def get_model(arch_type):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if arch_type == "fc1":
model = fc1.fc1().to(device)
elif arch_type == "lenet5":
model = LeNet5.LeNet5().to(device)
elif arch_type == "alexnet":
model = AlexNet.AlexNet().to(device)
elif arch_type == "vgg16":
model = vgg.vgg16().to(device)
elif arch_type == "resnet18":
model = resnet.resnet18().to(device)
elif arch_type == "densenet121":
model = densenet.densenet121().to(device)
# If you want to add extra model paste here
else:
print("\nWrong Model choice\n")
exit()
return model
def main(args, ITE=0):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
reinit = True if args.prune_type == "reinit" else False
if args.save_dir:
utils.checkdir(
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/"
)
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/"
)
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/"
)
else:
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
utils.checkdir(f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/")
# Data Loader
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
if args.dataset == "mnist":
traindataset = datasets.MNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.MNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar10":
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
traindataset = datasets.CIFAR10('../data',
train=True,
download=True,
transform=transform_train)
testdataset = datasets.CIFAR10('../data',
train=False,
transform=transform_test)
from archs.cifar10 import AlexNet, LeNet5, fc1, vgg, resnet, densenet
elif args.dataset == "fashionmnist":
traindataset = datasets.FashionMNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.FashionMNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar100":
traindataset = datasets.CIFAR100('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR100('../data',
train=False,
transform=transform)
from archs.cifar100 import AlexNet, fc1, LeNet5, vgg, resnet
# If you want to add extra datasets paste here
else:
print("\nWrong Dataset choice \n")
exit()
if args.dataset == "cifar10":
#trainsampler = torch.utils.data.RandomSampler(traindataset, replacement=True, num_samples=45000) # 45K train dataset
#train_loader = torch.utils.data.DataLoader(traindataset, batch_size=args.batch_size, shuffle=False, num_workers=0, drop_last=False, sampler=trainsampler)
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4)
else:
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
#train_loader = cycle(train_loader)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4)
# Importing Network Architecture
#Initalize hessian dataloader, default batch_num 1
for inputs, labels in train_loader:
hessian_dataloader = (inputs, labels)
break
global model
if args.arch_type == "fc1":
model = fc1.fc1().to(device)
elif args.arch_type == "lenet5":
model = LeNet5.LeNet5().to(device)
elif args.arch_type == "alexnet":
model = AlexNet.AlexNet().to(device)
elif args.arch_type == "vgg16":
model = vgg.vgg16().to(device)
elif args.arch_type == "resnet18":
model = resnet.resnet18().to(device)
elif args.arch_type == "densenet121":
model = densenet.densenet121().to(device)
# If you want to add extra model paste here
else:
print("\nWrong Model choice\n")
exit()
model = nn.DataParallel(model)
# Weight Initialization
model.apply(weight_init)
# Copying and Saving Initial State
initial_state_dict = copy.deepcopy(model.state_dict())
if args.save_dir:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/initial_state_dict_{args.prune_type}.pth"
)
else:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth"
)
# global total_params
total_params = 0
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
total_params += param.numel()
# Making Initial Mask
make_mask(model, total_params)
# Optimizer and Loss
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=1e-4)
# warm up schedule; scheduler_warmup is chained with schduler_steplr
scheduler_steplr = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[0, 15],
gamma=0.1,
last_epoch=-1)
if args.warmup:
scheduler_warmup = GradualWarmupScheduler(
optimizer,
multiplier=1,
total_epoch=50,
after_scheduler=scheduler_steplr) # 20K=(idx)56, 35K=70
criterion = nn.CrossEntropyLoss(
) # Default was F.nll_loss; why test, train different?
