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training_loop.py
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training_loop.py
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import os, time, psutil
from typing import Tuple
import pandas as pd
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
import numpy as np
import torchio
from medcam import medcam
from GANDLF.data import get_testing_loader
from GANDLF.grad_clipping.grad_scaler import GradScaler, model_parameters_exclude_head
from GANDLF.grad_clipping.clip_gradients import dispatch_clip_grad_
from GANDLF.utils import (
get_date_time,
best_model_path_end,
latest_model_path_end,
initial_model_path_end,
save_model,
optimize_and_save_model,
load_model,
version_check,
write_training_patches,
print_model_summary,
get_ground_truths_and_predictions_tensor,
get_model_dict,
print_and_format_metrics,
)
from GANDLF.metrics import overall_stats
from GANDLF.logger import Logger
from .step import step
from .forward_pass import validate_network
from .generic import create_pytorch_objects
# hides torchio citation request, see https://github.com/fepegar/torchio/issues/235
os.environ["TORCHIO_HIDE_CITATION_PROMPT"] = "1"
def train_network(
model: torch.nn.Module,
train_dataloader: DataLoader,
optimizer: torch.optim.Optimizer,
params: dict,
) -> Tuple[float, dict]:
"""
This function performs the training of the network.
Args:
model (torch.nn.Module): The model to process the input image with, it should support appropriate dimensions.
train_dataloader (DataLoader): The dataloader for the training epoch.
optimizer (torch.optim.Optimizer): Optimizer for optimizing network.
params (dict): The parameters dictionary.
Returns:
Tuple[float, dict]: The average epoch training loss and metrics.
"""
print("*" * 20)
print("Starting Training : ")
print("*" * 20)
# Initialize a few things
total_epoch_train_loss = 0
total_epoch_train_metric = {}
average_epoch_train_metric = {}
calculate_overall_metrics = (params["problem_type"] == "classification") or (
params["problem_type"] == "regression"
)
for metric in params["metrics"]:
if "per_label" in metric:
total_epoch_train_metric[metric] = []
else:
total_epoch_train_metric[metric] = 0
# automatic mixed precision - https://pytorch.org/docs/stable/amp.html
if params["model"]["amp"]:
scaler = GradScaler()
if params["verbose"]:
print("Using Automatic mixed precision", flush=True)
# get ground truths
if calculate_overall_metrics:
(
ground_truth_array,
predictions_array,
) = get_ground_truths_and_predictions_tensor(params, "training_data")
# Set the model to train
model.train()
for batch_idx, (subject) in enumerate(
tqdm(train_dataloader, desc="Looping over training data")
):
optimizer.zero_grad()
image = (
torch.cat(
[subject[key][torchio.DATA] for key in params["channel_keys"]], dim=1
)
.float()
.to(params["device"])
)
if "value_keys" in params:
label = torch.cat([subject[key] for key in params["value_keys"]], dim=0)
# min is needed because for certain cases, batch size becomes smaller than the total remaining labels
label = label.reshape(
min(params["batch_size"], len(label)), len(params["value_keys"])
)
else:
label = subject["label"][torchio.DATA]
label = label.to(params["device"])
if params["save_training"]:
write_training_patches(subject, params)
# ensure spacing is always present in params and is always subject-specific
if "spacing" in subject:
params["subject_spacing"] = subject["spacing"]
else:
params["subject_spacing"] = None
loss, calculated_metrics, output, _ = step(model, image, label, params)
# store predictions for classification
if calculate_overall_metrics:
predictions_array[
batch_idx
* params["batch_size"] : (batch_idx + 1)
* params["batch_size"]
] = (torch.argmax(output[0], 0).cpu().item())
nan_loss = torch.isnan(loss)
second_order = (
hasattr(optimizer, "is_second_order") and optimizer.is_second_order
)
if params["model"]["amp"]:
with torch.cuda.amp.autocast():
# if loss is nan, don't backprop and don't step optimizer
if not nan_loss:
scaler(
loss=loss,
optimizer=optimizer,
clip_grad=params["clip_grad"],
clip_mode=params["clip_mode"],
parameters=model_parameters_exclude_head(
model, clip_mode=params["clip_mode"]
),
