def subset_selection_test():
labels_path = '../../bdd100k/classification/labels/'
bdd100k_labels_path = "../../bdd100k/labels/"
val_labels_csv = '../../bdd100k/classification/labels/val_ground_truth.csv'
class_map_file = labels_path + 'class_mapping.csv'
val_json = '../../bdd100k/labels/bdd100k_labels_images_val.json'
attr_val_file = bdd100k_labels_path + 'bdd100k_labels_images_val_attributes.csv'
attr_tr_file = bdd100k_labels_path + 'bdd100k_labels_images_train_attributes.csv'
train_labels = '../../bdd100k/classification/labels/train_ground_truth.csv'
class_map_file = bu.class_mapping(input_json=val_json,
output_csv=class_map_file)
# Dataset for analysis
tr_partition, tr_labels = bu.get_ids_labels(train_labels, class_map_file)
w, s, tod, wst_dk2ak = bu.wst_attribute_mapping(attr_tr_file)
d_tod = analyse.DiscreteAttribute(tod)
d_s = analyse.DiscreteAttribute(s)
scene_tod_distrib = np.zeros(
(len(d_tod.get_labels()), len(d_s.get_labels())))
print(scene_tod_distrib)
for data_key in tr_partition:
attr_key = wst_dk2ak(data_key)
x = d_tod.index_of(d_tod.labelof(attr_key))
y = d_s.index_of(d_s.labelof(attr_key))
scene_tod_distrib[x][y] += 1
print(" " + " / ".join(d_s.get_labels()))
for k in xrange(len(scene_tod_distrib)):
print(k)
print(d_tod.get_labels()[k] + "\t" +
" ".join([str(val) for val in scene_tod_distrib[k]]))
def retinanet_train(tiny=False):
labels_path = bdd100k_labels_path
val_json = labels_path + 'bdd100k_labels_images_val.json'
train_json = labels_path + 'bdd100k_labels_images_train.json'
val_annot = labels_path + 'val_annotations.csv'
train_annot = labels_path + 'train_annotations.csv'
num_data = 70000
classes = bu.annotate(val_json, val_annot, labels_path, bdd100k_val_path)
cl_map_path = bu.class_mapping(classes,
output_csv=labels_path +
'class_mapping.csv')
bu.annotate(train_json, train_annot, bdd100k_labels_path,
bdd100k_train_path)
# Hyper-parameters
batch_size = 1
steps_per_epoch = np.ceil(num_data / batch_size)
for m in models:
print('Generating %s backbone...' % m)
backbone = kr_models.backbone(m)
weights = backbone.download_imagenet()
print('Creating generators...')
tr_gen, val_gen = mt.create_generators(
train_annotations=train_annot,
val_annotations=val_annot,
class_mapping=cl_map_path,
base_dir='',
preprocess_image=backbone.preprocess_image,
batch_size=batch_size)
print('Creating models...')
model, training_model, prediction_model = kr_train.create_models(
backbone.retinanet, tr_gen.num_classes(), weights)
print('Creating callbacks...')
callbacks = mt.create_callbacks(model,
batch_size,
'test',
tensorboard_dir=log_path)
print('Training...')
training_model.fit_generator(
generator=tr_gen,
steps_per_epoch=steps_per_epoch, # 10000,
epochs=2,
verbose=1,
callbacks=callbacks,
workers=4, # 1
use_multiprocessing=True, # False,
max_queue_size=10,
validation_data=val_gen)
def retinanet_tiny_train():
labels_path = bdd100k_labels_path
val_json = labels_path + 'bdd100k_labels_images_val.json'
num_data = 7000
batch_size = 1
steps_per_epoch = np.ceil(num_data / batch_size)
train_annot, val_annot = bu.annotate_tiny(val_json,
labels_path,
bdd100k_val_path,
overwrite=True)
cl_map_path = bu.class_mapping(input_json=val_json,
output_csv=labels_path +
'class_mapping.csv')
for m in models:
print('Generating %s backbone...' % m)
backbone = kr_models.backbone(m)
weights = backbone.download_imagenet()
print('Creating generators...')
tr_gen, val_gen = bu.create_generators(
train_annotations=train_annot,
val_annotations=val_annot,
class_mapping=cl_map_path,
base_dir='',
preprocess_image=backbone.preprocess_image,
batch_size=batch_size)
print('Creating models...')
