def __init__(self, pathModel, nnArchitecture, nnClassCount, transCrop):
#---- Initialize the network
if nnArchitecture == 'DENSE-NET-121': model = densenet121(False).cuda()
elif nnArchitecture == 'DENSE-NET-169':
model = densenet169(False).cuda()
elif nnArchitecture == 'DENSE-NET-201':
model = densenet201(False).cuda()
model = torch.nn.DataParallel(model).cuda()
modelCheckpoint = torch.load(pathModel)
model.load_state_dict(modelCheckpoint['best_model_wts'], strict=False)
self.model = model.module.features
self.model.eval()
#---- Initialize the weights
self.weights = list(self.model.parameters())[-2]
#---- Initialize the image transform - resize + normalize
normalize = transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
transformList = []
transformList.append(transforms.Resize(transCrop))
transformList.append(transforms.ToTensor())
transformList.append(normalize)
self.transformSequence = transforms.Compose(transformList)
def get_net(name):
if name == 'densenet121':
net = densenet121()
elif name == 'densenet161':
net = densenet161()
elif name == 'densenet169':
net = densenet169()
elif name == 'googlenet':
net = googlenet()
elif name == 'inception_v3':
net = inception_v3()
elif name == 'mobilenet_v2':
net = mobilenet_v2()
elif name == 'resnet18':
net = resnet18()
elif name == 'resnet34':
net = resnet34()
elif name == 'resnet50':
net = resnet50()
elif name == 'resnet_orig':
net = resnet_orig()
elif name == 'vgg11_bn':
net = vgg11_bn()
elif name == 'vgg13_bn':
net = vgg13_bn()
elif name == 'vgg16_bn':
net = vgg16_bn()
elif name == 'vgg19_bn':
net = vgg19_bn()
else:
print(f'{name} not a valid model name')
sys.exit(0)
return net.to(device)
def main():
test_dir = opt.test_dir
feature_param_file = opt.feat
class_param_file = opt.cls
bsize = opt.b
# models
if 'vgg' == opt.i:
feature = Vgg16()
elif 'resnet' == opt.i:
feature = resnet50()
elif 'densenet' == opt.i:
feature = densenet121()
feature.cuda()
# feature.load_state_dict(torch.load(feature_param_file))
feature.eval()
classifier = Classifier(opt.i)
classifier.cuda()
# classifier.load_state_dict(torch.load(class_param_file))
classifier.eval()
loader = torch.utils.data.DataLoader(MyClsTestData(test_dir,
transform=True),
batch_size=bsize,
shuffle=True,
num_workers=4,
pin_memory=True)
acc = eval_acc(feature, classifier, loader)
print acc
def make_model():
global og_req_grads
base = densenet121(pretrained=True)
selector = classSelector(classesToKeep)
model = nn.Sequential(base, selector)
og_req_grads = [p.requires_grad for p in model.parameters()]
return model
def __init__(self, out_size):
super(DenseNet121, self).__init__()
# self.densenet121 = torchvision.models.densenet121(pretrained=True,x='')
self.densenet121 = densenet.densenet121(pretrained=True)
num_ftrs = self.densenet121.classifier.in_features
self.densenet121.classifier = nn.Sequential(
nn.Linear(num_ftrs, out_size),
nn.Sigmoid()
)
def get_densenet_model(x, args, is_training):
with slim.arg_scope(densenet.densenet_arg_scope(weight_decay=args.weight_decay,
batch_norm_decay=0.99,
batch_norm_epsilon=1.1e-5,
data_format='NHWC')):
net, end_points = densenet.densenet121(x,
None,
is_training=is_training,
with_top=False)
net = slim.batch_norm(net, is_training=is_training, activation_fn=tf.nn.relu)
return get_classifier_and_reconstruct_model(net, args, is_training, end_points)
def __init__(self, out_size):
super(DenseNet121, self).__init__()
self.inplanes = 1024
self.densenet121 = densenet.densenet121(pretrained=True, small=args.small)
num_ftrs = self.densenet121.classifier.in_features
self.classifier_font = nn.Sequential(
# 这里可以用fc做分类
# nn.Linear(num_ftrs, out_size)
# 这里可以用1×1卷积做分类
nn.Conv2d(num_ftrs, out_size, kernel_size=1, bias=False)
)
self.train_params = []
self.unpool = nn.MaxUnpool2d(kernel_size=2, stride=2)
def main(args):
transform_train = transforms.Compose([
ReScale((args.image_size, args.image_size)),
transforms.ToTensor()
])
anno = pd.read_csv(args.anno_txt, sep=';', header=None)
image_paths0 = list(anno.ix[:, 1])
image_paths1 = list(anno.ix[:, 2])
targets = np.array(anno.ix[:,3])
targets = np.sign(targets - 3)
global plotter
plotter = VisdomLinePlotter(env_name=args.name)
train_set = SiameseData(args.data_dir, image_paths0,
image_paths1, targets, transform=transform_train)
train_loader = DataLoader(train_set, batch_size=
args.batch_size, #num_workers=1, # pin_memory=True,
collate_fn = collate_cat,
shuffle=True)
model = densenet121(drop_rate=args.drop_rate)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
model = model.cuda()
if args.ckpt:
if os.path.isfile(args.ckpt):
print('loading ckpt {}'.format(args.ckpt))
checkpoint = torch.load(args.ckpt)
state_dict = OrderedDict()
for k, v in checkpoint.items():
if k in model.state_dict().keys():
state_dict[k] = v
model.load_state_dict(state_dict)
print('checkpoint loaded')
cudnn.benchmark = True
optimizer = torch.optim.Adam(model.parameters(), args.lr,
weight_decay=args.weight_decay)
criterion = RankLoss()
for epoch in range(args.epoch):
# adjust learning rate
train(train_loader, model, criterion, optimizer, epoch)
save_ckpt(model, args.training_dir, epoch)
