remove unneeded functions
Edward Yang
parent
4ab674b6cc
commit
beb998fed3
220
export.py
220
export.py
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@ -117,22 +117,6 @@ def try_export(inner_func):
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return outer_func
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@try_export
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def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
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# YOLOv5 TorchScript model export
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LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
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f = file.with_suffix('.torchscript')
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ts = torch.jit.trace(model, im, strict=False)
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d = {'shape': im.shape, 'stride': int(max(model.stride)), 'names': model.names}
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extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
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if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
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optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
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else:
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ts.save(str(f), _extra_files=extra_files)
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return f, None
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@try_export
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def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
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# YOLOv5 ONNX export
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@ -211,44 +195,6 @@ def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
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return f, None
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@try_export
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def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
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# YOLOv5 Paddle export
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check_requirements(('paddlepaddle', 'x2paddle'))
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import x2paddle
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from x2paddle.convert import pytorch2paddle
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LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
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f = str(file).replace('.pt', f'_paddle_model{os.sep}')
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pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im]) # export
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yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
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return f, None
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@try_export
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def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
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# YOLOv5 CoreML export
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check_requirements('coremltools')
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import coremltools as ct
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LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
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f = file.with_suffix('.mlmodel')
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ts = torch.jit.trace(model, im, strict=False) # TorchScript model
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ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
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bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
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if bits < 32:
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if MACOS: # quantization only supported on macOS
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress numpy==1.20 float warning
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ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
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else:
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print(f'{prefix} quantization only supported on macOS, skipping...')
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ct_model.save(f)
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return f, ct_model
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@try_export
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def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
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# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
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@ -311,172 +257,6 @@ def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose
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return f, None
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@try_export
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def export_saved_model(model,
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im,
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file,
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dynamic,
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tf_nms=False,
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agnostic_nms=False,
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topk_per_class=100,
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topk_all=100,
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iou_thres=0.45,
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conf_thres=0.25,
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keras=False,
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prefix=colorstr('TensorFlow SavedModel:')):
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# YOLOv5 TensorFlow SavedModel export
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try:
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import tensorflow as tf
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except Exception:
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check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
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import tensorflow as tf
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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from models.tf import TFModel
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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f = str(file).replace('.pt', '_saved_model')
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batch_size, ch, *imgsz = list(im.shape) # BCHW
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tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
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im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
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_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
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inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
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outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
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keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
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keras_model.trainable = False
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keras_model.summary()
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if keras:
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keras_model.save(f, save_format='tf')
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else:
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spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
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m = tf.function(lambda x: keras_model(x)) # full model
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m = m.get_concrete_function(spec)
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frozen_func = convert_variables_to_constants_v2(m)
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tfm = tf.Module()
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tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])
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tfm.__call__(im)
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tf.saved_model.save(tfm,
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f,
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options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(
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tf.__version__, '2.6') else tf.saved_model.SaveOptions())
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return f, keras_model
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@try_export
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def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
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# YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
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import tensorflow as tf
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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f = file.with_suffix('.pb')
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m = tf.function(lambda x: keras_model(x)) # full model
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m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
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frozen_func = convert_variables_to_constants_v2(m)
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frozen_func.graph.as_graph_def()
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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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return f, None
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@try_export
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def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
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# YOLOv5 TensorFlow Lite export
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import tensorflow as tf
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LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
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batch_size, ch, *imgsz = list(im.shape) # BCHW
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f = str(file).replace('.pt', '-fp16.tflite')
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converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
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converter.target_spec.supported_types = [tf.float16]
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converter.optimizations = [tf.lite.Optimize.DEFAULT]
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if int8:
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from models.tf import representative_dataset_gen
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dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
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converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
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converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
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converter.target_spec.supported_types = []
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converter.inference_input_type = tf.uint8 # or tf.int8
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converter.inference_output_type = tf.uint8 # or tf.int8
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converter.experimental_new_quantizer = True
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f = str(file).replace('.pt', '-int8.tflite')
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if nms or agnostic_nms:
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converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
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tflite_model = converter.convert()
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open(f, 'wb').write(tflite_model)
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return f, None
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@try_export
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def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
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# YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
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cmd = 'edgetpu_compiler --version'
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help_url = 'https://coral.ai/docs/edgetpu/compiler/'
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assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
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if subprocess.run(f'{cmd} > /dev/null 2>&1', shell=True).returncode != 0:
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LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
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sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
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for c in (
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'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
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'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
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'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
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subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
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ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
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LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
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f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model
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f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model
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subprocess.run([
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'edgetpu_compiler',
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'-s',
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'-d',
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'-k',
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'10',
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'--out_dir',
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str(file.parent),
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f_tfl,], check=True)
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return f, None
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@try_export
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def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')):
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# YOLOv5 TensorFlow.js export
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check_requirements('tensorflowjs')
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import tensorflowjs as tfjs
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LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
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f = str(file).replace('.pt', '_web_model') # js dir
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f_pb = file.with_suffix('.pb') # *.pb path
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f_json = f'{f}/model.json' # *.json path
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args = [
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'tensorflowjs_converter',
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'--input_format=tf_frozen_model',
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'--quantize_uint8' if int8 else '',
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'--output_node_names=Identity,Identity_1,Identity_2,Identity_3',
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str(f_pb),
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str(f),]
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subprocess.run([arg for arg in args if arg], check=True)
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json = Path(f_json).read_text()
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with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
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subst = re.sub(
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r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
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r'"Identity.?.?": {"name": "Identity.?.?"}, '
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r'"Identity.?.?": {"name": "Identity.?.?"}, '
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r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
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r'"Identity_1": {"name": "Identity_1"}, '
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r'"Identity_2": {"name": "Identity_2"}, '
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r'"Identity_3": {"name": "Identity_3"}}}', json)
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j.write(subst)
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return f, None
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def add_tflite_metadata(file, metadata, num_outputs):
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# Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
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with contextlib.suppress(ImportError):
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