def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(
random.uniform(0, len(subjects))
) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if np.random.random() < 0.5:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"90g_125mm_peaks.nii.gz")).get_data()
# rnd_choice = np.random.random()
# if rnd_choice < 0.33:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# elif rnd_choice < 0.66:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
seg = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(
self.HP,
"\n\nWARNING: Could not load file. Trying again in 20s (Try number: "
+ str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(
data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(
seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(
seg, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False,
None)
# Randomly sample slice orientation
slice_direction = int(round(random.uniform(0, 2)))
if slice_direction == 0:
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(
0, 3, 1, 2
) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(2, 3, 0, 1)
sw = 5 #slice_window (only odd numbers allowed)
pad = int((sw - 1) / 2)
data_pad = np.zeros(
(data.shape[0] + sw - 1, data.shape[1] + sw - 1,
data.shape[2] + sw - 1, data.shape[3])).astype(data.dtype)
data_pad[
pad:-pad, pad:-pad,
pad:-pad, :] = data #padded with two slices of zeros on all sides
batch = []
for s_idx in slice_idxs:
if slice_direction == 0:
#(s_idx+2)-2:(s_idx+2)+3 = s_idx:s_idx+5
x = data_pad[s_idx:s_idx + sw:, pad:-pad, pad:-pad, :].astype(
np.float32) # (5, y, z, channels)
x = np.array(x).transpose(
0, 3, 1, 2
) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2],
x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 1:
x = data_pad[pad:-pad, s_idx:s_idx + sw, pad:-pad, :].astype(
np.float32) # (5, y, z, channels)
x = np.array(x).transpose(
1, 3, 0, 2
) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2],
x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 2:
x = data_pad[pad:-pad, pad:-pad, s_idx:s_idx + sw, :].astype(
np.float32) # (5, y, z, channels)
x = np.array(x).transpose(
2, 3, 0, 1
) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2],
x.shape[3])) # (5*channels, y, z)
batch.append(x)
data_dict = {
"data": np.array(batch), # (batch_size, channels, x, y, [z])
"seg": y
} # (batch_size, channels, x, y, [z])
return data_dict
def train(self, HP):
if HP.USE_VISLOGGER:
try:
from trixi.logger.visdom import PytorchVisdomLogger
except ImportError:
pass
trixi = PytorchVisdomLogger(port=8080, auto_start=True)
ExpUtils.print_and_save(HP, socket.gethostname())
epoch_times = []
nr_of_updates = 0
metrics = {}
for type in ["train", "test", "validate"]:
metrics_new = {
"loss_" + type: [0],
"f1_macro_" + type: [0],
}
metrics = dict(list(metrics.items()) + list(metrics_new.items()))
for epoch_nr in range(HP.NUM_EPOCHS):
start_time = time.time()
# current_lr = HP.LEARNING_RATE * (HP.LR_DECAY ** epoch_nr)
# current_lr = HP.LEARNING_RATE
batch_gen_time = 0
data_preparation_time = 0
network_time = 0
metrics_time = 0
saving_time = 0
plotting_time = 0
batch_nr = {
"train": 0,
"test": 0,
"validate": 0
}
if HP.LOSS_WEIGHT_LEN == -1:
weight_factor = float(HP.LOSS_WEIGHT)
else:
if epoch_nr < HP.LOSS_WEIGHT_LEN:
# weight_factor = -(9./100.) * epoch_nr + 10. #ep0: 10 -> linear decrease -> ep100: 1
weight_factor = -((HP.LOSS_WEIGHT-1)/float(HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
# weight_factor = -((HP.LOSS_WEIGHT-5)/float(HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
else:
weight_factor = 1.
# weight_factor = 5.
for type in ["train", "test", "validate"]:
print_loss = []
start_time_batch_gen = time.time()
batch_generator = self.dataManager.get_batches(batch_size=HP.BATCH_SIZE,
type=type, subjects=getattr(HP, type.upper() + "_SUBJECTS"))
batch_gen_time = time.time() - start_time_batch_gen
# print("batch_gen_time: {}s".format(batch_gen_time))
print("Start looping batches...")
start_time_batch_part = time.time()
for batch in batch_generator: #getting next batch takes around 0.14s -> second largest Time part after mode!
