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Move tests outside package (#104)

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This commit is contained in:
Ayush Chaurasia
2022-12-27 22:16:07 +05:30
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parent 8028e2b1b8
commit 8b6466f731
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49
tests/assets/dummy_model.yaml Normal file
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# Ultralytics, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]

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tests/data/dataloader/hyp_test.yaml Normal file
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lr0: 0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937 # SGD momentum/Adam beta1
weight_decay: 0.0005 # optimizer weight decay 5e-4
warmup_epochs: 3.0 # warmup epochs (fractions ok)
warmup_momentum: 0.8 # warmup initial momentum
warmup_bias_lr: 0.1 # warmup initial bias lr
box: 0.05 # box loss gain
cls: 0.5 # cls loss gain
cls_pw: 1.0 # cls BCELoss positive_weight
obj: 1.0 # obj loss gain (scale with pixels)
obj_pw: 1.0 # obj BCELoss positive_weight
iou_t: 0.20 # IoU training threshold
anchor_t: 4.0 # anchor-multiple threshold
# anchors: 3 # anchors per output layer (0 to ignore)
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
degrees: 0.0 # image rotation (+/- deg)
translate: 0.1 # image translation (+/- fraction)
scale: 0.5 # image scale (+/- gain)
shear: 0.0 # image shear (+/- deg)
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # image flip up-down (probability)
fliplr: 0.5 # image flip left-right (probability)
mosaic: 1.0 # image mosaic (probability)
mixup: 0.0 # image mixup (probability)
copy_paste: 0.5 # segment copy-paste (probability)

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tests/data/dataloader/yolodetection.py Normal file
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import cv2
import hydra
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.utils import ROOT
from ultralytics.yolo.utils.plotting import plot_images
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
class Colors:
# Ultralytics color palette https://ultralytics.com/
def __init__(self):
# hex = matplotlib.colors.TABLEAU_COLORS.values()
hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
self.n = len(self.palette)
def __call__(self, i, bgr=False):
c = self.palette[int(i) % self.n]
return (c[2], c[1], c[0]) if bgr else c
@staticmethod
def hex2rgb(h): # rgb order (PIL)
return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
colors = Colors() # create instance for 'from utils.plots import colors'
def plot_one_box(x, img, color=None, label=None, line_thickness=None):
import random
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(
img,
label,
(c1[0], c1[1] - 2),
0,
tl / 3,
[225, 255, 255],
thickness=tf,
lineType=cv2.LINE_AA,
)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def test(cfg):
cfg.task = "detect"
cfg.mode = "train"
dataloader, _ = build_dataloader(
cfg=cfg,
batch_size=4,
img_path="/d/dataset/COCO/coco128-seg/images",
stride=32,
label_path=None,
mode=cfg.mode,
)
for d in dataloader:
images = d["img"]
cls = d["cls"].squeeze(-1)
bboxes = d["bboxes"]
paths = d["im_file"]
batch_idx = d["batch_idx"]
result = plot_images(images, batch_idx, cls, bboxes, paths=paths)
cv2.imshow("p", result)
if cv2.waitKey(0) == ord("q"):
break
if __name__ == "__main__":
test()
# test(augment=True, rect=False)
# test(augment=False, rect=True)
# test(augment=False, rect=False)

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tests/data/dataloader/yolopose.py Normal file
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import cv2
import numpy as np
from omegaconf import OmegaConf
from ultralytics.yolo.data import build_dataloader
class Colors:
# Ultralytics color palette https://ultralytics.com/
def __init__(self):
# hex = matplotlib.colors.TABLEAU_COLORS.values()
hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
self.n = len(self.palette)
def __call__(self, i, bgr=False):
c = self.palette[int(i) % self.n]
return (c[2], c[1], c[0]) if bgr else c
@staticmethod
def hex2rgb(h): # rgb order (PIL)
return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
colors = Colors() # create instance for 'from utils.plots import colors'
def plot_one_box(x, img, keypoints=None, color=None, label=None, line_thickness=None):
import random
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(
img,
label,
(c1[0], c1[1] - 2),
0,
tl / 3,
[225, 255, 255],
thickness=tf,
lineType=cv2.LINE_AA,
)
if keypoints is not None:
plot_keypoint(img, keypoints, color, tl)
def plot_keypoint(img, keypoints, color, tl):
num_l = len(keypoints)
# clors = [(255, 0, 0),(0, 255, 0),(0, 0, 255),(255, 255, 0),(0, 255, 255)]
# clors = [[random.randint(0, 255) for _ in range(3)] for _ in range(num_l)]
for i in range(num_l):
point_x = int(keypoints[i][0])
point_y = int(keypoints[i][1])
cv2.circle(img, (point_x, point_y), tl + 3, color, -1)
with open("ultralytics/tests/data/dataloader/hyp_test.yaml") as f:
hyp = OmegaConf.load(f)
def test(augment, rect):
dataloader, _ = build_dataloader(
img_path="/d/dataset/COCO/images/val2017",
imgsz=640,
label_path=None,
cache=False,
hyp=hyp,
augment=augment,
prefix="",
rect=rect,
batch_size=4,
stride=32,
pad=0.5,
use_segments=False,
use_keypoints=True,
)
for d in dataloader:
idx = 1 # show which image inside one batch
img = d["img"][idx].numpy()
img = np.ascontiguousarray(img.transpose(1, 2, 0))
ih, iw = img.shape[:2]
# print(img.shape)
bidx = d["batch_idx"]
cls = d["cls"][bidx == idx].numpy()
bboxes = d["bboxes"][bidx == idx].numpy()
bboxes[:, [0, 2]] *= iw
bboxes[:, [1, 3]] *= ih
keypoints = d["keypoints"][bidx == idx]
keypoints[..., 0] *= iw
keypoints[..., 1] *= ih
# print(keypoints, keypoints.shape)
# print(d["im_file"])
for i, b in enumerate(bboxes):
x, y, w, h = b
x1 = x - w / 2
x2 = x + w / 2
y1 = y - h / 2
y2 = y + h / 2
c = int(cls[i][0])
# print(x1, y1, x2, y2)
plot_one_box([int(x1), int(y1), int(x2), int(y2)],
img,
keypoints=keypoints[i],
label=f"{c}",
color=colors(c))
cv2.imshow("p", img)
if cv2.waitKey(0) == ord("q"):
break
if __name__ == "__main__":
test(augment=True, rect=False)
test(augment=False, rect=True)
test(augment=False, rect=False)

