def experiment(exp, arch, loss, double_softmax, confidence_thresh, rampup,
teacher_alpha, fix_ema, unsup_weight, cls_bal_scale,
cls_bal_scale_range, cls_balance, cls_balance_loss,
combine_batches, learning_rate, standardise_samples,
src_affine_std, src_xlat_range, src_hflip, src_intens_flip,
src_intens_scale_range, src_intens_offset_range,
src_gaussian_noise_std, tgt_affine_std, tgt_xlat_range,
tgt_hflip, tgt_intens_flip, tgt_intens_scale_range,
tgt_intens_offset_range, tgt_gaussian_noise_std, num_epochs,
batch_size, epoch_size, seed, log_file, model_file, device):
settings = locals().copy()
import os
import sys
import pickle
import cmdline_helpers
if log_file == '':
log_file = 'output_aug_log_{}.txt'.format(exp)
elif log_file == 'none':
log_file = None
if log_file is not None:
if os.path.exists(log_file):
print('Output log file {} already exists'.format(log_file))
return
use_rampup = rampup > 0
src_intens_scale_range_lower, src_intens_scale_range_upper, src_intens_offset_range_lower, src_intens_offset_range_upper = \
cmdline_helpers.intens_aug_options(src_intens_scale_range, src_intens_offset_range)
tgt_intens_scale_range_lower, tgt_intens_scale_range_upper, tgt_intens_offset_range_lower, tgt_intens_offset_range_upper = \
cmdline_helpers.intens_aug_options(tgt_intens_scale_range, tgt_intens_offset_range)
import time
import math
import numpy as np
from batchup import data_source, work_pool
import data_loaders
import standardisation
import network_architectures
import augmentation
import torch, torch.cuda
from torch import nn
from torch.nn import functional as F
import optim_weight_ema
torch_device = torch.device(device)
pool = work_pool.WorkerThreadPool(2)
n_chn = 0
if exp == 'svhn_mnist':
d_source = data_loaders.load_svhn(zero_centre=False, greyscale=True)
d_target = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
val=False)
elif exp == 'mnist_svhn':
d_source = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True)
d_target = data_loaders.load_svhn(zero_centre=False,
greyscale=True,
val=False)
elif exp == 'svhn_mnist_rgb':
d_source = data_loaders.load_svhn(zero_centre=False, greyscale=False)
d_target = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
val=False,
rgb=True)
elif exp == 'mnist_svhn_rgb':
d_source = data_loaders.load_mnist(invert=False,
zero_centre=False,
pad32=True,
rgb=True)
d_target = data_loaders.load_svhn(zero_centre=False,
greyscale=False,
val=False)
elif exp == 'cifar_stl':
d_source = data_loaders.load_cifar10(range_01=False)
d_target = data_loaders.load_stl(zero_centre=False, val=False)
elif exp == 'stl_cifar':
d_source = data_loaders.load_stl(zero_centre=False)
d_target = data_loaders.load_cifar10(range_01=False, val=False)
elif exp == 'mnist_usps':
d_source = data_loaders.load_mnist(zero_centre=False)
d_target = data_loaders.load_usps(zero_centre=False,
scale28=True,
val=False)
elif exp == 'usps_mnist':
d_source = data_loaders.load_usps(zero_centre=False, scale28=True)
d_target = data_loaders.load_mnist(zero_centre=False, val=False)
elif exp == 'syndigits_svhn':
d_source = data_loaders.load_syn_digits(zero_centre=False)
d_target = data_loaders.load_svhn(zero_centre=False, val=False)
elif exp == 'synsigns_gtsrb':
d_source = data_loaders.load_syn_signs(zero_centre=False)
d_target = data_loaders.load_gtsrb(zero_centre=False, val=False)
else:
print('Unknown experiment type \'{}\''.format(exp))
return
# Delete the training ground truths as we should not be using them
del d_target.train_y
if standardise_samples:
standardisation.standardise_dataset(d_source)
standardisation.standardise_dataset(d_target)
n_classes = d_source.n_classes
print('Loaded data')
if arch == '':
if exp in {'mnist_usps', 'usps_mnist'}:
arch = 'mnist-bn-32-64-256'
if exp in {'svhn_mnist', 'mnist_svhn'}:
arch = 'grey-32-64-128-gp'
if exp in {
'cifar_stl', 'stl_cifar', 'syndigits_svhn', 'svhn_mnist_rgb',
'mnist_svhn_rgb'
}:
arch = 'rgb-128-256-down-gp'
if exp in {'synsigns_gtsrb'}:
arch = 'rgb40-96-192-384-gp'
net_class, expected_shape = network_architectures.get_net_and_shape_for_architecture(
arch)
if expected_shape != d_source.train_X.shape[1:]:
print(
'Architecture {} not compatible with experiment {}; it needs samples of shape {}, '
'data has samples of shape {}'.format(arch, exp, expected_shape,
d_source.train_X.shape[1:]))
return
student_net = net_class(n_classes).to(torch_device)
teacher_net = net_class(n_classes).to(torch_device)
student_params = list(student_net.parameters())
teacher_params = list(teacher_net.parameters())
for param in teacher_params:
param.requires_grad = False
student_optimizer = torch.optim.Adam(student_params, lr=learning_rate)
if fix_ema:
teacher_optimizer = optim_weight_ema.EMAWeightOptimizer(
teacher_net, student_net, alpha=teacher_alpha)
else:
teacher_optimizer = optim_weight_ema.OldWeightEMA(teacher_net,
student_net,
alpha=teacher_alpha)
classification_criterion = nn.CrossEntropyLoss()
print('Built network')
src_aug = augmentation.ImageAugmentation(
src_hflip,
src_xlat_range,
src_affine_std,
intens_flip=src_intens_flip,
intens_scale_range_lower=src_intens_scale_range_lower,
intens_scale_range_upper=src_intens_scale_range_upper,
intens_offset_range_lower=src_intens_offset_range_lower,
intens_offset_range_upper=src_intens_offset_range_upper,
gaussian_noise_std=src_gaussian_noise_std)
tgt_aug = augmentation.ImageAugmentation(
tgt_hflip,
tgt_xlat_range,
tgt_affine_std,
intens_flip=tgt_intens_flip,
intens_scale_range_lower=tgt_intens_scale_range_lower,
intens_scale_range_upper=tgt_intens_scale_range_upper,
intens_offset_range_lower=tgt_intens_offset_range_lower,
intens_offset_range_upper=tgt_intens_offset_range_upper,
gaussian_noise_std=tgt_gaussian_noise_std)
if combine_batches:
def augment(X_sup, y_src, X_tgt):
X_src_stu, X_src_tea = src_aug.augment_pair(X_sup)
X_tgt_stu, X_tgt_tea = tgt_aug.augment_pair(X_tgt)
return X_src_stu, X_src_tea, y_src, X_tgt_stu, X_tgt_tea
else:
def augment(X_src, y_src, X_tgt):
X_src = src_aug.augment(X_src)
X_tgt_stu, X_tgt_tea = tgt_aug.augment_pair(X_tgt)
return X_src, y_src, X_tgt_stu, X_tgt_tea
rampup_weight_in_list = [0]
cls_bal_fn = network_architectures.get_cls_bal_function(cls_balance_loss)
def compute_aug_loss(stu_out, tea_out):
# Augmentation loss
if use_rampup:
unsup_mask = None
conf_mask_count = None
unsup_mask_count = None
else:
conf_tea = torch.max(tea_out, 1)[0]
unsup_mask = conf_mask = (conf_tea > confidence_thresh).float()
unsup_mask_count = conf_mask_count = conf_mask.sum()
if loss == 'bce':
aug_loss = network_architectures.robust_binary_crossentropy(
stu_out, tea_out)
else:
d_aug_loss = stu_out - tea_out
aug_loss = d_aug_loss * d_aug_loss
# Class balance scaling
if cls_bal_scale:
if use_rampup:
n_samples = float(aug_loss.shape[0])
else:
n_samples = unsup_mask.sum()
avg_pred = n_samples / float(n_classes)
bal_scale = avg_pred / torch.clamp(tea_out.sum(dim=0), min=1.0)
if cls_bal_scale_range != 0.0:
bal_scale = torch.clamp(bal_scale,
min=1.0 / cls_bal_scale_range,
max=cls_bal_scale_range)
bal_scale = bal_scale.detach()
aug_loss = aug_loss * bal_scale[None, :]
aug_loss = aug_loss.mean(dim=1)
if use_rampup:
unsup_loss = aug_loss.mean() * rampup_weight_in_list[0]
else:
unsup_loss = (aug_loss * unsup_mask).mean()
# Class balance loss
if cls_balance > 0.0:
# Compute per-sample average predicated probability
# Average over samples to get average class prediction
avg_cls_prob = stu_out.mean(dim=0)
# Compute loss
equalise_cls_loss = cls_bal_fn(avg_cls_prob,
float(1.0 / n_classes))
equalise_cls_loss = equalise_cls_loss.mean() * n_classes
if use_rampup:
equalise_cls_loss = equalise_cls_loss * rampup_weight_in_list[0]
else:
if rampup == 0:
equalise_cls_loss = equalise_cls_loss * unsup_mask.mean(
dim=0)
unsup_loss += equalise_cls_loss * cls_balance
return unsup_loss, conf_mask_count, unsup_mask_count
if combine_batches:
def f_train(X_src0, X_src1, y_src, X_tgt0, X_tgt1):
X_src0 = torch.tensor(X_src0,
dtype=torch.float,
device=torch_device)
X_src1 = torch.tensor(X_src1,
dtype=torch.float,
device=torch_device)
y_src = torch.tensor(y_src, dtype=torch.long, device=torch_device)
X_tgt0 = torch.tensor(X_tgt0,
dtype=torch.float,
device=torch_device)
X_tgt1 = torch.tensor(X_tgt1,
dtype=torch.float,
device=torch_device)
n_samples = X_src0.size()[0]
n_total = n_samples + X_tgt0.size()[0]
student_optimizer.zero_grad()
student_net.train()
teacher_net.train()
# Concatenate source and target mini-batches
X0 = torch.cat([X_src0, X_tgt0], 0)
X1 = torch.cat([X_src1, X_tgt1], 0)
student_logits_out = student_net(X0)
student_prob_out = F.softmax(student_logits_out, dim=1)
src_logits_out = student_logits_out[:n_samples]
src_prob_out = student_prob_out[:n_samples]
teacher_logits_out = teacher_net(X1)
teacher_prob_out = F.softmax(teacher_logits_out, dim=1)
# Supervised classification loss
if double_softmax:
clf_loss = classification_criterion(src_prob_out, y_src)
else:
clf_loss = classification_criterion(src_logits_out, y_src)
unsup_loss, conf_mask_count, unsup_mask_count = compute_aug_loss(
student_prob_out, teacher_prob_out)
loss_expr = clf_loss + unsup_loss * unsup_weight
loss_expr.backward()
student_optimizer.step()
teacher_optimizer.step()
outputs = [
float(clf_loss) * n_samples,
float(unsup_loss) * n_total
]
if not use_rampup:
mask_count = float(conf_mask_count) * 0.5
unsup_count = float(unsup_mask_count) * 0.5
outputs.append(mask_count)
outputs.append(unsup_count)
return tuple(outputs)
else:
def f_train(X_src, y_src, X_tgt0, X_tgt1):
X_src = torch.tensor(X_src, dtype=torch.float, device=torch_device)
y_src = torch.tensor(y_src, dtype=torch.long, device=torch_device)
X_tgt0 = torch.tensor(X_tgt0,
dtype=torch.float,
device=torch_device)
X_tgt1 = torch.tensor(X_tgt1,
dtype=torch.float,
device=torch_device)
student_optimizer.zero_grad()
student_net.train()
teacher_net.train()
src_logits_out = student_net(X_src)
student_tgt_logits_out = student_net(X_tgt0)
student_tgt_prob_out = F.softmax(student_tgt_logits_out, dim=1)
teacher_tgt_logits_out = teacher_net(X_tgt1)
teacher_tgt_prob_out = F.softmax(teacher_tgt_logits_out, dim=1)
# Supervised classification loss
if double_softmax:
clf_loss = classification_criterion(
F.softmax(src_logits_out, dim=1), y_src)
else:
clf_loss = classification_criterion(src_logits_out, y_src)
unsup_loss, conf_mask_count, unsup_mask_count = compute_aug_loss(
student_tgt_prob_out, teacher_tgt_prob_out)
loss_expr = clf_loss + unsup_loss * unsup_weight
loss_expr.backward()
student_optimizer.step()
teacher_optimizer.step()
n_samples = X_src.size()[0]
outputs = [
float(clf_loss) * n_samples,
float(unsup_loss) * n_samples
]
if not use_rampup:
mask_count = float(conf_mask_count)
unsup_count = float(unsup_mask_count)
outputs.append(mask_count)
outputs.append(unsup_count)
return tuple(outputs)
print('Compiled training function')
def f_pred_src(X_sup):
X_var = torch.tensor(X_sup, dtype=torch.float, device=torch_device)
student_net.eval()
teacher_net.eval()
return (F.softmax(student_net(X_var), dim=1).detach().cpu().numpy(),
F.softmax(teacher_net(X_var), dim=1).detach().cpu().numpy())
def f_pred_tgt(X_sup):
X_var = torch.tensor(X_sup, dtype=torch.float, device=torch_device)
student_net.eval()
teacher_net.eval()
return (F.softmax(student_net(X_var), dim=1).detach().cpu().numpy(),
F.softmax(teacher_net(X_var), dim=1).detach().cpu().numpy())
def f_eval_src(X_sup, y_sup):
y_pred_prob_stu, y_pred_prob_tea = f_pred_src(X_sup)
y_pred_stu = np.argmax(y_pred_prob_stu, axis=1)
y_pred_tea = np.argmax(y_pred_prob_tea, axis=1)
return (float(
(y_pred_stu != y_sup).sum()), float((y_pred_tea != y_sup).sum()))
def f_eval_tgt(X_sup, y_sup):
y_pred_prob_stu, y_pred_prob_tea = f_pred_tgt(X_sup)
y_pred_stu = np.argmax(y_pred_prob_stu, axis=1)
y_pred_tea = np.argmax(y_pred_prob_tea, axis=1)
return (float(
(y_pred_stu != y_sup).sum()), float((y_pred_tea != y_sup).sum()))
print('Compiled evaluation function')
# Setup output
def log(text):
print(text)
if log_file is not None:
with open(log_file, 'a') as f:
f.write(text + '\n')
f.flush()
f.close()
cmdline_helpers.ensure_containing_dir_exists(log_file)
# Report setttings
log('Settings: {}'.format(', '.join([
'{}={}'.format(key, settings[key])
for key in sorted(list(settings.keys()))
])))
# Report dataset size
log('Dataset:')
log('SOURCE Train: X.shape={}, y.shape={}'.format(d_source.train_X.shape,
d_source.train_y.shape))
log('SOURCE Test: X.shape={}, y.shape={}'.format(d_source.test_X.shape,
d_source.test_y.shape))
log('TARGET Train: X.shape={}'.format(d_target.train_X.shape))
log('TARGET Test: X.shape={}, y.shape={}'.format(d_target.test_X.shape,
d_target.test_y.shape))
print('Training...')
