mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-10 23:48:01 -05:00
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
from models.efficientnet import EfficientNet
|
|
from tinygrad.tensor import Tensor
|
|
from extra.utils import fetch
|
|
import ast
|
|
|
|
def compile_net(run, special_names):
|
|
# functions that run the net
|
|
functions = {}
|
|
bufs = {}
|
|
bufnum = 0
|
|
statements = []
|
|
bufs_to_save = {}
|
|
for fxn,args in run.jit_cache:
|
|
functions[fxn.name] = fxn.prg # NOTE: this assumes all with the same name are the same
|
|
cargs = []
|
|
for i,arg in enumerate(args):
|
|
key = id(arg)
|
|
if key not in bufs:
|
|
if key in special_names:
|
|
bufs[key] = (special_names[key], len(arg._buf))
|
|
else:
|
|
bufs[key] = (f"buf_{bufnum}", len(arg._buf))
|
|
bufnum += 1
|
|
if i > 0: bufs_to_save[bufs[key][0]] = arg # if first usage of a buffer is not an output, and it's not a special name
|
|
cargs.append(bufs[key][0])
|
|
statements.append(f"{fxn.name}({', '.join(cargs)});")
|
|
|
|
return functions, statements, bufs, bufs_to_save
|
|
|
|
if __name__ == "__main__":
|
|
model = EfficientNet(0)
|
|
model.load_from_pretrained()
|
|
|
|
from tinygrad.jit import TinyJit
|
|
@TinyJit
|
|
def run(x): return model.forward(x).realize()
|
|
|
|
# twice to run the JIT
|
|
the_input = Tensor.randn(1,3,224,224)
|
|
the_output = run(the_input)
|
|
the_output = run(the_input)
|
|
|
|
# hack to put the inputs back
|
|
assert len(run.input_replace) == 1, f"didn't get one input to replace {run.input_replace}"
|
|
for (j,i),idx in run.input_replace.items():
|
|
run.jit_cache[j][1][i] = the_input.lazydata.realized.raw()
|
|
|
|
# TODO: fetch this from the jit in self.input_replace and self.ret (hint: use get_parameters on self.ret)
|
|
special_names = {id(the_input.lazydata.realized.raw()): "input", id(the_output.lazydata.realized.raw()): "outputs"}
|
|
|
|
functions, statements, bufs, bufs_to_save = compile_net(run, special_names)
|
|
|
|
# c header
|
|
cprog = ["#include <stdio.h>", "#include <math.h>", "#define max(x,y) ((x>y)?x:y)"]
|
|
|
|
# save the weights
|
|
for name,cl in bufs_to_save.items():
|
|
weight = ''.join(["\\x%02X"%x for x in bytes(cl._buf)])
|
|
cprog.append(f"unsigned char {name}_data[] = \"{weight}\";")
|
|
|
|
# image library!
|
|
cprog += ["#define STB_IMAGE_IMPLEMENTATION", fetch("https://raw.githubusercontent.com/nothings/stb/master/stb_image.h").decode('utf-8')]
|
|
|
|
# imagenet labels, move to datasets?
|
|
lbls = fetch("https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt")
|
|
lbls = ast.literal_eval(lbls.decode('utf-8'))
|
|
lbls = ['"'+lbls[i]+'"' for i in range(1000)]
|
|
cprog.append(f"char *lbls[] = {{{','.join(lbls)}}};")
|
|
|
|
# buffers (empty + weights)
|
|
cprog += [f"float {name}[{len}];" if name not in bufs_to_save else f"float *{name} = (float *){name}_data;" for name,len in bufs.values()]
|
|
|
|
# the functions
|
|
cprog += list(functions.values())
|
|
|
|
# the net
|
|
cprog += ["void net() {"] + statements + ["}"]
|
|
|
|
cprog += ["""
|
|
int main(int argc, char* argv[]) {
|
|
int DEBUG = getenv("DEBUG") != NULL ? atoi(getenv("DEBUG")) : 0;
|
|
int X=0, Y=0, chan=0;
|
|
stbi_uc *image = (argc > 1) ? stbi_load(argv[1], &X, &Y, &chan, 3) : stbi_load_from_file(stdin, &X, &Y, &chan, 3);
|
|
assert(image != NULL);
|
|
if (DEBUG) printf("loaded image %dx%d channels %d\\n", X, Y, chan);
|
|
assert(chan == 3);
|
|
// resize to input[1,3,224,224] and rescale
|
|
for (int y = 0; y < 224; y++) {
|
|
for (int x = 0; x < 224; x++) {
|
|
// get sample position
|
|
int tx = (x/224.)*X;
|
|
int ty = (y/224.)*Y;
|
|
for (int c = 0; c < 3; c++) {
|
|
input[c*224*224 + y*224 + x] = (image[ty*X*chan + tx*chan + c] / 255.0 - 0.45) / 0.225;
|
|
}
|
|
}
|
|
}
|
|
net();
|
|
float best = -INFINITY;
|
|
int best_idx = -1;
|
|
for (int i = 0; i < 1000; i++) {
|
|
if (outputs[i] > best) {
|
|
best = outputs[i];
|
|
best_idx = i;
|
|
}
|
|
}
|
|
if (DEBUG) printf("category : %d (%s) with %f\\n", best_idx, lbls[best_idx], best);
|
|
else printf("%s\\n", lbls[best_idx]);
|
|
}"""]
|
|
|
|
# CLANG=1 python3 examples/compile_efficientnet.py | clang -O2 -lm -x c - -o recognize && DEBUG=1 time ./recognize docs/stable_diffusion_by_tinygrad.jpg
|
|
print('\n'.join(cprog))
|