mirror of
https://github.com/firestar5683/StarPilot.git
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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.
75 lines
3.3 KiB
Python
75 lines
3.3 KiB
Python
from pathlib import Path
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from extra.models.efficientnet import EfficientNet
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from tinygrad.tensor import Tensor
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from tinygrad.device import Device
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from tinygrad.nn.state import get_state_dict, safe_save, safe_load, load_state_dict
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from extra.export_model import export_model
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from tinygrad.helpers import fetch
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import ast
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if __name__ == "__main__":
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model = EfficientNet(0)
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model.load_from_pretrained()
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dirname = Path(__file__).parent
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# exporting a model that's loaded from safetensors doesn't work without loading in from safetensors first
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# loading the state dict from a safetensor file changes the generated kernels
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if Device.DEFAULT == "WEBGPU":
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safe_save(get_state_dict(model), (dirname / "net.safetensors").as_posix())
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load_state_dict(model, safe_load(str(dirname / "net.safetensors")))
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mode = "clang" if Device.DEFAULT == "CPU" else "webgpu" if Device.DEFAULT == "WEBGPU" else ""
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prg, inp_sizes, out_sizes, state = export_model(model, mode, Tensor.randn(1,3,224,224))
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if Device.DEFAULT != "CPU":
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ext = "js" if Device.DEFAULT == "WEBGPU" else "json"
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with open(dirname / f"net.{ext}", "w") as text_file:
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text_file.write(prg)
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else:
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cprog = [prg]
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# image library!
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cprog += ["#define STB_IMAGE_IMPLEMENTATION", fetch("https://raw.githubusercontent.com/nothings/stb/master/stb_image.h").read_text().replace("half", "_half")]
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# imagenet labels, move to datasets?
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lbls = ast.literal_eval(fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt").read_text())
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lbls = ['"'+lbls[i]+'"' for i in range(1000)]
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inputs = "\n".join([f"float {inp}[{inp_size}];" for inp,inp_size in inp_sizes.items()])
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outputs = "\n".join([f"float {out}[{out_size}];" for out,out_size in out_sizes.items()])
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cprog.append(f"char *lbls[] = {{{','.join(lbls)}}};")
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cprog.append(inputs)
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cprog.append(outputs)
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# buffers (empty + weights)
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cprog.append("""
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int main(int argc, char* argv[]) {
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int DEBUG = getenv("DEBUG") != NULL ? atoi(getenv("DEBUG")) : 0;
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int X=0, Y=0, chan=0;
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stbi_uc *image = (argc > 1) ? stbi_load(argv[1], &X, &Y, &chan, 3) : stbi_load_from_file(stdin, &X, &Y, &chan, 3);
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assert(image != NULL);
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if (DEBUG) printf("loaded image %dx%d channels %d\\n", X, Y, chan);
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assert(chan == 3);
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// resize to input[1,3,224,224] and rescale
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for (int y = 0; y < 224; y++) {
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for (int x = 0; x < 224; x++) {
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// get sample position
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int tx = (x/224.)*X;
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int ty = (y/224.)*Y;
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for (int c = 0; c < 3; c++) {
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input0[c*224*224 + y*224 + x] = (image[ty*X*chan + tx*chan + c] / 255.0 - 0.45) / 0.225;
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}
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}
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}
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net(input0, output0);
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float best = -INFINITY;
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int best_idx = -1;
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for (int i = 0; i < 1000; i++) {
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if (output0[i] > best) {
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best = output0[i];
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best_idx = i;
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}
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}
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if (DEBUG) printf("category : %d (%s) with %f\\n", best_idx, lbls[best_idx], best);
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else printf("%s\\n", lbls[best_idx]);
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}""")
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# DEV=CPU python3 examples/compile_efficientnet.py | clang -O2 -lm -x c - -o recognize && DEBUG=1 time ./recognize docs/showcase/stable_diffusion_by_tinygrad.jpg
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# category : 281 (tabby, tabby cat) with 9.452788
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print('\n'.join(cprog))
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