def main():
use_cuda = args.use_cuda
train_data = UnlabeledContact(data=args.data_dir)
print('Number of samples: {}'.format(len(train_data)))
trainloader = DataLoader(train_data, batch_size=args.batch_size)
# Contact matrices are 21x21
input_size = 441
img_height = 21
img_width = 21
vae = AutoEncoder(code_size=20,
imgsize=input_size,
height=img_height,
width=img_width)
criterion = nn.BCEWithLogitsLoss()
if use_cuda:
#vae = nn.DataParallel(vae)
vae = vae.cuda() #.half()
criterion = criterion.cuda()
optimizer = optim.SGD(vae.parameters(), lr=0.01)
clock = AverageMeter(name='clock32single', rank=0)
epoch_loss = 0
total_loss = 0
end = time.time()
for epoch in range(15):
for batch_idx, data in enumerate(trainloader):
inputs = data['cont_matrix']
inputs = inputs.resize_(args.batch_size, 1, 21, 21)
inputs = inputs.float()
if use_cuda:
inputs = inputs.cuda() #.half()
inputs = Variable(inputs)
optimizer.zero_grad()
output, code = vae(inputs)
loss = criterion(output, inputs)
loss.backward()
optimizer.step()
epoch_loss += loss.data[0]
clock.update(time.time() - end)
end = time.time()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(trainloader.dataset),
100. * batch_idx / len(trainloader), loss.data[0]))
clock.save(
path=
'/home/ygx/libraries/mds/molecules/molecules/conv_autoencoder/runtimes'
)
def generate(model_path,model_name, generate_path, generate_name, piece):
"""Synthesize audio from an array of embeddings.
Args:
encodings: Numpy array with shape [batch_size, time, dim].
save_paths: Iterable of output file names.
checkpoint_path: Location of the pretrained model. [model.ckpt-200000]
samples_per_save: Save files after every amount of generated samples.
"""
# Create directory for encoding
if os.path.exists(generate_path) is False:
os.makedirs(generate_path)
net = AutoEncoder()
net = load_model(net,model_path,model_name)
cuda_available = torch.cuda.is_available()
if cuda_available is True:
net = net.cuda()
net.eval()
# Load audio for encoding
piece = audio.load_wav(piece)
spec = audio.spectrogram(piece).astype(np.float32)
spec = torch.from_numpy(spec.T)
spec = torch.FloatTensor(spec)
spec = torch.unsqueeze(spec, 0)
spec = Variable(spec, volatile=True).contiguous()
if cuda_available is True:
spec = spec.cuda()
generated_spec = net(spec)
generated_spec = generated_spec.data.cpu().numpy()
generated_spec = np.squeeze(generated_spec)
waveform = audio.inv_spectrogram(generated_spec.T)
wav_name = generate_path + generate_name + '.wav'
audio.save_wav(waveform , wav_name)
def decode(model_name, encoding, decoder_name):
"""Synthesize audio from an array of embeddings.
Args:
encodings: Numpy array with shape [batch_size, time, dim].
save_paths: Iterable of output file names.
checkpoint_path: Location of the pretrained model. [model.ckpt-200000]
samples_per_save: Save files after every amount of generated samples.
"""
decoder_path = './decoding/'
model_path = './restore/'
# Create directory for encoding
if os.path.exists(decoder_path) is False:
os.makedirs(decoder_path)
net = AutoEncoder()
net = load_model(net,model_path,model_name)
cuda_available = torch.cuda.is_available()
if cuda_available is True:
net = net.cuda()
net.eval()
# Load Encoding
encoding_ndarray = np.load(encoding)
encoding = torch.from_numpy(encoding_ndarray).float()
encoding = Variable(encoding, volatile=True)
generated_spec = net.decoder(encoding)
generated_spec = generated_spec.data.cpu().numpy()
generated_spec = np.squeeze(generated_spec)
dec_name = decoder_path + decoder_name
np.save(dec_name , generated_spec)
def encode(model_name, piece, encoding_name):
model_path = './restore/'
encoding_path = './encoding/'
# Create directory for encoding
if os.path.exists(encoding_path) is False:
os.makedirs(encoding_path)
net = AutoEncoder()
net = load_model(net,model_path,model_name)
cuda_available = torch.cuda.is_available()
if cuda_available is True:
net = net.cuda()
net.eval()
# Load audio for encoding
piece = audio.load_wav(piece)
spec = audio.spectrogram(piece).astype(np.float32)
spec = torch.from_numpy(spec.T)
spec = torch.FloatTensor(spec)
spec = torch.unsqueeze(spec, 0)
spec = Variable(spec, volatile=True).contiguous()
if cuda_available is True:
spec = spec.cuda()
# Pass input audio to net forward pass
encoding = net.encoder(spec)
encoding = encoding.data.cpu().numpy()
#encoding = np.squeeze(encoding)
encoding_ndarray = encoding_path + encoding_name+ '.npy'
np.save(encoding_ndarray, encoding)
def encode(spec):
model_path = './restore/'
model_name = 'Autoencoder1.model'
net = AutoEncoder()
net = load_model(net, model_path, model_name)
cuda_available = torch.cuda.is_available()
if cuda_available is True:
net = net.cuda()
net.eval()
spec = torch.FloatTensor(torch.from_numpy(spec))
spec = torch.unsqueeze(spec, 0)
spec = Variable(spec, volatile=True).contiguous()
if cuda_available is True:
spec = spec.cuda()
# Pass input audio to net forward pass
out = net.encoder(spec)
out = out.data.cpu().numpy()
out = np.squeeze(out)
return out
def main(args):
# ensures that weight initializations are all the same
torch.manual_seed(args.seed)
np.random.seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
logging = utils.Logger(args.global_rank, args.save)
writer = utils.Writer(args.global_rank, args.save)
