def run(train_dir,
val_dir,
test_dir,
img_size=[256, 256],
img_scale=None,
rescale_factor=None,
featurewise_center=True,
featurewise_mean=59.6,
equalize_hist=True,
augmentation=False,
class_list=['background', 'malignant', 'benign'],
batch_size=64,
train_bs_multiplier=.5,
nb_epoch=5,
top_layer_epochs=10,
all_layer_epochs=20,
load_val_ram=False,
load_train_ram=False,
net='resnet50',
use_pretrained=True,
nb_init_filter=32,
init_filter_size=5,
init_conv_stride=2,
pool_size=2,
pool_stride=2,
weight_decay=.0001,
weight_decay2=.0001,
alpha=.0001,
l1_ratio=.0,
inp_dropout=.0,
hidden_dropout=.0,
hidden_dropout2=.0,
optim='sgd',
init_lr=.01,
lr_patience=10,
es_patience=25,
resume_from=None,
auto_batch_balance=False,
pos_cls_weight=1.0,
neg_cls_weight=1.0,
top_layer_nb=None,
top_layer_multiplier=.1,
all_layer_multiplier=.01,
best_model='./modelState/patch_clf.h5',
final_model="NOSAVE"):
'''Train a deep learning model for patch classifications
'''
best_model_dir = os.path.dirname(best_model)
if not os.path.exists(best_model_dir):
os.makedirs(best_model_dir)
if final_model != "NOSAVE":
final_model_dir = os.path.dirname(final_model)
if not os.path.exists(final_model_dir):
os.makedirs(final_model_dir)
# ======= Environmental variables ======== #
random_seed = int(os.getenv('RANDOM_SEED', 12345))
nb_worker = int(os.getenv('NUM_CPU_CORES', 4))
gpu_count = int(os.getenv('NUM_GPU_DEVICES', 1))
# ========= Image generator ============== #
if featurewise_center:
print "Using feature-wise centering, mean:", featurewise_mean
train_imgen = DMImageDataGenerator(featurewise_center=True)
val_imgen = DMImageDataGenerator(featurewise_center=True)
test_imgen = DMImageDataGenerator(featurewise_center=True)
train_imgen.mean = featurewise_mean
val_imgen.mean = featurewise_mean
test_imgen.mean = featurewise_mean
else:
train_imgen = DMImageDataGenerator()
val_imgen = DMImageDataGenerator()
test_imgen = DMImageDataGenerator()
# Add augmentation options.
if augmentation:
train_imgen.horizontal_flip = True
train_imgen.vertical_flip = True
train_imgen.rotation_range = 25. # in degree.
train_imgen.shear_range = .2 # in radians.
train_imgen.zoom_range = [.8, 1.2] # in proportion.
train_imgen.channel_shift_range = 20. # in pixel intensity values.
# ================= Model creation ============== #
model, preprocess_input, top_layer_nb = get_dl_model(
net,
nb_class=len(class_list),
use_pretrained=use_pretrained,
resume_from=resume_from,
img_size=img_size,
top_layer_nb=top_layer_nb,
weight_decay=weight_decay,
hidden_dropout=hidden_dropout,
nb_init_filter=nb_init_filter,
init_filter_size=init_filter_size,
init_conv_stride=init_conv_stride,
pool_size=pool_size,
pool_stride=pool_stride,
alpha=alpha,
l1_ratio=l1_ratio,
inp_dropout=inp_dropout)
if featurewise_center:
preprocess_input = None
if gpu_count > 1:
model, org_model = make_parallel(model, gpu_count)
else:
org_model = model
# ============ Train & validation set =============== #
train_bs = int(batch_size * train_bs_multiplier)
if net != 'yaroslav':
dup_3_channels = True
else:
dup_3_channels = False
if load_train_ram:
raw_imgen = DMImageDataGenerator()
print "Create generator for raw train set"
raw_generator = raw_imgen.flow_from_directory(
train_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
batch_size=train_bs,
shuffle=False)
print "Loading raw train set into RAM.",
sys.stdout.flush()
raw_set = load_dat_ram(raw_generator, raw_generator.nb_sample)
print "Done."
sys.stdout.flush()
print "Create generator for train set"
train_generator = train_imgen.flow(
raw_set[0],
raw_set[1],
batch_size=train_bs,
auto_batch_balance=auto_batch_balance,
preprocess=preprocess_input,
shuffle=True,
seed=random_seed)
else:
print "Create generator for train set"
train_generator = train_imgen.flow_from_directory(
train_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
auto_batch_balance=auto_batch_balance,
batch_size=train_bs,
preprocess=preprocess_input,
shuffle=True,
seed=random_seed)
print "Create generator for val set"
validation_set = val_imgen.flow_from_directory(
val_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
batch_size=batch_size,
preprocess=preprocess_input,
shuffle=False)
sys.stdout.flush()
if load_val_ram:
print "Loading validation set into RAM.",
sys.stdout.flush()
validation_set = load_dat_ram(validation_set, validation_set.nb_sample)
print "Done."