def set_model():
if Config.backbone == 'resnet18':
model = resnet.resnet18(num_class=Config.out_class)
if Config.backbone == 'resnet34':
model = resnet.resnet34(num_class=Config.out_class, pretrained=Config.pretrain)
if Config.backbone == 'resnet50':
model = resnet.resnet50(num_class=Config.out_class, pretrained=Config.pretrain)
if Config.backbone == 'ncrf18':
model = ncrf.resnet18(num_class=Config.out_class)
if Config.backbone == 'ncrf34':
model = ncrf.resnet34(num_class=Config.out_class)
if Config.backbone == 'ncrf50':
model = ncrf.resnet50(num_class=Config.out_class)
if Config.backbone == 'densenet121':
model = densenet.densenet121(Config.out_class, pretrained=Config.pretrain, drop_rate=Config.drop_rate)
if Config.backbone == 'msdn18':
model = hardcore_msdn.msdn18(Config.out_class, ss=Config.ss, drop_rate=Config.drop_rate)
if Config.backbone == 'alexnet':
model = alexnet.alexnet(2)
return model
def CreatNet(name):
if name == 'LSTM':
return LSTM(100, 27 * 5, 5)
elif name == 'CNN':
return CNN()
elif name == 'resnet18_1d':
return resnet18_1d()
elif name == 'dfcnn':
return dfcnn()
elif name in ['resnet101', 'resnet50', 'resnet18']:
if name == 'resnet101':
net = torchvision.models.resnet101(pretrained=False)
net.fc = nn.Linear(2048, 5)
elif name == 'resnet50':
net = torchvision.models.resnet50(pretrained=False)
net.fc = nn.Linear(2048, 5)
elif name == 'resnet18':
net = torchvision.models.resnet18(pretrained=False)
net.fc = nn.Linear(512, 5)
net.conv1 = nn.Conv2d(1, 64, 7, 2, 3, bias=False)
return net
elif 'densenet' in name:
if name == 'densenet121':
net = densenet.densenet121(pretrained=False, num_classes=5)
elif name == 'densenet201':
net = densenet.densenet201(pretrained=False, num_classes=5)
# net.features = nn.Sequential(OrderedDict([
# ('conv0', nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)),
# ('norm0', nn.BatchNorm2d(64)),
# ('relu0', nn.ReLU(inplace=True)),
# ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
# ]))
# net.classifier = nn.Linear(64, 5)
return net
pin_memory=True,
num_workers=4)
if args.model == 'resnet18':
cnn = ResNet18(num_classes=num_classes)
elif args.model == 'resnet50':
from resnet import resnet50
cnn = resnet50(num_classes)
print(cnn)
elif args.model == 'resnext50':
from resnext import resnext50
cnn = resnext50(num_classes)
print(cnn)
elif args.model == 'densenet121':
from densenet import densenet121
cnn = densenet121(num_classes)
print(cnn)
elif args.model == 'wideresnet':
if args.dataset == 'svhn':
cnn = WideResNet(depth=16,
num_classes=num_classes,
widen_factor=8,
dropRate=0.4)
else:
cnn = WideResNet(depth=28,
num_classes=num_classes,
widen_factor=10,
dropRate=0.3)
elif args.model == 'wideresnet101':
cnn = torch.hub.load('pytorch/vision:v0.5.0',
'wide_resnet101_2',
help='Input image path')
args = parser.parse_args()
args.use_cuda = args.use_cuda and torch.cuda.is_available()
if args.use_cuda:
print("Using GPU for acceleration")
else:
print("Using CPU for computation")
return args
if __name__ == '__main__':
args = get_args()
# loading models
model = densenet121()
model = torch.nn.DataParallel(model)
checkpoint = torch.load(
"/Users/fredrickang/Desktop/github/ChestXray/logs/densenet121_model_best.pth"
)
model.load_state_dict(checkpoint['state_dict'])
grad_cam = GradCam(model=model,
target_layer_names=["denseblock3"],
use_cuda=True)
img = cv2.imread(
"/Users/fredrickang/Desktop/github/ChestXray/00009709_010.png", 1)
img = np.float32(cv2.resize(img, (256, 256))) / 255
inputs = preprocess_image(img)
def get_model_dics(device, model_list= None):
if model_list is None:
model_list = ['densenet121', 'densenet161', 'resnet50', 'resnet152',
'incept_v1', 'incept_v3', 'inception_v4', 'incept_resnet_v2',
'incept_v4_adv2', 'incept_resnet_v2_adv2',
'black_densenet161','black_resnet50','black_incept_v3',
'old_vgg','old_res','old_incept']
models = {}
for model in model_list:
if model=='densenet121':
models['densenet121'] = densenet121(num_classes=110)
load_model(models['densenet121'],"../pre_weights/ep_38_densenet121_val_acc_0.6527.pth",device)
if model=='densenet161':
models['densenet161'] = densenet161(num_classes=110)
load_model(models['densenet161'],"../pre_weights/ep_30_densenet161_val_acc_0.6990.pth",device)
if model=='resnet50':
models['resnet50'] = resnet50(num_classes=110)
load_model(models['resnet50'],"../pre_weights/ep_41_resnet50_val_acc_0.6900.pth",device)
if model=='incept_v3':
models['incept_v3'] = inception_v3(num_classes=110)
load_model(models['incept_v3'],"../pre_weights/ep_36_inception_v3_val_acc_0.6668.pth",device)
if model=='incept_v1':
models['incept_v1'] = googlenet(num_classes=110)
load_model(models['incept_v1'],"../pre_weights/ep_33_googlenet_val_acc_0.7091.pth",device)
#vgg16 = vgg16_bn(num_classes=110)
#load_model(vgg16, "./pre_weights/ep_30_vgg16_bn_val_acc_0.7282.pth",device)
if model=='incept_resnet_v2':
models['incept_resnet_v2'] = InceptionResNetV2(num_classes=110)
load_model(models['incept_resnet_v2'], "../pre_weights/ep_17_InceptionResNetV2_ori_0.8320.pth",device)
if model=='incept_v4':
models['incept_v4'] = InceptionV4(num_classes=110)