# Get data loaders.
train_queue, valid_queue, num_classes, _ = datasets.get_loaders(args)
args.num_total_iter = len(train_queue) * args.epochs
warmup_iters = len(train_queue) * args.warmup_epochs
swa_start = len(train_queue) * (args.epochs - 1)
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, writer, arch_instance)
model = model.cuda()
logging.info('args = %s', args)
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
logging.info('groups per scale: %s, total_groups: %d',
model.groups_per_scale, sum(model.groups_per_scale))
if args.fast_adamax:
# Fast adamax has the same functionality as torch.optim.Adamax, except it is faster.
cnn_optimizer = Adamax(model.parameters(),
args.learning_rate,
weight_decay=args.weight_decay,
eps=1e-3)
else:
cnn_optimizer = torch.optim.Adamax(model.parameters(),
args.learning_rate,
weight_decay=args.weight_decay,
eps=1e-3)
cnn_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
cnn_optimizer,
float(args.epochs - args.warmup_epochs - 1),
eta_min=args.learning_rate_min)
grad_scalar = GradScaler(2**10)
num_output = utils.num_output(args.dataset, args)
bpd_coeff = 1. / np.log(2.) / num_output
# if load
checkpoint_file = os.path.join(args.save, 'checkpoint.pt')
if args.cont_training:
logging.info('loading the model.')
checkpoint = torch.load(checkpoint_file, map_location='cpu')
init_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
model = model.cuda()
cnn_optimizer.load_state_dict(checkpoint['optimizer'])
grad_scalar.load_state_dict(checkpoint['grad_scalar'])
cnn_scheduler.load_state_dict(checkpoint['scheduler'])
global_step = checkpoint['global_step']
else:
global_step, init_epoch = 0, 0
for epoch in range(init_epoch, args.epochs):
# update lrs.
if args.distributed:
train_queue.sampler.set_epoch(global_step + args.seed)
valid_queue.sampler.set_epoch(0)
if epoch > args.warmup_epochs:
cnn_scheduler.step()
# Logging.
logging.info('epoch %d', epoch)
# Training.
train_nelbo, global_step = train(train_queue, model, cnn_optimizer,
grad_scalar, global_step,
warmup_iters, writer, logging)
logging.info('train_nelbo %f', train_nelbo)
writer.add_scalar('train/nelbo', train_nelbo, global_step)
model.eval()
# generate samples less frequently
eval_freq = 1 if args.epochs <= 50 else 20
if epoch % eval_freq == 0 or epoch == (args.epochs - 1):
with torch.no_grad():
num_samples = 16
n = int(np.floor(np.sqrt(num_samples)))
for t in [0.7, 0.8, 0.9, 1.0]:
logits = model.sample(num_samples, t)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(
output, torch.distributions.bernoulli.Bernoulli
) else output.sample(t)
output_tiled = utils.tile_image(output_img, n)
writer.add_image('generated_%0.1f' % t, output_tiled,
global_step)
valid_neg_log_p, valid_nelbo = test(valid_queue,
model,
num_samples=10,
args=args,
logging=logging)
logging.info('valid_nelbo %f', valid_nelbo)
logging.info('valid neg log p %f', valid_neg_log_p)
logging.info('valid bpd elbo %f', valid_nelbo * bpd_coeff)
logging.info('valid bpd log p %f', valid_neg_log_p * bpd_coeff)
writer.add_scalar('val/neg_log_p', valid_neg_log_p, epoch)
writer.add_scalar('val/nelbo', valid_nelbo, epoch)
writer.add_scalar('val/bpd_log_p', valid_neg_log_p * bpd_coeff,
epoch)
writer.add_scalar('val/bpd_elbo', valid_nelbo * bpd_coeff, epoch)
save_freq = int(np.ceil(args.epochs / 100))
if epoch % save_freq == 0 or epoch == (args.epochs - 1):
if args.global_rank == 0:
logging.info('saving the model.')
