def run_server(prediction_fn, gt_fn):
submission = load_gt(prediction_fn, rle_key='EncodedPixels')
gt = load_gt(gt_fn)
def compute_score(key):
yt = DicomDataset.rles_to_mask(gt[key], merge_masks=True)
yp = DicomDataset.rles_to_mask(submission[key], merge_masks=True)
return score(yt, yp)
scores = []
keys = list(submission)
with ThreadPoolExecutor(1) as e:
scores = list(tqdm(e.map(compute_score, keys), total=len(keys)))
empty_score = np.sum([s[0] for s in scores if s[1] == 'empty'])
num_empty = sum(1 for s in scores if s[1] == 'empty')
num_empty_pred = sum(1 for s in scores if s[-1] == 'empty')
num_non_empty_pred = sum(1 for s in scores if s[-1] == 'non-empty')
non_empty_score = np.sum([s[0] for s in scores if s[1] == 'non-empty'])
num_non_empty = len(scores) - num_empty
final_score = np.sum([s[0] for s in scores]) / len(scores)
print("[GT: %5d | P: %5d] %012s %.4f | %.4f" %
(num_empty, num_empty_pred, 'Empty: ', empty_score / num_empty,
empty_score / len(scores)))
print("[GT: %5d | P: %5d] %012s %.4f | %.4f" %
(num_non_empty, num_non_empty_pred, 'Non-Empty: ',
non_empty_score / num_non_empty, non_empty_score / len(scores)))
print("[%5d] Final: %.4f" % (len(scores), final_score))
return final_score
def main(config):
seed_all()
train_image_fns = sorted(glob(os.path.join(config.train_dir, '*/*/*.dcm')))
test_image_fns = sorted(glob(os.path.join(config.test_dir, '*/*/*.dcm')))
# assert len(train_image_fns) == 10712
# assert len(test_image_fns) == 1377
gt = load_gt(config.train_rle)
# create folds
np.random.shuffle(train_image_fns)
folds = np.arange(len(train_image_fns)) % config.num_folds
val_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] == config.fold
]
train_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] != config.fold
]
# remove not-used files:
# https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/discussion/98478#latest-572385 # noqa
train_image_fns = [
fn for fn in train_image_fns if DicomDataset.fn_to_id(fn) in gt
]
val_image_fns = [
fn for fn in val_image_fns if DicomDataset.fn_to_id(fn) in gt
]
print("VAL: ", len(val_image_fns), val_image_fns[0])
print("TRAIN: ", len(train_image_fns), train_image_fns[0])
if config.submit_val:
test_image_fns = val_image_fns
test_ds = DicomDataset(test_image_fns,
gt_rles=gt,
height=config.height,
width=config.height)
test_ds.cache()
test_loader = td.DataLoader(test_ds,
batch_size=config.batch_size,
shuffle=False,
num_workers=0,
pin_memory=False,
drop_last=False)
model = FPNSegmentation(config.slug)
print("Loading: %s" % config.weights)
r = model.load_state_dict(th.load(config.weight))
from IPython import embed
embed()
model = model.to(config.device).float()
# model = apex.amp.initialize(model, opt_level="O1")
model.eval()
sub = create_submission(model,
test_loader,
config,
pred_zip=config.pred_zip,
tta=False)
sub.to_csv(config.submission_fn, index=False)
print("Wrote to %s" % config.submission_fn)
def main(config):
seed_all()
train_image_fns = sorted(glob(os.path.join(config.train_dir, '*.jpg')))
test_image_fns = sorted(glob(os.path.join(config.test_dir, '*.jpg')))
gt, label_to_int = load_gt(config.train_csv)
int_to_label = {v: k for k, v in label_to_int.items()}
# create folds
np.random.shuffle(train_image_fns)
folds = np.arange(len(train_image_fns)) % config.num_folds
val_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] == config.fold
]
train_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] != config.fold
]
