def config_filenames(self, net_type, use_core, keep_spatial_dim=False):
# init file managers
Weights = File.Weights(net_type, output_dir=input_dir)
if use_core:
if keep_spatial_dim:
Embed = File.Embed('SP_' + net_type, output_dir=output_dir)
else:
Embed = File.Embed(net_type, output_dir=output_dir)
else:
if net_type == 'dir':
Embed = File.Pred(type='malig', pre='dir', output_dir=output_dir)
elif net_type == 'dirR':
Embed = File.Pred(type='rating', pre='dirR', output_dir=output_dir)
elif net_type == 'dirS':
Embed = File.Pred(type='size', pre='dirS', output_dir=output_dir)
elif net_type == 'dirRS':
# assert False # save rating and size in seperate files
Embed = {}
Embed['R'] = File.Pred(type='rating', pre='dirRS', output_dir=output_dir)
Embed['S'] = File.Pred(type='size', pre='dirRS', output_dir=output_dir)
else:
print('{} not recognized'.format(net_type))
assert False
return Weights, Embed
def __init__(self, network = 'dir', pooling='max', categorize=False):
self.network = network
self.Weights = FileManager.Weights(network)
self.Embed = FileManager.Embed(network)
self.data_size = 144
self.data_res = '0.5I' # 'Legacy'
self.data_sample = 'Normal'
self.net_in_size = 128
self.net_input_shape = (self.net_in_size, self.net_in_size, 1)
self.net_out_size = 128
self.net_normalize = True
self.net_pool = pooling
self.categorize = categorize
self.model = None
def run(choose_model="DIR",
epochs=200,
config=0,
skip_validation=False,
no_training=False,
config_name='LEGACY',
load_data_from_predications=False):
np.random.seed(1337)
random.seed(1337)
tf.set_random_seed(1234)
K.set_session(tf.Session(graph=tf.get_default_graph()))
## --------------------------------------- ##
## ------- General Setup ----------------- ##
## --------------------------------------- ##
#data
dataset_type = 'Primary'
data_size = 160
if no_training:
data_size = 160
res = 0.5 # 'Legacy' #0.7 #0.5 #'0.5I'
sample = 'Normal' # 'UniformNC' #'Normal' #'Uniform'
data_run = '813'
data_epoch = 70
return_predicted_ratings = not no_training
use_gen = True
#model
model_size = 128
input_shape = (model_size, model_size, 1)
normalize = True
out_size = 128
do_augment = True
if no_training:
do_augment = False
preload_weight = None
print("-" * 30)
print("Running {} for --** {} **-- model, with #{} configuration".format(
"training" if not no_training else "validation", choose_model, config))
if load_data_from_predications:
print(
"\tdata_run = {}, \n\tdata_epoch = {}, return_predicted_ratings = {}"
.format(data_run, data_epoch, return_predicted_ratings))
else:
print(
"\tdata_size = {},\n\tmodel_size = {},\n\tres = {},\n\tdo_augment = {}"
.format(data_size, model_size, res, do_augment))
print("\tdataset_type = {}".format(dataset_type))
print("-" * 30)
model = None
data_augment_params = {
'max_angle': 30,
'flip_ratio': 0.5,
'crop_stdev': 0.15,
'epoch': 0
}
data_loader = build_loader(
size=data_size,
res=res,
sample=sample,
dataset_type=dataset_type,
config_name=config_name,
configuration=config,
run=data_run,
epoch=data_epoch,
load_data_from_predictions=load_data_from_predications,
return_predicted_ratings=return_predicted_ratings)
## --------------------------------------- ##
## ------- Prepare Direct Architecture ------- ##
## --------------------------------------- ##
if choose_model is "DIR":
