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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-05-23 13:15:44 +02:00
parent 68b9df4dd4
commit 001193b9c7
2 changed files with 8 additions and 12 deletions
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6
train.py
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@ -55,7 +55,6 @@ hyp = {'xy': 0.2, # xy loss gain
# 'momentum': 0.9025, # SGD momentum # 'momentum': 0.9025, # SGD momentum
# 'weight_decay': 0.0005417} # optimizer weight decay # 'weight_decay': 0.0005417} # optimizer weight decay
def train( def train(
cfg, cfg,
data_cfg, data_cfg,
@ -143,7 +142,6 @@ def train(
batch_size, batch_size,
augment=True, augment=True,
rect=False, rect=False,
cache=True,
multi_scale=multi_scale) multi_scale=multi_scale)
# Initialize distributed training # Initialize distributed training
@ -168,7 +166,7 @@ def train(
model, optimizer = amp.initialize(model, optimizer, opt_level='O1') model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Remove old results # Remove old results
for f in glob.glob('train_batch*.jpg') + glob.glob('test_batch*.jpg') + ['results.txt']: for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'):
os.remove(f) os.remove(f)
# Start training # Start training
@ -307,7 +305,7 @@ if __name__ == '__main__':
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch') parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing') parser.add_argument('--accumulate', type=int, default=1, help='accumulate gradient x batches before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path') parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
parser.add_argument('--data-cfg', type=str, default='data/coco_32img.data', help='coco.data file path') parser.add_argument('--data-cfg', type=str, default='data/coco.data', help='coco.data file path')
parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608') parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--resume', action='store_true', help='resume training flag') parser.add_argument('--resume', action='store_true', help='resume training flag')

12
utils/datasets.py
View File

@ -130,7 +130,7 @@ class LoadWebcam: # for inference
class LoadImagesAndLabels(Dataset): # for training/testing class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False, cache=False, def __init__(self, path, img_size=416, batch_size=16, augment=False, rect=True, image_weights=False,
multi_scale=False): multi_scale=False):
with open(path, 'r') as f: with open(path, 'r') as f:
img_files = f.read().splitlines() img_files = f.read().splitlines()
@ -190,11 +190,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32.).astype(np.int) * 32 self.batch_shapes = np.ceil(np.array(shapes) * img_size / 32.).astype(np.int) * 32
# Preload images
if cache and (n < 1001): # preload all images into memory if possible
self.imgs = [cv2.imread(self.img_files[i]) for i in tqdm(range(n), desc='Reading images')]
# Preload labels (required for weighted CE training) # Preload labels (required for weighted CE training)
self.imgs = [None] * n
self.labels = [np.zeros((0, 5))] * n self.labels = [np.zeros((0, 5))] * n
iter = tqdm(self.label_files, desc='Reading labels') if n > 1000 else self.label_files iter = tqdm(self.label_files, desc='Reading labels') if n > 1000 else self.label_files
for i, file in enumerate(iter): for i, file in enumerate(iter):
@ -227,10 +224,11 @@ class LoadImagesAndLabels(Dataset): # for training/testing
label_path = self.label_files[index] label_path = self.label_files[index]
# Load image # Load image
if hasattr(self, 'imgs'): # preloaded
img = self.imgs[index] img = self.imgs[index]
else: if img is None:
img = cv2.imread(img_path) # BGR img = cv2.imread(img_path) # BGR
if self.n < 1001:
self.imgs[index] = img # cache image into memory
assert img is not None, 'File Not Found ' + img_path assert img is not None, 'File Not Found ' + img_path
# Augment colorspace # Augment colorspace