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381 lines
20 KiB
381 lines
20 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license |
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import json |
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import platform |
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from collections import OrderedDict, namedtuple |
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from pathlib import Path |
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from urllib.parse import urlparse |
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import cv2 |
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import numpy as np |
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import torch |
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import torch.nn as nn |
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from PIL import Image |
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from ultralytics.yolo.utils import LOGGER, ROOT, yaml_load |
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from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_version |
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from ultralytics.yolo.utils.downloads import attempt_download, is_url |
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from ultralytics.yolo.utils.ops import xywh2xyxy |
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class AutoBackend(nn.Module): |
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def __init__(self, weights='yolov8n.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True): |
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""" |
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Ultralytics YOLO MultiBackend class for python inference on various backends |
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Args: |
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weights: the path to the weights file. Defaults to yolov8n.pt |
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device: The device to run the model on. |
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dnn: If you want to use OpenCV's DNN module to run the inference, set this to True. Defaults to |
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False |
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data: a dictionary containing the following keys: |
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fp16: If true, will use half precision. Defaults to False |
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fuse: whether to fuse the model or not. Defaults to True |
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Supported format and their usage: |
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| Platform | weights | |
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|-----------------------|------------------| |
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| PyTorch | *.pt | |
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| TorchScript | *.torchscript | |
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| ONNX Runtime | *.onnx | |
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| ONNX OpenCV DNN | *.onnx --dnn | |
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| OpenVINO | *.xml | |
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| CoreML | *.mlmodel | |
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| TensorRT | *.engine | |
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| TensorFlow SavedModel | *_saved_model | |
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| TensorFlow GraphDef | *.pb | |
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| TensorFlow Lite | *.tflite | |
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| TensorFlow Edge TPU | *_edgetpu.tflite | |
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| PaddlePaddle | *_paddle_model | |
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""" |
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super().__init__() |
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w = str(weights[0] if isinstance(weights, list) else weights) |
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nn_module = isinstance(weights, torch.nn.Module) |
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pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) |
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fp16 &= pt or jit or onnx or engine or nn_module # FP16 |
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nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) |
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stride = 32 # default stride |
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cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA |
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if not (pt or triton or nn_module): |
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w = attempt_download(w) # download if not local |
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# NOTE: special case: in-memory pytorch model |
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if nn_module: |
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model = weights.to(device) |
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model = model.fuse() if fuse else model |
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names = model.module.names if hasattr(model, 'module') else model.names # get class names |
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model.half() if fp16 else model.float() |
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self.model = model # explicitly assign for to(), cpu(), cuda(), half() |
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pt = True |
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elif pt: # PyTorch |
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from ultralytics.nn.tasks import attempt_load_weights |
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model = attempt_load_weights(weights if isinstance(weights, list) else w, |
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device=device, |
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inplace=True, |
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fuse=fuse) |
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stride = max(int(model.stride.max()), 32) # model stride |
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names = model.module.names if hasattr(model, 'module') else model.names # get class names |
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model.half() if fp16 else model.float() |
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self.model = model # explicitly assign for to(), cpu(), cuda(), half() |
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elif jit: # TorchScript |
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LOGGER.info(f'Loading {w} for TorchScript inference...') |
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extra_files = {'config.txt': ''} # model metadata |
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model = torch.jit.load(w, _extra_files=extra_files, map_location=device) |
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model.half() if fp16 else model.float() |
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if extra_files['config.txt']: # load metadata dict |
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d = json.loads(extra_files['config.txt'], |
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object_hook=lambda d: {int(k) if k.isdigit() else k: v |
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for k, v in d.items()}) |
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stride, names = int(d['stride']), d['names'] |
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elif dnn: # ONNX OpenCV DNN |
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LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') |
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check_requirements('opencv-python>=4.5.4') |
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net = cv2.dnn.readNetFromONNX(w) |
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elif onnx: # ONNX Runtime |
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LOGGER.info(f'Loading {w} for ONNX Runtime inference...') |
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check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) |
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import onnxruntime |
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] |
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session = onnxruntime.InferenceSession(w, providers=providers) |
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output_names = [x.name for x in session.get_outputs()] |
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meta = session.get_modelmeta().custom_metadata_map # metadata |
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if 'stride' in meta: |
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stride, names = int(meta['stride']), eval(meta['names']) |
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elif xml: # OpenVINO |
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LOGGER.info(f'Loading {w} for OpenVINO inference...') |
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check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/ |
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from openvino.runtime import Core, Layout, get_batch # noqa |
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ie = Core() |
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if not Path(w).is_file(): # if not *.xml |
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w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir |
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network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin')) |
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if network.get_parameters()[0].get_layout().empty: |
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network.get_parameters()[0].set_layout(Layout("NCHW")) |
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batch_dim = get_batch(network) |
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if batch_dim.is_static: |
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batch_size = batch_dim.get_length() |
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executable_network = ie.compile_model(network, device_name="CPU") # device_name="MYRIAD" for Intel NCS2 |
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stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata |
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elif engine: # TensorRT |
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LOGGER.info(f'Loading {w} for TensorRT inference...') |
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import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download |
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check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 |
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if device.type == 'cpu': |
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device = torch.device('cuda:0') |