sys.stdout.flush()
# ==================== Model training ==================== #
# Do 3-stage training.
train_batches = int(train_generator.nb_sample / train_bs) + 1
if isinstance(validation_set, tuple):
val_samples = len(validation_set[0])
else:
val_samples = validation_set.nb_sample
validation_steps = int(val_samples / batch_size)
#### DEBUG ####
# val_samples = 100
#### DEBUG ####
# import pdb; pdb.set_trace()
model, loss_hist, acc_hist = do_3stage_training(
model,
org_model,
train_generator,
validation_set,
validation_steps,
best_model,
train_batches,
top_layer_nb,
net,
nb_epoch=nb_epoch,
top_layer_epochs=top_layer_epochs,
all_layer_epochs=all_layer_epochs,
use_pretrained=use_pretrained,
optim=optim,
init_lr=init_lr,
top_layer_multiplier=top_layer_multiplier,
all_layer_multiplier=all_layer_multiplier,
es_patience=es_patience,
lr_patience=lr_patience,
auto_batch_balance=auto_batch_balance,
nb_class=len(class_list),
pos_cls_weight=pos_cls_weight,
neg_cls_weight=neg_cls_weight,
nb_worker=nb_worker,
weight_decay2=weight_decay2,
hidden_dropout2=hidden_dropout2)
# Training report.
if len(loss_hist) > 0:
min_loss_locs, = np.where(loss_hist == min(loss_hist))
best_val_loss = loss_hist[min_loss_locs[0]]
best_val_accuracy = acc_hist[min_loss_locs[0]]
print "\n==== Training summary ===="
print "Minimum val loss achieved at epoch:", min_loss_locs[0] + 1
print "Best val loss:", best_val_loss
print "Best val accuracy:", best_val_accuracy
if final_model != "NOSAVE":
model.save(final_model)
# ==== Predict on test set ==== #
print "\n==== Predicting on test set ===="
test_generator = test_imgen.flow_from_directory(
test_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
batch_size=batch_size,
preprocess=preprocess_input,
shuffle=False)
if test_generator.nb_sample:
print "Test samples =", test_generator.nb_sample
print "Load saved best model:", best_model + '.',
sys.stdout.flush()
org_model.load_weights(best_model)
print "Done."
test_steps = int(test_generator.nb_sample / batch_size)
#### DEBUG ####
# test_samples = 10
#### DEBUG ####
test_res = model.evaluate_generator(
test_generator,
test_steps,
nb_worker=nb_worker,
pickle_safe=True if nb_worker > 1 else False)
print "Evaluation result on test set:", test_res
else:
print "Skip testing because no test sample is found."
def run(train_dir,
val_dir,
test_dir,
patch_model_state=None,
resume_from=None,
img_size=[1152, 896],
img_scale=None,
rescale_factor=None,
featurewise_center=True,
featurewise_mean=52.16,
equalize_hist=False,
augmentation=True,
class_list=['neg', 'pos'],
patch_net='resnet50',
block_type='resnet',
top_depths=[512, 512],
top_repetitions=[3, 3],
bottleneck_enlarge_factor=4,
add_heatmap=False,
avg_pool_size=[7, 7],
add_conv=True,
add_shortcut=False,
hm_strides=(1, 1),
hm_pool_size=(5, 5),
fc_init_units=64,
fc_layers=2,
top_layer_nb=None,
batch_size=64,
train_bs_multiplier=.5,
nb_epoch=5,
all_layer_epochs=20,
load_val_ram=False,
load_train_ram=False,
weight_decay=.0001,
hidden_dropout=.0,
weight_decay2=.0001,
hidden_dropout2=.0,
optim='sgd',
init_lr=.01,
lr_patience=10,
es_patience=25,
auto_batch_balance=False,
pos_cls_weight=1.0,
neg_cls_weight=1.0,
all_layer_multiplier=.1,
best_model='./modelState/image_clf.h5',
final_model="NOSAVE"):
'''Train a deep learning model for image classifications
'''
# ======= Environmental variables ======== #
random_seed = int(os.getenv('RANDOM_SEED', 12345))
nb_worker = int(os.getenv('NUM_CPU_CORES', 4))
gpu_count = int(os.getenv('NUM_GPU_DEVICES', 1))
# ========= Image generator ============== #
if featurewise_center:
train_imgen = DMImageDataGenerator(featurewise_center=True)
val_imgen = DMImageDataGenerator(featurewise_center=True)
test_imgen = DMImageDataGenerator(featurewise_center=True)
train_imgen.mean = featurewise_mean
val_imgen.mean = featurewise_mean
test_imgen.mean = featurewise_mean
else:
train_imgen = DMImageDataGenerator()
val_imgen = DMImageDataGenerator()
test_imgen = DMImageDataGenerator()
# Add augmentation options.
if augmentation:
train_imgen.horizontal_flip = True
train_imgen.vertical_flip = True
train_imgen.rotation_range = 25. # in degree.
train_imgen.shear_range = .2 # in radians.
train_imgen.zoom_range = [.8, 1.2] # in proportion.
train_imgen.channel_shift_range = 20. # in pixel intensity values.
