def test(choice, X_test, y_test):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
#Teacher Model
if choice['teacher'] == 'lnin':
model = LNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ae':
model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ssae':
model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
ssae = SSAE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'dnin':
model = DNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'tnin':
model = TNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'mnin':
model = MNIN(img_size, img_class, sess, choice)
else:
model = SN(img_size, img_class, sess, choice)
#Student Model
if choice['student'] == 'lsn':
model = LSN(img_size, img_class, sess, choice)
elif choice['student'] == 'dsn':
model = DSN(img_size, img_class, sess, choice)
elif choice['student'] == 'msn':
model = MSN(img_size, img_class, sess, choice)
elif choice['student'] == 'ssn':
model = SSN(img_size, img_class, sess, choice)
if not model.save:
#sess.run(tf.initialize_all_variables())
print "No Model to test"
return
a = 0
for i in range(100):
if choice['student'] == 'none':
if choice['teacher'] == 'lnin_ssae':
hidden = model.get_hidden(X_test[i * 100:(i + 1) * 100])
hidden = ae.get_embedding(hidden)
a += ssae.test(hidden, y_test[i * 100:(i + 1) * 100])
else:
a += model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
else:
a += model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
print " Test Average Accuracy: ", 1. * a / 100
return a
def main(model_name='AE',
embedding_size=128,
n_epochs=1000,
batch_size=32,
roi_size=128):
if model_name == 'VAE':
model = VAE(embedding_size=embedding_size)
criterion = VAELoss()
elif 'AE':
model = AE(embedding_size=embedding_size)
criterion = nn.MSELoss()
details = 'L{}'.format(embedding_size)
log_dir = os.path.join(
log_dir_root, '{}_{}_{}'.format(model_name, details,
time.strftime('%Y%m%d_%H%M%S')))
#criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
if is_cuda:
print('This is CUDA!!!!')
torch.backends.cudnn.benchmark = True #useful for arrays of fix dimension
model = model.cuda()
criterion = criterion.cuda()
generator = ROIFlowBatch(mask_file,
feat_file,
is_cuda=is_cuda,
batch_size=batch_size,
roi_size=roi_size,
is_shuffle=True)
t = TrainerAutoEncoder(model, optimizer, criterion, generator, n_epochs,
log_dir)
t.fit()
def init_fit(self, X1_train, X2_train, y_train, X1_val, X2_val, y_val, args, ):
self.train_loader = get_dataloader (X1_train, X2_train, y_train, args.batch_size)
self.test_loader = get_dataloader(X1_val, X2_val, y_val, args.batch_size)
self.predictor = AE(
encoder_layer_sizes=[X1_train.shape[1]],
latent_size=args.latent_size,
decoder_layer_sizes=[X2_train.shape[1]],
activation=args.activation,
batch_norm= args.batch_norm,
dropout=args.dropout,
mlp_type=self.mlp_type,
conditional=args.conditional,
num_labels=10 if args.conditional else 0).to(self.device)
self.optimizer = torch.optim.Adam(self.predictor.parameters(), lr=args.learning_rate)
self.scheduler = torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=0.8)
def make_ae(self) -> AE:
encoder = self.make_encoder(self.get_encoder_input_shape()[1:])
decoder = self.make_decoder(self.get_latent_code_shape(encoder)[1:])
model = AE(encoder=encoder,
decoder=decoder,
learning_rate=WarmupSchedule(1000, self.learning_rate))
return model
def build_model(self):
# pass
# 모델 생성하고 config 파일을 load, gpu에 올림
self.model = cc(AE(self.config)).to(self.get_default_device())
# 모델을 evaluation 모드로 전환
# print(self.model)
self.model.eval()
# load_state_dict를 이용하여 학습된 VC 모델의 checkpoint load
# print(f'Load model from /home/ubuntu/test/VC')
self.model.load_state_dict(
torch.load('/home/ubuntu/test/VC/model_140000_2.ckpt',
map_location=torch.device("cpu")))
return
def build_model(self):
# pass
# self.config 모델에 load한뒤 gpu에 올려서 self.model 변수 생성
self.model = cc(AE(self.config))
print(self.model)
# self.config을 참고하여 self.opt에 optimizer
optimizer = self.config['optimizer']
self.opt = torch.optim.Adam(self.model.parameters(),
lr=optimizer['lr'],
betas=(optimizer['beta1'],
optimizer['beta2']),
amsgrad=optimizer['amsgrad'],
weight_decay=optimizer['weight_decay'])
print(self.opt)
return
def test(data, folder, e):
label_col = list(data.columns)
result = data
model = AE()
model.load_state_dict(
torch.load(f'{folder}/ep{e}data_aug.pth', map_location='cpu'))
model.eval()
dataset = AEDataset(data)
dataloader = DataLoader(dataset=dataset, batch_size=128, shuffle=False)
for inputs in tqdm(dataloader):
outputs = model(inputs.float(), 'cpu')
for i in range(len(outputs)):
tmp = outputs[i].detach().numpy()
tmp = pd.DataFrame([tmp], columns=label_col)
result = pd.concat([result, tmp], ignore_index=True)
result.to_csv(f'{folder}/data_augment.csv',
mode='a',
header=True,
index=False)
return result
TC.on_train_end('_')
if __name__ == "__main__":
data_dir = 'data/coco2017'
log_dir = 'logs'
input_image_size = (256, 256, 3)
batch_size = 10
latent_dim = 32
optimizer = tf.keras.optimizers.Adam(1e-3)
# Initialize and compile models
incept_model = InceptionV3(include_top=False,
pooling='avg',
input_shape=input_image_size)
ae_model = AE(latent_dim, input_image_size)
cae_model = CAE(latent_dim, input_image_size)
vae_model = VAE(latent_dim, input_image_size)
cvae_model = CVAE(latent_dim, input_image_size)
memcae_model = MemCAE(latent_dim, True, input_image_size, batch_size, 500,
