Start export implementation (#110)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
@ -1,4 +1,77 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
|
||||
|
||||
Format | `export.py --include` | Model
|
||||
--- | --- | ---
|
||||
PyTorch | - | yolov8n.pt
|
||||
TorchScript | `torchscript` | yolov8n.torchscript
|
||||
ONNX | `onnx` | yolov8n.onnx
|
||||
OpenVINO | `openvino` | yolov5s_openvino_model/
|
||||
TensorRT | `engine` | yolov8n.engine
|
||||
CoreML | `coreml` | yolov8n.mlmodel
|
||||
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
|
||||
TensorFlow GraphDef | `pb` | yolov8n.pb
|
||||
TensorFlow Lite | `tflite` | yolov8n.tflite
|
||||
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
|
||||
TensorFlow.js | `tfjs` | yolov5s_web_model/
|
||||
PaddlePaddle | `paddle` | yolov5s_paddle_model/
|
||||
|
||||
Requirements:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
|
||||
|
||||
Usage:
|
||||
$ python export.py --weights yolov8n.pt --include torchscript onnx openvino engine coreml tflite ...
|
||||
|
||||
Inference:
|
||||
$ python detect.py --weights yolov8n.pt # PyTorch
|
||||
yolov8n.torchscript # TorchScript
|
||||
yolov8n.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s_openvino_model # OpenVINO
|
||||
yolov8n.engine # TensorRT
|
||||
yolov8n.mlmodel # CoreML (macOS-only)
|
||||
yolov5s_saved_model # TensorFlow SavedModel
|
||||
yolov8n.pb # TensorFlow GraphDef
|
||||
yolov8n.tflite # TensorFlow Lite
|
||||
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s_paddle_model # PaddlePaddle
|
||||
|
||||
TensorFlow.js:
|
||||
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
|
||||
$ npm install
|
||||
$ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
|
||||
$ npm start
|
||||
|
||||
|
||||
from ultralytics import YOLO
|
||||
model = YOLO().new('yolov8n.yaml')
|
||||
results = model.export(format='onnx')
|
||||
"""
|
||||
import contextlib
|
||||
import json
|
||||
import os
|
||||
import platform
|
||||
import re
|
||||
import subprocess
|
||||
import time
|
||||
import warnings
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
import torch
|
||||
from torch.utils.mobile_optimizer import optimize_for_mobile
|
||||
|
||||
from ultralytics.nn.modules import Detect, Segment
|
||||
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel
|
||||
from ultralytics.yolo.utils import LOGGER, ROOT, colorstr, get_default_args
|
||||
from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version
|
||||
from ultralytics.yolo.utils.files import file_size, yaml_save
|
||||
from ultralytics.yolo.utils.ops import Profile
|
||||
from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
MACOS = platform.system() == 'Darwin' # macOS environment
|
||||
|
||||
|
||||
def export_formats():
|
||||
@ -17,3 +90,519 @@ def export_formats():
|
||||
['TensorFlow.js', 'tfjs', '_web_model', False, False],
|
||||
['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
|
||||
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
|
||||
|
||||
|
||||
def try_export(inner_func):
|
||||
# YOLOv5 export decorator, i..e @try_export
|
||||
inner_args = get_default_args(inner_func)
|
||||
|
||||
def outer_func(*args, **kwargs):
|
||||
prefix = inner_args['prefix']
|
||||
try:
|
||||
with Profile() as dt:
|
||||
f, model = inner_func(*args, **kwargs)
|
||||
LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)')
|
||||
return f, model
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
|
||||
return None, None
|
||||
|
||||
return outer_func
|
||||
|
||||
|
||||
@try_export
|
||||
def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
|
||||
# YOLOv5 TorchScript model export
|
||||
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
|
||||
f = file.with_suffix('.torchscript')
|
||||
|
||||
ts = torch.jit.trace(model, im, strict=False)
|
||||
d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
|
||||
extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
|
||||
if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
|
||||
optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
|
||||
else:
|
||||
ts.save(str(f), _extra_files=extra_files)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
|
||||
# YOLOv5 ONNX export
|
||||
check_requirements('onnx>=1.12.0')
|
||||
import onnx # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
|
||||
f = file.with_suffix('.onnx')
|
||||
|
||||
output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
|
||||
if dynamic:
|
||||
dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640)
|
||||
if isinstance(model, SegmentationModel):
|
||||
dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
|
||||
dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160)
|
||||
elif isinstance(model, DetectionModel):
|
||||
dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
|
||||
|
||||
torch.onnx.export(
|
||||
model.cpu() if dynamic else model, # --dynamic only compatible with cpu