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float)
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
for _ite in range(args.start_iter, ITERATION):
if not _ite == 0:
prune_by_percentile(args.prune_percent,
resample=resample,
reinit=reinit,
total_params=total_params,
hessian_aware=args.hessian,
criterion=criterion,
dataloader=hessian_dataloader,
cuda=torch.cuda.is_available())
if reinit:
model.apply(weight_init)
#if args.arch_type == "fc1":
# model = fc1.fc1().to(device)
#elif args.arch_type == "lenet5":
# model = LeNet5.LeNet5().to(device)
#elif args.arch_type == "alexnet":
# model = AlexNet.AlexNet().to(device)
#elif args.arch_type == "vgg16":
# model = vgg.vgg16().to(device)
#elif args.arch_type == "resnet18":
# model = resnet.resnet18().to(device)
#elif args.arch_type == "densenet121":
# model = densenet.densenet121().to(device)
#else:
# print("\nWrong Model choice\n")
# exit()
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
param_frac = param.numel() / total_params
if param_frac > 0.01:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict, total_params)
# optimizer = torch.optim.SGD([{'params': model.parameters(), 'initial_lr': 0.03}], lr=args.lr, momentum=0.9, weight_decay=1e-4)
# scheduler_steplr = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[0, 14], gamma=0.1, last_epoch=-1)
# scheduler_warmup = GradualWarmupScheduler(optimizer, multiplier=1, total_epoch=56, after_scheduler=scheduler_steplr) # 20K=(idx)56, 35K=70
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Optimizer and Loss
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=1e-4)
# warm up schedule; scheduler_warmup is chained with schduler_steplr
scheduler_steplr = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[0, 15], gamma=0.1, last_epoch=-1)
if args.warmup:
scheduler_warmup = GradualWarmupScheduler(
optimizer,
multiplier=1,
total_epoch=50,
after_scheduler=scheduler_steplr) # 20K=(idx)56, 35K=70
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
pbar = tqdm(range(args.end_iter)) # process bar
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, test_loader, criterion)
# Save Weights for each _ite
if accuracy > best_accuracy:
best_accuracy = accuracy
if args.save_dir:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/{_ite}_model_{args.prune_type}.pth"
)
else:
# torch.save(model,f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth")
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth"
)
# Training
loss = train(model, train_loader, optimizer, criterion,
total_params)
all_loss[iter_] = loss
all_accuracy[iter_] = accuracy
# warm up
if args.warmup:
scheduler_warmup.step()
_lr = optimizer.param_groups[0]['lr']
# Save the model during training
if args.save_freq > 0 and iter_ % args.save_freq == 0:
if args.save_dir:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{args.save_dir}/{_ite}_model_{args.prune_type}_epoch{iter_}.pth"
)
else:
torch.save(
model.state_dict(),
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}_epoch{iter_}.pth"
)
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}% Learning Rate: {_lr:.6f}%'
)
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_loss - np.min(all_loss)) /
np.ptp(all_loss).astype(float),
c="blue",
label="Loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
all_accuracy,
c="red",
label="Accuracy")
plt.title(
f"Loss Vs Accuracy Vs Iterations ({args.dataset},{args.arch_type})"
)
plt.xlabel("Iterations")
plt.ylabel("Loss and Accuracy")
plt.legend()
plt.grid(color="gray")
if args.save_dir:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=1200)
else:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=1200)
plt.close()
# Dump Plot values
if args.save_dir:
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
else:
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
# Dumping mask
if args.save_dir:
with open(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
else:
with open(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
if args.save_dir:
comp.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_bestaccuracy.dat"
)
else:
comp.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat"
)
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(
f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{args.arch_type})"
)
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test accuracy")
plt.xticks(a, comp, rotation="vertical")
plt.ylim(0, 100)
plt.legend()
plt.grid(color="gray")
if args.save_dir:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.save_dir}/{args.prune_type}_AccuracyVsWeights.png",
dpi=1200)
else:
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png",
dpi=1200)
plt.close()
def main(args, ITE=0):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
reinit = True if args.prune_type == "reinit" else False
# Data Loader
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
if args.dataset == "mnist":
traindataset = datasets.MNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.MNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar10":
traindataset = datasets.CIFAR10('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR10('../data',
train=False,