sup_ds = data_source.ArrayDataSource([d_source.train_X, d_source.train_y],
repeats=-1)
tgt_train_ds = data_source.ArrayDataSource([d_target.train_X], repeats=-1)
train_ds = data_source.CompositeDataSource([sup_ds,
tgt_train_ds]).map(augment)
train_ds = pool.parallel_data_source(train_ds)
if epoch_size == 'large':
n_samples = max(d_source.train_X.shape[0], d_target.train_X.shape[0])
elif epoch_size == 'small':
n_samples = min(d_source.train_X.shape[0], d_target.train_X.shape[0])
elif epoch_size == 'target':
n_samples = d_target.train_X.shape[0]
n_train_batches = n_samples // batch_size
source_test_ds = data_source.ArrayDataSource(
[d_source.test_X, d_source.test_y])
target_test_ds = data_source.ArrayDataSource(
[d_target.test_X, d_target.test_y])
if seed != 0:
shuffle_rng = np.random.RandomState(seed)
else:
shuffle_rng = np.random
train_batch_iter = train_ds.batch_iterator(batch_size=batch_size,
shuffle=shuffle_rng)
best_teacher_model_state = {
k: v.cpu().numpy()
for k, v in teacher_net.state_dict().items()
}
best_conf_mask_rate = 0.0
best_src_test_err = 1.0
for epoch in range(num_epochs):
t1 = time.time()
if use_rampup:
if epoch < rampup:
p = max(0.0, float(epoch)) / float(rampup)
p = 1.0 - p
rampup_value = math.exp(-p * p * 5.0)
else:
rampup_value = 1.0
rampup_weight_in_list[0] = rampup_value
train_res = data_source.batch_map_mean(f_train,
train_batch_iter,
n_batches=n_train_batches)
train_clf_loss = train_res[0]
if combine_batches:
unsup_loss_string = 'unsup (both) loss={:.6f}'.format(train_res[1])
else:
unsup_loss_string = 'unsup (tgt) loss={:.6f}'.format(train_res[1])
src_test_err_stu, src_test_err_tea = source_test_ds.batch_map_mean(
f_eval_src, batch_size=batch_size * 2)
tgt_test_err_stu, tgt_test_err_tea = target_test_ds.batch_map_mean(
f_eval_tgt, batch_size=batch_size * 2)
if use_rampup:
unsup_loss_string = '{}, rampup={:.3%}'.format(
unsup_loss_string, rampup_value)
if src_test_err_stu < best_src_test_err:
best_src_test_err = src_test_err_stu
best_teacher_model_state = {
k: v.cpu().numpy()
for k, v in teacher_net.state_dict().items()
}
improve = '*** '
else:
improve = ''
else:
conf_mask_rate = train_res[-2]
unsup_mask_rate = train_res[-1]
if conf_mask_rate > best_conf_mask_rate:
best_conf_mask_rate = conf_mask_rate
improve = '*** '
best_teacher_model_state = {
k: v.cpu().numpy()
for k, v in teacher_net.state_dict().items()
}
else:
improve = ''
unsup_loss_string = '{}, conf mask={:.3%}, unsup mask={:.3%}'.format(
unsup_loss_string, conf_mask_rate, unsup_mask_rate)
t2 = time.time()
log('{}Epoch {} took {:.2f}s: TRAIN clf loss={:.6f}, {}; '
'SRC TEST ERR={:.3%}, TGT TEST student err={:.3%}, TGT TEST teacher err={:.3%}'
.format(improve, epoch, t2 - t1, train_clf_loss, unsup_loss_string,
src_test_err_stu, tgt_test_err_stu, tgt_test_err_tea))
# Save network
if model_file != '':
cmdline_helpers.ensure_containing_dir_exists(model_file)
with open(model_file, 'wb') as f:
torch.save(best_teacher_model_state, f)
def train_seg_semisup_aug_mt(
submit_config: job_helper.SubmitConfig, dataset, model, arch,
freeze_bn, opt_type, sgd_momentum, sgd_nesterov, sgd_weight_decay,
learning_rate, lr_sched, lr_step_epochs, lr_step_gamma, lr_poly_power,
teacher_alpha, bin_fill_holes, crop_size, aug_offset_range, aug_hflip,
aug_vflip, aug_hvflip, aug_scale_hung, aug_max_scale,
aug_scale_non_uniform, aug_rot_mag, aug_free_scale_rot, cons_loss_fn,
cons_weight, conf_thresh, conf_per_pixel, rampup, unsup_batch_ratio,
num_epochs, iters_per_epoch, batch_size, n_sup, n_unsup, n_val,
split_seed, split_path, val_seed, save_preds, save_model, num_workers):
settings = locals().copy()
del settings['submit_config']
import os
import math
import time
import itertools
import numpy as np
import torch.nn as nn, torch.nn.functional as F
from architectures import network_architectures
import torch.utils.data
from datapipe import datasets
from datapipe import seg_data, seg_transforms, seg_transforms_cv
import evaluation
import optim_weight_ema
import lr_schedules
from datapipe import torch_utils
affine_align_corners_kw = torch_utils.affine_align_corners_kw(True)
if crop_size == '':
crop_size = None
else:
crop_size = [int(x.strip()) for x in crop_size.split(',')]
torch_device = torch.device('cuda:0')
#
# Load data sets
#
ds_dict = datasets.load_dataset(dataset, n_val, val_seed, n_sup, n_unsup,
split_seed, split_path)
ds_src = ds_dict['ds_src']
ds_tgt = ds_dict['ds_tgt']
tgt_val_ndx = ds_dict['val_ndx_tgt']
src_val_ndx = ds_dict['val_ndx_src'] if ds_src is not ds_tgt else None
test_ndx = ds_dict['test_ndx_tgt']
sup_ndx = ds_dict['sup_ndx']
unsup_ndx = ds_dict['unsup_ndx']
n_classes = ds_src.num_classes
root_n_classes = math.sqrt(n_classes)
if bin_fill_holes and n_classes != 2:
print(
'Binary hole filling can only be used with binary (2-class) segmentation datasets'
)
return
print('Loaded data')
# Build network
NetClass = network_architectures.seg.get(arch)
student_net = NetClass(ds_src.num_classes).to(torch_device)
if opt_type == 'adam':
student_optim = torch.optim.Adam([
dict(params=student_net.pretrained_parameters(),
lr=learning_rate * 0.1),
dict(params=student_net.new_parameters(), lr=learning_rate)
])
elif opt_type == 'sgd':
student_optim = torch.optim.SGD([
dict(params=student_net.pretrained_parameters(),
lr=learning_rate * 0.1),
dict(params=student_net.new_parameters(), lr=learning_rate)
],
momentum=sgd_momentum,
nesterov=sgd_nesterov,
weight_decay=sgd_weight_decay)
else:
raise ValueError('Unknown opt_type {}'.format(opt_type))
if model == 'mean_teacher':
teacher_net = NetClass(ds_src.num_classes).to(torch_device)
for p in teacher_net.parameters():
p.requires_grad = False
teacher_optim = optim_weight_ema.EMAWeightOptimizer(
teacher_net, student_net, teacher_alpha)
eval_net = teacher_net
elif model == 'pi':
teacher_net = student_net
teacher_optim = None
eval_net = student_net
else:
print('Unknown model type {}'.format(model))
return
BLOCK_SIZE = student_net.BLOCK_SIZE
NET_MEAN, NET_STD = seg_transforms.get_mean_std(ds_tgt, student_net)
if freeze_bn:
if not hasattr(student_net, 'freeze_batchnorm'):
raise ValueError(
'Network {} does not support batchnorm freezing'.format(arch))
clf_crossent_loss = nn.CrossEntropyLoss(ignore_index=255)
print('Built network')
if iters_per_epoch == -1:
iters_per_epoch = len(unsup_ndx) // batch_size
total_iters = iters_per_epoch * num_epochs
lr_epoch_scheduler, lr_iter_scheduler = lr_schedules.make_lr_schedulers(
optimizer=student_optim,
total_iters=total_iters,
schedule_type=lr_sched,
step_epochs=lr_step_epochs,
step_gamma=lr_step_gamma,
poly_power=lr_poly_power)
# Train data pipeline: transforms
train_transforms = []
if crop_size is not None:
if aug_scale_hung:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCropScaleHung(
crop_size, (aug_offset_range, aug_offset_range),
uniform_scale=not aug_scale_non_uniform))
elif aug_max_scale != 1.0 or aug_rot_mag != 0.0:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCropRotateScale(
crop_size, (aug_offset_range, aug_offset_range),
rot_mag=aug_rot_mag,
max_scale=aug_max_scale,
uniform_scale=not aug_scale_non_uniform,
constrain_rot_scale=not aug_free_scale_rot))
else:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCrop(
crop_size, (aug_offset_range, aug_offset_range)))
else:
if aug_scale_hung:
raise NotImplementedError('aug_scale_hung requires a crop_size')
if aug_hflip or aug_vflip or aug_hvflip:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomFlip(aug_hflip, aug_vflip,
aug_hvflip))
train_transforms.append(
seg_transforms_cv.SegCVTransformNormalizeToTensor(NET_MEAN, NET_STD))
# Train data pipeline: supervised and unsupervised data sets
train_sup_ds = ds_src.dataset(
labels=True,
mask=False,
xf=False,
pair=False,
transforms=seg_transforms.SegTransformCompose(train_transforms),
pipeline_type='cv')
train_unsup_ds = ds_src.dataset(
labels=False,
mask=True,
xf=True,
pair=True,
transforms=seg_transforms.SegTransformCompose(train_transforms),
pipeline_type='cv')
collate_fn = seg_data.SegCollate(BLOCK_SIZE)
# Train data pipeline: data loaders
sup_sampler = seg_data.RepeatSampler(
torch.utils.data.SubsetRandomSampler(sup_ndx))
train_sup_loader = torch.utils.data.DataLoader(train_sup_ds,
batch_size,
sampler=sup_sampler,
collate_fn=collate_fn,
num_workers=num_workers)
if cons_weight > 0.0:
unsup_sampler = seg_data.RepeatSampler(
torch.utils.data.SubsetRandomSampler(unsup_ndx))
train_unsup_loader = torch.utils.data.DataLoader(
train_unsup_ds,
batch_size,
sampler=unsup_sampler,
collate_fn=collate_fn,
num_workers=num_workers)
else:
train_unsup_loader = None
# Eval pipeline
src_val_loader, tgt_val_loader, test_loader = datasets.eval_data_pipeline(
ds_src, ds_tgt, src_val_ndx, tgt_val_ndx, test_ndx, batch_size,
collate_fn, NET_MEAN, NET_STD, num_workers)
# Report setttings
print('Settings:')
print(', '.join([
'{}={}'.format(key, settings[key])
for key in sorted(list(settings.keys()))
]))
# Report dataset size
print('Dataset:')
print('len(sup_ndx)={}'.format(len(sup_ndx)))
print('len(unsup_ndx)={}'.format(len(unsup_ndx)))
if ds_src is not ds_tgt:
print('len(src_val_ndx)={}'.format(len(tgt_val_ndx)))
print('len(tgt_val_ndx)={}'.format(len(tgt_val_ndx)))
else:
print('len(val_ndx)={}'.format(len(tgt_val_ndx)))
if test_ndx is not None:
print('len(test_ndx)={}'.format(len(test_ndx)))
if n_sup != -1:
print('sup_ndx={}'.format(sup_ndx.tolist()))
# Track mIoU for early stopping
best_tgt_miou = None
best_epoch = 0
eval_net_state = {
key: value.detach().cpu().numpy()
for key, value in eval_net.state_dict().items()
}
# Create iterators
train_sup_iter = iter(train_sup_loader)
train_unsup_iter = iter(
train_unsup_loader) if train_unsup_loader is not None else None
iter_i = 0
print('Training...')
