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 train_model(Config, model, data_loader):
if Config.USE_VISLOGGER:
try:
from trixi.logger.visdom import PytorchVisdomLogger
except ImportError:
pass
trixi = PytorchVisdomLogger(port=8080, auto_start=True)
exp_utils.print_and_save(Config, socket.gethostname())
epoch_times = []
nr_of_updates = 0
metrics = {}
for type in ["train", "test", "validate"]:
for metric in Config.METRIC_TYPES:
metrics[metric + "_" + type] = [0]
batch_gen_train = data_loader.get_batch_generator(batch_size=Config.BATCH_SIZE, type="train",
subjects=getattr(Config, "TRAIN_SUBJECTS"))
batch_gen_val = data_loader.get_batch_generator(batch_size=Config.BATCH_SIZE, type="validate",
subjects=getattr(Config, "VALIDATE_SUBJECTS"))
for epoch_nr in range(Config.NUM_EPOCHS):
start_time = time.time()
timings = defaultdict(lambda: 0)
batch_nr = defaultdict(lambda: 0)
weight_factor = _get_weights_for_this_epoch(Config, epoch_nr)
types = ["validate"] if Config.ONLY_VAL else ["train", "validate"]
for type in types:
print_loss = []
if Config.DIM == "2D":
nr_of_samples = len(getattr(Config, type.upper() + "_SUBJECTS")) * Config.INPUT_DIM[0]
else:
nr_of_samples = len(getattr(Config, type.upper() + "_SUBJECTS"))
# *Config.EPOCH_MULTIPLIER needed to have roughly same number of updates/batches as with 2D U-Net
nr_batches = int(int(nr_of_samples / Config.BATCH_SIZE) * Config.EPOCH_MULTIPLIER)
print("Start looping batches...")
start_time_batch_part = time.time()
for i in range(nr_batches):
batch = next(batch_gen_train) if type == "train" else next(batch_gen_val)
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)
timings["data_preparation_time"] += time.time() - start_time_data_preparation
start_time_network = time.time()
if type == "train":
nr_of_updates += 1
probs, metr_batch = model.train(x, y, weight_factor=weight_factor)
elif type == "validate":
probs, metr_batch = model.test(x, y, weight_factor=weight_factor)
elif type == "test":
probs, metr_batch = model.test(x, y, weight_factor=weight_factor)
timings["network_time"] += time.time() - start_time_network
start_time_metrics = time.time()
metrics = _update_metrics(Config, metrics, metr_batch, type)
timings["metrics_time"] += time.time() - start_time_metrics
print_loss.append(metr_batch["loss"])
if batch_nr[type] % Config.PRINT_FREQ == 0:
time_batch_part = time.time() - start_time_batch_part
start_time_batch_part = time.time()
exp_utils.print_and_save(Config, "{} Ep {}, Sp {}, loss {}, t print {}s, t batch {}s".format(
type, epoch_nr, batch_nr[type] * Config.BATCH_SIZE, round(np.array(print_loss).mean(), 6),
round(time_batch_part, 3), round( time_batch_part / Config.PRINT_FREQ, 3)))
print_loss = []
if Config.USE_VISLOGGER:
plot_utils.plot_result_trixi(trixi, x, y, probs, metr_batch["loss"], metr_batch["f1_macro"], epoch_nr)
################################### Post Training tasks (each epoch) ###################################
if Config.ONLY_VAL:
metrics = metric_utils.normalize_last_element(metrics, batch_nr["validate"], type="validate")
print("f1 macro validate: {}".format(round(metrics["f1_macro_validate"][0], 4)))
return model
# Average loss per batch over entire epoch
metrics = metric_utils.normalize_last_element(metrics, batch_nr["train"], type="train")
metrics = metric_utils.normalize_last_element(metrics, batch_nr["validate"], type="validate")
print(" Epoch {}, Average Epoch loss = {}".format(epoch_nr, metrics["loss_train"][-1]))
print(" Epoch {}, nr_of_updates {}".format(epoch_nr, nr_of_updates))