# ================= Model creation ============== #
if resume_from is not None:
image_model = load_model(resume_from, compile=False)
else:
patch_model = load_model(patch_model_state, compile=False)
image_model, top_layer_nb = add_top_layers(
patch_model,
img_size,
patch_net,
block_type,
top_depths,
top_repetitions,
bottleneck_org,
nb_class=len(class_list),
shortcut_with_bn=True,
bottleneck_enlarge_factor=bottleneck_enlarge_factor,
dropout=hidden_dropout,
weight_decay=weight_decay,
add_heatmap=add_heatmap,
avg_pool_size=avg_pool_size,
add_conv=add_conv,
add_shortcut=add_shortcut,
hm_strides=hm_strides,
hm_pool_size=hm_pool_size,
fc_init_units=fc_init_units,
fc_layers=fc_layers)
if gpu_count > 1:
image_model, org_model = make_parallel(image_model, gpu_count)
else:
org_model = image_model
# ============ Train & validation set =============== #
train_bs = int(batch_size * train_bs_multiplier)
dup_3_channels = True
if load_train_ram:
raw_imgen = DMImageDataGenerator()
print "Create generator for raw train set"
raw_generator = raw_imgen.flow_from_directory(
train_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
batch_size=train_bs,
shuffle=False)
print "Loading raw train set into RAM.",
sys.stdout.flush()
raw_set = load_dat_ram(raw_generator, raw_generator.nb_sample)
print "Done."
sys.stdout.flush()
print "Create generator for train set"
train_generator = train_imgen.flow(
raw_set[0],
raw_set[1],
batch_size=train_bs,
auto_batch_balance=auto_batch_balance,
shuffle=True,
seed=random_seed)
else:
print "Create generator for train set"
train_generator = train_imgen.flow_from_directory(
train_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
auto_batch_balance=auto_batch_balance,
batch_size=train_bs,
shuffle=True,
seed=random_seed)
print "Create generator for val set"
validation_set = val_imgen.flow_from_directory(
val_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
batch_size=batch_size,
shuffle=False)
sys.stdout.flush()
if load_val_ram:
print "Loading validation set into RAM.",
sys.stdout.flush()
validation_set = load_dat_ram(validation_set, validation_set.nb_sample)
print "Done."
sys.stdout.flush()
# ==================== Model training ==================== #
# Do 2-stage training.
train_batches = int(train_generator.nb_sample / train_bs) + 1
if isinstance(validation_set, tuple):
val_samples = len(validation_set[0])
else:
val_samples = validation_set.nb_sample
validation_steps = int(val_samples / batch_size)
#### DEBUG ####
# train_batches = 1
# val_samples = batch_size*5
# validation_steps = 5
#### DEBUG ####
if load_val_ram:
auc_checkpointer = DMAucModelCheckpoint(best_model,
validation_set,
batch_size=batch_size)
else:
auc_checkpointer = DMAucModelCheckpoint(best_model,
validation_set,
test_samples=val_samples)
# import pdb; pdb.set_trace()
image_model, loss_hist, acc_hist = do_2stage_training(
image_model,
org_model,
train_generator,
validation_set,
validation_steps,
best_model,
train_batches,
top_layer_nb,
nb_epoch=nb_epoch,
all_layer_epochs=all_layer_epochs,
optim=optim,
init_lr=init_lr,
all_layer_multiplier=all_layer_multiplier,
es_patience=es_patience,
lr_patience=lr_patience,
auto_batch_balance=auto_batch_balance,
pos_cls_weight=pos_cls_weight,
neg_cls_weight=neg_cls_weight,
nb_worker=nb_worker,
auc_checkpointer=auc_checkpointer,
weight_decay=weight_decay,
hidden_dropout=hidden_dropout,
weight_decay2=weight_decay2,
hidden_dropout2=hidden_dropout2,
)
# Training report.
if len(loss_hist) > 0:
min_loss_locs, = np.where(loss_hist == min(loss_hist))
best_val_loss = loss_hist[min_loss_locs[0]]
best_val_accuracy = acc_hist[min_loss_locs[0]]
print "\n==== Training summary ===="
print "Minimum val loss achieved at epoch:", min_loss_locs[0] + 1
print "Best val loss:", best_val_loss
print "Best val accuracy:", best_val_accuracy
if final_model != "NOSAVE":
image_model.save(final_model)
# ==== Predict on test set ==== #
print "\n==== Predicting on test set ===="
test_generator = test_imgen.flow_from_directory(
test_dir,
target_size=img_size,
target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist,
dup_3_channels=dup_3_channels,
classes=class_list,
class_mode='categorical',
batch_size=batch_size,
shuffle=False)
test_samples = test_generator.nb_sample
#### DEBUG ####
# test_samples = 5
#### DEBUG ####
print "Test samples =", test_samples
print "Load saved best model:", best_model + '.',
sys.stdout.flush()
org_model.load_weights(best_model)
print "Done."