for epoch_i in range(num_epochs):
if lr_epoch_scheduler is not None:
lr_epoch_scheduler.step(epoch_i)
t1 = time.time()
if rampup > 0:
ramp_val = network_architectures.sigmoid_rampup(epoch_i, rampup)
else:
ramp_val = 1.0
student_net.train()
if teacher_net is not student_net:
teacher_net.train()
if freeze_bn:
student_net.freeze_batchnorm()
if teacher_net is not student_net:
teacher_net.freeze_batchnorm()
sup_loss_acc = 0.0
consistency_loss_acc = 0.0
conf_rate_acc = 0.0
n_sup_batches = 0
n_unsup_batches = 0
src_val_iter = iter(
src_val_loader) if src_val_loader is not None else None
tgt_val_iter = iter(
tgt_val_loader) if tgt_val_loader is not None else None
for sup_batch in itertools.islice(train_sup_iter, iters_per_epoch):
if lr_iter_scheduler is not None:
lr_iter_scheduler.step(iter_i)
student_optim.zero_grad()
#
# Supervised branch
#
batch_x = sup_batch['image'].to(torch_device)
batch_y = sup_batch['labels'].to(torch_device)
logits_sup = student_net(batch_x)
sup_loss = clf_crossent_loss(logits_sup, batch_y[:, 0, :, :])
sup_loss.backward()
if cons_weight > 0.0:
for _ in range(unsup_batch_ratio):
#
# Unsupervised branch
#
# Cut mode: batch consists of unsupervised samples and mask params
unsup_batch = next(train_unsup_iter)
# Input images to torch tensor
batch_ux0 = unsup_batch['sample0']['image'].to(
torch_device)
batch_um0 = unsup_batch['sample0']['mask'].to(torch_device)
batch_ux1 = unsup_batch['sample1']['image'].to(
torch_device)
batch_um1 = unsup_batch['sample1']['mask'].to(torch_device)
batch_ufx0_to_1 = unsup_batch['xf0_to_1'].to(torch_device)
# Get teacher predictions for image0
with torch.no_grad():
logits_cons_tea = teacher_net(batch_ux0).detach()
# Get student prediction for image1
logits_cons_stu = student_net(batch_ux1)
# Transformation from teacher to student space
grid_tea_to_stu = F.affine_grid(batch_ufx0_to_1,
batch_ux0.shape,
**affine_align_corners_kw)
# Transform teacher predicted logits to student space
logits_cons_tea_in_stu = F.grid_sample(
logits_cons_tea, grid_tea_to_stu,
**affine_align_corners_kw)
# Transform mask from teacher to student space and multiply by student space mask
mask_tea_in_stu = F.grid_sample(
batch_um0, grid_tea_to_stu, **
affine_align_corners_kw) * batch_um1
# Logits -> probs
prob_cons_tea = F.softmax(logits_cons_tea, dim=1)
prob_cons_stu = F.softmax(logits_cons_stu, dim=1)
# Transform teacher predicted probabilities to student space
prob_cons_tea_in_stu = F.grid_sample(
prob_cons_tea, grid_tea_to_stu,
**affine_align_corners_kw)
# for i in range(len(batch_ux0)):
# plt.figure(figsize=(18, 12))
#
# x_0_in_1 = F.grid_sample(batch_ux0, grid_tea_to_stu)
# d_x0_in_1 = torch.abs(x_0_in_1 - batch_ux1) * mask_tea_in_stu
# mask_tea_in_stu_np = mask_tea_in_stu.detach().cpu().numpy()
#
# plt.subplot(2, 4, 1)
# plt.imshow(batch_ux0[i].detach().cpu().numpy().transpose(1, 2, 0) * 0.25 + 0.5)
# plt.subplot(2, 4, 2)
# plt.imshow(batch_ux1[i].detach().cpu().numpy().transpose(1, 2, 0) * 0.25 + 0.5)
# plt.subplot(2, 4, 3)
# plt.imshow(x_0_in_1[i].detach().cpu().numpy().transpose(1, 2, 0) * 0.25 + 0.5)
# plt.subplot(2, 4, 4)
# plt.imshow(d_x0_in_1[i].detach().cpu().numpy().transpose(1, 2, 0) * 10 + 0.5, cmap='gray')
#
# plt.subplot(2, 4, 5)
# plt.imshow(batch_um0[i,0].detach().cpu().numpy(), cmap='gray')
# plt.subplot(2, 4, 6)
# plt.imshow(batch_um1[i,0].detach().cpu().numpy(), cmap='gray')
# plt.subplot(2, 4, 7)
# plt.imshow(mask_tea_in_stu[i,0].detach().cpu().numpy(), cmap='gray')
#
# plt.show()
loss_mask = mask_tea_in_stu
# Confidence thresholding
if conf_thresh > 0.0:
# Compute confidence of teacher predictions
conf_tea = prob_cons_tea_in_stu.max(dim=1)[0]
# Compute confidence mask
conf_mask = (conf_tea >=
conf_thresh).float()[:, None, :, :]
# Record rate for reporting
conf_rate_acc += float(conf_mask.mean())
# Average confidence mask if requested
if not conf_per_pixel:
conf_mask = conf_mask.mean()
loss_mask = loss_mask * conf_mask
elif rampup > 0:
conf_rate_acc += ramp_val
# Compute per-pixel consistency loss
# Note that the way we aggregate the loss across the class/channel dimension (1)
# depends on the loss function used. Generally, summing over the class dimension
# keeps the magnitude of the gradient of the loss w.r.t. the logits
# nearly constant w.r.t. the number of classes. When using logit-variance,
# dividing by `sqrt(num_classes)` helps.
if cons_loss_fn == 'var':
delta_prob = prob_cons_stu - prob_cons_tea_in_stu
consistency_loss = delta_prob * delta_prob
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
elif cons_loss_fn == 'logits_var':
delta_logits = logits_cons_stu - logits_cons_tea_in_stu
consistency_loss = delta_logits * delta_logits
consistency_loss = delta_prob * delta_prob
consistency_loss = consistency_loss.sum(
dim=1, keepdim=True) / root_n_classes
elif cons_loss_fn == 'logits_smoothl1':
consistency_loss = F.smooth_l1_loss(
logits_cons_stu,
logits_cons_tea_in_stu,
reduce=False)
consistency_loss = consistency_loss.sum(
dim=1, keepdim=True) / root_n_classes
elif cons_loss_fn == 'bce':
consistency_loss = network_architectures.robust_binary_crossentropy(
prob_cons_stu, prob_cons_tea_in_stu)
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
elif cons_loss_fn == 'kld':
consistency_loss = F.kl_div(F.log_softmax(
logits_cons_stu, dim=1),
prob_cons_tea_in_stu,
reduce=False)
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
else:
raise ValueError(
'Unknown consistency loss function {}'.format(
cons_loss_fn))
# Apply consistency loss mask and take the mean over pixels and images
consistency_loss = (consistency_loss * loss_mask).mean()
# Modulate with rampup if desired
if rampup > 0:
consistency_loss = consistency_loss * ramp_val
# Weight the consistency loss and back-prop
unsup_loss = consistency_loss * cons_weight
unsup_loss.backward()
consistency_loss_acc += float(consistency_loss.detach())
n_unsup_batches += 1
student_optim.step()
if teacher_optim is not None:
teacher_optim.step()
sup_loss_acc += float(sup_loss.detach())
n_sup_batches += 1
iter_i += 1
sup_loss_acc /= n_sup_batches
if n_unsup_batches > 0:
consistency_loss_acc /= n_unsup_batches
conf_rate_acc /= n_unsup_batches
eval_net.eval()
if src_val_iter is not None:
src_iou_eval = evaluation.EvaluatorIoU(ds_src.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in src_val_iter:
batch_x = batch['image'].to(torch_device)
batch_y = batch['labels'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i in range(len(batch_y)):
src_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
src_iou = src_iou_eval.score()
src_miou = src_iou.mean()
else:
src_iou_eval = src_iou = src_miou = None
tgt_iou_eval = evaluation.EvaluatorIoU(ds_tgt.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in tgt_val_iter:
batch_x = batch['image'].to(torch_device)
batch_y = batch['labels'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i in range(len(batch_y)):
tgt_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
tgt_iou = tgt_iou_eval.score()
tgt_miou = tgt_iou.mean()
t2 = time.time()
if ds_src is not ds_tgt:
print(
'Epoch {}: took {:.3f}s, TRAIN clf loss={:.6f}, consistency loss={:.6f}, conf rate={:.3%}, '
'SRC VAL mIoU={:.3%}, TGT VAL mIoU={:.3%}'.format(
epoch_i + 1, t2 - t1, sup_loss_acc, consistency_loss_acc,
conf_rate_acc, src_miou, tgt_miou))
print('-- SRC {}'.format(', '.join(
['{:.3%}'.format(x) for x in src_iou])))
print('-- TGT {}'.format(', '.join(
['{:.3%}'.format(x) for x in tgt_iou])))
else:
print(
'Epoch {}: took {:.3f}s, TRAIN clf loss={:.6f}, consistency loss={:.6f}, conf rate={:.3%}, VAL mIoU={:.3%}'
.format(epoch_i + 1, t2 - t1, sup_loss_acc,
consistency_loss_acc, conf_rate_acc, tgt_miou))
print('-- {}'.format(', '.join(
['{:.3%}'.format(x) for x in tgt_iou])))
if save_model:
model_path = os.path.join(submit_config.run_dir, "model.pth")
torch.save(eval_net, model_path)
if save_preds:
out_dir = os.path.join(submit_config.run_dir, 'preds')
os.makedirs(out_dir, exist_ok=True)
with torch.no_grad():
for batch in tgt_val_loader:
batch_x = batch['image'].to(torch_device)
batch_ndx = batch['index'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i, sample_ndx in enumerate(batch_ndx):
ds_tgt.save_prediction_by_index(
out_dir, pred_y[sample_i].astype(np.uint32),
sample_ndx)
else:
out_dir = None
if test_loader is not None:
test_iou_eval = evaluation.EvaluatorIoU(ds_tgt.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in test_loader:
batch_x = batch['image'].to(torch_device)
batch_ndx = batch['index'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i, sample_ndx in enumerate(batch_ndx):
if save_preds:
ds_tgt.save_prediction_by_index(
out_dir, pred_y[sample_i].astype(np.uint32),
sample_ndx)
test_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
test_iou = test_iou_eval.score()
test_miou = test_iou.mean()
print('FINAL TEST: mIoU={:.3%}'.format(test_miou))
print('-- TEST {}'.format(', '.join(
['{:.3%}'.format(x) for x in test_iou])))
def train_seg_semisup_vat_mt(
submit_config: job_helper.SubmitConfig, dataset, model, arch,
freeze_bn, opt_type, sgd_momentum, sgd_nesterov, sgd_weight_decay,
learning_rate, lr_sched, lr_step_epochs, lr_step_gamma, lr_poly_power,
teacher_alpha, bin_fill_holes, crop_size, aug_hflip, aug_vflip,
aug_hvflip, aug_scale_hung, aug_max_scale, aug_scale_non_uniform,
aug_rot_mag, vat_radius, adaptive_vat_radius, vat_dir_from_student,
cons_loss_fn, cons_weight, conf_thresh, conf_per_pixel, rampup,