|
||||
im.cpu() if dynamic else im,
|
||||
f,
|
||||
verbose=False,
|
||||
opset_version=opset,
|
||||
do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
|
||||
input_names=['images'],
|
||||
output_names=output_names,
|
||||
dynamic_axes=dynamic or None)
|
||||
|
||||
# Checks
|
||||
model_onnx = onnx.load(f) # load onnx model
|
||||
onnx.checker.check_model(model_onnx) # check onnx model
|
||||
|
||||
# Metadata
|
||||
d = {'stride': int(max(model.stride)), 'names': model.names}
|
||||
for k, v in d.items():
|
||||
meta = model_onnx.metadata_props.add()
|
||||
meta.key, meta.value = k, str(v)
|
||||
onnx.save(model_onnx, f)
|
||||
|
||||
# Simplify
|
||||
if simplify:
|
||||
try:
|
||||
cuda = torch.cuda.is_available()
|
||||
check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
|
||||
import onnxsim
|
||||
|
||||
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
|
||||
model_onnx, check = onnxsim.simplify(model_onnx)
|
||||
assert check, 'assert check failed'
|
||||
onnx.save(model_onnx, f)
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} simplifier failure: {e}')
|
||||
return f, model_onnx
|
||||
|
||||
|
||||
@try_export
|
||||
def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
|
||||
# YOLOv5 OpenVINO export
|
||||
check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
||||
import openvino.inference_engine as ie # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
|
||||
f = str(file).replace('.pt', f'_openvino_model{os.sep}')
|
||||
|
||||
cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
|
||||
subprocess.run(cmd.split(), check=True, env=os.environ) # export
|
||||
yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
|
||||
# YOLOv5 Paddle export
|
||||
check_requirements(('paddlepaddle', 'x2paddle'))
|
||||
import x2paddle # noqa
|
||||
from x2paddle.convert import pytorch2paddle # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
|
||||
f = str(file).replace('.pt', f'_paddle_model{os.sep}')
|
||||
|
||||
pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im]) # export
|
||||
yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
|
||||
# YOLOv5 CoreML export
|
||||
check_requirements('coremltools')
|
||||
import coremltools as ct # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
|
||||
f = file.with_suffix('.mlmodel')
|
||||
|
||||
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
|
||||
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
|
||||
bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
|
||||
if bits < 32:
|
||||
if MACOS: # quantization only supported on macOS
|
||||
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
|
||||
else:
|
||||
LOGGER.info(f'{prefix} quantization only supported on macOS, skipping...')
|
||||
ct_model.save(f)
|
||||
return f, ct_model
|
||||
|
||||
|
||||
@try_export
|
||||
def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
|
||||
# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
|
||||
assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
|
||||
try:
|
||||
import tensorrt as trt
|
||||
except Exception:
|
||||
if platform.system() == 'Linux':
|
||||
check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
|
||||
import tensorrt as trt
|
||||
|
||||
if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
|
||||
grid = model.model[-1].anchor_grid
|
||||
model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
|
||||
export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
|
||||
model.model[-1].anchor_grid = grid
|
||||
else: # TensorRT >= 8
|
||||
check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
|
||||
export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
|
||||
onnx = file.with_suffix('.onnx')
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
|
||||
assert onnx.exists(), f'failed to export ONNX file: {onnx}'
|
||||
f = file.with_suffix('.engine') # TensorRT engine file
|
||||
logger = trt.Logger(trt.Logger.INFO)
|
||||
if verbose:
|
||||
logger.min_severity = trt.Logger.Severity.VERBOSE
|
||||
|
||||
builder = trt.Builder(logger)
|
||||
config = builder.create_builder_config()
|
||||
config.max_workspace_size = workspace * 1 << 30
|
||||
# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
|
||||
|
||||
flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
|
||||
network = builder.create_network(flag)
|
||||
parser = trt.OnnxParser(network, logger)
|
||||
if not parser.parse_from_file(str(onnx)):
|
||||
raise RuntimeError(f'failed to load ONNX file: {onnx}')
|
||||
|
||||
inputs = [network.get_input(i) for i in range(network.num_inputs)]
|
||||
outputs = [network.get_output(i) for i in range(network.num_outputs)]
|
||||
for inp in inputs:
|
||||
LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
|
||||
for out in outputs:
|
||||
LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')
|
||||
|
||||
if dynamic:
|
||||
