Update `export.py` with Detect, Validate usages (#6280)
Glenn Jocher
GitHub
parent
f3085accd3
commit
80473a6551
53
export.py
53
export.py
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@ -82,6 +82,7 @@ def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:'
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ts.save(str(f), _extra_files=extra_files)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'{prefix} export failure: {e}')
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@ -125,7 +126,7 @@ def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorst
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except Exception as e:
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LOGGER.info(f'{prefix} simplifier failure: {e}')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
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return f
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except Exception as e:
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LOGGER.info(f'{prefix} export failure: {e}')
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@ -143,13 +144,13 @@ def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
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subprocess.check_output(cmd, shell=True)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
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# YOLOv5 CoreML export
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ct_model = None
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try:
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check_requirements(('coremltools',))
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import coremltools as ct
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@ -162,10 +163,10 @@ def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
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ct_model.save(f)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return ct_model, f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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return ct_model
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return None, None
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def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
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@ -216,7 +217,7 @@ def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=F
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with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
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t.write(engine.serialize())
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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@ -225,7 +226,6 @@ def export_saved_model(model, im, file, dynamic,
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tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
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conf_thres=0.25, prefix=colorstr('TensorFlow SavedModel:')):
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# YOLOv5 TensorFlow SavedModel export
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keras_model = None
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try:
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import tensorflow as tf
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from tensorflow import keras
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@ -247,10 +247,10 @@ def export_saved_model(model, im, file, dynamic,
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keras_model.save(f, save_format='tf')
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return keras_model, f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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return keras_model
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return None, None
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def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
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@ -269,6 +269,7 @@ def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
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tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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@ -300,7 +301,7 @@ def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('Te
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tflite_model = converter.convert()
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open(f, "wb").write(tflite_model)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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@ -328,6 +329,7 @@ def export_edgetpu(keras_model, im, file, prefix=colorstr('Edge TPU:')):
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subprocess.run(cmd, shell=True, check=True)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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@ -364,6 +366,7 @@ def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
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j.write(subst)
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LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
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return f
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except Exception as e:
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LOGGER.info(f'\n{prefix} export failure: {e}')
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@ -431,15 +434,15 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
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# Exports
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if 'torchscript' in include:
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export_torchscript(model, im, file, optimize)
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f = export_torchscript(model, im, file, optimize)
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if 'engine' in include: # TensorRT required before ONNX
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export_engine(model, im, file, train, half, simplify, workspace, verbose)
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f = export_engine(model, im, file, train, half, simplify, workspace, verbose)
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if ('onnx' in include) or ('openvino' in include): # OpenVINO requires ONNX
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export_onnx(model, im, file, opset, train, dynamic, simplify)
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f = export_onnx(model, im, file, opset, train, dynamic, simplify)
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if 'openvino' in include:
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export_openvino(model, im, file)
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f = export_openvino(model, im, file)
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if 'coreml' in include:
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export_coreml(model, im, file)
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_, f = export_coreml(model, im, file)
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# TensorFlow Exports
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if any(tf_exports):
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@ -447,22 +450,26 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
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if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
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check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
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assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
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model = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
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agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class, topk_all=topk_all,
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conf_thres=conf_thres, iou_thres=iou_thres) # keras model
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model, f = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
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agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class,
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topk_all=topk_all,
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conf_thres=conf_thres, iou_thres=iou_thres) # keras model
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if pb or tfjs: # pb prerequisite to tfjs
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export_pb(model, im, file)
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f = export_pb(model, im, file)
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if tflite or edgetpu:
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export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100)
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f = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100)
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if edgetpu:
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export_edgetpu(model, im, file)
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f = export_edgetpu(model, im, file)
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if tfjs:
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export_tfjs(model, im, file)
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f = export_tfjs(model, im, file)
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# Finish
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LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
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f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
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f'\nVisualize with https://netron.app')
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f"\nVisualize with https://netron.app"
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f"\nDetect with `python detect.py --weights {f}`"
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f" or `model = torch.hub.load('ultralytics/yolov5', 'custom', '{f}')"
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f"\nValidate with `python val.py --weights {f}`")
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def parse_opt():
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@ -490,7 +497,7 @@ def parse_opt():
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parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
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parser.add_argument('--include', nargs='+',
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default=['torchscript', 'onnx'],
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help='available formats are (torchscript, onnx, engine, coreml, saved_model, pb, tflite, tfjs)')
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help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
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opt = parser.parse_args()
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print_args(FILE.stem, opt)
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return opt
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