else:
pred_outs['conf'] = F.softmax(pred_outs['conf'], -1)
return self.detect(pred_outs)
# Some testing code
if __name__ == '__main__':
from utils.functions import init_console
init_console()
# Use the first argument to set the config if you want
import sys
if len(sys.argv) > 1:
from data.config import set_cfg
set_cfg(sys.argv[1])
net = Yolact()
net.train()
net.init_weights(backbone_path='weights/' + cfg.backbone.path)
# GPU
# net = net.cuda()
net = net
# cudnn.benchmark = True
torch.set_default_tensor_type('torch.FloatTensor')
x = torch.zeros((1, 3, cfg.max_size, cfg.max_size))
y = net(x)
for p in net.prediction_layers:
# Copyright (c) 2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from data.config import set_cfg
from yolact import Yolact
set_cfg('yolact_resnet50_config')
def create_model(weights):
yolact = Yolact()
yolact.load_weights(weights)
return yolact
#pub = rospy.Publisher('chatter',String,queue_size=10)
#rate = rospy.Rate(50) #10hz
#str_ += text_str
#rospy.loginfo(str_)
#pub.publish(str_)
#rate.sleep()
return img_numpy
if __name__ == '__main__':
parse_args()
if args.config is not None:
set_cfg(args.config)
if args.trained_model == 'interrupt':
args.trained_model = SavePath.get_interrupt('weights/')
elif args.trained_model == 'latest':
args.trained_model = SavePath.get_latest('weights/', cfg.name)
if args.config is None:
model_path = SavePath.from_str(args.trained_model)
# TODO: Bad practice? Probably want to do a name lookup instead.
args.config = model_path.model_name + '_config'
print('Config not specified. Parsed %s from the file name.\n' %
args.config)
set_cfg(args.config)
if args.detect:
iou_thresholds = [x / 100 for x in range(50, 100, 5)]
cuda = torch.cuda.is_available()
if __name__ == '__main__':
args = parser.parse_args()
json_path = 'results'
if not os.path.exists(json_path):
os.mkdir(json_path)
if args.config is None:
piece = args.trained_model.split('/')[1].split('_')
name = f'{piece[0]}_{piece[1]}_config'
print(
f'\nConfig not specified. Parsed \'{name}\' from the checkpoint name.\n'
)
set_cfg(name)
with torch.no_grad():
if cuda:
cudnn.benchmark = True
cudnn.fastest = True
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
dataset = COCODetection(cfg.dataset.valid_images,
cfg.dataset.valid_info,
augmentation=BaseTransform())
print('Loading model...')
net = Yolact()