mirror of
https://github.com/THU-MIG/yolov10.git
synced 2025-07-07 22:04:53 +08:00
Model coverage cleanup (#4585)
This commit is contained in:
Glenn Jocher
GitHub
parent
c635418a27
commit
deac7575b1
@ -40,14 +40,6 @@ def test_train(task, model, data):
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@pytest.mark.parametrize('task,model,data', TASK_ARGS)
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def test_val(task, model, data):
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# Download annotations to run pycocotools eval
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# from ultralytics.utils import SETTINGS, Path
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# from ultralytics.utils.downloads import download
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# url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
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# download(f'{url}instances_val2017.json', dir=Path(SETTINGS['datasets_dir']) / 'coco8/annotations')
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# download(f'{url}person_keypoints_val2017.json', dir=Path(SETTINGS['datasets_dir']) / 'coco8-pose/annotations')
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# Validate
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run(f'yolo val {task} model={WEIGHTS_DIR / model}.pt data={data} imgsz=32 save_txt save_json')
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@ -1,16 +1,18 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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import subprocess
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from pathlib import Path
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import pytest
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import torch
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from ultralytics import YOLO
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from ultralytics import YOLO, download
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from ultralytics.utils import ASSETS, SETTINGS
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CUDA_IS_AVAILABLE = torch.cuda.is_available()
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CUDA_DEVICE_COUNT = torch.cuda.device_count()
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DATASETS_DIR = Path(SETTINGS['datasets_dir'])
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WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
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MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt' # test spaces in path
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DATA = 'coco8.yaml'
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@ -37,13 +39,15 @@ def test_train_ddp():
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def test_utils_benchmarks():
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from ultralytics.utils.benchmarks import ProfileModels
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YOLO(MODEL).export(format='engine', imgsz=32, dynamic=True, batch=1) # pre-export engine model, auto-device
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# Pre-export a dynamic engine model to use dynamic inference
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YOLO(MODEL).export(format='engine', imgsz=32, dynamic=True, batch=1)
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ProfileModels([MODEL], imgsz=32, half=False, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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def test_predict_sam():
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from ultralytics import SAM
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from ultralytics.models.sam import Predictor as SAMPredictor
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# Load a model
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model = SAM(WEIGHTS_DIR / 'sam_b.pt')
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@ -60,10 +64,23 @@ def test_predict_sam():
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# Run inference with points prompt
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model(ASSETS / 'zidane.jpg', points=[900, 370], labels=[1], device=0)
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# Create SAMPredictor
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overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model='mobile_sam.pt')
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predictor = SAMPredictor(overrides=overrides)
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# Set image
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predictor.set_image('ultralytics/assets/zidane.jpg') # set with image file
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# predictor(bboxes=[439, 437, 524, 709])
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# predictor(points=[900, 370], labels=[1])
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# Reset image
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predictor.reset_image()
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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def test_model_tune():
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subprocess.run('pip install ray[tune]'.split(), check=True)
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with contextlib.suppress(RuntimeError): # RuntimeError may be caused by out-of-memory
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YOLO('yolov8n-cls.yaml').tune(data='imagenet10',
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grace_period=1,
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max_samples=1,
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@ -71,3 +88,39 @@ def test_model_tune():
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epochs=1,
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plots=False,
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device='cpu')
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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def test_pycocotools():
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from ultralytics.models.yolo.detect import DetectionValidator
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from ultralytics.models.yolo.pose import PoseValidator
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from ultralytics.models.yolo.segment import SegmentationValidator
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# Download annotations after each dataset downloads first
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url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
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validator = DetectionValidator(args={'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64})
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8/annotations')
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_ = validator.eval_json(validator.stats)
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validator = SegmentationValidator(args={
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'model': 'yolov8n-seg.pt',
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'data': 'coco8-seg.yaml',
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'save_json': True,
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'imgsz': 64})
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8-seg/annotations')
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_ = validator.eval_json(validator.stats)
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validator = PoseValidator(args={
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'model': 'yolov8n-pose.pt',
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'data': 'coco8-pose.yaml',
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'save_json': True,
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'imgsz': 64})
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validator()
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validator.is_coco = True
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download(f'{url}person_keypoints_val2017.json', dir=DATASETS_DIR / 'coco8-pose/annotations')
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_ = validator.eval_json(validator.stats)
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@ -1,7 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import contextlib