# test_steps = int(test_generator.nb_sample/batch_size)
# test_res = image_model.evaluate_generator(
# test_generator, test_steps, nb_worker=nb_worker,
# pickle_safe=True if nb_worker > 1 else False)
test_auc = DMAucModelCheckpoint.calc_test_auc(test_generator,
image_model,
test_samples=test_samples)
print "AUROC on test set:", test_auc
def run(train_dir, val_dir, test_dir, patch_model_state=None, resume_from=None,
img_size=[1152, 896], img_scale=None, rescale_factor=None,
featurewise_center=True, featurewise_mean=52.16,
equalize_hist=False, augmentation=True,
class_list=['neg', 'pos'], patch_net='resnet50',
block_type='resnet', top_depths=[512, 512], top_repetitions=[3, 3],
bottleneck_enlarge_factor=4,
add_heatmap=False, avg_pool_size=[7, 7],
add_conv=True, add_shortcut=False,
hm_strides=(1,1), hm_pool_size=(5,5),
fc_init_units=64, fc_layers=2,
top_layer_nb=None,
batch_size=64, train_bs_multiplier=.5,
nb_epoch=5, all_layer_epochs=20,
load_val_ram=False, load_train_ram=False,
weight_decay=.0001, hidden_dropout=.0,
weight_decay2=.0001, hidden_dropout2=.0,
optim='sgd', init_lr=.01, lr_patience=10, es_patience=25,
auto_batch_balance=False, pos_cls_weight=1.0, neg_cls_weight=1.0,
all_layer_multiplier=.1,
best_model='./modelState/image_clf.h5',
final_model="NOSAVE"):
'''Train a deep learning model for image classifications
'''
# ======= Environmental variables ======== #
random_seed = int(os.getenv('RANDOM_SEED', 12345))
nb_worker = int(os.getenv('NUM_CPU_CORES', 4))
gpu_count = int(os.getenv('NUM_GPU_DEVICES', 1))
# ========= Image generator ============== #
if featurewise_center:
train_imgen = DMImageDataGenerator(featurewise_center=True)
val_imgen = DMImageDataGenerator(featurewise_center=True)
test_imgen = DMImageDataGenerator(featurewise_center=True)
train_imgen.mean = featurewise_mean
val_imgen.mean = featurewise_mean
test_imgen.mean = featurewise_mean
else:
train_imgen = DMImageDataGenerator()
val_imgen = DMImageDataGenerator()
test_imgen = DMImageDataGenerator()
# Add augmentation options.
if augmentation:
train_imgen.horizontal_flip = True
train_imgen.vertical_flip = True
train_imgen.rotation_range = 25. # in degree.
train_imgen.shear_range = .2 # in radians.
train_imgen.zoom_range = [.8, 1.2] # in proportion.
train_imgen.channel_shift_range = 20. # in pixel intensity values.
# ================= Model creation ============== #
if resume_from is not None:
image_model = load_model(resume_from, compile=False)
else:
patch_model = load_model(patch_model_state, compile=False)
image_model, top_layer_nb = add_top_layers(
patch_model, img_size, patch_net, block_type,
top_depths, top_repetitions, bottleneck_org,
nb_class=len(class_list), shortcut_with_bn=True,
bottleneck_enlarge_factor=bottleneck_enlarge_factor,
dropout=hidden_dropout, weight_decay=weight_decay,
add_heatmap=add_heatmap, avg_pool_size=avg_pool_size,
add_conv=add_conv, add_shortcut=add_shortcut,
hm_strides=hm_strides, hm_pool_size=hm_pool_size,
fc_init_units=fc_init_units, fc_layers=fc_layers)
if gpu_count > 1:
image_model, org_model = make_parallel(image_model, gpu_count)
else:
org_model = image_model
# ============ Train & validation set =============== #
train_bs = int(batch_size*train_bs_multiplier)
if patch_net != 'yaroslav':
dup_3_channels = True
else:
dup_3_channels = False
if load_train_ram:
raw_imgen = DMImageDataGenerator()
print "Create generator for raw train set"
raw_generator = raw_imgen.flow_from_directory(
train_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
batch_size=train_bs, shuffle=False)
print "Loading raw train set into RAM.",
sys.stdout.flush()
raw_set = load_dat_ram(raw_generator, raw_generator.nb_sample)
print "Done."; sys.stdout.flush()
print "Create generator for train set"
train_generator = train_imgen.flow(
raw_set[0], raw_set[1], batch_size=train_bs,
auto_batch_balance=auto_batch_balance,
shuffle=True, seed=random_seed)
else:
print "Create generator for train set"
train_generator = train_imgen.flow_from_directory(
train_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
auto_batch_balance=auto_batch_balance, batch_size=train_bs,
shuffle=True, seed=random_seed)
print "Create generator for val set"
validation_set = val_imgen.flow_from_directory(
val_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
batch_size=batch_size, shuffle=False)
sys.stdout.flush()
if load_val_ram:
print "Loading validation set into RAM.",
sys.stdout.flush()
validation_set = load_dat_ram(validation_set, validation_set.nb_sample)
print "Done."; sys.stdout.flush()