# Adapt LR
if Config.LR_SCHEDULE:
if Config.LR_SCHEDULE_MODE == "min":
model.scheduler.step(metrics["loss_validate"][-1])
else:
model.scheduler.step(metrics["f1_macro_validate"][-1])
model.print_current_lr()
# Save Weights
start_time_saving = time.time()
if Config.SAVE_WEIGHTS:
model.save_model(metrics, epoch_nr, mode=Config.BEST_EPOCH_SELECTION)
timings["saving_time"] += time.time() - start_time_saving
# Create Plots
start_time_plotting = time.time()
pickle.dump(metrics, open(join(Config.EXP_PATH, "metrics.pkl"), "wb"))
plot_utils.create_exp_plot(metrics, Config.EXP_PATH, Config.EXP_NAME,
keys=["loss", "f1_macro"],
types=["train", "validate"],
selected_ax=["loss", "f1"],
fig_name="metrics_all.png")
plot_utils.create_exp_plot(metrics, Config.EXP_PATH, Config.EXP_NAME, without_first_epochs=True,
keys=["loss", "f1_macro"],
types=["train", "validate"],
selected_ax=["loss", "f1"],
fig_name="metrics.png")
if "angle_err" in Config.METRIC_TYPES:
plot_utils.create_exp_plot(metrics, Config.EXP_PATH, Config.EXP_NAME, without_first_epochs=True,
keys=["loss", "angle_err"],
types=["train", "validate"],
selected_ax=["loss", "f1"],
fig_name="metrics_angle.png")
timings["plotting_time"] += time.time() - start_time_plotting
epoch_time = time.time() - start_time
epoch_times.append(epoch_time)
exp_utils.print_and_save(Config, " Epoch {}, time total {}s".format(epoch_nr, epoch_time))
exp_utils.print_and_save(Config, " Epoch {}, time UNet: {}s".format(epoch_nr, timings["network_time"]))
exp_utils.print_and_save(Config, " Epoch {}, time metrics: {}s".format(epoch_nr, timings["metrics_time"]))
exp_utils.print_and_save(Config, " Epoch {}, time saving files: {}s".format(epoch_nr, timings["saving_time"]))
exp_utils.print_and_save(Config, str(datetime.datetime.now()))
# Adding next Epoch
if epoch_nr < Config.NUM_EPOCHS-1:
metrics = metric_utils.add_empty_element(metrics)
with open(join(Config.EXP_PATH, "Hyperparameters.txt"), "a") as f:
f.write("\n\nAverage Epoch time: {}s".format(sum(epoch_times) / float(len(epoch_times))))
def train_model(Config, model, data_loader):
if Config.USE_VISLOGGER:
try:
from trixi.logger.visdom import PytorchVisdomLogger
except ImportError:
pass
trixi = PytorchVisdomLogger(port=8080, auto_start=True)
exp_utils.print_and_save(Config, 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(Config.NUM_EPOCHS):
start_time = time.time()
# current_lr = Config.LEARNING_RATE * (Config.LR_DECAY ** epoch_nr)
# current_lr = Config.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 Config.LOSS_WEIGHT_LEN == -1:
weight_factor = float(Config.LOSS_WEIGHT)
else:
if epoch_nr < Config.LOSS_WEIGHT_LEN:
weight_factor = -(
(Config.LOSS_WEIGHT - 1) /
float(Config.LOSS_WEIGHT_LEN)) * epoch_nr + float(
Config.LOSS_WEIGHT)
else:
weight_factor = 1.
for type in ["train", "test", "validate"]:
print_loss = []
start_time_batch_gen = time.time()
batch_gen = data_loader.get_batch_generator(
batch_size=Config.BATCH_SIZE,
type=type,
subjects=getattr(Config,
type.upper() + "_SUBJECTS"))
batch_gen_time = time.time() - start_time_batch_gen
# print("batch_gen_time: {}s".format(batch_gen_time))
if Config.DIM == "2D":
nr_of_samples = len(getattr(
Config,
type.upper() + "_SUBJECTS")) * Config.INPUT_DIM[0]
else:
nr_of_samples = len(getattr(Config,
type.upper() + "_SUBJECTS"))
# *Config.EPOCH_MULTIPLIER needed to have roughly same number of updates/batches as with 2D U-Net
nr_batches = int(
int(nr_of_samples / Config.BATCH_SIZE) *
Config.EPOCH_MULTIPLIER)
print("Start looping batches...")
