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import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import trunc_normal_, DropPath
def get_num_layer_for_convnext_single(var_name, depths):
"""
Each layer is assigned distinctive layer ids
"""
if var_name.startswith("downsample_layers"):
stage_id = int(var_name.split(".")[1])
layer_id = sum(depths[:stage_id]) + 1
return layer_id
elif var_name.startswith("stages"):
stage_id = int(var_name.split(".")[1])
block_id = int(var_name.split(".")[2])
layer_id = sum(depths[:stage_id]) + block_id + 1
return layer_id
else:
return sum(depths) + 1
def get_num_layer_for_convnext(var_name):
"""
Divide [3, 3, 27, 3] layers into 12 groups; each group is three
consecutive blocks, including possible neighboring downsample layers;
adapted from https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py
"""
num_max_layer = 12
if var_name.startswith("downsample_layers"):
stage_id = int(var_name.split(".")[1])
if stage_id == 0:
layer_id = 0
elif stage_id == 1 or stage_id == 2:
layer_id = stage_id + 1
elif stage_id == 3:
layer_id = 12
return layer_id
elif var_name.startswith("stages"):
stage_id = int(var_name.split(".")[1])
block_id = int(var_name.split(".")[2])
if stage_id == 0 or stage_id == 1:
layer_id = stage_id + 1
elif stage_id == 2:
layer_id = 3 + block_id // 3
elif stage_id == 3:
layer_id = 12
return layer_id
else:
return num_max_layer + 1
def get_parameter_groups(model, lr, wd=1e-5, ld=0.9, skip_list=()):
parameter_group_names = {}
parameter_group_vars = {}
skip = {}
if skip_list is not None:
skip = skip_list
elif hasattr(model, "no_weight_decay"):
skip = model.no_weight_decay()
num_layers = 12 # sum(model.depths)
layer_scale = list(ld ** (num_layers + 1 - i) for i in range(num_layers + 2))
for name, param in model.named_parameters():
if not param.requires_grad:
continue # frozen weights
if (
len(param.shape) == 1
or name.endswith(".bias")
or name in skip
or name.endswith(".gamma")
or name.endswith(".beta")
):
group_name = "no_decay"
this_weight_decay = 0.0
else:
group_name = "decay"
this_weight_decay = wd
# layer_id = get_num_layer_for_convnext_single(name, model.depths)
layer_id = get_num_layer_for_convnext(name)
group_name = "layer_%d_%s" % (layer_id, group_name)
if group_name not in parameter_group_names:
scale = layer_scale[layer_id]
cur_lr = lr * scale
parameter_group_names[group_name] = {
"weight_decay": this_weight_decay,
"params": [],
"lr_scale": scale,
"lr": cur_lr,
}
parameter_group_vars[group_name] = {
"weight_decay": this_weight_decay,
"params": [],
"lr_scale": scale,
"lr": cur_lr,
}
parameter_group_vars[group_name]["params"].append(param)
parameter_group_names[group_name]["params"].append(name)
# if is_main_process():
# print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))
return list(parameter_group_vars.values()), [
v["lr"] for k, v in parameter_group_vars.items()
]
class LayerNorm(nn.Module):
"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
with shape (batch_size, channels, height, width).
"""
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
self.normalized_shape = (normalized_shape,)
def forward(self, x):
if self.data_format == "channels_last":
return F.layer_norm(
x, self.normalized_shape, self.weight, self.bias, self.eps
)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class GRN(nn.Module):
"""GRN (Global Response Normalization) layer"""
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
def forward(self, x):
Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
return self.gamma * (x * Nx) + self.beta + x
class Block(nn.Module):
"""ConvNeXtV2 Block.
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
"""
def __init__(self, dim, drop_path=0.0, mult=4, use_checkpoint=False):
super().__init__()
self.dwconv = nn.Conv2d(
dim, dim, kernel_size=7, padding=3, groups=dim
) # depthwise conv
self.norm = LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(
dim, mult * dim
) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.grn = GRN(mult * dim)
self.pwconv2 = nn.Linear(mult * dim, dim)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.use_checkpoint = use_checkpoint
def forward(self, x):
input = x
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.grn(x)
x = self.pwconv2(x)
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = input + self.drop_path(x)
return x
class ConvNeXtV2(nn.Module):
"""ConvNeXt V2
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
drop_path_rate (float): Stochastic depth rate. Default: 0.
head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
"""
def __init__(
self,
in_chans=3,
depths=[3, 3, 9, 3],
dims=96,
drop_path_rate=0.0,
output_idx=[],
use_checkpoint=False,
):
super().__init__()
self.num_layers = len(depths)
self.depths = output_idx
self.embed_dims = [
int(dim) for i, dim in enumerate(dims) for _ in range(depths[i])
]
self.embed_dim = dims[0]
self.downsample_layers = (
nn.ModuleList()
) # stem and 3 intermediate downsampling conv layers
stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
self.stages = (
nn.ModuleList()
) # 4 feature resolution stages, each consisting of multiple residual blocks
self.out_norms = nn.ModuleList()
dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
cur = 0
for i in range(4):
stage = nn.ModuleList(
[
Block(
dim=dims[i],
drop_path=dp_rates[cur + j],
use_checkpoint=use_checkpoint,
)
for j in range(depths[i])
]
)
self.stages.append(stage)
cur += depths[i]
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, (nn.Conv2d, nn.Linear)):
trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
def forward(self, x):
outs = []
for i in range(4):
x = self.downsample_layers[i](x)
for stage in self.stages[i]:
x = stage(x)
outs.append(x.permute(0, 2, 3, 1))
cls_tokens = [x.mean(dim=(1, 2)).unsqueeze(1).contiguous() for x in outs]
return outs, cls_tokens
def get_params(self, lr, wd, ld, *args, **kwargs):
encoder_p, encoder_lr = get_parameter_groups(self, lr, wd, ld)
return encoder_p, encoder_lr
def freeze(self) -> None:
for module in self.modules():
module.eval()
for parameters in self.parameters():
parameters.requires_grad = False
@classmethod
def build(cls, config):
obj = globals()[config["model"]["encoder"]["name"]](config)
return obj
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