load_model(models['incept_v4'],"../pre_weights/ep_17_InceptionV4_ori_0.8171.pth",device)
if model=='incept_resnet_v2_adv':
models['incept_resnet_v2_adv'] = InceptionResNetV2(num_classes=110)
load_model(models['incept_resnet_v2_adv'], "../pre_weights/ep_22_InceptionResNetV2_val_acc_0.8214.pth",device)
if model=='incept_v4_adv':
models['incept_v4_adv'] = InceptionV4(num_classes=110)
load_model(models['incept_v4_adv'],"../pre_weights/ep_24_InceptionV4_val_acc_0.6765.pth",device)
if model=='incept_resnet_v2_adv2':
models['incept_resnet_v2_adv2'] = InceptionResNetV2(num_classes=110)
#load_model(models['incept_resnet_v2_adv2'], "../test_weights/ep_29_InceptionResNetV2_adv2_0.8115.pth",device)
load_model(models['incept_resnet_v2_adv2'], "../test_weights/ep_13_InceptionResNetV2_val_acc_0.8889.pth",device)
if model=='incept_v4_adv2':
models['incept_v4_adv2'] = InceptionV4(num_classes=110)
# load_model(models['incept_v4_adv2'],"../test_weights/ep_32_InceptionV4_adv2_0.7579.pth",device)
load_model(models['incept_v4_adv2'],"../test_weights/ep_50_InceptionV4_val_acc_0.8295.pth",device)
if model=='resnet152':
models['resnet152'] = resnet152(num_classes=110)
load_model(models['resnet152'],"../pre_weights/ep_14_resnet152_ori_0.6956.pth",device)
if model=='resnet152_adv':
models['resnet152_adv'] = resnet152(num_classes=110)
load_model(models['resnet152_adv'],"../pre_weights/ep_29_resnet152_adv_0.6939.pth",device)
if model=='resnet152_adv2':
models['resnet152_adv2'] = resnet152(num_classes=110)
load_model(models['resnet152_adv2'],"../pre_weights/ep_31_resnet152_adv2_0.6931.pth",device)
if model=='black_resnet50':
models['black_resnet50'] = resnet50(num_classes=110)
load_model(models['black_resnet50'],"../test_weights/ep_0_resnet50_val_acc_0.7063.pth",device)
if model=='black_densenet161':
models['black_densenet161'] = densenet161(num_classes=110)
load_model(models['black_densenet161'],"../test_weights/ep_4_densenet161_val_acc_0.6892.pth",device)
if model=='black_incept_v3':
models['black_incept_v3']=inception_v3(num_classes=110)
load_model(models['black_incept_v3'],"../test_weights/ep_28_inception_v3_val_acc_0.6680.pth",device)
if model=='old_res':
MainModel = imp.load_source('MainModel', "./models_old/tf_to_pytorch_resnet_v1_50.py")
models['old_res'] = torch.load('./models_old/tf_to_pytorch_resnet_v1_50.pth').to(device)
if model=='old_vgg':
MainModel = imp.load_source('MainModel', "./models_old/tf_to_pytorch_vgg16.py")
models[model] = torch.load('./models_old/tf_to_pytorch_vgg16.pth').to(device)
if model=='old_incept':
MainModel = imp.load_source('MainModel', "./models_old/tf_to_pytorch_inception_v1.py")
models[model] = torch.load('./models_old/tf_to_pytorch_inception_v1.pth').to(device)
return models
os.system('rm -rf ./runs2/*')
writer = SummaryWriter('./runs2/' + datetime.now().strftime('%B%d %H:%M:%S'))
if not os.path.exists('./runs2'):
os.mkdir('./runs2')
if not os.path.exists(check_dir):
os.mkdir(check_dir)
# models
if 'vgg' == opt.i:
feature = Vgg16(pretrained=True)
elif 'resnet' == opt.i:
feature = resnet50(pretrained=True)
elif 'densenet' == opt.i:
feature = densenet121(pretrained=True)
feature.cuda()
classifier = Classifier(opt.i)
classifier.cuda()
if resume_ep >= 0:
feature_param_file = glob.glob('%s/feature-epoch-%d*.pth' %
(check_dir, resume_ep))
classifier_param_file = glob.glob('%s/classifier-epoch-%d*.pth' %
(check_dir, resume_ep))
feature.load_state_dict(torch.load(feature_param_file[0]))
classifier.load_state_dict(torch.load(classifier_param_file[0]))
train_loader = torch.utils.data.DataLoader(MyClsData(train_dir,
transform=True,
print(x.shape)
y = np.load("lab_train_normal_1234_random.npy")
print(y.shape)
x = torch.Tensor(x)
y = torch.Tensor(y)
#y = torch.topk(y, 1)[1].squeeze(1)
#print(y.size())
torch_dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
dataset=torch_dataset,
batch_size=BS,
shuffle=True,
)
net = densenet.densenet121(in_channel=6)
net = net.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(net)
cudnn.benchmark = True
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=LR, momentum=0.9, weight_decay=5e-4)
def train(epoch):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
parser.add_argument('--b', type=int, default=16) # batch size
opt = parser.parse_args()
print(opt)
test_dir = opt.test_dir
feature_param_file = opt.feat
class_param_file = opt.cls
bsize = opt.b
# models
if 'vgg' == opt.i:
feature = Vgg16()
elif 'resnet' == opt.i:
feature = resnet50()
elif 'densenet' == opt.i:
feature = densenet121()
feature.cuda()
feature.load_state_dict(torch.load(feature_param_file))
classifier = Classifier(opt.i)
classifier.cuda()
classifier.load_state_dict(torch.load(class_param_file))
loader = torch.utils.data.DataLoader(
MyClsTestData(test_dir, transform=True),
batch_size=bsize, shuffle=True, num_workers=4, pin_memory=True)
it = 0.0
num_correct = 0
for ib, (data, lbl) in enumerate(loader):
inputs = Variable(data.float()).cuda()
print('Best val Loss: {:4f}'.format(lowest_val_loss))
print(f'Best epoch: {best_epoch}')