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import shutil
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from copy import copy
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from pathlib import Path
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@ -15,7 +14,7 @@ from torchvision.transforms import ToTensor
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from ultralytics import RTDETR, YOLO
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from ultralytics.cfg import TASK2DATA
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from ultralytics.data.build import load_inference_source
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from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS, WINDOWS
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from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, MACOS, ONLINE, ROOT, SETTINGS, WINDOWS
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from ultralytics.utils.downloads import download
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from ultralytics.utils.torch_utils import TORCH_1_9
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@ -50,14 +49,22 @@ def test_model_methods():
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_ = model.task_map
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def test_model_profile():
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# Test profile=True model argument
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from ultralytics.nn.tasks import DetectionModel
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model = DetectionModel() # build model
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im = torch.randn(1, 3, 64, 64) # requires min imgsz=64
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_ = model.predict(im, profile=True)
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def test_predict_txt():
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# Write a list of sources (file, dir, glob, recursive glob) to a txt file
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txt_file = TMP / 'sources.txt'
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with open(txt_file, 'w') as f:
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for x in [ASSETS / 'bus.jpg', ASSETS, ASSETS / '*', ASSETS / '**/*.jpg']:
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f.write(f'{x}\n')
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model = YOLO(MODEL)
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model(source=txt_file, imgsz=32)
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_ = YOLO(MODEL)(source=txt_file, imgsz=32)
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def test_predict_img():
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@ -143,8 +150,7 @@ def test_track_stream():
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def test_val():
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model = YOLO(MODEL)
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model.val(data='coco8.yaml', imgsz=32, save_hybrid=True)
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YOLO(MODEL).val(data='coco8.yaml', imgsz=32, save_hybrid=True)
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def test_train_scratch():
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@ -160,29 +166,25 @@ def test_train_pretrained():
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def test_export_torchscript():
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model = YOLO(MODEL)
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f = model.export(format='torchscript', optimize=True)
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f = YOLO(MODEL).export(format='torchscript', optimize=True)
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YOLO(f)(SOURCE) # exported model inference
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def test_export_onnx():
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model = YOLO(MODEL)
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f = model.export(format='onnx', dynamic=True)
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f = YOLO(MODEL).export(format='onnx', dynamic=True)
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YOLO(f)(SOURCE) # exported model inference
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def test_export_openvino():
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model = YOLO(MODEL)
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f = model.export(format='openvino')
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f = YOLO(MODEL).export(format='openvino')
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YOLO(f)(SOURCE) # exported model inference
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def test_export_coreml():
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if not WINDOWS: # RuntimeError: BlobWriter not loaded with coremltools 7.0 on windows
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model = YOLO(MODEL)
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model.export(format='coreml', nms=True)
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# if MACOS:
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# YOLO(f)(SOURCE) # model prediction only supported on macOS
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f = YOLO(MODEL).export(format='coreml', nms=True)
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if MACOS:
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YOLO(f)(SOURCE) # model prediction only supported on macOS
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def test_export_tflite(enabled=False):
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@ -204,13 +206,11 @@ def test_export_pb(enabled=False):
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def test_export_paddle(enabled=False):
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# Paddle protobuf requirements conflicting with onnx protobuf requirements
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if enabled:
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model = YOLO(MODEL)
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model.export(format='paddle')
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YOLO(MODEL).export(format='paddle')
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def test_export_ncnn():
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model = YOLO(MODEL)
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f = model.export(format='ncnn')
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f = YOLO(MODEL).export(format='ncnn')
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YOLO(f)(SOURCE) # exported model inference
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@ -218,14 +218,14 @@ def test_all_model_yamls():
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for m in (ROOT / 'cfg' / 'models').rglob('*.yaml'):
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if 'rtdetr' in m.name:
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if TORCH_1_9: # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
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RTDETR(m.name)(SOURCE, imgsz=640) # must be 640
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_ = RTDETR(m.name)(SOURCE, imgsz=640) # must be 640
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else:
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YOLO(m.name)
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def test_workflow():
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model = YOLO(MODEL)
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model.train(data='coco8.yaml', epochs=1, imgsz=32)
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model.train(data='coco8.yaml', epochs=1, imgsz=32, optimizer='SGD')
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model.val(imgsz=32)
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model.predict(SOURCE, imgsz=32)
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model.export(format='onnx') # export a model to ONNX format
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@ -254,8 +254,7 @@ def test_predict_callback_and_setup():
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def test_results():
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for m in 'yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt':
