_index = feature_take_indices(len(self.dense_stages) + 1, indices)
# forward pass
feat_idx = 0 # stem is index 0
x = self.stem(x)
if feat_idx in take_indices:
intermediates.append(x)
if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
dense_stages = self.dense_stages
else:
dense_stages = self.dense_stages[:max_index]
for stage in dense_stages:
feat_idx += 1
x = stage(x)
if feat_idx in take_indices:
# NOTE not bothering to apply norm_pre when norm=True as almost no models have it enabled
intermediates.append(x)
if intermediates_only:
return intermediates
x = self.norm_pre(x)
return x, intermediates
def prune_intermediate_layers(
self,
indices: Union[int, List[int]] = 1,
prune_norm: bool = False,
prune_head: bool = True,
):
""" Prune layers not required for specified intermediates.
"""
take_indices, max_index = feature_take_indices(len(self.dense_stages) + 1, indices)
self.dense_stages = self.dense_stages[:max_index] # truncate blocks w/ stem as idx 0
if prune_norm:
self.norm_pre = nn.Identity()
if prune_head:
self.reset_classifier(0, '')
return take_indices
@torch.jit.ignore
def get_classifier(self) -> nn.Module:
return self.head.fc
def reset_classifier(self, num_classes: int, global_pool: Optional[str] = None):
self.head.reset(num_classes, global_pool)
def forward_features(self, x):
x = self.stem(x)
x = self.dense_stages(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.head(x)
return x
@torch.jit.ignore
def group_matcher(self, coarse=False):
assert not coarse, "coarse grouping is not implemented for RDNet"
return dict(
stem=r'^stem',
blocks=r'^dense_stages\.(\d+)',
)
@torch.jit.ignore
def set_grad_checkpointing(self, enable=True):
for s in self.dense_stages:
s.grad_checkpointing = enable
def _init_weights(module, name=None, head_init_scale=1.0):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight)
elif isinstance(module, nn.BatchNorm2d):
nn.init.constant_(module.weight, 1)
nn.init.constant_(module.bias, 0)
elif isinstance(module, nn.Linear):
nn.init.constant_(module.bias, 0)
if name and 'head.' in name:
module.weight.data.mul_(head_init_scale)
module.bias.data.mul_(head_init_scale)
def checkpoint_filter_fn(state_dict, model):
""" Remap NV checkpoints -> timm """
if 'stem.0.weight' in state_dict:
return state_dict # non-NV checkpoint
if 'model' in state_dict:
state_dict = state_dict['model']
out_dict = {}
for k, v in state_dict.items():
k = k.replace('stem.stem.', 'stem.')
out_dict[k] = v
return out_dict
def _create_rdnet(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
RDNet, variant, pretrained,
pretrained_filter_fn=checkpoint_filter_fn,
feature_cfg=dict(out_indices=(0, 1, 2, 3), flatten_sequential=True),
**kwargs)
return model
def _cfg(url='', **kwargs):
return {
"url": url,
"num_classes": 1000, "input_size": (3, 224, 224), "pool_size": (7, 7),
"crop_pct": 0.9, "interpolation": "bicubic",
"mean": IMAGENET_DEFAULT_MEAN, "std": IMAGENET_DEFAULT_STD,
"first_conv": "stem.0", "classifier": "head.fc",
"paper_ids": "arXiv:2403.19588",
"paper_name": "DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs",
"origin_url": "https://github.com/naver-ai/rdnet",
**kwargs,
}
default_cfgs = generate_default_cfgs({
'rdnet_tiny.nv_in1k': _cfg(
hf_hub_id='naver-ai/rdnet_tiny.nv_in1k'),
'rdnet_small.nv_in1k': _cfg(
hf_hub_id='naver-ai/rdnet_small.nv_in1k'),
'rdnet_base.nv_in1k': _cfg(
hf_hub_id='naver-ai/rdnet_base.nv_in1k'),
'rdnet_large.nv_in1k': _cfg(
hf_hub_id='naver-ai/rdnet_large.nv_in1k'),
'rdnet_large.nv_in1k_ft_in1k_384': _cfg(
hf_hub_id='naver-ai/rdnet_large.nv_in1k_ft_in1k_384',
input_size=(3, 384, 384), crop_pct=1.0, pool_size=(12, 12)),
})
@register_model
def rdnet_tiny(pretrained=False, **kwargs):
n_layer = 7
model_args = {
"num_init_features": 64,
"growth_rates": [64] + [104] + [128] * 4 + [224],
"num_blocks_list": [3] * n_layer,
"is_downsample_block": (None, True, True, False, False, False, True),
"transition_compression_ratio": 0.5,
"block_type": ["Block"] + ["Block"] + ["BlockESE"] * 4 + ["BlockESE"],
}
model = _create_rdnet("rdnet_tiny", pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def rdnet_small(pretrained=False, **kwargs):
n_layer = 11
model_args = {
"num_init_features": 72,
"growth_rates": [64] + [128] + [128] * (n_layer - 4) + [240] * 2,
"num_blocks_list": [3] * n_layer,
"is_downsample_block": (None, True, True, False, False, False, False, False, False, True, False),
"transition_compression_ratio": 0.5,
"block_type": ["Block"] + ["Block"] + ["BlockESE"] * (n_layer - 4) + ["BlockESE"] * 2,
}
model = _create_rdnet("rdnet_small", pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def rdnet_base(pretrained=False, **kwargs):
n_layer = 11
model_args = {
"num_init_features": 120,
"growth_rates": [96] + [128] + [168] * (n_layer - 4) + [336] * 2,
"num_blocks_list": [3] * n_layer,
"is_downsample_block": (None, True, True, False, False, False, False, False, False, True, False),
"transition_compression_ratio": 0.5,
"block_type": ["Block"] + ["Block"] + ["BlockESE"] * (n_layer - 4) + ["BlockESE"] * 2,
}
model = _create_rdnet("rdnet_base", pretrained=pretrained, **dict(model_args, **kwargs))
return model
@register_model
def rdnet_large(pretrained=False, **kwargs):
n_layer = 12
model_args = {
"num_init_features": 144,
"growth_rates": [128] + [192] + [256] * (n_layer - 4) + [360] * 2,
"num_blocks_list": [3] * n_layer,
"is_downsample_block": (None, True, True, False, False, False, False, False, False, False, True, False),
"transition_compression_ratio": 0.5,
"block_type": ["Block"] + ["Block"] + ["BlockESE"] * (n_layer - 4) + ["BlockESE"] * 2,
}
model = _create_rdnet("rdnet_large", pretrained=pretrained, **dict(model_args, **kwargs))
return model
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