if im.shape[0] <= 1:
|
||||
LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument")
|
||||
profile = builder.create_optimization_profile()
|
||||
for inp in inputs:
|
||||
profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
|
||||
config.add_optimization_profile(profile)
|
||||
|
||||
LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}')
|
||||
if builder.platform_has_fast_fp16 and half:
|
||||
config.set_flag(trt.BuilderFlag.FP16)
|
||||
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
|
||||
t.write(engine.serialize())
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_saved_model(model,
|
||||
im,
|
||||
file,
|
||||
dynamic,
|
||||
tf_nms=False,
|
||||
agnostic_nms=False,
|
||||
topk_per_class=100,
|
||||
topk_all=100,
|
||||
iou_thres=0.45,
|
||||
conf_thres=0.25,
|
||||
keras=False,
|
||||
prefix=colorstr('TensorFlow SavedModel:')):
|
||||
# YOLOv5 TensorFlow SavedModel export
|
||||
try:
|
||||
import tensorflow as tf
|
||||
except Exception:
|
||||
check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
|
||||
import tensorflow as tf
|
||||
from models.tf import TFModel
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = str(file).replace('.pt', '_saved_model')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
|
||||
_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
|
||||
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
|
||||
keras_model.trainable = False
|
||||
keras_model.summary()
|
||||
if keras:
|
||||
keras_model.save(f, save_format='tf')
|
||||
else:
|
||||
spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(spec)
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
tfm = tf.Module()
|
||||
tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])
|
||||
tfm.__call__(im)
|
||||
tf.saved_model.save(tfm,
|
||||
f,
|
||||
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(
|
||||
tf.__version__, '2.6') else tf.saved_model.SaveOptions())
|
||||
return f, keras_model
|
||||
|
||||
|
||||
@try_export
|
||||
def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
|
||||
# YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
|
||||
import tensorflow as tf # noqa
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = file.with_suffix('.pb')
|
||||
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
frozen_func.graph.as_graph_def()
|
||||
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
|
||||
# YOLOv5 TensorFlow Lite export
|
||||
import tensorflow as tf # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
f = str(file).replace('.pt', '-fp16.tflite')
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
|
||||
converter.target_spec.supported_types = [tf.float16]
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
if int8:
|
||||
# from models.tf import representative_dataset_gen
|
||||
# dataset = LoadImages(check_dataset(check_yaml(data))['train'], imgsz=imgsz, auto=False)
|
||||
# converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
||||
converter.target_spec.supported_types = []
|
||||
converter.inference_input_type = tf.uint8 # or tf.int8
|
||||
converter.inference_output_type = tf.uint8 # or tf.int8
|
||||
converter.experimental_new_quantizer = True
|
||||
f = str(file).replace('.pt', '-int8.tflite')
|
||||
if nms or agnostic_nms:
|
||||
converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
|
||||
|
||||
tflite_model = converter.convert()
|
||||
open(f, "wb").write(tflite_model)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
|
||||
# YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
|
||||
cmd = 'edgetpu_compiler --version'
|
||||
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
|
||||
assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
|
||||
if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
|
||||
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
|
||||
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
|
||||
for c in (
|
||||
'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
|
||||
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
|
||||
'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
|
||||
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
|
||||
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
|
||||
f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model
|
||||
f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model
|
||||
|
||||
cmd = f"edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}"
|
||||
subprocess.run(cmd.split(), check=True)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_tfjs(file, prefix=colorstr('TensorFlow.js:')):
|
||||
# YOLOv5 TensorFlow.js export
|
||||
check_requirements('tensorflowjs')
|
||||
import tensorflowjs as tfjs # noqa
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
|
||||
f = str(file).replace('.pt', '_web_model') # js dir
|
||||
f_pb = file.with_suffix('.pb') # *.pb path
|
||||
f_json = f'{f}/model.json' # *.json path