optimizer)
for classes in [['cat']]:
# Load and augment training data
ds_train = dataloader(classes, data_dir, input_image_size, batch_size,
'train2019')
ds_val = dataloader(classes, data_dir, input_image_size, batch_size,
'val2019')
class_label = classes[0] if len(classes) == 1 else "similar"
# Train each model for comparison
image_matrix.append(skt.unsqueeze(0).repeat(1, 3, 1, 1).clone())
image_matrix.append(gimg_ae.cpu())
image_matrix.append(
skt.unsqueeze(0).repeat(1, 3, 1, 1).clone().fill_(1))
image_matrix.append(torch.nn.functional.interpolate(g_images, 512))
image_matrix = torch.cat(image_matrix)
vutils.save_image(0.5 * (image_matrix + 1), im_name, nrow=BATCH_SIZE + 1)
if __name__ == "__main__":
device = 'cuda'
from models import AE, RefineGenerator_art, RefineGenerator_face
net_ae = AE()
net_ae.style_encoder.reset_cls()
net_ig = RefineGenerator_face()
ckpt = torch.load('./models/16.pth')
net_ae.load_state_dict(ckpt['ae'])
net_ig.load_state_dict(ckpt['ig'])
net_ae.to(device)
net_ig.to(device)
net_ae.eval()
#net_ig.eval()
data_root_colorful = './data/rgb/'
def main(args):
model_alias = 'DeepBiome_%s+%s_%s+fea1_%s+fea2_%s+bs_%s+%s' % (
args.model,
args.dataset_name, args.data_type,
args.fea1,args.fea2,
args.batch_size,
args.extra)
tl.configure("runs/ds.{}".format(model_alias))
tl.log_value(model_alias, 0)
""" no stat file needed for now
stat_alias = 'obj_DataStat+%s_%s' % (args.dataset_name, args.dataset_subset)
stat_path = os.path.join(
output_dir, '%s.pkl' % (stat_alias)
)
with open(stat_path,'rb') as sf:
data_stats = pickle.load(sf)
"""
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
data_path = os.path.join(DATA_ROOT, "ibd_{}.pkl".format(args.data_type))
logger.info("Initializing train dataset")
# load data
print('==> loading data'); print()
(X1_train, X2_train, y_train), (X1_val, X2_val, y_val) = load_data(data_path)
train_loader = get_dataloader (X1_train, X2_train, y_train, args.batch_size)
test_loader = get_dataloader(X1_val, X2_val, y_val, args.batch_size)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
ts = time.time()
def loss_fn(model, recon_x, x, mean, log_var):
if model =="AE":
mseloss = torch.nn.MSELoss()
return torch.sqrt(mseloss(recon_x, x))
elif model =="VAE":
BCE = torch.nn.functional.binary_cross_entropy(
recon_x.view(-1, 28*28), x.view(-1, 28*28), reduction='sum')
KLD = -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp())
return (BCE + KLD) / x.size(0)
if args.model == "AE":
predictor = AE(
encoder_layer_sizes=[X1_train.shape[1]],
latent_size=args.latent_size,
decoder_layer_sizes=[X2_train.shape[1]],
activation=args.activation,
batch_norm= args.batch_norm,
dropout=args.dropout,
conditional=args.conditional,
num_labels=10 if args.conditional else 0).to(device)
else:
predictor = VAE(
encoder_layer_sizes=args.encoder_layer_sizes,
latent_size=args.latent_size,
decoder_layer_sizes=args.decoder_layer_sizes,
activation=args.activation,
batch_norm=args.batch_norm,
dropout=args.dropout,
conditional=args.conditional,
num_labels=10 if args.conditional else 0).to(device)
optimizer = torch.optim.Adam(predictor.parameters(), lr=args.learning_rate)
logs = defaultdict(list)
for epoch in range(args.epochs):
tracker_epoch = defaultdict(lambda: defaultdict(dict))
for iteration, (x1, x2, y) in enumerate(train_loader):
x1, x2, y = x1.to(device), x2.to(device), y.to(device)
if args.conditional:
x2_hat, z, mean, log_var = predictor(x1, y)
else:
x2_hat, z, mean, log_var = predictor(x1)
for i, yi in enumerate(y):
id = len(tracker_epoch)
tracker_epoch[id]['x'] = z[i, 0].item()
tracker_epoch[id]['y'] = z[i, 1].item()
tracker_epoch[id]['label'] = yi.item()
loss = loss_fn(args.model, x2_hat, x2, mean, log_var)
optimizer.zero_grad()
loss.backward()
optimizer.step()
logs['loss'].append(loss.item())
if iteration % args.print_every == 0 or iteration == len(train_loader)-1:
print("Epoch {:02d}/{:02d} Batch {:04d}/{:d}, Loss {:9.4f}".format(
epoch, args.epochs, iteration, len(train_loader)-1, loss.item()))
if args.model =="VAE":
if args.conditional:
c = torch.arange(0, 10).long().unsqueeze(1)
x = predictor.inference(n=c.size(0), c=c)
else:
x = predictor.inference(n=10)
"""
plt.figure()
plt.figure(figsize=(5, 10))
for p in range(10):
plt.subplot(5, 2, p+1)
if args.conditional:
plt.text(
0, 0, "c={:d}".format(c[p].item()), color='black',
backgroundcolor='white', fontsize=8)
plt.imshow(x[p].view(28, 28).cpu().data.numpy())
plt.axis('off')
if not os.path.exists(os.path.join(args.fig_root, str(ts))):
if not(os.path.exists(os.path.join(args.fig_root))):
os.mkdir(os.path.join(args.fig_root))
os.mkdir(os.path.join(args.fig_root, str(ts)))
plt.savefig(
os.path.join(args.fig_root, str(ts),
"E{:d}I{:d}.png".format(epoch, iteration)),
dpi=300)
plt.clf()
plt.close('all')
"""
#Batch test
x1, x2, y = torch.FloatTensor(X1_val).to(device), torch.FloatTensor(X2_val).to(device), torch.FloatTensor(y_val).to(device)
if args.conditional:
x2_hat, z, mean, log_var = predictor(x1, y)
else:
x2_hat, z, mean, log_var = predictor(x1)
val_loss = loss_fn(args.model, x2_hat, x2, mean, log_var)
print("val_loss: {:9.4f}", val_loss.item())
"""
def __init__(self):
super(GNNq,self).__init__()
self.gnnql = AE(rnafeat.shape[1],256,args.hidden)
self.gnnqd = AE(gdi.shape[0],256,args.hidden)
('base_lr', args.base_lr), ('n_input', args.n_input),
('n_output', args.n_output), ('archi', args.archi)])
return config
config = parse_args()
### call data ###
mnist = Mnist()
n_samples = mnist.num_examples
### call models ###
if config['model_name'] == 'AE':
print('Run AE')
model = AE(config['n_input'], config['n_output'], config['archi'])
elif config['model_name'] == 'VAE':
print('Run VAE')
model = VAE(config['n_input'], config['n_output'], config['archi'])
### make folder ###
mother_folder = config['model_name']
try:
os.mkdir(mother_folder)
except OSError:
pass
### outputs ###
latent, reconstr, loss = model.Forward()
lr = config['base_lr']
import os
import sys
#Be sure to use abspath linux does not give the path if one uses __file__
_BASEDIR = os.path.dirname(os.path.abspath(__file__))
src_dir = os.path.join(_BASEDIR, 'src')
sys.path.append(src_dir)
from flow import ROIFlowBatch
from models import AE
mask_file = '/Volumes/behavgenom_archive$/Avelino/screening/CeNDR/MaskedVideos/CeNDR_Set1_160517/BRC20067_worms10_food1-10_Set10_Pos5_Ch6_16052017_165021.hdf5'
feat_file = '/Volumes/behavgenom_archive$/Avelino/screening/CeNDR/Results/CeNDR_Set1_160517/BRC20067_worms10_food1-10_Set10_Pos5_Ch6_16052017_165021_featuresN.hdf5'
if __name__ == '__main__':
ae = AE(64)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(ae.parameters(), lr=1e-2)
#%%
#optimizer = torch.optim.SGD(vae.parameters(), lr=1e-1, momentum=0.9)
n_epochs = 100
#%%
gen = ROIFlowBatch(mask_file, feat_file, batch_size=32, roi_size=128)
ae.train()
for epoch in range(n_epochs):
pbar = tqdm.tqdm(gen)
for X in pbar:
decoded = ae.forward(X)
config['io_type'] = config['i_type'] + '2' + config['o_type']
mnist = Mnist()
config['n_output'] = mnist.out_shape
n_samples = mnist.num_examples
iter_per_epoch = int(n_samples / config['batch_size'])
train_x, train_y = mnist.train_images, mnist.train_labels
test_x, test_y = mnist.test_images, mnist.test_labels
if config['model_name'] == 'AE':
print('Run AE')
model = AE(config['n_output'], config['archi'])
elif config['model_name'] == 'VAE':
print('Run VAE')
model = VAE(config['n_output'], config['archi'])
mother_folder = os.path.join(config['path_dir'], config['model_name'])
mother_folder = os.path.join(mother_folder, config['io_type'])
try:
os.mkdir(mother_folder)
except OSError:
pass
folder_name = os.path.join(mother_folder,
config['model_name'] + '_' + config['datasets'])
try:
os.mkdir(folder_name)
def build_model(self):
# create model, discriminator, optimizers
self.model = cc(AE(self.config))
print(self.model)
self.model.eval()
return
from utils import load_params
net_ig = RefineGenerator().cuda()
net_ig = nn.DataParallel(net_ig)
ckpt = './train_results/trial_refine_ae_as_gan_1024_2/models/4.pth'
if ckpt is not None:
ckpt = torch.load(ckpt)
#net_ig.load_state_dict(ckpt['ig'])
#net_id.load_state_dict(ckpt['id'])
net_ig_ema = ckpt['ig_ema']
load_params(net_ig, net_ig_ema)
net_ig = net_ig.module
#net_ig.eval()
net_ae = AE()
net_ae.load_state_dicts(
'./train_results/trial_vae_512_1/models/176000.pth')
net_ae.cuda()
net_ae.eval()
inception = load_patched_inception_v3().cuda()
inception.eval()
'''
real_features = extract_feature_from_generator_fn(
real_image_loader(dataloader, n_batches=1000), inception )
real_mean = np.mean(real_features, 0)
real_cov = np.cov(real_features, rowvar=False)
'''
#pickle.dump({'feats': real_features, 'mean': real_mean, 'cov': real_cov}, open('celeba_fid_feats.npy','wb') )
def data_aug(data, lr=0.001, epoch=800, batch_size=128):
folder = 'data_aug'
save_path = f'{folder}/data_augment.csv'
clean_file(save_path)
store_e = [700, 750, 800]
if not os.path.exists(folder):
os.makedirs(folder)
else:
for i in store_e:
result = test(data, folder, i)
return result
train_loss_curve = []
valid_loss_curve = []
# load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = AE()
model = model.to(device)
model.train()
dataset = AEDataset(data)
train_size = int(0.85 * len(dataset))
valid_size = len(dataset) - train_size
train_dataset, valid_dataset = random_split(dataset,
[train_size, valid_size])
train_dataloader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
valid_dataloader = DataLoader(dataset=valid_dataset,
batch_size=batch_size,
shuffle=True)
# loss function and optimizer
# can change loss function and optimizer you want
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
best = 100
# start training
for e in range(epoch):
train_loss = 0.0
valid_loss = 0.0
print(f'\nEpoch: {e+1}/{epoch}')
print('-' * len(f'Epoch: {e+1}/{epoch}'))
# tqdm to disply progress bar
for inputs in tqdm(train_dataloader):
# data from data_loader
inputs = inputs.float().to(device)
outputs = model(inputs, device)
loss = criterion(outputs, inputs)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
for inputs in tqdm(valid_dataloader):
# data from data_loader
inputs = inputs.float().to(device)
outputs = model(inputs, device)
# MSE loss
loss = criterion(outputs, inputs)
# loss calculate
valid_loss += loss.item()
# save the best model weights as .pth file
loss_epoch = train_loss / len(train_dataset)
valid_loss_epoch = valid_loss / len(valid_dataset)
# if valid_loss_epoch < best :
# best = valid_loss_epoch
# torch.save(model.state_dict(), 'data_aug.pth')
if e in store_e:
torch.save(model.state_dict(), f'{folder}/ep{e}data_aug.pth')
print(f"training in epoch {e},start augment data!!")