model, training_model, prediction_model = kr_train.create_models(
backbone.retinanet, tr_gen.num_classes(), weights)
print('Creating callbacks...')
callbacks = bu.create_callbacks(model,
batch_size,
snapshots_path=retinanet_h5_path,
tensorboard_dir=log_path,
backbone=m,
dataset_type='bdd10k')
print('Training...')
training_model.fit_generator(
generator=tr_gen,
steps_per_epoch=steps_per_epoch, # 10000,
epochs=50,
verbose=1,
callbacks=callbacks,
workers=1, # 1
use_multiprocessing=False, # False,
max_queue_size=10,
validation_data=val_gen)
def bdd100k_analysis(model_file, do_plot_boxes=False):
class_map_file = bu.class_mapping(input_json=val_json, output_csv=labels_path + 'class_mapping.csv')
# Dataset for analysis
val_partition, val_labels = bu.get_ids_labels(val_labels_csv, class_map_file)
# Attribute mapping and data_key to attr_key function (dk2ak)
weather, scene, timeofday, wst_dk2ak = bu.wst_attribute_mapping(attr_val_file)
box_size, box_size_dk2ak = bu.box_size_attribute_mapping(box_val_file, box_val_json)
# show_worst(model_file, 10, 'daytime', timeofday, dk2ak=wst_dk2ak)
# for attr in attributes.values():
# print(attr['d_attribute'].get_labels())
# print(attr['d_attribute'].get_distribution())
attributes = {'weather': {'name': 'weather',
'map': weather,
'dk2ak': wst_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
'scene': {'name': 'scene',
'map': scene,
'dk2ak': wst_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
'timeofday': {'name': 'timeofday',
'map': timeofday,
'dk2ak': wst_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
'box_size': {'name': 'box_size',
'map': box_size,
'dk2ak': box_size_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
}
bdd100k_model_analysis(model_file, attributes, val_labels)
if do_plot_boxes:
plotting.plot_discrete_attribute_scores(attributes, 'score', model_file)
# plotting.plot_discrete_attribute_scores(attributes, 'acc', model_file)
return attributes
def bdd100k_compare(model_file, ref_file, attribute, metric):
class_map_file = bu.class_mapping(input_json=val_json, output_csv=labels_path + 'class_mapping.csv')
# Dataset for analysis
val_partition, val_labels = bu.get_ids_labels(val_labels_csv, class_map_file)
# Attribute mapping and data_key to attr_key function (dk2ak)
weather, scene, timeofday, wst_dk2ak = bu.wst_attribute_mapping(attr_val_file)
box_size, box_size_dk2ak = bu.box_size_attribute_mapping(box_val_file, box_val_json)
attributes = {'weather': {'name': 'weather',
'map': weather,
'dk2ak': wst_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
'scene': {'name': 'scene',
'map': scene,
'dk2ak': wst_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
'timeofday': {'name': 'timeofday',
'map': timeofday,
'dk2ak': wst_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
'box_size': {'name': 'box_size',
'map': box_size,
'dk2ak': box_size_dk2ak,
'd_attribute': None,
'metrics': ['score', 'acc'],
'weaks': [[], []]},
}
bdd100k_model_analysis(model_file, attributes, val_labels)
labels = attributes[attribute]['d_attribute'].get_labels()
distrib = [int(v) for v in attributes[attribute]['d_attribute'].get_distribution()]
series = [attributes[attribute]['d_attribute'].get_metric_value_list(metric, label) for label in labels]
series_names = ["%s (%1.2f)" % (labels[k], distrib[k] / sum(distrib)) for k in xrange(len(labels))]
attributes[attribute]['d_attribute'].flush()
bdd100k_model_analysis(ref_file, attributes, val_labels)
series_ref = [attributes[attribute]['d_attribute'].get_metric_value_list(metric, label) for label in labels]
plotting.n_box_plot_compare(series, series_ref, series_names, metric)
def bdd100k_sel_partition_test():
labels_path = '../../bdd100k/classification/labels/'