torch.save(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': cnn_optimizer.state_dict(),
'global_step': global_step,
'args': args,
'arch_instance': arch_instance,
'scheduler': cnn_scheduler.state_dict(),
'grad_scalar': grad_scalar.state_dict()
}, checkpoint_file)
# Final validation
valid_neg_log_p, valid_nelbo = test(valid_queue,
model,
num_samples=1000,
args=args,
logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
writer.add_scalar('val/neg_log_p', valid_neg_log_p, epoch + 1)
writer.add_scalar('val/nelbo', valid_nelbo, epoch + 1)
writer.add_scalar('val/bpd_log_p', valid_neg_log_p * bpd_coeff, epoch + 1)
writer.add_scalar('val/bpd_elbo', valid_nelbo * bpd_coeff, epoch + 1)
writer.close()
def train_autoencoder(train_matrix, test_set):
num_users, num_items = train_matrix.shape
weight_matrix = log_surplus_confidence_matrix(train_matrix,
alpha=args.alpha,
epsilon=args.epsilon)
train_matrix[train_matrix > 0] = 1.0
place_correlation = scipy.sparse.load_npz(
'./data/Foursquare/place_correlation_gamma60.npz')
assert num_items == place_correlation.shape[0]
print(train_matrix.shape)
# Construct the model by instantiating the class defined in model.py
model = AutoEncoder(num_items,
args.inner_layers,
num_items,
da=args.num_attention,
dropout_rate=args.dropout_rate)
if torch.cuda.is_available():
model.cuda()
criterion = torch.nn.MSELoss(size_average=False, reduce=False)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay)
batch_size = args.batch_size
user_indexes = np.arange(num_users)
model.train()
for t in range(args.epoch):
print("epoch:{}".format(t))
np.random.shuffle(user_indexes)
avg_cost = 0.
for batchID in range(int(num_users / batch_size)):
start = batchID * batch_size
end = start + batch_size
batch_user_index = user_indexes[start:end]
batch_x, batch_x_weight, batch_item_index = get_mini_batch(
train_matrix, weight_matrix, batch_user_index)
batch_x_weight += 1
batch_x = Variable(torch.from_numpy(batch_x).type(T.FloatTensor),
requires_grad=False)
y_pred = model(batch_item_index, place_correlation)
# Compute and print loss
batch_x_weight = Variable(torch.from_numpy(batch_x_weight).type(
T.FloatTensor),
requires_grad=False)
loss = (batch_x_weight *
criterion(y_pred, batch_x)).sum() / batch_size
print(batchID, loss.data)
# Zero gradients, perform a backward pass, and update the weights.
optimizer.zero_grad()
loss.backward()
optimizer.step()
avg_cost += loss / num_users * batch_size
print("Avg loss:{}".format(avg_cost))
# print the prediction score for the user 0
print(
model([train_matrix.getrow(0).indices], place_correlation)
[:,
T.LongTensor(train_matrix.getrow(0).indices.astype(np.int32))])
print(model([train_matrix.getrow(0).indices], place_correlation))
# Evaluation
model.eval()
topk = 20
recommended_list = []
for user_id in range(num_users):
user_rating_vector = train_matrix.getrow(user_id).toarray()
pred_rating_vector = model([train_matrix.getrow(user_id).indices],
place_correlation)
pred_rating_vector = pred_rating_vector.cpu().data.numpy()
user_rating_vector = user_rating_vector[0]
pred_rating_vector = pred_rating_vector[0]
pred_rating_vector[user_rating_vector > 0] = 0
item_recommended_dict = dict()
for item_inner_id, score in enumerate(pred_rating_vector):
item_recommended_dict[item_inner_id] = score
sorted_item = heapq.nlargest(topk,
item_recommended_dict,
key=item_recommended_dict.get)
recommended_list.append(sorted_item)
print(test_set[user_id], sorted_item[:topk])
print(pred_rating_vector[sorted_item[0]],
pred_rating_vector[sorted_item[1]],
pred_rating_vector[sorted_item[2]],
pred_rating_vector[sorted_item[3]],
pred_rating_vector[sorted_item[4]])
print("user:%d, precision@5:%f, precision@10:%f" %
(user_id,
eval_metrics.precision_at_k_per_sample(test_set[user_id],
sorted_item[:5], 5),
eval_metrics.precision_at_k_per_sample(
test_set[user_id], sorted_item[:topk], topk)))
precision, recall, MAP = [], [], []
for k in [5, 10, 15, 20]:
precision.append(
eval_metrics.precision_at_k(test_set, recommended_list, k))
recall.append(eval_metrics.recall_at_k(test_set, recommended_list, k))
MAP.append(eval_metrics.mapk(test_set, recommended_list, k))
print(precision)
print(recall)
print(MAP)
def main():
cuda_available = torch.cuda.is_available()
train_params, dataset_params = get_arguments()
net = AutoEncoder()
epoch_trained = 0
if train_params['restore_model']:
net = load_model(net, train_params['restore_dir'],
train_params['restore_model'])
if net is None:
print("Initialize network and train from scratch.")