|
||||
|
||||
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
|
||||
f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
|
||||
subprocess.run(cmd.split())
|
||||
|
||||
json = Path(f_json).read_text()
|
||||
with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
|
||||
subst = re.sub(
|
||||
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
|
||||
r'"Identity_1": {"name": "Identity_1"}, '
|
||||
r'"Identity_2": {"name": "Identity_2"}, '
|
||||
r'"Identity_3": {"name": "Identity_3"}}}', json)
|
||||
j.write(subst)
|
||||
return f, None
|
||||
|
||||
|
||||
def add_tflite_metadata(file, metadata, num_outputs):
|
||||
# Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
|
||||
with contextlib.suppress(ImportError):
|
||||
# check_requirements('tflite_support')
|
||||
from tflite_support import flatbuffers # noqa
|
||||
from tflite_support import metadata as _metadata # noqa
|
||||
from tflite_support import metadata_schema_py_generated as _metadata_fb # noqa
|
||||
|
||||
tmp_file = Path('/tmp/meta.txt')
|
||||
with open(tmp_file, 'w') as meta_f:
|
||||
meta_f.write(str(metadata))
|
||||
|
||||
model_meta = _metadata_fb.ModelMetadataT()
|
||||
label_file = _metadata_fb.AssociatedFileT()
|
||||
label_file.name = tmp_file.name
|
||||
model_meta.associatedFiles = [label_file]
|
||||
|
||||
subgraph = _metadata_fb.SubGraphMetadataT()
|
||||
subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
|
||||
subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
|
||||
model_meta.subgraphMetadata = [subgraph]
|
||||
|
||||
b = flatbuffers.Builder(0)
|
||||
b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
||||
metadata_buf = b.Output()
|
||||
|
||||
populator = _metadata.MetadataPopulator.with_model_file(file)
|
||||
populator.load_metadata_buffer(metadata_buf)
|
||||
populator.load_associated_files([str(tmp_file)])
|
||||
populator.populate()
|
||||
tmp_file.unlink()
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def export_model(
|
||||
model, # model
|
||||
file=ROOT / 'yolov8n.pt',
|
||||
data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
|
||||
imgsz=(640, 640), # image (height, width)
|
||||
batch_size=1, # batch size
|
||||
device=torch.device('cpu'), # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
format='onnx', # export format
|
||||
half=False, # FP16 half-precision export
|
||||
keras=False, # use Keras
|
||||
optimize=False, # TorchScript: optimize for mobile
|
||||
int8=False, # CoreML/TF INT8 quantization
|
||||
dynamic=False, # ONNX/TF/TensorRT: dynamic axes
|
||||
simplify=False, # ONNX: simplify model
|
||||
opset=17, # ONNX: opset version
|
||||
verbose=False, # TensorRT: verbose log
|
||||
workspace=4, # TensorRT: workspace size (GB)
|
||||
nms=False, # TF: add NMS to model
|
||||
agnostic_nms=False, # TF: add agnostic NMS to model
|
||||
topk_per_class=100, # TF.js NMS: topk per class to keep
|
||||
topk_all=100, # TF.js NMS: topk for all classes to keep
|
||||
iou_thres=0.45, # TF.js NMS: IoU threshold
|
||||
conf_thres=0.25, # TF.js NMS: confidence threshold
|
||||
):
|
||||
t = time.time()
|
||||
format = format.lower() # to lowercase
|
||||
fmts = tuple(export_formats()['Argument'][1:]) # available export formats
|
||||
flags = [x == format for x in fmts]
|
||||
assert sum(flags), f'ERROR: Invalid format={format}, valid formats are {fmts}'
|
||||
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans
|
||||
|
||||
# Load PyTorch model
|
||||
device = select_device(device)
|
||||
if half:
|
||||
assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
|
||||
assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
|
||||
model = deepcopy(model).fuse() # load FP32 model
|
||||
|
||||
# Checks
|
||||
if isinstance(imgsz, int):
|
||||
imgsz = [imgsz]
|
||||
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
|
||||
if optimize:
|
||||
assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
|
||||
|
||||
# Input
|
||||
gs = int(max(model.stride)) # grid size (max stride)
|
||||
imgsz = [check_imgsz(x, gs) for x in imgsz] # verify img_size are gs-multiples
|
||||
im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
|
||||
|
||||
# Update model
|
||||
model.eval()
|
||||
for k, m in model.named_modules():
|
||||
if isinstance(m, (Detect, Segment)):
|
||||
m.dynamic = dynamic
|
||||
m.export = True
|
||||
|
||||
for _ in range(2):
|
||||
y = model(im) # dry runs
|
||||
if half and not coreml:
|
||||
im, model = im.half(), model.half() # to FP16
|
||||
shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
|
||||
metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata
|
||||
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
|
||||
|
||||
# Warnings
|
||||
warnings.filterwarnings('ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
|
||||
warnings.filterwarnings('ignore', category=UserWarning) # suppress shape prim::Constant type missing ONNX warning