# save best model weights
torch.save(model, f'./weights/full_{model_name}_epoch{best_epoch}.pth')
return model
if __name__ == "__main__":
now = datetime.now()
model_name = f'wide_resnet101_AutoWtdCE_{now.date()}_{now.hour}-{now.minute}'
# default `log_dir` is "runs" - we'll be more specific here
writer = SummaryWriter(f'runs/{model_name}')
model_ft = densenet121(pretrained=True)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs, 5)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss(weight=training.weights.to(device))
# Observe that all parameters are being optimized
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.01, momentum=0.9)
# Decay LR by a factor of 0.1 every 7 epochs
exp_lr_scheduler = lr_scheduler.StepLR(
optimizer_ft, step_size=10, gamma=0.1)
model_ft = train_model(model_ft, criterion, optimizer_ft,
exp_lr_scheduler, writer, model_name, batch_size=16, num_epochs=50)
def get_model(class_weights=None):
model = densenet121(pretrained=True, drop_rate=0.25)
# model_size: penultimate_layer_output_dim,
# 201: 1920, 169: 1664, 121: 1024
# make all params untrainable
for p in model.parameters():
p.requires_grad = False
# reset the last fc layer
model.classifier = nn.Linear(1024, 257)
normal(model.classifier.weight, 0.0, 0.01)
constant(model.classifier.bias, 0.0)
# make some other params trainable
trainable_params = []
trainable_params += [
n for n, p in model.named_parameters() if 'norm5' in n
]
trainable_params += [
n for n, p in model.named_parameters() if 'denseblock4' in n
]
for n, p in model.named_parameters():
if n in trainable_params:
p.requires_grad = True
for m in model.features.denseblock4.modules():
if isinstance(m, nn.ReLU):
m.inplace = False
# create different parameter groups
classifier_weights = [model.classifier.weight]
classifier_biases = [model.classifier.bias]
features_weights = [
p for n, p in model.named_parameters()
if n in trainable_params and 'conv' in n
]
features_bn_weights = [
p for n, p in model.named_parameters()
if n in trainable_params and 'norm' in n and 'weight' in n
]
features_bn_biases = [
p for n, p in model.named_parameters()
if n in trainable_params and 'bias' in n
]
# you can set different learning rates
classifier_lr = 1e-2
features_lr = 1e-2
# but they are not actually used (because lr_scheduler is used)
params = [{
'params': classifier_weights,
'lr': classifier_lr,
'weight_decay': 1e-4
}, {
'params': classifier_biases,
'lr': classifier_lr
}, {
'params': features_weights,
'lr': features_lr,
'weight_decay': 1e-4
}, {
'params': features_bn_weights,
'lr': features_lr
}, {
'params': features_bn_biases,
'lr': features_lr
}]
optimizer = optim.SGD(params, momentum=0.9, nesterov=True)
# loss function
criterion = nn.CrossEntropyLoss(weight=class_weights).cuda()
# move the model to gpu
model = model.cuda()
return model, criterion, optimizer
def get_model(args):
network = args.network
if network == 'vgg11':
model = vgg.vgg11(num_classes=args.class_num)
elif network == 'vgg13':
model = vgg.vgg13(num_classes=args.class_num)
elif network == 'vgg16':
model = vgg.vgg16(num_classes=args.class_num)
elif network == 'vgg19':
model = vgg.vgg19(num_classes=args.class_num)
elif network == 'vgg11_bn':
model = vgg.vgg11_bn(num_classes=args.class_num)
elif network == 'vgg13_bn':
model = vgg.vgg13_bn(num_classes=args.class_num)
elif network == 'vgg16_bn':
model = vgg.vgg16_bn(num_classes=args.class_num)
elif network == 'vgg19_bn':
model = vgg.vgg19_bn(num_classes=args.class_num)
elif network == 'resnet18':
model = models.resnet18(num_classes=args.class_num)
model.conv1 = torch.nn.Conv2d(in_channels=1,
out_channels=model.conv1.out_channels,
kernel_size=model.conv1.kernel_size,
stride=model.conv1.stride,
padding=model.conv1.padding,
bias=model.conv1.bias)
elif network == 'resnet34':
model = models.resnet34(num_classes=args.class_num)
model.conv1 = torch.nn.Conv2d(in_channels=1,
out_channels=model.conv1.out_channels,
kernel_size=model.conv1.kernel_size,
stride=model.conv1.stride,
padding=model.conv1.padding,
bias=model.conv1.bias)
elif network == 'resnet50':
model = models.resnet50(num_classes=args.class_num)
model.conv1 = torch.nn.Conv2d(in_channels=1,
out_channels=model.conv1.out_channels,
kernel_size=model.conv1.kernel_size,
stride=model.conv1.stride,
padding=model.conv1.padding,
bias=model.conv1.bias)
elif network == 'resnet101':
model = models.resnet101(num_classes=args.class_num)
model.conv1 = torch.nn.Conv2d(in_channels=1,
out_channels=model.conv1.out_channels,
kernel_size=model.conv1.kernel_size,
stride=model.conv1.stride,
padding=model.conv1.padding,
bias=model.conv1.bias)
elif network == 'resnet152':
model = models.resnet152(num_classes=args.class_num)
model.conv1 = torch.nn.Conv2d(in_channels=1,
out_channels=model.conv1.out_channels,
kernel_size=model.conv1.kernel_size,
stride=model.conv1.stride,
padding=model.conv1.padding,
bias=model.conv1.bias)
elif network == 'densenet121':
model = densenet.densenet121(num_classes=args.class_num)
elif network == 'densenet169':
model = densenet.densenet169(num_classes=args.class_num)
elif network == 'densenet161':
model = densenet.densenet161(num_classes=args.class_num)
