/
autobackend.py
659 lines (575 loc) 路 30.1 KB
/
autobackend.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
# Ultralytics YOLO 馃殌, AGPL-3.0 license
import ast
import contextlib
import json
import platform
import zipfile
from collections import OrderedDict, namedtuple
from pathlib import Path
import cv2
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from ultralytics.utils import ARM64, IS_JETSON, IS_RASPBERRYPI, LINUX, LOGGER, ROOT, yaml_load
from ultralytics.utils.checks import check_requirements, check_suffix, check_version, check_yaml
from ultralytics.utils.downloads import attempt_download_asset, is_url
def check_class_names(names):
"""
Check class names.
Map imagenet class codes to human-readable names if required. Convert lists to dicts.
"""
if isinstance(names, list): # names is a list
names = dict(enumerate(names)) # convert to dict
if isinstance(names, dict):
# Convert 1) string keys to int, i.e. '0' to 0, and non-string values to strings, i.e. True to 'True'
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
names = {k: names_map[v] for k, v in names.items()}
return names
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
class AutoBackend(nn.Module):
"""
Handles dynamic backend selection for running inference using Ultralytics YOLO models.
The AutoBackend class is designed to provide an abstraction layer for various inference engines. It supports a wide
range of formats, each with specific naming conventions as outlined below:
Supported Formats and Naming Conventions:
| Format | File Suffix |
|-----------------------|------------------|
| PyTorch | *.pt |
| TorchScript | *.torchscript |
| ONNX Runtime | *.onnx |
| ONNX OpenCV DNN | *.onnx (dnn=True)|
| OpenVINO | *openvino_model/ |
| CoreML | *.mlpackage |
| TensorRT | *.engine |
| TensorFlow SavedModel | *_saved_model |
| TensorFlow GraphDef | *.pb |
| TensorFlow Lite | *.tflite |
| TensorFlow Edge TPU | *_edgetpu.tflite |
| PaddlePaddle | *_paddle_model |
| NCNN | *_ncnn_model |
This class offers dynamic backend switching capabilities based on the input model format, making it easier to deploy
models across various platforms.
"""
@torch.no_grad()
def __init__(
self,
weights="yolov8n.pt",
device=torch.device("cpu"),
dnn=False,
data=None,
fp16=False,
batch=1,
fuse=True,
verbose=True,
):
"""
Initialize the AutoBackend for inference.
Args:
weights (str): Path to the model weights file. Defaults to 'yolov8n.pt'.
device (torch.device): Device to run the model on. Defaults to CPU.
dnn (bool): Use OpenCV DNN module for ONNX inference. Defaults to False.
data (str | Path | optional): Path to the additional data.yaml file containing class names. Optional.
fp16 (bool): Enable half-precision inference. Supported only on specific backends. Defaults to False.
batch (int): Batch-size to assume for inference.
fuse (bool): Fuse Conv2D + BatchNorm layers for optimization. Defaults to True.
verbose (bool): Enable verbose logging. Defaults to True.
"""
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)
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
if cuda and not any([nn_module, pt, jit, engine, onnx]): # GPU dataloader formats
device = torch.device("cpu")
cuda = False
# Download if not local
if not (pt or triton or nn_module):
w = attempt_download_asset(w)
# In-memory PyTorch model
if nn_module:
model = weights.to(device)
if fuse:
model = model.fuse(verbose=verbose)
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
model.half() if fp16 else model.float()
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
pt = True
# PyTorch
elif pt:
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"):
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
model.half() if fp16 else model.float()
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
# TorchScript
elif jit:
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()))
# ONNX OpenCV DNN
elif dnn:
LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...")
check_requirements("opencv-python>=4.5.4")
net = cv2.dnn.readNetFromONNX(w)
# ONNX Runtime
elif onnx:
LOGGER.info(f"Loading {w} for ONNX Runtime inference...")
check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime"))
if IS_RASPBERRYPI or IS_JETSON:
# Fix error: module 'numpy.linalg._umath_linalg' has no attribute '_ilp64' when exporting to Tensorflow SavedModel on RPi and Jetson
check_requirements("numpy==1.23.5")
import onnxruntime
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
# OpenVINO
elif xml:
LOGGER.info(f"Loading {w} for OpenVINO inference...")
check_requirements("openvino>=2024.0.0")
import openvino as ov
core = ov.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"))
if ov_model.get_parameters()[0].get_layout().empty:
ov_model.get_parameters()[0].set_layout(ov.Layout("NCHW"))
# OpenVINO inference modes are 'LATENCY', 'THROUGHPUT' (not recommended), or 'CUMULATIVE_THROUGHPUT'
inference_mode = "CUMULATIVE_THROUGHPUT" if batch > 1 else "LATENCY"
LOGGER.info(f"Using OpenVINO {inference_mode} mode for batch={batch} inference...")
