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Update auto3dseg algorithm templates #184

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merged 4 commits into from
Feb 10, 2023
Merged

Update auto3dseg algorithm templates #184

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Jan 19, 2023
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Jan 31, 2023
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6 changes: 3 additions & 3 deletions auto3dseg/algorithm_templates/dints/configs/hyper_parameters.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -49,10 +49,10 @@ training:
momentum: 0.9
weight_decay: 4.0e-05
lr_scheduler:
_target_: torch.optim.lr_scheduler.StepLR
_target_: torch.optim.lr_scheduler.PolynomialLR
optimizer: "$@training#optimizer"
step_size: "$max(@training#num_epochs // 5, 1)"
gamma: 0.5
power: 0.5,
total_iters: '$@training#num_epochs // @training#num_epochs_per_validation + 1'

# fine-tuning
finetune:
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2 changes: 1 addition & 1 deletion auto3dseg/algorithm_templates/dints/scripts/search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -445,7 +445,7 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
"train_loss_arch", loss.item(), epoch_len * epoch + step
)

lr_scheduler.step()
lr_scheduler.step()

if torch.cuda.device_count() > 1:
dist.barrier()
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254 changes: 129 additions & 125 deletions auto3dseg/algorithm_templates/dints/scripts/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,16 +143,18 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
validate_cache_rate = float(parser.get_parsed_content("validate_cache_rate"))

train_ds = monai.data.CacheDataset(
data=train_files,
data=train_files * num_epochs_per_validation,
transform=train_transforms,
cache_rate=train_cache_rate,
hash_as_key=True,
num_workers=parser.get_parsed_content("training#num_cache_workers"),
progress=False,
)
val_ds = monai.data.CacheDataset(
data=val_files,
transform=val_transforms,
cache_rate=validate_cache_rate,
hash_as_key=True,
num_workers=parser.get_parsed_content("training#num_cache_workers"),
progress=False,
)
Expand Down Expand Up @@ -253,11 +255,14 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
f.write("epoch\tmetric\tloss\tlr\ttime\titer\n")

start_time = time.time()
for epoch in range(num_epochs):

num_rounds = int(np.ceil(float(num_epochs) // float(num_epochs_per_validation)))
for _round in range(num_rounds):
epoch = (_round + 1) * num_epochs_per_validation
lr = lr_scheduler.get_last_lr()[0]
if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
print("-" * 10)
print(f"epoch {epoch + 1}/{num_epochs}")
print(f"epoch {_round * num_epochs_per_validation + 1}/{num_epochs}")
print(f"learning rate is set to {lr}")

model.train()
Expand Down Expand Up @@ -303,9 +308,9 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
f"[{str(datetime.now())[:19]}] "
+ f"{step}/{epoch_len}, train_loss: {loss.item():.4f}"
)
writer.add_scalar("Loss/train", loss.item(), epoch_len * epoch + step)
writer.add_scalar("Loss/train", loss.item(), epoch_len * num_rounds + step)

lr_scheduler.step()
lr_scheduler.step()

if torch.cuda.device_count() > 1:
dist.barrier()
Expand All @@ -315,144 +320,143 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
loss_torch_epoch = loss_torch[0] / loss_torch[1]
print(
f"epoch {epoch + 1} average loss: {loss_torch_epoch:.4f}, "
f"epoch {epoch} average loss: {loss_torch_epoch:.4f}, "
f"best mean dice: {best_metric:.4f} at epoch {best_metric_epoch}"
)

if (epoch + 1) % val_interval == 0 or (epoch + 1) == num_epochs:
torch.cuda.empty_cache()
model.eval()
with torch.no_grad():
metric = torch.zeros(metric_dim * 2, dtype=torch.float, device=device)
metric_sum = 0.0
metric_count = 0
metric_mat = []
val_images = None
val_labels = None
val_outputs = None

_index = 0
for val_data in val_loader:
val_images = (
val_data["image"].to(device)
if sw_input_on_cpu is False
else val_data["image"]
)
val_labels = (
val_data["label"].to(device)
if sw_input_on_cpu is False
else val_data["label"]
torch.cuda.empty_cache()
model.eval()
with torch.no_grad():
metric = torch.zeros(metric_dim * 2, dtype=torch.float, device=device)
metric_sum = 0.0
metric_count = 0
metric_mat = []
val_images = None
val_labels = None
val_outputs = None

_index = 0
for val_data in val_loader:
val_images = (
val_data["image"].to(device)
if sw_input_on_cpu is False
else val_data["image"]
)
val_labels = (
val_data["label"].to(device)
if sw_input_on_cpu is False
else val_data["label"]
)

with torch.cuda.amp.autocast(enabled=amp):
val_outputs = sliding_window_inference(
val_images,
patch_size_valid,
num_sw_batch_size,
model,
mode="gaussian",
overlap=overlap_ratio,
sw_device=device,
)

with torch.cuda.amp.autocast(enabled=amp):
val_outputs = sliding_window_inference(
val_images,
patch_size_valid,
num_sw_batch_size,
model,
mode="gaussian",
overlap=overlap_ratio,
sw_device=device,
)
val_outputs = post_pred(val_outputs[0, ...])
val_outputs = val_outputs[None, ...]

val_outputs = post_pred(val_outputs[0, ...])
val_outputs = val_outputs[None, ...]
if softmax:
val_labels = post_label(val_labels[0, ...])
val_labels = val_labels[None, ...]

if softmax:
val_labels = post_label(val_labels[0, ...])
val_labels = val_labels[None, ...]
value = compute_dice(
y_pred=val_outputs, y=val_labels, include_background=not softmax
)

value = compute_dice(
y_pred=val_outputs, y=val_labels, include_background=not softmax
)
print(_index + 1, "/", len(val_loader), value)

print(_index + 1, "/", len(val_loader), value)
metric_count += len(value)
metric_sum += value.sum().item()
metric_vals = value.cpu().numpy()
if len(metric_mat) == 0:
metric_mat = metric_vals
else:
metric_mat = np.concatenate((metric_mat, metric_vals), axis=0)

metric_count += len(value)
metric_sum += value.sum().item()
metric_vals = value.cpu().numpy()
if len(metric_mat) == 0:
metric_mat = metric_vals
else:
metric_mat = np.concatenate((metric_mat, metric_vals), axis=0)

for _c in range(metric_dim):
val0 = torch.nan_to_num(value[0, _c], nan=0.0)
val1 = 1.0 - torch.isnan(value[0, 0]).float()
metric[2 * _c] += val0 * val1
metric[2 * _c + 1] += val1

_index += 1

if torch.cuda.device_count() > 1:
dist.barrier()
dist.all_reduce(metric, op=torch.distributed.ReduceOp.SUM)

metric = metric.tolist()
if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
for _c in range(metric_dim):
print(
f"evaluation metric - class {_c + 1:d}:",
metric[2 * _c] / metric[2 * _c + 1],
)
avg_metric = 0
for _c in range(metric_dim):
avg_metric += metric[2 * _c] / metric[2 * _c + 1]
avg_metric = avg_metric / float(metric_dim)
print("avg_metric", avg_metric)

writer.add_scalar("Accuracy/validation", avg_metric, epoch)

if avg_metric > best_metric:
best_metric = avg_metric
best_metric_epoch = epoch + 1
if torch.cuda.device_count() > 1:
torch.save(
model.module.state_dict(),
os.path.join(ckpt_path, "best_metric_model.pt"),
)
else:
torch.save(
model.state_dict(),
os.path.join(ckpt_path, "best_metric_model.pt"),
)
print("saved new best metric model")

dict_file = {}
dict_file["best_avg_dice_score"] = float(best_metric)
dict_file["best_avg_dice_score_epoch"] = int(best_metric_epoch)
dict_file["best_avg_dice_score_iteration"] = int(idx_iter)
with open(
os.path.join(ckpt_path, "progress.yaml"), "a"
) as out_file:
yaml.dump([dict_file], stream=out_file)
for _c in range(metric_dim):
val0 = torch.nan_to_num(value[0, _c], nan=0.0)
val1 = 1.0 - torch.isnan(value[0, 0]).float()
metric[2 * _c] += val0 * val1
metric[2 * _c + 1] += val1

_index += 1

if torch.cuda.device_count() > 1:
dist.barrier()
dist.all_reduce(metric, op=torch.distributed.ReduceOp.SUM)

metric = metric.tolist()
if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
for _c in range(metric_dim):
print(
"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}".format(
epoch + 1, avg_metric, best_metric, best_metric_epoch
)
f"evaluation metric - class {_c + 1:d}:",
metric[2 * _c] / metric[2 * _c + 1],
)
avg_metric = 0
for _c in range(metric_dim):
avg_metric += metric[2 * _c] / metric[2 * _c + 1]
avg_metric = avg_metric / float(metric_dim)
print("avg_metric", avg_metric)

writer.add_scalar("Accuracy/validation", avg_metric, epoch)

if avg_metric > best_metric:
best_metric = avg_metric
best_metric_epoch = epoch
if torch.cuda.device_count() > 1:
torch.save(
model.module.state_dict(),
os.path.join(ckpt_path, "best_metric_model.pt"),
)
else:
torch.save(
model.state_dict(),
os.path.join(ckpt_path, "best_metric_model.pt"),
)
print("saved new best metric model")

current_time = time.time()
elapsed_time = (current_time - start_time) / 60.0
dict_file = {}
dict_file["best_avg_dice_score"] = float(best_metric)
dict_file["best_avg_dice_score_epoch"] = int(best_metric_epoch)
dict_file["best_avg_dice_score_iteration"] = int(idx_iter)
with open(
os.path.join(ckpt_path, "accuracy_history.csv"), "a"
) as f:
f.write(
"{:d}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.1f}\t{:d}\n".format(
epoch + 1,
avg_metric,
loss_torch_epoch,
lr,
elapsed_time,
idx_iter,
)
os.path.join(ckpt_path, "progress.yaml"), "a"
) as out_file:
yaml.dump([dict_file], stream=out_file)

print(
"current epoch: {} current mean dice: {:.4f} best mean dice: {:.4f} at epoch {}".format(
epoch, avg_metric, best_metric, best_metric_epoch
)
)

current_time = time.time()
elapsed_time = (current_time - start_time) / 60.0
with open(
os.path.join(ckpt_path, "accuracy_history.csv"), "a"
) as f:
f.write(
"{:d}\t{:.5f}\t{:.5f}\t{:.5f}\t{:.1f}\t{:d}\n".format(
epoch,
avg_metric,
loss_torch_epoch,
lr,
elapsed_time,
idx_iter,
)
)

if torch.cuda.device_count() > 1:
dist.barrier()
if torch.cuda.device_count() > 1:
dist.barrier()

torch.cuda.empty_cache()
torch.cuda.empty_cache()

if torch.cuda.device_count() == 1 or dist.get_rank() == 0:
print(
Expand Down
2 changes: 1 addition & 1 deletion auto3dseg/algorithm_templates/segresnet2d/scripts/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,7 +305,7 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
)
writer.add_scalar("Loss/train", loss.item(), epoch_len * epoch + step)

lr_scheduler.step()
lr_scheduler.step()

if torch.cuda.device_count() > 1:
dist.barrier()
Expand Down
2 changes: 1 addition & 1 deletion auto3dseg/algorithm_templates/swinunetr/scripts/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -324,7 +324,7 @@ def run(config_file: Optional[Union[str, Sequence[str]]] = None, **override):
)
writer.add_scalar("Loss/train", loss.item(), epoch_len * epoch + step)

lr_scheduler.step()
lr_scheduler.step()

if torch.cuda.device_count() > 1:
dist.barrier()
Expand Down