# run = '300' # SPIE avg-pool (data-aug, balanced=False,class_weight=True)
# run = '301' # SPIE max-pool (data-aug, balanced=False,class_weight=True)
# run = '302' # SPIE rmac-pool (data-aug, balanced=False,class_weight=True)
# run = 'zzz'
model = DirectArch(miniXception_loader,
input_shape,
output_size=out_size,
normalize=normalize,
pooling='msrmac')
model.model.summary()
model.compile(learning_rate=1e-3, decay=0)
if use_gen:
generator = DataGeneratorDir(
data_loader,
val_factor=0 if skip_validation else 1,
balanced=False,
data_size=data_size,
model_size=model_size,
batch_size=32,
do_augment=do_augment,
augment=data_augment_params,
use_class_weight=True,
use_confidence=False)
model.load_generator(generator)
else:
dataset = load_nodule_dataset(size=data_size,
res=res,
sample=sample)
images_train, labels_train, class_train, masks_train, _ = prepare_data_direct(
dataset[2], num_of_classes=2)
images_valid, labels_valid, class_valid, masks_valid, _ = prepare_data_direct(
dataset[1], num_of_classes=2)
images_train = np.array([
crop_center(im, msk, size=model_size)[0]
for im, msk in zip(images_train, masks_train)
])
images_valid = np.array([
crop_center(im, msk, size=model_size)[0]
for im, msk in zip(images_valid, masks_valid)
])
model.load_data(images_train,
labels_train,
images_valid,
labels_valid,
batch_size=32)
if choose_model is "DIR_RATING":
### CLEAN SET
# run = '800' # rmac conf:size
# run = '801' # rmac conf:none
# run = '802' # rmac conf:rating-std
# run = '803' # max conf:none
### PRIMARY SET
# run = '810' # rmac conf:size
# run = '811' # rmac conf:none
# run = '812' # rmac conf:rating-std
# run = '813' # max conf:none
# run = '814' # max separated_prediction
# run = '820' # dirD, max, logcoh-loss
# run = '821' # dirD, max, pearson-loss
# run = '822' # dirD, max, KL-rank-loss
# run = '823' # dirD, max, poisson-rank-loss
# run = '824' # dirD, max, categorical-cross-entropy-loss
# run = '825' # dirD, max, ranked-pearson-loss
# run = '826' # dirD, max, KL-normalized-rank-loss
# run = '827' # dirD, max, KL-normalized-rank-loss (local-scaled) softmax
# run = '828' # dirD, max, KL-normalized-rank-loss (local-scaled) l2
# run = '829' # dirD, max, ranked-pearson-loss (local-scaled)
# run = '830' # dirD, rmac, logcoh-loss
# run = '831' # dirD, rmac, pearson-loss
# run = '832' # dirD, rmac, KL-rank-loss
# run = '833' # dirD, rmac, poisson-rank-loss
# run = '834' # dirD, rmac, categorical-cross-entropy-loss
# run = '835' # dirD, rmac, ranked-pearson-loss
# run = '836' # dirD, rmac, KL-normalized-rank-loss
# run = '841' # dirD, max, pearson-loss pre:dirR813-50
# run = '842b' # dirD, max, KL-rank-loss pre:dirR813-50 (b:lr-4)
# run = '846' # dirD, max, KL-norm-loss pre:dirR813-50
# run = '851' # dirD, rmac, pearson-loss pre:dirR813-50
# run = '852' # dirD, rmac, KL-rank-loss pre:dirR813-50
# run = '856' # dirD, rmac, KL-norm-loss pre:dirR813-50
# run = '860' # dirD, max, KL-loss pre:dirR813-50 (b:lr-4, freeze:7)
# run = '861' # dirD, max, KL-loss pre:dirR813-50 (b:lr-4, freeze:17)
# run = '862' # dirD, max, KL-loss pre:dirR813-50 (b:lr-4, freeze:28)
# run = '863' # dirD, max, KL-loss pre:dirR813-50 (b:lr-4, freeze:39)
# run = '870' # dirRD, max, KL-loss schd: 00
# run = '871' # dirRD, max, KL-loss schd: 01
# run = '872' # dirRD, max, KL-loss schd: 02
# run = '873' # dirRD, max, KL-loss schd: 03
# run = '874' # dirRD, max, KL-loss schd: 04
# run = '875' # dirRD, max, KL-loss schd: 05
# run = '876' # dirRD, max, KL-loss schd: 06
# run = '877b' # dirRD, max, KL-loss schd: 07b
# run = '878' # dirRD, max, KL-loss schd: 08
# run = '879' # dirRD, max, KL-loss schd: 09
# run = '888' # dirRD, max, KL-loss schd: 08, on partial data SUP
# run = '882' # dirRD, max, KL-loss schd:
run = '898b' # dirRD, max, KL-loss schd: 08, on partial data UNSUP