# ==================== Model training ==================== #
# Do 2-stage training.
train_batches = int(train_generator.nb_sample/train_bs) + 1
if isinstance(validation_set, tuple):
val_samples = len(validation_set[0])
else:
val_samples = validation_set.nb_sample
validation_steps = int(val_samples/batch_size)
#### DEBUG ####
# train_batches = 1
# val_samples = batch_size*5
# validation_steps = 5
#### DEBUG ####
if load_val_ram:
auc_checkpointer = DMAucModelCheckpoint(
best_model, validation_set, batch_size=batch_size)
else:
auc_checkpointer = DMAucModelCheckpoint(
best_model, validation_set, test_samples=val_samples)
# import pdb; pdb.set_trace()
image_model, loss_hist, acc_hist = do_2stage_training(
image_model, org_model, train_generator, validation_set, validation_steps,
best_model, train_batches, top_layer_nb, nb_epoch=nb_epoch,
all_layer_epochs=all_layer_epochs,
optim=optim, init_lr=init_lr,
all_layer_multiplier=all_layer_multiplier,
es_patience=es_patience, lr_patience=lr_patience,
auto_batch_balance=auto_batch_balance,
pos_cls_weight=pos_cls_weight, neg_cls_weight=neg_cls_weight,
nb_worker=nb_worker, auc_checkpointer=auc_checkpointer,
weight_decay=weight_decay, hidden_dropout=hidden_dropout,
weight_decay2=weight_decay2, hidden_dropout2=hidden_dropout2,)
# Training report.
if len(loss_hist) > 0:
min_loss_locs, = np.where(loss_hist == min(loss_hist))
best_val_loss = loss_hist[min_loss_locs[0]]
best_val_accuracy = acc_hist[min_loss_locs[0]]
print "\n==== Training summary ===="
print "Minimum val loss achieved at epoch:", min_loss_locs[0] + 1
print "Best val loss:", best_val_loss
print "Best val accuracy:", best_val_accuracy
if final_model != "NOSAVE":
image_model.save(final_model)
# ==== Predict on test set ==== #
print "\n==== Predicting on test set ===="
test_generator = test_imgen.flow_from_directory(
test_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical', batch_size=batch_size,
shuffle=False)
test_samples = test_generator.nb_sample
#### DEBUG ####
# test_samples = 5
#### DEBUG ####
print "Test samples =", test_samples
print "Load saved best model:", best_model + '.',
sys.stdout.flush()
org_model.load_weights(best_model)
print "Done."
# test_steps = int(test_generator.nb_sample/batch_size)
# test_res = image_model.evaluate_generator(
# test_generator, test_steps, nb_worker=nb_worker,
# pickle_safe=True if nb_worker > 1 else False)
test_auc = DMAucModelCheckpoint.calc_test_auc(
test_generator, image_model, test_samples=test_samples)
print "AUROC on test set:", test_auc
def run(train_dir, val_dir, test_dir,
img_size=[256, 256], img_scale=None, rescale_factor=None,
featurewise_center=True, featurewise_mean=59.6,
equalize_hist=True, augmentation=False,
class_list=['background', 'malignant', 'benign'],
batch_size=64, train_bs_multiplier=.5, nb_epoch=5,
top_layer_epochs=10, all_layer_epochs=20,
load_val_ram=False, load_train_ram=False,
net='resnet50', use_pretrained=True,
nb_init_filter=32, init_filter_size=5, init_conv_stride=2,
pool_size=2, pool_stride=2,
weight_decay=.0001, weight_decay2=.0001,
alpha=.0001, l1_ratio=.0,
inp_dropout=.0, hidden_dropout=.0, hidden_dropout2=.0,
optim='sgd', init_lr=.01, lr_patience=10, es_patience=25,
resume_from=None, auto_batch_balance=False,
pos_cls_weight=1.0, neg_cls_weight=1.0,
top_layer_nb=None, top_layer_multiplier=.1, all_layer_multiplier=.01,
best_model='./modelState/patch_clf.h5',
final_model="NOSAVE"):
'''Train a deep learning model for patch classifications
'''
#给块分类训练一个深度学习模型
# ======= Environmental variables ======== #
random_seed = int(os.getenv('RANDOM_SEED', 12345))
nb_worker = int(os.getenv('NUM_CPU_CORES', 4))
gpu_count = int(os.getenv('NUM_GPU_DEVICES', 1))
# ========= Image generator ============== #图片生成
if featurewise_center:#数据集去中心化
train_imgen = DMImageDataGenerator(featurewise_center=True)
val_imgen = DMImageDataGenerator(featurewise_center=True)
test_imgen = DMImageDataGenerator(featurewise_center=True)
train_imgen.mean = featurewise_mean
val_imgen.mean = featurewise_mean
test_imgen.mean = featurewise_mean
else:
train_imgen = DMImageDataGenerator()
val_imgen = DMImageDataGenerator()
test_imgen = DMImageDataGenerator()
# Add augmentation options.
#图像增强
if augmentation:
train_imgen.horizontal_flip = True #进行随机水平翻转
train_imgen.vertical_flip = True#进行随机垂直翻转
train_imgen.rotation_range = 25. # in degree.#整数，数据提升时图片随机转动的角度
train_imgen.shear_range = .2 # in radians.浮点数，剪切强度（逆时针方向的剪切变换角度）
train_imgen.zoom_range = [.8, 1.2] # in proportion.