train_labels = '../../bdd100k/classification/labels/train_ground_truth.csv'
val_json = '../../bdd100k/labels/bdd100k_labels_images_val.json'
class_map_file = bu.class_mapping(input_json=val_json,
output_csv=labels_path +
'class_mapping.csv')
# Datasets
tr_partition, tr_labels = bu.get_ids_labels(train_labels, class_map_file)
ft_partition = tr_partition[500000:1000000]
sel_partition = analyse.select_ft_data(
'densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22.hdf5',
ft_partition)
print('selection res=', len(sel_partition))
def bdd100k_cc_analysis():
model_files = ['densenet121_bdd100k_cl0-500k_20ep_woda_ep20_vl0.22.hdf5',
]
# 'resnet50_bdd100k_cl0-500k_20ep_woda_ep13_vl0.27.hdf5',
# 'mobilenet_bdd100k_cl0-500k_20ep_woda_ep15_vl0.24.hdf5',
# 'mobilenetv2_bdd100k_cl0-500k_20ep_woda_ep17_vl0.22.hdf5',
# 'nasnet_bdd100k_cl0-500k_20ep_woda_ep17_vl0.24.hdf5']
class_map_file = bu.class_mapping(input_json=val_json, output_csv=labels_path + 'class_mapping.csv')
# Dataset for analysis
val_partition, val_labels = bu.get_ids_labels(val_labels_csv, class_map_file)
for m in model_files:
# test_subset creation
pr_file = '.'.join(m.split('.')[:-1]) + '_predictions.csv'
predictions, y_scores, img_ids = dt.get_scores_from_file(csv_path + pr_file, val_labels)
top_n_args, bot_n_args = dt.get_topbot_n_args(20000, y_scores)
cc_high = metrics_color.ColorDensityCube(resolution=4)
for arg in top_n_args:
cc_high.feed(cv2.imread(img_ids[arg]))
print('high sum', np.sum(cc_high.get_cube().flatten()))
cc_high.normalize()
cc_high.plot_cube()
cc_low = metrics_color.ColorDensityCube(resolution=4)
for arg in bot_n_args:
cc_low.feed(cv2.imread(img_ids[arg]))
print('low sum', np.sum(cc_low.get_cube().flatten()))
cc_low.normalize()
cc_low.plot_cube()
cc_diff = cc_high.substract(cc_low, 'value')
print('diff mean', np.sum(cc_diff.get_cube().flatten()))
print('diff mean', np.mean(cc_diff.get_cube().flatten()))
cc_diff.normalize()
# cc_diff.plot_cube()
# cc_diff.normalize()
cc_diff.plot_cube(title='Color cube analysis difference (' + str(20000) + ' images/series)', normalize=True,
save=True)
def bdd100k_local_finetune_test(model_files):
labels_path = '../../bdd100k/classification/labels/'
train_labels_csv = '../../bdd100k/classification/labels/train_ground_truth.csv'
val_labels_csv = '../../bdd100k/classification/labels/val_ground_truth.csv'
val_json = '../../bdd100k/labels/bdd100k_labels_images_val.json'
# Parameters
params = {
'dim': (64, 64, 3),
'batch_size': 32,
'n_classes': 10,
'shuffle': True
}
epochs = 30
class_map_file = bu.class_mapping(input_json=val_json,
output_csv=labels_path +
'class_mapping.csv')
# Datasets
tr_partition, tr_labels = bu.get_ids_labels(train_labels_csv,
class_map_file)
val_partition, val_labels = bu.get_ids_labels(val_labels_csv,
class_map_file)
for model_file in model_files:
ft_partition = tr_partition[500000:1000000]
if 'densenet121' in model_file:
ref_generator = mt.DataGenerator(tr_partition[:300000], tr_labels,
**params)
val_generator = mt.DataGenerator(val_partition[:100000],
val_labels, **params)
mt.ft(h5_path + model_file,
ref_generator,
val_generator,
epochs,
save_history=True,
tag='ref2')
mt.ft(h5_path + model_file,
ref_generator,
val_generator,
epochs,
save_history=True,
tag='ref3')
else:
ref_generator = mt.DataGenerator(tr_partition[:300000], tr_labels,
**params)
val_generator = mt.DataGenerator(val_partition[:100000],
val_labels, **params)
mt.ft(h5_path + model_file,
ref_generator,
val_generator,
epochs,
save_history=True,
tag='ref2')
mt.ft(h5_path + model_file,
ref_generator,
val_generator,
epochs,
save_history=True,
tag='ref3')
epochs = 15
# daytime timeofday finetuning
# Selected data partition
day_sel_partition = analyse.select_ft_data(model_file,
