You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
82 lines
3.7 KiB
82 lines
3.7 KiB
7 hours ago
|
import random, sys
|
||
|
|
import numpy as np
|
||
|
|
from extra.datasets.imagenet import get_imagenet_categories, get_val_files, center_crop
|
||
|
|
from examples.benchmark_onnx import load_onnx_model
|
||
|
|
from PIL import Image
|
||
|
|
from tinygrad import Tensor, dtypes, GlobalCounters
|
||
|
|
from tinygrad.helpers import fetch, getenv
|
||
|
|
|
||
|
|
# works:
|
||
|
|
# ~70% - https://github.com/onnx/models/raw/refs/heads/main/validated/vision/classification/resnet/model/resnet50-v2-7.onnx
|
||
|
|
# ~43% - https://github.com/onnx/models/raw/refs/heads/main/Computer_Vision/alexnet_Opset16_torch_hub/alexnet_Opset16.onnx
|
||
|
|
# ~72% - https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx
|
||
|
|
# ~71% - https://github.com/axinc-ai/onnx-quantization/raw/refs/heads/main/models/mobilenetv2_1.0.opt.onnx
|
||
|
|
# ~67% - https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7-quantized.onnx
|
||
|
|
# broken:
|
||
|
|
# https://github.com/MTlab/onnx2caffe/raw/refs/heads/master/model/MobileNetV2.onnx
|
||
|
|
# https://huggingface.co/qualcomm/MobileNet-v2-Quantized/resolve/main/MobileNet-v2-Quantized.onnx
|
||
|
|
# ~35% - https://github.com/axinc-ai/onnx-quantization/raw/refs/heads/main/models/mobilenev2_quantized.onnx
|
||
|
|
|
||
|
|
# QUANT=1 python3 examples/test_onnx_imagenet.py
|
||
|
|
# https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx
|
||
|
|
# DONT_REALIZE_EXPAND=1 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
|
||
|
|
# VIZ=1 DONT_REALIZE_EXPAND=1 python3 examples/benchmark_onnx.py /tmp/model.quant.onnx
|
||
|
|
|
||
|
|
def imagenet_dataloader(cnt=0):
|
||
|
|
input_mean = Tensor([0.485, 0.456, 0.406]).reshape(1, -1, 1, 1)
|
||
|
|
input_std = Tensor([0.229, 0.224, 0.225]).reshape(1, -1, 1, 1)
|
||
|
|
files = get_val_files()
|
||
|
|
random.shuffle(files)
|
||
|
|
if cnt != 0: files = files[:cnt]
|
||
|
|
cir = get_imagenet_categories()
|
||
|
|
for fn in files:
|
||
|
|
img = Image.open(fn)
|
||
|
|
img = img.convert('RGB') if img.mode != "RGB" else img
|
||
|
|
img = center_crop(img)
|
||
|
|
img = np.array(img)
|
||
|
|
img = Tensor(img).permute(2,0,1).reshape(1,3,224,224)
|
||
|
|
img = ((img.cast(dtypes.float32)/255.0) - input_mean) / input_std
|
||
|
|
y = cir[fn.split("/")[-2]]
|
||
|
|
yield img,y
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
fn = sys.argv[1]
|
||
|
|
if getenv("QUANT"):
|
||
|
|
from onnxruntime.quantization import quantize_dynamic, quantize_static, QuantFormat, QuantType, CalibrationDataReader
|
||
|
|
model_fp32 = fetch(fn)
|
||
|
|
fn = '/tmp/model.quant.onnx'
|
||
|
|
if getenv("DYNAMIC"):
|
||
|
|
quantize_dynamic(model_fp32, fn)
|
||
|
|
else:
|
||
|
|
class ImagenetReader(CalibrationDataReader):
|
||
|
|
def __init__(self):
|
||
|
|
self.iter = imagenet_dataloader(cnt=1000)
|
||
|
|
def get_next(self) -> dict:
|
||
|
|
try:
|
||
|
|
img,y = next(self.iter)
|
||
|
|
except StopIteration:
|
||
|
|
return None
|
||
|
|
return {"input": img.numpy()}
|
||
|
|
quantize_static(model_fp32, fn, ImagenetReader(), quant_format=QuantFormat.QDQ, per_channel=False,
|
||
|
|
activation_type=QuantType.QUInt8, weight_type=QuantType.QUInt8,
|
||
|
|
extra_options={"ActivationSymmetric": False})
|
||
|
|
|
||
|
|
run_onnx_jit, input_specs = load_onnx_model(fetch(fn))
|
||
|
|
t_name, t_spec = list(input_specs.items())[0]
|
||
|
|
assert t_spec.shape[1:] == (3,224,224), f"shape is {t_spec.shape}"
|
||
|
|
|
||
|
|
hit = 0
|
||
|
|
for i,(img,y) in enumerate(imagenet_dataloader(cnt=getenv("CNT", 100))):
|
||
|
|
GlobalCounters.reset()
|
||
|
|
p = run_onnx_jit(**{t_name:img})
|
||
|
|
assert p.shape == (1,1000)
|
||
|
|
t = p.argmax().item()
|
||
|
|
hit += y==t
|
||
|
|
print(f"target: {y:3d} pred: {t:3d} acc: {hit/(i+1)*100:.2f}%")
|
||
|
|
|
||
|
|
MS_TARGET = 13.4
|
||
|
|
print(f"need {GlobalCounters.global_ops/1e9*(1000/MS_TARGET):.2f} GFLOPS for {MS_TARGET:.2f} ms")
|
||
|
|
|
||
|
|
import pickle
|
||
|
|
with open("/tmp/im.pkl", "wb") as f: pickle.dump(run_onnx_jit, f)
|