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Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) |
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logger = trt.Logger(trt.Logger.INFO) |
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with open(w, 'rb') as f, trt.Runtime(logger) as runtime: |
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model = runtime.deserialize_cuda_engine(f.read()) |
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context = model.create_execution_context() |
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bindings = OrderedDict() |
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output_names = [] |
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fp16 = False # default updated below |
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dynamic = False |
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for i in range(model.num_bindings): |
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name = model.get_binding_name(i) |
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dtype = trt.nptype(model.get_binding_dtype(i)) |
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if model.binding_is_input(i): |
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if -1 in tuple(model.get_binding_shape(i)): # dynamic |
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dynamic = True |
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context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) |
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if dtype == np.float16: |
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fp16 = True |
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else: # output |
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output_names.append(name) |
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shape = tuple(context.get_binding_shape(i)) |
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im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) |
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bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) |
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binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) |
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batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size |
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elif coreml: # CoreML |
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LOGGER.info(f'Loading {w} for CoreML inference...') |
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import coremltools as ct |
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model = ct.models.MLModel(w) |
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elif saved_model: # TF SavedModel |
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LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') |
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import tensorflow as tf |
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keras = False # assume TF1 saved_model |
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model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) |
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elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt |
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LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') |
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import tensorflow as tf |
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def wrap_frozen_graph(gd, inputs, outputs): |
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x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped |
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ge = x.graph.as_graph_element |
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return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) |
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def gd_outputs(gd): |
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name_list, input_list = [], [] |
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for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef |
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name_list.append(node.name) |
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input_list.extend(node.input) |
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return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp')) |
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gd = tf.Graph().as_graph_def() # TF GraphDef |
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with open(w, 'rb') as f: |
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gd.ParseFromString(f.read()) |
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frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) |
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elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python |
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try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu |
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from tflite_runtime.interpreter import Interpreter, load_delegate |
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except ImportError: |
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import tensorflow as tf |
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Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate, |
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if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime |
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LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') |
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delegate = { |
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'Linux': 'libedgetpu.so.1', |
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'Darwin': 'libedgetpu.1.dylib', |
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'Windows': 'edgetpu.dll'}[platform.system()] |
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interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) |
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else: # TFLite |
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LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') |
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interpreter = Interpreter(model_path=w) # load TFLite model |
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interpreter.allocate_tensors() # allocate |
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input_details = interpreter.get_input_details() # inputs |
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output_details = interpreter.get_output_details() # outputs |
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elif tfjs: # TF.js |
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raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported') |
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elif paddle: # PaddlePaddle |
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LOGGER.info(f'Loading {w} for PaddlePaddle inference...') |
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check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle') |
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import paddle.inference as pdi |
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if not Path(w).is_file(): # if not *.pdmodel |
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w = next(Path(w).rglob('*.pdmodel')) # get *.xml file from *_openvino_model dir |
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weights = Path(w).with_suffix('.pdiparams') |
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config = pdi.Config(str(w), str(weights)) |
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if cuda: |
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config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) |
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predictor = pdi.create_predictor(config) |
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input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) |
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output_names = predictor.get_output_names() |
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elif triton: # NVIDIA Triton Inference Server |
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LOGGER.info('Triton Inference Server not supported...') |
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''' |
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TODO: |
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check_requirements('tritonclient[all]') |
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from utils.triton import TritonRemoteModel |
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model = TritonRemoteModel(url=w) |
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nhwc = model.runtime.startswith("tensorflow") |
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''' |
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else: |
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raise NotImplementedError(f'ERROR: {w} is not a supported format') |
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# class names |
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if 'names' not in locals(): |
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names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)} |
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if names[0] == 'n01440764' and len(names) == 1000: # ImageNet |
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names = yaml_load(ROOT / 'yolo/data/datasets/ImageNet.yaml')['names'] # human-readable names |
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self.__dict__.update(locals()) # assign all variables to self |
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def forward(self, im, augment=False, visualize=False): |
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""" |
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Runs inference on the given model |
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Args: |
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im: the image tensor |
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augment: whether to augment the image. Defaults to False |
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visualize: if True, then the network will output the feature maps of the last convolutional layer. |
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Defaults to False |
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""" |
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# YOLOv5 MultiBackend inference |
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b, ch, h, w = im.shape # batch, channel, height, width |
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if self.fp16 and im.dtype != torch.float16: |
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im = im.half() # to FP16 |
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if self.nhwc: |
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im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) |