'''
浮点数或形如[lower,upper]的列表，随机缩放的幅度，若为浮点数，则相当于[lower,upper] = [1 - zoom_range, 1+zoom_range]
'''
train_imgen.channel_shift_range = 20. # in pixel intensity values.
#.浮点数，随机通道偏移的幅度
#通过对颜色通道的数值偏移，改变图片的整体的颜色
# ================= Model creation ============== #模型创建
'''
一、weight decay（权值衰减）使用的目的是防止过拟合。
在损失函数中，weight decay是放在正则项（regularization）前面的一个系数，正则项一般指示模型的复杂度，
所以weight decay的作用是调节模型复杂度对损失函数的影响，若weight decay很大，则复杂的模型损失函数的值也就大。
hidden_dropout 防止过拟合
init_conv_stride 卷积核步幅大小
pool_size 池化层大小，pool_stride 池化层步幅（一般是最大值池化，和平均值）
alpha 给图像添加透明度
l1_ratio 交叉验证选择l1和l2惩罚之间的折中,类可以通过交叉验证来设置 alpha(α) 和 l1_ratio(ρ) **参数 :l1_ratio 参数来控制L1和L2的凸组合
inp_dropout 输入权重随机抛弃
'''
model, preprocess_input, top_layer_nb = get_dl_model(
net, nb_class=len(class_list), use_pretrained=use_pretrained,
resume_from=resume_from, img_size=img_size, top_layer_nb=top_layer_nb,
weight_decay=weight_decay, hidden_dropout=hidden_dropout,
nb_init_filter=nb_init_filter, init_filter_size=init_filter_size,
init_conv_stride=init_conv_stride, pool_size=pool_size,
pool_stride=pool_stride, alpha=alpha, l1_ratio=l1_ratio,
inp_dropout=inp_dropout)
if featurewise_center:
preprocess_input = None
if gpu_count > 1:
model, org_model = make_parallel(model, gpu_count)#并行计算
else:
org_model = model
# ============ Train & validation set =============== #
#训练和验证集
train_bs = int(batch_size*train_bs_multiplier)#每批数据量的大小*乘数
if net != 'yaroslav':#dm_keras_ext.py
dup_3_channels = True
else:
dup_3_channels = False
if load_train_ram:
raw_imgen = DMImageDataGenerator()#t图片数据生成器
#创建行训练集数据生成器
print ("Create generator for raw train set")
#以文件夹路径为参数,生成经过数据提升/归一化后的数据,在一个无限循环中无限产生batch数据
'''
equalize_hist 直方图均衡，
shuffle 随机打乱数据
'''
raw_generator = raw_imgen.flow_from_directory(
train_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
batch_size=train_bs, shuffle=False)
#加载行训练数据集到内存
print ("Loading raw train set into RAM.",sys.stdout.flush())
#行数据集
raw_set = load_dat_ram(raw_generator, raw_generator.nb_sample)
print ("Done."); sys.stdout.flush()
#为训练集创建生成器
print ("Create generator for train set")
#接收numpy数组和标签为参数,生成经过数据提升或标准化后的batch数据,并在一个无限循环中不断的返回batch数据
train_generator = train_imgen.flow(
raw_set[0], raw_set[1], batch_size=train_bs,
auto_batch_balance=auto_batch_balance, preprocess=preprocess_input,
shuffle=True, seed=random_seed)
else:
print ("Create generator for train set")
#以文件夹路径为参数,生成经过数据提升/归一化后的数据,在一个无限循环中无限产生batch数据
train_generator = train_imgen.flow_from_directory(
train_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
auto_batch_balance=auto_batch_balance, batch_size=train_bs,
preprocess=preprocess_input, shuffle=True, seed=random_seed)
#创建验证集生成器
print ("Create generator for val set")
# 以文件夹路径为参数,生成经过数据提升/归一化后的数据,在一个无限循环中无限产生batch数据
validation_set = val_imgen.flow_from_directory(
val_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
batch_size=batch_size, preprocess=preprocess_input, shuffle=False)
sys.stdout.flush()
#是否加载验证集到内存中
if load_val_ram:
print ("Loading validation set into RAM.",
sys.stdout.flush())
validation_set = load_dat_ram(validation_set, validation_set.nb_sample)
print ("Done."); sys.stdout.flush()
# ==================== Model training ==================== #模型训练
# Do 3-stage training.三个阶段训练
train_batches = int(train_generator.nb_sample/train_bs) + 1
#判断验证集是否三元组
if isinstance(validation_set, tuple):
val_samples = len(validation_set[0])
else:
val_samples = validation_set.nb_sample
validation_steps = int(val_samples/batch_size)
#### DEBUG ####
# val_samples = 100
#### DEBUG ####
# import pdb; pdb.set_trace()
#通过三阶段训练得到模型，损失率，准确率
model, loss_hist, acc_hist = do_3stage_training(
model, org_model, train_generator, validation_set, validation_steps,
best_model, train_batches, top_layer_nb, net, nb_epoch=nb_epoch,
top_layer_epochs=top_layer_epochs, all_layer_epochs=all_layer_epochs,
use_pretrained=use_pretrained, optim=optim, init_lr=init_lr,
top_layer_multiplier=top_layer_multiplier,
all_layer_multiplier=all_layer_multiplier,
es_patience=es_patience, lr_patience=lr_patience,
auto_batch_balance=auto_batch_balance, nb_class=len(class_list),
pos_cls_weight=pos_cls_weight, neg_cls_weight=neg_cls_weight,
nb_worker=nb_worker, weight_decay2=weight_decay2,
hidden_dropout2=hidden_dropout2)