elif network == 'densenet201':
model = densenet.densenet201(num_classes=args.class_num)
return model
def generate_model(opt):
assert opt.model in [
'resnet', 'preresnet', 'wideresnet', 'resnext', 'densenet'
]
if opt.model == 'resnet':
assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200]
from resnet import get_fine_tuning_parameters
if opt.model_depth == 10:
model = resnet.resnet10(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 18:
model = resnet.resnet18(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 34:
model = resnet.resnet34(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 50:
model = resnet.resnet50(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = resnet.resnet101(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = resnet.resnet152(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 200:
model = resnet.resnet200(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'wideresnet':
assert opt.model_depth in [50]
from models.wide_resnet import get_fine_tuning_parameters
if opt.model_depth == 50:
model = wide_resnet.resnet50(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
k=opt.wide_resnet_k,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'resnext':
assert opt.model_depth in [50, 101, 152]
from models.resnext import get_fine_tuning_parameters
if opt.model_depth == 50:
model = resnext.resnet50(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = resnext.resnet101(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = resnext.resnet152(num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
cardinality=opt.resnext_cardinality,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'preresnet':
assert opt.model_depth in [18, 34, 50, 101, 152, 200]
from models.pre_act_resnet import get_fine_tuning_parameters
if opt.model_depth == 18:
model = pre_act_resnet.resnet18(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 34:
model = pre_act_resnet.resnet34(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 50:
model = pre_act_resnet.resnet50(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 101:
model = pre_act_resnet.resnet101(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 152:
model = pre_act_resnet.resnet152(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 200:
model = pre_act_resnet.resnet200(
num_classes=opt.n_classes,
shortcut_type=opt.resnet_shortcut,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model == 'densenet':
assert opt.model_depth in [121, 169, 201, 264]
from models.densenet import get_fine_tuning_parameters
if opt.model_depth == 121:
model = densenet.densenet121(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 169:
model = densenet.densenet169(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 201:
model = densenet.densenet201(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
elif opt.model_depth == 264:
model = densenet.densenet264(num_classes=opt.n_classes,
sample_size=opt.sample_size,
sample_duration=opt.sample_duration)
if not opt.no_cuda:
model = model.cuda()
model = nn.DataParallel(model, device_ids=None)
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model == 'densenet':
model.module.classifier = nn.Linear(
model.module.classifier.in_features,
opt.n_finetune_classes)
model.module.classifier = model.module.classifier.cuda()
else:
model.module.fc = nn.Linear(model.module.fc.in_features,
opt.n_finetune_classes)
model.module.fc = model.module.fc.cuda()
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
return model, parameters
else:
if opt.pretrain_path:
print('loading pretrained model {}'.format(opt.pretrain_path))
pretrain = torch.load(opt.pretrain_path)
assert opt.arch == pretrain['arch']
model.load_state_dict(pretrain['state_dict'])
if opt.model == 'densenet':
model.classifier = nn.Linear(model.classifier.in_features,
opt.n_finetune_classes)
else:
model.fc = nn.Linear(model.fc.in_features,
opt.n_finetune_classes)
parameters = get_fine_tuning_parameters(model, opt.ft_begin_index)
return model, parameters
return model, model.parameters()
RootPath = "D:/project/shallowNN/dataset/"
df = pd.read_csv(RootPath + "CheXpert-v1.0-small/TEST.csv")
df = preprocess(df, RootPath)
# df = df.sample(100)
test_dl = DataLoader(CheXpert(df), batch_size=20, shuffle=True)
# model_f = torch.load("D:/project/shallowNN/ShallowDeepNN/scripts/models/Chexpert/checkpoints/m_0120_012145.pth.tar",
# map_location=torch.device("cpu"))
# model_f = model_f['state_dict']
model_f = torch.load(
"D:/project/shallowNN/ShallowDeepNN/scripts/models/Chexpert/checkpoints/epoch_31.pth"
)
model = densenet121(num_classes=df.shape[1] - 1)
model = torch.nn.DataParallel(model)
model.load_state_dict(model_f)
LABELS = []
OUTPUTS = []
model.eval()
for d, l in tqdm(test_dl):
print(d.shape)
o = model(d)
LABELS.extend(l.detach().cpu().numpy())
OUTPUTS.extend(o.detach().cpu().numpy())
LABELS = np.asarray(LABELS)
OUTPUTS = np.asarray(OUTPUTS)
def __init__(self,
base_model,
in_channels=3,
out_channels=1,
dropout_rate=0.2,
pretrained=False):
super().__init__()
assert base_model in [
'resnet50', 'resnet101', 'densenet121', 'densenet201',