# run = '890b' # dirR
# run = '892b' # dirRD, max, KL-loss
# run = 'ccc'
obj = 'rating_distance-matrix' # 'distance-matrix' 'rating' 'rating-size'
rating_scale = 'none'
reg_loss = None # {'SampleCorrelation': 0.0} # 'Dispersion', 'Std', 'FeatureCorrelation', 'SampleCorrelation'
batch_size = 32
epoch_pre = 50
preload_weight = None
# FileManager.Weights('dirR', output_dir=input_dir).name(run='813c{}'.format(config), epoch=epoch_pre)
# FileManager.Weights('dirR', output_dir=input_dir).name(run='251c{}'.format(config), epoch=epoch_pre)
model = DirectArch(miniXception_loader,
input_shape,
output_size=out_size,
objective=obj,
separated_prediction=False,
normalize=normalize,
pooling='max',
l1_regularization=None,
regularization_loss=reg_loss,
batch_size=batch_size)
if preload_weight is not None:
model.load_core_weights(preload_weight, 39)
# 7: freeze 1 blocks
# 17: freeze 2 blocks
# 28: freeze 3 blocks
# 39: freeze 4 blocks
model.model.summary()
should_use_scheduale = (reg_loss is not None) or (obj in [
'rating_size', 'rating_distance-matrix'
])
# scheduale 00: 870
# sched = [{'epoch': 00, 'weights': [0.9, 0.1]},
# {'epoch': 40, 'weights': [0.5, 0.5]},
# {'epoch': 80, 'weights': [0.1, 0.9]}] \
# if should_use_scheduale else []
# scheduale 01: 871
# sched = [{'epoch': 00, 'weights': [1.0, 0.0]},
# {'epoch': 50, 'weights': [0.0, 1.0]}] \
# if should_use_scheduale else []
# scheduale 02: 872
# sched = [{'epoch': 00, 'weights': [0.9, 0.1]},
# {'epoch': 50, 'weights': [0.1, 0.9]}] \
# if should_use_scheduale else []
# scheduale 03: 873
# sched = [{'epoch': 00, 'weights': [0.9, 0.1]},
# {'epoch': 50, 'weights': [0.5, 0.5]},
# {'epoch': 100, 'weights': [0.1, 0.9]}] \
# if should_use_scheduale else []
# scheduale 04: 874
# sched = [{'epoch': 00, 'weights': [1.0, 0.0]},
# {'epoch': 50, 'weights': [0.0, 0.1]}] \
# if should_use_scheduale else []
# scheduale 05: 875
# sched = [{'epoch': 00, 'weights': [1.0, 0.0]},
# {'epoch': 50, 'weights': [0.0, 1.0]},
# {'epoch': 100, 'weights': [0.0, 0.1]}] \
# if should_use_scheduale else []
# scheduale 06: 876
# sched = [{'epoch': 00, 'weights': [0.9, 0.1]},
# {'epoch': 40, 'weights': [0.5, 0.5]},
# {'epoch': 60, 'weights': [0.1, 0.1]},
# {'epoch': 80, 'weights': [0.0, 0.1]},
# {'epoch': 100, 'weights': [0.0, 0.05]}] \
# if should_use_scheduale else []
# scheduale 07b: 877b
# sched = [{'epoch': 00, 'weights': [1.0, 0.0]},
# {'epoch': 50, 'weights': [0.0, 1.0]},
# {'epoch': 80, 'weights': [0.0, 0.1]},
# {'epoch': 100, 'weights': [0.0, 0.05]}] \
# if should_use_scheduale else []
# scheduale 08b: 878
# sched = [{'epoch': 00, 'weights': [0.9, 0.1]},
# {'epoch': 40, 'weights': [0.5, 0.5]},
# {'epoch': 80, 'weights': [0.0, 0.1]}] \
# if should_use_scheduale else []
# scheduale 09: 879
# sched = [{'epoch': 00, 'weights': [0.9, 0.1]},
# {'epoch': 20, 'weights': [0.7, 0.3]},
# {'epoch': 40, 'weights': [0.5, 0.5]},
# {'epoch': 60, 'weights': [0.3, 0.3]},
# {'epoch': 80, 'weights': [0.0, 0.1]}] \
# if should_use_scheduale else []
# scheduale 892/882
sched = [{'epoch': 00, 'weights': [0.9, 0.1]},
{'epoch': 80, 'weights': [0.5, 0.5]},
{'epoch': 120, 'weights': [0.0, 0.1]}] \
if should_use_scheduale else []
loss = dict()
loss['predictions'] = 'logcosh'
loss['predictions_size'] = 'logcosh'
loss['distance_matrix'] = distance_matrix_rank_loss_adapter(
K_losses.kullback_leibler_divergence, 'KL')
# distance_matrix_logcosh
# pearson_correlation
# distance_matrix_rank_loss_adapter(K_losses.kullback_leibler_divergence, 'KL')
# distance_matrix_rank_loss_adapter(K_losses.poisson, 'poisson')
# distance_matrix_rank_loss_adapter(K_losses.categorical_crossentropy, 'entropy')
model.compile(
learning_rate=1e-3 if (preload_weight is None) else 1e-4,
loss=loss,
scheduale=sched
) # mean_squared_logarithmic_error, binary_crossentropy, logcosh
if use_gen:
generator = DataGeneratorDir(
data_loader,
val_factor=0 if skip_validation else 1,
data_size=data_size,
model_size=model_size,
batch_size=batch_size,
objective=obj,
rating_scale=rating_scale,
weighted_rating=('distance-matrix' in obj),
balanced=False,
do_augment=do_augment,
augment=data_augment_params,
use_class_weight=False,
use_confidence=False)
model.load_generator(generator)
else:
dataset = load_nodule_dataset(size=data_size,
res=res,
sample=sample,
dataset_type=dataset_type)
images_train, labels_train, masks_train = prepare_data_direct(
dataset[2], objective='rating', rating_scale=rating_scale)
images_valid, labels_valid, masks_valid = prepare_data_direct(
dataset[1], objective='rating', rating_scale=rating_scale)
images_train = np.array([
crop_center(im, msk, size=model_size)[0]
for im, msk in zip(images_train, masks_train)
])
images_valid = np.array([
crop_center(im, msk, size=model_size)[0]
for im, msk in zip(images_valid, masks_valid)
])
model.load_data(images_train,
labels_train,
images_valid,
labels_valid,
batch_size=batch_size)
## --------------------------------------- ##
## ------- Prepare Siamese Architecture ------ ##
## --------------------------------------- ##
if choose_model is "SIAM":
# run = '300' # l1, avg-pool (data-aug, balanced=True, class_weight=False)
# run = '301' # l1, max-pool (data-aug, balanced=True, class_weight=False)
# run = '302' # l1, rmac-pool (data-aug, balanced=True, class_weight=False)
# run = '310' # l2, avg-pool (data-aug, balanced=True, class_weight=False)
# run = '311' # l2, max-pool (data-aug, balanced=True, class_weight=False)
# run = '312' # l2, rmac-pool (data-aug, balanced=True, class_weight=False)
# run = '320' # cos, avg-pool (data-aug, balanced=True, class_weight=False)
# run = '321' # cos, max-pool (data-aug, balanced=True, class_weight=False)
# run = '322b' # cos, rmac-pool (data-aug, balanced=True, class_weight=False)
# b/c - changed margin-loss params
# run = '313c' # l2, max-pool MARGINAL-LOSS (data-aug, balanced=True, class_weight=False)
# run = '314c' # l2, rmac-pool MARGINAL-LOSS (data-aug, balanced=True, class_weight=False)
# run = '323c' # cos, max-pool MARGINAL-LOSS (data-aug, balanced=True, class_weight=False)
# run = '324c' # cos, rmac-pool MARGINAL-LOSS (data-aug, balanced=True, class_weight=False)
# run = 'zzz'
batch_size = 64 if local else 128
# model
generator = DataGeneratorSiam(data_loader,
data_size=data_size,
model_size=model_size,
batch_size=batch_size,
val_factor=0 if skip_validation else 3,
balanced=True,
objective="malignancy",
do_augment=do_augment,
augment=data_augment_params,