net = AutoEncoder()
else:
epoch_trained = 0
train_loader, validation = audio_data_loader(**dataset_params)
if cuda_available is False:
warnings.warn(
"Cuda is not avalable, can not train model using multi-gpu.")
if cuda_available:
# Remove train_params["device_ids"] for single GPU
if train_params["device_ids"]:
batch_size = dataset_params["batch_size"]
num_gpu = len(train_params["device_ids"])
assert batch_size % num_gpu == 0
net = nn.DataParallel(net, device_ids=train_params['device_ids'])
torch.backends.cudnn.benchmark = True
net = net.cuda()
criterion = nn.MSELoss()
optimizer = get_optimizer(net, train_params['optimizer'],
train_params['learning_rate'],
train_params['momentum'])
if cuda_available:
criterion = criterion.cuda()
if not os.path.exists(train_params['log_dir']):
os.makedirs(train_params['log_dir'])
if not os.path.exists(train_params['restore_dir']):
os.makedirs(train_params['restore_dir'])
train_loss_log_file = open(train_params['log_dir'] + 'train_loss_log.log',
'a')
test_loss_log_file = open(train_params['log_dir'] + 'test_loss_log.log',
'a')
# Add print for start of training time
time = str(datetime.now())
line = 'Training Started at' + str(time) + ' !!! \n'
train_loss_log_file.writelines(line)
train_loss_log_file.flush()
# Keep track of losses
train_losses = []
eval_losses = []
best_eval = float('inf')
# Begin!
for epoch in range(train_params['num_epochs']):
train(net, criterion, optimizer, train_losses, train_params,
train_loss_log_file, train_loader, cuda_available)
eval_loss = evaluate(net, criterion, epoch, eval_losses, validation,
test_loss_log_file, cuda_available)
if eval_loss < best_eval:
save_model(net, 1, train_params['restore_dir'])
torch.save(net.state_dict(),
train_params['restore_dir'] + 'bestmodel.pth')
best_eval = eval_loss
save_model(net, epoch_trained + epoch + 1, train_params['restore_dir'])
torch.save([train_losses, eval_losses, epoch],
train_params['restore_dir'] + 'data_params')
# Add print for end of training time
time = str(datetime.now())
line = 'Training Ended at' + str(time) + ' !!! \n'
train_loss_log_file.writelines(line)
train_loss_log_file.flush()
train_loss_log_file.close()
test_loss_log_file.close()
def test(self, config):
"""Testing routine"""
# Initialize Dataset for testing.
test_data = torchvision.datasets.ImageFolder(
root=os.path.join(config.data_dir, "test"),
transform=torchvision.transforms.ToTensor())
# Create data loader for the test dataset with two number of workers and no
# shuffling.
te_data_loader = torch.utils.data.DataLoader(
dataset=test_data,
batch_size=config.batch_size,
num_workers=config.numWorker,
shuffle=False)
# Create model
model = AutoEncoder()
# Move to GPU if you have one.
if torch.cuda.is_available():
model = model.cuda()
# Create loss objects
data_loss = nn.MSELoss()
# Fix gpu -> cpu bug
compute_device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load our best model and set model for testing
load_res = torch.load(os.path.join(config.save_dir, "best_model.pth"),
map_location=compute_device)
model.load_state_dict(load_res["model"])
model.eval()
# Implement The Test loop
prefix = "Testing: "
te_loss = []
te_acc = []
for data in tqdm(te_data_loader, desc=prefix):
# Split the data
x, y = data
# Send data to GPU if we have one
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
# Don't invoke gradient computation
with torch.no_grad():
# Compute logits
logits = model.forward(x)
# Compute loss and store as numpy
loss = data_loss(logits, x.float())
te_loss += [loss.cpu().numpy()]
# Compute accuracy and store as numpy
pred = torch.argmax(logits, dim=1)
acc = torch.mean(torch.eq(pred.vewi(x.size()),
x).float()) * 100.0
te_acc += [acc.cpu().numpy()]
# Report Test loss and accuracy
print("Test Loss = {}".format(np.mean(te_loss))) # TODO proper logging
print("Test Accuracy = {}%".format(
np.mean(te_acc))) # TODO proper logging
def train_autoencoder(train_matrix, test_set):
num_users, num_items = train_matrix.shape
weight_matrix = log_surplus_confidence_matrix(train_matrix,
alpha=args.alpha,
epsilon=args.epsilon)
train_matrix[train_matrix > 0] = 1.0
place_correlation = scipy.sparse.load_npz(
'Foursquare/place_correlation_gamma60.npz')
assert num_items == place_correlation.shape[0]
