def main():
device = torch.device("cuda" if not hyperparams.
hyperparameter_defaults['no_cuda'] else "cpu")
hyperparams.hyperparameter_defaults['run_name'] = fileutils.rand_run_name()
trainloader, testloader = dataloader.get_train_test_dataloader_cifar10()
print("Initializing datasets and dataloaders")
model_new = basemodelclass.ResNet18(
hyperparams.hyperparameter_defaults['dropout'])
wandb_run_init = wandb.init(
config=hyperparams.hyperparameter_defaults,
project=hyperparams.hyperparameter_defaults['project'])
wandb.watch_called = False
config = wandb.config
print(config)
wandb.watch(model_new, log="all")
trainloader, testloader = dataloader.get_train_test_dataloader_cifar10()
optimizer = optim.SGD(model_new.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
#optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss
#scheduler = None
#scheduler = CyclicLR(optimizer, base_lr=config.lr*0.01, max_lr=config.lr, mode='triangular', gamma=1.)
#, cycle_momentum=False)#,step_size_up=1000)#, scale_fn='triangular',step_size_up=200)
#scheduler = StepLR(optimizer, step_size=config.sched_lr_step, gamma=config.sched_lr_gamma)
#scheduler = MultiStepLR(optimizer, milestones=[10,20], gamma=config.sched_lr_gamma)
#scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.2, patience=2, verbose=True, threshold=0.0001)
scheduler = traintest.MyOwnReduceLROnPlateau(optimizer,
mode='min',
factor=0.2,
patience=2,
verbose=True,
threshold=0.0001)
#scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=config.factor, patience=4, verbose=True, threshold=config.lr_decay_threshold)
final_model_path = traintest.execute_model(
model_new,
hyperparams.hyperparameter_defaults,
trainloader,
testloader,
device,
dataloader.classes,
wandb=wandb,
optimizer_in=optimizer,
scheduler=scheduler,
prev_saved_model=saved_model_path,
criterion=criterion,
save_best=True,
lars_mode=False,
batch_step=False)
def main():
device = torch.device("cuda" if not hyperparams.
hyperparameter_defaults['no_cuda'] else "cpu")
hyperparams.hyperparameter_defaults['run_name'] = fileutils.rand_run_name()
transform_train = Compose([
PadIfNeeded(min_height=40, min_width=40, always_apply=True,
p=1.0), #,value=(0,0,0), border_mode=0),
RandomCrop(height=32, width=32, p=1),
#Flip(p=0.5),
IAAFliplr(p=0.5),
Cutout(num_holes=1,
max_h_size=8,
max_w_size=8,
always_apply=True,
p=1,
fill_value=[0.4914 * 255, 0.4826 * 255, 0.44653 * 255]),
Normalize(
mean=[0.4914, 0.4826, 0.44653],
std=[0.24703, 0.24349, 0.26519],
),
ToTensor()
])
trainloader, testloader = dataloader.get_train_test_dataloader_cifar10(
transform_train=transform_train)
print("Initializing datasets and dataloaders")
#model_new = basemodelclass.ResNet18(hyperparams.hyperparameter_defaults['dropout'])
model_new = basemodelclass.S11ResNet()
wandb_run_init = wandb.init(
config=hyperparams.hyperparameter_defaults,
project=hyperparams.hyperparameter_defaults['project'])
wandb.watch_called = False
config = wandb.config
print(config)
wandb.watch(model_new, log="all")
#trainloader, testloader = dataloader.get_train_test_dataloader_cifar10()
optimizer = optim.SGD(model_new.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
#optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss
#scheduler = None
cycle_momentum = True if config.cycle_momentum == "True" else False
print("Momentum cycling set to {}".format(cycle_momentum))
#scheduler = CyclicLR(optimizer, base_lr=config.lr*0.01, max_lr=config.lr, mode='triangular', gamma=1., cycle_momentum=True,step_size_up=2000)#, scale_fn='triangular',step_size_up=200)
scheduler = OneCycleLR(optimizer,
config.ocp_max_lr,
epochs=config.epochs,
cycle_momentum=cycle_momentum,
steps_per_epoch=len(trainloader),
base_momentum=config.momentum,
max_momentum=0.95,
pct_start=0.208,
anneal_strategy=config.anneal_strategy,
div_factor=config.div_factor,
final_div_factor=config.final_div_factor)
#scheduler =CyclicLR(optimizer, base_lr=config.lr*0.01, max_lr=config.lr, mode='triangular', gamma=1., cycle_momentum=True,step_size_up=2000)#, scale_fn='triangular',step_size_up=200)
#scheduler = StepLR(optimizer, step_size=config.sched_lr_step, gamma=config.sched_lr_gamma)
#scheduler = MultiStepLR(optimizer, milestones=[10,20], gamma=config.sched_lr_gamma)
#scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.2, patience=2, verbose=True, threshold=0.0001)
#scheduler = traintest.MyOwnReduceLROnPlateau(optimizer, mode='min', factor=0.2, patience=2, verbose=True, threshold=0.0001)
#scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=config.factor, patience=4, verbose=True, threshold=config.lr_decay_threshold)
final_model_path = traintest.execute_model(
model_new,
hyperparams.hyperparameter_defaults,
trainloader,
testloader,
device,
dataloader.classes,
wandb=wandb,
optimizer_in=optimizer,
scheduler=scheduler,
prev_saved_model=saved_model_path,
criterion=criterion,
save_best=True,
lars_mode=False,
batch_step=True)
def main():
device = torch.device("cuda" if not hyperparams.
