def load_model():
#return torch.load("D:\\deployment\\hotdog-not-hotdog-master\\uploads/finetunned_model")
'''model = models.vgg16(pretrained=True)
prepare_for_finetunning(model)
model.load_state_dict(torch.load("D:\\deployment\\hotdog-not-hotdog-master\\finetunned_model_state.pth"))'''
resume = './net_model/weights_31_501_0.9806_0.9973.pth'
model = MainModel()
model_dict = model.state_dict()
pretrained_dict = torch.load(resume)
pretrained_dict = {
k[7:]: v
for k, v in pretrained_dict.items() if k[7:] in model_dict
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
return model
dataloader['val'] = torch.utils.data.DataLoader(val_set,\
batch_size=args.val_batch,\
shuffle=False,\
num_workers=args.val_num_workers,\
collate_fn=collate_fn4test if not Config.use_backbone else collate_fn4backbone,
drop_last=True if Config.use_backbone else False,
pin_memory=True)
setattr(dataloader['val'], 'total_item_len', len(val_set))
setattr(dataloader['val'], 'num_cls', Config.numcls)
cudnn.benchmark = True
print('Choose model and train set', flush=True)
model = MainModel(Config)
# load model
if (args.resume is None) and (not args.auto_resume):
print('train from imagenet pretrained models ...', flush=True)
else:
if not args.resume is None:
resume = args.resume
print('load from pretrained checkpoint %s ...'% resume, flush=True)
elif args.auto_resume:
resume = auto_load_resume(Config.save_dir)
print('load from %s ...'%resume, flush=True)
else:
raise Exception("no checkpoints to load")
model_dict = model.state_dict()
type=int)
parser.add_argument('--tnw', dest='train_num_workers', default=0, type=int)
parser.add_argument('--vnw', dest='val_num_workers', default=32, type=int)
parser.add_argument('--detail', dest='discribe', default='', type=str)
parser.add_argument('--size',
dest='resize_resolution',
default=512,
type=int)
parser.add_argument('--crop',
dest='crop_resolution',
default=448,
type=int)
parser.add_argument('--cls_2', dest='cls_2', action='store_true')
parser.add_argument('--cls_mul', dest='cls_mul', action='store_true')
parser.add_argument('--swap_num',
default=[7, 7],
nargs=2,
metavar=('swap1', 'swap2'),
type=int,
help='specify a range')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print(args, flush=True)
Config = LoadConfig(args, 'train')
model = MainModel(Config).cuda()
summary(model, (3, 224, 224))
def startup(self, args):
i_debug = 18
if 1 == i_debug:
# 为无锡所招标预留功能开发
app = WxsApp()
app.startup(args)
return
print('模型热力图绘制应用 v0.1.0')
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
args = self.parse_args()
# arg_dict = vars(args)
args.train_num_workers = 0
args.val_num_workers = 0
print(args, flush=True)
Config = LoadConfig(args, 'train')
Config.cls_2 = args.cls_2
Config.cls_2xmul = args.cls_mul
assert Config.cls_2 ^ Config.cls_2xmul
transformers = load_data_transformers(args.resize_resolution,
args.crop_resolution,
args.swap_num)
# inital dataloader
train_set = dataset(Config = Config,\
anno = Config.train_anno,\
common_aug = transformers["common_aug"],\
swap = transformers["swap"],\
swap_size=args.swap_num, \
totensor = transformers["train_totensor"],\
train = True)
trainval_set = dataset(Config = Config,\
anno = Config.val_anno,\
common_aug = transformers["None"],\
swap = transformers["None"],\
swap_size=args.swap_num, \
totensor = transformers["val_totensor"],\
train = False,
train_val = True)
val_set = dataset(Config = Config,\
anno = Config.val_anno,\
common_aug = transformers["None"],\
swap = transformers["None"],\
swap_size=args.swap_num, \
totensor = transformers["test_totensor"],\
test=True)
dataloader = {}
dataloader['train'] = torch.utils.data.DataLoader(train_set,\
batch_size=args.train_batch,\
shuffle=True,\
num_workers=args.train_num_workers,\
collate_fn=collate_fn4train if not Config.use_backbone else collate_fn4backbone,
drop_last=True if Config.use_backbone else False,
pin_memory=True)
setattr(dataloader['train'], 'total_item_len', len(train_set))
dataloader['trainval'] = torch.utils.data.DataLoader(trainval_set,\
batch_size=args.val_batch,\
shuffle=False,\
num_workers=args.val_num_workers,\
collate_fn=collate_fn4val if not Config.use_backbone else collate_fn4backbone,
drop_last=True if Config.use_backbone else False,
pin_memory=True)
setattr(dataloader['trainval'], 'total_item_len', len(trainval_set))
setattr(dataloader['trainval'], 'num_cls', Config.num_brands)
dataloader['val'] = torch.utils.data.DataLoader(val_set,\
batch_size=args.val_batch,\
shuffle=False,\