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model = YOLO(m)
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results = model([SOURCE, SOURCE], imgsz=160)
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results = YOLO(m)([SOURCE, SOURCE], imgsz=160)
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for r in results:
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r = r.cpu().numpy()
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r = r.to(device='cpu', dtype=torch.float32)
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@ -278,8 +277,7 @@ def test_data_utils():
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for task in 'detect', 'segment', 'pose':
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file = Path(TASK2DATA[task]).with_suffix('.zip') # i.e. coco8.zip
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download(f'https://github.com/ultralytics/hub/raw/main/example_datasets/{file}', unzip=False)
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shutil.move(str(file), TMP) # Python 3.8 requires string input to shutil.move()
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download(f'https://github.com/ultralytics/hub/raw/main/example_datasets/{file}', unzip=False, dir=TMP)
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stats = HUBDatasetStats(TMP / file, task=task)
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stats.get_json(save=True)
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stats.process_images()
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@ -294,8 +292,7 @@ def test_data_converter():
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from ultralytics.data.converter import coco80_to_coco91_class, convert_coco
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file = 'instances_val2017.json'
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download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}')
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shutil.move(file, TMP)
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download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}', dir=TMP)
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convert_coco(labels_dir=TMP, use_segments=True, use_keypoints=False, cls91to80=True)
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coco80_to_coco91_class()
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@ -339,6 +339,8 @@ class Model:
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overrides['batch'] = 1 # default to 1 if not modified
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if 'data' not in kwargs:
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overrides['data'] = None # default to None if not modified (avoid int8 calibration with coco.yaml)
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if 'verbose' not in kwargs:
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overrides['verbose'] = False
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args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)
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args.task = self.task
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return Exporter(overrides=args, _callbacks=self.callbacks)(model=self.model)
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@ -51,9 +51,7 @@ class FastSAMPrompt:
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n = len(result.masks.data)
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for i in range(n):
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mask = result.masks.data[i] == 1.0
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if torch.sum(mask) < filter:
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continue
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if torch.sum(mask) >= filter:
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annotation = {
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'id': i,
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'segmentation': mask.cpu().numpy(),
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@ -63,20 +61,6 @@ class FastSAMPrompt:
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annotations.append(annotation)
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return annotations
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@staticmethod
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def filter_masks(annotations): # filter the overlap mask
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annotations.sort(key=lambda x: x['area'], reverse=True)
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to_remove = set()
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for i in range(len(annotations)):
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a = annotations[i]
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for j in range(i + 1, len(annotations)):
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b = annotations[j]
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if i != j and j not in to_remove and b['area'] < a['area'] and \
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(a['segmentation'] & b['segmentation']).sum() / b['segmentation'].sum() > 0.8:
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to_remove.add(j)
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return [a for i, a in enumerate(annotations) if i not in to_remove], to_remove
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@staticmethod
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def _get_bbox_from_mask(mask):
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mask = mask.astype(np.uint8)
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@ -242,15 +226,12 @@ class FastSAMPrompt:
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cropped_images = []
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not_crop = []
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filter_id = []
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# annotations, _ = filter_masks(annotations)
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# filter_id = list(_)
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for _, mask in enumerate(annotations):
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if np.sum(mask['segmentation']) <= 100:
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filter_id.append(_)
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continue
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bbox = self._get_bbox_from_mask(mask['segmentation']) # mask 的 bbox
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cropped_boxes.append(self._segment_image(image, bbox)) # 保存裁剪的图片
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# cropped_boxes.append(segment_image(image,mask["segmentation"]))
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cropped_images.append(bbox) # 保存裁剪的图片的bbox
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return cropped_boxes, cropped_images, not_crop, filter_id, annotations
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@ -267,10 +267,11 @@ class PositionEmbeddingRandom(nn.Module):
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super().__init__()
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if scale is None or scale <= 0.0:
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scale = 1.0
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self.register_buffer(
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'positional_encoding_gaussian_matrix',
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scale * torch.randn((2, num_pos_feats)),
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)
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self.register_buffer('positional_encoding_gaussian_matrix', scale * torch.randn((2, num_pos_feats)))
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# Set non-deterministic for forward() error 'cumsum_cuda_kernel does not have a deterministic implementation'
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torch.use_deterministic_algorithms(False)
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torch.backends.cudnn.deterministic = False
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def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor:
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"""Positionally encode points that are normalized to [0,1]."""