|
||||
warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress CoreML np.bool deprecation warning
|
||||
|
||||
# Exports
|
||||
f = [''] * len(fmts) # exported filenames
|
||||
if jit: # TorchScript
|
||||
f[0], _ = export_torchscript(model, im, file, optimize)
|
||||
if engine: # TensorRT required before ONNX
|
||||
f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
|
||||
if onnx or xml: # OpenVINO requires ONNX
|
||||
f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
|
||||
if xml: # OpenVINO
|
||||
f[3], _ = export_openvino(file, metadata, half)
|
||||
if coreml: # CoreML
|
||||
f[4], _ = export_coreml(model, im, file, int8, half)
|
||||
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
|
||||
assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
||||
assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
|
||||
f[5], s_model = export_saved_model(model.cpu(),
|
||||
im,
|
||||
file,
|
||||
dynamic,
|
||||
tf_nms=nms or agnostic_nms or tfjs,
|
||||
agnostic_nms=agnostic_nms or tfjs,
|
||||
topk_per_class=topk_per_class,
|
||||
topk_all=topk_all,
|
||||
iou_thres=iou_thres,
|
||||
conf_thres=conf_thres,
|
||||
keras=keras)
|
||||
if pb or tfjs: # pb prerequisite to tfjs
|
||||
f[6], _ = export_pb(s_model, file)
|
||||
if tflite or edgetpu:
|
||||
f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
|
||||
if edgetpu:
|
||||
f[8], _ = export_edgetpu(file)
|
||||
add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
|
||||
if tfjs:
|
||||
f[9], _ = export_tfjs(file)
|
||||
if paddle: # PaddlePaddle
|
||||
f[10], _ = export_paddle(model, im, file, metadata)
|
||||
|
||||
# Finish
|
||||
f = [str(x) for x in f if x] # filter out '' and None
|
||||
if any(f):
|
||||
cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type
|
||||
det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel)
|
||||
dir = Path('segment' if seg else 'classify' if cls else '')
|
||||
h = '--half' if half else '' # --half FP16 inference arg
|
||||
s = "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" if cls else \
|
||||
"# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" if seg else ''
|
||||
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
|
||||
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
|
||||
f"\nDetect: python {dir / 'predict.py'} --weights {f[-1]} {h}"
|
||||
f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}"
|
||||
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}"
|
||||
f"\nVisualize: https://netron.app")
|
||||
return f # return list of exported files/dirs
|
||||
|
||||
@ -1,13 +1,13 @@
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
import yaml
|
||||
|
||||
from ultralytics import yolo # noqa required for python usage
|
||||
from ultralytics.nn.tasks import ClassificationModel, DetectionModel, SegmentationModel, attempt_load_weights
|
||||
# from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
|
||||
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
|
||||
from ultralytics.yolo.utils import HELP_MSG, LOGGER
|
||||
from ultralytics.yolo.configs import get_config
|
||||
from ultralytics.yolo.engine.exporter import export_model
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, HELP_MSG, LOGGER
|
||||
from ultralytics.yolo.utils.checks import check_yaml
|
||||
from ultralytics.yolo.utils.configs import get_config
|
||||
from ultralytics.yolo.utils.files import yaml_load
|
||||
from ultralytics.yolo.utils.torch_utils import smart_inference_mode
|
||||
|
||||
@ -36,7 +36,7 @@ class YOLO:
|
||||
type (str): Type/version of models to use
|
||||
"""
|
||||
if init_key != YOLO.__init_key:
|
||||
raise Exception(HELP_MSG)
|
||||
raise SyntaxError(HELP_MSG)
|
||||
|
||||
self.type = type
|
||||
self.ModelClass = None
|
||||
@ -46,7 +46,8 @@ class YOLO:
|
||||
self.model = None
|
||||
self.trainer = None
|
||||
self.task = None
|
||||
self.ckpt = None
|
||||
self.ckpt = None # if loaded from *.pt
|
||||
self.cfg = None # if loaded from *.yaml
|
||||
self.overrides = {}
|
||||
self.init_disabled = False
|
||||
|
||||
@ -59,12 +60,12 @@ class YOLO:
|
||||
cfg (str): model configuration file
|
||||
"""
|
||||
cfg = check_yaml(cfg) # check YAML
|
||||
with open(cfg, encoding='ascii', errors='ignore') as f:
|
||||
cfg = yaml.safe_load(f) # model dict
|
||||
cfg_dict = yaml_load(cfg) # model dict
|
||||
obj = cls(init_key=cls.__init_key)
|
||||
obj.task = obj._guess_task_from_head(cfg["head"][-1][-2])
|
||||
obj.task = obj._guess_task_from_head(cfg_dict["head"][-1][-2])
|
||||
obj.ModelClass, obj.TrainerClass, obj.ValidatorClass, obj.PredictorClass = obj._guess_ops_from_task(obj.task)
|
||||
obj.model = obj.ModelClass(cfg) # initialize
|
||||
obj.model = obj.ModelClass(cfg_dict) # initialize
|
||||
obj.cfg = cfg
|
||||
|
||||
return obj
|
||||
|
||||
@ -116,13 +117,14 @@ class YOLO:
|
||||
LOGGER.info("model not initialized!")
