mirror of https://github.com/commaai/tinygrad.git
68 lines
2.8 KiB
Python
68 lines
2.8 KiB
Python
from models.efficientnet import EfficientNet
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from tinygrad.tensor import Tensor
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from tinygrad.state import safe_save
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from extra.utils import fetch
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from extra.export_model import export_model
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from tinygrad.helpers import getenv
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import ast, os
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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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mode = "clang" if getenv("CLANG", "") != "" else "webgpu" if getenv("WEBGPU", "") != "" else ""
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prg, inp_size, out_size, state = export_model(model, Tensor.randn(1,3,224,224), mode)
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if getenv("CLANG", "") == "":
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safe_save(state, os.path.join(os.path.dirname(__file__), "net.safetensors"))
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ext = "js" if getenv("WEBGPU", "") != "" else "json"
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with open(os.path.join(os.path.dirname(__file__), 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").decode('utf-8').replace("half", "_half")]
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# imagenet labels, move to datasets?
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lbls = fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt")
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lbls = ast.literal_eval(lbls.decode('utf-8'))
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lbls = ['"'+lbls[i]+'"' for i in range(1000)]
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cprog.append(f"char *lbls[] = {{{','.join(lbls)}}};")
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cprog.append(f"float input[{inp_size}];")
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cprog.append(f"float outputs[{out_size}];")
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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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input[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(input, outputs);
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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 (outputs[i] > best) {
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best = outputs[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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# CLANG=1 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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