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if self.pt or self.nn_module: # PyTorch |
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y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) |
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elif self.jit: # TorchScript |
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y = self.model(im) |
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elif self.dnn: # ONNX OpenCV DNN |
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im = im.cpu().numpy() # torch to numpy |
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self.net.setInput(im) |
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y = self.net.forward() |
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elif self.onnx: # ONNX Runtime |
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im = im.cpu().numpy() # torch to numpy |
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y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) |
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elif self.xml: # OpenVINO |
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im = im.cpu().numpy() # FP32 |
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y = list(self.executable_network([im]).values()) |
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elif self.engine: # TensorRT |
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if self.dynamic and im.shape != self.bindings['images'].shape: |
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i = self.model.get_binding_index('images') |
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self.context.set_binding_shape(i, im.shape) # reshape if dynamic |
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self.bindings['images'] = self.bindings['images']._replace(shape=im.shape) |
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for name in self.output_names: |
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i = self.model.get_binding_index(name) |
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self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) |
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s = self.bindings['images'].shape |
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assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" |
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self.binding_addrs['images'] = int(im.data_ptr()) |
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self.context.execute_v2(list(self.binding_addrs.values())) |
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y = [self.bindings[x].data for x in sorted(self.output_names)] |
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elif self.coreml: # CoreML |
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im = im.cpu().numpy() |
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im = Image.fromarray((im[0] * 255).astype('uint8')) |
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# im = im.resize((192, 320), Image.ANTIALIAS) |
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y = self.model.predict({'image': im}) # coordinates are xywh normalized |
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if 'confidence' in y: |
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box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels |
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conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) |
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y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) |
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else: |
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y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) |
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elif self.paddle: # PaddlePaddle |
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im = im.cpu().numpy().astype(np.float32) |
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self.input_handle.copy_from_cpu(im) |
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self.predictor.run() |
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y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] |
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elif self.triton: # NVIDIA Triton Inference Server |
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y = self.model(im) |
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else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) |
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im = im.cpu().numpy() |
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if self.saved_model: # SavedModel |
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y = self.model(im, training=False) if self.keras else self.model(im) |
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elif self.pb: # GraphDef |
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y = self.frozen_func(x=self.tf.constant(im)) |
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else: # Lite or Edge TPU |
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input = self.input_details[0] |
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int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model |
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if int8: |
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scale, zero_point = input['quantization'] |
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im = (im / scale + zero_point).astype(np.uint8) # de-scale |
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self.interpreter.set_tensor(input['index'], im) |
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self.interpreter.invoke() |
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y = [] |
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for output in self.output_details: |
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x = self.interpreter.get_tensor(output['index']) |
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if int8: |
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scale, zero_point = output['quantization'] |
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x = (x.astype(np.float32) - zero_point) * scale # re-scale |
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y.append(x) |
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y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] |
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y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels |
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if isinstance(y, (list, tuple)): |
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return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] |
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else: |
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return self.from_numpy(y) |
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def from_numpy(self, x): |
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""" |
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`from_numpy` converts a numpy array to a tensor |
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Args: |
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x: the numpy array to convert |
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""" |
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return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x |
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def warmup(self, imgsz=(1, 3, 640, 640)): |
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""" |
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Warmup model by running inference once |
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Args: |
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imgsz: the size of the image you want to run inference on. |
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""" |
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warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton, self.nn_module |
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if any(warmup_types) and (self.device.type != 'cpu' or self.triton): |
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im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input |
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for _ in range(2 if self.jit else 1): # |
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self.forward(im) # warmup |
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@staticmethod |
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def _model_type(p='path/to/model.pt'): |
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""" |
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This function takes a path to a model file and returns the model type |
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Args: |
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p: path to the model file. Defaults to path/to/model.pt |
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""" |
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# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx |
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# types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] |
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from ultralytics.yolo.engine.exporter import export_formats |
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sf = list(export_formats().Suffix) # export suffixes |
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if not is_url(p, check=False) and not isinstance(p, str): |
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check_suffix(p, sf) # checks |
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url = urlparse(p) # if url may be Triton inference server |
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types = [s in Path(p).name for s in sf] |
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types[8] &= not types[9] # tflite &= not edgetpu |
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triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc]) |
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return types + [triton] |
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@staticmethod |
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def _load_metadata(f=Path('path/to/meta.yaml')): |
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""" |
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> Loads the metadata from a yaml file |
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Args: |
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f: The path to the metadata file. |
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""" |
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from ultralytics.yolo.utils.files import yaml_load |
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# Load metadata from meta.yaml if it exists |
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if f.exists(): |
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d = yaml_load(f) |
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return d['stride'], d['names'] # assign stride, names |
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return None, None
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