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ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2024-01-10 03:16:08 +01:00 • committed by GitHub
parent e795277391
commit fe27db2f6e
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ultralytics/nn/autobackend.py
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@ -32,10 +32,12 @@ def check_class_names(names):
names = {int(k): str(v) for k, v in names.items()}
n = len(names)
if max(names.keys()) >= n:
raise KeyError(f'{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices '
f'{min(names.keys())}-{max(names.keys())} defined in your dataset YAML.')
if isinstance(names[0], str) and names[0].startswith('n0'): # imagenet class codes, i.e. 'n01440764'
names_map = yaml_load(ROOT / 'cfg/datasets/ImageNet.yaml')['map'] # human-readable names
raise KeyError(
f"{n}-class dataset requires class indices 0-{n - 1}, but you have invalid class indices "
f"{min(names.keys())}-{max(names.keys())} defined in your dataset YAML."
)
if isinstance(names[0], str) and names[0].startswith("n0"): # imagenet class codes, i.e. 'n01440764'
names_map = yaml_load(ROOT / "cfg/datasets/ImageNet.yaml")["map"] # human-readable names
names = {k: names_map[v] for k, v in names.items()}
return names
@ -44,8 +46,8 @@ def default_class_names(data=None):
"""Applies default class names to an input YAML file or returns numerical class names."""
if data:
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
return yaml_load(check_yaml(data))["names"]
return {i: f"class{i}" for i in range(999)} # return default if above errors
class AutoBackend(nn.Module):
@ -77,14 +79,16 @@ class AutoBackend(nn.Module):
"""
@torch.no_grad()
def __init__(self,
weights='yolov8n.pt',
device=torch.device('cpu'),
dnn=False,
data=None,
fp16=False,
fuse=True,
verbose=True):
def __init__(
self,
weights="yolov8n.pt",
device=torch.device("cpu"),
dnn=False,
data=None,
fp16=False,
fuse=True,
verbose=True,
):
"""
Initialize the AutoBackend for inference.
@ -100,17 +104,31 @@ class AutoBackend(nn.Module):
super().__init__()
w = str(weights[0] if isinstance(weights, list) else weights)
nn_module = isinstance(weights, torch.nn.Module)
pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, ncnn, triton = \
self._model_type(w)
(
pt,
jit,
onnx,
xml,
engine,
coreml,
saved_model,
pb,
tflite,
edgetpu,
tfjs,
paddle,
ncnn,
triton,
) = self._model_type(w)
fp16 &= pt or jit or onnx or xml or engine or nn_module or triton # FP16
nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
stride = 32 # default stride
model, metadata = None, None
# Set device
cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA
if cuda and not any([nn_module, pt, jit, engine, onnx]): # GPU dataloader formats
device = torch.device('cpu')
device = torch.device("cpu")
cuda = False
# Download if not local
@ -121,77 +139,79 @@ class AutoBackend(nn.Module):
if nn_module: # in-memory PyTorch model
model = weights.to(device)
model = model.fuse(verbose=verbose) if fuse else model
if hasattr(model, 'kpt_shape'):
if hasattr(model, "kpt_shape"):
kpt_shape = model.kpt_shape # pose-only
stride = max(int(model.stride.max()), 32) # model stride
names = model.module.names if hasattr(model, 'module') else model.names # get class names
names = model.module.names if hasattr(model, "module") else model.names # get class names
model.half() if fp16 else model.float()
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
pt = True
elif pt: # PyTorch
from ultralytics.nn.tasks import attempt_load_weights
model = attempt_load_weights(weights if isinstance(weights, list) else w,
device=device,
inplace=True,
fuse=fuse)
if hasattr(model, 'kpt_shape'):
model = attempt_load_weights(
weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse
)
if hasattr(model, "kpt_shape"):
kpt_shape = model.kpt_shape # pose-only
stride = max(int(model.stride.max()), 32) # model stride
names = model.module.names if hasattr(model, 'module') else model.names # get class names
names = model.module.names if hasattr(model, "module") else model.names # get class names
model.half() if fp16 else model.float()
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
elif jit: # TorchScript
LOGGER.info(f'Loading {w} for TorchScript inference...')
extra_files = {'config.txt': ''} # model metadata
LOGGER.info(f"Loading {w} for TorchScript inference...")