print(train_matrix.shape)
# Construct the model by instantiating the class defined in model.py
model = AutoEncoder(num_items,
args.inner_layers,
num_items,
da=args.num_attention,
dropout_rate=args.dropout_rate)
if torch.cuda.is_available():
model.cuda()
criterion = torch.nn.MSELoss(size_average=False, reduce=False)
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
weight_decay=args.weight_decay)
batch_size = args.batch_size
user_indexes = np.arange(num_users)
model.train()
torch.load('model.pkl')
# Evaluation
model.eval()
topk = 20
recommended_list = []
for user_id in range(num_users):
user_rating_vector = train_matrix.getrow(user_id).toarray()
pred_rating_vector = model([train_matrix.getrow(user_id).indices],
place_correlation)
pred_rating_vector = pred_rating_vector.cpu().data.numpy()
user_rating_vector = user_rating_vector[0]
pred_rating_vector = pred_rating_vector[0]
pred_rating_vector[user_rating_vector > 0] = 0
item_recommended_dict = dict()
for item_inner_id, score in enumerate(pred_rating_vector):
item_recommended_dict[item_inner_id] = score
sorted_item = heapq.nlargest(topk,
item_recommended_dict,
key=item_recommended_dict.get)
recommended_list.append(sorted_item)
print(test_set[user_id], sorted_item[:topk])
print(pred_rating_vector[sorted_item[0]],
pred_rating_vector[sorted_item[1]],
pred_rating_vector[sorted_item[2]],
pred_rating_vector[sorted_item[3]],
pred_rating_vector[sorted_item[4]])
print("user:%d, precision@5:%f, precision@10:%f" %
(user_id,
eval_metrics.precision_at_k_per_sample(test_set[user_id],
sorted_item[:5], 5),
eval_metrics.precision_at_k_per_sample(
test_set[user_id], sorted_item[:topk], topk)))
precision, recall, MAP = [], [], []
for k in [5, 10, 15, 20]:
precision.append(
eval_metrics.precision_at_k(test_set, recommended_list, k))
recall.append(eval_metrics.recall_at_k(test_set, recommended_list, k))
MAP.append(eval_metrics.mapk(test_set, recommended_list, k))
print(precision)
print(recall)
print(MAP)
def main(eval_args):
# ensures that weight initializations are all the same
logging = utils.Logger(eval_args.local_rank, eval_args.save)
# load a checkpoint
logging.info('loading the model at:')
logging.info(eval_args.checkpoint)
checkpoint = torch.load(eval_args.checkpoint, map_location='cpu')
args = checkpoint['args']
if not hasattr(args, 'ada_groups'):
logging.info('old model, no ada groups was found.')
args.ada_groups = False
if not hasattr(args, 'min_groups_per_scale'):
logging.info('old model, no min_groups_per_scale was found.')
args.min_groups_per_scale = 1
if not hasattr(args, 'num_mixture_dec'):
logging.info('old model, no num_mixture_dec was found.')
args.num_mixture_dec = 10
logging.info('loaded the model at epoch %d', checkpoint['epoch'])
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, None, arch_instance)
# Loading is not strict because of self.weight_normalized in Conv2D class in neural_operations. This variable
# is only used for computing the spectral normalization and it is safe not to load it. Some of our earlier models
# did not have this variable.
model.load_state_dict(checkpoint['state_dict'], strict=False)
model = model.cuda()
logging.info('args = %s', args)
logging.info('num conv layers: %d', len(model.all_conv_layers))
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
if eval_args.eval_mode == 'evaluate':
# load train valid queue
args.data = eval_args.data
train_queue, valid_queue, num_classes = datasets.get_loaders(args)
if eval_args.eval_on_train:
logging.info('Using the training data for eval.')
valid_queue = train_queue
# get number of bits
num_output = utils.num_output(args.dataset)
bpd_coeff = 1. / np.log(2.) / num_output
valid_neg_log_p, valid_nelbo = test(
valid_queue,
model,
num_samples=eval_args.num_iw_samples,
args=args,
logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
logging.info('final valid nelbo in bpd %f', valid_nelbo * bpd_coeff)
logging.info('final valid neg log p in bpd %f',
valid_neg_log_p * bpd_coeff)
else:
bn_eval_mode = not eval_args.readjust_bn
num_samples = 16
with torch.no_grad():
n = int(np.floor(np.sqrt(num_samples)))
set_bn(model,
bn_eval_mode,
num_samples=36,
t=eval_args.temp,
iter=500)
for ind in range(10): # sampling is repeated.
