def publish(self):
callback = RequestParams.get('callback', None)
if callback is None:
return '{0}({1})'.format(callback, {'msg': 'Param %s is required' % 'callback'})
if 'metric' not in RequestParams:
return '{0}({1})'.format(callback, {'msg': 'Param %s is required' % 'metric'})
if 'value' not in RequestParams:
return '{0}({1})'.format(callback, {'msg': 'Param %s is required' % 'value'})
if 'timestamp' not in RequestParams:
return '{0}({1})'.format(callback, {'msg': 'Param %s is required' % 'timestamp'})
metric = RequestParams.get('metric')
value = RequestParams.get('value')
timestamp = RequestParams.get('timestamp')
from lib import app
app = app.CarbonHttpApplication()
queue = app.queue
try:
m = Metric({
'metric': metric,
'value': value,
'timestamp': timestamp
})
m.enqueue(queue)
except ValueError:
return '{0}({1})'.format(callback, {'msg': 'Invalid Metric.'})
return '{0}({1})'.format(callback, {'msg': 'Publish success.'})
def publish(self):
if 'metric' not in RequestParams:
raise CParamsMissing('Param %s is required' % 'metric')
if 'value' not in RequestParams:
raise CParamsMissing('Param %s is required' % 'value')
if 'timestamp' not in RequestParams:
raise CParamsMissing('Param %s is required' % 'timestamp')
metric = RequestParams.get('metric')
value = RequestParams.get('value')
timestamp = RequestParams.get('timestamp')
from lib import app
app = app.CarbonHttpApplication()
queue = app.queue
try:
m = Metric({
'metric': metric,
'value': value,
'timestamp': timestamp
})
m.enqueue(queue)
return self.make_result()
except ValueError:
raise CParamsError('Invalid Metric.')
def main(config, Model, data, base_path, _run):
log("Start " + ex.get_experiment_info()["name"])
base_path = base_path + str(_run._id) + "/"
config["base_path"] = base_path
# Model
model = Model(config)
model.to(config["device"])
model.eval()
if config["model_state"] == "":
raise Exception("A model needs to be loaded")
model.load_state_dict(torch.load(config["model_state"]))
os.makedirs(config["base_path"] + "preds")
test_data = data("test", loss=config["loss_fn"], dev=config["device"])
stats = []
with torch.no_grad():
for i in range(len(test_data)): # Studying in Depth (first) dimension
if i % 10 == 0:
print("{}/{}".format(i, len(test_data)))
X, Y, id_, W = test_data[i]
pred = model(X)
pred = pred[0].cpu().numpy()
Y = Y.cpu().numpy()
# Stats
m = Metric(pred, Y)
dice_res = m.dice()[0]
haus_res = m.hausdorff()[0]
islands_res = m.islands()[0]
stats.append("{}\t{}\t{}\t{}\n".format(id_, dice_res, haus_res,
islands_res))
_out = np.argmax(np.moveaxis(np.reshape(pred, (2, 18, 256, 256)),
1, -1),
axis=0)
nib.save(nib.Nifti1Image(_out, np.eye(4)),
config["base_path"] + "preds/" + id_ + "_pred.nii.gz")
_out = np.argmax(np.moveaxis(np.reshape(Y, (2, 18, 256, 256)), 1,
-1),
axis=0)
nib.save(nib.Nifti1Image(_out, np.eye(4)),
config["base_path"] + "preds/" + id_ + "_label.nii.gz")
_out = np.moveaxis(np.reshape(X.cpu().numpy(), (18, 256, 256)), 0,
-1)
nib.save(nib.Nifti1Image(_out, np.eye(4)),
config["base_path"] + "preds/" + id_ + ".nii.gz")
with open(config["base_path"] + "results", "w") as f:
for s in stats:
f.write(s)
def publish(self):
if 'metrics' not in RequestParams:
raise CParamsMissing('Param %s is required' % 'metrics')
metrics = RequestParams.get('metrics')
if type(metrics) != list:
raise CParamsError('Param %s must be list' % 'metrics')
from lib import app
app = app.CarbonHttpApplication()
queue = app.queue
for metric_dict in metrics:
try:
m = Metric(metric_dict)
m.enqueue(queue)
except ValueError:
raise CParamsError('Invalid Metric.')
return self.make_result()
if te_i % 10 == 0:
print("Masks generated: {}/{}".format(te_i, len(test_data)))
X, Y, id_ = test_data[te_i]
output = model(X)
pred = output[0].cpu().numpy() # BCDHW
# Optional Post-processing
if removeSmallIslands_thr != -1:
pred = removeSmallIslands(pred, thr=removeSmallIslands_thr)
if type(Y) != type(None):
Y = Y.cpu().numpy()
with open(outputPath + "stats.csv", "a") as f:
measures = Metric(pred, Y)
f.write("{},{},{},{}\n".format(
id_,
measures.dice()[0, 1],
measures.compactness()[0],
measures.hausdorff_distance()[0]))
pred = pred[0] # CDHW
pred = np.argmax(pred, axis=0) # DHW
pred = np.moveaxis(pred, 0, -1) # HWD
# The filename will be the name of the last 3 folders
# Ideally Study_Timepoint_ScanID_predMask.nii.gz
filename = "_".join(id_[:-1].split("/")[-3:]) + "_predMask.nii.gz"
nib.save(nib.Nifti1Image(pred, np.eye(4)), outputPath + filename)
def main(config, Model, data, base_path, _run):
log("Start " + ex.get_experiment_info()["name"])
base_path = base_path + str(_run._id) + "/"
config["base_path"] = base_path
# Data
tr_data = data("train",
loss=config["loss_fn"],
brainmask=config["brainmask"],
overlap=config["overlap"],
dev=config["device"])
val_data = data("validation",
loss=config["loss_fn"],
brainmask=config["brainmask"],
overlap=config["overlap"],
dev=config["device"])
#tr_data = data("train", loss=config["loss_fn"], dev=config["device"])
#val_data = data("validation", loss=config["loss_fn"], dev=config["device"])
# Calculating compactness in the training data
"""
comp_vals = []
for tr_i in range(len(tr_data)):
X, Y, id_, W = tr_data[tr_i]
comp_vals.append(str(Metric(Y.detach().cpu().numpy(), None).compactness()[0][-1]))
with open("compactness_results", "a") as f:
f.write(",".join(comp_vals) + "\n")
import sys
sys.exit(1)
"""
# Model
model = Model(config)
#model.cuda()
model.to(config["device"])
# Weight initialization
def weight_init(m):
if isinstance(m, torch.nn.Conv3d):
config["initW"](m.weight)
config["initB"](m.bias)
model.apply(weight_init)
# Save graph
X, _, _, _ = tr_data[0]
tb_path = base_path[:-1].split("/")
tb_path = "/".join(tb_path[:-2]) + "/tensorboard/" + "_".join(tb_path[-2:])
writer = SummaryWriter(tb_path)
writer.add_graph(model, X)
writer.close()
# Test how long each operation take
with torch.autograd.profiler.profile(use_cuda=True) as prof:
model(X)
with open(base_path + "profile", "w") as f:
f.write(str(prof))
# Create folder for saving the model and validation results
if len(config["save_validation"]) > 0:
os.makedirs(config["base_path"] + "val_evol")
os.makedirs(config["base_path"] + "model")
# Config
ep = config["epochs"]
bs = config["batch"]
loss_fn = config["loss_fn"]
opt = config["opt"](model.parameters(),
lr=config["lr"],
weight_decay=config["weight_decay"])
lr_scheduler = config["lr_scheduler"]
if not lr_scheduler is None:
lr_scheduler.setOptimizer(opt)
# Save model and optimizer's state dict
param_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
log("Number of parameters: " + str(param_num))
with open(base_path + "state_dict", "w") as f:
f.write(">> Model's state dict:\n") # Important for debugging
for param_tensor in model.state_dict():
f.write(param_tensor + "\t" +
str(model.state_dict()[param_tensor].size()) + "\n")
f.write(
"\n>> Optimizer's state dict:\n") # Important for reproducibility
for var_name in opt.state_dict():
f.write(var_name + "\t" + str(opt.state_dict()[var_name]) + "\n")
# Load weights if necessary
if config["model_state"] != "":
log("Loading previous model")
model.load_state_dict(torch.load(config["model_state"]))
# Counters and flags
e = 0 # Epoch counter
it = 0 # Iteration counter
keep_training = True # Flag to stop training when overfitting
log("Training")
while e < ep and keep_training:
model.train()
tr_loss = 0
tr_islands = 0
tr_i = 0
while tr_i < len(tr_data) and keep_training:
X, Y, id_, W = tr_data[tr_i]
output = model(X)
pred = output[0]
if W is None:
tr_loss_tmp = loss_fn(output, Y, config)
else:
tr_loss_tmp = loss_fn(output, Y, config, W)
tr_loss += tr_loss_tmp
tr_islands += np.sum(Metric(pred.detach().cpu(), None).islands())
# Optimization
opt.zero_grad()
tr_loss_tmp.backward()
#writer = SummaryWriter(tb_path)
#record_grads = ["bottleneck4.seq.2.weight", "conv1.weight", "block3.seq.4.weight"]
#for n, p in model.named_parameters():
# if n in record_grads:
# writer.add_histogram(n, p.grad, e)
#writer.close()
#plot_grad_flow(model.named_parameters())
opt.step()
it += 1
tr_i += 1
tr_loss /= len(tr_data)
tr_islands /= len(tr_data)
#for n, p in named_parameters:
# if(p.requires_grad) and ("bias" not in n):
# layers.append(n)
# ave_grads.append(p.grad.abs().mean())
# max_grads.append(p.grad.abs().max()
# Tensorboard summaries
writer = SummaryWriter(tb_path)
writer.add_scalar("tr_loss", tr_loss, e)
writer.add_scalar("tr_islands", tr_islands, e)
writer.close()
log("Validation")
val_loss = 0
val_islands = 0
val_dice = 0
val_i = 0
model.eval()
with torch.no_grad():
while val_i < len(val_data) and keep_training:
X, Y, id_, W = val_data[val_i]
output = model(X)
pred = output[0]
if W is None:
val_loss_tmp = loss_fn(output, Y, config)
else:
val_loss_tmp = loss_fn(output, Y, config, W)
val_loss += val_loss_tmp
m = Metric(pred.cpu().numpy(), Y.cpu().numpy())
val_islands += np.sum(m.islands())
val_dice += m.dice()[:, 1] # Lesion Dice
if id_ in config["save_validation"]:
name = id_ + "_" + str(e)
pred = np.moveaxis(
np.moveaxis(
np.reshape(pred.cpu().numpy(), (2, 18, 256, 256)),
1, -1), 0, -1)
if config["save_npy"]:
np.save(config["base_path"] + "val_evol/" + name, pred)
pred = np.argmax(pred, axis=-1)
nib.save(
nib.Nifti1Image(pred, np.eye(4)),
config["base_path"] + "val_evol/" + name + ".nii.gz")
val_i += 1
val_loss /= len(val_data)
val_islands /= len(val_data)
val_dice /= len(val_data)
# Tensorboard summaries
writer = SummaryWriter(tb_path)
writer.add_scalar("val_loss", val_loss, e)
writer.add_scalar("val_islands", val_islands, e)
writer.add_scalar("val_dice", val_dice, e)
writer.close()
log("Epoch: {}. Loss: {}. Val Loss: {}".format(e, tr_loss, val_loss))
# Reduce learning rate if needed, and stop if limit is reached.
if lr_scheduler != None:
lr_scheduler.step(val_loss)
#keep_training = lr_scheduler.limit_cnt > -1 # -1 -> stop training
if lr_scheduler.limit_cnt < 0:
keep_training = False
lr_scheduler.limit_cnt = lr_scheduler.limit # Needed if we run ex. more than once!
# Save model after every epoch
torch.save(model.state_dict(),
config["base_path"] + "model/model-" + str(e))
if e > 4 and os.path.exists(config["base_path"] + "model/model-" +
str(e - 5)):
os.remove(config["base_path"] + "model/model-" + str(e - 5))
e += 1
log("Testing")
test_data = data("test",
loss=config["loss_fn"],
brainmask=config["brainmask"],
overlap=config["overlap"],
dev=config["device"])
if config["save_prediction_mask"] or config[
"save_prediction_softmaxprob"] or config["save_prediction_logits"]:
os.makedirs(config["base_path"] + "preds")
results = {}
results_post = {}
model.eval()
if config[
"pc_name"] != "sampo-tipagpu1": # Sampo seems to not deal well with multiprocess
pool = multiprocessing.Pool(processes=PROCESSES)
with torch.no_grad():
# Assuming that batch_size is 1
for test_i in range(len(test_data)):
X, Y, id_, _ = test_data[test_i]
output = model(X)
pred = output[0].cpu().numpy()
Y = Y.cpu().numpy() # NBWHC
if config["removeSmallIslands_thr"] == -1 and config[
"save_prediction_mask"]:
_out = np.argmax(np.moveaxis(
np.reshape(pred, (2, 18, 256, 256)), 1, -1),
axis=0)
nib.save(nib.Nifti1Image(_out, np.eye(4)),
config["base_path"] + "preds/" + id_ + "_mask.nii.gz")
if config["save_prediction_logits"] and len(output) > 1:
logits = output[1].cpu().numpy()
_out = np.moveaxis(
np.moveaxis(np.reshape(logits, (2, 18, 256, 256)), 1, -1),
0, -1)
nib.save(
nib.Nifti1Image(_out, np.eye(4)),
config["base_path"] + "preds/" + id_ + "_logits.nii.gz")
if config["save_prediction_softmaxprob"]:
_out = np.moveaxis(
np.moveaxis(np.reshape(pred, (2, 18, 256, 256)), 1, -1), 0,
-1)
nib.save(
nib.Nifti1Image(_out, np.eye(4)), config["base_path"] +
"preds/" + id_ + "_softmaxprob.nii.gz")
if config["pc_name"] != "sampo-tipagpu1":
results[id_] = pool.apply_async(Metric(pred, Y).all)
else:
results[id_] = Metric(pred, Y).all()
# Results after post-processing
if config["removeSmallIslands_thr"] != -1:
pred_post = pred.copy()
pred_post = removeSmallIslands(
pred_post, thr=config["removeSmallIslands_thr"])
if config["pc_name"] != "sampo-tipagpu1":
results_post[id_] = pool.apply_async(
Metric(pred_post, Y).all)
else:
results_post[id_] = Metric(pred_post, Y).all()
if config["save_prediction_mask"]:
_out = np.argmax(np.moveaxis(
np.reshape(pred_post, (2, 18, 256, 256)), 1, -1),
axis=0)
nib.save(
nib.Nifti1Image(_out, np.eye(4)),
config["base_path"] + "preds/" + id_ + "_mask.nii.gz")
if config["pc_name"] != "sampo-tipagpu1":
for k in results:
results[k] = results[k].get()
with open(config["base_path"] + "results.json", "w") as f:
f.write(json.dumps(results))
if config["removeSmallIslands_thr"] != -1:
if config["pc_name"] != "sampo-tipagpu1":
for k in results_post:
results_post[k] = results_post[k].get()
# Results after post-processing
with open(config["base_path"] + "results-post.json", "w") as f:
f.write(json.dumps(results_post))
if config["pc_name"] != "sampo-tipagpu1":
pool.close()
pool.join()
pool.terminate()
log("End")
def fit(self, tr_loader, val_loader, epochs, val_interval, loss,
val_metrics, opt):
"""Trains the NN.
Args:
`tr_loader`: DataLoader with the training set.
`val_loader`: DataLoader with the validaiton set.
`epochs`: Number of epochs to train the model. If 0, no train.
`val_interval`: After how many epochs to perform validation.
`loss`: Name of the loss function.
`val_metrics`: Which metrics to measure at validation time.
`opt`: Optimizer.
"""
t0 = time.time()
e = 1
# Expected classes of our dataset
measure_classes = {0: "background", 1: "contra", 2: "R_hemisphere"}
# Which classes will be reported during validation
measure_classes_mean = np.array([1, 2])
while e <= epochs:
self.train()
tr_loss = 0
for (tr_i), (X, Y, info, W) in enumerate(tr_loader):
X = [x.to(self.device) for x in X]
Y = [y.to(self.device) for y in Y]
W = [w.to(self.device) for w in W]
output = self(X)
pred = output[0]
tr_loss_tmp = loss(output, Y, W)
tr_loss += tr_loss_tmp
# Optimization
opt.zero_grad()
tr_loss_tmp.backward()
opt.step()
tr_loss /= len(tr_loader)
if len(val_loader) != 0 and e % val_interval == 0:
log("Validation", self.out_path)
self.eval()
val_loss = 0
# val_scores stores all needed metrics for assessing validation
val_scores = np.zeros(
(len(val_metrics), len(val_loader), len(measure_classes)))
Measure = Metric(val_metrics,
onehot=softmax2onehot,
classes=measure_classes,
multiprocess=False)
with torch.no_grad():
for (val_i), (X, Y, info, W) in enumerate(val_loader):
X = [x.to(self.device) for x in X]
Y = [y.to(self.device) for y in Y]
W = [w.to(self.device) for w in W]
output = self(X)
val_loss_tmp = loss(output, Y, W)
val_loss += val_loss_tmp
y_true_cpu = Y[0].cpu().numpy()
y_pred_cpu = output[0].cpu().numpy()