extra_files = {"config.txt": ""} # model metadata
model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
model.half() if fp16 else model.float()
if extra_files['config.txt']: # load metadata dict
metadata = json.loads(extra_files['config.txt'], object_hook=lambda x: dict(x.items()))
if extra_files["config.txt"]: # load metadata dict
metadata = json.loads(extra_files["config.txt"], object_hook=lambda x: dict(x.items()))
elif dnn: # ONNX OpenCV DNN
LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
check_requirements('opencv-python>=4.5.4')
LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...")
check_requirements("opencv-python>=4.5.4")
net = cv2.dnn.readNetFromONNX(w)
elif onnx: # ONNX Runtime
LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
LOGGER.info(f"Loading {w} for ONNX Runtime inference...")
check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime"))
import onnxruntime
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] if cuda else ["CPUExecutionProvider"]
session = onnxruntime.InferenceSession(w, providers=providers)
output_names = [x.name for x in session.get_outputs()]
metadata = session.get_modelmeta().custom_metadata_map # metadata
elif xml: # OpenVINO
LOGGER.info(f'Loading {w} for OpenVINO inference...')
check_requirements('openvino>=2023.0') # requires openvino-dev: https://pypi.org/project/openvino-dev/
LOGGER.info(f"Loading {w} for OpenVINO inference...")
check_requirements("openvino>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/
from openvino.runtime import Core, Layout, get_batch # noqa
core = Core()
w = Path(w)
if not w.is_file(): # if not *.xml
w = next(w.glob('*.xml')) # get *.xml file from *_openvino_model dir
ov_model = core.read_model(model=str(w), weights=w.with_suffix('.bin'))
w = next(w.glob("*.xml")) # get *.xml file from *_openvino_model dir
ov_model = core.read_model(model=str(w), weights=w.with_suffix(".bin"))
if ov_model.get_parameters()[0].get_layout().empty:
ov_model.get_parameters()[0].set_layout(Layout('NCHW'))
ov_model.get_parameters()[0].set_layout(Layout("NCHW"))
batch_dim = get_batch(ov_model)
if batch_dim.is_static:
batch_size = batch_dim.get_length()
ov_compiled_model = core.compile_model(ov_model, device_name='AUTO') # AUTO selects best available device
metadata = w.parent / 'metadata.yaml'
ov_compiled_model = core.compile_model(ov_model, device_name="AUTO") # AUTO selects best available device
metadata = w.parent / "metadata.yaml"
elif engine: # TensorRT
LOGGER.info(f'Loading {w} for TensorRT inference...')
LOGGER.info(f"Loading {w} for TensorRT inference...")
try:
import tensorrt as trt # noqa https://developer.nvidia.com/nvidia-tensorrt-download
except ImportError:
if LINUX:
check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com")
import tensorrt as trt # noqa
check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0
if device.type == 'cpu':
device = torch.device('cuda:0')
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
check_version(trt.__version__, "7.0.0", hard=True) # require tensorrt>=7.0.0
if device.type == "cpu":
device = torch.device("cuda:0")
Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr"))
logger = trt.Logger(trt.Logger.INFO)
# Read file
with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
meta_len = int.from_bytes(f.read(4), byteorder='little') # read metadata length
metadata = json.loads(f.read(meta_len).decode('utf-8')) # read metadata
with open(w, "rb") as f, trt.Runtime(logger) as runtime:
meta_len = int.from_bytes(f.read(4), byteorder="little") # read metadata length
metadata = json.loads(f.read(meta_len).decode("utf-8")) # read metadata
model = runtime.deserialize_cuda_engine(f.read()) # read engine
context = model.create_execution_context()
bindings = OrderedDict()
@ -213,116 +233,124 @@ class AutoBackend(nn.Module):
im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size
batch_size = bindings["images"].shape[0] # if dynamic, this is instead max batch size
elif coreml: # CoreML
LOGGER.info(f'Loading {w} for CoreML inference...')
LOGGER.info(f"Loading {w} for CoreML inference...")
import coremltools as ct
model = ct.models.MLModel(w)
metadata = dict(model.user_defined_metadata)
elif saved_model: # TF SavedModel
LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...")
import tensorflow as tf
keras = False # assume TF1 saved_model
model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
metadata = Path(w) / 'metadata.yaml'
metadata = Path(w) / "metadata.yaml"
elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...")
import tensorflow as tf
from ultralytics.engine.exporter import gd_outputs
def wrap_frozen_graph(gd, inputs, outputs):
"""Wrap frozen graphs for deployment."""