start_time_batch_part = time.time()
for i in range(nr_batches):
batch = next(batch_gen)
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)
data_preparation_time += time.time(
) - start_time_data_preparation
start_time_network = time.time()
if type == "train":
nr_of_updates += 1
loss, probs, f1 = model.train(x,
y,
weight_factor=weight_factor)
# loss, probs, f1, intermediate = model.train(x, y)
elif type == "validate":
loss, probs, f1 = model.test(x,
y,
weight_factor=weight_factor)
elif type == "test":
loss, probs, f1 = model.test(x,
y,
weight_factor=weight_factor)
network_time += time.time() - start_time_network
start_time_metrics = time.time()
if Config.CALC_F1:
if Config.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 = metric_utils.calculate_metrics(metrics, y_flat, probs, loss, f1=np.mean(f1),
# type=type, threshold=Config.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 = metric_utils.calc_peak_dice(Config, probs, y_right_order)
# peak_f1_mean = np.array([s for s in peak_f1.values()]).mean()
# import IPython
# IPython.embed()
#Pytorch
peak_f1_mean = np.array([
s.to('cpu') for s in list(f1.values())
]).mean() #if f1 for multiple bundles
metrics = metric_utils.calculate_metrics(
metrics,
None,
None,
loss,
f1=peak_f1_mean,
type=type,
threshold=Config.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 = metric_utils.calculate_metrics(metrics, None, None, loss, f1=peak_f1_mean_a,
# type=type, threshold=Config.THRESHOLD,
# f1_per_bundle={"LenF1": peak_f1_mean_b})
#Single Bundle
# metrics = metric_utils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"][0],
# type=type, threshold=Config.THRESHOLD,
# f1_per_bundle={"Thr1": f1["CST_right"][1],
# "Thr2": f1["CST_right"][2]})
# metrics = metric_utils.calculate_metrics(metrics, None, None, loss, f1=f1["CST_right"],
# type=type, threshold=Config.THRESHOLD)
else:
metrics = metric_utils.calculate_metrics(
metrics,
None,
None,
loss,
f1=np.mean(f1),
type=type,
threshold=Config.THRESHOLD)
else:
metrics = metric_utils.calculate_metrics_onlyLoss(
metrics, loss, type=type)
metrics_time += time.time() - start_time_metrics
print_loss.append(loss)
if batch_nr[type] % Config.PRINT_FREQ == 0:
time_batch_part = time.time() - start_time_batch_part
start_time_batch_part = time.time()
exp_utils.print_and_save(
Config, "{} Ep {}, Sp {}, loss {}, t print {}s, "
"t batch {}s".format(
type, epoch_nr, batch_nr[type] * Config.BATCH_SIZE,
round(np.array(print_loss).mean(), 6),
round(time_batch_part, 3),
round(time_batch_part / Config.PRINT_FREQ, 3)))
print_loss = []
if Config.USE_VISLOGGER:
plot_utils.plot_result_trixi(trixi, x, y, probs, loss, f1,
epoch_nr)
###################################
# Post Training tasks (each epoch)
###################################
# Average loss per batch over entire epoch
metrics = metric_utils.normalize_last_element(metrics,
batch_nr["train"],
type="train")
metrics = metric_utils.normalize_last_element(metrics,
batch_nr["validate"],
type="validate")
metrics = metric_utils.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))
# Adapt LR
if Config.LR_SCHEDULE:
if Config.LR_SCHEDULE_MODE == "min":
model.scheduler.step(metrics["loss_validate"][-1])
else:
model.scheduler.step(metrics["f1_macro_validate"][-1])
model.print_current_lr()
# Save Weights
start_time_saving = time.time()
if Config.SAVE_WEIGHTS:
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(Config.EXP_PATH, "metrics.pkl"), "wb"))
plot_utils.create_exp_plot(metrics, Config.EXP_PATH, Config.EXP_NAME)
plot_utils.create_exp_plot(metrics,
Config.EXP_PATH,
Config.EXP_NAME,
without_first_epochs=True)
plotting_time += time.time() - start_time_plotting
epoch_time = time.time() - start_time
epoch_times.append(epoch_time)
exp_utils.print_and_save(
Config, " Epoch {}, time total {}s".format(epoch_nr, epoch_time))
exp_utils.print_and_save(
Config,
" Epoch {}, time UNet: {}s".format(epoch_nr, network_time))
exp_utils.print_and_save(
Config,
" Epoch {}, time metrics: {}s".format(epoch_nr, metrics_time))
exp_utils.print_and_save(
Config,
" Epoch {}, time saving files: {}s".format(epoch_nr, saving_time))
exp_utils.print_and_save(Config, str(datetime.datetime.now()))
# Adding next Epoch
if epoch_nr < Config.NUM_EPOCHS - 1:
metrics = metric_utils.add_empty_element(metrics)
####################################
# After all epochs
###################################
with open(join(Config.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 model
def setUp(self):
self.visdomLogger = PytorchVisdomLogger()
class TestPytorchVisdomLogger(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestPytorchVisdomLogger, cls).setUpClass()
try:
start_visdom()
except:
print("Could not start visdom, it might be already running.")