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@ -20,12 +20,14 @@ class Sam(nn.Module):
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mask_threshold: float = 0.0
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image_format: str = 'RGB'
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def __init__(self,
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def __init__(
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self,
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image_encoder: ImageEncoderViT,
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prompt_encoder: PromptEncoder,
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mask_decoder: MaskDecoder,
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pixel_mean: List[float] = None,
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pixel_std: List[float] = None) -> None:
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pixel_mean: List[float] = (123.675, 116.28, 103.53),
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pixel_std: List[float] = (58.395, 57.12, 57.375)
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) -> None:
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"""
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SAM predicts object masks from an image and input prompts.
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@ -37,10 +39,6 @@ class Sam(nn.Module):
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pixel_mean (list(float)): Mean values for normalizing pixels in the input image.
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pixel_std (list(float)): Std values for normalizing pixels in the input image.
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"""
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if pixel_mean is None:
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pixel_mean = [123.675, 116.28, 103.53]
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if pixel_std is None:
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pixel_std = [58.395, 57.12, 57.375]
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super().__init__()
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self.image_encoder = image_encoder
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self.prompt_encoder = prompt_encoder
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@ -30,40 +30,6 @@ class Conv2d_BN(torch.nn.Sequential):
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torch.nn.init.constant_(bn.bias, 0)
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self.add_module('bn', bn)
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@torch.no_grad()
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def fuse(self):
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c, bn = self._modules.values()
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w = bn.weight / (bn.running_var + bn.eps) ** 0.5
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w = c.weight * w[:, None, None, None]
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b = bn.bias - bn.running_mean * bn.weight / (bn.running_var + bn.eps) ** 0.5
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m = torch.nn.Conv2d(w.size(1) * self.c.groups,
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w.size(0),
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w.shape[2:],
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stride=self.c.stride,
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padding=self.c.padding,
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dilation=self.c.dilation,
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groups=self.c.groups)
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m.weight.data.copy_(w)
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m.bias.data.copy_(b)
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return m
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# NOTE: This module and timm package is needed only for training.
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# from ultralytics.utils.checks import check_requirements
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# check_requirements('timm')
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# from timm.models.layers import DropPath as TimmDropPath
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# from timm.models.layers import trunc_normal_
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# class DropPath(TimmDropPath):
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#
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# def __init__(self, drop_prob=None):
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# super().__init__(drop_prob=drop_prob)
|
||||
# self.drop_prob = drop_prob
|
||||
#
|
||||
# def __repr__(self):
|
||||
# msg = super().__repr__()
|
||||
# msg += f'(drop_prob={self.drop_prob})'
|
||||
# return msg
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