start_time_data_preparation = time.time()
batch_nr[type] += 1
x = batch["data"] # (bs, nr_of_channels, x, y)
y = batch["seg"] # (bs, nr_of_classes, x, y)
# since using new BatchGenerator y is not int anymore but float -> would be good for Pytorch but not Lasagne
# y = y.astype(HP.LABELS_TYPE) #for bundle_peaks regression: is already float -> saves 0.2s/batch if left out
data_preparation_time += time.time() - start_time_data_preparation
# self.model.learning_rate.set_value(np.float32(current_lr))
start_time_network = time.time()
if type == "train":
nr_of_updates += 1
loss, probs, f1 = self.model.train(x, y, weight_factor=weight_factor) # probs: # (bs, x, y, nrClasses)
# loss, probs, f1, intermediate = self.model.train(x, y)
elif type == "validate":
loss, probs, f1 = self.model.predict(x, y, weight_factor=weight_factor)
elif type == "test":
loss, probs, f1 = self.model.predict(x, y, weight_factor=weight_factor)
network_time += time.time() - start_time_network
start_time_metrics = time.time()
if HP.CALC_F1:
if HP.EXPERIMENT_TYPE == "peak_regression":
#Following two lines increase metrics_time by 30s (without < 1s); time per batch increases by 1.5s by these lines
# y_flat = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# y_flat = np.reshape(y_flat, (-1, y_flat.shape[-1])) # (bs*x*y, nr_of_classes)
# metrics = MetricUtils.calculate_metrics(metrics, y_flat, probs, loss, f1=np.mean(f1), type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"CA": f1[5], "FX_left": f1[23], "FX_right": f1[24]})
#Numpy
# y_right_order = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# peak_f1 = MetricUtils.calc_peak_dice(HP, probs, y_right_order)
# peak_f1_mean = np.array([s for s in peak_f1.values()]).mean()
#Pytorch
peak_f1_mean = np.array([s for s in list(f1.values())]).mean() #if f1 for multiple bundles
metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=peak_f1_mean, type=type, threshold=HP.THRESHOLD)
#Pytorch 2 F1
# peak_f1_mean_a = np.array([s for s in f1[0].values()]).mean()
# peak_f1_mean_b = np.array([s for s in f1[1].values()]).mean()
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=peak_f1_mean_a, type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"LenF1": peak_f1_mean_b})
#Single Bundle
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"][0], type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"Thr1": f1["CST_right"][1], "Thr2": f1["CST_right"][2]})
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"], type=type, threshold=HP.THRESHOLD)
else:
metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=np.mean(f1), type=type, threshold=HP.THRESHOLD)
else:
metrics = MetricUtils.calculate_metrics_onlyLoss(metrics, loss, type=type)
metrics_time += time.time() - start_time_metrics
print_loss.append(loss)
if batch_nr[type] % HP.PRINT_FREQ == 0:
time_batch_part = time.time() - start_time_batch_part
start_time_batch_part = time.time()
ExpUtils.print_and_save(HP, "{} Ep {}, Sp {}, loss {}, t print {}s, t batch {}s".format(type, epoch_nr,
batch_nr[type] * HP.BATCH_SIZE,
round(np.array(print_loss).mean(), 6), round(time_batch_part, 3),
round(time_batch_part / HP.PRINT_FREQ, 3)))
print_loss = []
if HP.USE_VISLOGGER:
ExpUtils.plot_result_trixi(trixi, x, y, probs, loss, f1, epoch_nr)
###################################
# Post Training tasks (each epoch)
###################################
#Adapt LR
if HP.LR_SCHEDULE:
self.model.scheduler.step()
# self.model.scheduler.step(np.mean(f1))
self.model.print_current_lr()
# Average loss per batch over entire epoch
metrics = MetricUtils.normalize_last_element(metrics, batch_nr["train"], type="train")
metrics = MetricUtils.normalize_last_element(metrics, batch_nr["validate"], type="validate")
metrics = MetricUtils.normalize_last_element(metrics, batch_nr["test"], type="test")
print(" Epoch {}, Average Epoch loss = {}".format(epoch_nr, metrics["loss_train"][-1]))
print(" Epoch {}, nr_of_updates {}".format(epoch_nr, nr_of_updates))
# Save Weights
start_time_saving = time.time()
if HP.SAVE_WEIGHTS:
self.model.save_model(metrics, epoch_nr)
saving_time += time.time() - start_time_saving
# Create Plots
start_time_plotting = time.time()
pickle.dump(metrics, open(join(HP.EXP_PATH, "metrics.pkl"), "wb")) # wb -> write (override) and binary (binary only needed on windows, on unix also works without) # for loading: pickle.load(open("metrics.pkl", "rb"))
ExpUtils.create_exp_plot(metrics, HP.EXP_PATH, HP.EXP_NAME)
ExpUtils.create_exp_plot(metrics, HP.EXP_PATH, HP.EXP_NAME, without_first_epochs=True)
plotting_time += time.time() - start_time_plotting
epoch_time = time.time() - start_time