ft_partition,
'timeofday',
'daytime',
do_plot_boxes=False)
# Generators
sp = 4 * len(day_sel_partition) // 5 # split point
day_ft_generator = mt.DataGenerator(day_sel_partition[:sp],
tr_labels, **params)
day_val_generator = mt.DataGenerator(day_sel_partition[sp:],
tr_labels, **params)
mt.ft(h5_path + model_file,
day_ft_generator,
day_val_generator,
epochs,
save_history=True,
tag='daytime2')
mt.ft(h5_path + model_file,
day_ft_generator,
day_val_generator,
epochs,
save_history=True,
tag='daytime3')
# Night timeofday finetuning
night_sel_partition = analyse.select_ft_data(
model_file, ft_partition, 'timeofday', 'night')
sp = 4 * len(night_sel_partition) // 5 # split point
night_ft_generator = mt.DataGenerator(night_sel_partition[:sp],
tr_labels, **params)
night_val_generator = mt.DataGenerator(night_sel_partition[sp:],
tr_labels, **params)
mt.ft(h5_path + model_file,
night_ft_generator,
night_val_generator,
epochs,
save_history=True,
tag='night2')
mt.ft(h5_path + model_file,
night_ft_generator,
night_val_generator,
epochs,
save_history=True,
tag='night3')
# Highway scene finetuning
highway_sel_partition = analyse.select_ft_data(
model_file, ft_partition, 'scene', 'highway')
sp = 4 * len(highway_sel_partition) // 5 # split point
highway_ft_generator = mt.DataGenerator(highway_sel_partition[:sp],
tr_labels, **params)
highway_val_generator = mt.DataGenerator(
highway_sel_partition[sp:], tr_labels, **params)
mt.ft(h5_path + model_file,
highway_ft_generator,
highway_val_generator,
epochs,
save_history=True,
tag='highway2')
mt.ft(h5_path + model_file,
highway_ft_generator,
highway_val_generator,
epochs,
save_history=True,
tag='highway3')
# City street scene finetuning
city_street_sel_partition = analyse.select_ft_data(
model_file, ft_partition, 'scene', 'city street')
sp = 4 * len(city_street_sel_partition) // 5 # split point
city_street_ft_generator = mt.DataGenerator(
city_street_sel_partition[:sp], tr_labels, **params)
city_street_val_generator = mt.DataGenerator(
city_street_sel_partition[sp:], tr_labels, **params)
mt.ft(h5_path + model_file,
city_street_ft_generator,
city_street_val_generator,
epochs,
save_history=True,
tag='city_street2')
mt.ft(h5_path + model_file,
city_street_ft_generator,
city_street_val_generator,
epochs,
save_history=True,
tag='city_street3')
def bdd100k_global_finetune_test(model_files):
labels_path = '../../bdd100k/classification/labels/'
train_labels = '../../bdd100k/classification/labels/train_ground_truth.csv'
val_labels_csv = '../../bdd100k/classification/labels/val_ground_truth.csv'
# class_map_file = labels_path + 'class_mapping.csv'
val_json = '../../bdd100k/labels/bdd100k_labels_images_val.json'
# Parameters
params = {
'dim': (64, 64, 3),
'batch_size': 32,
'n_classes': 10,
'shuffle': False
}
n_test_data = 100000
epochs = 30
class_map_file = bu.class_mapping(input_json=val_json,
output_csv=labels_path +
'class_mapping.csv')
# Datasets
tr_partition, tr_labels = bu.get_ids_labels(train_labels, class_map_file)
val_partition, val_labels = bu.get_ids_labels(val_labels_csv,
class_map_file)
for model_file in model_files:
ft_partition = tr_partition[500000:1000000]
n_sel_data = 300000
sel_partition = analyse.select_ft_data(
model_file, ft_partition) # Selected data partition
# Generators
finetune_generator = mt.DataGenerator(sel_partition[:n_sel_data],
tr_labels, **params)
reference_generator = mt.DataGenerator(
tr_partition[500000:500000 + len(ft_partition)], tr_labels,
**params)
validation_generator = mt.DataGenerator(val_partition[:n_test_data],
val_labels, **params)
# finetune
model = load_model(h5_path + model_file)
model.compile('adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