def setUp(self):
self.visdomLogger = PytorchVisdomLogger()
def test_show_image(self):
image = np.random.random_sample((3, 128, 128))
tensor = torch.from_numpy(image)
self.visdomLogger.show_image(tensor.numpy(), title='image')
def test_show_images(self):
images = np.random.random_sample((4, 3, 128, 128))
tensors = torch.from_numpy(images)
self.visdomLogger.show_images(tensors.numpy(), title='images')
def test_show_image_grid(self):
images = np.random.random_sample((4, 3, 128, 128))
tensor = torch.from_numpy(images)
self.visdomLogger.show_image_grid(tensor, title="image_grid")
def test_show_image_grid_heatmap(self):
images = np.random.random_sample((4, 3, 128, 128))
self.visdomLogger.show_image_grid_heatmap(images,
title="image_grid_heatmap")
def test_show_barplot(self):
tensor = torch.from_numpy(np.random.random_sample(5))
self.visdomLogger.show_barplot(tensor, title="barplot")
def test_show_lineplot(self):
x = [0, 1, 2, 3, 4, 5]
y = np.random.random_sample(6)
self.visdomLogger.show_lineplot(y, x, title="lineplot1")
def test_show_piechart(self):
array = torch.from_numpy(np.random.random_sample(5))
self.visdomLogger.show_piechart(array, title="piechart")
def test_show_scatterplot(self):
array = torch.from_numpy(np.random.random_sample((5, 2)))
self.visdomLogger.show_scatterplot(array, title="scatterplot")
def test_show_value(self):
val = torch.from_numpy(np.random.random_sample(1))
self.visdomLogger.show_value(val, title="value")
val = torch.from_numpy(np.random.random_sample(1))
self.visdomLogger.show_value(val, title="value")
val = torch.from_numpy(np.random.random_sample(1))
self.visdomLogger.show_value(val, title="value", counter=4)
def test_show_text(self):
text = "\nTest 4 fun: zD ;-D 0o"
self.visdomLogger.show_text(text, title='text')
def test_get_roc_curve(self):
array = np.random.random_sample(100)
labels = np.random.choice((0, 1), 100)
self.visdomLogger.show_roc_curve(array, labels, name="roc")
def test_get_pr_curve(self):
array = np.random.random_sample(100)
labels = np.random.choice((0, 1), 100)
self.visdomLogger.show_roc_curve(array, labels, name="pr")
def test_get_classification_metric(self):
array = np.random.random_sample(100)
labels = np.random.choice((0, 1), 100)
self.visdomLogger.show_classification_metrics(
array,
labels,
metric=("roc-auc", "pr-score"),
name="classification-metrics")
def test_show_image_gradient(self):
net = Net()
random_input = torch.from_numpy(
np.random.randn(28 * 28).reshape((1, 1, 28, 28))).float()
fake_labels = torch.from_numpy(np.array([2])).long()
criterion = torch.nn.CrossEntropyLoss()
def err_fn(x):
x = net(x)
return criterion(x, fake_labels)
self.visdomLogger.show_image_gradient(name="grads-vanilla",
model=net,
inpt=random_input,
err_fn=err_fn,
grad_type="vanilla")
time.sleep(1)
self.visdomLogger.show_image_gradient(name="grads-svanilla",
model=net,
