def get_data(dataset,
dataroot,
augment,
resize=608,
split=0.15,
split_idx=0,
multinode=False,
target_lb=-1):
transform_train = transforms.Compose(
[Normalizer(), Augmenter(),
Resizer(min_side=resize)])
transform_test = transforms.Compose(
[Normalizer(), Resizer(min_side=resize)])
if isinstance(C.get().aug, list):
logger.debug('augmentation provided.')
policies = policy_decoder(augment, augment['num_policy'],
augment['num_op'])
transform_train.transforms.insert(
0, Augmentation(policies, detection=True))
if dataset == 'coco':
total_trainset = CocoDataset(dataroot,
set_name='train',
transform=transform_train)
testset = CocoDataset(dataroot,
set_name='val',
transform=transform_test)
return total_trainset, testset
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 = combine_values(checkpoint['model_specs']['training_configs'], checkpoint['hp_values'])
labels = checkpoint['labels']
num_classes = len(labels)
retinanet = model.resnet152(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
def preprocess(self, dataset='csv', csv_train=None, csv_val=None, csv_classes=None, coco_path=False,
train_set_name='train2017', val_set_name='val2017', resize=608):
self.dataset = dataset
if self.dataset == 'coco':
if coco_path is None:
raise ValueError('Must provide --home_path when training on COCO,')
self.dataset_train = CocoDataset(self.home_path, set_name=train_set_name,
transform=transforms.Compose(
[Normalizer(), Augmenter(), Resizer(min_side=resize)]))
self.dataset_val = CocoDataset(self.home_path, set_name=val_set_name,
transform=transforms.Compose([Normalizer(), Resizer(min_side=resize)]))
elif self.dataset == 'csv':
if csv_train is None:
raise ValueError('Must provide --csv_train when training on COCO,')
if csv_classes is None:
raise ValueError('Must provide --csv_classes when training on COCO,')
self.dataset_train = CSVDataset(train_file=csv_train, class_list=csv_classes,
transform=transforms.Compose(
[Normalizer(), Augmenter(), Resizer(min_side=resize)])
)
if csv_val is None:
self.dataset_val = None
print('No validation annotations provided.')
else:
self.dataset_val = CSVDataset(train_file=csv_val, class_list=csv_classes,
transform=transforms.Compose([Normalizer(), Resizer(min_side=resize)]))
else:
raise ValueError('Dataset type not understood (must be csv or coco), exiting.')
sampler = AspectRatioBasedSampler(self.dataset_train, batch_size=2, drop_last=False)
self.dataloader_train = DataLoader(self.dataset_train, num_workers=0, collate_fn=collater,
batch_sampler=sampler)
if self.dataset_val is not None:
sampler_val = AspectRatioBasedSampler(self.dataset_val, batch_size=1, drop_last=False)
self.dataloader_val = DataLoader(self.dataset_val, num_workers=3, collate_fn=collater,
batch_sampler=sampler_val)
print('Num training images: {}'.format(len(self.dataset_train)))
if len(self.dataset_val) == 0:
raise Exception('num val images is 0!')
print('Num val images: {}'.format(len(self.dataset_val)))
def get_dataloaders(dataset,
batch,
dataroot,
resize=608,
split=0.15,
split_idx=0,
multinode=False,
target_lb=-1):
multilabel = False
detection = False
if 'coco' in dataset:
transform_train = transforms.Compose(
[Normalizer(), Augmenter(),
Resizer(min_side=resize)])
transform_test = transforms.Compose(
[Normalizer(), Resizer(min_side=resize)])
multilabel = True
detection = True
elif 'cifar' in dataset or 'svhn' in dataset:
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(_CIFAR_MEAN, _CIFAR_STD),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(_CIFAR_MEAN, _CIFAR_STD),
])
elif 'imagenet' in dataset:
input_size = 224
sized_size = 256
if 'efficientnet' in C.get()['model']:
input_size = EfficientNet.get_image_size(C.get()['model'])
sized_size = input_size + 32 # TODO
# sized_size = int(round(input_size / 224. * 256))
# sized_size = input_size
logger.info('size changed to %d/%d.' % (input_size, sized_size))
transform_train = transforms.Compose([
EfficientNetRandomCrop(input_size),
transforms.Resize((input_size, input_size),
interpolation=Image.BICUBIC),
# transforms.RandomResizedCrop(input_size, scale=(0.1, 1.0), interpolation=Image.BICUBIC),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(
brightness=0.4,
contrast=0.4,
saturation=0.4,
),
transforms.ToTensor(),
Lighting(0.1, _IMAGENET_PCA['eigval'], _IMAGENET_PCA['eigvec']),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
EfficientNetCenterCrop(input_size),
transforms.Resize((input_size, input_size),
interpolation=Image.BICUBIC),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
else:
raise ValueError('dataset=%s' % dataset)
total_aug = augs = None
if isinstance(C.get().aug, list):
logger.debug('augmentation provided.')
