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Global export format sort (#6182)
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* Global export sort

* Cleanup
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@glenn-jocher
glenn-jocher authored Jan 4, 2022
1 parent 7cad659 commit 5bd6a97
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Showing 4 changed files with 120 additions and 120 deletions.
6 changes: 3 additions & 3 deletions detect.py
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Expand Up @@ -15,13 +15,13 @@
$ python path/to/detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.mlmodel # CoreML (under development)
yolov5s.xml # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (under development)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow protobuf
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s.engine # TensorRT
"""

import argparse
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148 changes: 74 additions & 74 deletions export.py
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Expand Up @@ -2,19 +2,19 @@
"""
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
Format | Example | `--include ...` argument
Format | `export.py --include` | Model
--- | --- | ---
PyTorch | yolov5s.pt | -
TorchScript | yolov5s.torchscript | `torchscript`
ONNX | yolov5s.onnx | `onnx`
CoreML | yolov5s.mlmodel | `coreml`
OpenVINO | yolov5s_openvino_model/ | `openvino`
TensorFlow SavedModel | yolov5s_saved_model/ | `saved_model`
TensorFlow GraphDef | yolov5s.pb | `pb`
TensorFlow Lite | yolov5s.tflite | `tflite`
TensorFlow Edge TPU | yolov5s_edgetpu.tflite | `edgetpu`
TensorFlow.js | yolov5s_web_model/ | `tfjs`
TensorRT | yolov5s.engine | `engine`
PyTorch | - | yolov5s.pt
TorchScript | `torchscript` | yolov5s.torchscript
ONNX | `onnx` | yolov5s.onnx
OpenVINO | `openvino` | yolov5s_openvino_model/
TensorRT | `engine` | yolov5s.engine
CoreML | `coreml` | yolov5s.mlmodel
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
TensorFlow GraphDef | `pb` | yolov5s.pb
TensorFlow Lite | `tflite` | yolov5s.tflite
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
TensorFlow.js | `tfjs` | yolov5s_web_model/
Usage:
$ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml openvino saved_model tflite tfjs
Expand All @@ -23,13 +23,13 @@
$ python path/to/detect.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
yolov5s.mlmodel # CoreML (under development)
yolov5s.xml # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (under development)
yolov5s_saved_model # TensorFlow SavedModel
yolov5s.pb # TensorFlow protobuf
yolov5s.pb # TensorFlow GraphDef
yolov5s.tflite # TensorFlow Lite
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
yolov5s.engine # TensorRT
TensorFlow.js:
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
Expand Down Expand Up @@ -126,6 +126,23 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
LOGGER.info(f'{prefix} export failure: {e}')


def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
# YOLOv5 OpenVINO export
try:
check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.inference_engine as ie

LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
f = str(file).replace('.pt', '_openvino_model' + os.sep)

cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}"
subprocess.check_output(cmd, shell=True)

LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
except Exception as e:
LOGGER.info(f'\n{prefix} export failure: {e}')


def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
# YOLOv5 CoreML export
ct_model = None
Expand All @@ -148,27 +165,57 @@ def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
return ct_model


def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
# YOLOv5 OpenVINO export
def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
try:
check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
import openvino.inference_engine as ie
check_requirements(('tensorrt',))
import tensorrt as trt

LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
f = str(file).replace('.pt', '_openvino_model' + os.sep)
opset = (12, 13)[trt.__version__[0] == '8'] # test on TensorRT 7.x and 8.x
export_onnx(model, im, file, opset, train, False, simplify)
onnx = file.with_suffix('.onnx')
assert onnx.exists(), f'failed to export ONNX file: {onnx}'

cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}"
subprocess.check_output(cmd, shell=True)
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
f = file.with_suffix('.engine') # TensorRT engine file
logger = trt.Logger(trt.Logger.INFO)
if verbose:
logger.min_severity = trt.Logger.Severity.VERBOSE

builder = trt.Builder(logger)
config = builder.create_builder_config()
config.max_workspace_size = workspace * 1 << 30

flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
network = builder.create_network(flag)
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(str(onnx)):
raise RuntimeError(f'failed to load ONNX file: {onnx}')

inputs = [network.get_input(i) for i in range(network.num_inputs)]
outputs = [network.get_output(i) for i in range(network.num_outputs)]
LOGGER.info(f'{prefix} Network Description:')
for inp in inputs:
LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
for out in outputs:
LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')

half &= builder.platform_has_fast_fp16
LOGGER.info(f'{prefix} building FP{16 if half else 32} engine in {f}')
if half:
config.set_flag(trt.BuilderFlag.FP16)
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
t.write(engine.serialize())
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')

except Exception as e:
LOGGER.info(f'\n{prefix} export failure: {e}')


def export_saved_model(model, im, file, dynamic,
tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
conf_thres=0.25, prefix=colorstr('TensorFlow saved_model:')):
# YOLOv5 TensorFlow saved_model export
conf_thres=0.25, prefix=colorstr('TensorFlow SavedModel:')):
# YOLOv5 TensorFlow SavedModel export
keras_model = None
try:
import tensorflow as tf
Expand Down Expand Up @@ -304,53 +351,6 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
LOGGER.info(f'\n{prefix} export failure: {e}')


def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
try:
check_requirements(('tensorrt',))
import tensorrt as trt

opset = (12, 13)[trt.__version__[0] == '8'] # test on TensorRT 7.x and 8.x
export_onnx(model, im, file, opset, train, False, simplify)
onnx = file.with_suffix('.onnx')
assert onnx.exists(), f'failed to export ONNX file: {onnx}'

LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
f = file.with_suffix('.engine') # TensorRT engine file
logger = trt.Logger(trt.Logger.INFO)
if verbose:
logger.min_severity = trt.Logger.Severity.VERBOSE

builder = trt.Builder(logger)
config = builder.create_builder_config()
config.max_workspace_size = workspace * 1 << 30

flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
network = builder.create_network(flag)
parser = trt.OnnxParser(network, logger)
if not parser.parse_from_file(str(onnx)):
raise RuntimeError(f'failed to load ONNX file: {onnx}')

inputs = [network.get_input(i) for i in range(network.num_inputs)]
outputs = [network.get_output(i) for i in range(network.num_outputs)]
LOGGER.info(f'{prefix} Network Description:')
for inp in inputs:
LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
for out in outputs:
LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')

half &= builder.platform_has_fast_fp16
LOGGER.info(f'{prefix} building FP{16 if half else 32} engine in {f}')
if half:
config.set_flag(trt.BuilderFlag.FP16)
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
t.write(engine.serialize())
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')

except Exception as e:
LOGGER.info(f'\n{prefix} export failure: {e}')


@torch.no_grad()
def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
weights=ROOT / 'yolov5s.pt', # weights path
Expand Down Expand Up @@ -417,12 +417,12 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
export_torchscript(model, im, file, optimize)
if ('onnx' in include) or ('openvino' in include): # OpenVINO requires ONNX
export_onnx(model, im, file, opset, train, dynamic, simplify)
if 'openvino' in include:
export_openvino(model, im, file)
if 'engine' in include:
export_engine(model, im, file, train, half, simplify, workspace, verbose)
if 'coreml' in include:
export_coreml(model, im, file)
if 'openvino' in include:
export_openvino(model, im, file)

# TensorFlow Exports
if any(tf_exports):
Expand Down
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