ov_compiled_model = core.compile_model(
ov_model,
device_name="AUTO", # AUTO selects best available device, do not modify
config={"PERFORMANCE_HINT": inference_mode},
)
input_name = ov_compiled_model.input().get_any_name()
metadata = w.parent / "metadata.yaml"
# TensorRT
elif engine:
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")
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"))
logger = trt.Logger(trt.Logger.INFO)
# Read file
with open(w, "rb") as f, trt.Runtime(logger) as runtime:
try:
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
except UnicodeDecodeError:
f.seek(0) # engine file may lack embedded Ultralytics metadata
model = runtime.deserialize_cuda_engine(f.read()) # read engine
# Model context
try:
context = model.create_execution_context()
except Exception as e: # model is None
LOGGER.error(f"ERROR: TensorRT model exported with a different version than {trt.__version__}\n")
raise e
bindings = OrderedDict()
output_names = []
fp16 = False # default updated below
dynamic = False
is_trt10 = not hasattr(model, "num_bindings")
num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings)
for i in num:
if is_trt10:
name = model.get_tensor_name(i)
dtype = trt.nptype(model.get_tensor_dtype(name))
is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT
if is_input:
if -1 in tuple(model.get_tensor_shape(name)):
dynamic = True
context.set_input_shape(name, tuple(model.get_tensor_profile_shape(name, 0)[1]))
if dtype == np.float16:
fp16 = True
else:
output_names.append(name)
shape = tuple(context.get_tensor_shape(name))
else: # TensorRT < 10.0
name = model.get_binding_name(i)
dtype = trt.nptype(model.get_binding_dtype(i))
is_input = model.binding_is_input(i)
if model.binding_is_input(i):
if -1 in tuple(model.get_binding_shape(i)): # dynamic
dynamic = True
context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[1]))
if dtype == np.float16:
fp16 = True
else:
output_names.append(name)
shape = tuple(context.get_binding_shape(i))
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
# CoreML
elif coreml:
LOGGER.info(f"Loading {w} for CoreML inference...")
import coremltools as ct
model = ct.models.MLModel(w)
metadata = dict(model.user_defined_metadata)
# TF SavedModel
elif saved_model:
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"
# TF GraphDef
elif pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
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
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:
gd.ParseFromString(f.read())
frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
# TFLite or TFLite Edge TPU
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()
]
interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
else: # TFLite
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:
meta_file = model.namelist()[0]
metadata = ast.literal_eval(model.read(meta_file).decode("utf-8"))
# TF.js
elif tfjs:
raise NotImplementedError("YOLOv8 TF.js inference is not currently supported.")
# PaddlePaddle
elif paddle:
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")))
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"
# NCNN
elif 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
net.load_param(str(w))
net.load_model(str(w.with_suffix(".bin")))
metadata = w.parent / "metadata.yaml"
# NVIDIA Triton Inference Server
elif triton:
check_requirements("tritonclient[all]")
from ultralytics.utils.triton import TritonRemoteModel
model = TritonRemoteModel(w)
# Any other format (unsupported)
else:
from ultralytics.engine.exporter import export_formats
raise TypeError(
f"model='{w}' is not a supported model format. "
f"See https://docs.ultralytics.com/modes/predict for help.\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"}:
metadata[k] = int(v)
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")
elif not (pt or triton or nn_module):
LOGGER.warning(f"WARNING 鈿狅笍 Metadata not found for 'model={weights}'")
# Check names
if "names" not in locals(): # names missing
names = default_class_names(data)
names = check_class_names(names)
# Disable gradients
if pt:
for p in model.parameters():
p.requires_grad = False
self.__dict__.update(locals()) # assign all variables to self
def forward(self, im, augment=False, visualize=False, embed=None):
"""
Runs inference on the YOLOv8 MultiBackend model.
Args:
im (torch.Tensor): The image tensor to perform inference on.
augment (bool): whether to perform data augmentation during inference, defaults to False
visualize (bool): whether to visualize the output predictions, defaults to False
embed (list, optional): A list of feature vectors/embeddings to return.