hyperparameter_defaults['no_cuda'] else "cpu")
hyperparams.hyperparameter_defaults['run_name'] = fileutils.rand_run_name()
print("Initializing datasets and dataloaders")
train_csv_file = "/content/drive/My Drive/EVA4/S2_Train.csv"
test_csv_file = "/content/drive/My Drive/EVA4/S2_Test.csv"
#model_new = basemodelclass.ResNet18(hyperparams.hyperparameter_defaults['dropout'], num_classes=200)
#trainloader, testloader = dataloader.get_imagenet_loaders(train_path, test_path, transform_train=None, transform_test=None)
transform_train = resize_bg_train_rrs(224, 224, [0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
transform_test = resize_bg(224, 224, [0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])
default_model = torch.hub.load('pytorch/vision:v0.6.0',
'mobilenet_v2',
pretrained=True)
model_new = basemodelclass.MobileNetV2New(default_model, 4)
#model_new = basemodelclass.MobileNetV24C(default_model, 4)
updatable_params = model_new.unfreeze_core_layer(
hyperparams.hyperparameter_defaults['unfreeze_layer'])
trainset = dataloader.QDFDataSet(
'/content/drive/My Drive/EVA4/S2_Train.csv', transform=transform_train)
trainloader = dataloader.get_dataloader(
trainset,
hyperparams.hyperparameter_defaults['batch_size'],
shuffle=True,
num_workers=4)
testset = dataloader.QDFDataSet('/content/drive/My Drive/EVA4/S2_Test.csv',
transform=transform_test)
testloader = dataloader.get_dataloader(
testset,
hyperparams.hyperparameter_defaults['batch_size'],
shuffle=False,
num_workers=4)
wandb_run_init = wandb.init(
config=hyperparams.hyperparameter_defaults,
project=hyperparams.hyperparameter_defaults['project'])
wandb.watch_called = False
config = wandb.config
print(config)
wandb.watch(model_new, log="all")
#trainloader, testloader = dataloader.get_train_test_dataloader_cifar10()
optimizer = optim.SGD(updatable_params,
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
#optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
#criterion=nn.CrossEntropyLoss
criterion = nn.NLLLoss
scheduler = None
cycle_momentum = True if config.cycle_momentum == "True" else False
print("Momentum cycling set to {}".format(cycle_momentum))
if (config.lr_policy == "clr"):
scheduler = CyclicLR(
optimizer,
base_lr=config.lr * 0.01,
max_lr=config.lr,
mode='triangular',
gamma=1.,
cycle_momentum=True,
step_size_up=256) #, scale_fn='triangular',step_size_up=200)
else:
scheduler = OneCycleLR(optimizer,
config.ocp_max_lr,
epochs=config.epochs,
cycle_momentum=cycle_momentum,
steps_per_epoch=len(trainloader),
base_momentum=config.momentum,
max_momentum=0.95,
pct_start=config.split_pct,
anneal_strategy=config.anneal_strategy,
div_factor=config.div_factor,
final_div_factor=config.final_div_factor)
local_classes = [
'Large QuadCopters', 'Flying Birds', 'Winged Drones',
'Small QuadCopters'
]
final_model_path = traintest.execute_model(
model_new,
hyperparams.hyperparameter_defaults,
trainloader,
testloader,
device,
local_classes,
wandb=wandb,
optimizer_in=optimizer,
scheduler=scheduler,
prev_saved_model=saved_model_path,
criterion=criterion,
save_best=True,
lars_mode=False,
batch_step=True)
def main():
device = torch.device("cuda" if not hyperparams.