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tests/data/dataloader/yolosegment.py Normal file
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import cv2
import hydra
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.utils import ROOT
from ultralytics.yolo.utils.plotting import plot_images
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
class Colors:
# Ultralytics color palette https://ultralytics.com/
def __init__(self):
# hex = matplotlib.colors.TABLEAU_COLORS.values()
hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
self.n = len(self.palette)
def __call__(self, i, bgr=False):
c = self.palette[int(i) % self.n]
return (c[2], c[1], c[0]) if bgr else c
@staticmethod
def hex2rgb(h): # rgb order (PIL)
return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
colors = Colors() # create instance for 'from utils.plots import colors'
def plot_one_box(x, img, color=None, label=None, line_thickness=None):
import random
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
cv2.putText(
img,
label,
(c1[0], c1[1] - 2),
0,
tl / 3,
[225, 255, 255],
thickness=tf,
lineType=cv2.LINE_AA,
)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def test(cfg):
cfg.task = "segment"
cfg.mode = "train"
dataloader, _ = build_dataloader(
cfg=cfg,
batch_size=4,
img_path="/d/dataset/COCO/coco128-seg/images",
stride=32,
label_path=None,
mode=cfg.mode,
)
for d in dataloader:
images = d["img"]
masks = d["masks"]
cls = d["cls"].squeeze(-1)
bboxes = d["bboxes"]
paths = d["im_file"]
batch_idx = d["batch_idx"]
result = plot_images(images, batch_idx, cls, bboxes, masks, paths=paths)
cv2.imshow("p", result)
if cv2.waitKey(0) == ord("q"):
break
if __name__ == "__main__":
test()
# test(augment=True, rect=False)
# test(augment=False, rect=True)
# test(augment=False, rect=False)

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tests/functional/test_loaders.py Normal file
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from ultralytics.nn.tasks import DetectionModel
from ultralytics.yolo.utils.checks import check_yaml
def test_model_parser():
cfg = check_yaml("../assets/dummy_model.yaml") # check YAML
# Create model
model = DetectionModel(cfg)
print(model)
'''
# Options
if opt.line_profile: # profile layer by layer
model(im, profile=True)
elif opt.profile: # profile forward-backward
results = profile(input=im, ops=[model], n=3)
elif opt.test: # test all models
for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
try:
_ = Model(cfg)
except Exception as e:
print(f'Error in {cfg}: {e}')
else: # report fused model summary
model.fuse()
'''
if __name__ == "__main__":
test_model_parser()

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tests/test_model.py Normal file
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import torch
from ultralytics.yolo import YOLO
def test_model_forward():
model = YOLO()
model.new("yolov8n-seg.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.info()
model.load("balloon-detect.pt")
model.info(verbose=True)
def test_model_fuse():
model = YOLO()
model.new("yolov8n.yaml")
model.fuse()
model.load("balloon-detect.pt")
model.fuse()
def test_visualize_preds():
model = YOLO()
model.load("balloon-segment.pt")
model.predict(source="ultralytics/assets")
def test_val():
model = YOLO()
model.load("balloon-segment.pt")
model.val(data="coco128-seg.yaml", imgsz=32)
def test_model_resume():
model = YOLO()
model.new("yolov8n-seg.yaml")
model.train(epochs=1, imgsz=32, data="coco128-seg.yaml")
try:
model.resume(task="segment")
except AssertionError:
print("Successfully caught resume assert!")
def test_model_train_pretrained():
model = YOLO()
model.load("balloon-detect.pt")
model.train(data="coco128.yaml", epochs=1, imgsz=32)
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)
def test():
test_model_forward()
test_model_info()
test_model_fuse()
test_visualize_preds()
test_val()
test_model_resume()
test_model_train_pretrained()
if __name__ == "__main__":
test()