Returns:
(tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)
"""
b, ch, h, w = im.shape # batch, channel, height, width
if self.fp16 and im.dtype != torch.float16:
im = im.half() # to FP16
if self.nhwc:
im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)
# PyTorch
if self.pt or self.nn_module:
y = self.model(im, augment=augment, visualize=visualize, embed=embed)
# TorchScript
elif self.jit:
y = self.model(im)
# ONNX OpenCV DNN
elif self.dnn:
im = im.cpu().numpy() # torch to numpy
self.net.setInput(im)
y = self.net.forward()
# ONNX Runtime
elif self.onnx:
im = im.cpu().numpy() # torch to numpy
y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
# OpenVINO
elif self.xml:
im = im.cpu().numpy() # FP32
if self.inference_mode in {"THROUGHPUT", "CUMULATIVE_THROUGHPUT"}: # optimized for larger batch-sizes
n = im.shape[0] # number of images in batch
results = [None] * n # preallocate list with None to match the number of images
def callback(request, userdata):
"""Places result in preallocated list using userdata index."""
results[userdata] = request.results
# Create AsyncInferQueue, set the callback and start asynchronous inference for each input image
async_queue = self.ov.runtime.AsyncInferQueue(self.ov_compiled_model)
async_queue.set_callback(callback)
for i in range(n):
# Start async inference with userdata=i to specify the position in results list
async_queue.start_async(inputs={self.input_name: im[i : i + 1]}, userdata=i) # keep image as BCHW
async_queue.wait_all() # wait for all inference requests to complete
y = np.concatenate([list(r.values())[0] for r in results])
else: # inference_mode = "LATENCY", optimized for fastest first result at batch-size 1
y = list(self.ov_compiled_model(im).values())
# TensorRT
elif self.engine:
if self.dynamic or im.shape != self.bindings["images"].shape:
if self.is_trt10:
self.context.set_input_shape("images", im.shape)
self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape)
for name in self.output_names:
self.bindings[name].data.resize_(tuple(self.context.get_tensor_shape(name)))
else:
i = self.model.get_binding_index("images")
self.context.set_binding_shape(i, 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
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.context.execute_v2(list(self.binding_addrs.values()))
y = [self.bindings[x].data for x in sorted(self.output_names)]
# CoreML
elif self.coreml:
im = im[0].cpu().numpy()
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."
)
# TODO: CoreML NMS inference handling
# from ultralytics.utils.ops import xywh2xyxy
# box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
# conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float32)
# y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
elif len(y) == 1: # classification model
y = list(y.values())
elif len(y) == 2: # segmentation model
y = list(reversed(y.values())) # reversed for segmentation models (pred, proto)
# PaddlePaddle
elif self.paddle:
im = im.cpu().numpy().astype(np.float32)
self.input_handle.copy_from_cpu(im)
self.predictor.run()
y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
# NCNN
elif self.ncnn:
mat_in = self.pyncnn.Mat(im[0].cpu().numpy())
with self.net.create_extractor() as ex:
ex.input(self.net.input_names()[0], mat_in)
# WARNING: 'output_names' sorted as a temporary fix for https://github.com/pnnx/pnnx/issues/130
y = [np.array(ex.extract(x)[1])[None] for x in sorted(self.net.output_names())]
# NVIDIA Triton Inference Server
elif self.triton:
im = im.cpu().numpy() # torch to numpy
y = self.model(im)
# TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
else:
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
details = self.input_details[0]
is_int = details["dtype"] in {np.int8, np.int16} # is TFLite quantized int8 or int16 model
if is_int:
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"])
if is_int:
scale, zero_point = output["quantization"]
x = (x.astype(np.float32) - zero_point) * scale # re-scale
if x.ndim == 3: # if task is not classification, excluding masks (ndim=4) as well
# Denormalize xywh by image size. See https://github.com/ultralytics/ultralytics/pull/1695
# xywh are normalized in TFLite/EdgeTPU to mitigate quantization error of integer models
x[:, [0, 2]] *= w
x[:, [1, 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)
"""
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 _model_type(p="path/to/model.pt"):
"""
This function takes a path to a model file and returns the model type. Possibles types are pt, jit, onnx, xml,
engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, ncnn or paddle.
Args:
p: path to the model file. Defaults to path/to/model.pt
Examples:
>>> model = AutoBackend(weights="path/to/model.onnx")
>>> model_type = model._model_type() # returns "onnx"
"""
from ultralytics.engine.exporter import export_formats
sf = list(export_formats().Suffix) # export suffixes
if not is_url(p) 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[8] &= not types[9] # tflite &= not edgetpu
if any(types):
triton = False
else:
from urllib.parse import urlsplit
url = urlsplit(p)
triton = bool(url.netloc) and bool(url.path) and url.scheme in {"http", "grpc"}
return types + [triton]