epoch_times.append(epoch_time)
ExpUtils.print_and_save(HP, " Epoch {}, time total {}s".format(epoch_nr, epoch_time))
ExpUtils.print_and_save(HP, " Epoch {}, time UNet: {}s".format(epoch_nr, network_time))
ExpUtils.print_and_save(HP, " Epoch {}, time metrics: {}s".format(epoch_nr, metrics_time))
ExpUtils.print_and_save(HP, " Epoch {}, time saving files: {}s".format(epoch_nr, saving_time))
ExpUtils.print_and_save(HP, str(datetime.datetime.now()))
# Adding next Epoch
if epoch_nr < HP.NUM_EPOCHS-1:
metrics = MetricUtils.add_empty_element(metrics)
####################################
# After all epochs
###################################
with open(join(HP.EXP_PATH, "Hyperparameters.txt"), "a") as f: # a for append
f.write("\n\n")
f.write("Average Epoch time: {}s".format(sum(epoch_times) / float(len(epoch_times))))
return metrics
def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(
random.uniform(0, len(subjects))
) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if self.HP.FEATURES_FILENAME == "12g90g270g":
# if np.random.random() < 0.5:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
rnd_choice = np.random.random()
if rnd_choice < 0.33:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"90g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"12g_125mm_peaks.nii.gz")).get_data()
elif self.HP.FEATURES_FILENAME == "T1_Peaks270g":
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
elif self.HP.FEATURES_FILENAME == "T1_Peaks12g90g270g":
rnd_choice = np.random.random()
if rnd_choice < 0.33:
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"90g_125mm_peaks.nii.gz")).get_data()
else:
peaks = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
"12g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
else:
data = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx], self.HP.FEATURES_FILENAME +
".nii.gz")).get_data()
seg = nib.load(
join(C.DATA_PATH, self.HP.DATASET_FOLDER,
subjects[subject_idx],
self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(
self.HP,
"\n\nWARNING: Could not load file. Trying again in 20s (Try number: "
+ str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(
data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.LABELS_FILENAME not in [
"bundle_peaks_11_808080", "bundle_peaks_20_808080",
"bundle_peaks_808080", "bundle_masks_20_808080",
"bundle_masks_72_808080"
]:
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(
seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(
seg, self.HP.DATASET,
self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False,
None)
# Randomly sample slice orientation
if self.HP.TRAINING_SLICE_DIRECTION == "xyz":
slice_direction = int(round(random.uniform(0, 2)))
else:
slice_direction = 1 #always use Y
if slice_direction == 0:
x = data[slice_idxs, :, :].astype(
np.float32) # (batch_size, y, z, channels)
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(
0, 3, 1, 2
) # depth-channel has to be before width and height for Unet (but after batches)
y = np.array(y).transpose(
0, 3, 1, 2
) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
x = data[:, slice_idxs, :].astype(
np.float32) # (x, batch_size, z, channels)
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(1, 3, 0, 2)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
x = data[:, :, slice_idxs].astype(
np.float32) # (x, y, batch_size, channels)
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(2, 3, 0, 1)
y = np.array(y).transpose(2, 3, 0, 1)
data_dict = {
"data": x, # (batch_size, channels, x, y, [z])
"seg": y
} # (batch_size, channels, x, y, [z])
return data_dict
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(
self.HP,
"current learning rate: {}".format(param_group['lr']))
def create_network(self):
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda()) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None #faster
return loss.data[0], probs, f1
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(), volatile=True), Variable(y.cuda(), volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(self.HP.EXP_PATH, "best_weights_ep" + str(epoch_nr) + ".npz"), unet=net)
except IOError:
print("\nERROR: Could not save weights because of IO Error\n")
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3*self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(self.HP, "Loading weights ... ({})".format(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
def create_network(self):
# torch.backends.cudnn.benchmark = True #not faster
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda()) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs, intermediate = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
