def GetAppropriateModel(arch, dataset, dumpData=False):
if arch == "resnet18":
if dataset == "cifar10" :
print("Going for cifar10")
return resnet18(32, num_classes=10)
elif dataset == "cifar100":
print("Going for cifar100")
return resnet18(32, num_classes=100)
elif arch == "resnet152":
if dataset == "cifar10":
print("Going for resnet152 cifar10")
return resnet152(num_classes=10)
elif dataset == "cifar100":
print("Going for resnet152 cifar100")
return resnet152(num_classes=100)
elif arch == "resnet50":
if dataset == "cifar10":
print("Going for cifar10 resnet50")
print("DumpData ",dumpData)
return resnet50( num_classes=10)
elif dataset == "cifar100":
print("Going for cifar100 resnet50")
return resnet50(num_classes=100)
def load_model(model_name, num_classes, pretrain=True, require_grad=True):
print('==> Building model..')
if model_name == 'resnet50_pmg':
net = resnet50(pretrained=pretrain)
for param in net.parameters():
param.requires_grad = require_grad
net = PMG(net, 512, num_classes)
return net
def GetAppropriateModelFreeze(arch, dataset, dumpData=False, dumpPath="emptyPath", dumpLayer=0):
if arch == "resnet18":
if dataset == "cifar10" :
print("Going for cifar10")
return resnet18(32, num_classes=10, dumpData=dumpData, dumpPath=dumpPath, dumpLayer=dumpLayer)
elif dataset == "cifar100":
return resnet18(32, num_classes=100, dumpData=dumpData, dumpPath=dumpPath, dumpLayer=dumpLayer)
elif arch == "resnet50":
if dataset == "cifar10" :
print("Going for cifar10")
return resnet50(32, num_classes=10, dumpData=dumpData, dumpPath=dumpPath,dumpLayer=dumpLayer)
elif dataset == "cifar100":
print("Going for cifar100")
return resnet50(32, num_classes=100, dumpData=dumpData, dumpPath=dumpPath, dumpLayer=dumpLayer)
elif arch == "resnet152":
if dataset == "cifar10":
print("Going for resnet152 cifar10")
return resnet152(num_classes=10, dumpData=dumpData, dumpPath=dumpPath, dumpLayer=dumpLayer)
elif dataset == "cifar100":
print("Going for resnet152 cifar101")
return resnet152(num_classes=100, dumpData=dumpData, dumpPath=dumpPath, dumpLayer=dumpLayer)
def load_ms_layer(model_name, classes_nums, pretrain=True, require_grad=True):
'''
MS-DeJOR
'''
print('==> Building model..')
if model_name == 'resnet50_ms':
net = resnet50(pretrained=pretrain)
for param in net.parameters():
param.requires_grad = require_grad
net = MS_resnet_layer(net, 50, 512, classes_nums)
elif model_name == 'resnet18_ms':
net = resnet18(pretrained=pretrain)
for param in net.parameters():
param.requires_grad = require_grad
net = MS_resnet_layer(net, 18, 512, classes_nums)
elif model_name == 'vgg16_ms':
net = VGG16(pretrained=pretrain)
for param in net.parameters():
param.requires_grad = require_grad
net = MS_vgg16_layer(net, 16, 512, classes_nums)
return net
def train(
epochs=120,
init_lr=0.001,
lr_coefficient=5,
weight_decay=1e-8,
model_num=1,
batch_size=64,
train_dir='s3://classifier-gar/train_try/',
test_dir='s3://classifier-gar/test_try/',
log_dir='s3://classifier-gar/log/', #用之前记着写默认路径
version='V0_0_0'):
#loading_data
print("data loading...\n")
transform = enhance_transforms()
transform_std = transform_standard()
trainset = DataClassify(train_dir, transforms=transform)
testset = DataClassify(test_dir, transforms=transform_std)
total_train = len(trainset)
total_test = len(testset)
data_loader_train = t.utils.data.DataLoader(dataset=trainset,
batch_size=batch_size,
shuffle=True)
data_loader_test = t.utils.data.DataLoader(dataset=testset,
batch_size=batch_size,
shuffle=False)
print("data loading complete\n")
##################################
#TO DO
##################################
if model_num == 0: #写不同模型的分支
exit(0)
else:
net = resnet50()
##################################
cost = t.nn.CrossEntropyLoss()
train_loss_list = []
train_accurate_list = []
test_loss_list = []
test_accurate_list = []
for epoch in range(epochs):
print("epoch " + str(epoch + 1) + " start training...\n")
net.train()
learning_rate = dloss(train_loss_list, init_lr, lr_coefficient,
init_lr)
optimizer = t.optim.Adam(list(net.parameters()),
lr=learning_rate,
weight_decay=weight_decay)
run_loss, corr = train_once(data_loader_train, net, optimizer, cost)
train_loss_list.append(run_loss / total_train)
train_accurate_list.append(corr / total_train)
print('epoch %d, training loss %.6f, training accuracy %.4f ------\n' %
(epoch + 1, run_loss / total_train, corr / total_train))
print("epoch " + str(epoch + 1) + " finish training\n")
print("-----------------------------------------------\n")
print("epoch " + str(epoch + 1) + " start testing...\n")
net.eval()
test_corr = evaluate(net, data_loader_test)
test_accurate_list.append(test_corr / total_test)
print('epoch %d, testing accuracy %.4f ------\n' %
(epoch + 1, test_corr / total_test))
print("epoch " + str(epoch + 1) + " finish testing\n")
print("-----------------------------------------------\n")
#torch.save(net.module, net_name)#保存模型全部内容，用于进行模型的导入导出，net_name后缀为pkl
#这种方式保存的模型需要使用net = torch.load(net_name)进行加载
#torch.save(net.state_dict(), net_name_para)#只保存参数，用于进行模型的迁移，net_name后缀为pkl
#这种方式保存的模型加载时需要定义网络，并且需要加载的参数名称与保存模型中一致
