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ultralytics 8.0.37 add TFLite metadata in AutoBackend (#953)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>
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Glenn Jocher 2023-02-14 14:28:23 +04:00 • committed by GitHub
parent 20fe708f31
commit bdc6cd4d8b
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18 changed files with 86 additions and 46 deletions
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15
ultralytics/nn/autobackend.py
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@ -1,7 +1,9 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
import ast
import contextlib
import json
import platform
import zipfile
from collections import OrderedDict, namedtuple
from pathlib import Path
from urllib.parse import urlparse
@ -207,6 +209,12 @@ class AutoBackend(nn.Module):
interpreter.allocate_tensors() # allocate
input_details = interpreter.get_input_details() # inputs
output_details = interpreter.get_output_details() # outputs
# load metadata
with contextlib.suppress(zipfile.BadZipFile):
with zipfile.ZipFile(w, "r") as model:
meta_file = model.namelist()[0]
meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
stride, names = int(meta['stride']), meta['names']
elif tfjs: # TF.js
raise NotImplementedError('ERROR: YOLOv8 TF.js inference is not supported')
elif paddle: # PaddlePaddle
@ -214,7 +222,7 @@ class AutoBackend(nn.Module):
check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
import paddle.inference as pdi
if not Path(w).is_file(): # if not *.pdmodel
w = next(Path(w).rglob('*.pdmodel')) # get *.xml file from *_openvino_model dir
w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir
weights = Path(w).with_suffix('.pdiparams')
config = pdi.Config(str(w), str(weights))
if cuda:
@ -328,6 +336,9 @@ class AutoBackend(nn.Module):
scale, zero_point = output['quantization']
x = (x.astype(np.float32) - zero_point) * scale # re-scale
y.append(x)
# TF segment fixes: export is reversed vs ONNX export and protos are transposed
if len(self.output_details) == 2: # segment
y = [y[1], np.transpose(y[0], (0, 3, 1, 2))]
y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels