class ModelsHandler:
input_shape: tuple
num_actions: int
lr: float = field(default=0.001)
def __post_init__(self):
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = ConvNet(self.input_shape, self.num_actions,
self.lr).to(self.device)
self.tgt_model = ConvNet(self.input_shape, self.num_actions,
self.lr).to(self.device)
self.model_update_count = 0
self.current_loss = 0
def train_step(self, rb: ReplayBuffer, sample_size=300):
# loss calcualation
trans_sts = rb.sample(sample_size)
states = torch.stack([trans.state_tensor
for trans in trans_sts]).to(self.device)
next_states = torch.stack(
[trans.next_state_tensor for trans in trans_sts]).to(self.device)
not_done = torch.Tensor([trans.not_done_tensor
for trans in trans_sts]).to(self.device)
actions = [trans.action for trans in trans_sts]
rewards = torch.stack([trans.reward_tensor
for trans in trans_sts]).to(self.device)
with torch.no_grad():
qvals_predicted = self.tgt_model(next_states).max(-1)
self.model.optimizer.zero_grad()
qvals_current = self.model(states)
one_hot_actions = torch.nn.functional.one_hot(
torch.LongTensor(actions), self.num_actions).to(self.device)
loss = ((rewards + (not_done * qvals_predicted.values) -
torch.sum(qvals_current * one_hot_actions, -1))**2).mean()
loss.backward()
self.model.optimizer.step()
return loss.detach().item()
def update_target_model(self):
state_dict = deepcopy(self.model.state_dict())
self.tgt_model.load_state_dict(state_dict)
self.model_update_count += 1
def save_target_model(self):
file_name = f"{datetime.now().strftime('%H:%M:%S')}.pth"
temp_dir = os.environ.get('TMPDIR', '/tmp')
file_name = os.path.join(temp_dir, file_name)
torch.save(self.model, file_name)
wandb.save(file_name)
#net.set_masks(masks)
#print("--- {}% parameters pruned ---".format(param['pruning_perc']))
test(new_net, loader_test)
# Retraining
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(new_net.parameters(),
lr=param['learning_rate'],
weight_decay=param['weight_decay'])
train(new_net, criterion, optimizer, param, loader_train)
# Check accuracy and nonzeros weights in each layer
print("--- After retraining ---")
test(new_net, loader_test)
# Save and load the entire model
#torch.save(net.state_dict(), 'models/convnet_pruned.pkl')
import os
torch.save({
'cfg': cfg,
'state_dict': new_net.state_dict()
}, os.path.join('models', 'conv-pruned1.pth.tar'))
checkpoint = torch.load('models/conv-pruned1.pth.tar')
net2 = ConvNet(checkpoint['cfg'])
net2.load_state_dict(checkpoint['state_dict'])
print(sum([param.nelement() for param in net2.parameters()]))
test(net2, loader_test)
# Load the pretrained model
net = ConvNet()
net.load_state_dict(torch.load('models/convnet_pretrained.pkl'))
if torch.cuda.is_available():
print('CUDA ensabled.')