result = test(data, folder, e)
print(f'Training loss: {loss_epoch:.4f}')
print(f'Valid loss: {valid_loss_epoch:.4f}')
# save loss every epoch
train_loss_curve.append(loss_epoch)
valid_loss_curve.append(valid_loss_epoch)
# generate training curve
# visualize(train_loss_curve,valid_loss_curve, 'Data Augmentation')
return result
def train(choice, X_train, y_train, X_test, y_test):
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
student_model = None
teacher_model = None
with tf.Session(config=config) as sess:
#Teacher Model
global ssae, ae
if choice['teacher'] == 'lnin':
teacher_model = LNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ae':
teacher_model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'lnin_ssae':
teacher_model = DNIN(img_size, img_class, sess, choice)
ae = AE(img_size, img_class, sess, choice)
ssae = SSAE(img_size, img_class, sess, choice)
elif choice['teacher'] == 'dnin':
teacher_model = DNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'mnin':
teacher_model = MNIN(img_size, img_class, sess, choice)
elif choice['teacher'] == 'tnin':
teacher_model = TNIN(img_size, img_class, sess, choice)
else:
teacher_model = SN(img_size, img_class, sess, choice)
#Student Model
if choice['student'] == 'lsn':
student_model = LSN(img_size, img_class, sess, choice)
elif choice['student'] == 'dsn':
student_model = DSN(img_size, img_class, sess, choice)
elif choice['student'] == 'msn':
student_model = MSN(img_size, img_class, sess, choice)
if choice['student'] != 'none':
if not student_model.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
else:
if choice['teacher'] == 'lnin_ssae':
if not ssae.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
elif choice['teacher'] == 'lnin_ae':
if not ae.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
else:
if not teacher_model.save:
sess.run(tf.initialize_all_variables())
print "Initialize"
max_epochs = choice['epochs']
l = 0
a = 0
test_accuracy = []
step = 0
epoch = 0
loss = [
] #np.load("loss_"+choice['teacher'] +'_'+choice['student']+"_run"+str(choice['run'])+".npy").tolist()
accuracy = [
] #np.load("accuracy_"+choice['teacher']+'_'+choice['student']+"_run"+str(choice['run'])+".npy").tolist()
while epoch <= choice['epochs']:
if step * batch_size > X_train.shape[0] or step == 0:
s = np.arange(X_train.shape[0])
np.random.shuffle(s)
X_train = X_train[s]
y_train = y_train[s]
step = 0
print "Epoch:%d, loss: %f, accuracy: %f" % (epoch, l, a)
save_results(choice, teacher_model, student_model, loss,
accuracy)
epoch += 1
X_batch = X_train[step *
batch_size:min(X_train.shape[0], (step + 1) *
batch_size), :]
y_batch = y_train[step *
batch_size:min(y_train.shape[0], (step + 1) *
batch_size), :]
if choice['student'] != 'none':
s = np.random.choice(X_batch.shape[0], X_batch.shape[0])
X_batch2 = X_batch[s]
eps = np.random.rand(X_batch.shape[0])[:, None, None, None]
eps = np.tile(eps, [1, 32, 32, 3])
X_batch3 = eps * X_batch + (1 - eps) * X_batch2
X_batch = np.concatenate(
(X_batch[:X_batch.shape[0]], X_batch3[X_batch3.shape[0]:]))
if choice['student'] != 'none':
if choice['teacher'] == 'lnin_ssae':
#X_batch, y_batch = teacher_model.correct(X_batch, y_batch)
hidden = teacher_model.get_hidden(X_batch)
hidden = ae.get_embedding(hidden)
labels = ssae.get_embedding(hidden)
elif choice['teacher'] == 'dnin':
labels = teacher_model.predict(X_batch)
#labels = y_batch
elif choice['teacher'] == 'tnin':
labels = teacher_model.get_embedding(X_batch)
labels[labels >= 0.5] = 1
labels[labels < 0.5] = 0
elif choice['teacher'] == 'mnin':
labels = teacher_model.get_sub_prediction(X_batch)
elif choice['teacher'] == 'lnin':
labels = teacher_model.get_logits(X_batch)
l, a = student_model.train(X_batch, y_batch, labels)
else:
if choice['teacher'] == 'lnin_ae':
#X_batch, y_batch = teacher_model.correct(X_batch, y_batch)
hidden = teacher_model.get_hidden(X_batch)
l, a = ae.train(hidden)
elif choice['teacher'] == 'lnin_ssae':
#X_batch, y_batch = teacher_model.correct(X_batch, y_batch)
hidden = teacher_model.get_hidden(X_batch)
hidden = ae.get_embedding(hidden)
l, a = ssae.train(hidden, y_batch)
else:
l, a = teacher_model.train(X_batch, y_batch)
loss.append(l)
accuracy.append(a)
if epoch == choice['epochs']:
a = 0
for i in range(100):
if choice['student'] != 'none':
a += student_model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
else:
a += teacher_model.test(X_test[i * 100:(i + 1) * 100],
y_test[i * 100:(i + 1) * 100])
a = 1. * a / 100
print "Test Accuracy ", a
test_accuracy.append(a)
if step == 100:
np.save("test_accuracy_"+choice['teacher']+'_'+choice['student']\
+"_run"+str(choice['run'])+".npy",np.asarray(test_accuracy))
print "Student Done"
break
step += 1
def train():
from benchmark import calc_fid, extract_feature_from_generator_fn, load_patched_inception_v3, real_image_loader, image_generator, image_generator_perm
import lpips
from config import IM_SIZE_GAN, BATCH_SIZE_GAN, NFC, NBR_CLS, DATALOADER_WORKERS, EPOCH_GAN, ITERATION_AE, GAN_CKECKPOINT
from config import SAVE_IMAGE_INTERVAL, SAVE_MODEL_INTERVAL, LOG_INTERVAL, SAVE_FOLDER, TRIAL_NAME, DATA_NAME, MULTI_GPU
from config import FID_INTERVAL, FID_BATCH_NBR, PRETRAINED_AE_PATH
from config import data_root_colorful, data_root_sketch_1, data_root_sketch_2, data_root_sketch_3
real_features = None
inception = load_patched_inception_v3().cuda()
inception.eval()
percept = lpips.PerceptualLoss(model='net-lin', net='vgg', use_gpu=True)
saved_image_folder = saved_model_folder = None
log_file_path = None
if saved_image_folder is None:
saved_image_folder, saved_model_folder = make_folders(
SAVE_FOLDER, 'GAN_' + TRIAL_NAME)
log_file_path = saved_image_folder + '/../gan_log.txt'
log_file = open(log_file_path, 'w')
log_file.close()
dataset = PairedMultiDataset(data_root_colorful,