unsup_batch_ratio, num_epochs, iters_per_epoch, batch_size, n_sup,
n_unsup, n_val, split_seed, split_path, val_seed, save_preds,
save_model, num_workers):
settings = locals().copy()
del settings['submit_config']
import os
import time
import itertools
import math
import numpy as np
import torch, torch.nn as nn, torch.nn.functional as F
from architectures import network_architectures
import torch.utils.data
from datapipe import datasets
from datapipe import seg_data, seg_transforms, seg_transforms_cv
import evaluation
import optim_weight_ema
import lr_schedules
if crop_size == '':
crop_size = None
else:
crop_size = [int(x.strip()) for x in crop_size.split(',')]
torch_device = torch.device('cuda:0')
#
# Load data sets
#
ds_dict = datasets.load_dataset(dataset, n_val, val_seed, n_sup, n_unsup,
split_seed, split_path)
ds_src = ds_dict['ds_src']
ds_tgt = ds_dict['ds_tgt']
tgt_val_ndx = ds_dict['val_ndx_tgt']
src_val_ndx = ds_dict['val_ndx_src'] if ds_src is not ds_tgt else None
test_ndx = ds_dict['test_ndx_tgt']
sup_ndx = ds_dict['sup_ndx']
unsup_ndx = ds_dict['unsup_ndx']
n_classes = ds_src.num_classes
root_n_classes = math.sqrt(n_classes)
if bin_fill_holes and n_classes != 2:
print(
'Binary hole filling can only be used with binary (2-class) segmentation datasets'
)
return
print('Loaded data')
# Build network
NetClass = network_architectures.seg.get(arch)
student_net = NetClass(ds_src.num_classes).to(torch_device)
if opt_type == 'adam':
student_optim = torch.optim.Adam([
dict(params=student_net.pretrained_parameters(),
lr=learning_rate * 0.1),
dict(params=student_net.new_parameters(), lr=learning_rate)
])
elif opt_type == 'sgd':
student_optim = torch.optim.SGD([
dict(params=student_net.pretrained_parameters(),
lr=learning_rate * 0.1),
dict(params=student_net.new_parameters(), lr=learning_rate)
],
momentum=sgd_momentum,
nesterov=sgd_nesterov,
weight_decay=sgd_weight_decay)
else:
raise ValueError('Unknown opt_type {}'.format(opt_type))
if model == 'mean_teacher':
teacher_net = NetClass(ds_src.num_classes).to(torch_device)
for p in teacher_net.parameters():
p.requires_grad = False
teacher_optim = optim_weight_ema.EMAWeightOptimizer(
teacher_net, student_net, teacher_alpha)
eval_net = teacher_net
elif model == 'pi':
teacher_net = student_net
teacher_optim = None
eval_net = student_net
else:
print('Unknown model type {}'.format(model))
return
if vat_dir_from_student:
vat_dir_net = student_net
else:
vat_dir_net = teacher_net
BLOCK_SIZE = student_net.BLOCK_SIZE
NET_MEAN, NET_STD = seg_transforms.get_mean_std(ds_tgt, student_net)
if freeze_bn:
if not hasattr(student_net, 'freeze_batchnorm'):
raise ValueError(
'Network {} does not support batchnorm freezing'.format(arch))
clf_crossent_loss = nn.CrossEntropyLoss(ignore_index=255)
print('Built network')
if iters_per_epoch == -1:
iters_per_epoch = len(unsup_ndx) // batch_size
total_iters = iters_per_epoch * num_epochs
lr_epoch_scheduler, lr_iter_scheduler = lr_schedules.make_lr_schedulers(
optimizer=student_optim,
total_iters=total_iters,
schedule_type=lr_sched,
step_epochs=lr_step_epochs,
step_gamma=lr_step_gamma,
poly_power=lr_poly_power)
# Train data pipeline: transforms
train_transforms = []
if crop_size is not None:
if aug_scale_hung:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCropScaleHung(
crop_size, (0, 0),
uniform_scale=not aug_scale_non_uniform))
elif aug_max_scale != 1.0 or aug_rot_mag != 0.0:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCropRotateScale(
crop_size, (0, 0),
rot_mag=aug_rot_mag,
max_scale=aug_max_scale,
uniform_scale=not aug_scale_non_uniform,
constrain_rot_scale=True))
else:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCrop(crop_size, (0, 0)))
else:
if aug_scale_hung:
raise NotImplementedError('aug_scale_hung requires a crop_size')
if aug_hflip or aug_vflip or aug_hvflip:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomFlip(aug_hflip, aug_vflip,
aug_hvflip))
train_transforms.append(
seg_transforms_cv.SegCVTransformNormalizeToTensor(NET_MEAN, NET_STD))
# Train data pipeline: supervised and unsupervised data sets
train_sup_ds = ds_src.dataset(
labels=True,
mask=False,
xf=False,
pair=False,
transforms=seg_transforms.SegTransformCompose(train_transforms),
pipeline_type='cv')
train_unsup_ds = ds_src.dataset(
labels=False,
mask=True,
xf=False,
pair=False,
transforms=seg_transforms.SegTransformCompose(train_transforms),
pipeline_type='cv')
collate_fn = seg_data.SegCollate(BLOCK_SIZE)
# Train data pipeline: data loaders
sup_sampler = seg_data.RepeatSampler(
torch.utils.data.SubsetRandomSampler(sup_ndx))
train_sup_loader = torch.utils.data.DataLoader(train_sup_ds,
batch_size,
sampler=sup_sampler,
collate_fn=collate_fn,
num_workers=num_workers)
if cons_weight > 0.0:
unsup_sampler = seg_data.RepeatSampler(
torch.utils.data.SubsetRandomSampler(unsup_ndx))
train_unsup_loader = torch.utils.data.DataLoader(
train_unsup_ds,
batch_size,
sampler=unsup_sampler,
collate_fn=collate_fn,
num_workers=num_workers)
else:
train_unsup_loader = None
# Eval pipeline
src_val_loader, tgt_val_loader, test_loader = datasets.eval_data_pipeline(
ds_src, ds_tgt, src_val_ndx, tgt_val_ndx, test_ndx, batch_size,
collate_fn, NET_MEAN, NET_STD, num_workers)
# Report setttings
print('Settings:')
print(', '.join([
'{}={}'.format(key, settings[key])
for key in sorted(list(settings.keys()))
]))
# Report dataset size
print('Dataset:')
print('len(sup_ndx)={}'.format(len(sup_ndx)))
print('len(unsup_ndx)={}'.format(len(unsup_ndx)))
if ds_src is not ds_tgt:
print('len(src_val_ndx)={}'.format(len(tgt_val_ndx)))
print('len(tgt_val_ndx)={}'.format(len(tgt_val_ndx)))
else:
print('len(val_ndx)={}'.format(len(tgt_val_ndx)))
if test_ndx is not None:
print('len(test_ndx)={}'.format(len(test_ndx)))
if n_sup != -1:
print('sup_ndx={}'.format(sup_ndx.tolist()))
def t_dot(a, b):
return (a * b).sum(dim=1, keepdim=True)
def normalize_eps(x):
x_flat = x.view(len(x), -1)
mag = torch.sqrt((x_flat * x_flat).sum(dim=1))
return x / (mag[:, None, None, None] + 1e-12)
def normalized_noise(x, requires_grad=False, scale=1.0):
eps = torch.randn(x.shape, dtype=torch.float, device=x.device)
eps = normalize_eps(eps) * scale
if requires_grad:
eps = eps.clone().detach().requires_grad_(True)
return eps
def vat_direction(x):
"""
Compute the VAT perturbation direction vector
:param x: input image as a `(N, C, H, W)` tensor
:return: VAT direction as a `(N, C, H, W)` tensor
"""
# Put the network used to get the VAT direction in eval mode and get the predicted
# logits and probabilities for the batch of samples x
vat_dir_net.eval()
with torch.no_grad():
y_pred_logits = vat_dir_net(x).detach()
y_pred_prob = F.softmax(y_pred_logits, dim=1)
# Initial noise offset vector with requires_grad=True
noise_scale = 1.0e-6 * x.shape[2] * x.shape[3] / 1000
eps = normalized_noise(x, requires_grad=True, scale=noise_scale)
# Predict logits and probs for sample perturbed by eps
eps_pred_logits = vat_dir_net(x.detach() + eps)
eps_pred_prob = F.softmax(eps_pred_logits, dim=1)
# Choose our loss function
if cons_loss_fn == 'var':
delta = (eps_pred_prob - y_pred_prob)
loss = (delta * delta).sum()
elif cons_loss_fn == 'bce':
loss = network_architectures.robust_binary_crossentropy(
eps_pred_prob, y_pred_prob).sum()
elif cons_loss_fn == 'kld':
loss = F.kl_div(F.log_softmax(eps_pred_logits, dim=1),
y_pred_prob,
reduce=False).sum()
elif cons_loss_fn == 'logits_var':
delta = (eps_pred_logits - y_pred_logits)
loss = (delta * delta).sum()
else:
raise ValueError(
'Unknown consistency loss function {}'.format(cons_loss_fn))
# Differentiate the loss w.r.t. the perturbation
eps_adv = torch.autograd.grad(outputs=loss,
inputs=eps,
create_graph=True,
retain_graph=True,
only_inputs=True)[0]
# Normalize the adversarial perturbation
return normalize_eps(eps_adv), y_pred_logits, y_pred_prob
def vat_perburbation(x, m):
eps_adv_nrm, y_pred_logits, y_pred_prob = vat_direction(x)
if adaptive_vat_radius:
# We view semantic segmentation as predicting the class of a pixel
# given a patch centred on that pixel.
# The most similar patch in terms of pixel content to a patch P
# is a patch Q whose central pixel is an immediate neighbour
# of the central pixel P.
# We therefore use the image Jacobian (gradient w.r.t. x and y) to
# get a sense of the distance between neighbouring patches
# so we can scale the VAT radius according to the image content.
# Delta in vertical and horizontal directions
delta_v = x[:, :, 2:, :] - x[:, :, :-2, :]
delta_h = x[:, :, :, 2:] - x[:, :, :, :-2]
# delta_h and delta_v are the difference between pixels where the step size is 2, rather than 1
# So divide by 2 to get the magnitude of the Jacobian
delta_v = delta_v.view(len(delta_v), -1)
delta_h = delta_h.view(len(delta_h), -1)
adv_radius = vat_radius * torch.sqrt(
(delta_v**2).sum(dim=1) +
(delta_h**2).sum(dim=1))[:, None, None, None] * 0.5
else:
scale = math.sqrt(float(x.shape[1] * x.shape[2] * x.shape[3]))
adv_radius = vat_radius * scale
return (eps_adv_nrm * adv_radius).detach(), y_pred_logits, y_pred_prob
# Track mIoU for early stopping
best_tgt_miou = None
best_epoch = 0
eval_net_state = {
key: value.detach().cpu().numpy()
for key, value in eval_net.state_dict().items()
}
# Create iterators
train_sup_iter = iter(train_sup_loader)
train_unsup_iter = iter(
train_unsup_loader) if train_unsup_loader is not None else None
iter_i = 0
print('Training...')