hyperparameter_defaults['no_cuda'] else "cpu")
hyperparams.hyperparameter_defaults['run_name'] = fileutils.rand_run_name()
print("Initializing datasets and dataloaders")
train_path = "/content/t2/train"
test_path = "/content/t2/val"
#model_new = basemodelclass.ResNet18(hyperparams.hyperparameter_defaults['dropout'], num_classes=200)
trainloader, testloader = dataloader.get_imagenet_loaders(
train_path, test_path, transform_train=None, transform_test=None)
model_new = basemodelclass.S11ResNet()
wandb_run_init = wandb.init(
config=hyperparams.hyperparameter_defaults,
project=hyperparams.hyperparameter_defaults['project'])
wandb.watch_called = False
config = wandb.config
print(config)
wandb.watch(model_new, log="all")
#trainloader, testloader = dataloader.get_train_test_dataloader_cifar10()
optimizer = optim.SGD(model_new.parameters(),
lr=config.lr,
momentum=config.momentum,
weight_decay=config.weight_decay)
#optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss
#scheduler = None
cycle_momentum = True if config.cycle_momentum == "True" else False
print("Momentum cycling set to {}".format(cycle_momentum))
if (config.lr_policy == "clr"):
scheduler = CyclicLR(
optimizer,
base_lr=config.lr * 0.01,
max_lr=config.lr,
mode='triangular',
gamma=1.,
cycle_momentum=True,
step_size_up=256) #, scale_fn='triangular',step_size_up=200)
else:
scheduler = OneCycleLR(optimizer,
config.ocp_max_lr,
epochs=config.epochs,
cycle_momentum=cycle_momentum,
steps_per_epoch=len(trainloader),
base_momentum=config.momentum,
max_momentum=0.95,
pct_start=config.split_pct,
anneal_strategy=config.anneal_strategy,
div_factor=config.div_factor,
final_div_factor=config.final_div_factor)
final_model_path = traintest.execute_model(
model_new,
hyperparams.hyperparameter_defaults,
trainloader,
testloader,
device,
dataloader.classes,
wandb=wandb,
optimizer_in=optimizer,
scheduler=scheduler,
prev_saved_model=saved_model_path,
criterion=criterion,
save_best=True,
lars_mode=False,
batch_step=True)
def main():
device = torch.device("cuda" if not hyperparams.
hyperparameter_defaults['no_cuda'] else "cpu")
fileutils.rand_run_name()
# print(len(trainloader))
# dataiter = iter(trainloader)
# images, labels = dataiter.next()
# print(images.shape)
#hyperparams.print_hyperparams()
# fileutils.get_image_samples(trainloader, classes)
# model_new = basemodelclass.CIFARModelDepthDilate().to(device)
# summary(model_new,input_size=(3, 32, 32))
# type(model_new)
print("Initializing datasets and dataloaders")
torch.manual_seed(hyperparams.hyperparameter_defaults['seed'])
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data',
train=True,
download=True,
transform=transform_train)
trainloader = dataloader.get_dataloader(
trainset,
hyperparams.hyperparameter_defaults['batch_size'],
shuffle=True,
num_workers=2)
# torch.utils.data.DataLoader(trainset, batch_size=hyperparameter_defaults['batch_size'],
# shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data',
train=False,
download=True,
transform=transform_test)
testloader = dataloader.get_dataloader(
testset,
hyperparams.hyperparameter_defaults['batch_size'],
shuffle=False,
num_workers=2)
# torch.utils.data.DataLoader(testset, batch_size=hyperparameter_defaults['batch_size'],
# shuffle=False, num_workers=2)
optimizer = optim.SGD #(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss
#model = basemodelclass.CIFARModelBuilder()#.to(device)
#model_new = basemodelclass.CIFARModelDepthDilate#.to(device)
model_new = basemodelclass.ResNet18(
hyperparams.hyperparameter_defaults['dropout'])
#execute_model(model, hyperparameter_defaults, )
final_model_path = traintest.execute_model(
model_new,
hyperparams.hyperparameter_defaults,
trainloader,
testloader,
device,
dataloader.classes,
optimizer=optimizer,
prev_saved_model=saved_model_path,
criterion=criterion,
save_best=True)