def build(self,
depth=50,
learning_rate=1e-5,
ratios=[0.5, 1, 2],
scales=[2**0, 2**(1.0 / 3.0), 2**(2.0 / 3.0)]):
# Create the model
if depth == 18:
retinanet = ret18(num_classes=self.dataset_train.num_classes,
ratios=ratios,
scales=scales,
weights_dir=self.weights_dir_path,
pretrained=True)
elif depth == 34:
retinanet = ret34(num_classes=self.dataset_train.num_classes,
ratios=ratios,
scales=scales,
weights_dir=self.weights_dir_path,
pretrained=True)
elif depth == 50:
retinanet = ret50(num_classes=self.dataset_train.num_classes,
ratios=ratios,
scales=scales,
weights_dir=self.weights_dir_path,
pretrained=True)
elif depth == 101:
retinanet = ret101(num_classes=self.dataset_train.num_classes,
ratios=ratios,
scales=scales,
weights_dir=self.weights_dir_path,
pretrained=True)
elif depth == 152:
retinanet = ret152(num_classes=self.dataset_train.num_classes,
ratios=ratios,
scales=scales,
weights_dir=self.weights_dir_path,
pretrained=True)
else:
raise ValueError(
'Unsupported model depth, must be one of 18, 34, 50, 101, 152')
self.retinanet = retinanet.to(device=self.device)
self.retinanet.training = True
self.optimizer = optim.Adam(self.retinanet.parameters(),
lr=learning_rate)
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(self.optimizer,
patience=3,
verbose=True)
if self.checkpoint is not None:
self.retinanet.load_state_dict(self.checkpoint['model'])
self.optimizer.load_state_dict(self.checkpoint['optimizer'])
self.scheduler.load_state_dict(
self.checkpoint['scheduler']
) # TODO: test this, is it done right?
# TODO is it right to resume_read_trial optimizer and schedular like this???
self.ratios = ratios
self.scales = scales
self.depth = depth
def detect(checkpoint,
pred_on_path,
output_path,
threshold=0.5,
visualize=False,
red_label='sick'):
device = torch.device(
type='cuda') if torch.cuda.is_available() else torch.device(type='cpu')
if os.path.exists(output_path):
shutil.rmtree(output_path)
os.makedirs(output_path)
logger.info('inside ' + str(pred_on_path) + ': ' +
str(os.listdir(pred_on_path)))
dataset_val = PredDataset(pred_on_path=pred_on_path,
transform=transforms.Compose([
Normalizer(),
Resizer(min_side=608)
])) #TODO make resize an input param
logger.info('dataset prepared')
dataloader_val = DataLoader(dataset_val,
num_workers=0,
collate_fn=collater,
batch_sampler=None)
logger.info('data loader initialized')
labels = checkpoint['labels']
logger.info('labels are: ' + str(labels))
num_classes = len(labels)
configs = deepcopy(checkpoint['model_specs']['training_configs'])
configs.update(checkpoint['hp_values'])
logger.info('initializing object_detection model')
retinanet = ret50(
num_classes=num_classes,
scales=configs['anchor_scales'],
ratios=configs['anchor_ratios']) #TODO: make depth an input parameter
logger.info('loading weights')
retinanet.load_state_dict(checkpoint['model'])
retinanet = retinanet.to(device=device)
logger.info('model to device: ' + str(device))
retinanet.eval()
unnormalize = UnNormalizer()
def draw_caption(image, box, caption):
b = np.array(box).astype(int)
cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN,
1, (0, 0, 0), 2)
cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN,
1, (255, 255, 255), 1)
inference_times = []
for idx, data in enumerate(dataloader_val):
scale = data['scale'][0]
with torch.no_grad():
st = time.time()
scores, classification, transformed_anchors = retinanet(
data['img'].to(device=device).float())
elapsed_time = time.time() - st
print('Elapsed time: {}'.format(elapsed_time))
inference_times.append(elapsed_time)
idxs = np.where(scores.cpu() > threshold)[0]
if visualize:
img = np.array(255 *
unnormalize(data['img'][0, :, :, :])).copy()
img[img < 0] = 0
img[img > 255] = 255
img = np.transpose(img, (1, 2, 0))
img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB)
detections_list = []
for j in range(idxs.shape[0]):
bbox = transformed_anchors[idxs[j], :]
if visualize:
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
label_idx = int(classification[idxs[j]])
label_name = labels[label_idx]
score = scores[idxs[j]].item()
if visualize:
draw_caption(img, (x1, y1, x2, y2), label_name)
if red_label in label_name:
cv2.rectangle(img, (x1, y1), (x2, y2),
color=(0, 0, 255),
thickness=2)
else:
cv2.rectangle(img, (x1, y1), (x2, y2),
color=(0, 255, 0),
thickness=2)
print(label_name)
# un resize for eval against gt
bbox /= scale
bbox.round()
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
detections_list.append([
label_name,
str(score),
str(x1),
str(y1),
str(x2),
str(y2)
])
img_name = dataset_val.image_names[idx].split('/')[-1]
i_name = img_name.split('.')[0]
filename = i_name + '.txt'
filepathname = os.path.join(output_path, filename)
with open(filepathname, 'w', encoding='utf8') as f:
for single_det_list in detections_list:
for i, x in enumerate(single_det_list):
f.write(str(x))
f.write(' ')
f.write('\n')
if visualize:
save_to_path = os.path.join(output_path, img_name)
cv2.imwrite(save_to_path, img)
cv2.waitKey(0)
print('average inference time per image: ', np.mean(inference_times))
return output_path
def detect_single_image(checkpoint, image_path, visualize=False):
device = torch.device(
type='cuda') if torch.cuda.is_available() else torch.device(type='cpu')
configs = deepcopy(checkpoint['model_specs']['training_configs'])
configs = configs.update(checkpoint['hp_values'])
labels = checkpoint['labels']
num_classes = len(labels)
retinanet = ret50(
num_classes=num_classes,
scales=configs['anchor_scales'],
ratios=configs['anchor_ratios']) #TODO: make depth an input parameter
retinanet.load_state_dict(checkpoint['model'])
retinanet = retinanet.to(device=device)
retinanet.eval()
img = skimage.io.imread(image_path)
if len(img.shape) == 2:
img = skimage.color.gray2rgb(img)
img = img.astype(np.float32) / 255.0
transform = transforms.Compose([Normalizer(),
Resizer(min_side=608)
]) #TODO: make this dynamic
data = transform({'img': img, 'annot': np.zeros((0, 5))})
img = data['img']
img = img.unsqueeze(0)
img = img.permute(0, 3, 1, 2)
with torch.no_grad():
scores, classification, transformed_anchors = retinanet(
img.to(device=device).float())
idxs = np.where(scores.cpu() > 0.5)[0]
scale = data['scale']
detections_list = []
for j in range(idxs.shape[0]):
bbox = transformed_anchors[idxs[j], :]
label_idx = int(classification[idxs[j]])
label_name = labels[label_idx]
score = scores[idxs[j]].item()
# un resize for eval against gt
bbox /= scale
bbox.round()
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
detections_list.append(
[label_name,
str(score),
str(x1),
str(y1),
str(x2),
str(y2)])
img_name = image_path.split('/')[-1].split('.')[0]
filename = img_name + '.txt'
path = os.path.dirname(image_path)
filepathname = os.path.join(path, filename)
with open(filepathname, 'w', encoding='utf8') as f:
for single_det_list in detections_list:
for i, x in enumerate(single_det_list):
f.write(str(x))
f.write(' ')
f.write('\n')
if visualize:
unnormalize = UnNormalizer()
return filepathname