transform_train.transforms.insert(
0, Augmentation(C.get().aug, detection=detection))
else:
logger.debug('augmentation: %s' % C.get().aug)
if C.get().aug == 'fa_reduced_cifar10':
transform_train.transforms.insert(
0, Augmentation(fa_reduced_cifar10()))
elif C.get().aug == 'fa_reduced_imagenet':
transform_train.transforms.insert(
0, Augmentation(fa_resnet50_rimagenet()))
elif C.get().aug == 'fa_reduced_svhn':
transform_train.transforms.insert(0,
Augmentation(fa_reduced_svhn()))
elif C.get().aug == 'arsaug':
transform_train.transforms.insert(0, Augmentation(arsaug_policy()))
elif C.get().aug == 'autoaug_cifar10':
transform_train.transforms.insert(
0, Augmentation(autoaug_paper_cifar10()))
elif C.get().aug == 'autoaug_extend':
transform_train.transforms.insert(0,
Augmentation(autoaug_policy()))
elif C.get().aug in ['default']:
pass
else:
raise ValueError('not found augmentations. %s' % C.get().aug)
if C.get()['cutout'] > 0:
transform_train.transforms.append(CutoutDefault(C.get()['cutout']))
if dataset == 'coco':
total_trainset = CocoDataset(dataroot,
set_name='train2017',
transform=transform_train)
testset = CocoDataset(dataroot,
set_name='val2017',
transform=transform_test)
elif dataset == 'cifar10':
total_trainset = torchvision.datasets.CIFAR10(
root=dataroot,
train=True,
download=True,
transform=transform_train)
testset = torchvision.datasets.CIFAR10(root=dataroot,
train=False,
download=True,
transform=transform_test)
elif dataset == 'reduced_cifar10':
total_trainset = torchvision.datasets.CIFAR10(
root=dataroot,
train=True,
download=True,
transform=transform_train)
sss = StratifiedShuffleSplit(n_splits=1,
test_size=46000,
random_state=0) # 4000 trainset
sss = sss.split(list(range(len(total_trainset))),
total_trainset.targets)
train_idx, valid_idx = next(sss)
targets = [total_trainset.targets[idx] for idx in train_idx]
total_trainset = Subset(total_trainset, train_idx)
total_trainset.targets = targets
testset = torchvision.datasets.CIFAR10(root=dataroot,
train=False,
download=True,
transform=transform_test)
elif dataset == 'cifar100':
total_trainset = torchvision.datasets.CIFAR100(
root=dataroot,
train=True,
download=True,
transform=transform_train)
testset = torchvision.datasets.CIFAR100(root=dataroot,
train=False,
download=True,
transform=transform_test)
elif dataset == 'svhn':
trainset = torchvision.datasets.SVHN(root=dataroot,
split='train',
download=True,
transform=transform_train)
extraset = torchvision.datasets.SVHN(root=dataroot,
split='extra',
download=True,
transform=transform_train)
total_trainset = ConcatDataset([trainset, extraset])
testset = torchvision.datasets.SVHN(root=dataroot,
split='test',
download=True,
transform=transform_test)
elif dataset == 'reduced_svhn':
total_trainset = torchvision.datasets.SVHN(root=dataroot,
split='train',
download=True,
transform=transform_train)
sss = StratifiedShuffleSplit(n_splits=1,
test_size=73257 - 1000,
random_state=0) # 1000 trainset
sss = sss.split(list(range(len(total_trainset))),
total_trainset.targets)
train_idx, valid_idx = next(sss)
targets = [total_trainset.targets[idx] for idx in train_idx]
total_trainset = Subset(total_trainset, train_idx)
total_trainset.targets = targets
testset = torchvision.datasets.SVHN(root=dataroot,
split='test',
download=True,
transform=transform_test)
elif dataset == 'imagenet':
total_trainset = ImageNet(root=os.path.join(dataroot,
'imagenet-pytorch'),
transform=transform_train,
download=True)
testset = ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'),
split='val',
transform=transform_test)
# compatibility
total_trainset.targets = [lb for _, lb in total_trainset.samples]
elif dataset == 'reduced_imagenet':
# randomly chosen indices
# idx120 = sorted(random.sample(list(range(1000)), k=120))
idx120 = [
16, 23, 52, 57, 76, 93, 95, 96, 99, 121, 122, 128, 148, 172, 181,
189, 202, 210, 232, 238, 257, 258, 259, 277, 283, 289, 295, 304,
307, 318, 322, 331, 337, 338, 345, 350, 361, 375, 376, 381, 388,
399, 401, 408, 424, 431, 432, 440, 447, 462, 464, 472, 483, 497,
506, 512, 530, 541, 553, 554, 557, 564, 570, 584, 612, 614, 619,
626, 631, 632, 650, 657, 658, 660, 674, 675, 680, 682, 691, 695,
