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495 lines
26 KiB
495 lines
26 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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import ast
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import contextlib
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import json
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import os
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import platform
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import zipfile
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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.utils import ARM64, LINUX, LOGGER, ROOT, yaml_load
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from ultralytics.utils.checks import check_requirements, check_suffix, check_version, check_yaml
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from ultralytics.utils.downloads import attempt_download_asset, is_url
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from ultralytics.utils.ops import xywh2xyxy
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def check_class_names(names):
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"""Check class names. Map imagenet class codes to human-readable names if required. Convert lists to dicts."""
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if isinstance(names, list): # names is a list
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names = dict(enumerate(names)) # convert to dict
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if isinstance(names, dict):
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# Convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'
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names = {int(k): str(v) for k, v in names.items()}
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n = len(names)
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if max(names.keys()) >= n:
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raise KeyError(f'{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices '
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f'{min(names.keys())}-{max(names.keys())} defined in your dataset YAML.')
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if isinstance(names[0], str) and names[0].startswith('n0'): # imagenet class codes, i.e. 'n01440764'
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map = yaml_load(ROOT / 'cfg/datasets/ImageNet.yaml')['map'] # human-readable names
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names = {k: map[v] for k, v in names.items()}
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return names
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class AutoBackend(nn.Module):
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def __init__(self,
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weights='yolov8n.pt',
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device=torch.device('cpu'),
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dnn=False,
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data=None,
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fp16=False,
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fuse=True,
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verbose=True):
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"""
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MultiBackend class for python inference on various platforms using Ultralytics YOLO.
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Args:
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weights (str): The path to the weights file. Default: 'yolov8n.pt'
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device (torch.device): The device to run the model on.
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dnn (bool): Use OpenCV DNN module for inference if True, defaults to False.
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data (str | Path | optional): Additional data.yaml file for class names.
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fp16 (bool): If True, use half precision. Default: False
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fuse (bool): Whether to fuse the model or not. Default: True
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verbose (bool): Whether to run in verbose mode or not. Default: True
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Supported formats and their naming conventions:
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| Format | Suffix |
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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=True |
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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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| ncnn | *_ncnn_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, ncnn, triton = \
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self._model_type(w)
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fp16 &= pt or jit or onnx or xml or engine or nn_module or triton # 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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model, metadata = None, None
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# Set device
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cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
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if cuda and not any([nn_module, pt, jit, engine]): # GPU dataloader formats
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device = torch.device('cpu')
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cuda = False
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# Download if not local
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if not (pt or triton or nn_module):
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w = attempt_download_asset(w)
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# Load model
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if nn_module: # in-memory PyTorch model
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model = weights.to(device)
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model = model.fuse(verbose=verbose) if fuse else model
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if hasattr(model, 'kpt_shape'):
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kpt_shape = model.kpt_shape # pose-only
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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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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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if hasattr(model, 'kpt_shape'):
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kpt_shape = model.kpt_shape # pose-only
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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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metadata = json.loads(extra_files['config.txt'], object_hook=lambda x: dict(x.items()))
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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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metadata = session.get_modelmeta().custom_metadata_map # metadata
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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>=2023.0') # 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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core = Core()
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w = Path(w)
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if not w.is_file(): # if not *.xml
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w = next(w.glob('*.xml')) # get *.xml file from *_openvino_model dir
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ov_model = core.read_model(model=str(w), weights=w.with_suffix('.bin'))
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if ov_model.get_parameters()[0].get_layout().empty:
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ov_model.get_parameters()[0].set_layout(Layout('NCHW'))
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batch_dim = get_batch(ov_model)
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if batch_dim.is_static:
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batch_size = batch_dim.get_length()
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ov_compiled_model = core.compile_model(ov_model, device_name='AUTO') # AUTO selects best available device
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metadata = w.parent / 'metadata.yaml'
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elif engine: # TensorRT
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LOGGER.info(f'Loading {w} for TensorRT inference...')
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try:
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import tensorrt as trt # noqa https://developer.nvidia.com/nvidia-tensorrt-download
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except ImportError:
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if LINUX:
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check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
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import tensorrt as trt # noqa
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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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# Read file
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with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
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meta_len = int.from_bytes(f.read(4), byteorder='little') # read metadata length
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metadata = json.loads(f.read(meta_len).decode('utf-8')) # read metadata
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model = runtime.deserialize_cuda_engine(f.read()) # read engine
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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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metadata = dict(model.user_defined_metadata)
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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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metadata = Path(w) / 'metadata.yaml'
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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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from ultralytics.engine.exporter import gd_outputs
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def wrap_frozen_graph(gd, inputs, outputs):
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"""Wrap frozen graphs for deployment."""