transform=transform)
from archs.cifar10 import AlexNet, LeNet5, fc1, vgg, resnet, densenet
elif args.dataset == "fashionmnist":
traindataset = datasets.FashionMNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.FashionMNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar100":
traindataset = datasets.CIFAR100('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR100('../data',
train=False,
transform=transform)
from archs.cifar100 import AlexNet, fc1, LeNet5, vgg, resnet
# If you want to add extra datasets paste here
else:
print("\nWrong Dataset choice \n")
exit()
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
#train_loader = cycle(train_loader)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
drop_last=True)
# Importing Network Architecture
global model
if args.arch_type == "fc1":
model = fc1.fc1().to(device)
elif args.arch_type == "lenet5":
model = LeNet5.LeNet5().to(device)
elif args.arch_type == "alexnet":
model = AlexNet.AlexNet().to(device)
elif args.arch_type == "vgg16":
model = vgg.vgg16().to(device)
elif args.arch_type == "resnet18":
model = resnet.resnet18().to(device)
elif args.arch_type == "densenet121":
model = densenet.densenet121().to(device)
# If you want to add extra model paste here
else:
print("\nWrong Model choice\n")
exit()
# Weight Initialization
model.apply(weight_init)
# Copying and Saving Initial State
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/")
torch.save(
model,
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar"
)
# Making Initial Mask
make_mask(model)
# Optimizer and Loss
optimizer = torch.optim.Adam(model.parameters(), weight_decay=1e-4)
criterion = nn.CrossEntropyLoss() # Default was F.nll_loss
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float)
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
for _ite in range(args.start_iter, ITERATION):
if not _ite == 0:
prune_by_percentile(args.prune_percent,
resample=resample,
reinit=reinit)
if reinit:
model.apply(weight_init)
#if args.arch_type == "fc1":
# model = fc1.fc1().to(device)
#elif args.arch_type == "lenet5":
# model = LeNet5.LeNet5().to(device)
#elif args.arch_type == "alexnet":
# model = AlexNet.AlexNet().to(device)
#elif args.arch_type == "vgg16":
# model = vgg.vgg16().to(device)
#elif args.arch_type == "resnet18":
# model = resnet.resnet18().to(device)
#elif args.arch_type == "densenet121":
# model = densenet.densenet121().to(device)
#else:
# print("\nWrong Model choice\n")
# exit()
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
pbar = tqdm(range(args.end_iter))
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, test_loader, criterion)
# Save Weights
if accuracy > best_accuracy:
best_accuracy = accuracy
utils.checkdir(
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/"
)
torch.save(
model,
f"{os.getcwd()}/saves/{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth.tar"
)
# Training
if _ite == 0:
loss = train(model, train_loader, optimizer, criterion)
#needed to be completed
#teacher_model = ...
else:
loss = train_with_distill(model, train_loader, optimizer,
teacher_model)
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
pbar.set_description(
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%'
)
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_loss - np.min(all_loss)) /
np.ptp(all_loss).astype(float),
c="blue",
label="Loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
all_accuracy,
c="red",
label="Accuracy")
plt.title(
f"Loss Vs Accuracy Vs Iterations ({args.dataset},{args.arch_type})"
)
plt.xlabel("Iterations")
plt.ylabel("Loss and Accuracy")
plt.legend()
plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=1200)
plt.close()
# Dump Plot values
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
# Dumping mask
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
with open(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
utils.checkdir(f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/")
comp.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat"
)
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(
f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{args.arch_type})"
)
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test accuracy")
plt.xticks(a, comp, rotation="vertical")
plt.ylim(0, 100)
plt.legend()
plt.grid(color="gray")
utils.checkdir(f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png",
dpi=1200)
plt.close()
def main(args, ITE=0):
import pandas as pd
pd.set_option('display.width', 400)
pd.set_option('display.max_columns', 10)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
reinit = True if args.prune_type == "reinit" else False
layerwise = True if args.prune_type == "layerwise" else False
# Data Loader
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])
transform_cifar10 = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
if args.dataset == "mnist":
traindataset = datasets.MNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.MNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar10":
traindataset = datasets.CIFAR10('../data',
train=True,
download=True,
transform=transform_cifar10)
testdataset = datasets.CIFAR10('../data',