699, 711, 734, 736, 741, 754, 757, 764, 769, 770, 780, 781, 787,
797, 799, 811, 822, 829, 830, 835, 837, 842, 843, 845, 873, 883,
897, 900, 902, 905, 913, 920, 925, 937, 938, 940, 941, 944, 949,
959
]
total_trainset = ImageNet(root=os.path.join(dataroot,
'imagenet-pytorch'),
transform=transform_train)
testset = ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'),
split='val',
transform=transform_test)
# compatibility
total_trainset.targets = [lb for _, lb in total_trainset.samples]
sss = StratifiedShuffleSplit(n_splits=1,
test_size=len(total_trainset) - 50000,
random_state=0) # 4000 trainset
sss = sss.split(list(range(len(total_trainset))),
total_trainset.targets)
train_idx, valid_idx = next(sss)
# filter out
train_idx = list(
filter(lambda x: total_trainset.labels[x] in idx120, train_idx))
valid_idx = list(
filter(lambda x: total_trainset.labels[x] in idx120, valid_idx))
test_idx = list(
filter(lambda x: testset.samples[x][1] in idx120,
range(len(testset))))
targets = [
idx120.index(total_trainset.targets[idx]) for idx in train_idx
]
for idx in range(len(total_trainset.samples)):
if total_trainset.samples[idx][1] not in idx120:
continue
total_trainset.samples[idx] = (total_trainset.samples[idx][0],
idx120.index(
total_trainset.samples[idx][1]))
total_trainset = Subset(total_trainset, train_idx)
total_trainset.targets = targets
for idx in range(len(testset.samples)):
if testset.samples[idx][1] not in idx120:
continue
testset.samples[idx] = (testset.samples[idx][0],
idx120.index(testset.samples[idx][1]))
testset = Subset(testset, test_idx)
print('reduced_imagenet train=', len(total_trainset))
else:
raise ValueError('invalid dataset name=%s' % dataset)
if total_aug is not None and augs is not None:
total_trainset.set_preaug(augs, total_aug)
print('set_preaug-')
train_sampler = None
if split > 0.0:
if multilabel:
# not sure how important stratified is, especially for val,
# might want to test with and without and add it for multilabel in the future
sss = ShuffleSplit(n_splits=5, test_size=split, random_state=0)
sss = sss.split(list(range(len(total_trainset))))
for _ in range(split_idx + 1):
train_idx, valid_idx = next(sss)
else:
sss = StratifiedShuffleSplit(n_splits=5,
test_size=split,
random_state=0)
sss = sss.split(list(range(len(total_trainset))),
total_trainset.targets)
for _ in range(split_idx + 1):
train_idx, valid_idx = next(sss)
if target_lb >= 0:
train_idx = [
i for i in train_idx if total_trainset.targets[i] == target_lb
]
valid_idx = [
i for i in valid_idx if total_trainset.targets[i] == target_lb
]
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetSampler(valid_idx)
if multinode:
train_sampler = torch.utils.data.distributed.DistributedSampler(
Subset(total_trainset, train_idx),
num_replicas=dist.get_world_size(),
rank=dist.get_rank())
else:
valid_sampler = SubsetSampler([])
if multinode:
train_sampler = torch.utils.data.distributed.DistributedSampler(
total_trainset,
num_replicas=dist.get_world_size(),
rank=dist.get_rank())
logger.info(
f'----- dataset with DistributedSampler {dist.get_rank()}/{dist.get_world_size()}'
)
trainloader = DataLoader(total_trainset,
batch_size=batch,
shuffle=True if train_sampler is None else False,
num_workers=8,
pin_memory=True,
sampler=train_sampler,
drop_last=True,
collate_fn=collater)
validloader = DataLoader(total_trainset,
batch_size=batch,
shuffle=False,
num_workers=4,
pin_memory=True,
sampler=valid_sampler,
drop_last=False,
collate_fn=collater)
testloader = DataLoader(testset,
batch_size=batch,
shuffle=False,
num_workers=8,
pin_memory=True,
drop_last=False,
collate_fn=collater)
return train_sampler, trainloader, validloader, testloader
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(checkpoint, output_dir, home_path=None, visualize=False):
device = torch.device(type='cuda') if torch.cuda.is_available() else torch.device(type='cpu')
if home_path is None:
home_path = checkpoint['model_specs']['data']['home_path']
if os.getcwd().split('/')[-1] == 'ObjectDetNet':
home_path = os.path.join('..', home_path)
# must have a file to predict on called "predict_on"
pred_on_path = os.path.join(home_path, 'predict_on')
#create output path