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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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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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# Load metadata
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with contextlib.suppress(zipfile.BadZipFile):
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with zipfile.ZipFile(w, 'r') as model:
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meta_file = model.namelist()[0]
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metadata = ast.literal_eval(model.read(meta_file).decode('utf-8'))
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elif tfjs: # TF.js
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raise NotImplementedError('YOLOv8 TF.js inference is not currently 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 # noqa
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w = Path(w)
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if not w.is_file(): # if not *.pdmodel
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w = next(w.rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir
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config = pdi.Config(str(w), str(w.with_suffix('.pdiparams')))
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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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metadata = w.parents[1] / 'metadata.yaml'
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elif ncnn: # ncnn
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LOGGER.info(f'Loading {w} for ncnn inference...')
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check_requirements('git+https://github.com/Tencent/ncnn.git' if ARM64 else 'ncnn') # requires ncnn
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import ncnn as pyncnn
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net = pyncnn.Net()
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net.opt.num_threads = os.cpu_count()
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net.opt.use_vulkan_compute = cuda
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w = Path(w)
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if not w.is_file(): # if not *.param
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w = next(w.glob('*.param')) # get *.param file from *_ncnn_model dir
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net.load_param(str(w))
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net.load_model(str(w.with_suffix('.bin')))
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metadata = w.parent / 'metadata.yaml'
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elif triton: # NVIDIA Triton Inference Server
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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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raise NotImplementedError('Triton Inference Server is not currently supported.')
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else:
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from ultralytics.engine.exporter import export_formats
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raise TypeError(f"model='{w}' is not a supported model format. "
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'See https://docs.ultralytics.com/modes/predict for help.'
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f'\n\n{export_formats()}')
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# Load external metadata YAML
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if isinstance(metadata, (str, Path)) and Path(metadata).exists():
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metadata = yaml_load(metadata)
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if metadata:
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for k, v in metadata.items():
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if k in ('stride', 'batch'):
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metadata[k] = int(v)
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elif k in ('imgsz', 'names', 'kpt_shape') and isinstance(v, str):
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metadata[k] = eval(v)
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stride = metadata['stride']
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task = metadata['task']
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batch = metadata['batch']
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imgsz = metadata['imgsz']
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names = metadata['names']
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kpt_shape = metadata.get('kpt_shape')
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elif not (pt or triton or nn_module):
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LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'")
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# Check names
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if 'names' not in locals(): # names missing
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names = self._apply_default_class_names(data)
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names = check_class_names(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 YOLOv8 MultiBackend model.
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Args:
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im (torch.Tensor): The image tensor to perform inference on.
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augment (bool): whether to perform data augmentation during inference, defaults to False
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visualize (bool): whether to visualize the output predictions, defaults to False
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Returns:
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(tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)
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"""