if self.HP.USE_VISLOGGER:
probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
else:
probs = None #faster
return loss.data[0], probs, f1, intermediate
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(), volatile=True), Variable(y.cuda(), volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data, outputs.data, per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(self.HP.EXP_PATH, "best_weights_ep" + str(epoch_nr) + ".npz"), unet=net)
except IOError:
print("\nERROR: Could not save weights because of IO Error\n")
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(self.HP, "current learning rate: {}".format(param_group['lr']))
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
# NR_OF_GRADIENTS = 9 * 5
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3*self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
# net = nn.DataParallel(net, device_ids=[0,1])
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
criterion = nn.BCEWithLogitsLoss()
optimizer = Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
# optimizer = Adam(net.parameters(), lr=self.HP.LEARNING_RATE) #very slow (half speed of Adamax) -> strange
# scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max")
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(self.HP, "Loading weights ... ({})".format(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
#plot feature weights
# weights = list(list(net.children())[0].children())[0].weight.cpu().data.numpy() # sequential -> conv2d # (64, 9, 3, 3)
# weights = weights[:, 0:1, :, :] # select one input channel to plot # (64, 1, 3, 3)
# weights = (weights*100).astype(np.uint8) # can not plot negative values (and if float only 0-1 allowed) -> not good: we remove negatives
# plot_kernels(weights)
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
self.print_current_lr = print_current_lr
# self.scheduler = scheduler
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(self.HP, "current learning rate: {}".format(param_group['lr']))
def create_network(self):
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda(
)) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None #faster
return loss.data[0], probs, f1
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(),
volatile=True), Variable(y.cuda(),
volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")
): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(
self.HP.EXP_PATH,
"best_weights_ep" + str(epoch_nr) + ".npz"),
unet=net)
except IOError:
print(
"\nERROR: Could not save weights because of IO Error\n"
)
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3 * self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT).cuda()
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT)
# net = UNet_Skip(n_input_channels=NR_OF_GRADIENTS, n_classes=self.HP.NR_OF_CLASSES, n_filt=self.HP.UNET_NR_FILT)
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(
self.HP, "Loading weights ... ({})".format(
join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
def train(self, HP):
if HP.USE_VISLOGGER:
nvl = Nvl(name="Training")
ExpUtils.print_and_save(HP, socket.gethostname())
epoch_times = []
nr_of_updates = 0
metrics = {}
for type in ["train", "test", "validate"]:
metrics_new = {
"loss_" + type: [0],
"f1_macro_" + type: [0],
}
metrics = dict(list(metrics.items()) + list(metrics_new.items()))
for epoch_nr in range(HP.NUM_EPOCHS):
start_time = time.time()
# current_lr = HP.LEARNING_RATE * (HP.LR_DECAY ** epoch_nr)
# current_lr = HP.LEARNING_RATE
batch_gen_time = 0
data_preparation_time = 0
network_time = 0
metrics_time = 0
saving_time = 0
plotting_time = 0
batch_nr = {"train": 0, "test": 0, "validate": 0}
if HP.LOSS_WEIGHT_LEN == -1:
weight_factor = float(HP.LOSS_WEIGHT)
else:
if epoch_nr < HP.LOSS_WEIGHT_LEN:
# weight_factor = -(9./100.) * epoch_nr + 10. #ep0: 10 -> linear decrease -> ep100: 1
weight_factor = -((HP.LOSS_WEIGHT - 1) / float(
HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
# weight_factor = -((HP.LOSS_WEIGHT-5)/float(HP.LOSS_WEIGHT_LEN)) * epoch_nr + float(HP.LOSS_WEIGHT)
else:
weight_factor = 1.
# weight_factor = 5.
for type in ["train", "test", "validate"]:
print_loss = []
start_time_batch_gen = time.time()
batch_generator = self.dataManager.get_batches(
batch_size=HP.BATCH_SIZE,
type=type,
subjects=getattr(HP,
type.upper() + "_SUBJECTS"))
batch_gen_time = time.time() - start_time_batch_gen
# print("batch_gen_time: {}s".format(batch_gen_time))
print("Start looping batches...")
start_time_batch_part = time.time()
for batch in batch_generator: #getting next batch takes around 0.14s -> second largest Time part after UNet!