torch.cuda.synchronize()
start = time()
with autocast():
logits = model.sample(num_samples, eval_args.temp)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(output, torch.distributions.bernoulli.Bernoulli) \
else output.sample()
torch.cuda.synchronize()
end = time()
# save images to 'results/eval-x/images/epochn' where x is exp id and n is epoch muber
# print("tensor shape: {}".format(output_img.shape))
# try saving the images one my one
path_to_images = '/content/gdrive/MyDrive/pipeline_results/NVAE/results/eval-1/images'
if not os.path.exists(path_to_images):
os.makedirs(path_to_images)
for i in range(output_img.size(0)):
vutils.save_image(output_img[i, :, :, :],
'%s/sample_batch%03d_img%03d.png' %
(path_to_images, ind + 1, i + 1),
normalize=True)
type=int,
default=144,
help='batch size')
parser.add_argument('--epoch', type=int, default=1, help='epoch size')
opt = parser.parse_args()
# 超参数
LR = opt.lr
BATCH_SIZE = opt.batch_size
EPOCHES = opt.epoch
LOG_INTERVAL = 5
# 获取gpu是不是可用
cuda_available = torch.cuda.is_available()
# 实例化网络
auto = AutoEncoder()
if cuda_available:
auto.cuda()
# 定义优化器和损失函数
optimizer = torch.optim.Adam(auto.parameters(), lr=LR)
# 数据准备
root_dir = "./celeba_select"
image_files = os.listdir(root_dir)
train_dataset = CelebaDataset(root_dir, image_files, (64, 64),
transforms.Compose([ToTensor()]))
train_loader = DataLoader(train_dataset,
batch_size=32,
num_workers=1,
shuffle=True)
for i in range(EPOCHES):
# 打乱数据
auto.train()
train_loss = 0
def main(eval_args):
# ensures that weight initializations are all the same
logging = utils.Logger(eval_args.local_rank, eval_args.save)
# load a checkpoint
logging.info('loading the model at:')
logging.info(eval_args.checkpoint)
checkpoint = torch.load(eval_args.checkpoint, map_location='cpu')
args = checkpoint['args']
logging.info('loaded the model at epoch %d', checkpoint['epoch'])
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, None, arch_instance)
model.load_state_dict(checkpoint['state_dict'])
model = model.cuda()
logging.info('args = %s', args)
logging.info('num conv layers: %d', len(model.all_conv_layers))
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
if eval_args.eval_mode == 'evaluate':
# load train valid queue
args.data = eval_args.data
train_queue, valid_queue, num_classes, test_queue = datasets.get_loaders(args)
if eval_args.eval_on_train:
logging.info('Using the training data for eval.')
valid_queue = train_queue
if eval_args.eval_on_test:
logging.info('Using the test data for eval.')
valid_queue = test_queue
# get number of bits
num_output = utils.num_output(args.dataset, args)
bpd_coeff = 1. / np.log(2.) / num_output
valid_neg_log_p, valid_nelbo = test(valid_queue, model, num_samples=eval_args.num_iw_samples, args=args, logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
logging.info('final valid nelbo in bpd %f', valid_nelbo * bpd_coeff)
logging.info('final valid neg log p in bpd %f', valid_neg_log_p * bpd_coeff)
else:
bn_eval_mode = not eval_args.readjust_bn
num_samples = 16
with torch.no_grad():
n = int(np.floor(np.sqrt(num_samples)))
set_bn(model, bn_eval_mode, num_samples=36, t=eval_args.temp, iter=500)
for ind in range(eval_args.repetition): # sampling is repeated.
torch.cuda.synchronize()
start = time()
with autocast():
logits = model.sample(num_samples, eval_args.temp)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(output, torch.distributions.bernoulli.Bernoulli) \
else output.sample()
torch.cuda.synchronize()
end = time()
# save to file
total_name = "{}/data_to_save_{}_{}.pickle".format(eval_args.save, eval_args.name_to_save, ind)
with open(total_name, 'wb') as handle:
pickle.dump(output_img.deatach().numpy(), handle, protocol=pickle.HIGHEST_PROTOCOL)
output_tiled = utils.tile_image(output_img, n).cpu().numpy().transpose(1, 2, 0)
logging.info('sampling time per batch: %0.3f sec', (end - start))
output_tiled = np.asarray(output_tiled * 255, dtype=np.uint8)
output_tiled = np.squeeze(output_tiled)
plt.imshow(output_tiled)
plt.savefig("{}/generation_{}_{}".format(eval_args.save, eval_args.name_to_save, ind))
def main(eval_args):
# ensures that weight initializations are all the same
logging = utils.Logger(eval_args.local_rank, eval_args.save)
# load a checkpoint
logging.info('loading the model at:')
logging.info(eval_args.checkpoint)
checkpoint = torch.load(eval_args.checkpoint, map_location='cpu')
args = checkpoint['args']
if not hasattr(args, 'ada_groups'):
logging.info('old model, no ada groups was found.')
args.ada_groups = False
if not hasattr(args, 'min_groups_per_scale'):
logging.info('old model, no min_groups_per_scale was found.')
args.min_groups_per_scale = 1
if not hasattr(args, 'num_mixture_dec'):
logging.info('old model, no num_mixture_dec was found.')
