Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-12-04 23:02:32 -08:00
parent e27b124828
commit 63c2736c12
3 changed files with 45 additions and 31 deletions
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10
test.py
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@ -17,7 +17,8 @@ def test(cfg,
conf_thres=0.001,
nms_thres=0.5,
save_json=False,
model=None):
model=None,
dataloader=None):
# Initialize/load model and set device
if model is None:
device = torch_utils.select_device(opt.device, batch_size=batch_size)
@ -46,11 +47,12 @@ def test(cfg,
names = load_classes(data['names']) # class names
# Dataloader
dataset = LoadImagesAndLabels(test_path, img_size, batch_size)
if dataloader is None:
dataset = LoadImagesAndLabels(test_path, img_size, batch_size, rect=True)
batch_size = min(batch_size, len(dataset))
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 16]),
num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
pin_memory=True,
collate_fn=dataset.collate_fn)
@ -167,7 +169,7 @@ def test(cfg,
# Save JSON
if save_json and map and len(jdict):
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataset.img_files]
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
with open('results.json', 'w') as file:
json.dump(jdict, file)

26
train.py
View File

@ -72,6 +72,7 @@ def train():
# Configure run
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
test_path = data_dict['valid']
nc = int(data_dict['classes']) # number of classes
# Remove previous results
@ -187,19 +188,17 @@ def train():
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
dataset = LoadImagesAndLabels(train_path, img_size, batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
image_weights=opt.img_weights,
cache_labels=True if epochs > 10 else False,
cache_images=False if opt.prebias else opt.cache_images)
cache_labels=epochs > 10,
cache_images=opt.cache_images and not opt.prebias)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 16]) # number of workers
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
print('Using %g dataloader workers' % nw)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
@ -208,13 +207,23 @@ def train():
pin_memory=True,
collate_fn=dataset.collate_fn)
# Test Dataloader
if not opt.prebias:
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size, batch_size, hyp=hyp,
cache_labels=True,
cache_images=opt.cache_images),
batch_size=batch_size,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
# Start training
nb = len(dataloader)
model.nc = nc # attach number of classes to model
model.arc = opt.arc # attach yolo architecture
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
torch_utils.model_info(model, report='summary') # 'full' or 'summary'
nb = len(dataloader)
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
@ -321,7 +330,8 @@ def train():
img_size=opt.img_size,
model=model,
conf_thres=0.001 if final_epoch and epoch > 0 else 0.1, # 0.1 for speed
save_json=final_epoch and epoch > 0 and 'coco.data' in data)
save_json=final_epoch and epoch > 0 and 'coco.data' in data,
dataloader=testloader)
# Write epoch results
with open(results_file, 'a') as f:

30
utils/datasets.py
View File

@ -255,7 +255,7 @@ class LoadStreams: # multiple IP or RTSP cameras
class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=True, image_weights=False,
def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
cache_labels=False, cache_images=False):
path = str(Path(path)) # os-agnostic
with open(path, 'r') as f:
@ -319,7 +319,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
self.labels = [np.zeros((0, 5))] * n
extract_bounding_boxes = False
create_datasubset = False
pbar = tqdm(self.label_files, desc='Reading labels')
pbar = tqdm(self.label_files, desc='Caching labels')
nm, nf, ne, ns = 0, 0, 0, 0 # number missing, number found, number empty, number datasubset
for i, file in enumerate(pbar):
try:
@ -370,13 +370,17 @@ class LoadImagesAndLabels(Dataset): # for training/testing
ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty
# os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove
pbar.desc = 'Reading labels (%g found, %g missing, %g empty for %g images)' % (nf, nm, ne, n)
pbar.desc = 'Caching labels (%g found, %g missing, %g empty for %g images)' % (nf, nm, ne, n)
assert nf > 0, 'No labels found. Recommend correcting image and label paths.'
# Cache images into memory for faster training (~5GB)
if cache_images and augment: # if training
for i in tqdm(range(min(len(self.img_files), 10000)), desc='Reading images'): # max 10k images
# Cache images into memory for faster training (WARNING: Large datasets may exceed system RAM)
if cache_images: # if training
gb = 0 # Gigabytes of cached images
pbar = tqdm(range(len(self.img_files)), desc='Caching images')
for i in pbar: # max 10k images
self.imgs[i] = load_image(self, i)
gb += self.imgs[i].nbytes
pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
# Detect corrupted images https://medium.com/joelthchao/programmatically-detect-corrupted-image-8c1b2006c3d3
detect_corrupted_images = False
@ -503,10 +507,10 @@ def load_image(self, index):
img_path = self.img_files[index]
img = cv2.imread(img_path) # BGR
assert img is not None, 'Image Not Found ' + img_path
r = self.img_size / max(img.shape) # size ratio
if self.augment: # if training (NOT testing), downsize to inference shape
r = self.img_size / max(img.shape) # resize image to img_size
if (r < 1) or ((r > 1) and self.augment): # always resize down, only resize up if training with augmentation
h, w = img.shape[:2]
img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest
return cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_LINEAR) # _LINEAR fastest
return img
@ -569,13 +573,11 @@ def load_mosaic(self, index):
# Concat/clip labels
if len(labels4):
labels4 = np.concatenate(labels4, 0)
np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use before random_affine
# np.clip(labels4[:, 1:], s / 2, 1.5 * s, out=labels4[:, 1:])
# labels4[:, 1:] -= s / 2
# img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)]
# np.clip(labels4[:, 1:] - s / 2, 0, s, out=labels4[:, 1:]) # use with center crop
np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_affine
# Augment
# img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)] # center crop (WARNING, requires box pruning)
img4, labels4 = random_affine(img4, labels4,
degrees=self.hyp['degrees'],
translate=self.hyp['translate'],