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
ge = x.graph.as_graph_element
return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
gd = tf.Graph().as_graph_def() # TF GraphDef
with open(w, 'rb') as f:
with open(w, "rb") as f:
gd.ParseFromString(f.read())
frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd))
frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
from tflite_runtime.interpreter import Interpreter, load_delegate
except ImportError:
import tensorflow as tf
Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate
if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
delegate = {
'Linux': 'libedgetpu.so.1',
'Darwin': 'libedgetpu.1.dylib',
'Windows': 'edgetpu.dll'}[platform.system()]
LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...")
delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[
platform.system()
]
interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
else: # TFLite
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
LOGGER.info(f"Loading {w} for TensorFlow Lite inference...")
interpreter = Interpreter(model_path=w) # load TFLite model
interpreter.allocate_tensors() # allocate
input_details = interpreter.get_input_details() # inputs
output_details = interpreter.get_output_details() # outputs
# Load metadata
with contextlib.suppress(zipfile.BadZipFile):
with zipfile.ZipFile(w, 'r') as model:
with zipfile.ZipFile(w, "r") as model:
meta_file = model.namelist()[0]
metadata = ast.literal_eval(model.read(meta_file).decode('utf-8'))
metadata = ast.literal_eval(model.read(meta_file).decode("utf-8"))
elif tfjs: # TF.js
raise NotImplementedError('YOLOv8 TF.js inference is not currently supported.')
raise NotImplementedError("YOLOv8 TF.js inference is not currently supported.")
elif paddle: # PaddlePaddle
LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
LOGGER.info(f"Loading {w} for PaddlePaddle inference...")
check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle")
import paddle.inference as pdi # noqa
w = Path(w)
if not w.is_file(): # if not *.pdmodel
w = next(w.rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir
config = pdi.Config(str(w), str(w.with_suffix('.pdiparams')))
w = next(w.rglob("*.pdmodel")) # get *.pdmodel file from *_paddle_model dir
config = pdi.Config(str(w), str(w.with_suffix(".pdiparams")))
if cuda:
config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
predictor = pdi.create_predictor(config)
input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
output_names = predictor.get_output_names()
metadata = w.parents[1] / 'metadata.yaml'
metadata = w.parents[1] / "metadata.yaml"
elif ncnn: # ncnn
LOGGER.info(f'Loading {w} for ncnn inference...')
check_requirements('git+https://github.com/Tencent/ncnn.git' if ARM64 else 'ncnn') # requires ncnn
LOGGER.info(f"Loading {w} for ncnn inference...")
check_requirements("git+https://github.com/Tencent/ncnn.git" if ARM64 else "ncnn") # requires ncnn
import ncnn as pyncnn
net = pyncnn.Net()
net.opt.use_vulkan_compute = cuda
w = Path(w)
if not w.is_file(): # if not *.param
w = next(w.glob('*.param')) # get *.param file from *_ncnn_model dir
w = next(w.glob("*.param")) # get *.param file from *_ncnn_model dir
net.load_param(str(w))
net.load_model(str(w.with_suffix('.bin')))
metadata = w.parent / 'metadata.yaml'
net.load_model(str(w.with_suffix(".bin")))
metadata = w.parent / "metadata.yaml"
elif triton: # NVIDIA Triton Inference Server
check_requirements('tritonclient[all]')
check_requirements("tritonclient[all]")
from ultralytics.utils.triton import TritonRemoteModel
model = TritonRemoteModel(w)
else:
from ultralytics.engine.exporter import export_formats
raise TypeError(f"model='{w}' is not a supported model format. "
'See https://docs.ultralytics.com/modes/predict for help.'
f'\n\n{export_formats()}')
raise TypeError(
f"model='{w}' is not a supported model format. "
"See https://docs.ultralytics.com/modes/predict for help."