args.num_mixture_dec = 10
logging.info('loaded the model at epoch %d', checkpoint['epoch'])
arch_instance = utils.get_arch_cells(args.arch_instance)
model = AutoEncoder(args, None, arch_instance)
# Loading is not strict because of self.weight_normalized in Conv2D class in neural_operations. This variable
# is only used for computing the spectral normalization and it is safe not to load it. Some of our earlier models
# did not have this variable.
model.load_state_dict(checkpoint['state_dict'], strict=False)
model = model.cuda()
logging.info('args = %s', args)
logging.info('num conv layers: %d', len(model.all_conv_layers))
logging.info('param size = %fM ', utils.count_parameters_in_M(model))
if eval_args.eval_mode == 'evaluate':
# load train valid queue
args.data = eval_args.data
train_queue, valid_queue, num_classes = datasets.get_loaders(args)
if eval_args.eval_on_train:
logging.info('Using the training data for eval.')
valid_queue = train_queue
# get number of bits
num_output = utils.num_output(args.dataset)
bpd_coeff = 1. / np.log(2.) / num_output
valid_neg_log_p, valid_nelbo = test(
valid_queue,
model,
num_samples=eval_args.num_iw_samples,
args=args,
logging=logging)
logging.info('final valid nelbo %f', valid_nelbo)
logging.info('final valid neg log p %f', valid_neg_log_p)
logging.info('final valid nelbo in bpd %f', valid_nelbo * bpd_coeff)
logging.info('final valid neg log p in bpd %f',
valid_neg_log_p * bpd_coeff)
else:
bn_eval_mode = not eval_args.readjust_bn
num_samples = 16
with torch.no_grad():
n = int(np.floor(np.sqrt(num_samples)))
set_bn(model,
bn_eval_mode,
num_samples=36,
t=eval_args.temp,
iter=500)
for ind in range(10): # sampling is repeated.
torch.cuda.synchronize()
start = time()
with autocast():
logits = model.sample(num_samples, eval_args.temp)
output = model.decoder_output(logits)
output_img = output.mean if isinstance(output, torch.distributions.bernoulli.Bernoulli) \
else output.sample()
torch.cuda.synchronize()
end = time()
output_tiled = utils.tile_image(output_img,
n).cpu().numpy().transpose(
1, 2, 0)
logging.info('sampling time per batch: %0.3f sec',
(end - start))
output_tiled = np.asarray(output_tiled * 255, dtype=np.uint8)
output_tiled = np.squeeze(output_tiled)
plt.imshow(output_tiled)
plt.show()
train=True,
transform=transforms.ToTensor(),
download=True)
test_set = datasets.MNIST(root='./data',
train=False,
transform=transforms.ToTensor(),
download=True)
train_loader = torch.utils.data.DataLoader(dataset=train_set,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_set,
batch_size=batch_size,
shuffle=False)
model = AutoEncoder(input_size=input_size)
model.cuda()
optimizer = optim.Adam(
model.parameters(),
lr=LR,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=WD,
amsgrad=False) # write so many arguments to get a clear view
criterion = nn.MSELoss()
losses_adam = []
for i in range(EP):
loss_ep = 0
idx = 0
for batch_idx, (inputs, targets) in enumerate(
train_loader): # Notice that we will not use target here.
if __name__ == '__main__':
opts = get_args()
if not os.path.exists(opts.outDir):
os.makedirs(opts.outDir)
# mnist digits dataset
transforms_ = torchvision.transforms.ToTensor()
traindata = torchvision.datasets.MNIST(root=opts.root, train=True,
transform=transforms_, download=True)
trainset = DataLoader(dataset=traindata, batch_size=opts.bs, shuffle=False)
# getting the structure of your autoencoder
autoencoder = AutoEncoder(opts.nz)
# useful when you have a GPU (link pytorch to CUDA)
if torch.cuda.is_available():
autoencoder.cuda()
print("test the data")
test_dataset(traindata=traindata, trainset=trainset)
print("begin training ...")
train(autoencoder=autoencoder, outDir=opts.outDir, trainset=trainset,
traindata=traindata)
print("visualize latent space representation")
visualize(autoencoder=autoencoder, outDir=opts.outDir, trainset=trainset,
traindata=traindata)
def train(self, config):
"""Training routine"""
# Initialize datasets for both training and validation
train_data = torchvision.datasets.ImageFolder(
root=os.path.join(config.data_dir, "train"),
transform=torchvision.transforms.ToTensor())
valid_data = torchvision.datasets.ImageFolder(
root=os.path.join(config.data_dir, "valid"),
transform=torchvision.transforms.ToTensor())
# Create data loader for training and validation.
tr_data_loader = torch.utils.data.DataLoader(
dataset=train_data,
batch_size=config.batch_size,
num_workers=config.numWorker,
shuffle=True)
va_data_loader = torch.utils.data.DataLoader(
dataset=valid_data,
batch_size=config.batch_size,
num_workers=config.numWorker,
shuffle=False)