# TODO: drop empty images <- is this helpful?
train_image_fns = [
fn for fn in train_image_fns if KuzushijiDataset.fn_to_id(fn) in gt
]
val_image_fns = [
fn for fn in val_image_fns if KuzushijiDataset.fn_to_id(fn) in gt
]
print("VAL: ", len(val_image_fns), val_image_fns[123])
print("TRAIN: ", len(train_image_fns), train_image_fns[456])
if config.submit_val:
test_image_fns = val_image_fns
test_ds = MultiScaleInferenceKuzushijiDataset(test_image_fns, 1536, 1536,
config.scales)
test_loader = td.DataLoader(test_ds,
batch_size=config.batch_size,
shuffle=False,
num_workers=0,
pin_memory=False,
drop_last=False)
model = FPNSegmentation(config.slug, pretrained=False)
print("Loading: %s" % config.weight)
model.load_state_dict(th.load(config.weight))
model = model.to(config.device)
# model = apex.amp.initialize(model, opt_level="O1")
model.eval()
sub = create_submission(model,
test_loader,
int_to_label,
config,
pred_zip=config.pred_zip,
tta=config.tta)
sub.to_csv(config.submission_fn, index=False)
print("Wrote to %s" % config.submission_fn)
def main(config):
os.makedirs('cache', exist_ok=True)
os.makedirs(config.logdir, exist_ok=True)
print("Logging to: %s" % config.logdir)
if not os.path.exists(config.train_dir):
print("KERNEL ENV")
config.train_dicom_dir = '../input/siim-train-test/siim/dicom-images-train'
config.test_dicom_dir = '../input/siim-train-test/siim/dicom-images-test'
config.train_dir = '../input/l2-images/l2-images/l2-images-train'
config.test_dir = '../input/l2-images/l2-images/l2-images-test'
config.sample_submission = '../input/siim-acr-pneumothorax-segmentation/' \
'sample_submission.csv'
config.train_rle = '../input/siim-train-test/siim/train-rle.csv'
train_image_fns = sorted(glob(os.path.join(config.train_dir, '*.png')))
test_image_fns = sorted(glob(os.path.join(config.test_dir, '*.png')))
assert len(train_image_fns) == 10675, len(train_image_fns)
assert len(test_image_fns) in (1372, 1377), len(test_image_fns)
gt = load_gt(config.train_rle)
# create folds
if not config.stratify:
# random folds
np.random.shuffle(train_image_fns)
else:
# folds stratified by mask size
train_mask_sizes = [
L2DicomDataset.rles_to_mask(gt[L2DicomDataset.fn_to_id(fn)]).sum()
for fn in tqdm(train_image_fns)
]
sorted_inds = [
k for k in sorted(range(len(train_image_fns)),
key=lambda k: train_mask_sizes[k])
]
train_image_fns = [train_image_fns[k] for k in sorted_inds]
folds = np.arange(len(train_image_fns)) % config.num_folds
val_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] == config.fold
]
train_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] != config.fold
]
# remove not-used files:
# https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/discussion/98478#latest-572385 # noqa
train_image_fns = [
fn for fn in train_image_fns if L2DicomDataset.fn_to_id(fn) in gt
]
val_image_fns = [
fn for fn in val_image_fns if L2DicomDataset.fn_to_id(fn) in gt
]
if config.drop_empty:
# remove empty masks from training data
non_empty_gt = {k: v for k, v in gt.items() if v[0] != ' -1'}
train_image_fns = [
fn for fn in train_image_fns
if L2DicomDataset.fn_to_id(fn) in non_empty_gt
]
print("[Non-EMPTY] TRAIN: ", len(train_image_fns),
os.path.basename(train_image_fns[0]))
print("VAL: ", len(val_image_fns), os.path.basename(val_image_fns[0]))
print("TRAIN: ", len(train_image_fns),
os.path.basename(train_image_fns[0]))
train_ds = L2DicomDataset(train_image_fns,
gt_rles=gt,
height=config.height,
width=config.height,
to_ram=True,
augment=True,
write_cache=not config.is_kernel,
train_dicom_dir=config.train_dicom_dir,
test_dicom_dir=config.test_dicom_dir)
val_ds = L2DicomDataset(val_image_fns,
gt_rles=gt,
height=config.height,
width=config.height,
to_ram=True,
write_cache=not config.is_kernel,
train_dicom_dir=config.train_dicom_dir,
test_dicom_dir=config.test_dicom_dir)
val_loader = data.DataLoader(val_ds,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.num_workers,