f"\n\n{export_formats()}"
)
# Load external metadata YAML
if isinstance(metadata, (str, Path)) and Path(metadata).exists():
metadata = yaml_load(metadata)
if metadata:
for k, v in metadata.items():
if k in ('stride', 'batch'):
if k in ("stride", "batch"):
metadata[k] = int(v)
elif k in ('imgsz', 'names', 'kpt_shape') and isinstance(v, str):
elif k in ("imgsz", "names", "kpt_shape") and isinstance(v, str):
metadata[k] = eval(v)
stride = metadata['stride']
task = metadata['task']
batch = metadata['batch']
imgsz = metadata['imgsz']
names = metadata['names']
kpt_shape = metadata.get('kpt_shape')
stride = metadata["stride"]
task = metadata["task"]
batch = metadata["batch"]
imgsz = metadata["imgsz"]
names = metadata["names"]
kpt_shape = metadata.get("kpt_shape")
elif not (pt or triton or nn_module):
LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'")
# Check names
if 'names' not in locals(): # names missing
if "names" not in locals(): # names missing
names = default_class_names(data)
names = check_class_names(names)
@ -367,26 +395,28 @@ class AutoBackend(nn.Module):
im = im.cpu().numpy() # FP32
y = list(self.ov_compiled_model(im).values())
elif self.engine: # TensorRT
if self.dynamic and im.shape != self.bindings['images'].shape:
i = self.model.get_binding_index('images')
if self.dynamic and im.shape != self.bindings["images"].shape:
i = self.model.get_binding_index("images")
self.context.set_binding_shape(i, im.shape) # reshape if dynamic
self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
for name in self.output_names:
i = self.model.get_binding_index(name)
self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
s = self.bindings['images'].shape
s = self.bindings["images"].shape
assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
self.binding_addrs['images'] = int(im.data_ptr())
self.binding_addrs["images"] = int(im.data_ptr())
self.context.execute_v2(list(self.binding_addrs.values()))
y = [self.bindings[x].data for x in sorted(self.output_names)]
elif self.coreml: # CoreML
im = im[0].cpu().numpy()
im_pil = Image.fromarray((im * 255).astype('uint8'))
im_pil = Image.fromarray((im * 255).astype("uint8"))
# im = im.resize((192, 320), Image.BILINEAR)
y = self.model.predict({'image': im_pil}) # coordinates are xywh normalized
if 'confidence' in y:
raise TypeError('Ultralytics only supports inference of non-pipelined CoreML models exported with '
f"'nms=False', but 'model={w}' has an NMS pipeline created by an 'nms=True' export.")
y = self.model.predict({"image": im_pil}) # coordinates are xywh normalized
if "confidence" in y:
raise TypeError(
"Ultralytics only supports inference of non-pipelined CoreML models exported with "
f"'nms=False', but 'model={w}' has an NMS pipeline created by an 'nms=True' export."
)
# TODO: CoreML NMS inference handling
# from ultralytics.utils.ops import xywh2xyxy
# box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
@ -425,20 +455,20 @@ class AutoBackend(nn.Module):
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)}
self.names = {i: f"class{i}" for i in range(nc)}
else: # Lite or Edge TPU
details = self.input_details[0]
integer = details['dtype'] in (np.int8, np.int16) # is TFLite quantized int8 or int16 model
integer = details["dtype"] in (np.int8, np.int16) # is TFLite quantized int8 or int16 model
if integer:
scale, zero_point = details['quantization']
im = (im / scale + zero_point).astype(details['dtype']) # de-scale
self.interpreter.set_tensor(details['index'], im)
scale, zero_point = details["quantization"]
im = (im / scale + zero_point).astype(details["dtype"]) # de-scale
self.interpreter.set_tensor(details["index"], im)
self.interpreter.invoke()
y = []
for output in self.output_details:
x = self.interpreter.get_tensor(output['index'])
x = self.interpreter.get_tensor(output["index"])
if integer:
scale, zero_point = output['quantization']
scale, zero_point = output["quantization"]
x = (x.astype(np.float32) - zero_point) * scale # re-scale
if x.ndim > 2: # if task is not classification
# Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695
@ -483,13 +513,13 @@ class AutoBackend(nn.Module):
(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):
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 _model_type(p='path/to/model.pt'):
def _model_type(p="path/to/model.pt"):
"""
This function takes a path to a model file and returns the model type.
@ -499,18 +529,20 @@ class AutoBackend(nn.Module):
# 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
name = Path(p).name
types = [s in name for s in sf]
types[5] |= name.endswith('.mlmodel') # retain support for older Apple CoreML *.mlmodel formats
types[5] |= name.endswith(".mlmodel") # retain support for older Apple CoreML *.mlmodel formats
types[8] &= not types[9] # tflite &= not edgetpu
if any(types):
triton = False
else:
from urllib.parse import urlsplit
url = urlsplit(p)
triton = url.netloc and url.path and url.scheme in {'http', 'grpc'}
triton = url.netloc and url.path and url.scheme in {"http", "grpc"}
return types + [triton]