|
||||
self.model.fuse()
|
||||
|
||||
@smart_inference_mode()
|
||||
def predict(self, source, **kwargs):
|
||||
"""
|
||||
Visualize prection.
|
||||
Visualize prediction.
|
||||
|
||||
Args:
|
||||
source (str): Accepts all source types accepted by yolo
|
||||
**kwargs : Any other args accepted by the predictors. Too see all args check 'configuration' section in the docs
|
||||
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in the docs
|
||||
"""
|
||||
overrides = self.overrides.copy()
|
||||
overrides.update(kwargs)
|
||||
@ -131,7 +133,7 @@ class YOLO:
|
||||
|
||||
# check size type
|
||||
sz = predictor.args.imgsz
|
||||
if type(sz) != int: # recieved listConfig
|
||||
if type(sz) != int: # received listConfig
|
||||
predictor.args.imgsz = [sz[0], sz[0]] if len(sz) == 1 else [sz[0], sz[1]] # expand
|
||||
else:
|
||||
predictor.args.imgsz = [sz, sz]
|
||||
@ -139,16 +141,17 @@ class YOLO:
|
||||
predictor.setup(model=self.model, source=source)
|
||||
predictor()
|
||||
|
||||
@smart_inference_mode()
|
||||
def val(self, data=None, **kwargs):
|
||||
"""
|
||||
Validate a model on a given dataset
|
||||
|
||||
Args:
|
||||
data (str): The dataset to validate on. Accepts all formats accepted by yolo
|
||||
kwargs: Any other args accepted by the validators. Too see all args check 'configuration' section in the docs
|
||||
kwargs: Any other args accepted by the validators. To see all args check 'configuration' section in the docs
|
||||
"""
|
||||
if not self.model:
|
||||
raise Exception("model not initialized!")
|
||||
raise ModuleNotFoundError("model not initialized!")
|
||||
|
||||
overrides = self.overrides.copy()
|
||||
overrides.update(kwargs)
|
||||
@ -160,6 +163,51 @@ class YOLO:
|
||||
validator = self.ValidatorClass(args=args)
|
||||
validator(model=self.model)
|
||||
|
||||
@smart_inference_mode()
|
||||
def export(self, format='', save_dir='', **kwargs):
|
||||
"""
|
||||
Export model.