pin_memory=config.pin,
drop_last=False)
model = FPNSegmentation(config.slug, num_input_channels=2)
if config.weight is not None:
model.load_state_dict(th.load(config.weight))
model = model.to(config.device)
optimizer = th.optim.Adam(model.parameters(),
lr=config.lr,
weight_decay=config.weight_decay)
if config.apex:
model, optimizer = apex.amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
updates_per_epoch = len(train_ds) // config.batch_size
num_updates = int(config.epochs * updates_per_epoch)
scheduler = WarmupLinearSchedule(warmup=config.warmup, t_total=num_updates)
# training loop
smooth = 0.1
best_dice = 0.0
best_fn = None
global_step = 0
for epoch in range(config.epochs):
smooth_loss = None
smooth_accuracy = None
model.train()
train_loader = data.DataLoader(train_ds,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.num_workers,
pin_memory=config.pin,
drop_last=True)
progress = tqdm(total=len(train_ds), smoothing=0.01)
for i, (X, y_true) in enumerate(train_loader):
X = X.to(config.device)
y_true = y_true.to(config.device)
y_pred = model(X)
loss = siim_loss(y_true, y_pred, weights=None)
if config.apex:
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
lr_this_step = None
if (i + 1) % config.accumulation_step == 0:
optimizer.step()
optimizer.zero_grad()
lr_this_step = config.lr * scheduler.get_lr(
global_step, config.warmup)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
global_step += 1
smooth_loss = loss.item() if smooth_loss is None else \
smooth * loss.item() + (1. - smooth) * smooth_loss
# print((y_true >= 0.5).sum().item())
accuracy = th.mean(
((y_pred >= 0.5) == (y_true >= 0.5)).to(th.float)).item()
smooth_accuracy = accuracy if smooth_accuracy is None else \
smooth * accuracy + (1. - smooth) * smooth_accuracy
progress.set_postfix(
loss='%.4f' % smooth_loss,
accuracy='%.4f' % (smooth_accuracy),
lr='%.6f' %
(config.lr if lr_this_step is None else lr_this_step))
progress.update(len(X))
# validation loop
model.eval()
thresholds = np.arange(0.1, 0.7, 0.1)
dice_coeffs = [[] for _ in range(len(thresholds))]
progress = tqdm(enumerate(val_loader), total=len(val_loader))
with th.no_grad():
for i, (X, y_trues) in progress:
X = X.to(config.device)
y_trues = y_trues.to(config.device)
y_preds = model(X)
for yt, yp in zip(y_trues, y_preds):
yt = (yt.squeeze().cpu().numpy() >= 0.5).astype('uint8')
yp = yp.squeeze().cpu().numpy()
for dind, threshold in enumerate(thresholds):
yp_ = (yp >= threshold).astype(np.uint8)
sc = score(yt, yp_)
dice_coeffs[dind].append(sc)
best_threshold_ind = -1
dice_coeff = -1
for dind, threshold in enumerate(thresholds):
dc = np.mean(
[x[0] for x in dice_coeffs[dind] if x[1] == 'non-empty'])
# progress.write("Dice @%.2f: %.4f" % (threshold, dc))
if dc > dice_coeff:
dice_coeff = dc
best_threshold_ind = dind
dice_coeffs = dice_coeffs[best_threshold_ind]
num_empty = sum(1 for x in dice_coeffs if x[1] == 'empty')
num_total = len(dice_coeffs)
num_non_empty = num_total - num_empty
empty_sum = np.sum([d[0] for d in dice_coeffs if d[1] == 'empty'])
non_empty_sum = np.sum(
[d[0] for d in dice_coeffs if d[1] == 'non-empty'])
dice_coeff_empty = empty_sum / num_empty
dice_coeff_non_empty = non_empty_sum / num_non_empty
progress.write(
'[Empty: %d]: %.3f | %.3f, [Non-Empty: %d]: %.3f | %.3f' %
(num_empty, dice_coeff_empty, empty_sum / num_total, num_non_empty,
dice_coeff_non_empty, non_empty_sum / num_total))
dice_coeff = float(dice_coeff)
summary_str = 'f%02d-ep-%04d-val_dice-%.4f@%.2f' % (
config.fold, epoch, dice_coeff, thresholds[best_threshold_ind])
progress.write(summary_str)
if dice_coeff > best_dice:
weight_fn = os.path.join(config.logdir, summary_str + '.pth')
th.save(model.state_dict(), weight_fn)
best_dice = dice_coeff
best_fn = weight_fn
fns = sorted(
glob(os.path.join(config.logdir, 'f%02d-*.pth' % config.fold)))
for fn in fns[:-config.n_keep]:
os.remove(fn)
# create submission
test_ds = L2DicomDataset(test_image_fns,
height=config.height,
width=config.height,
write_cache=not config.is_kernel,
train_dicom_dir=config.train_dicom_dir,
test_dicom_dir=config.test_dicom_dir)
test_loader = data.DataLoader(test_ds,
batch_size=config.batch_size,
shuffle=False,
num_workers=0,
pin_memory=False,
drop_last=False)
if best_fn is not None:
model.load_state_dict(th.load(best_fn))
model.eval()
sub = create_submission(model,
test_loader,
test_image_fns,
config,
pred_zip=config.pred_zip)
sub.to_csv(config.submission_fn, index=False)
print("Wrote to: %s" % config.submission_fn)
# create val submission
val_fn = config.submission_fn.replace('.csv', '_VAL.csv')
model.eval()
sub = []
sub = create_submission(model,
val_loader,
val_image_fns,
config,
pred_zip=config.pred_zip.replace(
'.zip', '_VAL.zip'))
sub.to_csv(val_fn, index=False)
print("Wrote to: %s" % val_fn)
def main(config):
seed_all()
os.makedirs('cache', exist_ok=True)
os.makedirs(config.logdir, exist_ok=True)
print("Logging to: %s" % config.logdir)
src_files = sorted(glob('*.py'))
for src_fn in src_files:
dst_fn = os.path.join(config.logdir, src_fn)
copyfile(src_fn, dst_fn)
train_image_fns = sorted(glob(os.path.join(config.train_dir, '*.jpg')))
test_image_fns = sorted(glob(os.path.join(config.test_dir, '*.jpg')))
assert len(train_image_fns) == 3881
assert len(test_image_fns) == 4150
gt, label_to_int = load_gt(config.train_rle)
int_to_label = {v: k for k, v in label_to_int.items()}
# create folds
np.random.shuffle(train_image_fns)
if config.subset > 0:
train_image_fns = train_image_fns[:config.subset]
folds = np.arange(len(train_image_fns)) % config.num_folds
val_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] == config.fold
]
train_image_fns = [
fn for k, fn in enumerate(train_image_fns) if folds[k] != config.fold
]
if config.add_val:
print("Training on validation set")
train_image_fns = train_image_fns + val_image_fns[:]
print(len(val_image_fns), len(train_image_fns))
# TODO: drop empty images <- is this helpful?
train_image_fns = [
fn for fn in train_image_fns if KuzushijiDataset.fn_to_id(fn) in gt
]
val_image_fns = [
fn for fn in val_image_fns if KuzushijiDataset.fn_to_id(fn) in gt
]
print("VAL: ", len(val_image_fns), val_image_fns[123])
print("TRAIN: ", len(train_image_fns), train_image_fns[456])
train_ds = KuzushijiDataset(train_image_fns,
gt_boxes=gt,
label_to_int=label_to_int,
augment=True)
val_ds = KuzushijiDataset(val_image_fns,
gt_boxes=gt,
label_to_int=label_to_int)
if config.cache:
train_ds.cache()
val_ds.cache()
val_loader = data.DataLoader(val_ds,
batch_size=config.batch_size // 8,
shuffle=False,
num_workers=config.num_workers,
pin_memory=config.pin,
drop_last=False)
model = FPNSegmentation(config.slug)
if config.weight is not None:
print("Loading: %s" % config.weight)
model.load_state_dict(th.load(config.weight))
model = model.to(config.device)
no_decay = ['mean', 'std', 'bias'] + ['.bn%d.' % i for i in range(100)]
grouped_parameters = [{
'params': [],
'weight_decay': config.weight_decay
}, {
'params': [],
'weight_decay': 0.0
}]
for n, p in model.named_parameters():
if not any(nd in n for nd in no_decay):
# print("Decay: %s" % n)
grouped_parameters[0]['params'].append(p)
else:
# print("No Decay: %s" % n)
grouped_parameters[1]['params'].append(p)
optimizer = AdamW(grouped_parameters, lr=config.lr)
if config.apex:
model, optimizer = apex.amp.initialize(model,
optimizer,
opt_level="O1",
verbosity=0)
updates_per_epoch = len(train_ds) // config.batch_size
num_updates = int(config.epochs * updates_per_epoch)
scheduler = WarmupLinearSchedule(warmup=config.warmup, t_total=num_updates)
# training loop
smooth = 0.1
best_acc = 0.0
best_fn = None
global_step = 0
for epoch in range(1, config.epochs + 1):
smooth_loss = None
smooth_accuracy = None
model.train()
train_loader = data.DataLoader(train_ds,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.num_workers,
pin_memory=config.pin,
drop_last=True)
progress = tqdm(total=len(train_ds), smoothing=0.01)
if True:
for i, (X, fns, hm, centers, classes) in enumerate(train_loader):
X = X.to(config.device).float()
hm = hm.to(config.device)
centers = centers.to(config.device)
classes = classes.to(config.device)
hm_pred, classes_pred = model(X, centers=centers)
loss = kuzushiji_loss(hm, centers, classes, hm_pred,
classes_pred)
if config.apex:
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
lr_this_step = None
if (i + 1) % config.accumulation_step == 0:
optimizer.step()
optimizer.zero_grad()
lr_this_step = config.lr * scheduler.get_lr(
global_step, config.warmup)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
global_step += 1
smooth_loss = loss.item() if smooth_loss is None else \
smooth * loss.item() + (1. - smooth) * smooth_loss
# print((y_true >= 0.5).sum().item())
accuracy = th.mean(
((th.sigmoid(hm_pred) >= 0.5) == (hm == 1)).to(
th.float)).item()
smooth_accuracy = accuracy if smooth_accuracy is None else \
smooth * accuracy + (1. - smooth) * smooth_accuracy
progress.set_postfix(
ep='%d/%d' % (epoch, config.epochs),
loss='%.4f' % smooth_loss,
accuracy='%.4f' % (smooth_accuracy),
lr='%.6f' %
(config.lr if lr_this_step is None else lr_this_step))
progress.update(len(X))
# skip validation
if epoch not in [10, 20, 30, 40, 50]:
if 1 < epoch <= 65:
continue
# validation loop
model.eval()
progress = tqdm(enumerate(val_loader), total=len(val_loader))
hm_correct, classes_correct = 0, 0
num_hm, num_classes = 0, 0
with th.no_grad():
for i, (X, fns, hm, centers, classes) in progress:
X = X.to(config.device).float()
hm = hm.cuda()
centers = centers.cuda()
classes = classes.cuda()
hm_pred, classes_pred = model(X)
hm_pred = th.sigmoid(hm_pred)
classes_pred = th.nn.functional.softmax(classes_pred, 1)
hm_cuda = hm.cuda()
# PyTorch 1.2 has `bool`
if hasattr(hm_cuda, 'bool'):
hm_cuda = hm_cuda.bool()
hm_correct += (hm_cuda == (hm_pred >=
0.5)).float().sum().item()
num_hm += np.prod(hm.shape)
num_samples = len(X)
for sample_ind in range(num_samples):
center_mask = centers[sample_ind, :, 0] != -1
per_image_letters = center_mask.sum().item()
if per_image_letters == 0:
continue
num_classes += per_image_letters
centers_per_img = centers[sample_ind][center_mask]
classes_per_img = classes[sample_ind][center_mask]
classes_per_img_pred = classes_pred[
sample_ind][:, centers_per_img[:, 1],
centers_per_img[:, 0]].argmax(0)
classes_correct += (
classes_per_img_pred == classes_per_img).sum().item()
num_classes += per_image_letters
val_hm_acc = hm_correct / num_hm
val_classes_acc = classes_correct / num_classes
summary_str = 'f%02d-ep-%04d-val_hm_acc-%.4f-val_classes_acc-%.4f' % (
config.fold, epoch, val_hm_acc, val_classes_acc)
progress.write(summary_str)
if val_classes_acc >= best_acc:
weight_fn = os.path.join(config.logdir, summary_str + '.pth')
progress.write("New best: %s" % weight_fn)
th.save(model.state_dict(), weight_fn)
best_acc = val_classes_acc
best_fn = weight_fn
fns = sorted(
glob(os.path.join(config.logdir, 'f%02d-*.pth' % config.fold)))
for fn in fns[:-config.n_keep]:
os.remove(fn)
# create submission
test_ds = KuzushijiDataset(test_image_fns)
test_loader = data.DataLoader(test_ds,
batch_size=config.batch_size // 8,
shuffle=False,
num_workers=config.num_workers,
pin_memory=False,
drop_last=False)
if best_fn is not None:
model.load_state_dict(th.load(best_fn))
model.eval()
sub = create_submission(model,
test_loader,
int_to_label,
config,
pred_zip=config.pred_zip)
sub.to_csv(config.submission_fn, index=False)
print("Wrote to: %s" % config.submission_fn)
# create val submission
val_fn = config.submission_fn.replace('.csv', '_VAL.csv')
model.eval()
sub = []
sub = create_submission(model,
val_loader,
int_to_label,
config,
pred_zip=config.pred_zip.replace(
'.zip', '_VAL.zip'))
sub.to_csv(val_fn, index=False)
print("Wrote to: %s" % val_fn)
def main(config):
seed_all()
os.makedirs('cache', exist_ok=True)
os.makedirs(config.logdir, exist_ok=True)
print("Logging to: %s" % config.logdir)
src_files = sorted(glob('*.py'))
for src_fn in src_files:
dst_fn = os.path.join(config.logdir, src_fn)
copyfile(src_fn, dst_fn)
train_image_fns = sorted(glob(os.path.join(config.train_dir, '*/*/*.dcm')))
test_image_fns = sorted(glob(os.path.join(config.test_dir, '*/*/*.dcm')))
# assert len(train_image_fns) == 10712
# assert len(test_image_fns) == 1377
gt = load_gt(config.train_rle)
# create folds
np.random.shuffle(train_image_fns)
if config.subset > 0:
train_image_fns = train_image_fns[:config.subset]
folds = np.arange(len(train_image_fns)) % config.num_folds
val_image_fns = [fn for k, fn in enumerate(train_image_fns)
if folds[k] == config.fold]
train_image_fns = [fn for k, fn in enumerate(train_image_fns)
if folds[k] != config.fold]
# remove not-used files:
# https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/discussion/98478#latest-572385 # noqa
train_image_fns = [fn for fn in train_image_fns
if DicomDataset.fn_to_id(fn) in gt]
val_image_fns = [fn for fn in val_image_fns
if DicomDataset.fn_to_id(fn) in gt]
print("VAL: ", len(val_image_fns), os.path.basename(val_image_fns[0]))
print("TRAIN: ", len(train_image_fns), os.path.basename(train_image_fns[0]))
train_ds = DicomDataset(train_image_fns, gt_rles=gt, augment=True)
val_ds = DicomDataset(val_image_fns, gt_rles=gt)
if config.cache:
train_ds.cache()
val_ds.cache()
val_loader = data.DataLoader(val_ds, batch_size=config.batch_size,
shuffle=False, num_workers=config.num_workers,
pin_memory=config.pin, drop_last=False)
model = FPNSegmentation(config.slug, ema=config.ema)
if config.weight is not None:
print("Loading: %s" % config.weight)
model.load_state_dict(th.load(config.weight))
model = model.to(config.device)
no_decay = ['mean', 'std', 'bias'] + ['.bn%d.' % i for i in range(100)]
grouped_parameters = [{'params': [], 'weight_decay': config.weight_decay},
{'params': [], 'weight_decay': 0.0}]
for n, p in model.named_parameters():
if not any(nd in n for nd in no_decay):
print("Decay: %s" % n)
grouped_parameters[0]['params'].append(p)
else:
print("No Decay: %s" % n)
grouped_parameters[1]['params'].append(p)
optimizer = AdamW(grouped_parameters, lr=config.lr)
if config.apex:
model, optimizer = apex.amp.initialize(model, optimizer, opt_level="O1",
verbosity=0)
updates_per_epoch = len(train_ds) // config.batch_size
num_updates = int(config.epochs * updates_per_epoch)
scheduler = WarmupLinearSchedule(warmup=config.warmup, t_total=num_updates)
# training loop
smooth = 0.1
best_dice = 0.0
best_fn = None
global_step = 0
for epoch in range(1, config.epochs + 1):
smooth_loss = None
smooth_accuracy = None
model.train()
train_loader = data.DataLoader(train_ds, batch_size=config.batch_size,
shuffle=True, num_workers=config.num_workers,
pin_memory=config.pin, drop_last=True)
progress = tqdm(total=len(train_ds), smoothing=0.01)
for i, (X, _, y_true) in enumerate(train_loader):
X = X.to(config.device).float()
y_true = y_true.to(config.device)
y_pred = model(X)
loss = siim_loss(y_true, y_pred, weights=None)
if config.apex:
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
lr_this_step = None
if (i + 1) % config.accumulation_step == 0:
optimizer.step()
optimizer.zero_grad()
lr_this_step = config.lr * scheduler.get_lr(global_step, config.warmup)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
global_step += 1
smooth_loss = loss.item() if smooth_loss is None else \
smooth * loss.item() + (1. - smooth) * smooth_loss
# print((y_true >= 0.5).sum().item())
accuracy = th.mean(((y_pred >= 0.5) == (y_true == 1)).to(
th.float)).item()
smooth_accuracy = accuracy if smooth_accuracy is None else \
smooth * accuracy + (1. - smooth) * smooth_accuracy
progress.set_postfix(ep='%d/%d' % (epoch, config.epochs),
loss='%.4f' % smooth_loss, accuracy='%.4f' %
(smooth_accuracy), lr='%.6f' % (config.lr if lr_this_step is None
else lr_this_step))
progress.update(len(X))
if epoch <= 12:
continue
# validation loop
model.eval()
thresholds = [0.1, 0.2]
dice_coeffs = [[] for _ in range(len(thresholds))]
progress = tqdm(enumerate(val_loader), total=len(val_loader))
with th.no_grad():
for i, (X, _, y_trues) in progress:
X = X.to(config.device).float()
y_trues = y_trues.to(config.device)
y_preds = model(X)
y_preds_flip = th.flip(model(th.flip(X, (-1, ))), (-1, ))
y_preds = 0.5 * (y_preds + y_preds_flip)
y_trues = y_trues.cpu().numpy()
y_preds = y_preds.cpu().numpy()
for yt, yp in zip(y_trues, y_preds):
yt = (yt.squeeze() >= 0.5).astype('uint8')
yp = yp.squeeze()
for dind, threshold in enumerate(thresholds):
yp_ = (yp >= threshold).astype(np.uint8)
sc = score(yt, yp_)
dice_coeffs[dind].append(sc)
best_threshold_ind = -1
dice_coeff = -1
for dind, threshold in enumerate(thresholds):
dc = np.mean([x[0] for x in dice_coeffs[dind] if x[1] == 'non-empty'])
# progress.write("Dice @%.2f: %.4f" % (threshold, dc))
if dc > dice_coeff:
dice_coeff = dc
best_threshold_ind = dind
dice_coeffs = dice_coeffs[best_threshold_ind]
num_empty = sum(1 for x in dice_coeffs if x[1] == 'empty')
num_total = len(dice_coeffs)
num_non_empty = num_total - num_empty
empty_sum = np.sum([d[0] for d in dice_coeffs if d[1] == 'empty'])
non_empty_sum = np.sum([d[0] for d in dice_coeffs if d[1] == 'non-empty'])
dice_coeff_empty = empty_sum / num_empty
dice_coeff_non_empty = non_empty_sum / num_non_empty
progress.write('[Empty: %d]: %.3f | %.3f, [Non-Empty: %d]: %.3f | %.3f' % (
num_empty, dice_coeff_empty, empty_sum / num_total,
num_non_empty, dice_coeff_non_empty, non_empty_sum / num_total))
dice_coeff = float(dice_coeff)
summary_str = 'f%02d-ep-%04d-val_dice-%.4f@%.2f' % (config.fold, epoch,
dice_coeff, thresholds[best_threshold_ind])
progress.write(summary_str)
if dice_coeff > best_dice:
weight_fn = os.path.join(config.logdir, summary_str + '.pth')
th.save(model.state_dict(), weight_fn)
best_dice = dice_coeff
best_fn = weight_fn
fns = sorted(glob(os.path.join(config.logdir, 'f%02d-*.pth' %
config.fold)))
for fn in fns[:-config.n_keep]:
os.remove(fn)
# create submission
test_ds = DicomDataset(test_image_fns)
test_loader = data.DataLoader(test_ds, batch_size=config.batch_size,
shuffle=False, num_workers=0,
pin_memory=False, drop_last=False)
if best_fn is not None:
model.load_state_dict(th.load(best_fn))
model.eval()
sub = create_submission(model, test_loader, config, pred_zip=config.pred_zip)
sub.to_csv(config.submission_fn, index=False)
print("Wrote to: %s" % config.submission_fn)
# create val submission
val_fn = config.submission_fn.replace('.csv', '_VAL.csv')
model.eval()
sub = []
sub = create_submission(model, val_loader, config,
pred_zip=config.pred_zip.replace('.zip', '_VAL.zip'))
sub.to_csv(val_fn, index=False)
print("Wrote to: %s" % val_fn)
'Both_SEG_logdir_073_f00/Both_SEG_sub_73_f00_VAL.csv',
'Both_SEG_logdir_073_f01/Both_SEG_sub_73_f01_VAL.csv',
'Both_SEG_logdir_073_f02/Both_SEG_sub_73_f02_VAL.csv',
'Both_SEG_logdir_073_f03/Both_SEG_sub_73_f03_VAL.csv',
'Both_SEG_logdir_073_f04/Both_SEG_sub_73_f04_VAL.csv',
]
with open('nih_ptx_hashes.p', 'rb') as f:
nih = pickle.load(f)
ptx_hashes = set(nih.values())
with open('current_ptx_hashes.p', 'rb') as f:
hh = pickle.load(f)
preds = [pd.read_csv(fn) for fn in oof_fns]
gts = load_gt('train-rle.csv')
np.random.seed(123)
pred = pd.concat(preds)
pred = {k: v for k, v in zip(pred['ImageId'], pred['EncodedPixels'])}
train_image_fns = sorted(glob(os.path.join('dicom-images-train', '*/*/*.dcm')))
np.random.shuffle(train_image_fns)
num_fp, num_fp_in_ptx, num_fp_not_in_ptx = 0, 0, 0
image_ids, rles = [], []
num_missing = 0