data_root_sketch_1,
data_root_sketch_2,
data_root_sketch_3,
im_size=IM_SIZE_GAN,
rand_crop=True)
print('the dataset contains %d images.' % len(dataset))
dataloader = iter(
DataLoader(dataset,
BATCH_SIZE_GAN,
sampler=InfiniteSamplerWrapper(dataset),
num_workers=DATALOADER_WORKERS,
pin_memory=True))
from datasets import ImageFolder
from datasets import trans_maker_augment as trans_maker
dataset_rgb = ImageFolder(data_root_colorful, trans_maker(512))
dataset_skt = ImageFolder(data_root_sketch_3, trans_maker(512))
net_ae = AE(nfc=NFC, nbr_cls=NBR_CLS)
if PRETRAINED_AE_PATH is None:
PRETRAINED_AE_PATH = 'train_results/' + 'AE_' + TRIAL_NAME + '/models/%d.pth' % ITERATION_AE
else:
from config import PRETRAINED_AE_ITER
PRETRAINED_AE_PATH = PRETRAINED_AE_PATH + '/models/%d.pth' % PRETRAINED_AE_ITER
net_ae.load_state_dicts(PRETRAINED_AE_PATH)
net_ae.cuda()
net_ae.eval()
RefineGenerator = None
if DATA_NAME == 'celeba':
from models import RefineGenerator_face as RefineGenerator
elif DATA_NAME == 'art' or DATA_NAME == 'shoe':
from models import RefineGenerator_art as RefineGenerator
net_ig = RefineGenerator(nfc=NFC, im_size=IM_SIZE_GAN).cuda()
net_id = Discriminator(nc=3).cuda(
) # we use the patch_gan, so the im_size for D should be 512 even if training image size is 1024
if MULTI_GPU:
net_ae = nn.DataParallel(net_ae)
net_ig = nn.DataParallel(net_ig)
net_id = nn.DataParallel(net_id)
net_ig_ema = copy_G_params(net_ig)
opt_ig = optim.Adam(net_ig.parameters(), lr=2e-4, betas=(0.5, 0.999))
opt_id = optim.Adam(net_id.parameters(), lr=2e-4, betas=(0.5, 0.999))
if GAN_CKECKPOINT is not None:
ckpt = torch.load(GAN_CKECKPOINT)
net_ig.load_state_dict(ckpt['ig'])
net_id.load_state_dict(ckpt['id'])
net_ig_ema = ckpt['ig_ema']
opt_ig.load_state_dict(ckpt['opt_ig'])
opt_id.load_state_dict(ckpt['opt_id'])
## create a log file
losses_g_img = AverageMeter()
losses_d_img = AverageMeter()
losses_mse = AverageMeter()
losses_rec_s = AverageMeter()
losses_rec_ae = AverageMeter()
fixed_skt = fixed_rgb = fixed_perm = None
fid = [[0, 0]]
for epoch in range(EPOCH_GAN):
for iteration in tqdm(range(10000)):
rgb_img, skt_img_1, skt_img_2, skt_img_3 = next(dataloader)
rgb_img = rgb_img.cuda()
rd = random.randint(0, 3)
if rd == 0:
skt_img = skt_img_1.cuda()
elif rd == 1:
skt_img = skt_img_2.cuda()
else:
skt_img = skt_img_3.cuda()
if iteration == 0:
fixed_skt = skt_img_3[:8].clone().cuda()
fixed_rgb = rgb_img[:8].clone()
fixed_perm = true_randperm(fixed_rgb.shape[0], 'cuda')
### 1. train D
gimg_ae, style_feats = net_ae(skt_img, rgb_img)
g_image = net_ig(gimg_ae, style_feats)
pred_r = net_id(rgb_img)
pred_f = net_id(g_image.detach())
loss_d = d_hinge_loss(pred_r, pred_f)
net_id.zero_grad()
loss_d.backward()
opt_id.step()
loss_rec_ae = F.mse_loss(gimg_ae, rgb_img) + F.l1_loss(
gimg_ae, rgb_img)
losses_rec_ae.update(loss_rec_ae.item(), BATCH_SIZE_GAN)
### 2. train G
pred_g = net_id(g_image)
loss_g = g_hinge_loss(pred_g)
if DATA_NAME == 'shoe':
loss_mse = 10 * (F.l1_loss(g_image, rgb_img) +
F.mse_loss(g_image, rgb_img))
else:
loss_mse = 10 * percept(
F.adaptive_avg_pool2d(g_image, output_size=256),
F.adaptive_avg_pool2d(rgb_img, output_size=256)).sum()
losses_mse.update(loss_mse.item() / BATCH_SIZE_GAN, BATCH_SIZE_GAN)
loss_all = loss_g + loss_mse
if DATA_NAME == 'shoe':
### the grey image reconstruction
perm = true_randperm(BATCH_SIZE_GAN)
img_ae_perm, style_feats_perm = net_ae(skt_img, rgb_img[perm])
gimg_grey = net_ig(img_ae_perm, style_feats_perm)
gimg_grey = gimg_grey.mean(dim=1, keepdim=True)
real_grey = rgb_img.mean(dim=1, keepdim=True)
loss_rec_grey = F.mse_loss(gimg_grey, real_grey)
loss_all += 10 * loss_rec_grey
net_ig.zero_grad()
loss_all.backward()
opt_ig.step()
for p, avg_p in zip(net_ig.parameters(), net_ig_ema):
avg_p.mul_(0.999).add_(p.data, alpha=0.001)
### 3. logging
losses_g_img.update(pred_g.mean().item(), BATCH_SIZE_GAN)
losses_d_img.update(pred_r.mean().item(), BATCH_SIZE_GAN)
if iteration % SAVE_IMAGE_INTERVAL == 0: #show the current images
with torch.no_grad():
backup_para_g = copy_G_params(net_ig)
load_params(net_ig, net_ig_ema)
gimg_ae, style_feats = net_ae(fixed_skt, fixed_rgb)
gmatch = net_ig(gimg_ae, style_feats)
gimg_ae_perm, style_feats = net_ae(fixed_skt,
fixed_rgb[fixed_perm])
gmismatch = net_ig(gimg_ae_perm, style_feats)
gimg = torch.cat([
F.interpolate(fixed_rgb, IM_SIZE_GAN),
F.interpolate(fixed_skt.repeat(1, 3, 1, 1),
IM_SIZE_GAN), gmatch,
F.interpolate(gimg_ae, IM_SIZE_GAN), gmismatch,
F.interpolate(gimg_ae_perm, IM_SIZE_GAN)
])
vutils.save_image(
gimg,
f'{saved_image_folder}/img_iter_{epoch}_{iteration}.jpg',
normalize=True,
range=(-1, 1))
del gimg
make_matrix(
dataset_rgb, dataset_skt, net_ae, net_ig, 5,
f'{saved_image_folder}/img_iter_{epoch}_{iteration}_matrix.jpg'
)
load_params(net_ig, backup_para_g)
if iteration % LOG_INTERVAL == 0:
log_msg = 'Iter: [{0}/{1}] G: {losses_g_img.avg:.4f} D: {losses_d_img.avg:.4f} MSE: {losses_mse.avg:.4f} Rec: {losses_rec_s.avg:.5f} FID: {fid:.4f}'.format(
epoch,
iteration,
losses_g_img=losses_g_img,
losses_d_img=losses_d_img,
losses_mse=losses_mse,
losses_rec_s=losses_rec_s,
fid=fid[-1][0])
print(log_msg)
print('%.5f' % (losses_rec_ae.avg))
if log_file_path is not None:
log_file = open(log_file_path, 'a')
log_file.write(log_msg + '\n')
log_file.close()
losses_g_img.reset()
losses_d_img.reset()
losses_mse.reset()
losses_rec_s.reset()
losses_rec_ae.reset()
if iteration % SAVE_MODEL_INTERVAL == 0 or iteration + 1 == 10000:
print('Saving history model')
torch.save(
{
'ig': net_ig.state_dict(),
'id': net_id.state_dict(),
'ae': net_ae.state_dict(),
'ig_ema': net_ig_ema,
'opt_ig': opt_ig.state_dict(),
'opt_id': opt_id.state_dict(),
}, '%s/%d.pth' % (saved_model_folder, epoch))
if iteration % FID_INTERVAL == 0 and iteration > 1:
print("calculating FID ...")
