def net_fn(inputs, theta=None, is_training=True):
if args.model == 'mlp':
mlp = models.MLP(
nlayers=args.nlayers, nhid=args.nhid, with_bias=True, batch_norm=False)
return mlp(inputs, theta, is_training)
elif args.model == 'resnet':
network = models.Net(args.model_size)
return network(inputs, is_training)
def __init__(self, ex_name=experiment_name, m_name=model_name):
net = models.Net(name=experiment_name)
net.load_state_dict(
torch.load(os.path.join(root, experiment_name, model_name)))
use_cuda = torch.cuda.is_available()
if use_cuda:
net.cuda()
self.net = net
self.transforms = transforms.Compose([transforms.ToTensor()])
self.cuda = use_cuda
def run_experiments(finetune, kernel_sizes, filters, lr, pooling, weight_decay,
other_params):
global embeddings_matrix, training_set, validation_set
other_params['commit_hash'] = commit_hash
(vocab_size, dimensions) = embeddings_matrix.shape
net = models.Net(dimensions=dimensions,
finetune=finetune,
vocab_size=vocab_size,
kernel_sizes=kernel_sizes,
filters=filters,
dropout_rate=0.5,
pooling=pooling,
lr=lr,
weight_decay=weight_decay,
embeddings_matrix=embeddings_matrix)
hyperparams = util.fill_dict(net.hyperparameters, other_params)
logger.info('experiment with hyperparameters: {}'.format(
json.dumps(hyperparams, sort_keys=True, indent=None)))
with get_archiver(datadir='data/models',
suffix="_" + commit_hash[:6]) as a1, get_archiver(
datadir='data/results',
suffix="_" + commit_hash[:6]) as a:
save_model(hyperparams, net, a.getFilePath)
early_stopping = train.EarlyStopping(c.monitor, c.patience,
c.monitor_objective)
model_checkpoint = train.ModelCheckpoint(a1.getFilePath('checkpoint'))
csv_logger = train.CSVLogger(a.getFilePath('logger.csv'))
adam_config = train.AdamConfig(
lr=net.hyperparameters['lr'],
beta_1=net.hyperparameters['beta_1'],
beta_2=net.hyperparameters['beta_2'],
epsilon=net.hyperparameters['epsilon'],
weight_decay=net.hyperparameters['weight_decay'])
history = train.fit(
net,
training_set,
validation_set,
batch_size=c.batch_size,
epochs=c.epochs,
validation_split=0.2,
callbacks=[early_stopping, model_checkpoint, csv_logger],
optimizer=adam_config)
save_history(history, a.getDirPath())
return
def main():
global args, best_micro, base_sum
args = parser.parse_args()
base_transf, embed = data_got.one_sample_base2avg(
batch_size=args.batch_size,
sample_num=args.num_sample,
samp_freq=args.samp_freq,
num_class=args.num_class)
Ftest_loader, novel_loader, novelall_loader, _ = data_got.Nload_data(
batch_size=args.batch_size,
sample_num=args.num_sample,
num_class=args.num_class)
embed = torch.from_numpy(embed).float()
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
model = models.Net(embed, args.lstm_hid_dim,
num_classes=args.num_class).cuda()
print('==> Reading from model checkpoint..')
assert os.path.isfile(
args.model), 'Error: no model checkpoint directory found!'
checkpoint = torch.load(args.model)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded model checkpoint '{}' (epoch {})".format(
args.model, checkpoint['epoch']))
cudnn.benchmark = True
real_weight = model.classifier.fc.weight.data
criterion = nn.MSELoss()
trans_model = models.Transfer().cuda()
optimizer = torch.optim.Adam(trans_model.parameters(),
lr=0.012,
betas=(0.9, 0.99))
for epoch in range(args.epochs):
train_loss, base_sum = train(base_transf, trans_model, model,
criterion, optimizer, real_weight)
print("loss", train_loss)
imprint(novel_loader, model, trans_model, base_sum)
model_criterion = nn.BCELoss()
model_optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
betas=(0.9, 0.99))
for i in range(2):
print("ft start")
fine_tuning(novelall_loader, model, model_criterion, model_optimizer)
validate(Ftest_loader, model)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('expert_policy_file', type=str)
parser.add_argument('envname', type=str)
parser.add_argument('--num_its', type=int, default=10)
parser.add_argument('--num_train_rolls', type=int, default=20)
parser.add_argument('--num_epochs', type=int, default=5)
args = parser.parse_args()
with open(
op.join(
'expert_data', args.envname + '_' +
'{}_eps.pkl'.format(args.num_train_rolls)),
"rb") as input_file:
train_data = pickle.load(input_file)
print("Observations : ", train_data['observations'].shape)
print("Actions : ", train_data['actions'].shape)
data = {}
data['X'], data['y'] = {}, {}
data['X']['train'], data['X']['test'], data['y']['train'], data['y'][
'test'] = train_test_split(train_data['observations'],
np.squeeze(train_data['actions']),
test_size=0.2)
# Store checkpoints as well
net = models.Net(obs_size=train_data['observations'].shape[1],
hidden=256,
act_space_size=train_data['actions'].shape[2])
net.compile(optimizer='adam', loss='mean_squared_error')
env = gym.make(args.envname)
max_steps = env.spec.max_episode_steps
policy_fn = load_policy.load_policy(args.expert_policy_file)
train_dagger(env,
expert=policy_fn,
policy=net,
num_its=args.num_its,
num_epochs=args.num_epochs,
data=data,
cp_dir='checkpoints/Dagger',
cp_name='{}_train_rolls_{}_epochs_'.format(
args.num_train_rolls, args.num_epochs))
def main():
global args, best_micro
args = parser.parse_args()
base_transf, embed = data_got.one_sample_base2avg(batch_size=args.batch_size,sample_num=args.num_sample,samp_freq=args.Bsamp_freq,num_class=args.num_class)
Ftest_loader, novel_loader,novelall_loader,test_y = data_got.Nload_data(batch_size=args.batch_size,sample_num=args.num_sample,samp_freq=args.Nsamp_freq,num_class=args.num_class)
embed = torch.from_numpy(embed).float()
model = models.Net(embed, args.lstm_hid_dim, num_classes=args.num_class).cuda()
print('==> Reading from model checkpoint..')
assert os.path.isfile(args.model), 'Error: no model checkpoint directory found!'
checkpoint = torch.load(args.model)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded model checkpoint '{}' (epoch {})"
.format(args.model, checkpoint['epoch']))
cudnn.benchmark = True
real_weight=model.classifier.fc.weight.data
criterion = nn.MSELoss()
trans_model = models.Transfer().cuda()
optimizer = torch.optim.Adam( trans_model.parameters(), lr=0.013, betas=(0.9, 0.99))
for epoch in range(args.epochs):
train_loss= train(base_transf, trans_model,model, criterion, optimizer,real_weight)
print("loss",train_loss)
tail_weight=imprint(novel_loader, model,trans_model)
# model_criterion= nn.BCELoss()
#
# model_optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.99))
# for i in range(3):
# train_loss, trn_micro, trn_macro = fine_tuning(novelall_loader, model, model_criterion, model_optimizer)
output_all=[]
F1 = np.zeros(54)
for i in range(args.Nsamp_freq):
print("ensemble start!!!!!!!! this is calssifier",i)
model.classifier.fc.weight.data = tail_weight[i]
output = validate(Ftest_loader, model)
output_all.append(output)
output_all =(torch.sum(torch.tensor(output_all),0))/args.Nsamp_freq
output_all[output_all > 0.5] = 1
output_all[output_all<= 0.5] = 0
for l in range(54):
F1[l] = f1_score(test_y[:, l], output_all[:, l], average='binary')
print("each class result f1!!!!!!!!!!!!!!")
print(F1)
def main():
train_env = envs.make_cartpole(100)
test_env = envs.make_cartpole(3000)
set_random_seeds(train_env, test_env)
net = models.Net()
optimizer = optim.Rprop(net.parameters())
# TODO Initialize weights randomly within [-0.5, 0.5]
for epoch in range(500):
rollout = generate_rollout(train_env, net)
if epoch % 10 == 9:
print('Epoch {:4d} | Steps: {:3d}'.format(epoch + 1, len(rollout)))
train(net, optimizer, rollout)
hint_to_goal(net, optimizer)
# test(test_env, net)
train_env.close()
test_env.close()
n = 0
dirs = []
dirs.append(name)
dirs = np.concatenate(dirs)
for i in pred:
result = Image.fromarray((i).astype(np.uint8))
result.save(args.save_img + '/' + dirs[n])
#torchvision.utils.save_image(i, 'preds/' + dirs[n])
n += 1
if __name__ == '__main__':
args = parser_2.arg_parse()
''' setup GPU '''
torch.cuda.set_device(args.gpu)
''' prepare data_loader '''
print('===> prepare data loader ...')
test_loader2 = torch.utils.data.DataLoader(data2.DATA2(args),
batch_size=args.test_batch,
num_workers=args.workers,
shuffle=False)
''' prepare mode '''
model = models.Net(args)
model_std = torch.load(os.path.join('model_best.pth.tar'))
model.load_state_dict(model_std)
model.cuda()
print("model loaded")
save_imgs(model, test_loader2, args)
def main():
global args, best_prec1
args = parser.parse_args()
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
model = models.Net(num_classes=200, norm=False, scale=False).cuda()
cudnn.benchmark = True
# Data loading code
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
train_dataset = loader.ImageLoader(args.data,
transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),
train=True,
num_classes=200,
num_train_sample=args.num_sample)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
sampler=train_dataset.get_balanced_sampler(),
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(loader.ImageLoader(
args.data,
transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]),
num_classes=200,
novel_only=args.test_novel_only),
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
print(' Total params: %.2fM' %
(sum(p.numel() for p in model.parameters()) / 1000000.0))
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
extractor_params = list(map(id, model.extractor.parameters()))
classifier_params = filter(lambda p: id(p) not in extractor_params,
model.parameters())
optimizer = torch.optim.SGD([{
'params': model.extractor.parameters()
}, {
'params': classifier_params,
'lr': args.lr * 10
}],
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=4,
gamma=0.94)
title = 'AllJoint'
if args.resume:
print('==> Resuming from checkpoint..')
assert os.path.isfile(
args.resume), 'Error: no checkpoint directory found!'
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
logger = Logger(os.path.join(args.checkpoint, 'log.txt'),
title=title,
resume=True)
else:
logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)
logger.set_names([
'Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.',
'Valid Acc.'
])
if args.evaluate:
validate(val_loader, model, criterion)
return
for epoch in range(args.start_epoch, args.epochs):
scheduler.step()
lr = optimizer.param_groups[1]['lr']
print('\nEpoch: [%d | %d] LR: %f' % (epoch + 1, args.epochs, lr))
# train for one epoch
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch)
# evaluate on validation set
test_loss, test_acc = validate(val_loader, model, criterion)
# append logger file
logger.append([lr, train_loss, test_loss, train_acc, test_acc])
# remember best prec@1 and save checkpoint
is_best = test_acc > best_prec1
best_prec1 = max(test_acc, best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
},
is_best,
checkpoint=args.checkpoint)
logger.close()
logger.plot()
savefig(os.path.join(args.checkpoint, 'log.eps'))
print('Best acc:')
print(best_prec1)
import models
import data
import torch
import os
from tqdm import tqdm
import wandb
import numpy as np
import matplotlib.pyplot as plt
from utils import compute_accuracy, visualize_predictions
from setup import (BATCH_SIZE, MODEL, DEVICE, LEARNING_RATE, NB_EPOCHS,
FILE_NAME, DROPOUT, WEIGHTS_INIT, ALPHA, KEEP_WEIGHTS, DENOISER)
train_loader, test_loader = data.get_loaders(BATCH_SIZE)
model = models.Net(MODEL, dropout=DROPOUT)
if WEIGHTS_INIT:
model.apply(models.init_weights)
model.to(DEVICE)
wandb.watch(model)
if KEEP_WEIGHTS and os.path.exists(f"weights/{FILE_NAME}.pt"):
print("Weights found")
model.load_state_dict(torch.load(f"weights/{FILE_NAME}.pt"))
else:
os.makedirs("weights/", exist_ok=True)
print("Weights not found")
if DENOISER:
denoiser = models.DeNoiser()
final_skill_sample.append(Flat_input)
print("LENGTH OF ACTUAL MODEL IS", len(Flat_input))
if len(task_samples) == 0:
accuracies = CAE_AE_TRAIN(net_shapes,
task_samples + final_skill_sample, 50)
else:
accuracies = CAE_AE_TRAIN(net_shapes,
task_samples + final_skill_sample, 250)
return accuracies
######################################################################################################
# GLOBAL VARIABLES
######################################################################################################
#net=models.Net().to(device)
net_reset = models.Net().to(device)
Actual_Accuracy = []
criterion = nn.CrossEntropyLoss()
student_model = models.SPLIT_CIFAR_CAE_TWO_SKILLS().to(device)
teacher_model = models.SPLIT_CIFAR_CAE_TWO_SKILLS().to(device)
vae_optimizer = optim.Adam(student_model.parameters(),
lr=0.0001) #, amsgrad=True)
lam = 0.0001
Actual_Accuracy = []
threshold_batchid = []
#biased training variables
nSamples = 10
nBiased = min(nSamples, 10)
trainBias = 0.5
minReps = 1
nReps = 20
torch.backends.cudnn.benchmark = True
source_dataset, target_dataset = get_dataset(args.task)
source_loader = torch.utils.data.DataLoader(source_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
target_loader = torch.utils.data.DataLoader(target_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
model = models.Net(task=args.task).cuda()
if args.task == 's2m':
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
else:
optimizer = torch.optim.Adam(model.parameters(), args.lr)
if args.resume:
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
idx_permute = [np.random.permutation(28**2) for _ in range(10)]
#=vis.bar(X=[1,2,3])
options_task_network = dict(fillarea=True,
width=400,
height=400,
xlabel='Task Network',
ylabel='T',
title='TASK_NETWORK')
#win_task_network = vis.line(Y=np.array([0.001,0.001]),win='Task Net Dist',name='TASK NET DIST',opts=options_task_network)
######################################################################################################
# TRAINING STARTS HERE - MNIST + CAE
######################################################################################################
allAcc = []
for permuatation in range(0, 10):
model = models.Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
task_samples = []
print("########## \n Threshold id is", threshold_batchid, "\n#########")
Train_loader, Test_loader = RELOAD_DATASET(idx_permute[permuatation])
SHOW_TEST_TRAIN_IMAGES_SAMPLE(permuatation)
accuracy = train(model, Train_loader, Test_loader, optimizer, 3,
'MNIST Skill ' + str(permuatation))
options = dict(fillarea=True,
width=400,
height=400,
xlabel='Skill',
ylabel='Actual_Accuracy',
title='Actual_Accuracy')
Actual_Accuracy.append(int(accuracy))
print("Actual acc", Actual_Accuracy)
help='LR is multiplied by gamma on schedule.')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
#model = Resnet.ResNet18()
net = models.Net(
) #dense_net2.DenseNet( growthRate=12)#Resnet.ResNet18()Resnet.Net() #
print(net)
criterion = nn.CrossEntropyLoss()
# if Flags['CIFAR_100'] :
# optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9,weight_decay=1e-4)
# else:
# optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
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([
def main():
global args
args = parser.parse_args()
debug = 0 # 0: normal mode 1: debug mode
# Data loading code
# args.data: path to the dataset
traindir = os.path.join(args.data, 'RealChallengeFree/train')
testdir = os.path.join(args.data, 'RealChallengeFree/Test')
train_dataset = utils.CURETSRDataset(
traindir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
test_dataset = utils.CURETSRDataset(
testdir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
model = models.Net()
model = torch.nn.DataParallel(model).cuda()
print("=> creating model %s " % model.__class__.__name__)
savedir = 'CNN_iter'
checkpointdir = os.path.join('./checkpoints', savedir)
if not debug:
os.mkdir(checkpointdir)
print('log directory: %s' % os.path.join('./logs', savedir))
print('checkpoints directory: %s' % checkpointdir)
# Set the logger
if not debug:
logger = Logger(os.path.join('./logs/', savedir))
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# 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_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print(
"=> loaded checkpoint '{}' (epoch {}, best_prec1 @ Source {})".
format(args.resume, checkpoint['epoch'], best_prec1))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
if args.evaluate:
evaluate(test_loader, model, criterion)
return
cudnn.benchmark = True
timestart = time.time()
best_prec1 = 0
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
print('\n*** Start Training *** \n')
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
print('\n*** Start Testing *** \n')
test_loss, test_prec1, _ = evaluate(test_loader, model, criterion)
info = {'Testing loss': test_loss, 'Testing Accuracy': test_prec1}
# remember best prec@1 and save checkpoint
is_best = test_prec1 > best_prec1
best_prec1 = max(test_prec1, best_prec1)
if is_best:
best_epoch = epoch + 1
if not debug:
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch + 1)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'last_prec1': test_prec1,
'optimizer': optimizer.state_dict()
}, is_best, checkpointdir)
print('Best epoch: ', best_epoch)
print('Total processing time: %.4f' % (time.time() - timestart))
def main():
#The main() is used to finish all the training operation.
#Record the training log using Tensorboard
writer = SummaryWriter()
#Load model
net = models.Net(name=experiment_name)
#Use CUDA or not
use_cuda = torch.cuda.is_available()
if use_cuda:
net.cuda()
#Define the transform for loading image
trans = transforms.Compose([transforms.RandomHorizontalFlip(),
transforms.Scale(size=(64,64)),
transforms.ToTensor(),])
#Set Adam as our optimizer
optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay=5e-4)
#Load train and test data and set corresponding parameters
TrainData = MyLoader('./train.txt', trans)
trainloader = DataLoader(dataset=TrainData, batch_size=batch_size, num_workers=16, shuffle=True, )
TestData = MyLoader('./test.txt', trans)
testloader = DataLoader(dataset=TestData, batch_size=batch_size, num_workers=16, shuffle=True)
#Set log path to save the training log.
log_path = os.path.join(logdir, experiment_name+extra_name)
if not os.path.exists(log_path):
os.mkdir(log_path)
#Petient is used to decide when to end training.
step = 1
duration = 0.
precision = 0.
petient = 0
#Use CrossEntropyLoss
criterion = nn.CrossEntropyLoss().cuda()
for epoch in range(1, 501):
#Train
for batch_idx, (img, target) in enumerate(trainloader):
start_time = time.time()
if use_cuda:
img, target = img.cuda(), target.cuda()
img, target = Variable(img), Variable(target)
pred = net.train()(img)
loss = criterion(pred, target)
learning_rate = adjust_lr(optimizer=optimizer, cur_stp=step)
#Show the information about training
duration += time.time() - start_time
if step%steps_to_show == 0:
speed = batch_size*steps_to_show/duration
duration = 0.
print '=>%s: epoch: %d, step: %d, loss=%f, lr=%f, (%.1f examples/sec)' % (datetime.now().strftime('%Y-%m-%d %H:%M:%S'), epoch, step, loss.data[0], learning_rate, speed)
#Save model
if step%steps_to_save==0:
model_path = os.path.join(log_path, 'model_%d.tar'%(step))
torch.save(net.state_dict(), model_path)
print 'model saved at:', model_path
writer.add_scalar('loss', loss.data[0], step)
writer.add_scalar('lr', learning_rate, step)
step += 1
optimizer.zero_grad()
loss.backward()
optimizer.step()
correct_num = 0.
count = 0
#Evaluation
for batch_idx, (img, target) in enumerate(testloader):
if use_cuda:
img, target = img.cuda(), target.cuda()
img = Variable(img)
pred = net.eval()(img)
correct_num += accuracy(pred, target)
count += target.size(0)
#Decide whether to end training according to the precision and petient
precision_ = float(correct_num)/float(count)
if precision_ > precision:
precision = precision_
model_path = os.path.join(log_path, 'model_%.4f.tar'%(precision))
torch.save(net.state_dict(), model_path)
print 'accuracy: ', precision
print 'model saved at:', model_path
else:
petient += 1
if petient==100:
print "Time to stop"
writer.close()
quit()
writer.close()
model_path = os.path.join(log_path, 'model_final.tar')
torch.save(net.state_dict(), model_path)
######################################################################################################
# LOADING OF VAE OUTPUT SKILL WEIGHTS BACK INTO THE MNSIT NETWORK
######################################################################################################
def loadWeights_mnsit(weights_to_load, net):
model.conv1.weight.data = torch.from_numpy(weights_to_load[0]).cuda()
model.conv1.bias.data = torch.from_numpy(weights_to_load[1]).cuda()
model.conv2.weight.data = torch.from_numpy(weights_to_load[2]).cuda()
model.conv2.bias.data = torch.from_numpy(weights_to_load[3]).cuda()
model.fc1.weight.data = torch.from_numpy(weights_to_load[4]).cuda()
model.fc1.bias.data = torch.from_numpy(weights_to_load[5]).cuda()
model.fc2.weight.data = torch.from_numpy(weights_to_load[6]).cuda()
model.fc2.bias.data = torch.from_numpy(weights_to_load[7]).cuda()
return model
print("device is ",device)
model = models.Net().to(device)
model_reset= models.Net().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
student_model=models.CAE().to(device)#nn.DataParallel(models.CAE().to(device))
teacher_model=models.CAE().to(device)#nn.DataParallel(models.CAE().to(device))
vae_optimizer = optim.Adam(student_model.parameters(), lr = 0.0001)
lam = 0.001
Actual_Accuracy=[]
threshold_batchid=[]
#####################################################################################################################
# For Viewing the test/train images-just for confirmation
#####################################################################################################################
classes = ('0','1', '2', '3', '4','5','6','7','8','9')
# functions to show an image
#plt.show()
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('expert_policy_file', type=str)
parser.add_argument('envname', type=str)
parser.add_argument('--num_train_rolls', type=int, default=20)
parser.add_argument('--num_its', type=int, default=10)
parser.add_argument('--render', action='store_true')
parser.add_argument('--train', action='store_true')
args = parser.parse_args()
env = gym.make(args.envname)
max_steps = env.spec.max_episode_steps
policy_fn = load_policy.load_policy(args.expert_policy_file)
policies = {}
policies['expert'] = policy_fn
# Store checkpoints as well
policies['BC'] = models.Net(obs_size=env.observation_space.shape[0],
hidden=256,
act_space_size=env.action_space.shape[0])
policies['Dagger'] = models.Net(obs_size=env.observation_space.shape[0],
hidden=256,
act_space_size=env.action_space.shape[0])
policy_wts = {}
regex = re.compile('.*{}_iters'.format(args.num_its - 1))
for root, dirs, files in sorted(os.walk('./checkpoints/Dagger/{}'.format(args.envname))):
for dir in dirs:
if regex.match(dir):
policy_wts['Dagger'] = op.join(root, dir, 'cp.ckpt')
regex = re.compile('.*{}_train_rolls'.format(args.num_train_rolls))
for root, dirs, files in sorted(os.walk('./checkpoints/BC/{}'.format(args.envname))):
for dir in dirs:
if regex.match(dir):
policy_wts['BC'] = op.join(root, dir, 'cp.ckpt')
print(policy_wts['Dagger'], policy_wts['BC'])
checkpoint_path = policy_wts['Dagger']
checkpoint_dir = os.path.dirname(checkpoint_path)
restore_checkpoint = tf.train.Checkpoint(model=policies['Dagger'])
restore_checkpoint.restore(checkpoint_path).expect_partial()
checkpoint_path = policy_wts['BC']
checkpoint_dir = os.path.dirname(checkpoint_path)
restore_checkpoint = tf.train.Checkpoint(model=policies['BC'])
restore_checkpoint.restore(checkpoint_path).expect_partial()
# Visualize the trained policy and calculate returns
returns, avg_returns = {}, {}
num_episodes = 20
for policy in ['expert', 'BC', 'Dagger']:
returns[policy] = []
for i in range(num_episodes):
returns[policy].append(run_policy(env, policies[policy], policy=policy))
avg_returns[policy] = np.mean(returns[policy])
# with open('{}_{}_eps_{}_epochs.pkl', 'wb') as file:
# pickle.dump(avg_returns, file, protocol=2)
print("Average return from {} : ".format(policy), avg_returns[policy])
def main():
global args
args = parser.parse_args()
traindir = os.path.join(args.data, 'RealChallengeFree/train')
testdir = os.path.join(args.data, 'RealChallengeFree/Test')
train_dataset = utils.CURETSRDataset(
traindir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
test_dataset = utils.CURETSRDataset(
testdir,
transforms.Compose([
transforms.Resize([28, 28]),
transforms.ToTensor(), utils.l2normalize, utils.standardization
]))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
model = models.AutoEncoder()
model = torch.nn.DataParallel(model).cuda()
print("=> creating model %s " % model.__class__.__name__)
criterion = nn.MSELoss().cuda()
savedir = 'AutoEncoder'
checkpointdir = os.path.join('./checkpoints', savedir)
os.makedirs(checkpointdir, exist_ok=True)
print('log directory: %s' % os.path.join('./logs', savedir))
print('checkpoints directory: %s' % checkpointdir)
logger = Logger(os.path.join('./logs/', savedir))
if args.evaluate:
print("=> loading checkpoint ")
checkpoint = torch.load(
os.path.join(checkpointdir, 'model_best.pth.tar'))
model.load_state_dict(checkpoint['AE_state_dict'], strict=False)
modelCNN = models.Net()
modelCNN = torch.nn.DataParallel(modelCNN).cuda()
checkpoint2 = torch.load('./checkpoints/CNN_iter/model_best.pth.tar')
modelCNN.load_state_dict(checkpoint2['state_dict'], strict=False)
evaluate(test_loader, model, modelCNN, criterion)
return
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
cudnn.benchmark = True
timestart = time.time()
if args.finetune:
print("=> loading checkpoint ")
checkpoint = torch.load(
os.path.join(checkpointdir, 'model_best.pth.tar'))
model.load_state_dict(checkpoint['AE_state_dict'], strict=False)
optimizer.load_state_dict(checkpoint['optimizer'])
best_loss = 10e10
# train_accs = []
# test_accs = []
loss_epochs = []
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
print('\n*** Start Training *** \n')
loss_train = train(train_loader, test_loader, model, criterion,
optimizer, epoch)
print(loss_train)
loss_epochs.append(loss_train)
is_best = loss_train < best_loss
print(best_loss)
best_loss = min(loss_train, best_loss)
info = {
'Loss': loss_train
# 'Testing Accuracy': test_prec1
}
# if not debug:
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch + 1)
if is_best:
best_epoch = epoch + 1
save_checkpoint(
{
'epoch': epoch + 1,
'AE_state_dict': model.state_dict(),
'optimizer': optimizer.state_dict()
}, is_best, checkpointdir)
generate_plots(range(args.start_epoch, args.epochs), loss_epochs)
print('Best epoch: ', best_epoch)
print('Total processing time: %.4f' % (time.time() - timestart))
print('Best loss:', best_loss)
# # print(imgset.shape)
# if label != 1:
# for count, img in enumerate(imgset):
# plt.subplot(1, len(imgset) + 1, count + 1)
# plt.imshow(img[0][0])
# # print(img.shape)
# print(mainImg.shape)
# plt.subplot(1, len(imgset) + 1, len(imgset) + 1)
# plt.imshow(mainImg[0][0])
# count += 1
# break
# # break
# creating the original network and couting the paramenters of different networks
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
siameseBaseLine = models.Net()
siameseBaseLine = siameseBaseLine.to(device)
count_parameters(siameseBaseLine)
# actual training
import torch.optim as optim
optimizer = optim.Adam(siameseBaseLine.parameters(), lr=0.0006)
num_epochs = 5
criterion = nn.BCEWithLogitsLoss()
save_path = 'siameseNet-batchnorm50.pt'
train_losses, val_losses = train(siameseBaseLine, train_loader, val_loader, num_epochs, criterion, save_path)
# Evaluation on previously saved models
import torch.optim as optim
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size',
type=int,
default=64,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size',
type=int,
default=1000,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs',
type=int,
default=10,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
type=float,
default=0.01,
metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
parser.add_argument(
'--log-interval',
type=int,
default=10,
metavar='N',
help='how many batches to wait before logging training status')
args = parser.parse_args()
use_cuda = not args.no_cuda and torch.cuda.is_available(
) # 根据输入参数和实际cuda的有无决定是否使用GPU
torch.manual_seed(args.seed) # 设置随机种子，保证可重复
device = torch.device("cuda" if use_cuda else "cpu") # 设置使用CPU or GPU
kwargs = {
'num_workers': 1,
'pin_memory': True
} if use_cuda else {} # 设置数据加载的子进程数；是否返回之前将张量复制到cuda的页锁定内存
dst = Dataset()
train_loader = torch.utils.data.DataLoader(dst.data_train,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(dst.data_test,
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
model = models.Net().to(device)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum) # 实例化求解器
for epoch in range(1, args.epochs + 1): # 循环调用train() and test() 进行epoch迭代
train(args, model, device, train_loader, optimizer, epoch)
test(args, model, device, test_loader)
vis.plot({'train_loss': train_loss_meter.value()[0]})
vis.plot({'test_acc': test_acc_meter.value()[0]}) # 为了可视化增加的内容
def main():
global args, best_prec1
args = parser.parse_args()
conf_name = args.data
conf_name += '_ortho' if args.ortho else ''
conf_name += ('_pre_' + args.net_type) if args.net_type != 'default' else ''
args.checkpoint = os.path.join(args.checkpoint, conf_name, 'imprint_checkpoint')
print(args.data)
print(args.checkpoint)
if not os.path.isdir(args.checkpoint):
mkdir_p(args.checkpoint)
model = models.Net(extractor_type=args.net_type).cuda()
print('==> Reading from model checkpoint..')
assert os.path.isfile(args.model), 'Error: no model checkpoint directory found!'
checkpoint = torch.load(args.model)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded model checkpoint '{}' (epoch {})"
.format(args.model, checkpoint['epoch']))
cudnn.benchmark = True
## check orthogonality
#import numpy as np
#W = model.classifier.fc.weight.data
#print(W.size())
#d_list = []
#for i in range(W.size(0)):
# for j in range(i,W.size(0)):
# if i==j:
# continue
# r1 = W[i]
# r2 = W[j]
#r1 = torch.nn.functional.normalize(r1,p=2,dim=0)
#r2 = torch.nn.functional.normalize(r2,p=2,dim=0)
# d = torch.dot(r1,r2)
# d_list.append(d.item())
#d_list = np.array(d_list)
#np.save('ortho_dotprod_hist.npy', d_list)
#
#import matplotlib.pyplot as plt
#plt.hist(d_list, bins=200, range=(-0.25,0.25), normed=True) # arguments are passed to np.histogram
#plt.title('Dot product histogram $\sigma$ = {:02f}'.format(np.std(d_list)))
#plt.show()
# Data loading code
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
novel_trasforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize]) if not args.aug else transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
novel_dataset = loader.ImageLoader(
args.data,
novel_trasforms,
train=True, num_classes=200,
num_train_sample=args.num_sample,
novel_only=True, aug=args.aug)
novel_loader = torch.utils.data.DataLoader(
novel_dataset, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
loader.ImageLoader(args.data, transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]), num_classes=200, novel_only=args.test_novel_only),
batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
imprint(novel_loader, model)
test_acc = validate(val_loader, model)
save_checkpoint({
'state_dict': model.state_dict(),
'best_prec1': test_acc,
}, checkpoint=args.checkpoint)
def load_model(self, model_path: str = 'mnist_0.49_error_rate.pt'):
self.model = models.Net()
self.model.load_state_dict(torch.load(model_path)) # load weights
if torch.cuda.is_available():
self.model = self.model.to(device)
self.model.eval()
traindataset = MyCustomFSDD(data_path=data_path, train=True)
trainloader = DataLoader(traindataset,
batch_size=batch_size,
shuffle=True,
pin_memory=True,
num_workers=0)
testdataset = MyCustomFSDD(data_path=data_path, train=False)
testloader = DataLoader(testdataset,
batch_size=1,
shuffle=True,
pin_memory=True,
num_workers=0)
# Initialize the NN model
net = models.Net()
net = net.to(device)
# Optimizer and Loss function
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=learning_rate)
start = timer() # start the timer
# Training
if __name__ == "__main__":
for epoch in range(max_epoch + 1):
batchiter = 0
for batch in trainloader:
batchiter += 1
def main():
from datetime import datetime
# this has been changed to run jupyter
#
# non jupyter ##############################################################
if len(sys.argv) > 1:
name = ' '.join(sys.argv[1:])
else:
name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
############################################################################
# remove line below if not running on jupyter
name = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
target_name = os.path.join('logs', '{}.pth'.format(name))
print('will save to {}'.format(target_name))
cudnn.benchmark = True
train_loader = data.get_loader(train=True)
val_loader = data.get_loader(val=True)
net = nn.DataParallel(models.Net(
train_loader.dataset.num_tokens)).cuda() #change made here
optimizer = optim.Adam([p for p in net.parameters() if p.requires_grad],
weight_decay=0.01)
tracker = utils.Tracker()
config_as_dict = {
k: v
for k, v in vars(config).items() if not k.startswith('__')
}
for i in range(config.epochs):
_ = run(net,
train_loader,
optimizer,
tracker,
train=True,
prefix='train',
epoch=i)
r = run(net,
val_loader,
optimizer,
tracker,
train=False,
prefix='val',
epoch=i)
results = {
'name': name,
'tracker': tracker.to_dict(),
'config': config_as_dict,
'weights': net.state_dict(),
'eval': {
'answers': r[0],
'accuracies': r[1],
'idx': r[2],
},
'vocab': train_loader.dataset.vocab,
}
torch.save(results, target_name)
torch.cuda.set_device(args.gpu)
data_test =data.DATA_TEST(args, mode='test')
#data_SVHN =data.SVHN(args, mode='test')
''' prepare data_loader '''
print('===> prepare data loader ...')
test_loader = torch.utils.data.DataLoader(data_test,
batch_size=args.test_batch,
num_workers=args.workers,
shuffle=False)
''' prepare mode '''
#load best model
if(args.target_set == 'svhn'):
if(args.resume_svhn =='model_target_svhn.pth.tar?dl=1'):
model = models.Net(args).cuda()
else:
model = models_best.Net(args).cuda()
print('checkpoint schn')
checkpoint = torch.load(args.resume_svhn)
elif (args.target_set == 'mnistm'):
if(args.resume_mnistm =='model_target_mnistm.pth.tar?dl=1'):
model = models.Net(args).cuda()
else:
model = models_best.Net(args).cuda()
print('checkpoint mnistm')
checkpoint = torch.load(args.resume_mnistm)
#save predictions
''' resume save model '''
sql=args.seq_len)
testInsts[i], testLabelPis[i], testLabelEmos[i] = batchify(
data.test_insts[i],
data.test_label_pis[i],
data.test_label_emos[i],
sql=args.seq_len)
#####################################################
# Build the model
#####################################################
net = [0] * 3
opt = [0] * 3
for i in range(3):
net[i] = models.Net(62, args.nhid, args.nlayer, args.seq_len,
args.batch_size, args.rnn_type, args.total_layer,
args.g_dropout, args.pi_dropout, args.emo_dropout,
args.ntask).double().to(device)
opt[i] = torch.optim.Adam(net[i].parameters(), lr=args.lr)
if args.way_loss == 0:
weigh_loss = models.Weigh_Loss_Simple().double().to(device)
elif args.way_loss == 1:
weigh_loss = models.Weigh_Loss_MLM().double().to(device)
else:
weigh_loss = models.Sum_Loss().double().to(device)
criterion = nn.CrossEntropyLoss()
#####################################################
# Training code
#####################################################
# Get a batch of training data
inputs, classes = next(iter(train_loader))
inputs = inputs * std_tensor + mean_tensor
# Make a grid from batch
out = torchvision.utils.make_grid(inputs) # CHW
outn = out.numpy().transpose(1, 2, 0) # HWC
plt.figure()
plt.imshow(outn)
#plt.title(",".join([classnames[i] for i in classes]))
plt.axis('off')
plt.savefig('augmentations.pdf', bbox_inches='tight')
plt.show()
model = models.Net(use_dropout, use_bn, l2_reg)
model = model.to(device)
# Display information about the model
summary_text = "Summary of the model architecture\n"+ \
"=================================\n" + \
f"{deepcs.display.torch_summarize(model)}\n"
print(summary_text)
"""
Adapted from :
https://github.com/seominseok0429/label-smoothing-visualization-pytorch
"""
class LabelSmoothingCrossEntropy(nn.Module):
transforms.Normalize((0.1722, ), (0.3309, ))])),
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_loader = torch.utils.data.DataLoader(datasets.EMNIST(
'data',
'letters',
train=False,
transform=transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1722, ), (0.3309, ))])),
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
model = models.Net()
# D_in = 28*28
# H = 500
# D_out = 27
# model = torch.nn.Sequential(
# torch.nn.Linear(D_in, H), torch.nn.ReLU(),
# torch.nn.Linear(H, H), torch.nn.ReLU(),
# torch.nn.Linear(H, D_out),
# )
if args.cuda:
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
import os
import shutil
from torch.utils.tensorboard import SummaryWriter
if __name__ == '__main__':
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', type=int, default=8)
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--epoch', type=int, default=50)
args = parser.parse_args()
train_loader, test_loader = datasets.prepare(batch_size=args.batch_size)
model = models.Net(num_classes=8).to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
log_dir = 'data/runs'
if os.path.exists(log_dir):
shutil.rmtree(log_dir)
os.makedirs(log_dir)
else:
os.makedirs(log_dir)
writer = SummaryWriter(log_dir=log_dir)
epoch_digit = len(list(str(args.epoch)))
for epoch in range(args.epoch):
model.train()
train_loss = 0
train_acc = 0