|
||||
|
||||
Args:
|
||||
format (str): Export format
|
||||
**kwargs : Any other args accepted by the predictors. To see all args check 'configuration' section in the docs
|
||||
"""
|
||||
|
||||
overrides = self.overrides.copy()
|
||||
overrides.update(kwargs)
|
||||
args = get_config(config=DEFAULT_CONFIG, overrides=overrides)
|
||||
args.task = self.task
|
||||
args.format = format
|
||||
|
||||
file = self.ckpt or Path(Path(self.cfg).name)
|
||||
if save_dir:
|
||||
file = Path(save_dir) / file.name
|
||||
file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
export_model(
|
||||
model=self.model,
|
||||
file=file,
|
||||
data=args.data, # 'dataset.yaml path'
|
||||
imgsz=args.imgsz or (640, 640), # image (height, width)
|
||||
batch_size=1, # batch size
|
||||
device=args.device, # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
format=args.format, # include formats
|
||||
half=args.half or False, # FP16 half-precision export
|
||||
keras=args.keras or False, # use Keras
|
||||
optimize=args.optimize or False, # TorchScript: optimize for mobile
|
||||
int8=args.int8 or False, # CoreML/TF INT8 quantization
|
||||
dynamic=args.dynamic or False, # ONNX/TF/TensorRT: dynamic axes
|
||||
opset=args.opset or 17, # ONNX: opset version
|
||||
verbose=False, # TensorRT: verbose log
|
||||
workspace=args.workspace or 4, # TensorRT: workspace size (GB)
|
||||
nms=False, # TF: add NMS to model
|
||||
agnostic_nms=False, # TF: add agnostic NMS to model
|
||||
topk_per_class=100, # TF.js NMS: topk per class to keep
|
||||
topk_all=100, # TF.js NMS: topk for all classes to keep
|
||||
iou_thres=0.45, # TF.js NMS: IoU threshold
|
||||
conf_thres=0.25, # TF.js NMS: confidence threshold
|
||||
)
|
||||
|
||||
def train(self, **kwargs):
|
||||
"""
|
||||
Trains the model on given dataset.
|
||||
@ -178,7 +226,7 @@ class YOLO:
|
||||
overrides["task"] = self.task
|
||||
overrides["mode"] = "train"
|
||||
if not overrides.get("data"):
|
||||
raise AttributeError("dataset not provided! Please check if you have defined `data` in you configs")
|
||||
raise AttributeError("dataset not provided! Please define `data` in config.yaml or pass as an argument.")
|
||||
|
||||
self.trainer = self.TrainerClass(overrides=overrides)
|
||||
self.trainer.model = self.trainer.load_model(weights=self.ckpt,
|
||||
@ -189,11 +237,11 @@ class YOLO:
|
||||
|
||||
def resume(self, task=None, model=None):
|
||||
"""
|
||||
Resume a training task. Requires either `task` or `model`. `model` takes the higher precederence.
|
||||
Resume a training task. Requires either `task` or `model`. `model` takes the higher precedence.
|
||||
Args:
|
||||
task (str): The task type you want to resume. Automatically finds the last run to resume if `model` is not specified.
|
||||
model (str): The model checkpoint to resume from. If not found, the last run of the given task type is resumed.
|
||||
If `model` is speficied
|
||||
If `model` is specified
|
||||
"""
|
||||
if task:
|
||||
if task.lower() not in MODEL_MAP:
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# predictor engine by Ultralytics
|
||||
"""
|
||||
Run prection on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
||||
Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
||||
Usage - sources:
|
||||
$ yolo task=... mode=predict model=s.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
@ -13,15 +13,15 @@ Usage - sources:
|
||||
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
Usage - formats:
|
||||
$ yolo task=... mode=predict --weights yolov5s.pt # PyTorch
|
||||
yolov5s.torchscript # TorchScript
|
||||
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
$ yolo task=... mode=predict --weights yolov8n.pt # PyTorch
|
||||
yolov8n.torchscript # TorchScript
|
||||
yolov8n.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s_openvino_model # OpenVINO
|
||||
yolov5s.engine # TensorRT
|
||||
yolov5s.mlmodel # CoreML (macOS-only)
|
||||
yolov8n.engine # TensorRT
|
||||
yolov8n.mlmodel # CoreML (macOS-only)
|
||||
yolov5s_saved_model # TensorFlow SavedModel
|
||||
yolov5s.pb # TensorFlow GraphDef
|
||||
yolov5s.tflite # TensorFlow Lite
|
||||
yolov8n.pb # TensorFlow GraphDef
|
||||
yolov8n.tflite # TensorFlow Lite
|
||||
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s_paddle_model # PaddlePaddle
|
||||
"""
|
||||
@ -31,16 +31,14 @@ from pathlib import Path
|
||||
import cv2
|
||||
|