fid_batch_images = FID_BATCH_NBR
if real_features is None:
if os.path.exists('%s_fid_feats.npy' % (DATA_NAME)):
real_features = pickle.load(
open('%s_fid_feats.npy' % (DATA_NAME), 'rb'))
else:
real_features = extract_feature_from_generator_fn(
real_image_loader(dataloader,
n_batches=fid_batch_images),
inception)
real_mean = np.mean(real_features, 0)
real_cov = np.cov(real_features, rowvar=False)
pickle.dump(
{
'feats': real_features,
'mean': real_mean,
'cov': real_cov
}, open('%s_fid_feats.npy' % (DATA_NAME), 'wb'))
real_features = pickle.load(
open('%s_fid_feats.npy' % (DATA_NAME), 'rb'))
sample_features = extract_feature_from_generator_fn(
image_generator(dataset,
net_ae,
net_ig,
n_batches=fid_batch_images),
inception,
total=fid_batch_images)
cur_fid = calc_fid(sample_features,
real_mean=real_features['mean'],
real_cov=real_features['cov'])
sample_features_perm = extract_feature_from_generator_fn(
image_generator_perm(dataset,
net_ae,
net_ig,
n_batches=fid_batch_images),
inception,
total=fid_batch_images)
cur_fid_perm = calc_fid(sample_features_perm,
real_mean=real_features['mean'],
real_cov=real_features['cov'])
fid.append([cur_fid, cur_fid_perm])
print('fid:', fid)
if log_file_path is not None:
log_file = open(log_file_path, 'a')
log_msg = 'fid: %.5f, %.5f' % (fid[-1][0], fid[-1][1])
log_file.write(log_msg + '\n')
log_file.close()
tmp = pickle.load(f, encoding='latin1')
edge_index_list = [utils.to_edge_index(adj).to(device) for adj in tmp['adj']]
down_transform_list = [
utils.to_sparse(down_transform).to(device)
for down_transform in tmp['down_transform']
]
up_transform_list = [
utils.to_sparse(up_transform).to(device)
for up_transform in tmp['up_transform']
]
model = AE(args.in_channels,
args.out_channels,
args.latent_channels,
edge_index_list,
down_transform_list,
up_transform_list,
K=args.K).to(device)
print(model)
if args.optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'SGD':
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
momentum=0.9)
else:
class BiomeAE():
def __init__(self, args):
if args.model in ["BiomeAEL0"]:
self.mlp_type = "L0"
else:
self.mlp_type = None
self.model_alias = args.model_alias
self.model= args.model
self.snap_loc = os.path.join(args.vis_dir, "snap.pt")
#tl.configure("runs/ds.{}".format(model_alias))
#tl.log_value(model_alias, 0)
""" no stat file needed for now
stat_alias = 'obj_DataStat+%s_%s' % (args.dataset_name, args.dataset_subset)
stat_path = os.path.join(
output_dir, '%s.pkl' % (stat_alias)
)
with open(stat_path,'rb') as sf:
data_stats = pickle.load(sf)
"""
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_num
self.predictor = None
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def get_transformation(self):
return None
def loss_fn(self, recon_x, x, mean, log_var):
if self.model in ["BiomeAE","BiomeAESnip"]:
mseloss = torch.nn.MSELoss()
return torch.sqrt(mseloss(recon_x, x))
if self.model in ["BiomeAEL0"]:
mseloss = torch.nn.MSELoss()
return torch.sqrt(mseloss(recon_x, x))+self.predictor.regularization()
elif self.model =="BiomeVAE":
BCE = torch.nn.functional.binary_cross_entropy(
recon_x.view(-1, 28*28), x.view(-1, 28*28), reduction='sum')
KLD = -0.5 * torch.sum(1 + log_var - mean.pow(2) - log_var.exp())
return (BCE + KLD) / x.size(0)
def param_l0(self):
return self.predictor.param_l0()
def init_fit(self, X1_train, X2_train, y_train, X1_val, X2_val, y_val, args, ):
self.train_loader = get_dataloader (X1_train, X2_train, y_train, args.batch_size)
self.test_loader = get_dataloader(X1_val, X2_val, y_val, args.batch_size)
self.predictor = AE(
encoder_layer_sizes=[X1_train.shape[1]],
latent_size=args.latent_size,
decoder_layer_sizes=[X2_train.shape[1]],
activation=args.activation,
batch_norm= args.batch_norm,
dropout=args.dropout,
mlp_type=self.mlp_type,
conditional=args.conditional,
num_labels=10 if args.conditional else 0).to(self.device)
self.optimizer = torch.optim.Adam(self.predictor.parameters(), lr=args.learning_rate)
self.scheduler = torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=0.8)
def train(self, args):
if args.contr:
print("Loading from ", self.snap_loc)
loaded_model_para = torch.load(self.snap_loc)
self.predictor.load_state_dict(loaded_model_para)
t = 0
logs = defaultdict(list)
iterations_per_epoch = len(self.train_loader.dataset) / args.batch_size
num_iterations = int(iterations_per_epoch * args.epochs)
for epoch in range(args.epochs):
tracker_epoch = defaultdict(lambda: defaultdict(dict))
for iteration, (x1, x2, y) in enumerate(self.train_loader):
t+=1
x1, x2, y = x1.to(self.device), x2.to(self.device), y.to(self.device)
if args.conditional:
x2_hat, z, mean, log_var = self.predictor(x1, y)
else:
x2_hat, z, mean, log_var = self.predictor(x1)
for i, yi in enumerate(y):
id = len(tracker_epoch)
tracker_epoch[id]['x'] = z[i, 0].item()
tracker_epoch[id]['y'] = z[i, 1].item()
tracker_epoch[id]['label'] = yi.item()
loss = self.loss_fn(x2_hat, x2, mean, log_var)
self.optimizer.zero_grad()
loss.backward()
if (t + 1) % int(num_iterations / 10) == 0:
self.scheduler.step()
self.optimizer.step()
#enforce non-negative
if args.nonneg_weight:
for layer in self.predictor.modules():
if isinstance(layer, nn.Linear):
layer.weight.data.clamp_(0.0)
logs['loss'].append(loss.item())
if iteration % args.print_every == 0 or iteration == len(self.train_loader)-1:
print("Epoch {:02d}/{:02d} Batch {:04d}/{:d}, Loss {:9.4f}".format(
epoch, args.epochs, iteration, len(self.train_loader)-1, loss.item()))
