def __init__(self, args, config):
self.args = args
self.config = config
self.model_dir = utils.get_model_dir(config)
self.category = utils.get_category(config)
self.anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
self.dnn = utils.parse_attr(config.get('model',
'dnn'))(config, self.anchors,
len(self.category))
self.dnn.eval()
logging.info(
humanize.naturalsize(
sum(var.cpu().numpy().nbytes
for var in self.dnn.state_dict().values())))
if torch.cuda.is_available():
self.dnn.cuda()
self.height, self.width = tuple(
map(int,
config.get('image', 'size').split()))
output = self.dnn(
torch.autograd.Variable(
utils.ensure_device(torch.zeros(1, 3, self.height,
self.width))))
_, _, self.rows, self.cols = output.size()
self.i, self.j = self.rows // 2, self.cols // 2
self.output = output[:, :, self.i, self.j]
dataset = Dataset(self.height, self.width)
try:
workers = self.config.getint('data', 'workers')
except configparser.NoOptionError:
workers = multiprocessing.cpu_count()
self.loader = torch.utils.data.DataLoader(
dataset, batch_size=self.args.batch_size, num_workers=workers)
def __init__(self, args, config):
self.args = args
self.config = config
self.model_dir = utils.get_model_dir(config)
self.cache_dir = utils.get_cache_dir(config)
self.category = utils.get_category(config, self.cache_dir)
self.draw_bbox = utils.visualize.DrawBBox(config, self.category)
self.loader = self.get_loader()
self.anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
dnn = utils.parse_attr(config.get('model', 'dnn'))(config,
self.anchors,
len(self.category))
path, self.step, self.epoch = utils.train.load_model(self.model_dir)
checkpoint = torch.load(path,
map_location=lambda storage, loc: storage)
dnn.load_state_dict(checkpoint['dnn'])
logging.info(
humanize.naturalsize(
sum(var.cpu().numpy().nbytes
for var in dnn.state_dict().values())))
self.inference = model.Inference(config, dnn, self.anchors)
self.inference.eval()
if torch.cuda.is_available():
self.inference.cuda()
path = self.model_dir + '.ini'
if os.path.exists(path):
self._config = configparser.ConfigParser()
self._config.read(path)
else:
logging.warning('training config (%s) not found' % path)
self.now = datetime.datetime.now()
self.mapper = utils.load_functions(self.config.get('eval', 'mapper'))
def __init__(self, args, config):
self.args = args
self.config = config
self.model_dir = utils.get_model_dir(config)
self.cache_dir = utils.get_cache_dir(config)
self.category = utils.get_category(config, self.cache_dir)
self.draw_bbox = utils.visualize.DrawBBox(config, self.category)
self.loader = self.get_loader()
self.anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
self.path, self.step, self.epoch = utils.train.load_model(self.model_dir)
state_dict = torch.load(self.path, map_location=lambda storage, loc: storage)
dnn = utils.parse_attr(config.get('model', 'dnn'))(model.ConfigChannels(config, state_dict), self.anchors, len(self.category))
dnn.load_state_dict(state_dict)
logging.info(humanize.naturalsize(sum(var.cpu().numpy().nbytes for var in dnn.state_dict().values())))
self.inference = model.Inference(config, dnn, self.anchors)
self.inference.eval()
if torch.cuda.is_available():
self.inference.cuda()
path = self.model_dir + '.ini'
if os.path.exists(path):
self._config = configparser.ConfigParser()
self._config.read(path)
else:
logging.warning('training config (%s) not found' % path)
self.now = datetime.datetime.now()
self.mapper = dict([(inflection.underscore(name), member()) for name, member in inspect.getmembers(importlib.machinery.SourceFileLoader('', self.config.get('eval', 'mapper')).load_module()) if inspect.isclass(member)])
def norm_bbox(data, pred, keys='yx_min, yx_max'):
size, image = (data[key] for key in 'size, image'.split(', '))
_size = size.float()
height, width = image.size()[1:3]
scale = _size.view(-1, 1, 2) / utils.ensure_device(
torch.from_numpy(
np.reshape(np.array([height, width], dtype=np.float32),