start_time_data_preparation = time.time()
batch_nr[type] += 1
x = batch["data"] # (bs, nr_of_channels, x, y)
y = batch["seg"] # (bs, nr_of_classes, x, y)
# since using new BatchGenerator y is not int anymore but float -> would be good for Pytorch but not Lasagne
# y = y.astype(HP.LABELS_TYPE) #for bundle_peaks regression: is already float -> saves 0.2s/batch if left out
data_preparation_time += time.time(
) - start_time_data_preparation
# self.model.learning_rate.set_value(np.float32(current_lr))
start_time_network = time.time()
if type == "train":
nr_of_updates += 1
loss, probs, f1 = self.model.train(
x, y, weight_factor=weight_factor
) # probs: # (bs, x, y, nrClasses)
# loss, probs, f1, intermediate = self.model.train(x, y)
elif type == "validate":
loss, probs, f1 = self.model.predict(
x, y, weight_factor=weight_factor)
elif type == "test":
loss, probs, f1 = self.model.predict(
x, y, weight_factor=weight_factor)
network_time += time.time() - start_time_network
start_time_metrics = time.time()
if HP.CALC_F1:
if HP.LABELS_TYPE == np.int16:
metrics = MetricUtils.calculate_metrics(
metrics,
None,
None,
loss,
f1=np.mean(f1),
type=type,
threshold=HP.THRESHOLD)
else: #Regression
#Following two lines increase metrics_time by 30s (without < 1s); time per batch increases by 1.5s by these lines
# y_flat = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# y_flat = np.reshape(y_flat, (-1, y_flat.shape[-1])) # (bs*x*y, nr_of_classes)
# metrics = MetricUtils.calculate_metrics(metrics, y_flat, probs, loss, f1=np.mean(f1), type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"CA": f1[5], "FX_left": f1[23], "FX_right": f1[24]})
#Numpy
# y_right_order = y.transpose(0, 2, 3, 1) # (bs, x, y, nr_of_classes)
# peak_f1 = MetricUtils.calc_peak_dice(HP, probs, y_right_order)
# peak_f1_mean = np.array([s for s in peak_f1.values()]).mean()
#Pytorch
peak_f1_mean = np.array([
s for s in list(f1.values())
]).mean() #if f1 for multiple bundles
metrics = MetricUtils.calculate_metrics(
metrics,
None,
None,
loss,
f1=peak_f1_mean,
type=type,
threshold=HP.THRESHOLD)
#Pytorch 2 F1
# peak_f1_mean_a = np.array([s for s in f1[0].values()]).mean()
# peak_f1_mean_b = np.array([s for s in f1[1].values()]).mean()
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=peak_f1_mean_a, type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"LenF1": peak_f1_mean_b})
#Single Bundle
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"][0], type=type, threshold=HP.THRESHOLD,
# f1_per_bundle={"Thr1": f1["CST_right"][1], "Thr2": f1["CST_right"][2]})
# metrics = MetricUtils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"], type=type, threshold=HP.THRESHOLD)
else:
metrics = MetricUtils.calculate_metrics_onlyLoss(
metrics, loss, type=type)
metrics_time += time.time() - start_time_metrics
print_loss.append(loss)
if batch_nr[type] % HP.PRINT_FREQ == 0:
time_batch_part = time.time() - start_time_batch_part
start_time_batch_part = time.time()
ExpUtils.print_and_save(
HP,
"{} Ep {}, Sp {}, loss {}, t print {}s, t batch {}s"
.format(type, epoch_nr,
batch_nr[type] * HP.BATCH_SIZE,
round(np.array(print_loss).mean(), 6),
round(time_batch_part, 3),
round(time_batch_part / HP.PRINT_FREQ, 3)))
print_loss = []
if HP.USE_VISLOGGER:
x_norm = (x - x.min()) / (x.max() - x.min())
nvl.show_images(
x_norm[0:1, :, :, :].transpose((1, 0, 2, 3)),
name="input batch",
title="Input batch") #all channels of one batch
probs_shaped = probs[:, :, :, 15:16].transpose(
(0, 3, 1, 2)) # (bs, 1, x, y)
probs_shaped_bin = (probs_shaped > 0.5).astype(
np.int16)
nvl.show_images(probs_shaped,
name="predictions",
title="Predictions Probmap")
# nvl.show_images(probs_shaped_bin, name="predictions_binary", title="Predictions Binary")
# Show GT and Prediction in one image (bundle: CST)
# GREEN: GT; RED: prediction (FP); YELLOW: prediction (TP)
combined = np.zeros(
(y.shape[0], 3, y.shape[2], y.shape[3]))
combined[:, 0:1, :, :] = probs_shaped_bin #Red
combined[:, 1:2, :, :] = y[:, 15:16, :, :] #Green
nvl.show_images(combined,
name="predictions_combined",
title="Combined")
#Show feature activations
contr_1_2 = intermediate[2].data.cpu().numpy(
) # (bs, nr_feature_channels=64, x, y)
contr_1_2 = contr_1_2[0:1, :, :, :].transpose(
(1, 0, 2, 3)) # (nr_feature_channels=64, 1, x, y)
contr_1_2 = (contr_1_2 - contr_1_2.min()) / (
contr_1_2.max() - contr_1_2.min())