if args.model =="VAE":
if args.conditional:
c = torch.arange(0, 10).long().unsqueeze(1)
x = self.predictor.inference(n=c.size(0), c=c)
else:
x = self.predictor.inference(n=10)
if not args.contr:
print("Saving to ", self.snap_loc)
torch.save(self.predictor.state_dict(), self.snap_loc)
def fit(self,X1_train, X2_train, y_train, X1_val, X2_val, y_val, args,):
self.init_fit(X1_train, X2_train, y_train, X1_val, X2_val, y_val, args)
self.train(args)
def get_graph(self):
"""
return nodes and weights
:return:
"""
nodes = []
weights = []
for l, layer in enumerate(self.predictor.modules()):
if isinstance(layer, nn.Linear):
lin_layer =layer
nodes.append(["%s"%(x) for x in list(range(lin_layer.in_features))])
weights.append(lin_layer.weight.detach().cpu().numpy().T)
nodes.append(["%s"%(x) for x in list(range(lin_layer.out_features))]) #last linear layer
return (nodes, weights)
def predict(self,X1_val, X2_val, y_val, args):
#Batch test
x1, x2, y = torch.FloatTensor(X1_val).to(self.device), torch.FloatTensor(X2_val).to(self.device), torch.FloatTensor(y_val).to(self.device)
if args.conditional:
x2_hat, z, mean, log_var = self.predictor(x1, y)
else:
x2_hat, z, mean, log_var = self.predictor(x1)
val_loss = self.loss_fn( x2_hat, x2, mean, log_var)
print("val_loss: {:9.4f}", val_loss.item())
return x2_hat.detach().cpu().numpy()
def transform(self,X1_val, X2_val, y_val, args):
x1, x2, y = torch.FloatTensor(X1_val).to(self.device), torch.FloatTensor(X2_val).to(
self.device), torch.FloatTensor(y_val).to(self.device)
if args.conditional:
x2_hat, z, mean, log_var = self.predictor(x1, y)
else:
x2_hat, z, mean, log_var = self.predictor(x1)
return z.detach().cpu().numpy()
def get_influence_matrix(self):
return self.predictor.get_influence_matrix()
from worm_models import SkeletonsMaps, EigenWorms
if __name__ == '__main__':
import pandas as pd
import tables
from tierpsy.helper.params import read_microns_per_pixel
from tierpsy.analysis.ske_create.helperIterROI import getROIfromInd
#load model
model_dir_root = '/data/ajaver/onedrive/classify_strains/logs/worm_autoencoder'
dnames = glob.glob(os.path.join(model_dir_root, 'AE_L64*'))
d = dnames[0]
embedding_size = int(d.split('AE_L')[-1].partition('_')[0])
model_path = os.path.join(d, 'checkpoint.pth.tar')
print(embedding_size)
model = AE(embedding_size)
checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'])
model.eval()
#%%
mask_file = '/data/ajaver/onedrive/aggregation/N2_1_Ch1_29062017_182108_comp3.hdf5'
feat_file = mask_file.replace('.hdf5', '_featuresN.hdf5')
w_ind = 264
ini_f = 1947
microns_per_pixel = read_microns_per_pixel(feat_file)
tmp = pickle.load(f, encoding='latin1')
edge_index_list = [utils.to_edge_index(adj).to(device) for adj in tmp['adj']]
down_transform_list = [
utils.to_sparse(down_transform).to(device)
for down_transform in tmp['down_transform']
]
up_transform_list = [
utils.to_sparse(up_transform).to(device)
for up_transform in tmp['up_transform']
]
model = AE(args.in_channels,
args.out_channels,
args.latent_channels,
edge_index_list,
down_transform_list,
up_transform_list,
K=args.K).to(device)
print(model)
if args.optimizer == 'Adam':
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer == 'SGD':
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay,
momentum=0.9)
else:
SAVE_IMAGES_IN_RESULTS = True
### LOAD DATA
data = datasets.ImageFolder(root=DATA_DIRECTORY,
transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
]))
data_loader = torch.utils.data.DataLoader(dataset=data,
batch_size=BATCH_SIZE,
shuffle=True)
### INIT MODEL
device = torch.device("cpu") #change if on GPU, also need to use .cuda()
model = AE().to(device)
### MSE LOSS AND ADAM OPTIMIZER
criterion = nn.MSELoss(size_average=True, reduce=True)
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
### TRAIN
for epoch in range(EPOCHS):
for idx, (sample, _) in enumerate(data_loader):
if sample.shape[0] < BATCH_SIZE: break
reconstruction, z = model(sample)
loss = criterion(reconstruction, sample)
optimizer.zero_grad()
loss.sum().backward()
optimizer.step()
parser.add_argument('--encoded', type=str, default=None,
help='Encoded file')
args = parser.parse_args()
np.random.seed(int(time.time()))
torch.manual_seed(int(time.time()))
# Load data
adj, inv_adj, features, labels, n_feat, n_class = load_data(n_conf=args.n_conf)
# Model and optimizer
if args.model == 'AE' or args.model == 'PCA':
model = AE(n_feat=n_feat,
n_hid=args.hidden,
n_lat=args.latent,
dropout=args.dropout)
elif args.model == 'GCAE':
model = GCAE(n_feat=n_feat,
n_hid=args.hidden,
n_lat=args.latent,
dropout=args.dropout)
else:
raise ValueError("You choose wrong network model")
optimizer = optim.Adam(model.parameters(), lr=args.lr)
criterion = nn.MSELoss()
if args.checkpoint is not None:
checkpoint = torch.load(args.checkpoint)
model.load_state_dict(checkpoint['model_state_dict'])
def __init__(self,
domain,
train_data_file,
validation_data_file,
test_data_file,
minibatch_size,
rng,
device,
behav_policy_file_wDemo,
behav_policy_file,
context_input=False,
context_dim=0,
drop_smaller_than_minibatch=True,
folder_name='/Name',
autoencoder_saving_period=20,
resume=False,
sided_Q='negative',
autoencoder_num_epochs=50,
autoencoder_lr=0.001,
autoencoder='AIS',
hidden_size=16,
ais_gen_model=1,
ais_pred_model=1,
embedding_dim=4,
state_dim=42,
num_actions=25,
corr_coeff_param=10,
dst_hypers={},
cde_hypers={},
odernn_hypers={},
**kwargs):
'''
We assume discrete actions and scalar rewards!