create_graph=second_order,
)
else:
if not nan_loss:
loss.backward(create_graph=second_order)
if params["clip_grad"] is not None:
dispatch_clip_grad_(
parameters=model_parameters_exclude_head(
model, clip_mode=params["clip_mode"]
),
value=params["clip_grad"],
mode=params["clip_mode"],
)
optimizer.step()
# Non network training related
if not nan_loss:
total_epoch_train_loss += loss.detach().cpu().item()
for metric in calculated_metrics.keys():
if isinstance(total_epoch_train_metric[metric], list):
if len(total_epoch_train_metric[metric]) == 0:
total_epoch_train_metric[metric] = np.array(
calculated_metrics[metric]
)
else:
total_epoch_train_metric[metric] += np.array(
calculated_metrics[metric]
)
else:
total_epoch_train_metric[metric] += calculated_metrics[metric]
if params["verbose"]:
# For printing information at halftime during an epoch
if ((batch_idx + 1) % (len(train_dataloader) / 2) == 0) and (
(batch_idx + 1) < len(train_dataloader)
):
print(
"\nHalf-Epoch Average train loss : ",
total_epoch_train_loss / (batch_idx + 1),
)
for metric in params["metrics"]:
if isinstance(total_epoch_train_metric[metric], np.ndarray):
to_print = (
total_epoch_train_metric[metric] / (batch_idx + 1)
).tolist()
else:
to_print = total_epoch_train_metric[metric] / (batch_idx + 1)
print("Half-Epoch Average train " + metric + " : ", to_print)
average_epoch_train_loss = total_epoch_train_loss / len(train_dataloader)
print(" Epoch Final train loss : ", average_epoch_train_loss)
# get overall stats for classification
if calculate_overall_metrics:
average_epoch_train_metric = overall_stats(
predictions_array, ground_truth_array, params
)
average_epoch_train_metric = print_and_format_metrics(
average_epoch_train_metric,
total_epoch_train_metric,
params["metrics"],
"train",
len(train_dataloader),
)
return average_epoch_train_loss, average_epoch_train_metric
def training_loop(
training_data: pd.DataFrame,
validation_data: pd.DataFrame,
device: str,
params: dict,
output_dir: str,
testing_data: bool = None,
epochs: bool = None,
) -> None:
"""
The main training loop.
Args:
training_data (pd.DataFrame): The data to use for training.
validation_data (pd.DataFrame): The data to use for validation.
device (str): The device to perform computations on.
params (dict): The parameters dictionary.
output_dir (str): The output directory.
testing_data (pd.DataFrame): The data to use for testing.
epochs (int): The number of epochs to train; if None, take from params.
"""
# Some autodetermined factors
if epochs is None:
epochs = params["num_epochs"]
params["device"] = device
params["output_dir"] = output_dir
params["training_data"] = training_data
params["validation_data"] = validation_data
params["testing_data"] = testing_data
testingDataDefined = True
if params["testing_data"] is None:
# testing_data = validation_data
testingDataDefined = False
# Setup a few variables for tracking
best_loss = 1e7
patience, start_epoch = 0, 0
first_model_saved = False
model_paths = {
"best": os.path.join(
output_dir, params["model"]["architecture"] + best_model_path_end
),
"initial": os.path.join(
output_dir, params["model"]["architecture"] + initial_model_path_end
),
"latest": os.path.join(
output_dir, params["model"]["architecture"] + latest_model_path_end
),
}
# if previous model file is present, load it up for sanity checks
main_dict = None
if os.path.exists(model_paths["best"]):
main_dict = load_model(model_paths["best"], params["device"])
version_check(params["version"], version_to_check=main_dict["version"])
params["previous_parameters"] = main_dict.get("parameters", None)
# Defining our model here according to parameters mentioned in the configuration file
print("Number of channels : ", params["model"]["num_channels"])
(
model,
optimizer,
train_dataloader,
val_dataloader,
scheduler,
params,
) = create_pytorch_objects(params, training_data, validation_data, device)
# save the initial model
if not os.path.exists(model_paths["initial"]):
save_model(
{
"epoch": 0,
"model_state_dict": get_model_dict(model, params["device_id"]),
"optimizer_state_dict": optimizer.state_dict(),
"loss": best_loss,
},
model,
params,
model_paths["initial"],
onnx_export=False,
)
print("Initial model saved.")
# if previous model file is present, load it up
if main_dict is not None:
try:
model.load_state_dict(main_dict["model_state_dict"])
start_epoch = main_dict["epoch"]
optimizer.load_state_dict(main_dict["optimizer_state_dict"])
best_loss = main_dict["loss"]
params["previous_parameters"] = main_dict.get("parameters", None)
print("Previous model successfully loaded.")
except RuntimeWarning:
RuntimeWarning("Previous model could not be loaded, initializing model")
if params["model"]["print_summary"]:
print_model_summary(
model,
params["batch_size"],
params["model"]["num_channels"],
params["patch_size"],
params["device"],
)
if testingDataDefined:
test_dataloader = get_testing_loader(params)
# Start training time here
start_time = time.time()
if not (os.environ.get("HOSTNAME") is None):
print("Hostname :", os.environ.get("HOSTNAME"))
# datetime object containing current date and time
print("Initializing training at :", get_date_time(), flush=True)
calculate_overall_metrics = (params["problem_type"] == "classification") or (
params["problem_type"] == "regression"
)
# get the overall metrics that are calculated automatically for classification/regression problems
if params["problem_type"] == "regression":
overall_metrics = overall_stats(torch.Tensor([1]), torch.Tensor([1]), params)
elif params["problem_type"] == "classification":
# this is just used to generate the headers for the overall stats
temp_tensor = torch.randint(0, params["model"]["num_classes"], (5,))
overall_metrics = overall_stats(
temp_tensor.to(dtype=torch.int32), temp_tensor.to(dtype=torch.int32), params
)
metrics_log = params["metrics"].copy()
if calculate_overall_metrics:
for metric in overall_metrics:
if metric not in metrics_log:
metrics_log[metric] = 0
# Setup a few loggers for tracking
train_logger = Logger(
logger_csv_filename=os.path.join(output_dir, "logs_training.csv"),
metrics=metrics_log,
)
valid_logger = Logger(
logger_csv_filename=os.path.join(output_dir, "logs_validation.csv"),
metrics=metrics_log,
)
if testingDataDefined:
test_logger = Logger(
logger_csv_filename=os.path.join(output_dir, "logs_testing.csv"),
metrics=metrics_log,
)
train_logger.write_header(mode="train")
valid_logger.write_header(mode="valid")
if testingDataDefined:
test_logger.write_header(mode="test")
if "medcam" in params:
model = medcam.inject(
model,
output_dir=os.path.join(
output_dir, "attention_maps", params["medcam"]["backend"]
),
backend=params["medcam"]["backend"],
layer=params["medcam"]["layer"],
save_maps=False,
return_attention=True,
enabled=False,
)
params["medcam_enabled"] = False
print("Using device:", device, flush=True)
# Iterate for number of epochs
for epoch in range(start_epoch, epochs):
if params["track_memory_usage"]:
file_to_write_mem = os.path.join(output_dir, "memory_usage.csv")
if os.path.exists(file_to_write_mem):
# append to previously generated file
file_mem = open(file_to_write_mem, "a")
outputToWrite_mem = ""
else:
# if file was absent, write header information
file_mem = open(file_to_write_mem, "w")
outputToWrite_mem = "Epoch,Memory_Total,Memory_Available,Memory_Percent_Free,Memory_Usage," # used to write output
if params["device"] == "cuda":
outputToWrite_mem += "CUDA_active.all.peak,CUDA_active.all.current,CUDA_active.all.allocated"
outputToWrite_mem += "\n"
mem = psutil.virtual_memory()
outputToWrite_mem += (
str(epoch)
+ ","
+ str(mem[0])
+ ","
+ str(mem[1])
+ ","
+ str(mem[2])
+ ","
+ str(mem[3])
)
if params["device"] == "cuda":
mem_cuda = torch.cuda.memory_stats()
outputToWrite_mem += (
","
+ str(mem_cuda["active.all.peak"])
+ ","
+ str(mem_cuda["active.all.current"])
+ ","
+ str(mem_cuda["active.all.allocated"])
)
outputToWrite_mem += ",\n"
file_mem.write(outputToWrite_mem)
file_mem.close()
# Printing times
epoch_start_time = time.time()
print("*" * 20)
print("*" * 20)
print("Starting Epoch : ", epoch)
if params["verbose"]:
print("Epoch start time : ", get_date_time())
params["current_epoch"] = epoch
epoch_train_loss, epoch_train_metric = train_network(
model, train_dataloader, optimizer, params
)
epoch_valid_loss, epoch_valid_metric = validate_network(
model, val_dataloader, scheduler, params, epoch, mode="validation"
)
patience += 1
# Write the losses to a logger
train_logger.write(epoch, epoch_train_loss, epoch_train_metric)
valid_logger.write(epoch, epoch_valid_loss, epoch_valid_metric)
if testingDataDefined:
epoch_test_loss, epoch_test_metric = validate_network(
model, test_dataloader, scheduler, params, epoch, mode="testing"
)
test_logger.write(epoch, epoch_test_loss, epoch_test_metric)
if params["verbose"]:
print("Epoch end time : ", get_date_time())
epoch_end_time = time.time()
print(
"Time taken for epoch : ",
(epoch_end_time - epoch_start_time) / 60,
" mins",
flush=True,
)
model_dict = get_model_dict(model, params["device_id"])
# Start to check for loss
if not (first_model_saved) or (epoch_valid_loss <= torch.tensor(best_loss)):
best_loss = epoch_valid_loss
best_train_idx = epoch
patience = 0
model.eval()
save_model(
{
"epoch": best_train_idx,
"model_state_dict": model_dict,
"optimizer_state_dict": optimizer.state_dict(),
"loss": best_loss,
},
model,
params,
model_paths["best"],
onnx_export=False,
)
model.train()
first_model_saved = True
if params["model"]["save_at_every_epoch"]:
save_model(
{
"epoch": epoch,
"model_state_dict": model_dict,
"optimizer_state_dict": optimizer.state_dict(),
"loss": epoch_valid_loss,
},
model,
params,
os.path.join(
output_dir,
params["model"]["architecture"]
+ "_epoch_"
+ str(epoch)
+ ".pth.tar",
),
onnx_export=False,
)
model.train()
# save the latest model
if os.path.exists(model_paths["latest"]):
os.remove(model_paths["latest"])
save_model(
{
"epoch": epoch,
"model_state_dict": model_dict,
"optimizer_state_dict": optimizer.state_dict(),
"loss": best_loss,
},
model,
params,
model_paths["latest"],
onnx_export=False,
)
print("Latest model saved.")
print("Current Best epoch: ", best_train_idx)
if patience > params["patience"]:
print(
"Performance Metric has not improved for %d epochs, exiting training loop!"
% (patience),
flush=True,
)
break
# End train time
end_time = time.time()
print(
"Total time to finish Training : ",
(end_time - start_time) / 60,
" mins",
flush=True,
)
# once the training is done, optimize the best model
if os.path.exists(model_paths["best"]):
optimize_and_save_model(model, params, model_paths["best"], onnx_export=True)
if __name__ == "__main__":
import argparse, pickle
torch.multiprocessing.freeze_support()
# parse the cli arguments here
parser = argparse.ArgumentParser(description="Training Loop of GANDLF")
parser.add_argument(
"-train_loader_pickle", type=str, help="Train loader pickle", required=True
)
parser.add_argument(
"-val_loader_pickle", type=str, help="Validation loader pickle", required=True
)
parser.add_argument(
"-testing_loader_pickle",
type=str,
help="Testing loader pickle",
required=False,
default=None,
)
parser.add_argument(
"-parameter_pickle", type=str, help="Parameters pickle", required=True
)
parser.add_argument("-outputDir", type=str, help="Output directory", required=True)
parser.add_argument("-device", type=str, help="Device to train on", required=True)
args = parser.parse_args()
# # write parameters to pickle - this should not change for the different folds, so keeping is independent
parameters = pickle.load(open(args.parameter_pickle, "rb"))
trainingDataFromPickle = pd.read_pickle(args.train_loader_pickle)
validationDataFromPickle = pd.read_pickle(args.val_loader_pickle)
testingData_str = args.testing_loader_pickle
testingDataFromPickle = pd.read_pickle(testingData_str) if testingData_str else None
training_loop(
training_data=trainingDataFromPickle,
validation_data=validationDataFromPickle,
output_dir=args.outputDir,
device=args.device,
params=parameters,
testing_data=testingDataFromPickle,
)