mirror of
https://github.com/firestar5683/StarPilot.git
synced 2026-11-09 22:13:43 +08:00
firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
from pathlib import Path
|
|
import torch
|
|
from tinygrad import nn
|
|
from tinygrad.tensor import Tensor
|
|
from tinygrad.helpers import fetch, get_child
|
|
|
|
class DownsampleBlock:
|
|
def __init__(self, c0, c1, stride=2):
|
|
self.conv1 = [nn.Conv2d(c0, c1, kernel_size=(3,3,3), stride=stride, padding=(1,1,1,1,1,1), bias=False), nn.InstanceNorm(c1), Tensor.relu]
|
|
self.conv2 = [nn.Conv2d(c1, c1, kernel_size=(3,3,3), padding=(1,1,1,1,1,1), bias=False), nn.InstanceNorm(c1), Tensor.relu]
|
|
|
|
def __call__(self, x):
|
|
return x.sequential(self.conv1).sequential(self.conv2)
|
|
|
|
class UpsampleBlock:
|
|
def __init__(self, c0, c1):
|
|
self.upsample_conv = [nn.ConvTranspose2d(c0, c1, kernel_size=(2,2,2), stride=2)]
|
|
self.conv1 = [nn.Conv2d(2 * c1, c1, kernel_size=(3,3,3), padding=(1,1,1,1,1,1), bias=False), nn.InstanceNorm(c1), Tensor.relu]
|
|
self.conv2 = [nn.Conv2d(c1, c1, kernel_size=(3,3,3), padding=(1,1,1,1,1,1), bias=False), nn.InstanceNorm(c1), Tensor.relu]
|
|
|
|
def __call__(self, x, skip):
|
|
x = x.sequential(self.upsample_conv)
|
|
x = Tensor.cat(x, skip, dim=1)
|
|
return x.sequential(self.conv1).sequential(self.conv2)
|
|
|
|
class UNet3D:
|
|
def __init__(self, in_channels=1, n_class=3):
|
|
filters = [32, 64, 128, 256, 320]
|
|
inp, out = filters[:-1], filters[1:]
|
|
self.input_block = DownsampleBlock(in_channels, filters[0], stride=1)
|
|
self.downsample = [DownsampleBlock(i, o) for i, o in zip(inp, out)]
|
|
self.bottleneck = DownsampleBlock(filters[-1], filters[-1])
|
|
self.upsample = [UpsampleBlock(filters[-1], filters[-1])] + [UpsampleBlock(i, o) for i, o in zip(out[::-1], inp[::-1])]
|
|
self.output = {"conv": nn.Conv2d(filters[0], n_class, kernel_size=(1, 1, 1))}
|
|
|
|
def __call__(self, x):
|
|
x = self.input_block(x)
|
|
outputs = [x]
|
|
for downsample in self.downsample:
|
|
x = downsample(x)
|
|
outputs.append(x)
|
|
x = self.bottleneck(x)
|
|
for upsample, skip in zip(self.upsample, outputs[::-1]):
|
|
x = upsample(x, skip)
|
|
x = self.output["conv"](x)
|
|
return x
|
|
|
|
def load_from_pretrained(self):
|
|
fn = Path(__file__).parents[1] / "weights" / "unet-3d.ckpt"
|
|
fetch("https://zenodo.org/record/5597155/files/3dunet_kits19_pytorch.ptc?download=1", fn)
|
|
state_dict = torch.jit.load(fn, map_location=torch.device("cpu")).state_dict()
|
|
for k, v in state_dict.items():
|
|
obj = get_child(self, k)
|
|
assert obj.shape == v.shape, (k, obj.shape, v.shape)
|
|
obj.assign(v.numpy())
|
|
|
|
if __name__ == "__main__":
|
|
mdl = UNet3D()
|
|
mdl.load_from_pretrained()
|