[1, 1, 2])))
for key in keys.split(', '):
data[key] = data[key] * scale
rows, cols = pred['feature'].size()[-2:]
scale = _size.view(-1, 1, 1, 2) / utils.ensure_device(
torch.from_numpy(
np.reshape(np.array([rows, cols], dtype=np.float32),
[1, 1, 1, 2])))
for key in keys.split(', '):
pred[key] = pred[key] * scale
def norm_bbox_pred(pred, keys='yx_min, yx_max'.split(', ')):
rows, cols = pred['feature'].size()[-2:]
scale = utils.ensure_device(
torch.from_numpy(
np.reshape(np.array([rows, cols], dtype=np.float32),
[1, 1, 1, 2])))
for key in keys:
pred[key] = pred[key] / scale
return keys
def norm_bbox_data(data, keys='yx_min, yx_max'.split(', ')):
height, width = data['image'].size()[1:3]
scale = utils.ensure_device(
torch.from_numpy(
np.reshape(np.array([height, width], dtype=np.float32),
[1, 1, 2])))
for key in keys:
data[key] = data[key] / scale
return keys
def __init__(self, args, config):
self.args = args
self.config = config
self.model_dir = utils.get_model_dir(config)
self.cache_dir = utils.get_cache_dir(config)
self.category = utils.get_category(config, self.cache_dir)
self.draw_bbox = utils.visualize.DrawBBox(self.category)
self.loader = self.get_loader()
self.anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
self.path, self.step, self.epoch = utils.train.load_model(
self.model_dir)
state_dict = torch.load(self.path,
map_location=lambda storage, loc: storage)
dnn = utils.parse_attr(config.get('model', 'dnn'))(
model.ConfigChannels(config, state_dict), self.anchors,
len(self.category))
dnn.load_state_dict(state_dict)
logging.info(
humanize.naturalsize(
sum(var.cpu().numpy().nbytes
for var in dnn.state_dict().values())))
self.inference = model.Inference(config, dnn, self.anchors)
self.inference.eval()
if torch.cuda.is_available():
self.inference.cuda()
path = self.model_dir + '.ini'
if os.path.exists(path):
self._config = configparser.ConfigParser()
self._config.read(path)
else:
logging.warning('training config (%s) not found' % path)
self.now = datetime.datetime.now()
self.mapper = dict([
(inflection.underscore(name), member())
for name, member in inspect.getmembers(
importlib.machinery.SourceFileLoader(
'', self.config.get('eval', 'mapper')).load_module())
if inspect.isclass(member)
])
def __init__(self, args, config):
self.args = args
self.config = config
self.model_dir = utils.get_model_dir(config)
self.category = utils.get_category(config)
self.anchors = torch.from_numpy(utils.get_anchors(config)).contiguous()
self.dnn = utils.parse_attr(config.get('model', 'dnn'))(model.ConfigChannels(config), self.anchors, len(self.category))
self.dnn.eval()
logging.info(humanize.naturalsize(sum(var.cpu().numpy().nbytes for var in self.dnn.state_dict().values())))
if torch.cuda.is_available():
self.dnn.cuda()
self.height, self.width = tuple(map(int, config.get('image', 'size').split()))
output = self.dnn(torch.autograd.Variable(utils.ensure_device(torch.zeros(1, 3, self.height, self.width)), volatile=True))
_, _, self.rows, self.cols = output.size()
self.i, self.j = self.rows // 2, self.cols // 2
self.output = output[:, :, self.i, self.j]
dataset = Dataset(self.height, self.width)
try:
workers = self.config.getint('data', 'workers')
except configparser.NoOptionError:
workers = multiprocessing.cpu_count()
self.loader = torch.utils.data.DataLoader(dataset, batch_size=self.args.batch_size, num_workers=workers)
def norm_bbox_pred(pred, keys='yx_min, yx_max'.split(', ')):
rows, cols = pred['feature'].size()[-2:]
scale = utils.ensure_device(torch.from_numpy(np.reshape(np.array([rows, cols], dtype=np.float32), [1, 1, 1, 2])))
for key in keys:
pred[key] = pred[key] / scale
return keys
def norm_bbox_data(data, keys='yx_min, yx_max'.split(', ')):
height, width = data['image'].size()[1:3]
scale = utils.ensure_device(torch.from_numpy(np.reshape(np.array([height, width], dtype=np.float32), [1, 1, 2])))
for key in keys:
data[key] = data[key] / scale
return keys