#并通过net.load_state_dict(torch.load(net_name_para))进行加载
curve_draw(train_loss_list, train_accurate_list, test_accurate_list,
log_dir, version)
print("mission complete")
def train(epochs=epochs,
init_lr=init_lr,
lr_coefficient=lr_coefficient,
weight_decay=weight_decay,
model_num=model_num,
batch_size=batch_size,
train_dir=train_dir,
test_dir=test_dir,
log_dir=log_dir):
#loading_data
print("data loading...\n")
transform = enhance_transforms()
transform_std = transform_standard()
trainset = DataClassify(train_dir, transforms=transform)
testset = DataClassify(test_dir, transforms=transform_std)
total_train = len(trainset)
total_test = len(testset)
data_loader_train = t.utils.data.DataLoader(dataset=trainset,
batch_size=batch_size,
shuffle=True)
data_loader_test = t.utils.data.DataLoader(dataset=testset,
batch_size=batch_size,
shuffle=False)
print("data loading complete\n")
##################################
#TO DO
##################################
if model_num == 0:
exit(0)
elif model_num == 18:
net = resnet18()
elif model_num == 34:
net = resnet34()
elif model_num == 50:
net = resnet50()
elif model_num == 101:
net = resnet101()
elif model_num == 152:
net = resnet152()
##################################
#确定网络基于cpu还是gpu
device = t.device("cuda:0" if t.cuda.is_available() else "cpu")
net.to(device)
cost = t.nn.CrossEntropyLoss()
train_loss_list = []
train_accurate_list = []
test_loss_list = []
test_accurate_list = []
for epoch in range(epochs):
print("epoch " + str(epoch + 1) + " start training...\n")
net.train()
learning_rate = dloss(train_loss_list, init_lr, lr_coefficient,
init_lr)
optimizer = t.optim.Adam(list(net.parameters()),
lr=learning_rate,
weight_decay=weight_decay)
run_loss, corr = train_once(data_loader_train, net, optimizer, cost,
device)
train_loss_list.append(run_loss / total_train)
train_accurate_list.append(corr / total_train)
print('epoch %d, training loss %.6f, training accuracy %.4f ------\n' %
(epoch + 1, run_loss / total_train, corr / total_train))
print("epoch " + str(epoch + 1) + " finish training\n")
print("-----------------------------------------------\n")
print("epoch " + str(epoch + 1) + " start testing...\n")
net.eval()
test_corr = evaluate(net, data_loader_test, device)
test_accurate_list.append(test_corr / total_test)
print('epoch %d, testing accuracy %.4f ------\n' %
(epoch + 1, test_corr / total_test))
print("epoch " + str(epoch + 1) + " finish testing\n")
print("-----------------------------------------------\n")
t.save(net, save_trained_net)
t.save(net.state_dict(), save_trained_net_params)
curve_draw(train_loss_list, train_accurate_list, test_accurate_list,
log_dir)
print("mission complete")
def train(epochs=120,
init_lr=0.001,
lr_coefficient=5,
weight_decay = 1e-8,
model_num=1,
batch_size=64,
train_dir='s3://classifier-gar/train_try/',
test_dir='s3://classifier-gar/test_try/',
log_dir='s3://classifier-gar/log/',
version = 'V0_0_0'):
#loading_data
print("data loading...\n")
transform = enhance_transforms()
transform_std = transform_standard()
trainset = DataClassify(train_dir, transforms=transform)
testset = DataClassify(test_dir, transforms=transform_std)
total_train = len(trainset)
total_test = len(testset)
data_loader_train = t.utils.data.DataLoader(dataset=trainset, batch_size=batch_size, shuffle=True)
data_loader_test = t.utils.data.DataLoader(dataset=testset, batch_size=batch_size, shuffle=False)
print("data loading complete\n")
##################################
#TO DO
##################################
if model_num==0:
exit(0)
else:
net = resnet50()
##################################
##################
#cuda
##################
device = t.device("cuda:0" if t.cuda.is_available() else "cpu")
net.to(device)
cost = t.nn.CrossEntropyLoss()
train_loss_list = []
train_accurate_list = []
test_loss_list = []
test_accurate_list = []
for epoch in range(epochs):
print("epoch " + str(epoch+1) + " start training...\n")
net.train()
learning_rate = dloss(train_loss_list, init_lr, lr_coefficient, init_lr)
optimizer = t.optim.Adam(list(net.parameters()), lr=learning_rate, weight_decay=weight_decay)
run_loss, corr = train_once(data_loader_train,net, optimizer, cost, device)
train_loss_list.append(run_loss/total_train)
train_accurate_list.append(corr/total_train)
print('epoch %d, training loss %.6f, training accuracy %.4f ------\n' %(epoch+1, run_loss/total_train, corr/total_train))
print("epoch " + str(epoch+1) + " finish training\n")
print("-----------------------------------------------\n")
print("epoch " + str(epoch+1) + " start testing...\n")
net.eval()
test_corr = evaluate(net, data_loader_test, device)
test_accurate_list.append(test_corr/total_test)
print('epoch %d, testing accuracy %.4f ------\n' %(epoch+1, test_corr/total_test))
print("epoch " + str(epoch+1) + " finish testing\n")
print("-----------------------------------------------\n")
#torch.save(net.module, net_name)
#torch.save(net.state_dict(), net_name_para)
curve_draw(train_loss_list, train_accurate_list, test_accurate_list, log_dir, version)
print("mission complete")