for epoch_i in range(num_epochs):
if lr_epoch_scheduler is not None:
lr_epoch_scheduler.step(epoch_i)
t1 = time.time()
if rampup > 0:
ramp_val = network_architectures.sigmoid_rampup(epoch_i, rampup)
else:
ramp_val = 1.0
student_net.train()
if teacher_net is not student_net:
teacher_net.train()
if freeze_bn:
student_net.freeze_batchnorm()
if teacher_net is not student_net:
teacher_net.freeze_batchnorm()
sup_loss_acc = 0.0
consistency_loss_acc = 0.0
conf_rate_acc = 0.0
n_sup_batches = 0
n_unsup_batches = 0
src_val_iter = iter(
src_val_loader) if src_val_loader is not None else None
tgt_val_iter = iter(
tgt_val_loader) if tgt_val_loader is not None else None
for sup_batch in itertools.islice(train_sup_iter, iters_per_epoch):
if lr_iter_scheduler is not None:
lr_iter_scheduler.step(iter_i)
student_optim.zero_grad()
#
# Supervised branch
#
batch_x = sup_batch['image'].to(torch_device)
batch_y = sup_batch['labels'].to(torch_device)
logits_sup = student_net(batch_x)
sup_loss = clf_crossent_loss(logits_sup, batch_y[:, 0, :, :])
sup_loss.backward()
if cons_weight > 0.0:
for _ in range(unsup_batch_ratio):
#
# Unsupervised branch
#
unsup_batch = next(train_unsup_iter)
# Input images to torch tensor
batch_ux = unsup_batch['image'].to(torch_device)
batch_um = unsup_batch['mask'].to(torch_device)
# batch_um is a mask that is 1 for valid pixels, 0 for invalid pixels.
# It us used later on to scale the consistency loss, so that consistency loss is
# only computed for valid pixels.
# Explanation:
# When using geometric augmentations such as rotations, some pixels in the training
# crop may come from outside the bounds of the input image. These pixels will have a value
# of 0 in these masks. Similarly, when using scaled crops, the size of the crop
# from the input image that must be scaled to the size of the training crop may be
# larger than one/both of the input image dimensions. Pixels in the training crop
# that arise from outside the input image bounds will once again be given a value
# of 0 in these masks.
# Compute VAT perburbation
x_perturb, logits_cons_tea, prob_cons_tea = vat_perburbation(
batch_ux, batch_um)
# Perturb image
batch_ux_adv = batch_ux + x_perturb
# Get teacher predictions for original image
with torch.no_grad():
logits_cons_tea = teacher_net(batch_ux).detach()
# Get student prediction for cut image
logits_cons_stu = student_net(batch_ux_adv)
# Logits -> probs
prob_cons_tea = F.softmax(logits_cons_tea, dim=1)
prob_cons_stu = F.softmax(logits_cons_stu, dim=1)
loss_mask = batch_um
# Confidence thresholding
if conf_thresh > 0.0:
# Compute confidence of teacher predictions
conf_tea = prob_cons_tea.max(dim=1)[0]
# Compute confidence mask
conf_mask = (conf_tea >=
conf_thresh).float()[:, None, :, :]
# Record rate for reporting
conf_rate_acc += float(conf_mask.mean())
# Average confidence mask if requested
if not conf_per_pixel:
conf_mask = conf_mask.mean()
loss_mask = loss_mask * conf_mask
elif rampup > 0:
conf_rate_acc += ramp_val
# Compute per-pixel consistency loss
# Note that the way we aggregate the loss across the class/channel dimension (1)
# depends on the loss function used. Generally, summing over the class dimension
# keeps the magnitude of the gradient of the loss w.r.t. the logits
# nearly constant w.r.t. the number of classes. When using logit-variance,
# dividing by `sqrt(num_classes)` helps.
if cons_loss_fn == 'var':
delta_prob = prob_cons_stu - prob_cons_tea
consistency_loss = delta_prob * delta_prob
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
elif cons_loss_fn == 'logits_var':
delta_logits = logits_cons_stu - logits_cons_tea
consistency_loss = delta_logits * delta_logits
consistency_loss = consistency_loss.sum(
dim=1, keepdim=True) / root_n_classes
elif cons_loss_fn == 'bce':
consistency_loss = network_architectures.robust_binary_crossentropy(
prob_cons_stu, prob_cons_tea)
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
elif cons_loss_fn == 'kld':
consistency_loss = F.kl_div(F.log_softmax(
logits_cons_stu, dim=1),
prob_cons_tea,
reduce=False)
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
else:
raise ValueError(
'Unknown consistency loss function {}'.format(
cons_loss_fn))
# Apply consistency loss mask and take the mean over pixels and images
consistency_loss = (consistency_loss * loss_mask).mean()
# Modulate with rampup if desired
if rampup > 0:
consistency_loss = consistency_loss * ramp_val
# Weight the consistency loss and back-prop
unsup_loss = consistency_loss * cons_weight
unsup_loss.backward()
consistency_loss_val = float(consistency_loss.detach())
consistency_loss_acc += consistency_loss_val
if np.isnan(consistency_loss_val):
print(
'NaN detected in consistency loss; bailing out...')
return
n_unsup_batches += 1
student_optim.step()
if teacher_optim is not None:
teacher_optim.step()
sup_loss_val = float(sup_loss.detach())
sup_loss_acc += sup_loss_val
if np.isnan(sup_loss_val):
print('NaN detected in supervised loss; bailing out...')
return
n_sup_batches += 1
iter_i += 1
sup_loss_acc /= n_sup_batches
if n_unsup_batches > 0:
consistency_loss_acc /= n_unsup_batches
conf_rate_acc /= n_unsup_batches
eval_net.eval()
if src_val_iter is not None:
src_iou_eval = evaluation.EvaluatorIoU(ds_src.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in src_val_iter:
batch_x = batch['image'].to(torch_device)
batch_y = batch['labels'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i in range(len(batch_y)):
src_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
src_iou = src_iou_eval.score()
src_miou = src_iou.mean()
else:
src_iou_eval = src_iou = src_miou = None
tgt_iou_eval = evaluation.EvaluatorIoU(ds_tgt.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in tgt_val_iter:
batch_x = batch['image'].to(torch_device)
batch_y = batch['labels'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i in range(len(batch_y)):
tgt_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
tgt_iou = tgt_iou_eval.score()
tgt_miou = tgt_iou.mean()
t2 = time.time()
if ds_src is not ds_tgt:
print(
'Epoch {}: took {:.3f}s, TRAIN clf loss={:.6f}, consistency loss={:.6f}, conf rate={:.3%}, '
'SRC VAL mIoU={:.3%}, TGT VAL mIoU={:.3%}'.format(
epoch_i + 1, t2 - t1, sup_loss_acc, consistency_loss_acc,
conf_rate_acc, src_miou, tgt_miou))
print('-- SRC {}'.format(', '.join(
['{:.3%}'.format(x) for x in src_iou])))
print('-- TGT {}'.format(', '.join(
['{:.3%}'.format(x) for x in tgt_iou])))
else:
print(
'Epoch {}: took {:.3f}s, TRAIN clf loss={:.6f}, consistency loss={:.6f}, conf rate={:.3%}, VAL mIoU={:.3%}'
.format(epoch_i + 1, t2 - t1, sup_loss_acc,
consistency_loss_acc, conf_rate_acc, tgt_miou))
print('-- {}'.format(', '.join(
['{:.3%}'.format(x) for x in tgt_iou])))
if save_model:
model_path = os.path.join(submit_config.run_dir, "model.pth")
torch.save(eval_net, model_path)
if save_preds:
out_dir = os.path.join(submit_config.run_dir, 'preds')
os.makedirs(out_dir, exist_ok=True)
with torch.no_grad():
for batch in tgt_val_loader:
batch_x = batch['image'].to(torch_device)
batch_ndx = batch['index'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i, sample_ndx in enumerate(batch_ndx):
ds_tgt.save_prediction_by_index(
out_dir, pred_y[sample_i].astype(np.uint32),
sample_ndx)
else:
out_dir = None
if test_loader is not None:
test_iou_eval = evaluation.EvaluatorIoU(ds_tgt.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in test_loader:
batch_x = batch['image'].to(torch_device)
batch_ndx = batch['index'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i, sample_ndx in enumerate(batch_ndx):
if save_preds:
ds_tgt.save_prediction_by_index(
out_dir, pred_y[sample_i].astype(np.uint32),
sample_ndx)
test_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
test_iou = test_iou_eval.score()
test_miou = test_iou.mean()
print('FINAL TEST: mIoU={:.3%}'.format(test_miou))
print('-- TEST {}'.format(', '.join(
['{:.3%}'.format(x) for x in test_iou])))
def train_toy2d(submit_config: job_helper.SubmitConfig, dataset,
region_erode_radius, img_noise_std, n_sup, balance_classes,
seed, sup_path, model, n_hidden, hidden_size, hidden_act,
norm_layer, perturb_noise_std, dist_contour_range, conf_thresh,
conf_avg, cons_weight, cons_loss_fn, cons_no_dropout,
learning_rate, teacher_alpha, num_epochs, batch_size,
render_cons_grad, render_pred, device, save_output):
settings = locals().copy()
del settings['submit_config']
import sys
print('Command line:')
print(' '.join(sys.argv))
print('Settings:')
print(', '.join(
['{}={}'.format(k, settings[k]) for k in sorted(settings.keys())]))
import os
import numpy as np
import time
import cv2
from scipy.ndimage.morphology import distance_transform_edt
import optim_weight_ema
from toy2d import generate_data
from datapipe.seg_data import RepeatSampler
import torch, torch.nn as nn, torch.nn.functional as F
import torch.utils.data
rng = np.random.RandomState(seed)
# Generate/load the dataset
if dataset.startswith('img:'):
# Generate a dataset from a black and white image
image_path = dataset[4:]
ds = generate_data.classification_dataset_from_image(
image_path, region_erode_radius, img_noise_std, n_sup,
balance_classes, rng)
image = ds.image
elif dataset == 'spiral':
# Generate a spiral dataset
ds = generate_data.spiral_classification_dataset(
n_sup, balance_classes, rng)
image = None
else:
print('Unknown dataset {}, should be spiral or img:<path>'.format(
dataset))
return
# If a path to a supervised dataset has been provided, load it
if sup_path is not None:
ds.load_supervised(sup_path)
# If we are constraining perturbations to lie along the contours of the distance map to the ground truth class boundary
if dist_contour_range > 0.0:
if image is None:
print(
'Constraining perturbations to lying on distance map contours is only supported for \'image\' experiments'
)
return
img_1 = image >= 0.5
# Compute signed distance map to boundary
dist_1 = distance_transform_edt(img_1)
dist_0 = distance_transform_edt(~img_1)
dist_map = dist_1 * img_1 + -dist_0 * (~img_1)
else:
dist_map = None
# PyTorch device
torch_device = torch.device(device)
# Convert perturbation noise std-dev to [y,x]
try:
perturb_noise_std = np.array(
[float(x.strip()) for x in perturb_noise_std.split(',')])
except ValueError:
perturb_noise_std = np.array([6.0, 6.0])
# Assume that perturbation noise std-dev is in pixel space (for image experiments), so convert
perturb_noise_std_real_scale = perturb_noise_std / ds.img_scale * 2.0
perturb_noise_std_real_scale = torch.tensor(perturb_noise_std_real_scale,
dtype=torch.float,
device=torch_device)
# Define the neural network model (an MLP)
class Network(nn.Module):
def __init__(self):
super(Network, self).__init__()
self.drop = nn.Dropout()
hidden = []
chn_in = 2
for i in range(n_hidden):
if norm_layer == 'spectral_norm':
hidden.append(
nn.utils.spectral_norm(nn.Linear(chn_in, hidden_size)))
elif norm_layer == 'weight_norm':
hidden.append(
nn.utils.weight_norm(nn.Linear(chn_in, hidden_size)))
else:
hidden.append(nn.Linear(chn_in, hidden_size))
if norm_layer == 'batch_norm':
hidden.append(nn.BatchNorm1d(hidden_size))
elif norm_layer == 'group_norm':
hidden.append(nn.GroupNorm(4, hidden_size))
if hidden_act == 'relu':
hidden.append(nn.ReLU())
elif hidden_act == 'lrelu':
hidden.append(nn.LeakyReLU(0.01))
else:
raise ValueError
chn_in = hidden_size
self.hidden = nn.Sequential(*hidden)
# Final layer; 2-class output
self.l_final = nn.Linear(chn_in, 2)
def forward(self, x, use_dropout=True):
x = self.hidden(x)
if use_dropout:
x = self.drop(x)
x = self.l_final(x)
return x
# Build student network, optimizer and supervised loss criterion
student_net = Network().to(torch_device)
student_params = list(student_net.parameters())
student_optimizer = torch.optim.Adam(student_params, lr=learning_rate)
classification_criterion = nn.CrossEntropyLoss()
# Build teacher network and optimizer
if model == 'mean_teacher':
teacher_net = Network().to(torch_device)
teacher_params = list(teacher_net.parameters())
for param in teacher_params:
param.requires_grad = False
teacher_optimizer = optim_weight_ema.EMAWeightOptimizer(
teacher_net, student_net, ema_alpha=teacher_alpha)
pred_net = teacher_net
else:
teacher_net = None
teacher_optimizer = None
pred_net = student_net
# Robust BCE helper
def robust_binary_crossentropy(pred, tgt):
inv_tgt = -tgt + 1.0
inv_pred = -pred + 1.0 + 1e-6
return -(tgt * torch.log(pred + 1.0e-6) +
inv_tgt * torch.log(inv_pred))
# If we are constraining perturbations to lie on distance map contours, load the distance map as a Torch tensor
if dist_contour_range > 0.0:
t_dist_map = torch.tensor(dist_map[None, None, ...],
dtype=torch.float,
device=torch_device)
else:
t_dist_map = None
# Helper function to compute confidence thresholding factor
def conf_factor(teacher_pred_prob):
# Compute confidence
conf_tea = torch.max(teacher_pred_prob, 1)[0]
conf_tea = conf_tea.detach()
# Compute factor based on threshold and `conf_avg` flag
if conf_thresh > 0.0:
conf_fac = (conf_tea >= conf_thresh).float()
else:
conf_fac = torch.ones(conf_tea.shape,
dtype=torch.float,
device=conf_tea.device)
if conf_avg:
conf_fac = torch.ones_like(conf_fac) * conf_fac.mean()