# Training report.
#训练报告
if len(loss_hist) > 0:
min_loss_locs, = np.where(loss_hist == min(loss_hist))
best_val_loss = loss_hist[min_loss_locs[0]]
best_val_accuracy = acc_hist[min_loss_locs[0]]
print ("\n==== Training summary ====")
print ("Minimum val loss achieved at epoch:", min_loss_locs[0] + 1)
print ("Best val loss:", best_val_loss)
print ("Best val accuracy:", best_val_accuracy)
#保存模型
if final_model != "NOSAVE":
model.save(final_model)
# ==== Predict on test set ==== #
#基于测试集的预测
print ("\n==== Predicting on test set ====")
# 以文件夹路径为参数,生成经过数据提升/归一化后的数据,在一个无限循环中无限产生batch数据
test_generator = test_imgen.flow_from_directory(
test_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical', batch_size=batch_size,
preprocess=preprocess_input, shuffle=False)
print ("Test samples =", test_generator.nb_sample)
#加载最好的模型
print ("Load saved best model:", best_model + '.',
sys.stdout.flush())
#原始模型加载最好模型的权重
org_model.load_weights(best_model)
print ("Done.")
#测试的步数
test_steps = int(test_generator.nb_sample/batch_size)
#### DEBUG ####
# test_samples = 10
#### DEBUG ####
test_res = model.evaluate_generator(
test_generator, test_steps, nb_worker=nb_worker,
pickle_safe=True if nb_worker > 1 else False)
print ("Evaluation result on test set:", test_res)
def run(train_dir, val_dir, test_dir,
img_size=[256, 256], img_scale=None, rescale_factor=None,
featurewise_center=True, featurewise_mean=59.6,
equalize_hist=True, augmentation=False,
class_list=['background', 'malignant', 'benign'],
batch_size=64, train_bs_multiplier=.5, nb_epoch=5,
top_layer_epochs=10, all_layer_epochs=20,
load_val_ram=False, load_train_ram=False,
net='resnet50', use_pretrained=True,
nb_init_filter=32, init_filter_size=5, init_conv_stride=2,
pool_size=2, pool_stride=2,
weight_decay=.0001, weight_decay2=.0001,
alpha=.0001, l1_ratio=.0,
inp_dropout=.0, hidden_dropout=.0, hidden_dropout2=.0,
optim='sgd', init_lr=.01, lr_patience=10, es_patience=25,
resume_from=None, auto_batch_balance=False,
pos_cls_weight=1.0, neg_cls_weight=1.0,
top_layer_nb=None, top_layer_multiplier=.1, all_layer_multiplier=.01,
best_model='./modelState/patch_clf.h5',
final_model="NOSAVE"):
'''Train a deep learning model for patch classifications
'''
# ======= Environmental variables ======== #
random_seed = int(os.getenv('RANDOM_SEED', 12345))
nb_worker = int(os.getenv('NUM_CPU_CORES', 4))
gpu_count = int(os.getenv('NUM_GPU_DEVICES', 1))
# ========= Image generator ============== #
if featurewise_center:
train_imgen = DMImageDataGenerator(featurewise_center=True)
val_imgen = DMImageDataGenerator(featurewise_center=True)
test_imgen = DMImageDataGenerator(featurewise_center=True)
train_imgen.mean = featurewise_mean
val_imgen.mean = featurewise_mean
test_imgen.mean = featurewise_mean
else:
train_imgen = DMImageDataGenerator()
val_imgen = DMImageDataGenerator()
test_imgen = DMImageDataGenerator()
# Add augmentation options.
if augmentation:
train_imgen.horizontal_flip = True
train_imgen.vertical_flip = True
train_imgen.rotation_range = 25. # in degree.
train_imgen.shear_range = .2 # in radians.
train_imgen.zoom_range = [.8, 1.2] # in proportion.
train_imgen.channel_shift_range = 20. # in pixel intensity values.