'efficientnetb0', 'efficientnetb4'
]
self._in_channels = in_channels
self._out_channels = out_channels
if base_model == 'resnet50':
if pretrained:
assert in_channels == 3
self._base_model = resnet50(pretrained=True,
drop_rate=dropout_rate)
else:
self._base_model = resnet50(pretrained=False,
in_channels=in_channels,
drop_rate=dropout_rate)
fc_in_features = 2048
if base_model == 'resnet101':
if pretrained:
assert in_channels == 3
self._base_model = resnet101(pretrained=True,
drop_rate=dropout_rate)
else:
self._base_model = resnet101(pretrained=False,
in_channels=in_channels,
drop_rate=dropout_rate)
fc_in_features = 2048
if base_model == 'densenet121':
if pretrained:
assert in_channels == 3
self._base_model = densenet121(pretrained=True,
drop_rate=dropout_rate)
else:
self._base_model = densenet121(pretrained=False,
drop_rate=dropout_rate,
in_channels=in_channels)
fc_in_features = 1024
if base_model == 'densenet201':
if pretrained:
assert in_channels == 3
self._base_model = densenet201(pretrained=True,
drop_rate=dropout_rate)
else:
self._base_model = densenet201(pretrained=False,
drop_rate=dropout_rate,
in_channels=in_channels)
fc_in_features = 1920
if base_model == 'efficientnetb0':
if pretrained:
assert in_channels == 3
self._base_model = EfficientNet.from_pretrained(
'efficientnet-b0')
else:
self._base_model = EfficientNet.from_name(
'efficientnet-b0', {'in_channels': in_channels})
fc_in_features = 1280
if base_model == 'efficientnetb4':
if pretrained:
assert in_channels == 3
self._base_model = EfficientNet.from_pretrained(
'efficientnet-b4')
else:
self._base_model = EfficientNet.from_name(
'efficientnet-b4', {'in_channels': in_channels})
fc_in_features = 1792
self._fc_mu1 = torch.nn.Linear(fc_in_features, fc_in_features)
self._fc_mu2 = torch.nn.Linear(fc_in_features, out_channels)
self._fc_logvar1 = torch.nn.Linear(fc_in_features, fc_in_features)
# self._fc_logvar2 = torch.nn.Linear(fc_in_features, out_channels)
self._fc_logvar2 = torch.nn.Linear(fc_in_features, 1)
if 'resnet' in base_model:
self._base_model.fc = torch.nn.Identity()
elif 'densenet' in base_model: # densenet
self._base_model.classifier = torch.nn.Identity()
elif 'efficientnet' in base_model:
self._base_model._fc = torch.nn.Identity()
self._dropout_T = 25
self._dropout_p = 0.5
np.random.seed(seed)
##################
# Initialization #
##################
# TODO: Put into command line args
num_classes = 10
batch_size = 16
num_epochs = 10
base_lr = 0.1
use_cuda = False
device = torch.device("cuda" if use_cuda else "cpu")
pathlib.Path('./snapshots').mkdir(parents=True, exist_ok=True)
# Load a model pretrained on ImageNet
model = densenet.densenet121(pretrained=True)
# Replace classification layer
model.classifier = torch.nn.Linear(model.classifier.in_features,
num_classes)
model.to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=base_lr, momentum=0.9)
############
# Training #
############
train_loader, val_loader = get_train_val_split(batch_size)
# Use model that performs best on validation for testing
best_val_accuracy = 0
n=5).reset_index() # split train - test set.
tensor_transform_test = transforms.Compose([
CenterCrop(896),
transforms.ToTensor(),
lambda x: x.float(),
])
dataset_test = KneeGradingDataset(val,
HOME_PATH,
tensor_transform_test,
stage='val')
test_loader = data.DataLoader(dataset_test, batch_size=2)
print('Validation data:', len(dataset_test))
# Network
#net = DenseNet(121,True,pool)
net = dn.densenet121(pretrained=True)
net.classifier = nn.Sequential(nn.Dropout(0.4), nn.Linear(1024, 5))
print(net)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in net.parameters()])))
print('############### Model Finished ####################')
criterion = nn.CrossEntropyLoss()
if USE_CUDA:
state_dict = torch.load(model_file_path)
net.load_state_dict(state_dict)
criterion.cuda()
net.cuda()
else:
state_dict = torch.load(model_file_path, map_location='cpu')
net.load_state_dict(state_dict)
def testRecord():
rootpath = 'data/Test/'
csv_path = 'Tests/question.csv'
csv_file = pd.read_csv(rootpath + csv_path, header=None).values
count = 0
anno = {}
outlist = []
print('Data Preprocessing!')
for row in tqdm(csv_file):
singlelist = []
for unit in row:
singlelist.append(unit)
anno[row[0]] = count
count += 1
outlist.append(singlelist)
print('Start Testing!')
for attribute_type in _ATTRIBUTE_INFO.keys():
test_dataset = attribute.Attribute(csv_path,
True,
attribute_type,
train=False,
is_full_mode=True,
csv_prefix=rootpath)
test_loader = DataLoader(dataset=test_dataset,
batch_size=_BATCH_SIZE,
shuffle=False)
if _NETWORK_TYPE == 'resnet-v2':
Network = resnet.resnet50(pretrained=True, v2=True,
dp_ratio=0.5).cuda()
Network.fc = torch.nn.Linear(
2048, _ATTRIBUTE_INFO[attribute_type]).cuda()
elif _NETWORK_TYPE == 'resnet':
Network = resnet.resnet50(pretrained=True, v2=False,
dp_ratio=0.5).cuda()
Network.fc = torch.nn.Linear(
2048, _ATTRIBUTE_INFO[attribute_type]).cuda()
elif _NETWORK_TYPE == 'densenet':
Network = densenet.densenet121(
pretrained=True,
num_classes=_ATTRIBUTE_INFO[attribute_type],
dp_ratio=0.5).cuda()
else:
raise 'Unknown network type error!'