return conf_fac
# Helper function that constrains consistency loss to operate only when perturbations lie along
# distance map contours.
# When this feature is enabled, it masks to zero the loss for any unsupervised sample whose random perturbation
# deviates too far from the distance map contour
def dist_map_weighting(t_dist_map, batch_u_X, batch_u_X_1):
if t_dist_map is not None and dist_contour_range > 0:
# For each sample in `batch_u_X` and `batch_u_X_1`, both of which are
# of shape `[n_points, [y,x]]` we want to get the value from the
# distance map. For this we use `torch.nn.functional.grid_sample`.
# This function expects grid look-up co-ordinates to have
# the shape `[batch, height, width, [x, y]]`.
# We reshape `batch_u_X` and `batch_u_X_1` to `[1, 1, n_points, [x,y]]` and stack along
# the height dimension, making two rows to send to `grid_sample`.
# The final shape will be `[1, 2, n_points, [x,y]]`:
# 1 sample (1 image)
# 2 rows; batch_u_X and batch_u_X_1
# n_points columns
# (x,y)
# `[n_points, [y,x]]` -> `[1, 1, n_points, [x,y]]`
sample_points_0 = torch.cat([
batch_u_X[:, 1].view(1, 1, -1, 1), batch_u_X[:, 0].view(
1, 1, -1, 1)
],
dim=3)
# `[n_points, [y,x]]` -> `[1, 1, n_points, [x,y]]`
sample_points_1 = torch.cat([
batch_u_X_1[:, 1].view(1, 1, -1, 1), batch_u_X_1[:, 0].view(
1, 1, -1, 1)
],
dim=3)
# -> `[1, 2, n_points, [x,y]]`
sample_points = torch.cat([sample_points_0, sample_points_1],
dim=1)
# Get distance to class boundary from distance map
dist_from_boundary = F.grid_sample(t_dist_map, sample_points)
# Get the squared difference between the distances from `batch_u_X` to the boundary
# and the distances from `batch_u_X_1` to the boundary.
delta_dist_sqr = (dist_from_boundary[0, 0, 0, :] -
dist_from_boundary[0, 0, 1, :]).pow(2)
# Per-sample loss mask based on difference between distances
weight = (delta_dist_sqr <=
(dist_contour_range * dist_contour_range)).float()
return weight
else:
return torch.ones(len(batch_u_X),
dtype=torch.float,
device=batch_u_X.device)
# Supervised dataset, sampler and loader
sup_dataset = torch.utils.data.TensorDataset(
torch.tensor(ds.sup_X, dtype=torch.float),
torch.tensor(ds.sup_y, dtype=torch.long))
sup_sampler = RepeatSampler(torch.utils.data.RandomSampler(sup_dataset))
sup_sep_loader = torch.utils.data.DataLoader(sup_dataset,
batch_size,
sampler=sup_sampler,
num_workers=1)
# Unsupervised dataset, sampler and loader
unsup_dataset = torch.utils.data.TensorDataset(
torch.tensor(ds.unsup_X, dtype=torch.float))
unsup_sampler = torch.utils.data.RandomSampler(unsup_dataset)
unsup_loader = torch.utils.data.DataLoader(unsup_dataset,
batch_size,
sampler=unsup_sampler,
num_workers=1)
# Complete dataset and loader
all_dataset = torch.utils.data.TensorDataset(
torch.tensor(ds.X, dtype=torch.float))
all_loader = torch.utils.data.DataLoader(all_dataset,
16384,
shuffle=False,
num_workers=1)
# Grid points used to render visualizations
vis_grid_dataset = torch.utils.data.TensorDataset(
torch.tensor(ds.px_grid_vis, dtype=torch.float))
vis_grid_loader = torch.utils.data.DataLoader(vis_grid_dataset,
16384,
shuffle=False,
num_workers=1)
# Evaluation mode initially
student_net.eval()
if teacher_net is not None:
teacher_net.eval()
# Compute the magnitude of the gradient of the consistency loss at the logits
def consistency_loss_logit_grad_mag(batch_u_X):
u_shape = batch_u_X.shape
batch_u_X_1 = batch_u_X + torch.randn(u_shape, dtype=torch.float, device=torch_device) * \
perturb_noise_std_real_scale[None, :]
student_optimizer.zero_grad()
grads = [None]
if teacher_net is not None:
teacher_unsup_logits = teacher_net(batch_u_X).detach()
else:
teacher_unsup_logits = student_net(batch_u_X)
teacher_unsup_prob = F.softmax(teacher_unsup_logits, dim=1)
student_unsup_logits = student_net(batch_u_X_1)
def grad_hook(grad):
grads[0] = torch.sqrt((grad * grad).sum(dim=1))
student_unsup_logits.register_hook(grad_hook)
student_unsup_prob = F.softmax(student_unsup_logits, dim=1)
weight = dist_map_weighting(t_dist_map, batch_u_X, batch_u_X_1)
mod_fac = conf_factor(teacher_unsup_prob) * weight
if cons_loss_fn == 'bce':
aug_loss = robust_binary_crossentropy(student_unsup_prob,
teacher_unsup_prob)
aug_loss = aug_loss.mean(dim=1) * mod_fac
unsup_loss = aug_loss.mean()
elif cons_loss_fn == 'var':
d_aug_loss = student_unsup_prob - teacher_unsup_prob
aug_loss = d_aug_loss * d_aug_loss
aug_loss = aug_loss.mean(dim=1) * mod_fac
unsup_loss = aug_loss.mean()
elif cons_loss_fn == 'logits_var':
d_aug_loss = student_unsup_logits - teacher_unsup_logits
aug_loss = d_aug_loss * d_aug_loss
aug_loss = aug_loss.mean(dim=1) * mod_fac
unsup_loss = aug_loss.mean()
else:
raise ValueError
unsup_loss.backward()
return (grads[0].cpu().numpy(), )
# Helper function for rendering an output image for visualization
def render_output_image():
# Generate output for plotting
with torch.no_grad():
vis_pred = []
vis_grad = [] if render_cons_grad else None
for (batch_X, ) in vis_grid_loader:
batch_X = batch_X.to(torch_device)
batch_pred_logits = pred_net(batch_X)
if render_pred == 'prob':
batch_vis = F.softmax(batch_pred_logits, dim=1)[:, 1]
elif render_pred == 'class':
batch_vis = torch.argmax(batch_pred_logits, dim=1)
else:
raise ValueError(
'Unknown prediction render {}'.format(render_pred))
batch_vis = batch_vis.detach().cpu().numpy()
vis_pred.append(batch_vis)
if render_cons_grad:
batch_grad = consistency_loss_logit_grad_mag(batch_X)
vis_grad.append(batch_grad.detach().cpu().numpy())
vis_pred = np.concatenate(vis_pred, axis=0)
if render_cons_grad:
vis_grad = np.concatenate(vis_grad, axis=0)
out_image = ds.semisup_image_plot(vis_pred, vis_grad)
return out_image
# Output image for first frame
if save_output and submit_config.run_dir is not None:
plot_path = os.path.join(submit_config.run_dir,
'epoch_{:05d}.png'.format(0))
cv2.imwrite(plot_path, render_output_image())
else:
cv2.imshow('Vis', render_output_image())
k = cv2.waitKey(1)
# Train
print('|sup|={}'.format(len(ds.sup_X)))
print('|unsup|={}'.format(len(ds.unsup_X)))
print('|all|={}'.format(len(ds.X)))
print('Training...')
