class SSD:
name = NAME
@classmethod
def mk_hash(cls, path):
'''
Create an MD5 hash from a models weight file.
Arguments:
path : str - path to TensorBox checkpoint
'''
dirs = path.split('/')
if 'ssd.pytorch' in dirs:
dirs = dirs[dirs.index('ssd.pytorch'):]
path = '/'.join(dirs)
else:
path = os.path.join('ssd.pytorch', path)
md5 = hashlib.md5()
md5.update(path.encode('utf-8'))
return md5.hexdigest()
@classmethod
def zip_weights(cls, path, base_dir='./'):
if os.path.splitext(path)[1] != '.pth':
raise ValueError('Invalid checkpoint')
dirs = path.split('/')
res = {
'name': 'TensorBox',
'instance': '_'.join(dirs[-2:]),
'id': cls.mk_hash(path)
}
zipfile = os.path.join(base_dir, res['id'] + '.zip')
if os.path.exists(zipfile):
os.remove(zipfile)
weight_dir = os.path.dirname(path)
with ZipFile(zipfile, 'w') as z:
z.write(path, os.path.join(res['id'], os.path.basename(file)))
return zipfile
def __init__(self, weights, classes=['building']):
self.net = Retina(classes).eval().cuda()
chkpnt = torch.load(weights)
self.net.load_state_dict(chkpnt['state_dict'])
self.transform = transforms.Compose([
transforms.Resize((300, 300)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
def predict_image(self, image, threshold, eval_mode=False):
"""
Infer buildings for a single image.
Inputs:
image :: n x m x 3 ndarray - Should be in RGB format
"""
t0 = time.time()
img = self.transform(image)
out = self.net(Variable(img.unsqueeze(0).cuda(),
volatile=True)).squeeze().data.cpu()
total_time = time.time() - t0
scores = out[:, :,
0] # class X top K X (score, minx, miny, maxx, maxy)
max_scores, inds = scores.max(dim=0)
linear = torch.arange(0, out.shape[1]).long()
boxes = out[inds, linear].numpy()
boxes[:, (1, 3)] = np.clip(boxes[:, (1, 3)] * image.width,
a_min=0,
a_max=image.width)
boxes[:, (2, 4)] = np.clip(boxes[:, (2, 4)] * image.height,
a_min=0,
a_max=image.height)
df = pandas.DataFrame(boxes, columns=['score', 'x1', 'y1', 'x2', 'y2'])
if eval_mode:
return df[df['score'] > threshold], df, total_time
else:
return df[df['score'] > threshold]
pdb.set_trace()
def predict_all(self, test_boxes_file, threshold, data_dir=None):
test_boxes = json.load(open(test_boxes_file))
true_annolist = al.parse(test_boxes_file)
if data_dir is None:
data_dir = os.path.join(os.path.dirname(test_boxes_file))
total_time = 0.0
for i in range(len(true_annolist)):
true_anno = true_annolist[i]
orig_img = imread('%s/%s' %
(data_dir, true_anno.imageName))[:, :, :3]
pred, all_rects, time = self.predict_image(orig_img,
threshold,
eval_mode=True)
pred['image_id'] = i
all_rects['image_id'] = i
yield pred, all_rects, test_boxes[i]
class RetinaNet:
name = NAME
@classmethod
def mk_hash(cls, path):
'''
Create an MD5 hash from a models weight file.
Arguments:
path : str - path to RetinaNet checkpoint
'''
dirs = path.split('/')
if 'retina_net' in dirs:
dirs = dirs[dirs.index('retina_net'):]
path = '/'.join(dirs)
else:
path = os.path.join('retina_net', path)
md5 = hashlib.md5()
md5.update(path.encode('utf-8'))
return md5.hexdigest()
@classmethod
def zip_weights(cls, path, base_dir='./'):
if os.path.splitext(path)[1] != '.pth':
raise ValueError('Invalid checkpoint')
dirs = path.split('/')
res = {
'name' : 'RetinaNet',
'instance' : '_'.join(dirs[-2:]),
'id' : cls.mk_hash(path)
}
zipfile = os.path.join(base_dir, res['id'] + '.zip')
if os.path.exists(zipfile):
os.remove(zipfile)
weight_dir = os.path.dirname(path)
with ZipFile(zipfile, 'w') as z:
z.write(path, os.path.join(res['id'], os.path.basename(path)))
return zipfile
def __init__(self, weights, classes=['building'], cuda = True):
chkpnt = torch.load(weights)
self.config = chkpnt['args']
self.net = Retina(self.config).eval()
self.net.load_state_dict(chkpnt['state_dict'])
self.transform = transforms.Compose([
transforms.Resize((self.config.model_input_size, self.config.model_input_size)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
self.net = self.net.cuda()
self.net.anchors.anchors = self.net.anchors.anchors.cuda()
torch.set_default_tensor_type('torch.cuda.FloatTensor')
self.cuda = cuda
def predict_image(self, image, eval_mode = False):
"""
Infer buildings for a single image.
Inputs:
image :: n x m x 3 ndarray - Should be in RGB format
"""
t0 = time.time()
img = self.transform(image)
if self.cuda:
img = img.cuda()
out = self.net(Variable(img.unsqueeze(0), requires_grad=False)).squeeze().data.cpu().numpy()
total_time = time.time() - t0
out = out[1] # ignore background class
out[:, (1, 3)] = np.clip(out[:, (1, 3)] * image.width, a_min=0, a_max=image.width)
out[:, (2, 4)] = np.clip(out[:, (2, 4)] * image.height, a_min=0, a_max=image.height)
out = out[out[:, 0] > 0]
return pandas.DataFrame(out, columns=['score', 'x1' ,'y1', 'x2', 'y2'])
def predict_all(self, test_boxes_file, batch_size=8, data_dir = None):
if data_dir is None:
data_dir = os.path.join(os.path.dirname(test_boxes_file))
annos = json.load(open(test_boxes_file))
total_time = 0.0
for batch in range(0, len(annos), batch_size):
images, sizes = [], []
for i in range(min(batch_size, len(annos) - batch)):
img = Image.open(os.path.join(data_dir, annos[batch + i]['image_path']))
images.append(self.transform(img))
sizes.append(torch.Tensor([img.width, img.height]))
images = torch.stack(images)
sizes = torch.stack(sizes)
if self.cuda:
images = images.cuda()
sizes = sizes.cuda()
out = self.net(Variable(images, requires_grad=False)).data
hws = torch.cat([sizes, sizes], dim=1).view(-1, 1, 1, 4).expand(-1, out.shape[1], out.shape[2], -1)
out[:, :, :, 1:] *= hws
out = out[:, 1, :, :].cpu().numpy()
for i, detections in enumerate(out):
anno = annos[batch + i]
pred = cv2.imread('../data/' + anno['image_path'])
detections = detections[detections[:, 0] > 0]
df = pandas.DataFrame(detections, columns=['score', 'x1', 'y1', 'x2', 'y2'])
df['image_id'] = anno['image_path']
truth = pred.copy()
for box in df[['x1', 'y1', 'x2', 'y2']].values.round().astype(int):
cv2.rectangle(pred, tuple(box[:2]), tuple(box[2:4]), (0,0,255))
for r in anno['rects']:
box = list(map(lambda x: int(r[x]), ['x1', 'y1', 'x2', 'y2']))
cv2.rectangle(truth, tuple(box[:2]), tuple(box[2:]), (0, 0, 255))
data = np.concatenate([pred, truth], axis=1)
cv2.imwrite('samples/image_%d.jpg' % (batch + i), data)
yield df