train=False,
transform=transform_cifar10)
from archs.cifar10 import AlexNet, LeNet5, fc1, vgg, resnet, densenet, minivgg
elif args.dataset == "fashionmnist":
traindataset = datasets.FashionMNIST('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.FashionMNIST('../data',
train=False,
transform=transform)
from archs.mnist import AlexNet, LeNet5, fc1, vgg, resnet
elif args.dataset == "cifar100":
traindataset = datasets.CIFAR100('../data',
train=True,
download=True,
transform=transform)
testdataset = datasets.CIFAR100('../data',
train=False,
transform=transform)
from archs.cifar100 import AlexNet, fc1, LeNet5, vgg, resnet
# If you want to add extra datasets paste here
else:
print("\nWrong Dataset choice \n")
exit()
# obtain training indices that will be used for validation
if args.early_stopping:
print(' Splitting Validation sets ')
trainset_size = int((1 - args.valid_size) * len(traindataset))
valset_size = len(traindataset) - trainset_size
trainset, valset = torch.utils.data.random_split(
traindataset, [trainset_size, valset_size])
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
valid_loader = torch.utils.data.DataLoader(valset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
else:
print(' Eww, no validation set? ')
train_loader = torch.utils.data.DataLoader(traindataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False)
# train_loader = cycle(train_loader)
test_loader = torch.utils.data.DataLoader(testdataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
drop_last=True)
# Importing Network Architecture
global model
if args.arch_type == "fc1":
model = fc1.fc1().to(device)
elif args.arch_type == "lenet5":
model = LeNet5.LeNet5().to(device)
elif args.arch_type == "alexnet":
model = AlexNet.AlexNet().to(device)
elif args.arch_type == "vgg16":
model = vgg.vgg16().to(device)
elif args.arch_type == "resnet18":
model = resnet.resnet18().to(device)
elif args.arch_type == "densenet121":
model = densenet.densenet121().to(device)
# If you want to add extra model paste here
elif args.arch_type == "conv2":
model = minivgg.conv2().to(device)
elif args.arch_type == "conv4":
model = minivgg.conv4().to(device)
elif args.arch_type == "conv6":
model = minivgg.conv6().to(device)
else:
print("\nWrong Model choice\n")
exit()
# Weight Initialization. Warning! This drops test acc, so i'm examining this function.
model.apply(weight_init)
# get time for file path
import datetime
now = datetime.datetime.now()
now_ = now.strftime("%02m%02d%02H%02M_")
# Copying and Saving Initial State
print(' saving initial model... ')
initial_state_dict = copy.deepcopy(model.state_dict())
utils.checkdir(
f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/")
torch.save(
model,
f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar"
)
print(
" initial model saved in ",
f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/initial_state_dict_{args.prune_type}.pth.tar"
)
# Making Initial Mask
make_mask(model)
# Optimizer and Loss
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
criterion = nn.CrossEntropyLoss() # Default was F.nll_loss
# Layer Looper
for name, param in model.named_parameters():
print(name, param.size())
# Pruning
# NOTE First Pruning Iteration is of No Compression
bestacc = 0.0
best_accuracy = 0
ITERATION = args.prune_iterations
comp = np.zeros(ITERATION, float)
bestacc = np.zeros(ITERATION, float)
step = 0
all_loss = np.zeros(args.end_iter, float)
all_vloss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
for _ite in range(args.start_iter, ITERATION):
# Early stopping parameter for each pruning iteration
early_stopping = EarlyStopping(
patience=99,
verbose=True) ######### we don't stop, party all night
if not _ite == 0:
prune_by_percentile(args.prune_percent,
args.fc_prune_percent,
resample=resample,
reinit=reinit,
layerwise=layerwise,
if_split=args.split_conv_and_fc)
if reinit:
model.apply(weight_init)
#if args.arch_type == "fc1":
# model = fc1.fc1().to(device)
#elif args.arch_type == "lenet5":
# model = LeNet5.LeNet5().to(device)
#elif args.arch_type == "alexnet":
# model = AlexNet.AlexNet().to(device)
#elif args.arch_type == "vgg16":
# model = vgg.vgg16().to(device)
#elif args.arch_type == "resnet18":
# model = resnet.resnet18().to(device)
#elif args.arch_type == "densenet121":
# model = densenet.densenet121().to(device)
#else:
# print("\nWrong Model choice\n")
# exit()
step = 0
for name, param in model.named_parameters():
if 'weight' in name:
weight_dev = param.device
param.data = torch.from_numpy(
param.data.cpu().numpy() *
mask[step]).to(weight_dev)
step = step + 1
step = 0
else:
original_initialization(mask, initial_state_dict)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=1e-4)
time.sleep(0.25)
print(f"\n--- Pruning Level [{ITE}:{_ite}/{ITERATION}]: ---")
# Print the table of Nonzeros in each layer
comp1 = utils.print_nonzeros(model)
comp[_ite] = comp1
# pbar = range(args.end_iter)
pbar = tqdm(range(args.end_iter))
stop_flag = False
for iter_ in pbar:
# Frequency for Testing
if iter_ % args.valid_freq == 0:
accuracy = test(model, test_loader, criterion)
# Save Weights
if accuracy > best_accuracy:
best_accuracy = accuracy
# We don't save model per test-acc, will use validation-acc!
# utils.checkdir(f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/")
# torch.save(model,f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth.tar")
# Training
loss = train(model, train_loader, optimizer, criterion)
all_loss[iter_] = loss
all_accuracy[iter_] = accuracy
# Validating
valid_loss, loss_v = validate(model, valid_loader, optimizer,
criterion)
all_vloss[iter_] = valid_loss #loss_v
# early stopping
checkpoint_path = f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/"
save_path = f"{os.getcwd()}/saves/{now_}{args.arch_type}/{args.dataset}/{_ite}_model_{args.prune_type}.pth.tar"
# msg = early_stopping(valid_loss, model, checkpoint_path, save_path)
early_stopping(valid_loss, model, checkpoint_path, save_path)
# Frequency for Printing Accuracy and Loss
if iter_ % args.print_freq == 0:
time.sleep(0.25)
pbar.set_description(
# f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}% \t' + msg)
f'Train Epoch: {iter_}/{args.end_iter} Loss: {loss:.6f} V-Loss: {valid_loss:.6f} Accuracy: {accuracy:.2f}% Best Accuracy: {best_accuracy:.2f}%'
)
if iter_ % 5 == 4:
print('')
if early_stopping.early_stop and not stop_flag:
print("Early stopping")
stop_flag = True
# break
writer.add_scalar('Accuracy/test', best_accuracy, comp1)
bestacc[_ite] = best_accuracy
# Plotting Loss (Training), Accuracy (Testing), Iteration Curve
#NOTE Loss is computed for every iteration while Accuracy is computed only for every {args.valid_freq} iterations. Therefore Accuracy saved is constant during the uncomputed iterations.
#NOTE Normalized the accuracy to [0,100] for ease of plotting.
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_loss - np.min(all_loss)) /
np.ptp(all_loss).astype(float),
c="blue",
label="Train loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
100 * (all_vloss - np.min(all_vloss)) /
np.ptp(all_vloss).astype(float),
c="green",
label="Valid loss")
plt.plot(np.arange(1, (args.end_iter) + 1),
all_accuracy,
c="red",
label="Accuracy")
plt.title(
f"Loss Vs Accuracy Vs Iterations ({args.dataset},{now_}{args.arch_type})"
)
plt.xlabel("Iterations")
plt.ylabel("Loss and Accuracy")
plt.legend()
plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_LossVsAccuracy_{comp1}.png",
dpi=300)
plt.close()
# Dump Plot values
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/")
all_loss.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_all_loss_{comp1}.dat"
)
all_accuracy.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_all_accuracy_{comp1}.dat"
)
# Dumping mask
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/")
with open(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_mask_{comp1}.pkl",
'wb') as fp:
pickle.dump(mask, fp)
# Making variables into 0
best_accuracy = 0
all_loss = np.zeros(args.end_iter, float)
all_accuracy = np.zeros(args.end_iter, float)
# Dumping Values for Plotting
utils.checkdir(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/")
comp.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_compression.dat"
)
bestacc.dump(
f"{os.getcwd()}/dumps/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_bestaccuracy.dat"
)
# Plotting
a = np.arange(args.prune_iterations)
plt.plot(a, bestacc, c="blue", label="Winning tickets")
plt.title(
f"Test Accuracy vs Unpruned Weights Percentage ({args.dataset},{now_}{args.arch_type})"
)
plt.xlabel("Unpruned Weights Percentage")
plt.ylabel("test accuracy")
plt.xticks(a, comp, rotation="vertical")
plt.ylim(0, 100)
plt.legend()
plt.grid(color="gray")
utils.checkdir(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/")
plt.savefig(
f"{os.getcwd()}/plots/lt/{now_}{args.arch_type}/{args.dataset}/{args.prune_type}_AccuracyVsWeights.png",
dpi=300)
plt.close()
print('Training ended~~~')