# ================= Model creation ============== #
model, preprocess_input, top_layer_nb = get_dl_model(
net, nb_class=len(class_list), use_pretrained=use_pretrained,
resume_from=resume_from, img_size=img_size, top_layer_nb=top_layer_nb,
weight_decay=weight_decay, hidden_dropout=hidden_dropout,
nb_init_filter=nb_init_filter, init_filter_size=init_filter_size,
init_conv_stride=init_conv_stride, pool_size=pool_size,
pool_stride=pool_stride, alpha=alpha, l1_ratio=l1_ratio,
inp_dropout=inp_dropout)
if featurewise_center:
preprocess_input = None
if gpu_count > 1:
model, org_model = make_parallel(model, gpu_count)
else:
org_model = model
# ============ Train & validation set =============== #
train_bs = int(batch_size*train_bs_multiplier)
if net != 'yaroslav':
dup_3_channels = True
else:
dup_3_channels = False
if load_train_ram:
raw_imgen = DMImageDataGenerator()
print "Create generator for raw train set"
raw_generator = raw_imgen.flow_from_directory(
train_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
batch_size=train_bs, shuffle=False)
print "Loading raw train set into RAM.",
sys.stdout.flush()
raw_set = load_dat_ram(raw_generator, raw_generator.nb_sample)
print "Done."; sys.stdout.flush()
print "Create generator for train set"
train_generator = train_imgen.flow(
raw_set[0], raw_set[1], batch_size=train_bs,
auto_batch_balance=auto_batch_balance, preprocess=preprocess_input,
shuffle=True, seed=random_seed)
else:
print "Create generator for train set"
train_generator = train_imgen.flow_from_directory(
train_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
auto_batch_balance=auto_batch_balance, batch_size=train_bs,
preprocess=preprocess_input, shuffle=True, seed=random_seed)
print "Create generator for val set"
validation_set = val_imgen.flow_from_directory(
val_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical',
batch_size=batch_size, preprocess=preprocess_input, shuffle=False)
sys.stdout.flush()
if load_val_ram:
print "Loading validation set into RAM.",
sys.stdout.flush()
validation_set = load_dat_ram(validation_set, validation_set.nb_sample)
print "Done."; sys.stdout.flush()
# ==================== Model training ==================== #
# Do 3-stage training.
train_batches = int(train_generator.nb_sample/train_bs) + 1
if isinstance(validation_set, tuple):
val_samples = len(validation_set[0])
else:
val_samples = validation_set.nb_sample
validation_steps = int(val_samples/batch_size)
#### DEBUG ####
# val_samples = 100
#### DEBUG ####
# import pdb; pdb.set_trace()
model, loss_hist, acc_hist = do_3stage_training(
model, org_model, train_generator, validation_set, validation_steps,
best_model, train_batches, top_layer_nb, net, nb_epoch=nb_epoch,
top_layer_epochs=top_layer_epochs, all_layer_epochs=all_layer_epochs,
use_pretrained=use_pretrained, optim=optim, init_lr=init_lr,
top_layer_multiplier=top_layer_multiplier,
all_layer_multiplier=all_layer_multiplier,
es_patience=es_patience, lr_patience=lr_patience,
auto_batch_balance=auto_batch_balance, nb_class=len(class_list),
pos_cls_weight=pos_cls_weight, neg_cls_weight=neg_cls_weight,
nb_worker=nb_worker, weight_decay2=weight_decay2,
hidden_dropout2=hidden_dropout2)
# Training report.
if len(loss_hist) > 0:
min_loss_locs, = np.where(loss_hist == min(loss_hist))
best_val_loss = loss_hist[min_loss_locs[0]]
best_val_accuracy = acc_hist[min_loss_locs[0]]
print "\n==== Training summary ===="
print "Minimum val loss achieved at epoch:", min_loss_locs[0] + 1
print "Best val loss:", best_val_loss
print "Best val accuracy:", best_val_accuracy
if final_model != "NOSAVE":
model.save(final_model)
# ==== Predict on test set ==== #
print "\n==== Predicting on test set ===="
test_generator = test_imgen.flow_from_directory(
test_dir, target_size=img_size, target_scale=img_scale,
rescale_factor=rescale_factor,
equalize_hist=equalize_hist, dup_3_channels=dup_3_channels,
classes=class_list, class_mode='categorical', batch_size=batch_size,
preprocess=preprocess_input, shuffle=False)
print "Test samples =", test_generator.nb_sample
print "Load saved best model:", best_model + '.',
sys.stdout.flush()
org_model.load_weights(best_model)
print "Done."
test_steps = int(test_generator.nb_sample/batch_size)
#### DEBUG ####
# test_samples = 10
#### DEBUG ####
test_res = model.evaluate_generator(
test_generator, test_steps, nb_worker=nb_worker,
pickle_safe=True if nb_worker > 1 else False)
print "Evaluation result on test set:", test_res
