def maskData(self, data):
"""
Args:
data:
Returns:
"""
msk = nib.load(self.mask)
mskD = msk.get_data()
if not np.all(np.bitwise_or(mskD == 0, mskD == 1)):
raise ValueError("Mask has incorrect values.")
# nVox = np.sum(mskD.flatten())
if data.shape[0:3] != mskD.shape:
raise ValueError((data.shape, mskD.shape))
msk_f = mskD.flatten()
msk_idx = np.where(msk_f == 1)[0]
if len(data.shape) == 3:
data_masked = data.flatten()[msk_idx]
if len(data.shape) == 4:
data = np.transpose(data, (3, 0, 1, 2))
data_masked = np.zeros((data.shape[0], int(mskD.sum())))
for i, x in enumerate(data):
data_masked[i] = x.flatten()[msk_idx]
img = data_masked
return np.array(img)
def train(epoch, model, optimizer, train_loader, device, scaling, vlog, elog,
log_var_std):
model.train()
train_loss = 0
for batch_idx, (data, _) in enumerate(train_loader):
data = data.to(device)
data_flat = data.flatten(start_dim=1).repeat(1, scaling)
optimizer.zero_grad()
recon_batch, mu, logvar = model(data_flat)
loss, kl, rec = loss_function(recon_batch, data_flat, mu, logvar,
log_var_std)
loss.backward()
train_loss += loss.item()
optimizer.step()
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader),
loss.item() / len(data)))
# vlog.show_value(torch.mean(kl).item(), name="Kl-loss", tag="Losses")
# vlog.show_value(torch.mean(rec).item(), name="Rec-loss", tag="Losses")
# vlog.show_value(loss.item(), name="Total-loss", tag="Losses")
print('====> Epoch: {} Average loss: {:.4f}'.format(
epoch, train_loss / len(train_loader.dataset)))
def maskData(self, data):
"""
Args:
data:
Returns:
"""
msk = nib.load(self.mask)
mskD = msk.get_data()
if not np.all(np.bitwise_or(mskD == 0, mskD == 1)):
raise ValueError("Mask has incorrect values.")
# nVox = np.sum(mskD.flatten())
if data.shape[0:3] != mskD.shape:
raise ValueError((data.shape, mskD.shape))
msk_f = mskD.flatten()
msk_idx = np.where(msk_f == 1)[0]
if len(data.shape) == 3:
data_masked = data.flatten()[msk_idx]
if len(data.shape) == 4:
data = np.transpose(data, (3, 0, 1, 2))
data_masked = np.zeros((data.shape[0], int(mskD.sum())))
for i, x in enumerate(data):
data_masked[i] = x.flatten()[msk_idx]
img = data_masked
return np.array(img)
def get_values(data_frame):
n = len(data_frame)
data = np.zeros((n, MAXLEN, 21), dtype=np.float32)
for i, (_, row) in enumerate(data_frame.iterrows()):
ind = row['indexes']
m = len(row['indexes'])
# s = (MAXLEN - m) // 2
s = 0
data[i, s:s + m, ind] = 1
return data.flatten()
def prep():
images_path = f'{utilz.PARENT_DIR}data/images/'
print(images_path)
images = image_loader.Images(images_path, transforms=edits)
data = images[0]
dataloader = DataLoader(images, shuffle=True, batch_size=BATCH_SIZE)
print(data.shape)
dim = data.flatten().shape[0]
if USE_LOGGER:
writer = SummaryWriter(
f"runs/image_gen_test_MID_{MIDDLE}_BOTTLE_{BOTTLENECK}_{TIME}")
else:
writer = None
model = models.VAEConv2d(dim, middle=MIDDLE,
bottleneck=BOTTLENECK).to(device)
print(model)
if LOAD_MODEL:
model = utilz.load_model(model, MODEL_FN)
if LR is not None:
optimizer = optim.Adam(model.parameters(), lr=LR)
else:
optimizer = optim.Adam(model.parameters())
write_to = f'samples/images/image_gen_{TIME}'
os.makedirs(write_to)
d = {
'write_to': write_to,
'writer': writer,
'dataloader': dataloader,
'model': model,
'optimizer': optimizer,
'set': images,
'model_fn': MODEL_FN
}
return d
def plot_log_dist(data,
logger,
name="undefined",
save_path="./",
cumulative=False,
hist=True,
kde=False,
bins=60):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if (isinstance(data, torch.Tensor)):
data = data.numpy()
data = data.flatten()
data_log = []
count_0 = 0
count = data.shape[0]
for dat in data:
if (dat > 0.0):
data_log.append(math.log(dat, 10))
elif (dat < 0.0):
print("%s is not a positive data" % (name))
return False
else:
count_0 += 1
data_log = np.array(data_log)
note = "non-zero rate: %.4f(%d/%d)" % (
(count - count_0) / count, count - count_0, count)
if count_0 == count:
logger.write("%s is all-zero." % (name))
return True
plot_dist(data=data_log,
logger=logger,
name=name + "(log)",
save_dir=save_dir,
notes=note,
cumulative=cumulative,
hist=hist,
kde=kde,
bins=bins)
return True
def update_model(participate, n_total):
"""
Update local model with all models selected clients.
