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import timm
import torch.nn.functional as F
from torch import nn, Tensor
from functools import partial
from typing import Optional
from ..utils import ConvRefine, _get_norm_layer, _get_activation
available_hrnets = [
"hrnet_w18", "hrnet_w18_small", "hrnet_w18_small_v2",
"hrnet_w30", "hrnet_w32", "hrnet_w40", "hrnet_w44", "hrnet_w48", "hrnet_w64",
]
class HRNet(nn.Module):
def __init__(
self,
model_name: str,
block_size: Optional[int] = None,
norm: str = "none",
act: str = "none"
) -> None:
super().__init__()
assert model_name in available_hrnets, f"Model name should be one of {available_hrnets}"
assert block_size is None or block_size in [8, 16, 32], f"block_size should be one of [8, 16, 32], but got {block_size}."
self.model_name = model_name
self.block_size = block_size if block_size is not None else 32
# model = timm.create_model(model_name, pretrained=True)
model = timm.create_model(model_name, pretrained=False)
self.conv1 = model.conv1
self.bn1 = model.bn1
self.act1 = model.act1
self.conv2 = model.conv2
self.bn2 = model.bn2
self.act2 = model.act2
self.layer1 = model.layer1
self.transition1 = model.transition1
self.stage2 = model.stage2
self.transition2 = model.transition2
self.stage3 = model.stage3
self.transition3 = model.transition3
self.stage4 = model.stage4
incre_modules = model.incre_modules
downsamp_modules = model.downsamp_modules
assert len(incre_modules) == 4, f"Expected 4 incre_modules, got {len(self.incre_modules)}"
assert len(downsamp_modules) == 3, f"Expected 3 downsamp_modules, got {len(self.downsamp_modules)}"
self.out_channels_4 = incre_modules[0][0].downsample[0].out_channels
self.out_channels_8 = incre_modules[1][0].downsample[0].out_channels
self.out_channels_16 = incre_modules[2][0].downsample[0].out_channels
self.out_channels_32 = incre_modules[3][0].downsample[0].out_channels
if self.block_size == 8:
self.encoder_reduction = 8
self.encoder_channels = self.out_channels_8
self.incre_modules = incre_modules[:2]
self.downsamp_modules = downsamp_modules[:1]
self.refiner = nn.Identity()
self.refiner_reduction = 8
self.refiner_channels = self.out_channels_8
elif self.block_size == 16:
self.encoder_reduction = 16
self.encoder_channels = self.out_channels_16
self.incre_modules = incre_modules[:3]
self.downsamp_modules = downsamp_modules[:2]
self.refiner = nn.Identity()
self.refiner_reduction = 16
self.refiner_channels = self.out_channels_16
else: # self.block_size == 32
self.encoder_reduction = 32
self.encoder_channels = self.out_channels_32
self.incre_modules = incre_modules
self.downsamp_modules = downsamp_modules
self.refiner = nn.Identity()
self.refiner_reduction = 32
self.refiner_channels = self.out_channels_32
# define the decoder
if self.refiner_channels <= 512:
groups = 1
elif self.refiner_channels <= 1024:
groups = 2
elif self.refiner_channels <= 2048:
groups = 4
else:
groups = 8
if norm == "bn":
norm_layer = nn.BatchNorm2d
elif norm == "ln":
norm_layer = nn.LayerNorm
else:
norm_layer = _get_norm_layer(model)
if act == "relu":
activation = nn.ReLU(inplace=True)
elif act == "gelu":
activation = nn.GELU()
else:
activation = _get_activation(model)
decoder_block = partial(ConvRefine, groups=groups, norm_layer=norm_layer, activation=activation)
if self.refiner_channels <= 256:
self.decoder = nn.Identity()
self.decoder_channels = self.refiner_channels
elif self.refiner_channels <= 512:
self.decoder = decoder_block(self.refiner_channels, self.refiner_channels // 2)
self.decoder_channels = self.refiner_channels // 2
elif self.refiner_channels <= 1024:
self.decoder = nn.Sequential(
decoder_block(self.refiner_channels, self.refiner_channels // 2),
decoder_block(self.refiner_channels // 2, self.refiner_channels // 4),
)
self.decoder_channels = self.refiner_channels // 4
else:
self.decoder = nn.Sequential(
decoder_block(self.refiner_channels, self.refiner_channels // 2),
decoder_block(self.refiner_channels // 2, self.refiner_channels // 4),
decoder_block(self.refiner_channels // 4, self.refiner_channels // 8),
)
self.decoder_channels = self.refiner_channels // 8
self.decoder_reduction = self.refiner_reduction
def _interpolate(self, x: Tensor) -> Tensor:
# This method adjust the spatial dimensions of the input tensor so that it can be divided by 32.
if x.shape[-1] % 32 != 0 or x.shape[-2] % 32 != 0:
new_h = int(round(x.shape[-2] / 32) * 32)
new_w = int(round(x.shape[-1] / 32) * 32)
return F.interpolate(x, size=(new_h, new_w), mode="bicubic", align_corners=False)
return x
def encode(self, x: Tensor) -> Tensor:
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.act2(x)
x = self.layer1(x)
x = [t(x) for t in self.transition1]
x = self.stage2(x)
x = [t(x[-1]) if not isinstance(t, nn.Identity) else x[i] for i, t in enumerate(self.transition2)]
x = self.stage3(x)
x = [t(x[-1]) if not isinstance(t, nn.Identity) else x[i] for i, t in enumerate(self.transition3)]
x = self.stage4(x)
assert len(x) == 4, f"Expected 4 outputs, got {len(x)}"
feats = None
for i, incre in enumerate(self.incre_modules):
if feats is None:
feats = incre(x[i])
else:
down = self.downsamp_modules[i - 1] # needed for torchscript module indexing
feats = incre(x[i]) + down.forward(feats)
return feats
def refine(self, x: Tensor) -> Tensor:
return self.refiner(x)
def decode(self, x: Tensor) -> Tensor:
return self.decoder(x)
def forward(self, x: Tensor) -> Tensor:
x = self._interpolate(x)
x = self.encode(x)
x = self.refine(x)
x = self.decode(x)
return x
def _hrnet(model_name: str, block_size: Optional[int] = None, norm: str = "none", act: str = "none") -> HRNet:
return HRNet(model_name, block_size, norm, act) |