net.cuda()
print("--- Pretrained network loaded ---")
test(net, loader_test)
# prune the weights
masks = filter_prune(net, param['pruning_perc'])
net.set_masks(masks)
print("--- {}% parameters pruned ---".format(param['pruning_perc']))
test(net, loader_test)
# Retraining
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.RMSprop(net.parameters(),
lr=param['learning_rate'],
weight_decay=param['weight_decay'])
train(net, criterion, optimizer, param, loader_train)
# Check accuracy and nonzeros weights in each layer
print("--- After retraining ---")
test(net, loader_test)
prune_rate(net)
# Save and load the entire model
torch.save(net.state_dict(), 'models/convnet_pruned.pkl')
def main():
# data normalization
input_size = 224
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# data loaders
kwargs = {'num_workers': 8, 'pin_memory': True} if args.cuda else {}
if args.da:
train_transforms = transforms.Compose([
random_transform,
transforms.ToPILImage(),
transforms.Resize((input_size, input_size)),
transforms.ToTensor(), normalize
])
else:
train_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((input_size, input_size)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
test_transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((input_size, input_size)),
transforms.ToTensor(), normalize
])
train_loader = torch.utils.data.DataLoader(DataLoader(df_train,
train_transforms,
root=args.data_dir,
mode=args.mode),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(DataLoader(df_gal,
test_transforms,
root=args.data_dir,
mode=args.mode),
batch_size=args.batch_size,
shuffle=False,
**kwargs)
# instanciate the models
output_shape, backbone = get_backbone(args)
embed = LinearProjection(output_shape, args.dim_embed)
model = ConvNet(backbone, embed)
# instanciate the proxies
fsem = get_semantic_fname(args.word)
path_semantic = os.path.join('aux', 'Semantic', args.dataset, fsem)
train_proxies = get_proxies(path_semantic, df_train['cat'].cat.categories)
test_proxies = get_proxies(path_semantic, df_gal['cat'].cat.categories)
train_proxynet = ProxyNet(args.n_classes,
args.dim_embed,
proxies=torch.from_numpy(train_proxies))
test_proxynet = ProxyNet(args.n_classes_gal,
args.dim_embed,
proxies=torch.from_numpy(test_proxies))
# criterion
criterion = ProxyLoss(args.temperature)
if args.multi_gpu:
model = nn.DataParallel(model)
if args.cuda:
backbone.cuda()
embed.cuda()
model.cuda()
train_proxynet.cuda()
test_proxynet.cuda()
parameters_set = []
low_layers = []
upper_layers = []
for c in backbone.children():
low_layers.extend(list(c.parameters()))
for c in embed.children():
upper_layers.extend(list(c.parameters()))
parameters_set.append({
'params': low_layers,
'lr': args.lr * args.factor_lower
})
parameters_set.append({'params': upper_layers, 'lr': args.lr * 1.})
optimizer = optim.SGD(parameters_set,
lr=args.lr,
momentum=0.9,
nesterov=True,
weight_decay=args.wd)
n_parameters = sum([p.data.nelement() for p in model.parameters()])
print(' + Number of params: {}'.format(n_parameters))
scheduler = CosineAnnealingLR(optimizer,
args.epochs * len(train_loader),
eta_min=3e-6)
print('Starting training...')
for epoch in range(args.start_epoch, args.epochs + 1):
# update learning rate
scheduler.step()
# train for one epoch
train(train_loader, model, train_proxynet.proxies.weight, criterion,
optimizer, epoch, scheduler)
val_acc = evaluate(test_loader, model, test_proxynet.proxies.weight,
criterion)
# saving
if epoch == args.epochs:
save_checkpoint({'epoch': epoch, 'state_dict': model.state_dict()})
print('\nResults on test set (end of training)')
write_logs('\nResults on test set (end of training)')
test_acc = evaluate(test_loader, model, test_proxynet.proxies.weight,
criterion)
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import copy
import time
from models import ConvNet, nCrossEntropyLoss
from config import DefaultConfig
from data.dataset import data_loader, data, dataset_size
from utils.utils import equal
net = ConvNet()
optimizer = torch.optim.Adam(net.parameters(), lr=0.001)
loss_func = nCrossEntropyLoss()
best_model_wts = copy.deepcopy(net.state_dict())
best_acc = 0.0
since = time.time()
for epoch in range(DefaultConfig.EPOCH):
running_loss = 0.0
running_corrects = 0
for step, (inputs, label) in enumerate(data_loader):
# 用 0 填充 LongTensor
pred = torch.LongTensor(DefaultConfig.BATCH_SIZE, 1).zero_()
inputs = Variable(inputs) # (bs, 3, 60, 160)