'''
self.rng = rng
self.device = device
self.train_data_file = train_data_file
self.validation_data_file = validation_data_file
self.test_data_file = test_data_file
self.minibatch_size = minibatch_size
self.drop_smaller_than_minibatch = drop_smaller_than_minibatch
self.autoencoder_num_epochs = autoencoder_num_epochs
self.autoencoder = autoencoder
self.autoencoder_lr = autoencoder_lr
self.saving_period = autoencoder_saving_period
self.resume = resume
self.sided_Q = sided_Q
self.num_actions = num_actions
self.state_dim = state_dim
self.corr_coeff_param = corr_coeff_param
self.context_input = context_input # Check to see if we'll one-hot encode the categorical contextual input
self.context_dim = context_dim # Check to see if we'll remove the context from the input and only use it for decoding
self.hidden_size = hidden_size
if self.context_input:
self.input_dim = self.state_dim + self.context_dim + self.num_actions
else:
self.input_dim = self.state_dim + self.num_actions
self.autoencoder_lower = self.autoencoder.lower()
self.data_folder = folder_name + f'/{self.autoencoder_lower}_data'
self.checkpoint_file = folder_name + f'/{self.autoencoder_lower}_checkpoints/checkpoint.pt'
if not os.path.exists(folder_name +
f'/{self.autoencoder_lower}_checkpoints'):
os.mkdir(folder_name + f'/{self.autoencoder_lower}_checkpoints')
if not os.path.exists(folder_name + f'/{self.autoencoder_lower}_data'):
os.mkdir(folder_name + f'/{self.autoencoder_lower}_data')
self.store_path = folder_name
self.gen_file = folder_name + f'/{self.autoencoder_lower}_data/{self.autoencoder_lower}_gen.pt'
self.pred_file = folder_name + f'/{self.autoencoder_lower}_data/{self.autoencoder_lower}_pred.pt'
if self.autoencoder == 'AIS':
self.container = AIS.ModelContainer(device, ais_gen_model,
ais_pred_model)
self.gen = self.container.make_encoder(
self.hidden_size,
self.state_dim,
self.num_actions,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.hidden_size,
self.state_dim,
self.num_actions)
elif self.autoencoder == 'AE':
self.container = AE.ModelContainer(device)
self.gen = self.container.make_encoder(
self.hidden_size,
self.state_dim,
self.num_actions,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.hidden_size,
self.state_dim,
self.num_actions)
elif self.autoencoder == 'DST':
self.dst_hypers = dst_hypers
self.container = DST.ModelContainer(device)
self.gen = self.container.make_encoder(
self.input_dim,
self.hidden_size,
gru_n_layers=self.dst_hypers['gru_n_layers'],
augment_chs=self.dst_hypers['augment_chs'])
self.pred = self.container.make_decoder(
self.hidden_size, self.state_dim,
self.dst_hypers['decoder_hidden_units'])
elif self.autoencoder == 'DDM':
self.container = DDM.ModelContainer(device)
self.gen = self.container.make_encoder(
self.state_dim,
self.hidden_size,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.state_dim,
self.hidden_size)
self.dyn = self.container.make_dyn(self.num_actions,
self.hidden_size)
self.all_params = chain(self.gen.parameters(),
self.pred.parameters(),
self.dyn.parameters())
self.inv_loss_coef = 10
self.dec_loss_coef = 0.1
self.max_grad_norm = 50
self.dyn_file = folder_name + '/ddm_data/ddm_dyn.pt'
elif self.autoencoder == 'RNN':
self.container = RNN.ModelContainer(device)
self.gen = self.container.make_encoder(
self.hidden_size,
self.state_dim,
self.num_actions,
context_input=self.context_input,
context_dim=self.context_dim)
self.pred = self.container.make_decoder(self.hidden_size,
self.state_dim,
self.num_actions)
elif self.autoencoder == 'CDE':
self.cde_hypers = cde_hypers
self.container = CDE.ModelContainer(device)
self.gen = self.container.make_encoder(
self.input_dim + 1,
self.hidden_size,
hidden_hidden_channels=self.
cde_hypers['encoder_hidden_hidden_channels'],
num_hidden_layers=self.cde_hypers['encoder_num_hidden_layers'])
self.pred = self.container.make_decoder(
self.hidden_size, self.state_dim,
self.cde_hypers['decoder_num_layers'],
self.cde_hypers['decoder_num_units'])
elif self.autoencoder == 'ODERNN':
self.odernn_hypers = odernn_hypers
self.container = ODERNN.ModelContainer(device)
self.gen = self.container.make_encoder(self.input_dim,
self.hidden_size,
self.odernn_hypers)
self.pred = self.container.make_decoder(
self.hidden_size, self.state_dim,
self.odernn_hypers['decoder_n_layers'],
self.odernn_hypers['decoder_n_units'])
else:
raise NotImplementedError
self.buffer_save_file = self.data_folder + '/ReplayBuffer'
self.next_obs_pred_errors_file = self.data_folder + '/test_next_obs_pred_errors.pt'
self.test_representations_file = self.data_folder + '/test_representations.pt'
self.test_correlations_file = self.data_folder + '/test_correlations.pt'
self.policy_eval_save_file = self.data_folder + '/dBCQ_policy_eval'
self.policy_save_file = self.data_folder + '/dBCQ_policy'
self.behav_policy_file_wDemo = behav_policy_file_wDemo
self.behav_policy_file = behav_policy_file
# Read in the data csv files
assert (domain == 'sepsis')
self.train_demog, self.train_states, self.train_interventions, self.train_lengths, self.train_times, self.acuities, self.rewards = torch.load(
self.train_data_file)
train_idx = torch.arange(self.train_demog.shape[0])
self.train_dataset = TensorDataset(self.train_demog, self.train_states,
self.train_interventions,
self.train_lengths,
self.train_times, self.acuities,
self.rewards, train_idx)
self.train_loader = DataLoader(self.train_dataset,
batch_size=self.minibatch_size,
shuffle=True)
self.val_demog, self.val_states, self.val_interventions, self.val_lengths, self.val_times, self.val_acuities, self.val_rewards = torch.load(
self.validation_data_file)
val_idx = torch.arange(self.val_demog.shape[0])
self.val_dataset = TensorDataset(self.val_demog, self.val_states,
self.val_interventions,
self.val_lengths, self.val_times,
self.val_acuities, self.val_rewards,
val_idx)
self.val_loader = DataLoader(self.val_dataset,
batch_size=self.minibatch_size,
shuffle=False)
self.test_demog, self.test_states, self.test_interventions, self.test_lengths, self.test_times, self.test_acuities, self.test_rewards = torch.load(
self.test_data_file)
test_idx = torch.arange(self.test_demog.shape[0])
self.test_dataset = TensorDataset(self.test_demog, self.test_states,
self.test_interventions,
self.test_lengths, self.test_times,
self.test_acuities,
self.test_rewards, test_idx)
self.test_loader = DataLoader(self.test_dataset,
batch_size=self.minibatch_size,
shuffle=False)
# encode CDE data first to save time
if self.autoencoder == 'CDE':
self.train_coefs = load_cde_data('train', self.train_dataset,
self.cde_hypers['coefs_folder'],
self.context_input, device)
self.val_coefs = load_cde_data('val', self.val_dataset,
self.cde_hypers['coefs_folder'],
self.context_input, device)
self.test_coefs = load_cde_data('test', self.test_dataset,
self.cde_hypers['coefs_folder'],
self.context_input, device)