use_class_weight=False)
model = SiamArch(miniXception_loader,
input_shape,
output_size=out_size,
batch_size=batch_size,
distance='l2',
normalize=normalize,
pooling='msrmac')
model.model.summary()
model.compile(learning_rate=1e-3, decay=0)
if use_gen:
model.load_generator(generator)
else:
imgs_trn, lbl_trn = generator.next_train().__next__()
imgs_val, lbl_val = generator.next_val().__next__()
model.load_data(imgs_trn, lbl_trn, imgs_val, lbl_val)
if choose_model is "SIAM_RATING":
### clean set
# run = '400' # l2-rmac no-conf
# run = '401' # cosine-rmac no-conf
# run = '402' # l2-rmac conf
# run = '403' # cosine-rmac conf
# run = '404' # l2-max no-conf
# run = '405' # cosine-max no-conf
### primary set
# run = '410' # l2-rmac no-conf
# run = '411' # cosine-rmac no-conf
# run = '412' # l2-rmac conf
# run = '413' # cosine-rmac conf
# run = '414' # l2-max no-conf
# run = '415' # cosine-max no-conf
run = 'zzz'
obj = 'rating' # rating / size / rating_size
batch_size = 16 if local else 64
reg_loss = None # {'SampleCorrating_clusters_distance_and_stdrelation': 0.1} # 'Dispersion', 'Std', 'FeatureCorrelation', 'SampleCorrelation'
epoch_pre = 60
preload_weight = None # FileManager.Weights('dirR', output_dir=input_dir).name(run='251c{}'.format(config), epoch=70)
should_use_scheduale = (reg_loss is not None) or (obj == 'rating_size')
'''
sched = [{'epoch': 00, 'weights': [0.1, 0.9]},
{'epoch': 30, 'weights': [0.4, 0.6]},
{'epoch': 60, 'weights': [0.6, 0.4]},
{'epoch': 80, 'weights': [0.9, 0.1]},
{'epoch': 100, 'weights': [1.0, 0.0]}] \
if should_use_scheduale else []
'''
sched = [{'epoch': 00, 'weights': [0.1, 0.9]},
{'epoch': 20, 'weights': [0.4, 0.6]},
{'epoch': 30, 'weights': [0.6, 0.4]},
{'epoch': 50, 'weights': [0.9, 0.1]},
{'epoch': 80, 'weights': [1.0, 0.0]}] \
if should_use_scheduale else []
# model
generator = DataGeneratorSiam(data_loader,
data_size=data_size,
model_size=model_size,
batch_size=batch_size,
train_facotr=2,
val_factor=0 if skip_validation else 3,
balanced=False,
objective=obj,
weighted_rating=True,
do_augment=do_augment,
augment=data_augment_params,
use_class_weight=False,
use_confidence=False)
model = SiamArch(miniXception_loader,
input_shape,
output_size=out_size,
objective=obj,
batch_size=batch_size,
distance='cosine',
normalize=normalize,
pooling='rmac',
regularization_loss=reg_loss,
l1_regularization=False)
if preload_weight is not None:
model.load_core_weights(preload_weight)
model.model.summary()
model.compile(learning_rate=1e-3,
decay=0,
loss='logcosh',
scheduale=sched) # mean_squared_error, logcosh
model.load_generator(generator)
## --------------------------------------- ##
## ------- Prepare Triplet Architecture ------ ##
## --------------------------------------- ##
if choose_model is "TRIPLET":
# run = '000' # rmac softplus, b16
# run = '001' # rmac hinge, b16, pre:dirR813-50
# run = '002' # rmac hinge, b32, pre:dirR813-50
# run = '003' # rmac hinge, b64, pre:dirR813-50
# run = '004' # rmac hinge, b128, pre:dirR813-50
# run = '005' # rmac hinge, b64, pre:dirR813-50
run = '006' # rmac rank, b64, pre:dirR813-50
# run = 'zzz'
objective = 'rating'
use_rank_loss = True
batch_size = 16 if local else 64
gen = True
epoch_pre = 50
preload_weight = FileManager.Weights(
'dirR', output_dir=input_dir).name(run='813c{}'.format(config),
epoch=epoch_pre)
# model
model = TripArch(miniXception_loader,