terminated = False
for epoch in range(num_epochs):
t1 = time.time()
student_net.train()
if teacher_net is not None:
teacher_net.train()
batch_sup_loss_accum = 0.0
batch_conf_mask_sum_accum = 0.0
batch_cons_loss_accum = 0.0
batch_N_accum = 0.0
for sup_batch, unsup_batch in zip(sup_sep_loader, unsup_loader):
(batch_X, batch_y) = sup_batch
(batch_u_X, ) = unsup_batch
batch_X = batch_X.to(torch_device)
batch_y = batch_y.to(torch_device)
batch_u_X = batch_u_X.to(torch_device)
# Apply perturbation to generate `batch_u_X_1`
aug_perturbation = torch.randn(batch_u_X.shape,
dtype=torch.float,
device=torch_device)
batch_u_X_1 = batch_u_X + aug_perturbation * perturb_noise_std_real_scale[
None, :]
# Supervised loss path
student_optimizer.zero_grad()
student_sup_logits = student_net(batch_X)
sup_loss = classification_criterion(student_sup_logits, batch_y)
if cons_weight > 0.0:
# Consistency loss path
# Logits are computed differently depending on model
if model == 'mean_teacher':
teacher_unsup_logits = teacher_net(
batch_u_X, use_dropout=not cons_no_dropout).detach()
student_unsup_logits = student_net(
batch_u_X_1, use_dropout=not cons_no_dropout)
elif model == 'pi':
teacher_unsup_logits = student_net(
batch_u_X, use_dropout=not cons_no_dropout)
student_unsup_logits = student_net(
batch_u_X_1, use_dropout=not cons_no_dropout)
elif model == 'pi_onebatch':
batch_both = torch.cat([batch_u_X, batch_u_X_1], dim=0)
both_unsup_logits = student_net(
batch_both, use_dropout=not cons_no_dropout)
teacher_unsup_logits = both_unsup_logits[:len(batch_u_X)]
student_unsup_logits = both_unsup_logits[len(batch_u_X):]
else:
raise RuntimeError
# Compute predicted probabilities
teacher_unsup_prob = F.softmax(teacher_unsup_logits, dim=1)
student_unsup_prob = F.softmax(student_unsup_logits, dim=1)
# Distance map weighting
# (if dist_contour_range is 0 then weight will just be 1)
weight = dist_map_weighting(t_dist_map, batch_u_X, batch_u_X_1)
# Confidence thresholding
conf_fac = conf_factor(teacher_unsup_prob)
mod_fac = conf_fac * weight
# Compute consistency loss
if cons_loss_fn == 'bce':
aug_loss = robust_binary_crossentropy(
student_unsup_prob, teacher_unsup_prob)
aug_loss = aug_loss.mean(dim=1) * mod_fac
cons_loss = aug_loss.sum() / weight.sum()
elif cons_loss_fn == 'var':
d_aug_loss = student_unsup_prob - teacher_unsup_prob
aug_loss = d_aug_loss * d_aug_loss
aug_loss = aug_loss.mean(dim=1) * mod_fac
cons_loss = aug_loss.sum() / weight.sum()
elif cons_loss_fn == 'logits_var':
d_aug_loss = student_unsup_logits - teacher_unsup_logits
aug_loss = d_aug_loss * d_aug_loss
aug_loss = aug_loss.mean(dim=1) * mod_fac
cons_loss = aug_loss.sum() / weight.sum()
else:
raise ValueError
# Combine supervised and consistency loss
loss = sup_loss + cons_loss * cons_weight
conf_rate = float(conf_fac.sum())
else:
loss = sup_loss
conf_rate = 0.0
cons_loss = 0.0
loss.backward()
student_optimizer.step()
if teacher_optimizer is not None:
teacher_optimizer.step()
batch_sup_loss_accum += float(sup_loss)
batch_conf_mask_sum_accum += conf_rate
batch_cons_loss_accum += float(cons_loss)
batch_N_accum += len(batch_X)
if batch_N_accum > 0:
batch_sup_loss_accum /= batch_N_accum
batch_conf_mask_sum_accum /= batch_N_accum
batch_cons_loss_accum /= batch_N_accum
student_net.eval()
if teacher_net is not None:
teacher_net.eval()
# Generate output for plotting
if save_output and submit_config.run_dir is not None:
plot_path = os.path.join(submit_config.run_dir,
'epoch_{:05d}.png'.format(epoch + 1))
cv2.imwrite(plot_path, render_output_image())
else:
cv2.imshow('Vis', render_output_image())
k = cv2.waitKey(1)
if (k & 255) == 27:
terminated = True
break
t2 = time.time()
# print('Epoch {}: took {:.3f}s: clf loss={:.6f}'.format(epoch, t2-t1, clf_loss))
print(
'Epoch {}: took {:.3f}s: clf loss={:.6f}, conf rate={:.3%}, cons loss={:.6f}'
.format(epoch + 1, t2 - t1, batch_sup_loss_accum,
batch_conf_mask_sum_accum, batch_cons_loss_accum))
# Get final score based on all samples
all_pred_y = []
with torch.no_grad():
for (batch_X, ) in all_loader:
batch_X = batch_X.to(torch_device)
batch_pred_logits = pred_net(batch_X)
batch_pred_cls = torch.argmax(batch_pred_logits, dim=1)
all_pred_y.append(batch_pred_cls.detach().cpu().numpy())
all_pred_y = np.concatenate(all_pred_y, axis=0)
err_rate = (all_pred_y != ds.y).mean()
print(
'FINAL RESULT: Error rate={:.6%} (supervised and unsupervised samples)'
.format(err_rate))
if not save_output:
# Close output window
if not terminated:
cv2.waitKey()
cv2.destroyAllWindows()
def train_seg_semisup_mask_mt(
submit_config: job_helper.SubmitConfig, dataset, model, arch,
freeze_bn, opt_type, sgd_momentum, sgd_nesterov, sgd_weight_decay,
learning_rate, lr_sched, lr_step_epochs, lr_step_gamma, lr_poly_power,
teacher_alpha, bin_fill_holes, crop_size, aug_hflip, aug_vflip,
aug_hvflip, aug_scale_hung, aug_max_scale, aug_scale_non_uniform,
aug_rot_mag, mask_mode, mask_prop_range, boxmask_n_boxes,
boxmask_fixed_aspect_ratio, boxmask_by_size, boxmask_outside_bounds,
boxmask_no_invert, cons_loss_fn, cons_weight, conf_thresh,
conf_per_pixel, rampup, unsup_batch_ratio, num_epochs, iters_per_epoch,
batch_size, n_sup, n_unsup, n_val, split_seed, split_path, val_seed,
save_preds, save_model, num_workers):
settings = locals().copy()
del settings['submit_config']
if ':' in mask_prop_range:
a, b = mask_prop_range.split(':')
mask_prop_range = (float(a.strip()), float(b.strip()))
del a, b
else:
mask_prop_range = float(mask_prop_range)
if mask_mode == 'zero':
mask_mix = False
elif mask_mode == 'mix':
mask_mix = True
else:
raise ValueError('Unknown mask_mode {}'.format(mask_mode))
del mask_mode
import os
import math
import time
import itertools
import numpy as np
import torch.nn as nn, torch.nn.functional as F
from architectures import network_architectures
import torch.utils.data
from datapipe import datasets
from datapipe import seg_data, seg_transforms, seg_transforms_cv
import evaluation
import optim_weight_ema
import mask_gen
import lr_schedules
if crop_size == '':
crop_size = None
else:
crop_size = [int(x.strip()) for x in crop_size.split(',')]
torch_device = torch.device('cuda:0')
#
# Load data sets
#
ds_dict = datasets.load_dataset(dataset, n_val, val_seed, n_sup, n_unsup,
split_seed, split_path)
ds_src = ds_dict['ds_src']
ds_tgt = ds_dict['ds_tgt']
tgt_val_ndx = ds_dict['val_ndx_tgt']
src_val_ndx = ds_dict['val_ndx_src'] if ds_src is not ds_tgt else None
test_ndx = ds_dict['test_ndx_tgt']
sup_ndx = ds_dict['sup_ndx']
unsup_ndx = ds_dict['unsup_ndx']
n_classes = ds_src.num_classes
root_n_classes = math.sqrt(n_classes)
if bin_fill_holes and n_classes != 2:
print(
'Binary hole filling can only be used with binary (2-class) segmentation datasets'
)
return
print('Loaded data')
# Build network
NetClass = network_architectures.seg.get(arch)
student_net = NetClass(ds_src.num_classes).to(torch_device)
if opt_type == 'adam':
student_optim = torch.optim.Adam([
dict(params=student_net.pretrained_parameters(),
lr=learning_rate * 0.1),
dict(params=student_net.new_parameters(), lr=learning_rate)
])
elif opt_type == 'sgd':
student_optim = torch.optim.SGD([
dict(params=student_net.pretrained_parameters(),
lr=learning_rate * 0.1),
dict(params=student_net.new_parameters(), lr=learning_rate)
],
momentum=sgd_momentum,
nesterov=sgd_nesterov,
weight_decay=sgd_weight_decay)
else:
raise ValueError('Unknown opt_type {}'.format(opt_type))
if model == 'mean_teacher':
teacher_net = NetClass(ds_src.num_classes).to(torch_device)
for p in teacher_net.parameters():
p.requires_grad = False
teacher_optim = optim_weight_ema.EMAWeightOptimizer(
teacher_net, student_net, teacher_alpha)
eval_net = teacher_net
elif model == 'pi':
teacher_net = student_net
teacher_optim = None
eval_net = student_net
else:
print('Unknown model type {}'.format(model))
return
BLOCK_SIZE = student_net.BLOCK_SIZE
NET_MEAN, NET_STD = seg_transforms.get_mean_std(ds_tgt, student_net)
if freeze_bn:
if not hasattr(student_net, 'freeze_batchnorm'):
raise ValueError(
'Network {} does not support batchnorm freezing'.format(arch))
clf_crossent_loss = nn.CrossEntropyLoss(ignore_index=255)
print('Built network')
mask_generator = mask_gen.BoxMaskGenerator(
prop_range=mask_prop_range,
n_boxes=boxmask_n_boxes,
random_aspect_ratio=not boxmask_fixed_aspect_ratio,
prop_by_area=not boxmask_by_size,
within_bounds=not boxmask_outside_bounds,
invert=not boxmask_no_invert)
if iters_per_epoch == -1:
iters_per_epoch = len(unsup_ndx) // batch_size
total_iters = iters_per_epoch * num_epochs
lr_epoch_scheduler, lr_iter_scheduler = lr_schedules.make_lr_schedulers(
optimizer=student_optim,
total_iters=total_iters,
schedule_type=lr_sched,
step_epochs=lr_step_epochs,
step_gamma=lr_step_gamma,
poly_power=lr_poly_power)
train_transforms = []
eval_transforms = []
if crop_size is not None:
if aug_scale_hung:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCropScaleHung(
crop_size, (0, 0),
uniform_scale=not aug_scale_non_uniform))
elif aug_max_scale != 1.0 or aug_rot_mag != 0.0:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCropRotateScale(
crop_size, (0, 0),
rot_mag=aug_rot_mag,
max_scale=aug_max_scale,
uniform_scale=not aug_scale_non_uniform,
constrain_rot_scale=True))
else:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomCrop(crop_size, (0, 0)))
else:
if aug_scale_hung:
raise NotImplementedError('aug_scale_hung requires a crop_size')
if aug_hflip or aug_vflip or aug_hvflip:
train_transforms.append(
seg_transforms_cv.SegCVTransformRandomFlip(aug_hflip, aug_vflip,
aug_hvflip))
train_transforms.append(
seg_transforms_cv.SegCVTransformNormalizeToTensor(NET_MEAN, NET_STD))
eval_transforms.append(
seg_transforms_cv.SegCVTransformNormalizeToTensor(NET_MEAN, NET_STD))
train_sup_ds = ds_src.dataset(
labels=True,
mask=False,
xf=False,
pair=False,
transforms=seg_transforms.SegTransformCompose(train_transforms),
pipeline_type='cv')
train_unsup_ds = ds_src.dataset(
labels=False,
mask=True,
xf=False,
pair=False,
transforms=seg_transforms.SegTransformCompose(train_transforms),
pipeline_type='cv')
eval_ds = ds_src.dataset(
labels=True,
mask=False,
xf=False,
pair=False,
transforms=seg_transforms.SegTransformCompose(eval_transforms),
pipeline_type='cv')
add_mask_params_to_batch = mask_gen.AddMaskParamsToBatch(mask_generator)
collate_fn = seg_data.SegCollate(BLOCK_SIZE)
mask_collate_fn = seg_data.SegCollate(
BLOCK_SIZE, batch_aug_fn=add_mask_params_to_batch)
# Train data pipeline: data loaders
sup_sampler = seg_data.RepeatSampler(
torch.utils.data.SubsetRandomSampler(sup_ndx))
train_sup_loader = torch.utils.data.DataLoader(train_sup_ds,
batch_size,
sampler=sup_sampler,
collate_fn=collate_fn,
num_workers=num_workers)
if cons_weight > 0.0:
unsup_sampler = seg_data.RepeatSampler(
torch.utils.data.SubsetRandomSampler(unsup_ndx))
train_unsup_loader_0 = torch.utils.data.DataLoader(
train_unsup_ds,
batch_size,
sampler=unsup_sampler,
collate_fn=mask_collate_fn,
num_workers=num_workers)
if mask_mix:
train_unsup_loader_1 = torch.utils.data.DataLoader(
train_unsup_ds,
batch_size,
sampler=unsup_sampler,
collate_fn=collate_fn,
num_workers=num_workers)
else:
train_unsup_loader_1 = None
else:
train_unsup_loader_0 = None
train_unsup_loader_1 = None
# Eval pipeline
src_val_loader, tgt_val_loader, test_loader = datasets.eval_data_pipeline(
ds_src, ds_tgt, src_val_ndx, tgt_val_ndx, test_ndx, batch_size,
collate_fn, NET_MEAN, NET_STD, num_workers)
# Report setttings
print('Settings:')
print(', '.join([
'{}={}'.format(key, settings[key])
for key in sorted(list(settings.keys()))
]))
# Report dataset size
print('Dataset:')
print('len(sup_ndx)={}'.format(len(sup_ndx)))
print('len(unsup_ndx)={}'.format(len(unsup_ndx)))
if ds_src is not ds_tgt:
print('len(src_val_ndx)={}'.format(len(tgt_val_ndx)))
print('len(tgt_val_ndx)={}'.format(len(tgt_val_ndx)))
else:
print('len(val_ndx)={}'.format(len(tgt_val_ndx)))
if test_ndx is not None:
print('len(test_ndx)={}'.format(len(test_ndx)))
if n_sup != -1:
print('sup_ndx={}'.format(sup_ndx.tolist()))
# Track mIoU for early stopping
best_tgt_miou = None
best_epoch = 0
eval_net_state = {
key: value.detach().cpu().numpy()
for key, value in eval_net.state_dict().items()
}
# Create iterators
train_sup_iter = iter(train_sup_loader)
train_unsup_iter_0 = iter(
train_unsup_loader_0) if train_unsup_loader_0 is not None else None
train_unsup_iter_1 = iter(
train_unsup_loader_1) if train_unsup_loader_1 is not None else None
iter_i = 0
print('Training...')