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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.ov_compiled_model(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[0].cpu().numpy()
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im_pil = Image.fromarray((im * 255).astype('uint8'))
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# im = im.resize((192, 320), Image.BILINEAR)
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y = self.model.predict({'image': im_pil}) # 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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elif len(y) == 1: # classification model
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y = list(y.values())
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elif len(y) == 2: # segmentation model
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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.ncnn: # ncnn
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mat_in = self.pyncnn.Mat(im[0].cpu().numpy())
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ex = self.net.create_extractor()
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input_names, output_names = self.net.input_names(), self.net.output_names()
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ex.input(input_names[0], mat_in)
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y = []
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for output_name in output_names:
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mat_out = self.pyncnn.Mat()
|
|
ex.extract(output_name, mat_out)
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|
y.append(np.array(mat_out)[None])
|
|
elif self.triton: # NVIDIA Triton Inference Server
|
|
y = self.model(im)
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|
else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
|
|
im = im.cpu().numpy()
|
|
if self.saved_model: # SavedModel
|
|
y = self.model(im, training=False) if self.keras else self.model(im)
|
|
if not isinstance(y, list):
|
|
y = [y]
|
|
elif self.pb: # GraphDef
|
|
y = self.frozen_func(x=self.tf.constant(im))
|
|
if len(y) == 2 and len(self.names) == 999: # segments and names not defined
|
|
ip, ib = (0, 1) if len(y[0].shape) == 4 else (1, 0) # index of protos, boxes
|
|
nc = y[ib].shape[1] - y[ip].shape[3] - 4 # y = (1, 160, 160, 32), (1, 116, 8400)
|
|
self.names = {i: f'class{i}' for i in range(nc)}
|
|
else: # Lite or Edge TPU
|
|
input = self.input_details[0]
|
|
int8 = input['dtype'] == np.int8 # is TFLite quantized int8 model
|
|
if int8:
|
|
scale, zero_point = input['quantization']
|
|
im = (im / scale + zero_point).astype(np.int8) # de-scale
|
|
self.interpreter.set_tensor(input['index'], im)
|
|
self.interpreter.invoke()
|
|
y = []
|
|
for output in self.output_details:
|
|
x = self.interpreter.get_tensor(output['index'])
|
|
if int8:
|
|
scale, zero_point = output['quantization']
|
|
x = (x.astype(np.float32) - zero_point) * scale # re-scale
|
|
if x.ndim > 2: # if task is not classification
|
|
# Unnormalize xywh with input image size
|
|
# xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models
|
|
# See this PR for details: https://github.com/ultralytics/ultralytics/pull/1695
|
|
x[:, 0] *= w
|
|
x[:, 1] *= h
|
|
x[:, 2] *= w
|
|
x[:, 3] *= h
|
|
y.append(x)
|
|
# TF segment fixes: export is reversed vs ONNX export and protos are transposed
|
|
if len(y) == 2: # segment with (det, proto) output order reversed
|
|
if len(y[1].shape) != 4:
|
|
y = list(reversed(y)) # should be y = (1, 116, 8400), (1, 160, 160, 32)
|
|
y[1] = np.transpose(y[1], (0, 3, 1, 2)) # should be y = (1, 116, 8400), (1, 32, 160, 160)
|
|
y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
|
|
|
|
# for x in y:
|
|
# print(type(x), len(x)) if isinstance(x, (list, tuple)) else print(type(x), x.shape) # debug shapes
|
|
if isinstance(y, (list, tuple)):
|
|
return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
|
|
else:
|
|
return self.from_numpy(y)
|
|
|
|
def from_numpy(self, x):
|
|
"""
|
|
Convert a numpy array to a tensor.
|
|
|
|
Args:
|
|
x (np.ndarray): The array to be converted.
|
|
|
|
Returns:
|
|
(torch.Tensor): The converted tensor
|
|
"""
|
|
return torch.tensor(x).to(self.device) if isinstance(x, np.ndarray) else x
|
|
|
|
def warmup(self, imgsz=(1, 3, 640, 640)):
|
|
"""
|
|
Warm up the model by running one forward pass with a dummy input.
|
|
|
|
Args:
|
|
imgsz (tuple): The shape of the dummy input tensor in the format (batch_size, channels, height, width)
|
|
|
|
Returns:
|
|
(None): This method runs the forward pass and don't return any value
|
|
"""
|
|
warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton, self.nn_module
|
|
if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
|
|
im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input
|
|
for _ in range(2 if self.jit else 1): #
|
|
self.forward(im) # warmup
|
|
|
|
@staticmethod
|
|
def _apply_default_class_names(data):
|
|
"""Applies default class names to an input YAML file or returns numerical class names."""
|
|
with contextlib.suppress(Exception):
|
|
return yaml_load(check_yaml(data))['names']
|
|
return {i: f'class{i}' for i in range(999)} # return default if above errors
|
|
|
|
@staticmethod
|
|
def _model_type(p='path/to/model.pt'):
|
|
"""
|
|
This function takes a path to a model file and returns the model type
|
|
|
|
Args:
|
|
p: path to the model file. Defaults to path/to/model.pt
|
|
"""
|
|
# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
|
|
# types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
|
|
from ultralytics.engine.exporter import export_formats
|
|
sf = list(export_formats().Suffix) # export suffixes
|
|
if not is_url(p, check=False) and not isinstance(p, str):
|
|
check_suffix(p, sf) # checks
|
|
url = urlparse(p) # if url may be Triton inference server
|
|
types = [s in Path(p).name for s in sf]
|
|
types[8] &= not types[9] # tflite &= not edgetpu
|
|
triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
|
|
return types + [triton]
|