output_path = os.path.join(home_path, 'predictions', output_dir)
try:
os.makedirs(output_path)
except FileExistsError:
if output_dir != 'check0':
raise Exception('there are already predictions for model: ' + output_dir)
else:
logger.info('there was already a check0 in place, erasing and predicting again from scratch')
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 = combine_values(checkpoint['model_specs']['training_configs'], checkpoint['hp_values'])
logger.info('initializing retinanet model')
if checkpoint['model_specs']['training_configs']['depth'] == 50:
retinanet = model.resnet50(num_classes=num_classes, scales=configs['anchor_scales'], ratios=configs['anchor_ratios']) #TODO: make depth an input parameter
elif checkpoint['model_specs']['training_configs']['depth'] == 152:
retinanet = model.resnet152(num_classes=num_classes, scales=configs['anchor_scales'], ratios=configs['anchor_ratios'])
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)
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())
print('Elapsed time: {}'.format(time.time() - st))
idxs = np.where(scores.cpu() > 0.5)[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)
cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), 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)
return output_path
def detect(home_path, checkpoint_path):
class_names_path = os.path.join(home_path, "d.names")
# compute number of classes
num_classes = sum(1 for line in open(class_names_path))
# must have a file to predict on called "predict_on"
pred_on_path = os.path.join(home_path, 'predict_on')
#create output path
checkpoint_name = checkpoint_path.split('.')[0]
output_path = os.path.join(home_path, 'predictions', checkpoint_name)
if not os.path.exists(os.path.join(home_path, 'predictions')):
os.mkdir(os.path.join(home_path, 'predictions'))
if os.path.exists(output_path):
raise Exception('there are already predictions for model: ' + checkpoint_name)
os.mkdir(output_path)
#copy annotations to predictions
gt_file = glob.glob(os.path.join(pred_on_path, '*.json'))[0]
set_name = gt_file.split('/')[-1].split('.')[0].split('_')[1]
if os.path.exists(gt_file):
if not os.path.exists(os.path.join(home_path, 'predictions', 'annotations')):
os.mkdir(os.path.join(home_path, 'predictions', 'annotations'))
copyfile(gt_file, os.path.join(home_path, 'predictions', 'annotations', gt_file.split('/')[-1]))
# dataset_val = PredDataset(pred_on_path=pred_on_path, class_list_path=class_names_path,
# transform=transforms.Compose([Normalizer(), Resizer(min_side=608)])) #TODO make resize an input param
dataset_val = PredDataset(pred_on_path, set_name=set_name,
transform=transforms.Compose([Normalizer(), Resizer(min_side=608)]))
# sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False)
dataloader_val = DataLoader(dataset_val, num_workers=0, collate_fn=collater, batch_sampler=None)
if torch.cuda.is_available():
checkpoint = torch.load(checkpoint_path)
else:
checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
scales = checkpoint['scales']
ratios = checkpoint['ratios']
retinanet = model.resnet152(num_classes=num_classes, scales=scales, ratios=ratios) #TODO: make depth an input parameter
retinanet.load_state_dict(checkpoint['model'])
retinanet = retinanet.cuda()
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)
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'].cuda().float())
print('Elapsed time: {}'.format(time.time() - st))
idxs = np.where(scores.cpu() > 0.5)[0]
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], :]
x1 = int(bbox[0])
y1 = int(bbox[1])
x2 = int(bbox[2])
y2 = int(bbox[3])
label_idx = int(classification[idxs[j]])
label_name = dataset_val.labels[label_idx]
score = scores[idxs[j]].item()
draw_caption(img, (x1, y1, x2, y2), label_name)
cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), 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.coco.dataset['images'][idx]['file_name'].split('.')[0]
filename = img_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')
img_save_name = dataset_val.coco.dataset['images'][idx]['file_name']
save_to_path = os.path.join(output_path, img_save_name)
cv2.imwrite(save_to_path, img)
cv2.waitKey(0)