for epoch_i in range(num_epochs):
if lr_epoch_scheduler is not None:
lr_epoch_scheduler.step(epoch_i)
t1 = time.time()
if rampup > 0:
ramp_val = network_architectures.sigmoid_rampup(epoch_i, rampup)
else:
ramp_val = 1.0
student_net.train()
if teacher_net is not student_net:
teacher_net.train()
if freeze_bn:
student_net.freeze_batchnorm()
if teacher_net is not student_net:
teacher_net.freeze_batchnorm()
sup_loss_acc = 0.0
consistency_loss_acc = 0.0
conf_rate_acc = 0.0
n_sup_batches = 0
n_unsup_batches = 0
src_val_iter = iter(
src_val_loader) if src_val_loader is not None else None
tgt_val_iter = iter(
tgt_val_loader) if tgt_val_loader is not None else None
for sup_batch in itertools.islice(train_sup_iter, iters_per_epoch):
if lr_iter_scheduler is not None:
lr_iter_scheduler.step(iter_i)
student_optim.zero_grad()
#
# Supervised branch
#
batch_x = sup_batch['image'].to(torch_device)
batch_y = sup_batch['labels'].to(torch_device)
logits_sup = student_net(batch_x)
sup_loss = clf_crossent_loss(logits_sup, batch_y[:, 0, :, :])
sup_loss.backward()
if cons_weight > 0.0:
for _ in range(unsup_batch_ratio):
#
# Unsupervised branch
#
if mask_mix:
# Mix mode: batch consists of paired unsupervised samples and mask parameters
unsup_batch0 = next(train_unsup_iter_0)
unsup_batch1 = next(train_unsup_iter_1)
batch_ux0 = unsup_batch0['image'].to(torch_device)
batch_um0 = unsup_batch0['mask'].to(torch_device)
batch_ux1 = unsup_batch1['image'].to(torch_device)
batch_um1 = unsup_batch1['mask'].to(torch_device)
batch_mask_params = unsup_batch0['mask_params'].to(
torch_device)
# batch_um0 and batch_um1 are masks that are 1 for valid pixels, 0 for invalid pixels.
# They are used later on to scale the consistency loss, so that consistency loss is
# only computed for valid pixels.
# Explanation:
# When using geometric augmentations such as rotations, some pixels in the training
# crop may come from outside the bounds of the input image. These pixels will have a value
# of 0 in these masks. Similarly, when using scaled crops, the size of the crop
# from the input image that must be scaled to the size of the training crop may be
# larger than one/both of the input image dimensions. Pixels in the training crop
# that arise from outside the input image bounds will once again be given a value
# of 0 in these masks.
# Convert mask parameters to masks of shape (N,1,H,W)
batch_mix_masks = mask_generator.torch_masks_from_params(
batch_mask_params, batch_ux0.shape[2:4],
torch_device)
# Mix images with masks
batch_ux_mixed = batch_ux0 * (
1 - batch_mix_masks) + batch_ux1 * batch_mix_masks
batch_um_mixed = batch_um0 * (
1 - batch_mix_masks) + batch_um1 * batch_mix_masks
# Get teacher predictions for original images
with torch.no_grad():
logits_u0_tea = teacher_net(batch_ux0).detach()
logits_u1_tea = teacher_net(batch_ux1).detach()
# Get student prediction for mixed image
logits_cons_stu = student_net(batch_ux_mixed)
# Mix teacher predictions using same mask
# It makes no difference whether we do this with logits or probabilities as
# the mask pixels are either 1 or 0
logits_cons_tea = logits_u0_tea * (
1 -
batch_mix_masks) + logits_u1_tea * batch_mix_masks
# Logits -> probs
prob_cons_tea = F.softmax(logits_cons_tea, dim=1)
prob_cons_stu = F.softmax(logits_cons_stu, dim=1)
loss_mask = batch_um_mixed
else:
# Cut mode: batch consists of unsupervised samples and mask params
unsup_batch = next(train_unsup_iter_0)
batch_ux = unsup_batch['image'].to(torch_device)
batch_um = unsup_batch['mask'].to(torch_device)
batch_mask_params = unsup_batch['mask_params'].to(
torch_device)
# Convert mask parameters to masks of shape (N,1,H,W)
batch_cut_masks = mask_generator.torch_masks_from_params(
batch_mask_params, batch_ux.shape[2:4],
torch_device)
# Cut image with mask (mask regions to zero)
batch_ux_cut = batch_ux * batch_cut_masks
# Get teacher predictions for original image
with torch.no_grad():
logits_cons_tea = teacher_net(batch_ux).detach()
# Get student prediction for cut image
logits_cons_stu = student_net(batch_ux_cut)
# Logits -> probs
prob_cons_tea = F.softmax(logits_cons_tea, dim=1)
prob_cons_stu = F.softmax(logits_cons_stu, dim=1)
loss_mask = batch_cut_masks * batch_um
# -- shared by mix and cut --
# Confidence thresholding
if conf_thresh > 0.0:
# Compute confidence of teacher predictions
conf_tea = prob_cons_tea.max(dim=1)[0]
# Compute confidence mask
conf_mask = (conf_tea >=
conf_thresh).float()[:, None, :, :]
# Record rate for reporting
conf_rate_acc += float(conf_mask.mean())
# Average confidence mask if requested
if not conf_per_pixel:
conf_mask = conf_mask.mean()
loss_mask = loss_mask * conf_mask
elif rampup > 0:
conf_rate_acc += ramp_val
# Compute per-pixel consistency loss
# Note that the way we aggregate the loss across the class/channel dimension (1)
# depends on the loss function used. Generally, summing over the class dimension
# keeps the magnitude of the gradient of the loss w.r.t. the logits
# nearly constant w.r.t. the number of classes. When using logit-variance,
# dividing by `sqrt(num_classes)` helps.
if cons_loss_fn == 'var':
delta_prob = prob_cons_stu - prob_cons_tea
consistency_loss = delta_prob * delta_prob
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
elif cons_loss_fn == 'logits_var':
delta_logits = logits_cons_stu - logits_cons_tea
consistency_loss = delta_logits * delta_logits
consistency_loss = consistency_loss.sum(
dim=1, keepdim=True) / root_n_classes
elif cons_loss_fn == 'logits_smoothl1':
consistency_loss = F.smooth_l1_loss(logits_cons_stu,
logits_cons_tea,
reduce=False)
consistency_loss = consistency_loss.sum(
dim=1, keepdim=True) / root_n_classes
elif cons_loss_fn == 'bce':
consistency_loss = network_architectures.robust_binary_crossentropy(
prob_cons_stu, prob_cons_tea)
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
elif cons_loss_fn == 'kld':
consistency_loss = F.kl_div(F.log_softmax(
logits_cons_stu, dim=1),
prob_cons_tea,
reduce=False)
consistency_loss = consistency_loss.sum(dim=1,
keepdim=True)
else:
raise ValueError(
'Unknown consistency loss function {}'.format(
cons_loss_fn))
# Apply consistency loss mask and take the mean over pixels and images
consistency_loss = (consistency_loss * loss_mask).mean()
# Modulate with rampup if desired
if rampup > 0:
consistency_loss = consistency_loss * ramp_val
# Weight the consistency loss and back-prop
unsup_loss = consistency_loss * cons_weight
unsup_loss.backward()
consistency_loss_acc += float(consistency_loss.detach())
n_unsup_batches += 1
student_optim.step()
if teacher_optim is not None:
teacher_optim.step()
sup_loss_val = float(sup_loss.detach())
if np.isnan(sup_loss_val):
print('NaN detected; network dead, bailing.')
return
sup_loss_acc += sup_loss_val
n_sup_batches += 1
iter_i += 1
sup_loss_acc /= n_sup_batches
if n_unsup_batches > 0:
consistency_loss_acc /= n_unsup_batches
conf_rate_acc /= n_unsup_batches
eval_net.eval()
if ds_src is not ds_tgt:
src_iou_eval = evaluation.EvaluatorIoU(ds_src.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in src_val_iter:
batch_x = batch['image'].to(torch_device)
batch_y = batch['labels'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i in range(len(batch_y)):
src_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
src_iou = src_iou_eval.score()
src_miou = src_iou.mean()
else:
src_iou_eval = src_iou = src_miou = None
tgt_iou_eval = evaluation.EvaluatorIoU(ds_tgt.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in tgt_val_iter:
batch_x = batch['image'].to(torch_device)
batch_y = batch['labels'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i in range(len(batch_y)):
tgt_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
tgt_iou = tgt_iou_eval.score()
tgt_miou = tgt_iou.mean()
t2 = time.time()
if ds_src is not ds_tgt:
print(
'Epoch {}: took {:.3f}s, TRAIN clf loss={:.6f}, consistency loss={:.6f}, conf rate={:.3%}, '
'SRC VAL mIoU={:.3%}, TGT VAL mIoU={:.3%}'.format(
epoch_i + 1, t2 - t1, sup_loss_acc, consistency_loss_acc,
conf_rate_acc, src_miou, tgt_miou))
print('-- SRC {}'.format(', '.join(
['{:.3%}'.format(x) for x in src_iou])))
print('-- TGT {}'.format(', '.join(
['{:.3%}'.format(x) for x in tgt_iou])))
else:
print(
'Epoch {}: took {:.3f}s, TRAIN clf loss={:.6f}, consistency loss={:.6f}, conf rate={:.3%}, VAL mIoU={:.3%}'
.format(epoch_i + 1, t2 - t1, sup_loss_acc,
consistency_loss_acc, conf_rate_acc, tgt_miou))
print('-- {}'.format(', '.join(
['{:.3%}'.format(x) for x in tgt_iou])))
if save_model:
model_path = os.path.join(submit_config.run_dir, "model.pth")
torch.save(eval_net, model_path)
if save_preds:
out_dir = os.path.join(submit_config.run_dir, 'preds')
os.makedirs(out_dir, exist_ok=True)
with torch.no_grad():
for batch in tgt_val_loader:
batch_x = batch['image'].to(torch_device)
batch_ndx = batch['index'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i, sample_ndx in enumerate(batch_ndx):
ds_tgt.save_prediction_by_index(
out_dir, pred_y[sample_i].astype(np.uint32),
sample_ndx)
else:
out_dir = None
if test_loader is not None:
test_iou_eval = evaluation.EvaluatorIoU(ds_tgt.num_classes,
bin_fill_holes)
with torch.no_grad():
for batch in test_loader:
batch_x = batch['image'].to(torch_device)
batch_ndx = batch['index'].numpy()
logits = eval_net(batch_x)
pred_y = torch.argmax(logits, dim=1).detach().cpu().numpy()
for sample_i, sample_ndx in enumerate(batch_ndx):
if save_preds:
ds_tgt.save_prediction_by_index(
out_dir, pred_y[sample_i].astype(np.uint32),
sample_ndx)
test_iou_eval.sample(batch_y[sample_i, 0],
pred_y[sample_i],
ignore_value=255)
test_iou = test_iou_eval.score()
test_miou = test_iou.mean()
print('FINAL TEST: mIoU={:.3%}'.format(test_miou))
print('-- TEST {}'.format(', '.join(
['{:.3%}'.format(x) for x in test_iou])))