||||
from ultralytics.nn.autobackend import AutoBackend
|
||||
from ultralytics.yolo.configs import get_config
|
||||
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
|
||||
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS, check_dataset, check_dataset_yaml
|
||||
from ultralytics.yolo.utils import LOGGER, ROOT, colorstr, ops
|
||||
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, colorstr, ops
|
||||
from ultralytics.yolo.utils.checks import check_file, check_imshow
|
||||
from ultralytics.yolo.utils.configs import get_config
|
||||
from ultralytics.yolo.utils.files import increment_path
|
||||
from ultralytics.yolo.utils.torch_utils import check_imgsz, select_device, smart_inference_mode
|
||||
|
||||
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
|
||||
|
||||
|
||||
class BasePredictor:
|
||||
|
||||
|
||||
@ -23,16 +23,14 @@ from tqdm import tqdm
|
||||
import ultralytics.yolo.utils as utils
|
||||
import ultralytics.yolo.utils.callbacks as callbacks
|
||||
from ultralytics import __version__
|
||||
from ultralytics.yolo.configs import get_config
|
||||
from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
|
||||
from ultralytics.yolo.utils import LOGGER, RANK, ROOT, TQDM_BAR_FORMAT, colorstr
|
||||
from ultralytics.yolo.utils import DEFAULT_CONFIG, LOGGER, RANK, TQDM_BAR_FORMAT, colorstr
|
||||
from ultralytics.yolo.utils.checks import check_file, print_args
|
||||
from ultralytics.yolo.utils.configs import get_config
|
||||
from ultralytics.yolo.utils.dist import ddp_cleanup, generate_ddp_command
|
||||
from ultralytics.yolo.utils.files import get_latest_run, increment_path, save_yaml
|
||||
from ultralytics.yolo.utils.files import get_latest_run, increment_path, yaml_save
|
||||
from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds, one_cycle, strip_optimizer
|
||||
|
||||
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
|
||||
|
||||
|
||||
class BaseTrainer:
|
||||
|
||||
@ -53,8 +51,7 @@ class BaseTrainer:
|
||||
self.wdir = self.save_dir / 'weights' # weights dir
|
||||
if RANK in {-1, 0}:
|
||||
self.wdir.mkdir(parents=True, exist_ok=True) # make dir
|
||||
# Save run settings
|
||||
save_yaml(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True))
|
||||
yaml_save(self.save_dir / 'args.yaml', OmegaConf.to_container(self.args, resolve=True)) # save run args
|
||||
self.last, self.best = self.wdir / 'last.pt', self.wdir / 'best.pt' # checkpoint paths
|
||||
|
||||
self.batch_size = self.args.batch_size
|
||||
@ -452,8 +449,9 @@ class BaseTrainer:
|
||||
self.ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA
|
||||
self.ema.updates = ckpt['updates']
|
||||
if self.args.resume:
|
||||
assert start_epoch > 0, f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
|
||||
f"Start a new training without --resume, i.e. 'yolo task=... mode=train model={self.args.model}'"
|
||||
assert start_epoch > 0, \
|
||||
f'{self.args.model} training to {self.epochs} epochs is finished, nothing to resume.\n' \
|
||||
f"Start a new training without --resume, i.e. 'yolo task=... mode=train model={self.args.model}'"
|
||||
LOGGER.info(
|
||||
f'Resuming training from {self.args.model} from epoch {start_epoch} to {self.epochs} total epochs')
|
||||
if self.epochs < start_epoch:
|
||||
|
||||
@ -66,7 +66,7 @@ class BaseValidator:
|
||||
self.args.batch_size = model.batch_size
|
||||
else:
|
||||
self.device = model.device
|
||||
if not (pt or jit):
|
||||
if not pt and not jit:
|
||||
self.args.batch_size = 1 # export.py models default to batch-size 1
|
||||
self.logger.info(
|
||||
f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
|
||||
@ -75,8 +75,8 @@ class BaseValidator:
|
||||
data = check_dataset_yaml(self.args.data)
|
||||
else:
|
||||
data = check_dataset(self.args.data)
|
||||
self.dataloader = self.get_dataloader(data.get("val") or data.set("test"),
|
||||
self.args.batch_size) if not self.dataloader else self.dataloader
|
||||
self.dataloader = self.dataloader or \
|
||||
self.get_dataloader(data.get("val") or data.set("test"), self.args.batch_size)
|
||||
|
||||
model.eval()
|
||||
|
||||
@ -139,7 +139,7 @@ class BaseValidator:
|
||||
def postprocess(self, preds):
|
||||
return preds
|
||||
|
||||
def init_metrics(self):
|
||||
def init_metrics(self, model):
|
||||
pass
|
||||
|
||||
def update_metrics(self, preds, batch):
|
||||
|
||||
Reference in New Issue
Block a user