Args:
participate: if to participate this round of averaging.
n_total: total number of samples used in this averaging round.
Returns:
"""
global model, client_socket, worker_number, args, dataset_length
parameters = model.state_dict()
manager = multiprocessing.Manager()
return_dict = manager.dict()
for key in parameters.keys():
data = parameters[key].cpu().numpy()
shape = parameters[key].shape
if participate == 0:
data = np.zeros(shape=shape)
data = data.flatten()
ratio = dataset_length / n_total
data = data * ratio
if participate == 1:
print("Worker {} conducting secret sharing on key {}".format(worker_number, key))
p = multiprocessing.Process(target=get_secret_sum, args=(client_socket, data, return_dict))
p.start()
p.join()
result = np.reshape(return_dict[0], shape)
if args.gpu == -1:
sum_result = torch.from_numpy(result).cpu()
else:
sum_result = torch.from_numpy(result).cuda()
parameters[key] = sum_result
if participate == 1:
print("Worker {} finished secret sharing on key {}".format(worker_number, key))
model.load_state_dict(parameters)
def __init__(self,
memmap_item,
indices,
dimension,
mean=None,
var=None,
min_=None,
max_=None,
normalize=False,
normalization_type="global_scale"):
self.memmap_item = memmap_item
self.indices = list(indices)
self.dimension = dimension
self.normalize = normalize
self.ntype = normalization_type
self.max_ = max_
self.min_ = min_
self.mean = mean
self.var = var
if self.normalize:
if self.ntype == "global_scale":
if (self.max_ is None) or (self.min_ is None):
print("Computing global max and min ... ")
self.max_ = float(np.finfo(np.float32).min)
self.min_ = float(np.finfo(np.float32).max)
for index in indices:
data = self.memmap_item[index]
max_ = float(np.amax(data.flatten()))
min_ = float(np.amin(data.flatten()))
if self.max_ < max_:
self.max_ = max_
if self.min_ > min_:
self.min_ = min_
print("Obtained global max and min ", self.max_, self.min_)
elif self.ntype == "global":
if (self.mean is None) or (self.var is None):
print("Computing global mean and standard deviation")
mean = 0
num_items = 0
for index in indices:
data = self.memmap_item[index]
mean, num_items = update_mean(mean,
num_items,
data,
lambda x: x,
offset=0)
var = 0
num_items = 0
for index in indices:
data = self.memmap_item[index]
var, num_items = update_mean(var,
num_items,
data,
lambda x: x**2,
offset=mean)
var = var**(0.5)
print("Obtained global mean and standard deviation", mean,
var)
self.mean = mean
self.var = var
def plot_dist(data,
logger,
name="undefined",
ran=[],
save_path="./",
bins=100,
kde=False,
hist=True,
notes="",
redo=False,
rug_max=False,
stat=True,
cumulative=False):
if not os.path.exists(save_dir):
os.makedirs(save_dir)
if not isinstance(data, np.ndarray):
data = np.array(data)
data = data.flatten()
data = np.sort(data)
stat_data = "mean=%.4f var=%.4f, min=%.4f, max=%.4f mid=%.4f" % (np.mean(
data), np.var(data), np.min(data), np.max(data), np.median(data))
num_max = 100000
if data.shape[0] > num_max:
print("data is too large. sample %d elements.")
