roidb = imdb.roidb
data_layer = RoIDataLayer(roidb, imdb.num_classes)
#pdb.set_trace()
# Create network and initialize
net = WSDDN(classes=imdb.classes, debug=_DEBUG)
network.weights_normal_init(net, dev=0.001)
if os.path.exists('pretrained_alexnet.pkl'):
pret_net = pkl.load(open('pretrained_alexnet.pkl', 'r'))
else:
pret_net = model_zoo.load_url(
'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth')
pkl.dump(pret_net, open('pretrained_alexnet.pkl', 'wb'),
pkl.HIGHEST_PROTOCOL)
own_state = net.state_dict()
for name, param in pret_net.items():
if name not in own_state:
continue
if isinstance(param, Parameter):
param = param.data
try:
own_state[name].copy_(param)
print('Copied {}'.format(name))
except:
print('Did not find {}'.format(name))
continue
# Move model to GPU and set train mode
net.cuda()
net.train()
test_roidb = test_imdb.roidb
data_layer_test = RoIDataLayer(test_roidb, test_imdb.num_classes)
# Create network and initialize
net = WSDDN(classes=imdb.classes, debug=_DEBUG)
network.weights_normal_init(net, dev=0.0001)
if os.path.exists('pretrained_alexnet.pkl'):
pret_net = pkl.load(open('pretrained_alexnet.pkl', 'r'))
# pret_net = pkl.load(open('./models/saved_model/wsdnn_50000.h5','r'))
else:
pret_net = model_zoo.load_url(
'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth')
pkl.dump(pret_net, open('pretrained_alexnet.pkl', 'wb'),
pkl.HIGHEST_PROTOCOL)
own_state = net.state_dict()
for name, param in pret_net.items():
if name not in own_state:
continue
if isinstance(param, Parameter):
param = param.data
try:
own_state[name].copy_(param)
print('Copied {}'.format(name))
except:
print('Did not find {}'.format(name))
continue
if resume and os.path.isfile("./wsddn_test_checkpoint"):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
imdb = get_imdb(imdb_name)
rdl_roidb.prepare_roidb(imdb)
roidb = imdb.roidb
data_layer = RoIDataLayer(roidb, imdb.num_classes)
# Create network and initialize
net = WSDDN(classes=imdb.classes, debug=_DEBUG)
network.weights_normal_init(net, dev=0.001)
if os.path.exists('pretrained_alexnet.pkl'):
pret_net = pkl.load(open('pretrained_alexnet.pkl', 'r'))
else:
pret_net = model_zoo.load_url(
'https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth')
pkl.dump(pret_net, open('pretrained_alexnet.pkl', 'wb'),
pkl.HIGHEST_PROTOCOL)
own_state = net.state_dict()
print(own_state.keys())
print(pret_net.keys())
for name, param in pret_net.items():
if name not in own_state:
continue
if isinstance(param, Parameter):
param = param.data
try:
own_state[name].copy_(param)
print('Copied {}'.format(name))
except:
print('Did not find {}'.format(name))
continue
# Move model to GPU and set train mode
# Create network and initialize
net = WSDDN(classes=imdb.classes, debug=_DEBUG)
net.features = torch.nn.DataParallel(net.features)
net.roi_pool = torch.nn.DataParallel(net.roi_pool)
net.classifier = torch.nn.DataParallel(net.classifier)
net.score_cls = torch.nn.DataParallel(net.score_cls)
net.score_det = torch.nn.DataParallel(net.score_det)
network.weights_normal_init(net, dev=0.001)
if os.path.exists('pretrained_alexnet.pkl'):
pret_net = pkl.load(open('pretrained_alexnet.pkl','r'))
else:
pret_net = model_zoo.load_url('https://download.pytorch.org/models/alexnet-owt-4df8aa71.pth')
pkl.dump(pret_net, open('pretrained_alexnet.pkl','wb'), pkl.HIGHEST_PROTOCOL)
own_state = net.state_dict() # net.state_dict(),keys =
for name, param in pret_net.items():
if name not in own_state:
continue
if isinstance(param, Parameter):
param = param.data
try:
own_state[name].copy_(param)
print('Copied {}'.format(name))
except:
print('Did not find {}'.format(name))
continue
if 'features' in name:
name = name.replace('features.','features.module.')
if 'classifier' in name:
m = re.search('\d', name)