|
||||
|
||||
@ -153,8 +153,7 @@ class Predictor(BasePredictor):
|
||||
bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes
|
||||
bboxes *= r
|
||||
if masks is not None:
|
||||
masks = torch.as_tensor(masks, dtype=torch.float32, device=self.device)
|
||||
masks = masks[:, None, :, :]
|
||||
masks = torch.as_tensor(masks, dtype=torch.float32, device=self.device).unsqueeze(1)
|
||||
|
||||
points = (points, labels) if points is not None else None
|
||||
# Embed prompts
|
||||
@ -257,9 +256,7 @@ class Predictor(BasePredictor):
|
||||
pred_bbox = batched_mask_to_box(pred_mask).float()
|
||||
keep_mask = ~is_box_near_crop_edge(pred_bbox, crop_region, [0, 0, iw, ih])
|
||||
if not torch.all(keep_mask):
|
||||
pred_bbox = pred_bbox[keep_mask]
|
||||
pred_mask = pred_mask[keep_mask]
|
||||
pred_score = pred_score[keep_mask]
|
||||
pred_bbox, pred_mask, pred_score = pred_bbox[keep_mask], pred_mask[keep_mask], pred_score[keep_mask]
|
||||
|
||||
crop_masks.append(pred_mask)
|
||||
crop_bboxes.append(pred_bbox)
|
||||
@ -288,9 +285,7 @@ class Predictor(BasePredictor):
|
||||
if len(crop_regions) > 1:
|
||||
scores = 1 / region_areas
|
||||
keep = torchvision.ops.nms(pred_bboxes, scores, crop_nms_thresh)
|
||||
pred_masks = pred_masks[keep]
|
||||
pred_bboxes = pred_bboxes[keep]
|
||||
pred_scores = pred_scores[keep]
|
||||
pred_masks, pred_bboxes, pred_scores = pred_masks[keep], pred_bboxes[keep], pred_scores[keep]
|
||||
|
||||
return pred_masks, pred_scores, pred_bboxes
|
||||
|
||||
|
||||
@ -82,8 +82,7 @@ class DETRLoss(nn.Module):
|
||||
loss[name_giou] = 1.0 - bbox_iou(pred_bboxes, gt_bboxes, xywh=True, GIoU=True)
|
||||
loss[name_giou] = loss[name_giou].sum() / len(gt_bboxes)
|
||||
loss[name_giou] = self.loss_gain['giou'] * loss[name_giou]
|
||||
loss = {k: v.squeeze() for k, v in loss.items()}
|
||||
return loss
|
||||
return {k: v.squeeze() for k, v in loss.items()}
|
||||
|
||||
def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
|
||||
# masks: [b, query, h, w], gt_mask: list[[n, H, W]]
|
||||
@ -105,7 +104,8 @@ class DETRLoss(nn.Module):
|
||||
loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
|
||||
return loss
|
||||
|
||||
def _dice_loss(self, inputs, targets, num_gts):
|
||||
@staticmethod
|
||||
def _dice_loss(inputs, targets, num_gts):
|
||||
inputs = F.sigmoid(inputs)
|
||||
inputs = inputs.flatten(1)
|
||||
targets = targets.flatten(1)
|
||||
@ -163,7 +163,8 @@ class DETRLoss(nn.Module):
|
||||
# loss[f'loss_dice_aux{postfix}'] = loss[4]
|
||||
return loss
|
||||
|
||||
def _get_index(self, match_indices):
|
||||
@staticmethod
|
||||
def _get_index(match_indices):
|
||||
batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(match_indices)])
|
||||
src_idx = torch.cat([src for (src, _) in match_indices])
|
||||
dst_idx = torch.cat([dst for (_, dst) in match_indices])
|
||||
@ -257,10 +258,10 @@ class RTDETRDetectionLoss(DETRLoss):
|
||||
dn_pos_idx, dn_num_group = dn_meta['dn_pos_idx'], dn_meta['dn_num_group']
|
||||
assert len(batch['gt_groups']) == len(dn_pos_idx)
|
||||
|
||||
# denoising match indices
|
||||
# Denoising match indices
|
||||
match_indices = self.get_dn_match_indices(dn_pos_idx, dn_num_group, batch['gt_groups'])
|
||||
|
||||
# compute denoising training loss
|
||||
# Compute denoising training loss
|
||||
dn_loss = super().forward(dn_bboxes, dn_scores, batch, postfix='_dn', match_indices=match_indices)
|
||||
total_loss.update(dn_loss)
|
||||
else:
|
||||
@ -270,7 +271,8 @@ class RTDETRDetectionLoss(DETRLoss):
|
||||
|
||||
@staticmethod
|
||||
def get_dn_match_indices(dn_pos_idx, dn_num_group, gt_groups):
|
||||
"""Get the match indices for denoising.
|
||||
"""
|
||||
Get the match indices for denoising.
|
||||
|
||||
Args:
|
||||
dn_pos_idx (List[torch.Tensor]): A list includes positive indices of denoising.
|
||||
@ -279,7 +281,6 @@ class RTDETRDetectionLoss(DETRLoss):
|
||||
|
||||
Returns:
|
||||
dn_match_indices (List(tuple)): Matched indices.
|
||||
|
||||
"""
|
||||
dn_match_indices = []
|
||||
idx_groups = torch.as_tensor([0, *gt_groups[:-1]]).cumsum_(0)
|
||||
|
||||
@ -51,8 +51,7 @@ class TransformerEncoderLayer(nn.Module):
|
||||
src = self.norm1(src)
|
||||
src2 = self.fc2(self.dropout(self.act(self.fc1(src))))
|
||||
src = src + self.dropout2(src2)
|
||||
src = self.norm2(src)
|
||||
return src
|
||||
return self.norm2(src)
|
||||
|
||||
def forward_pre(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
|
||||
src2 = self.norm1(src)
|
||||
@ -61,8 +60,7 @@ class TransformerEncoderLayer(nn.Module):
|
||||
src = src + self.dropout1(src2)
|
||||
src2 = self.norm2(src)
|
||||
src2 = self.fc2(self.dropout(self.act(self.fc1(src2))))
|
||||
src = src + self.dropout2(src2)
|
||||
return src
|
||||
return src + self.dropout2(src2)
|
||||
|
||||
def forward(self, src, src_mask=None, src_key_padding_mask=None, pos=None):
|
||||
"""Forward propagates the input through the encoder module."""