num_workers=args.val_num_workers,\
collate_fn=collate_fn4test if not Config.use_backbone else collate_fn4backbone,
drop_last=True if Config.use_backbone else False,
pin_memory=True)
setattr(dataloader['val'], 'total_item_len', len(val_set))
setattr(dataloader['val'], 'num_cls', Config.num_brands)
cudnn.benchmark = True
print('Choose model and train set', flush=True)
print('Choose model and train set', flush=True)
model = MainModel(Config)
# load model
if (args.resume is None) and (not args.auto_resume):
print('train from imagenet pretrained models ...', flush=True)
else:
if not args.resume is None:
resume = args.resume
print('load from pretrained checkpoint %s ...' % resume,
flush=True)
elif args.auto_resume:
resume = self.auto_load_resume(Config.save_dir)
print('load from %s ...' % resume, flush=True)
else:
raise Exception("no checkpoints to load")
model_dict = model.state_dict()
pretrained_dict = torch.load(resume)
print('train.py Ln193 resume={0};'.format(resume))
pretrained_dict = {
k[7:]: v
for k, v in pretrained_dict.items() if k[7:] in model_dict
}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
print('Set cache dir', flush=True)
time = datetime.datetime.now()
filename = '%s_%d%d%d_%s' % (args.cam, time.month, time.day, time.hour,
Config.dataset)
save_dir = os.path.join(Config.save_dir, filename)
print('save_dir: {0} + {1};'.format(Config.save_dir, filename))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
model.cuda()
cam_main_model = model
cam_model = model.model
model = nn.DataParallel(model)
# optimizer prepare
if Config.use_backbone:
ignored_params = list(map(id, model.module.classifier.parameters())) \
+ list(map(id, model.module.brand_clfr.parameters()))
else:
ignored_params1 = list(
map(id, model.module.classifier.parameters()))
ignored_params1x = list(
map(id, model.module.brand_clfr.parameters()))
ignored_params2 = list(
map(id, model.module.classifier_swap.parameters()))
ignored_params3 = list(map(id, model.module.Convmask.parameters()))
ignored_params = ignored_params1 + ignored_params1x + ignored_params2 + ignored_params3
print('the num of new layers:', len(ignored_params), flush=True)
base_params = filter(lambda p: id(p) not in ignored_params,
model.module.parameters())
lr_ratio = args.cls_lr_ratio
base_lr = args.base_lr
momentum = 0.9
if Config.use_backbone:
optimizer = optim.SGD(
[{
'params': base_params
}, {
'params': model.module.classifier.parameters(),
'lr': base_lr
}, {
'params': model.module.brand_clfr.parameters(),
'lr': base_lr
}],
lr=base_lr,
momentum=momentum)
else:
optimizer = optim.SGD([
{
'params': base_params
},
{
'params': model.module.classifier.parameters(),
'lr': lr_ratio * base_lr
},
{
'params': model.module.brand_clfr.parameters(),
'lr': lr_ratio * base_lr
},
{
'params': model.module.classifier_swap.parameters(),
'lr': lr_ratio * base_lr
},
{
'params': model.module.Convmask.parameters(),
'lr': lr_ratio * base_lr
},
],
lr=base_lr,
momentum=momentum)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.decay_step,
gamma=0.1)
# *******************
# *******************
print('model: {0};'.format(cam_model))
print('avgpoo: {0};'.format(cam_main_model.avgpool))
headers = {
'avgpool': cam_main_model.avgpool,
'classifier': cam_main_model.brand_clfr
}
grad_cam = GradCam(model=cam_model, feature_module=cam_model[7], \
target_layer_names=["2"], headers=headers, use_cuda=True)
# 读入图片数据
img = None
img_file = '/media/ps/0A9AD66165F33762/yantao/dcl/support/ds_files/wxs_ds/head/car/d00/d00/d00/d00/d96/SC7168CH5_冀B591C5_02_120000100604_120000702916290242.jpg'
with open(img_file, 'rb') as f:
with Image.open(f) as img:
img = img.convert('RGB')
crop_reso = 224
to_tensor = transforms.Compose([
transforms.Resize((crop_reso, crop_reso)),
# ImageNetPolicy(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
img_obj = to_tensor(img)
input = img_obj.reshape(1, 3, 224, 224)
input.cuda()
input.requires_grad_(True)
print('input: {0};'.format(input.shape))
# If None, returns the map for the highest scoring category.
# Otherwise, targets the requested index.
target_index = None
mask = grad_cam(input, target_index)
#
self.show_cam_on_image(img_file, mask)
#
gb_model = GuidedBackpropReLUModel(model=cam_main_model, use_cuda=True)
gb = gb_model(input, index=target_index)
gb = gb.transpose((1, 2, 0))
cam_mask = cv2.merge([mask, mask, mask])
cam_gb = self.deprocess_image(cam_mask * gb)
gb = self.deprocess_image(gb)
cv2.imwrite('gb.jpg', gb)
cv2.imwrite('cam_gb.jpg', cam_gb)
print('^_^ The End! 002 ^_^')