# Create model instance.
#model = Model()
model = AutoEncoder()
# Move model to gpu if cuda is available
if torch.cuda.is_available():
model = model.cuda()
# Make sure that the model is set for training
model.train()
# Create loss objects
data_loss = nn.MSELoss()
# Create optimizier
optimizer = optim.Adam(model.parameters(), lr=config.learn_rate)
# No need to move the optimizer (as of PyTorch 1.0), it lies in the same
# space as the model
# Create summary writer
tr_writer = SummaryWriter(
log_dir=os.path.join(config.log_dir, "train"))
va_writer = SummaryWriter(
log_dir=os.path.join(config.log_dir, "valid"))
# Create log directory and save directory if it does not exist
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.save_dir):
os.makedirs(config.save_dir)
# Initialize training
iter_idx = -1 # make counter start at zero
best_va_acc = 0 # to check if best validation accuracy
# Prepare checkpoint file and model file to save and load from
checkpoint_file = os.path.join(config.save_dir, "checkpoint.pth")
bestmodel_file = os.path.join(config.save_dir, "best_model.pth")
# Check for existing training results. If it existst, and the configuration
# is set to resume `config.resume==True`, resume from previous training. If
# not, delete existing checkpoint.
if os.path.exists(checkpoint_file):
if config.resume:
# Use `torch.load` to load the checkpoint file and the load the
# things that are required to continue training. For the model and
# the optimizer, use `load_state_dict`. It's actually a good idea
# to code the saving part first and then code this part.
print("Checkpoint found! Resuming") # TODO proper logging
# Read checkpoint file.
# Fix gpu -> cpu bug
compute_device = 'cuda' if torch.cuda.is_available() else 'cpu'
load_res = torch.load(checkpoint_file,
map_location=compute_device)
# Resume iterations
iter_idx = load_res["iter_idx"]
# Resume best va result
best_va_acc = load_res["best_va_acc"]
# Resume model
model.load_state_dict(load_res["model"])
# Resume optimizer
optimizer.load_state_dict(load_res["optimizer"])
# Note that we do not resume the epoch, since we will never be able
# to properly recover the shuffling, unless we remember the random
# seed, for example. For simplicity, we will simply ignore this,
# and run `config.num_epoch` epochs regardless of resuming.
else:
os.remove(checkpoint_file)
# Training loop
for epoch in range(config.num_epoch):
# For each iteration
prefix = "Training Epoch {:3d}: ".format(epoch)
for data in tqdm(tr_data_loader, desc=prefix):
# Counter
iter_idx += 1
# Split the data
# x is img, y is label
x, y = data
#print(x)
# Send data to GPU if we have one
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
# Apply the model to obtain scores (forward pass)
logits = model.forward(x)
# Compute the loss
loss = data_loss(logits, x.float())
# Compute gradients
loss.backward()
# Update parameters
optimizer.step()
# Zero the parameter gradients in the optimizer
optimizer.zero_grad()
# Monitor results every report interval
if iter_idx % config.rep_intv == 0:
# Compute accuracy (No gradients required). We'll wrapp this
# part so that we prevent torch from computing gradients.
with torch.no_grad():
pred = torch.argmax(logits, dim=1)
acc = torch.mean(
torch.eq(pred.view(x.size()), x).float()) * 100.0
# Write loss and accuracy to tensorboard, using keywords `loss`
# and `accuracy`.
tr_writer.add_scalar("loss", loss, global_step=iter_idx)
tr_writer.add_scalar("accuracy", acc, global_step=iter_idx)
# Save
torch.save(
{
"iter_idx": iter_idx,
"best_va_acc": best_va_acc,
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"loss": loss,
"epoch": epoch,
"acc": acc
}, checkpoint_file)
# Validate results every validation interval
if iter_idx % config.val_intv == 0:
# List to contain all losses and accuracies for all the
# training batches
va_loss = []
va_acc = []
# Set model for evaluation
model = model.eval()
for data in va_data_loader:
# Split the data
x, y = data
# Send data to GPU if we have one
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
# Apply forward pass to compute the losses
# and accuracies for each of the validation batches
with torch.no_grad():
# Compute logits
logits = model.forward(x)
# Compute loss and store as numpy
loss = data_loss(logits, x.float())
va_loss += [loss.cpu().numpy()]
# Compute accuracy and store as numpy
pred = torch.argmax(logits, dim=1)
acc = torch.mean(
torch.eq(pred.view(x.size()),
x).float()) * 100.0
va_acc += [acc.cpu().numpy()]
# Set model back for training
model = model.train()
# Take average
va_loss = np.mean(va_loss)
va_acc = np.mean(va_acc)
# Write to tensorboard using `va_writer`
va_writer.add_scalar("loss", va_loss, global_step=iter_idx)
va_writer.add_scalar("accuracy",
va_acc,
global_step=iter_idx)
# Check if best accuracy
if va_acc > best_va_acc:
best_va_acc = va_acc
# Save best model using torch.save. Similar to previous
# save but at location defined by `bestmodel_file`
torch.save(
{
"iter_idx": iter_idx,
"best_va_acc": best_va_acc,
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"loss": loss,
"acc": acc
}, bestmodel_file)