nvl.show_images(contr_1_2,
name="contr_1_2",
title="contr_1_2")
# Show feature activations
contr_3_2 = intermediate[1].data.cpu().numpy(
) # (bs, nr_feature_channels=64, x, y)
contr_3_2 = contr_3_2[0:1, :, :, :].transpose(
(1, 0, 2, 3)) # (nr_feature_channels=64, 1, x, y)
contr_3_2 = (contr_3_2 - contr_3_2.min()) / (
contr_3_2.max() - contr_3_2.min())
nvl.show_images(contr_3_2,
name="contr_3_2",
title="contr_3_2")
# Show feature activations
deconv_2 = intermediate[0].data.cpu().numpy(
) # (bs, nr_feature_channels=64, x, y)
deconv_2 = deconv_2[0:1, :, :, :].transpose(
(1, 0, 2, 3)) # (nr_feature_channels=64, 1, x, y)
deconv_2 = (deconv_2 - deconv_2.min()) / (
deconv_2.max() - deconv_2.min())
nvl.show_images(deconv_2,
name="deconv_2",
title="deconv_2")
nvl.show_value(float(loss), name="loss")
nvl.show_value(float(np.mean(f1)), name="f1")
###################################
# Post Training tasks (each epoch)
###################################
#Adapt LR
# self.model.scheduler.step()
# self.model.scheduler.step(np.mean(f1))
# self.model.print_current_lr()
# Average loss per batch over entire epoch
metrics = MetricUtils.normalize_last_element(metrics,
batch_nr["train"],
type="train")
metrics = MetricUtils.normalize_last_element(metrics,
batch_nr["validate"],
type="validate")
metrics = MetricUtils.normalize_last_element(metrics,
batch_nr["test"],
type="test")
print(" Epoch {}, Average Epoch loss = {}".format(
epoch_nr, metrics["loss_train"][-1]))
print(" Epoch {}, nr_of_updates {}".format(
epoch_nr, nr_of_updates))
# Save Weights
start_time_saving = time.time()
if HP.SAVE_WEIGHTS:
self.model.save_model(metrics, epoch_nr)
saving_time += time.time() - start_time_saving
# Create Plots
start_time_plotting = time.time()
pickle.dump(
metrics, open(join(HP.EXP_PATH, "metrics.pkl"), "wb")
) # wb -> write (override) and binary (binary only needed on windows, on unix also works without) # for loading: pickle.load(open("metrics.pkl", "rb"))
ExpUtils.create_exp_plot(metrics, HP.EXP_PATH, HP.EXP_NAME)
ExpUtils.create_exp_plot(metrics,
HP.EXP_PATH,
HP.EXP_NAME,
without_first_epochs=True)
plotting_time += time.time() - start_time_plotting
epoch_time = time.time() - start_time
epoch_times.append(epoch_time)
ExpUtils.print_and_save(
HP, " Epoch {}, time total {}s".format(epoch_nr, epoch_time))
ExpUtils.print_and_save(
HP,
" Epoch {}, time UNet: {}s".format(epoch_nr, network_time))
ExpUtils.print_and_save(
HP,
" Epoch {}, time metrics: {}s".format(epoch_nr, metrics_time))
ExpUtils.print_and_save(
HP, " Epoch {}, time saving files: {}s".format(
epoch_nr, saving_time))
ExpUtils.print_and_save(HP, str(datetime.datetime.now()))
# Adding next Epoch
if epoch_nr < HP.NUM_EPOCHS - 1:
metrics = MetricUtils.add_empty_element(metrics)
####################################
# After all epochs
###################################
with open(join(HP.EXP_PATH, "Hyperparameters.txt"),
"a") as f: # a for append
f.write("\n\n")
f.write("Average Epoch time: {}s".format(
sum(epoch_times) / float(len(epoch_times))))
return metrics
def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(random.uniform(0, len(subjects))) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if np.random.random() < 0.5:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
# rnd_choice = np.random.random()
# if rnd_choice < 0.33:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# elif rnd_choice < 0.66:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
seg = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(self.HP, "\n\nWARNING: Could not load file. Trying again in 20s (Try number: " + str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(seg, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False, None)
# Randomly sample slice orientation
slice_direction = int(round(random.uniform(0,2)))
if slice_direction == 0:
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(0, 3, 1, 2) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
y = np.array(y).transpose(2, 3, 0, 1)
sw = 5 #slice_window (only odd numbers allowed)
pad = int((sw-1) / 2)
data_pad = np.zeros((data.shape[0]+sw-1, data.shape[1]+sw-1, data.shape[2]+sw-1, data.shape[3])).astype(data.dtype)
data_pad[pad:-pad, pad:-pad, pad:-pad, :] = data #padded with two slices of zeros on all sides
batch=[]
for s_idx in slice_idxs:
if slice_direction == 0:
#(s_idx+2)-2:(s_idx+2)+3 = s_idx:s_idx+5