# Record all needed metrics
# If batch_size > 1, Measure.all() returns an avg.
tmp_res = Measure.all(y_pred_cpu, y_true_cpu)
for i, m in enumerate(val_metrics):
val_scores[i, val_i] = tmp_res[m]
# Validation loss
val_loss /= len(val_loader)
val_str = " Val Loss: {}".format(val_loss)
# val_metrics shape: num_metrics x num_batches x num_classes
for i, m in enumerate(val_metrics):
# tmp shape: num_classes (averaged over num_batches when val != -1)
tmp = np.array(Measure._getMean(val_scores[i]))
# Mean validation value in metric m (all interesting classes)
tmp_val = tmp[measure_classes_mean]
# Note: if tmp_val is NaN, it means that the classes I am
# interested in (check lib/data/whatever, measure_classes_mean)
# were not found in the validation set.
tmp_val = np.mean(tmp_val[tmp_val != -1])
val_str += ". Val " + m + ": " + str(tmp_val)
else:
val_str = ""
eta = " ETA: " + datetime.fromtimestamp(
time.time() + (epochs - e) *
(time.time() - t0) / e).strftime("%Y-%m-%d %H:%M:%S")
log("Epoch: {}. Loss: {}.".format(e, tr_loss) + val_str + eta,
self.out_path)
# Save model after every epoch
torch.save(
self.state_dict(),
self.out_path + "model/MedicDeepLabv3Plus-model-" + str(e))
if e > 1 and os.path.exists(self.out_path +
"model/MedicDeepLabv3Plus-model-" +
str(e - 1)):
os.remove(self.out_path + "model/MedicDeepLabv3Plus-model-" +
str(e - 1))
e += 1
def evaluate(self, test_loader, metrics, remove_islands, save_output=True):
"""Tests/Evaluates the NN.
Args:
`test_loader`: DataLoader containing the test set. Batch_size = 1.
`metrics`: Metrics to measure.
`save_output`: (bool) whether to save the output segmentations.
`remove_islands`: (bool) whether to apply post-processing.
"""
# Expected classes of our dataset
measure_classes = {0: "background", 1: "contra", 2: "R_hemisphere"}
results = {}
self.eval()
Measure = Metric(metrics,
onehot=sigmoid2onehot,
classes=measure_classes,
multiprocess=True)
# Pool to store pieces of output that will be put together
# before evaluating the whole image.
# This is useful when the entire image doesn't fit into mem.
with torch.no_grad():
for (test_i), (X, Y, info, W) in enumerate(test_loader):
print("{}/{}".format(test_i + 1, len(test_loader)))
X = [x.to(self.device) for x in X]
Y = [y.to(self.device) for y in Y]
W = [w.to(self.device) for w in W]
id_ = info["id"][0]
output = self(X)
y_pred_cpu = output[0].cpu().numpy()
y_true_cpu = Y[0].cpu().numpy()
if remove_islands:
y_pred_cpu = removeSmallIslands(y_pred_cpu, thr=20)
# Predictions (and GT) separate the two hemispheres
# combineLabels will combine these such that it creates
# brainmask and contra-hemisphere ROIs instead of
# two different hemisphere ROIs.
y_pred_cpu = combineLabels(y_pred_cpu)
# If GT was provided it measures the performance
if len(y_true_cpu.shape) > 1:
y_true_cpu = combineLabels(y_true_cpu)
results[id_] = Measure.all(y_pred_cpu, y_true_cpu)
test_loader.dataset.save(y_pred_cpu[0], info,
self.out_path + id_)
# Gather results (multiprocessing)
for k in results:
results[k] = results[k].get()
if len(results) > 0:
with open(self.out_path + "stats.json", "w") as f:
f.write(json.dumps(results))
# If we are using multiprocessing we need to close the pool
Measure.close()