inpt=random_input,
err_fn=err_fn,
grad_type="smooth-vanilla")
time.sleep(1)
self.visdomLogger.show_image_gradient(name="grads-guided",
model=net,
inpt=random_input,
err_fn=err_fn,
grad_type="guided")
time.sleep(1)
self.visdomLogger.show_image_gradient(name="grads-sguided",
model=net,
inpt=random_input,
err_fn=err_fn,
grad_type="smooth-guided")
time.sleep(1)
def test_plot_model_structure(self):
net = Net()
self.visdomLogger.plot_model_structure(net, [(1, 1, 28, 28)])
def test_plot_model_statistics(self):
net = Net()
self.visdomLogger.plot_model_statistics(net, plot_grad=False)
self.visdomLogger.plot_model_statistics(net, plot_grad=True)
def test_show_embedding(self):
array = torch.from_numpy(np.random.random_sample((100, 100)))
self.visdomLogger.show_embedding(array, method="tsne")
self.visdomLogger.show_embedding(array, method="umap")
def train_model(Config, model, data_loader):
if Config.USE_VISLOGGER:
try:
from trixi.logger.visdom import PytorchVisdomLogger
except ImportError:
pass
trixi = PytorchVisdomLogger(port=8080, auto_start=True)
exp_utils.print_and_save(Config, socket.gethostname())
epoch_times = []
nr_of_updates = 0
metrics = {}
for type in ["train", "test", "validate"]:
metrics_new = {}
for metric in Config.METRIC_TYPES:
metrics_new[metric + "_" + type] = [0]
metrics = dict(list(metrics.items()) + list(metrics_new.items()))
batch_gen_train = data_loader.get_batch_generator(
batch_size=Config.BATCH_SIZE,
type="train",
subjects=getattr(Config, "TRAIN_SUBJECTS"))
batch_gen_val = data_loader.get_batch_generator(
batch_size=Config.BATCH_SIZE,
type="validate",
subjects=getattr(Config, "VALIDATE_SUBJECTS"))
for epoch_nr in range(Config.NUM_EPOCHS):
start_time = time.time()
# current_lr = Config.LEARNING_RATE * (Config.LR_DECAY ** epoch_nr)
# current_lr = Config.LEARNING_RATE
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 Config.LOSS_WEIGHT is None:
weight_factor = None
elif Config.LOSS_WEIGHT_LEN == -1:
weight_factor = float(Config.LOSS_WEIGHT)
else:
# Linearly decrease from LOSS_WEIGHT to 1 over LOSS_WEIGHT_LEN epochs
if epoch_nr < Config.LOSS_WEIGHT_LEN:
weight_factor = -(
(Config.LOSS_WEIGHT - 1) /
float(Config.LOSS_WEIGHT_LEN)) * epoch_nr + float(
Config.LOSS_WEIGHT)
else:
weight_factor = 1.
exp_utils.print_and_save(
Config, "Current weight_factor: {}".format(weight_factor))
if Config.ONLY_VAL:
types = ["validate"]
else:
types = ["train", "validate"]
for type in types:
print_loss = []
if Config.DIM == "2D":
nr_of_samples = len(getattr(
Config,
type.upper() + "_SUBJECTS")) * Config.INPUT_DIM[0]
else:
nr_of_samples = len(getattr(Config,
type.upper() + "_SUBJECTS"))
# *Config.EPOCH_MULTIPLIER needed to have roughly same number of updates/batches as with 2D U-Net
nr_batches = int(
int(nr_of_samples / Config.BATCH_SIZE) *
Config.EPOCH_MULTIPLIER)
print("Start looping batches...")