x = data_pad[s_idx:s_idx+sw:, pad:-pad, pad:-pad, :].astype(np.float32) # (5, y, z, channels)
x = np.array(x).transpose(0, 3, 1, 2) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 1:
x = data_pad[pad:-pad, s_idx:s_idx+sw, pad:-pad, :].astype(np.float32) # (5, y, z, channels)
x = np.array(x).transpose(1, 3, 0, 2) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) # (5*channels, y, z)
batch.append(x)
elif slice_direction == 2:
x = data_pad[pad:-pad, pad:-pad, s_idx:s_idx+sw, :].astype(np.float32) # (5, y, z, channels)
x = np.array(x).transpose(2, 3, 0, 1) # channels dim has to be before width and height for Unet (but after batches)
x = np.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3])) # (5*channels, y, z)
batch.append(x)
data_dict = {"data": np.array(batch), # (batch_size, channels, x, y, [z])
"seg": y} # (batch_size, channels, x, y, [z])
return data_dict
def generate_train_batch(self):
subjects = self._data[0]
subject_idx = int(random.uniform(0, len(subjects))) # len(subjects)-1 not needed because int always rounds to floor
for i in range(20):
try:
if self.HP.FEATURES_FILENAME == "12g90g270g":
# if np.random.random() < 0.5:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
# else:
# data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
rnd_choice = np.random.random()
if rnd_choice < 0.33:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
else:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
elif self.HP.FEATURES_FILENAME == "T1_Peaks270g":
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
elif self.HP.FEATURES_FILENAME == "T1_Peaks12g90g270g":
rnd_choice = np.random.random()
if rnd_choice < 0.33:
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "270g_125mm_peaks.nii.gz")).get_data()
elif rnd_choice < 0.66:
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "90g_125mm_peaks.nii.gz")).get_data()
else:
peaks = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "12g_125mm_peaks.nii.gz")).get_data()
t1 = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], "T1.nii.gz")).get_data()
data = np.concatenate((peaks, t1), axis=3)
else:
data = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], self.HP.FEATURES_FILENAME + ".nii.gz")).get_data()
seg = nib.load(join(C.DATA_PATH, self.HP.DATASET_FOLDER, subjects[subject_idx], self.HP.LABELS_FILENAME + ".nii.gz")).get_data()
break
except IOError:
ExpUtils.print_and_save(self.HP, "\n\nWARNING: Could not load file. Trying again in 20s (Try number: " + str(i) + ").\n\n")
ExpUtils.print_and_save(self.HP, "Sleeping 20s")
sleep(20)
# ExpUtils.print_and_save(self.HP, "Successfully loaded input.")
data = np.nan_to_num(data) # Needed otherwise not working
seg = np.nan_to_num(seg)
data = DatasetUtils.scale_input_to_unet_shape(data, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, channels)
if self.HP.LABELS_FILENAME not in ["bundle_peaks_11_808080", "bundle_peaks_20_808080", "bundle_peaks_808080",
"bundle_masks_20_808080", "bundle_masks_72_808080"]:
if self.HP.DATASET in ["HCP_2mm", "HCP_2.5mm", "HCP_32g"]:
# By using "HCP" but lower resolution scale_input_to_unet_shape will automatically downsample the HCP sized seg_mask to the lower resolution
seg = DatasetUtils.scale_input_to_unet_shape(seg, "HCP", self.HP.RESOLUTION)
else:
seg = DatasetUtils.scale_input_to_unet_shape(seg, self.HP.DATASET, self.HP.RESOLUTION) # (x, y, z, classes)
slice_idxs = np.random.choice(data.shape[0], self.BATCH_SIZE, False, None)
# Randomly sample slice orientation
if self.HP.TRAINING_SLICE_DIRECTION == "xyz":
slice_direction = int(round(random.uniform(0,2)))
else:
slice_direction = 1 #always use Y
if slice_direction == 0:
x = data[slice_idxs, :, :].astype(np.float32) # (batch_size, y, z, channels)
y = seg[slice_idxs, :, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(0, 3, 1, 2) # depth-channel has to be before width and height for Unet (but after batches)
y = np.array(y).transpose(0, 3, 1, 2) # nr_classes channel has to be before with and height for DataAugmentation (bs, nr_of_classes, x, y)
elif slice_direction == 1:
x = data[:, slice_idxs, :].astype(np.float32) # (x, batch_size, z, channels)
y = seg[:, slice_idxs, :].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(1, 3, 0, 2)
y = np.array(y).transpose(1, 3, 0, 2)
elif slice_direction == 2:
x = data[:, :, slice_idxs].astype(np.float32) # (x, y, batch_size, channels)