for ind, fn in tqdm(enumerate(train_image_fns), total=len(train_image_fns)):
img_id = DicomDataset.fn_to_id(fn)
try:
p = pred[img_id]
except:
num_missing += 1
print(img_id)
import pandas as pd
import numpy as np
import os
from tqdm import tqdm
tqdm.monitor_interval = 0 # noqa
from data import DicomDataset, load_mask_counts, load_gt
sub_fn = 'x_ensemble/CLF_ADJUST_Both_ENS_0024.csv'
out_fn = os.path.join(os.path.dirname(sub_fn),
'ADJUST_V2_' + os.path.basename(sub_fn))
sub = load_gt(sub_fn, rle_key='EncodedPixels')
val = '_VAL' in sub_fn
if val:
ids = load_mask_counts('train-rle.csv')
else:
ids = load_mask_counts('sample_submission.csv')
adjusted_sub = {'ImageId': [], 'EncodedPixels': []}
num_removed = 0
num_added = 0
num_missed = 0
for image_id in tqdm(sub):
rles = sub[image_id]
num_masks = ids.get(image_id, 1)
masks = DicomDataset.rles_to_mask(rles, merge_masks=False)
num_pred = masks.max()
if num_pred > num_masks:
sizes = np.float32([(masks == i).sum()
for i in range(1, num_pred + 1)])
inds = np.argsort(-sizes)[:num_masks]
inds = [range(1, num_pred + 1)[ind] for ind in inds]
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--fn', type=str)
parser.add_argument('--show-empty', action='store_true')
parser.add_argument('--seed', type=int, default=32)
parser.add_argument('--height', type=int, default=1024)
args = parser.parse_args()
sub = pd.read_csv(args.fn)
np.random.seed(args.seed)
if ' EncodedPixels' in sub.columns:
sub['EncodedPixels'] = sub[' EncodedPixels']
sub = sub[['ImageId', 'EncodedPixels']]
sub['EncodedPixels'] = sub['EncodedPixels'].apply(lambda x: x
if x != ' -1' else '-1')
gt = load_gt('train-rle.csv')
pred_gt = load_gt('sub_8730.csv', rle_key='EncodedPixels')
for k, v in pred_gt.items():
gt[k] = v
train_fns = sorted(glob('dicom-images-train/*/*/*.dcm'))
test_fns = sorted(glob('dicom-images-test/*/*/*.dcm'))
all_fns = train_fns + test_fns
id_to_fn = {DicomDataset.fn_to_id(fn): fn for fn in all_fns}
sub_ = defaultdict(list)
for iid, rle in zip(sub['ImageId'], sub['EncodedPixels']):
sub_[iid].append(rle)
sub = sub_
num_mask = sum(1 for k, v in sub.items() if v[0] != '-1')
num_one_mask = sum(1 for k, v in sub.items()
if v[0] != '-1' and len(v) == 1)
num_more_mask = sum(1 for k, v in sub.items()
if v[0] != '-1' and len(v) >= 2)
print("%d of %d have a mask" % (num_mask, len(sub)))
print("%d have 1, %d 2 or more" % (num_one_mask, num_more_mask))
img_ids = sorted(sub.keys())
np.random.shuffle(img_ids)
for img_id in img_ids:
img_fn = id_to_fn[img_id]
rles = sub[img_id]
if not args.show_empty:
if rles[0] == '-1':
continue
print("%d masks" % len(rles))
dcm = pydicom.dcmread(img_fn)
view = dcm.ViewPosition
print(view)
img = dcm.pixel_array
mask = DicomDataset.rles_to_mask(rles, merge_masks=False)
if args.height != 1024:
img = cv2.resize(img, (args.height, args.height),
interpolation=cv2.INTER_NEAREST)
mask = cv2.resize(mask, (args.height, args.height),
interpolation=cv2.INTER_NEAREST)
gt_mask = None
if img_id in gt:
gt_rles = gt[img_id]
gt_mask = DicomDataset.rles_to_mask(gt_rles, merge_masks=False)
gt_mask = cv2.resize(gt_mask, (args.height, args.height),
interpolation=cv2.INTER_NEAREST)
if gt_mask.max() == 0:
continue
# for j in range(0, 512, 16):
# img[:, j] = 255
# img[j, :] = 255
# mask[:, j] = mask.max()
# mask[j, :] = mask.max()
nc = 2 if gt_mask is None else 3
plt.subplot(1, nc, 1)
plt.title(os.path.splitext(img_id)[-1])
plt.imshow(img, cmap='bone')
plt.axis('off')
plt.subplot(1, nc, 2)
plt.title('PRED: ' + str(mask.max()))
plt.imshow(mask, cmap='bone', alpha=0.4)
plt.axis('off')
if gt_mask is not None:
vis = np.dstack([img.copy()] * 3)
vis[gt_mask > 0] = (0, 255, 0)
vis[mask > 0] = 0.3 * vis[mask > 0] + 0.7 * np.float32([255, 0, 0])
plt.subplot(1, nc, 3)
plt.title('%sGT: ' % ('PRED-' if img_id in pred_gt else 'GT-') +
str(gt_mask.max()))
plt.imshow(vis, cmap='bone')
plt.axis('off')
plt.show()