label = Variable(label) # (bs, 4)
# 梯度清零
optimizer.zero_grad()
def main(args):
init_process_group(backend='nccl')
with open(args.config) as file:
config = json.load(file)
config.update(vars(args))
config = apply_dict(Dict, config)
backends.cudnn.benchmark = True
backends.cudnn.fastest = True
cuda.set_device(distributed.get_rank() % cuda.device_count())
train_dataset = ImageDataset(root=config.train_root,
meta=config.train_meta,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ) * 3,
(0.5, ) * 3)
]))
val_dataset = ImageDataset(root=config.val_root,
meta=config.val_meta,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, ) * 3,
(0.5, ) * 3)
]))
train_sampler = utils.data.distributed.DistributedSampler(train_dataset)
val_sampler = utils.data.distributed.DistributedSampler(val_dataset)
train_data_loader = utils.data.DataLoader(
dataset=train_dataset,
batch_size=config.local_batch_size,
sampler=train_sampler,
num_workers=config.num_workers,
pin_memory=True)
val_data_loader = utils.data.DataLoader(dataset=val_dataset,
batch_size=config.local_batch_size,
sampler=val_sampler,
num_workers=config.num_workers,
pin_memory=True)
model = ConvNet(conv_params=[
Dict(in_channels=3,
out_channels=32,
kernel_size=5,
padding=2,
stride=2,
bias=False),
Dict(in_channels=32,
out_channels=64,
kernel_size=5,
padding=2,
stride=2,
bias=False),
],
linear_params=[
Dict(in_channels=3136,
out_channels=1024,
kernel_size=1,
bias=False),
Dict(in_channels=1024,
out_channels=10,
kernel_size=1,
bias=True),
])
config.global_batch_size = config.local_batch_size * distributed.get_world_size(
)
config.optimizer.lr *= config.global_batch_size / config.global_batch_denom
optimizer = optim.Adam(model.parameters(), **config.optimizer)
epoch = 0
global_step = 0
if config.checkpoint:
checkpoint = Dict(torch.load(config.checkpoint))
model.load_state_dict(checkpoint.model_state_dict)
optimizer.load_state_dict(checkpoint.optimizer_state_dict)
epoch = checkpoint.last_epoch + 1
global_step = checkpoint.global_step
def train(data_loader):
nonlocal global_step
model.train()
for images, labels in data_loader:
images = images.cuda()
labels = labels.cuda()
optimizer.zero_grad()
logits = model(images)
loss = nn.functional.cross_entropy(logits, labels)
loss.backward(retain_graph=True)
average_gradients(model.parameters())
optimizer.step()
predictions = logits.topk(k=1, dim=1)[1].squeeze()
accuracy = torch.mean((predictions == labels).float())
average_tensors([loss, accuracy])
global_step += 1
dprint(f'[training] epoch: {epoch} global_step: {global_step} '
f'loss: {loss:.4f} accuracy: {accuracy:.4f}')
@torch.no_grad()
def validate(data_loader):
model.eval()
losses = []
accuracies = []
for images, labels in data_loader:
images = images.cuda()
labels = labels.cuda()
logits = model(images)
loss = nn.functional.cross_entropy(logits, labels)
predictions = logits.topk(k=1, dim=1)[1].squeeze()
accuracy = torch.mean((predictions == labels).float())
average_tensors([loss, accuracy])
losses.append(loss)
accuracies.append(accuracy)
loss = torch.mean(torch.stack(losses)).item()
accuracy = torch.mean(torch.stack(accuracies)).item()
dprint(f'[validation] epoch: {epoch} global_step: {global_step} '
f'loss: {loss:.4f} accuracy: {accuracy:.4f}')
@torch.no_grad()
def feed(data_loader):
model.eval()
for images, _ in data_loader:
images = images.cuda()
logits = model(images)
def save():
if not distributed.get_rank():
os.makedirs('checkpoints', exist_ok=True)
torch.save(
dict(model_state_dict=model.state_dict(),
optimizer_state_dict=optimizer.state_dict(),
last_epoch=epoch,
global_step=global_step),
os.path.join('checkpoints', f'epoch_{epoch}'))
if config.training:
model.cuda()
broadcast_tensors(model.state_dict().values())
for epoch in range(epoch, config.num_training_epochs):
train_sampler.set_epoch(epoch)
train(train_data_loader)
validate(val_data_loader)
save()
if config.validation:
model.cuda()
broadcast_tensors(model.state_dict().values())
validate(val_data_loader)
if config.quantization:
model.cuda()
broadcast_tensors(model.state_dict().values())
with QuantizationEnabler(model):
with BatchStatsUser(model):
for epoch in range(epoch, config.num_quantization_epochs):
train_sampler.set_epoch(epoch)
train(train_data_loader)
validate(val_data_loader)
save()
with AverageStatsUser(model):
for epoch in range(epoch, config.num_quantization_epochs):
train_sampler.set_epoch(epoch)
train(train_data_loader)
validate(val_data_loader)
save()
