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, '*/*/*.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)
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 __init__(self,
training_dataloader,
validate_dataloaer,
optimizer,
loss_func,
model,
num_epoch = 100,
lr = 0.0002,
gpus=None,
pretrained_path = None,
checkpoint_save_path = "best_model.pt",
is_apex = False,
is_scheduler = True):
"""
config data loader and gpus using for training.
:param training_dataloader:
:param validate_dataloaer:
:param optimizer:
:param loss_func:
:param gpus:
# :param _model:
# """
# init data
self.training_data = training_dataloader
self.validate_data = validate_dataloaer
self.optimizer = optimizer
self.loss_func = loss_func
self.model = model
self.checkpoint_path = checkpoint_save_path
# support vars
self.name_model = model.__class__.__name__
self.writer = SummaryWriter()
self.best_current_loss = 1000000000
self.current_delay_overfit = 0
self.is_apex = is_apex
self.lr = lr
self.num_epoch = num_epoch
self.scheduler = WarmupLinearSchedule(warmup=0.03, t_total=len(training_dataloader)* num_epoch) if is_scheduler else None
# load pre-trained _model
os.environ["CUDA_VISIBLE_DEVICES"] = gpus
if pretrained_path is not None:
print("loaded model %s"%pretrained_path)
# checkpoint = torch.load(pretrained_path, map_location='cpu')
# source_state = checkpoint['state_dict']
# target_state = self.model.state_dict()
# new_target_state = collections.OrderedDict()
# for target_key, target_value in target_state.items():
# if target_key in source_state and source_state[target_key].size() == target_state[
# target_key].size():
# new_target_state[target_key] = source_state[target_key]
# else:
# new_target_state[target_key] = target_state[target_key]
# print('[WARNING] Not found pre-trained parameters for {}'.format(target_key))
#
# self.model.load_state_dict(new_target_state)
self.model.load_state_dict(torch.load(pretrained_path))
# setting cuda if needed
self.gpus, self.model = setting_cuda(gpus, self.model)
if self.is_apex:
# self.model, self.optimizer = apex.amp.initialize(self.model, self.optimizer, opt_level="O1",
# verbosity=0)
pass
self.is_cuda = len(self.gpus) >= 1