data = np.array(random.sample(data.tolist(), num_max))
logger.write(stat_data)
img_dir = {}
y_interv = 0.05
y_line = 0.95
#hist_plot
if hist:
sig = True
sig_check = False
while sig:
try:
title = name + " distribution"
if (cumulative == True):
title += "(cumu)"
hist_dir = save_dir + title + ".jpg"
img_dir["hist"] = hist_dir
if (redo == False and os.path.exists(hist_dir)):
print("image already exists.")
else:
plt.figure()
if (ran != []):
plt.xlim(ran[0], ran[1])
else:
set_lim(data, None, plt)
#sns.distplot(data, bins=bins, color='b',kde=False)
#method="sns"
plt.hist(data,
bins=bins,
color="b",
density=True,
cumulative=cumulative)
method = "hist"
plt.title(title, fontsize=large_fontsize)
if (stat == True):
plt.annotate(stat_data,
xy=(0.02, y_line),
xycoords='axes fraction')
y_line -= y_interv
print_notes(notes, y_line, y_interv)
plt.savefig(hist_dir)
plt.close()
sig = False
except Exception:
if (sig_check == True):
raise Exception("exception in plot dist.")
data, note = check_array_1d(data, logger)
if (isinstance(notes, str)):
notes = [notes, note]
elif (isinstance(notes, list)):
notes = notes + [note]
sig_check = True
try: #kde_plot
y_line = 0.95
if kde:
title = name + " dist(kde)"
if (cumulative == True):
title += "(cumu)"
kde_dir = save_dir + title + ".jpg"
img_dir["kde"] = kde_dir
if (redo == False and os.path.exists(kde_dir)):
print("image already exists.")
else:
plt.figure()
if (ran != []):
plt.xlim(ran[0], ran[1])
else:
set_lim(data, None, plt)
sns.kdeplot(data, shade=True, color='b', cumulative=cumulative)
if rug_max:
sns.rugplot(data[(data.shape[0] -
data.shape[0] // 500):-1],
height=0.2,
color='r')
plt.title(title, fontsize=large_fontsize)
if stat:
plt.annotate(stat_data,
xy=(0.02, y_line),
xycoords='axes fraction')
y_line -= 0.05
print_notes(notes, y_line, y_interv)
plt.savefig(kde_dir)
plt.close()
except Exception:
print("exception in kde plot %s" % (kde_dir))
return img_dir
def test(model, test_loader, test_loader_abnorm, device, scaling, vlog, elog,
image_size, batch_size, log_var_std):
model.eval()
test_loss = []
kl_loss = []
rec_loss = []
with torch.no_grad():
for i, (data, _) in enumerate(test_loader):
data = data.to(device)
data_flat = data.flatten(start_dim=1).repeat(1, scaling)
recon_batch, mu, logvar = model(data_flat)
loss, kl, rec = loss_function(recon_batch, data_flat, mu, logvar,
log_var_std)
test_loss += (kl + rec).tolist()
kl_loss += kl.tolist()
rec_loss += rec.tolist()
if i == 0:
n = min(data.size(0), 8)
comparison = torch.cat([
data[:n],
recon_batch[:, :image_size].view(batch_size, 1, 28, 28)[:n]
])
# vlog.show_image_grid(comparison.cpu(), name='reconstruction')
# vlog.show_value(np.mean(kl_loss), name="Norm-Kl-loss", tag="Anno")
# vlog.show_value(np.mean(rec_loss), name="Norm-Rec-loss", tag="Anno")
# vlog.show_value(np.mean(test_loss), name="Norm-Total-loss", tag="Anno")
# elog.show_value(np.mean(kl_loss), name="Norm-Kl-loss", tag="Anno")
# elog.show_value(np.mean(rec_loss), name="Norm-Rec-loss", tag="Anno")
# elog.show_value(np.mean(test_loss), name="Norm-Total-loss", tag="Anno")
test_loss_ab = []