Network.eval()
# print(resnet.state_dict().keys())
para_dict = torch.load(_ATTRIBUTE_MODELS[attribute_type] +
attribute_type + '/' +
str(_ATTRIBUTE_EPOCH[attribute_type]) +
'-params.pk1')
# print('para_dict')
# print(para_dict.keys())
Network.load_state_dict(para_dict)
attribute_prob = []
attribute_mark = []
for test_num in range(_TEST_AUGTIMES):
test_count = 0
print('===>' + attribute_type + ' aug-testing ' +
str(test_num + 1))
for batch_x, batch_y, meta in tqdm(test_loader):
batch_x = Variable(batch_x, volatile=True).cuda()
out = Network(batch_x)
out = torch.nn.functional.softmax(out, dim=1)
out = out.cpu().data.numpy()
for i in range(len(out)):
if test_count >= len(attribute_prob):
attribute_prob.append([])
attribute_mark.append(meta['img_path'][i])
attribute_prob[test_count].append(out[i])
test_count += 1
for i in range(len(attribute_prob)):
mean_prob = np.sum(attribute_prob[i], axis=0) / _TEST_AUGTIMES
resultstr = ''
for j in range(len(mean_prob)):
resultstr += '{:.4f};'.format(mean_prob[j])
resultstr = resultstr[0:len(resultstr) - 1]
outlist[anno[attribute_mark[i][
len(rootpath):len(attribute_mark[i])]]].append(resultstr)
# print(meta)
print(attribute_type + ' done!')
out_csv = pd.DataFrame(np.array(outlist))
out_csv.to_csv('answer_full_label.csv', index=False, header=None)
def main():
rootpath = 'data/test/'
txtpath = 'data/test.txt'
inp_pre = 556
inp_res = 512
print('Start Testing!')
test_dataset = brand.Brand(txtpath,
rootpath,
num_classes=_NUM_CLASSES,
inp_pre=inp_pre,
inp_res=inp_res,
train=False,
val=False)
test_loader = DataLoader(dataset=test_dataset,
batch_size=_BATCH_SIZE,
shuffle=False)
if _NETWORK_TYPE == 'resnet-v2':
Network = resnet.resnet50(pretrained=True, v2=True,
dp_ratio=0.5).cuda()
Network.fc = torch.nn.Linear(2048,
_ATTRIBUTE_INFO[attribute_type]).cuda()
elif _NETWORK_TYPE == 'resnet':
Network = resnet.resnet50(pretrained=True, v2=False,
dp_ratio=0.5).cuda()
Network.fc = torch.nn.Linear(2048,
_ATTRIBUTE_INFO[attribute_type]).cuda()
elif _NETWORK_TYPE == 'senet':
Network = pretrainedmodels.__dict__['se_resnet50'](
pretrained=False).cuda()
Network.avg_pool = torch.nn.AvgPool2d(16, stride=1)
Network.last_linear = torch.nn.Linear(2048, _NUM_CLASSES).cuda()
elif _NETWORK_TYPE == 'senet154':
Network = pretrainedmodels.__dict__['senet154']().cuda()
Network.avg_pool = torch.nn.AvgPool2d(16, stride=1)
Network.last_linear = torch.nn.Linear(2048, _NUM_CLASSES).cuda()
elif _NETWORK_TYPE == 'densenet':
Network = densenet.densenet121(pretrained=False,
num_classes=_NUM_CLASSES).cuda()
else:
raise 'Unknown network type error!'
Network.eval()
# print(resnet.state_dict().keys())
para_dict = torch.load(_MODELS_PATH + '/' + str(_EPOCH) + '-params.pk1')
# print('para_dict')
# print(para_dict.keys())
Network.load_state_dict(para_dict)
attribute_prob = []
attribute_mark = []
for test_num in range(_TEST_AUGTIMES):
test_count = 0
print('===> aug-testing ' + str(test_num + 1))
for batch_x, batch_y, meta in tqdm(test_loader):
batch_x = Variable(batch_x, volatile=True).cuda()
out = Network(batch_x)
out = torch.nn.functional.softmax(out, dim=1)
out = out.cpu().data.numpy()
for i in range(len(out)):
if test_count >= len(attribute_prob):
attribute_prob.append([])
attribute_mark.append(meta['img_path'][i])
attribute_prob[test_count].append(out[i])
test_count += 1
results = []
for i in range(len(attribute_prob)):
mean_prob = np.sum(attribute_prob[i], axis=0) / _TEST_AUGTIMES
resultstr = attribute_mark[i][len(rootpath):len(
attribute_mark[i])] + ' ' + str(np.argmax(mean_prob) + 1)
results.append(resultstr)
print('done!')