|
||||
@ -116,8 +114,7 @@ class TransformerLayer(nn.Module):
|
||||
def forward(self, x):
|
||||
"""Apply a transformer block to the input x and return the output."""
|
||||
x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
|
||||
x = self.fc2(self.fc1(x)) + x
|
||||
return x
|
||||
return self.fc2(self.fc1(x)) + x
|
||||
|
||||
|
||||
class TransformerBlock(nn.Module):
|
||||
@ -185,8 +182,7 @@ class LayerNorm2d(nn.Module):
|
||||
u = x.mean(1, keepdim=True)
|
||||
s = (x - u).pow(2).mean(1, keepdim=True)
|
||||
x = (x - u) / torch.sqrt(s + self.eps)
|
||||
x = self.weight[:, None, None] * x + self.bias[:, None, None]
|
||||
return x
|
||||
return self.weight[:, None, None] * x + self.bias[:, None, None]
|
||||
|
||||
|
||||
class MSDeformAttn(nn.Module):
|
||||
@ -271,8 +267,7 @@ class MSDeformAttn(nn.Module):
|
||||
else:
|
||||
raise ValueError(f'Last dim of reference_points must be 2 or 4, but got {num_points}.')
|
||||
output = multi_scale_deformable_attn_pytorch(value, value_shapes, sampling_locations, attention_weights)
|
||||
output = self.output_proj(output)
|
||||
return output
|
||||
return self.output_proj(output)
|
||||
|
||||
|
||||
class DeformableTransformerDecoderLayer(nn.Module):
|
||||
@ -309,8 +304,7 @@ class DeformableTransformerDecoderLayer(nn.Module):
|
||||
def forward_ffn(self, tgt):
|
||||
tgt2 = self.linear2(self.dropout3(self.act(self.linear1(tgt))))
|
||||
tgt = tgt + self.dropout4(tgt2)
|
||||
tgt = self.norm3(tgt)
|
||||
return tgt
|
||||
return self.norm3(tgt)
|
||||
|
||||
def forward(self, embed, refer_bbox, feats, shapes, padding_mask=None, attn_mask=None, query_pos=None):
|
||||
# self attention
|
||||
@ -327,9 +321,7 @@ class DeformableTransformerDecoderLayer(nn.Module):
|
||||
embed = self.norm2(embed)
|
||||
|
||||
# ffn
|
||||
embed = self.forward_ffn(embed)
|
||||
|
||||
return embed
|
||||
return self.forward_ffn(embed)
|
||||
|
||||
|
||||
class DeformableTransformerDecoder(nn.Module):
|
||||
|
||||
@ -322,31 +322,10 @@ class PoseModel(DetectionModel):
|
||||
class ClassificationModel(BaseModel):
|
||||
"""YOLOv8 classification model."""
|
||||
|
||||
def __init__(self,
|
||||
cfg='yolov8n-cls.yaml',
|
||||
model=None,
|
||||
ch=3,
|
||||
nc=None,
|
||||
cutoff=10,
|
||||
verbose=True): # YAML, model, channels, number of classes, cutoff index, verbose flag
|
||||
def __init__(self, cfg='yolov8n-cls.yaml', ch=3, nc=None, verbose=True):
|
||||
"""Init ClassificationModel with YAML, channels, number of classes, verbose flag."""
|
||||
super().__init__()
|
||||
self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg, ch, nc, verbose)
|
||||
|
||||
def _from_detection_model(self, model, nc=1000, cutoff=10):
|
||||
"""Create a YOLOv5 classification model from a YOLOv5 detection model."""
|
||||
from ultralytics.nn.autobackend import AutoBackend
|
||||
if isinstance(model, AutoBackend):
|
||||
model = model.model # unwrap DetectMultiBackend
|
||||
model.model = model.model[:cutoff] # backbone
|
||||
m = model.model[-1] # last layer
|
||||
ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module
|
||||
c = Classify(ch, nc) # Classify()
|
||||
c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type
|
||||
model.model[-1] = c # replace
|
||||
self.model = model.model
|
||||
self.stride = model.stride
|
||||
self.save = []
|
||||
self.nc = nc
|
||||
self._from_yaml(cfg, ch, nc, verbose)
|
||||
|
||||
def _from_yaml(self, cfg, ch, nc, verbose):
|
||||
"""Set YOLOv8 model configurations and define the model architecture."""
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user