input_shape,
objective=objective,
output_size=out_size,
distance='l2',
normalize=True,
pooling='msrmac',
categorize=use_rank_loss)
if preload_weight is not None:
model.load_core_weights(preload_weight)
model.model.summary()
model.compile(learning_rate=1e-3, decay=0)
generator = DataGeneratorTrip(data_loader,
data_size=data_size,
model_size=model_size,
batch_size=batch_size,
objective=objective,
balanced=(objective == 'malignancy'),
categorize=use_rank_loss,
val_factor=0 if skip_validation else 1,
train_factor=2,
do_augment=do_augment,
augment=data_augment_params,
use_class_weight=False,
use_confidence=False)
if gen:
model.load_generator(generator)
else:
imgs_trn, lbl_trn = generator.next_train().__next__()
imgs_val, lbl_val = generator.next_val().__next__()
model.load_data(imgs_trn, lbl_trn, imgs_val, lbl_val)
## --------------------------------------- ##
## ------- RUN ------ ##
## --------------------------------------- ##
cnf_id = config if config_name == 'LEGACY' else CrossValidationManager(
config_name).get_run_id(config)
run_name = '{}{}c{}'.format('', run, cnf_id)
print('Current Run: {}'.format(run_name))
if no_training:
model.last_epoch = epochs
model.run = run_name
else:
model.train(run=run_name,
epoch=(0 if preload_weight is None else epoch_pre),
n_epoch=epochs,
gen=use_gen,
do_graph=False)
return model
for conf in [1]: # range(n_groups):
run = run_id + 'c{}'.format(conf)
if True:
print("Predicting Rating for " + run)
PredRating = PredictRating(pooling=pooling)
PredRating.load_dataset(data_subset_id=DataSubSet,
full=full,
include_unknown=include_unknown,
size=128,
rating_scale=rating_scale,
configuration=conf)
preds = []
valid_epochs = []
for e in epochs:
WeightsFile = FileManager.Weights('dirR').name(run, epoch=e)
PredFile = FileManager.Pred(type='rating', pre='dirR')
out_file = PredFile(run=run, dset=post)
data, out_filename = PredRating.predict_rating(
WeightsFile, out_file)
images_test, pred, meta_test, classes_test, labels_test, masks_test = data
preds.append(np.expand_dims(pred, axis=0))
preds = np.concatenate(preds, axis=0)
pickle.dump((preds, np.array(epochs), meta_test, images_test,
classes_test, labels_test, masks_test),
open(out_filename, 'bw'))
else:
print("Predicting Malignancy for " + run)
PredMal = PredictMal(pooling=pooling)
for e in epochs:
in_size = 128
out_size = 128
normalize = True
load = False
evaluate = False
force = False
# 0 Test
# 1 Validation
# 2 Training
DataSubSet = 2
run = '000'
epoch = 5
WeightsFile = FileManager.Weights('siamR').name(run, epoch=epoch)
pred_file_format = '.\output\embed\pred_siam{}_E{}_{}.p'
def pred_filename(run, epoch, post):
return pred_file_format.format(run, epoch, post)
## ========================= ##
## ======= Load Data ======= ##
## ========================= ##
if DataSubSet == 0:
post = "Test"
elif DataSubSet == 1:
# ===================
inp_size = 144
net_size = 128
out_size = 128
input_shape = (net_size, net_size, 1)
res = 'Legacy'
sample = 'Normal' #'UniformNC'
# 0 Test
# 1 Validation
# 2 Training
DataSubSet = 1
dsets = ['Test', 'Valid', 'Train']
Weights = FileManager.Weights('siam')
wRuns = ['078X']
wEpchs= [24]
run = wRuns[0]
epoch = wEpchs[0]
# Load Data
# =================
images, labels, masks, meta = \
prepare_data(load_nodule_dataset(size=inp_size, res=res, sample=sample)[DataSubSet],
categorize=False,
reshuffle=False,
return_meta=True,