kl_loss_ab = []
rec_loss_ab = []
with torch.no_grad():
for i, (data, _) in enumerate(test_loader_abnorm):
data = data.to(device)
data_flat = data.flatten(start_dim=1).repeat(1, scaling)
recon_batch, mu, logvar = model(data_flat)
loss, kl, rec = loss_function(recon_batch, data_flat, mu, logvar,
log_var_std)
test_loss_ab += (kl + rec).tolist()
kl_loss_ab += kl.tolist()
rec_loss_ab += rec.tolist()
if i == 0:
n = min(data.size(0), 8)
comparison = torch.cat([
data[:n],
recon_batch[:, :image_size].view(batch_size, 1, 28, 28)[:n]
])
# vlog.show_image_grid(comparison.cpu(), name='reconstruction2')
print('====> Test set loss: {:.4f}'.format(np.mean(test_loss)))
# vlog.show_value(np.mean(kl_loss_ab), name="Unorm-Kl-loss", tag="Anno")
# vlog.show_value(np.mean(rec_loss_ab), name="Unorm-Rec-loss", tag="Anno")
# vlog.show_value(np.mean(test_loss_ab), name="Unorm-Total-loss", tag="Anno")
# elog.show_value(np.mean(kl_loss_ab), name="Unorm-Kl-loss", tag="Anno")
# elog.show_value(np.mean(rec_loss_ab), name="Unorm-Rec-loss", tag="Anno")
# elog.show_value(np.mean(test_loss_ab), name="Unorm-Total-loss", tag="Anno")
kl_roc, kl_pr = elog.get_classification_metrics(
kl_loss + kl_loss_ab,
[0] * len(kl_loss) + [1] * len(kl_loss_ab),
)[0]
rec_roc, rec_pr = elog.get_classification_metrics(
rec_loss + rec_loss_ab,
[0] * len(rec_loss) + [1] * len(rec_loss_ab),
)[0]
loss_roc, loss_pr = elog.get_classification_metrics(
test_loss + test_loss_ab,
[0] * len(test_loss) + [1] * len(test_loss_ab),
)[0]
# vlog.show_value(np.mean(kl_roc), name="KL-loss", tag="ROC")
# vlog.show_value(np.mean(rec_roc), name="Rec-loss", tag="ROC")
# vlog.show_value(np.mean(loss_roc), name="Total-loss", tag="ROC")
# elog.show_value(np.mean(kl_roc), name="KL-loss", tag="ROC")
# elog.show_value(np.mean(rec_roc), name="Rec-loss", tag="ROC")
# elog.show_value(np.mean(loss_roc), name="Total-loss", tag="ROC")
# vlog.show_value(np.mean(kl_pr), name="KL-loss", tag="PR")
# vlog.show_value(np.mean(rec_pr), name="Rec-loss", tag="PR")
# vlog.show_value(np.mean(loss_pr), name="Total-loss", tag="PR")
return kl_roc, rec_roc, loss_roc, kl_pr, rec_pr, loss_pr
if __name__ == "__main__":
WORKER_SIZE = 2
BATCH_SIZE = 20
kwargs = {'num_workers': WORKER_SIZE, 'pin_memory': True}
train_loader = torch.utils.data.DataLoader(OPLoader(
WORKER_SIZE, BATCH_SIZE),
batch_size=WORKER_SIZE,
shuffle=False,
**kwargs)
for batch_idx, (data, label) in enumerate(train_loader):
data = data.flatten(0, 1)
label = label.flatten(0, 1)
time.sleep(2)
print("BatchIDX: " + str(batch_idx), data.shape, label.shape)
for i in range(0, data.shape[0]):
img_viz = data.detach().cpu().numpy().copy()[i, 0, :, :]
cv2.putText(img_viz, str(batch_idx), (0, 50),
cv2.FONT_HERSHEY_SIMPLEX, 2, 255)
cv2.imshow("img", img_viz + 0.5)
cv2.waitKey(15)
break
#break