start_time_batch_part = time.time()
for i in range(nr_batches):
if type == "train":
batch = next(batch_gen_train)
else:
batch = next(batch_gen_val)
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)
# print("x.shape: {}".format(x.shape))
# print("y.shape: {}".format(y.shape))
data_preparation_time += time.time(
) - start_time_data_preparation
start_time_network = time.time()
if type == "train":
nr_of_updates += 1
probs, metr_batch = model.train(
x, y, weight_factor=weight_factor)
elif type == "validate":
probs, metr_batch = model.test(x,
y,
weight_factor=weight_factor)
elif type == "test":
probs, metr_batch = model.test(x,
y,
weight_factor=weight_factor)
network_time += time.time() - start_time_network
start_time_metrics = time.time()
if Config.CALC_F1:
if Config.EXPERIMENT_TYPE == "peak_regression":
peak_f1_mean = np.array([
s.to('cpu')
for s in list(metr_batch["f1_macro"].values())
]).mean()
metr_batch["f1_macro"] = peak_f1_mean
metrics = metric_utils.add_to_metrics(
metrics, metr_batch, type, Config.METRIC_TYPES)
else:
metr_batch["f1_macro"] = np.mean(
metr_batch["f1_macro"])
metrics = metric_utils.add_to_metrics(
metrics, metr_batch, type, Config.METRIC_TYPES)
else:
metrics = metric_utils.calculate_metrics_onlyLoss(
metrics, metr_batch["loss"], type=type)
metrics_time += time.time() - start_time_metrics
print_loss.append(metr_batch["loss"])
if batch_nr[type] % Config.PRINT_FREQ == 0:
time_batch_part = time.time() - start_time_batch_part
start_time_batch_part = time.time()
exp_utils.print_and_save(
Config, "{} Ep {}, Sp {}, loss {}, t print {}s, "
"t batch {}s".format(
type, epoch_nr, batch_nr[type] * Config.BATCH_SIZE,
round(np.array(print_loss).mean(), 6),
round(time_batch_part, 3),
round(time_batch_part / Config.PRINT_FREQ, 3)))
print_loss = []
if Config.USE_VISLOGGER:
plot_utils.plot_result_trixi(trixi, x, y, probs,
metr_batch["loss"],
metr_batch["f1_macro"],
epoch_nr)
###################################
# Post Training tasks (each epoch)
###################################
if Config.ONLY_VAL:
metrics = metric_utils.normalize_last_element(metrics,
batch_nr["validate"],
type="validate")
print("f1 macro validate: {}".format(
round(metrics["f1_macro_validate"][0], 4)))
return model
# Average loss per batch over entire epoch
metrics = metric_utils.normalize_last_element(metrics,
batch_nr["train"],
type="train")
metrics = metric_utils.normalize_last_element(metrics,
batch_nr["validate"],
type="validate")
# metrics = metric_utils.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))
# Adapt LR
if Config.LR_SCHEDULE:
if Config.LR_SCHEDULE_MODE == "min":
model.scheduler.step(metrics["loss_validate"][-1])
else:
model.scheduler.step(metrics["f1_macro_validate"][-1])
model.print_current_lr()
# Save Weights
start_time_saving = time.time()
if Config.SAVE_WEIGHTS:
model.save_model(metrics,
epoch_nr,
mode=Config.BEST_EPOCH_SELECTION)
saving_time += time.time() - start_time_saving
# Create Plots
start_time_plotting = time.time()
pickle.dump(metrics, open(join(Config.EXP_PATH, "metrics.pkl"), "wb"))
plot_utils.create_exp_plot(metrics,
Config.EXP_PATH,
Config.EXP_NAME,
keys=["loss", "f1_macro"],
types=["train", "validate"],
selected_ax=["loss", "f1"],
fig_name="metrics_all.png")
plot_utils.create_exp_plot(metrics,
Config.EXP_PATH,
Config.EXP_NAME,
without_first_epochs=True,
keys=["loss", "f1_macro"],
types=["train", "validate"],
selected_ax=["loss", "f1"],
fig_name="metrics.png")
if "angle_err" in Config.METRIC_TYPES:
plot_utils.create_exp_plot(metrics,
Config.EXP_PATH,
Config.EXP_NAME,
without_first_epochs=True,
keys=["loss", "angle_err"],
types=["train", "validate"],
selected_ax=["loss", "f1"],
fig_name="metrics_angle.png")
plotting_time += time.time() - start_time_plotting
epoch_time = time.time() - start_time
epoch_times.append(epoch_time)
exp_utils.print_and_save(
Config, " Epoch {}, time total {}s".format(epoch_nr, epoch_time))
exp_utils.print_and_save(
Config,
" Epoch {}, time UNet: {}s".format(epoch_nr, network_time))
exp_utils.print_and_save(
Config,
" Epoch {}, time metrics: {}s".format(epoch_nr, metrics_time))
exp_utils.print_and_save(
Config,
" Epoch {}, time saving files: {}s".format(epoch_nr, saving_time))
exp_utils.print_and_save(Config, str(datetime.datetime.now()))
# Adding next Epoch
if epoch_nr < Config.NUM_EPOCHS - 1:
metrics = metric_utils.add_empty_element(metrics)
####################################
# After all epochs
###################################
with open(join(Config.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 model