y = seg[:, :, slice_idxs].astype(self.HP.LABELS_TYPE)
x = np.array(x).transpose(2, 3, 0, 1)
y = np.array(y).transpose(2, 3, 0, 1)
data_dict = {"data": x, # (batch_size, channels, x, y, [z])
"seg": y} # (batch_size, channels, x, y, [z])
return data_dict
def create_network(self):
# torch.backends.cudnn.benchmark = True #not faster
def train(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda()), Variable(y.cuda(
)) # X: (bs, features, x, y) y: (bs, classes, x, y)
else:
X, y = Variable(X), Variable(y)
optimizer.zero_grad()
net.train()
outputs = net(X) # forward # outputs: (bs, classes, x, y)
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
loss.backward() # backward
optimizer.step() # optimise
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
if self.HP.USE_VISLOGGER:
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
else:
probs = None #faster
return loss.data[0], probs, f1
def test(X, y):
X = torch.from_numpy(X.astype(np.float32))
y = torch.from_numpy(y.astype(np.float32))
if torch.cuda.is_available():
X, y = Variable(X.cuda(),
volatile=True), Variable(y.cuda(),
volatile=True)
else:
X, y = Variable(X, volatile=True), Variable(y, volatile=True)
net.train(False)
outputs = net(X) # forward
loss = criterion(outputs, y)
# loss = PytorchUtils.soft_dice(outputs, y)
f1 = PytorchUtils.f1_score_macro(y.data,
outputs.data,
per_class=True)
# probs = outputs.data.cpu().numpy().transpose(0,2,3,1) # (bs, x, y, classes)
probs = None # faster
return loss.data[0], probs, f1
def predict(X):
X = torch.from_numpy(X.astype(np.float32))
if torch.cuda.is_available():
X = Variable(X.cuda(), volatile=True)
else:
X = Variable(X, volatile=True)
net.train(False)
outputs = net(X) # forward
probs = outputs.data.cpu().numpy().transpose(
0, 2, 3, 1) # (bs, x, y, classes)
return probs
def save_model(metrics, epoch_nr):
max_f1_idx = np.argmax(metrics["f1_macro_validate"])
max_f1 = np.max(metrics["f1_macro_validate"])
if epoch_nr == max_f1_idx and max_f1 > 0.01: # saving to network drives takes 5s (to local only 0.5s) -> do not save so often
print(" Saving weights...")
for fl in glob.glob(join(self.HP.EXP_PATH, "best_weights_ep*")
): # remove weights from previous epochs
os.remove(fl)
try:
#Actually is a pkl not a npz
PytorchUtils.save_checkpoint(join(
self.HP.EXP_PATH,
"best_weights_ep" + str(epoch_nr) + ".npz"),
unet=net)
except IOError:
print(
"\nERROR: Could not save weights because of IO Error\n"
)
self.HP.BEST_EPOCH = epoch_nr
def load_model(path):
PytorchUtils.load_checkpoint(path, unet=net)
def print_current_lr():
for param_group in optimizer.param_groups:
ExpUtils.print_and_save(
self.HP,
"current learning rate: {}".format(param_group['lr']))
if self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "single_direction":
NR_OF_GRADIENTS = 9
# NR_OF_GRADIENTS = 9 * 5
# NR_OF_GRADIENTS = 9 * 9
# NR_OF_GRADIENTS = 33
elif self.HP.SEG_INPUT == "Peaks" and self.HP.TYPE == "combined":
NR_OF_GRADIENTS = 3 * self.HP.NR_OF_CLASSES
else:
NR_OF_GRADIENTS = 33
if torch.cuda.is_available():
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT).cuda()
else:
net = UNet(n_input_channels=NR_OF_GRADIENTS,
n_classes=self.HP.NR_OF_CLASSES,
n_filt=self.HP.UNET_NR_FILT)
# net = nn.DataParallel(net, device_ids=[0,1])
if self.HP.TRAIN:
ExpUtils.print_and_save(self.HP, str(net), only_log=True)
# weights = torch.ones((self.HP.BATCH_SIZE, self.HP.NR_OF_CLASSES, self.HP.INPUT_DIM[0], self.HP.INPUT_DIM[1])).cuda()
# weights[:, 5, :, :] *= 10 #CA
# weights[:, 21, :, :] *= 10 #FX_left
# weights[:, 22, :, :] *= 10 #FX_right
# criterion = nn.BCEWithLogitsLoss(weight=weights)
criterion = nn.BCEWithLogitsLoss()
optimizer = Adamax(net.parameters(), lr=self.HP.LEARNING_RATE)
# optimizer = Adam(net.parameters(), lr=self.HP.LEARNING_RATE) #very slow (half speed of Adamax) -> strange
# scheduler = lr_scheduler.StepLR(optimizer, step_size=100, gamma=0.1)
# scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode="max")
if self.HP.LOAD_WEIGHTS:
ExpUtils.print_verbose(
self.HP, "Loading weights ... ({})".format(
join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH)))
load_model(join(self.HP.EXP_PATH, self.HP.WEIGHTS_PATH))
self.train = train
self.predict = test
self.get_probs = predict
self.save_model = save_model
self.load_model = load_model
self.print_current_lr = print_current_lr