xdobro23/YOLOv8-16bit
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Make YOLO a module (#111)

Browse Source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Ayush Chaurasia
2022-12-29 00:08:37 +05:30
committed by GitHub
parent 0303ced8ab
commit 34829a6b29
4 changed files with 82 additions and 39 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
tests/test_model.py
View File

@ -3,45 +3,49 @@ import torch
from ultralytics import YOLO
def test_model_init():
model = YOLO.new("yolov8n.yaml")
model.info()
try:
YOLO()
except Exception:
print("Successfully caught constructor assert!")
raise Exception("constructor error didn't occur")
def test_model_forward():
model = YOLO()
model.new("yolov8n.yaml")
model = YOLO.new("yolov8n.yaml")
img = torch.rand(512 * 512 * 3).view(1, 3, 512, 512)
model.forward(img)
model(img)
def test_model_info():
model = YOLO()
model.new("yolov8n.yaml")
model = YOLO.new("yolov8n.yaml")
model.info()
model.load("best.pt")
model = model.load("best.pt")
model.info(verbose=True)
def test_model_fuse():
model = YOLO()
model.new("yolov8n.yaml")
model = YOLO.new("yolov8n.yaml")
model.fuse()
model.load("best.pt")
model.fuse()
def test_visualize_preds():
model = YOLO()
model.load("best.pt")
model = YOLO.load("best.pt")
model.predict(source="ultralytics/assets")
def test_val():
model = YOLO()
model.load("best.pt")
model = YOLO.load("best.pt")
model.val(data="coco128.yaml", imgsz=32)
def test_model_resume():
model = YOLO()
model.new("yolov8n.yaml")
model = YOLO.new("yolov8n.yaml")
model.train(epochs=1, imgsz=32, data="coco128.yaml")
try:
model.resume(task="detect")
@ -50,10 +54,9 @@ def test_model_resume():
def test_model_train_pretrained():
model = YOLO()
model.load("best.pt")
model = YOLO.load("best.pt")
model.train(data="coco128.yaml", epochs=1, imgsz=32)
model.new("yolov8n.yaml")
model = model.new("yolov8n.yaml")
model.train(data="coco128.yaml", epochs=1, imgsz=32)
img = torch.rand(512 * 512 * 3).view(1, 3, 512, 512)
model(img)