# print(meta)
with open('results/result.txt', 'w') as f:
for i in range(len(results)):
f.write(results[i] + '\n')
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils,datasets, models
from PIL import Image
import time
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from densenet import densenet121
import torch.nn.functional as F
from sklearn.metrics import roc_curve
from get_data import train_model
use_gpu = torch.cuda.is_available()
#model_ft = models.densenet121(pretrained = False)
model_ft = densenet121(pretrained = False)
#torch.nn.Linear(in_features,out_features,bias = True)
num_ftrs = model_ft.classifier.in_features
model_ft.classifier = nn.Linear(num_ftrs,1)
if use_gpu:
model_ft = model_ft.cuda()
criterion = nn.BCELoss()
optimizer_ft = optim.Adam(model_ft.parameters(), lr = 0.001, betas=(0.9, 0.999))
exp_lr_scheduler = lr_scheduler.ReduceLROnPlateau(optimizer_ft, mode='min', factor=0.1, patience=2,verbose=True)
def main():
global args, best_acc1
args = parser.parse_args()
if args.logging:
filename = 'resnet' if args.resnet else 'densenet'
filename += 'pretrained' if args.pretrained else ''
f = open(filename + '.txt', 'w')
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
# create model
if args.resnet:
print("=> creating resnet model")
model = resnet18(pretrained=args.pretrained)
f.write("Model: resnet \n")
else:
print("=> creating densenet model")
model = densenet121(pretrained=args.pretrained)
f.write("Model: densenet \n")
if args.gpu:
model = model.cuda()
model = torch.nn.DataParallel(model).cuda()
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
betas=(0.9, 0.999))
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
f.write("File_directory: " + args.data + "\n")
args.data = os.path.expanduser(args.data)
traindir = os.path.join(args.data, 'train')
valdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset = datasets.ImageFolder(
traindir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]))
val_dataset = datasets.ImageFolder(
valdir,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
validate(val_loader, model, criterion, args.gpu, args.print_freq, f)
return
mode = 'evaluate' if args.evaluate else 'training'
f.write("Mode :" + mode + "\n")
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, args.lr, epoch, args.epoch_decay)
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args.gpu,
args.print_freq, f)
# evaluate on validation set
acc1 = validate(val_loader, model, criterion, args.gpu,
args.print_freq, f)
# remember best acc@1 and save checkpoint
is_best = acc1 > best_acc1
best_acc1 = max(acc1, best_acc1)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc1,
'optimizer': optimizer.state_dict(),
},
is_best,
model='resnet18' if args.resnet else 'densenet121')
f.write("Best accuracy :" + str(best_acc1))
f.close()
def __init__(self, base):
super(Model, self).__init__()
self.base = base
self.bn1 = nn.BatchNorm1d(1)
self.classifier = nn.Linear(1024 + 112, 34)
def forward(self, x1, x2, x3):
x1 = self.base(x1)
x2 = self.bn1(x2)
x2 = x2.view(x2.size(0), -1)
x = torch.cat([x1, x2, x3], 1)
x = self.classifier(x)
return x
model = Model(densenet121()).cuda()
testres = []
for foldNum in range(5):
testData = TestData(subA,
'testA',
Feat_RR,
transform=transforms.Compose([ToTensor()]))
testloader = DataLoader(testData, batch_size=64, shuffle=False)
model.load_state_dict(
torch.load('dense121/weight_best_%s.pt' % str(foldNum)))
model.cuda()
model.eval()
test_outputs = []
test_fnames = []
for images, fea, fea2, fnames in testloader:
preds = torch.sigmoid(
def main():
resume_ep = opt.r
train_dir = opt.train_dir
check_dir = opt.check_dir
val_dir = opt.val_dir
bsize = opt.b
iter_num = opt.e
label_weight = [4.858, 17.57]
std = [.229, .224, .225]
mean = [.485, .456, .406]
os.system('rm -rf ./runs2/*')
writer = SummaryWriter('./runs2/' +
datetime.now().strftime('%B%d %H:%M:%S'))
if not os.path.exists('./runs2'):
os.mkdir('./runs2')
if not os.path.exists(check_dir):
os.mkdir(check_dir)
# models
if 'vgg' == opt.i:
feature = Vgg16(pretrained=True)
elif 'resnet' == opt.i:
feature = resnet50(pretrained=True)
elif 'densenet' == opt.i:
feature = densenet121(pretrained=True)
feature.cuda()
classifier = Classifier(opt.i)
classifier.cuda()
if resume_ep >= 0:
feature_param_file = glob.glob('%s/feature-epoch-%d*.pth' %
(check_dir, resume_ep))
classifier_param_file = glob.glob('%s/classifier-epoch-%d*.pth' %
(check_dir, resume_ep))
feature.load_state_dict(torch.load(feature_param_file[0]))
classifier.load_state_dict(torch.load(classifier_param_file[0]))
train_loader = torch.utils.data.DataLoader(MyClsData(train_dir,
transform=True,
crop=True,
hflip=True,
vflip=False),
batch_size=bsize,
shuffle=True,
num_workers=4,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(MyClsTestData(val_dir,
transform=True),
batch_size=1,
shuffle=True,
num_workers=4,
pin_memory=True)
criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor(label_weight))
criterion.cuda()
optimizer_classifier = torch.optim.Adam(classifier.parameters(), lr=1e-3)
optimizer_feature = torch.optim.Adam(feature.parameters(), lr=1e-4)
acc = 0.0
for it in range(resume_ep + 1, iter_num):
for ib, (data, lbl) in enumerate(train_loader):
inputs = Variable(data.float()).cuda()
lbl = Variable(lbl.long()).cuda()
feats = feature(inputs)
output = classifier(feats)
loss = criterion(output, lbl)
classifier.zero_grad()
feature.zero_grad()
loss.backward()
optimizer_feature.step()
optimizer_classifier.step()
if ib % 20 == 0:
# image = make_image_grid(inputs.data[:4, :3], mean, std)
# writer.add_image('Image', torchvision.utils.make_grid(image), ib)
writer.add_scalar('M_global', loss.data[0], ib)
print('loss: %.4f (epoch: %d, step: %d), acc: %.4f' %
(loss.data[0], it, ib, acc))
del inputs, lbl, loss, feats
gc.collect()
new_acc = eval_acc(feature, classifier, val_loader)
if new_acc > acc:
filename = ('%s/classifier-epoch-%d-step-%d.pth' %
(check_dir, it, ib))
torch.save(classifier.state_dict(), filename)
filename = ('%s/feature-epoch-%d-step-%d.pth' %
(check_dir, it, ib))
torch.save(feature.state_dict(), filename)
print('save: (epoch: %d, step: %d)' % (it, ib))
acc = new_acc