checkpoint = ModelCheckpoint(model_file.rstrip('.hdf5') +
'_ftep{epoch:02d}_vl{val_loss:.2f}.hdf5',
monitor='val_acc',
verbose=0,
save_best_only=True,
save_weights_only=False,
mode='auto')
# Train model on selected dataset
ft_history = model.fit_generator(generator=finetune_generator,
validation_data=validation_generator,
verbose=1,
epochs=epochs,
use_multiprocessing=True,
workers=6,
callbacks=[checkpoint])
with open(model_file.rstrip('.hdf5') + '_ft_hist.pkl', 'w') as fd:
pickle.dump(ft_history, fd)
# reference
model = load_model(h5_path + model_file)
model.compile('adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
checkpoint = ModelCheckpoint(model_file.rstrip('.h5') +
'_refep{epoch:02d}_vl{val_loss:.2f}.hdf5',
monitor='val_acc',
verbose=0,
save_best_only=True,
save_weights_only=False,
mode='auto')
# Train model on ref dataset
ref_history = model.fit_generator(generator=reference_generator,
validation_data=validation_generator,
verbose=1,
epochs=epochs,
use_multiprocessing=True,
workers=6,
callbacks=[checkpoint])
with open(model_file.rstrip('.hdf5') + '_ref_hist.pkl', 'w') as fd:
pickle.dump(ref_history, fd)
def load_model_test(model_files, overwrite=False):
for model_file in model_files:
m = load_model(h5_path + model_file)
m.summary()
return
labels_path = '../../bdd100k/classification/labels/'
val_labels_csv = '../../bdd100k/classification/labels/val_ground_truth.csv'
val_json = '../../bdd100k/labels/bdd100k_labels_images_val.json'
# Parameters
params = {
'dim': (64, 64, 3),
'batch_size': 32,
'n_classes': 10,
'shuffle': False
}
n_test_data = 100000
class_map_file = bu.class_mapping(input_json=val_json,
output_csv=labels_path +
'class_mapping.csv')
# Datasets
val_partition, val_labels = bu.get_ids_labels(val_labels_csv,
class_map_file)
# Generators
validation_generator = mt.DataGenerator(val_partition[:n_test_data],
val_labels, **params)
label_distrib = [
val_labels.values()[:n_test_data].count(k) / n_test_data
for k in xrange(params['n_classes'])
]
print(label_distrib)
for model_file in model_files:
predictions_file = '.'.join(
model_file.split('.')[:-1]) + '_predictions.csv'
if os.path.isfile(predictions_file) and not overwrite:
print('File ' + predictions_file + ' already exists. Not written.')
return
start_time = datetime.now()
m = load_model(h5_path + model_file)
print('File successfully loaded', model_file, 'in',
str(datetime.now() - start_time))
# print("Validation ")
# start_time = datetime.now()
# print(m.metrics_names)
# print(m.evaluate_generator(validation_generator))
# print('Model successfully evaluated', model_file, 'in (s)', str(datetime.now() - start_time))
print('Writing predictions')
out_pr = open(csv_path + predictions_file, 'w')
start_time = datetime.now()
y_predicted = m.predict_generator(validation_generator)
# prediction
for i in xrange(len(y_predicted)):
out_pr.write(val_partition[i] + ',' +
str(y_predicted[i].tolist()) + '\n')
out_pr.close()
predicted_classes = np.argmax(y_predicted, axis=1)
print('Predictions successfully written', model_file, 'in',
str(datetime.now() - start_time))
true_classes = [
val_labels[id] for id in val_partition[:len(y_predicted)]
]
acc = metrics.accuracy(predicted_classes, true_classes)
print('acc=', acc)
print(sk_metrics.confusion_matrix(true_classes, predicted_classes))
def train_bdd100k_cl():
labels_path = '../../bdd100k/classification/labels/'
train_labels = '../../bdd100k/classification/labels/train_ground_truth.csv'
val_labels = '../../bdd100k/classification/labels/val_ground_truth.csv'
# class_map_file = labels_path + 'class_mapping.csv'
val_json = '../../bdd100k/labels/bdd100k_labels_images_val.json'
epochs = 20
# Parameters
params = {
'dim': (64, 64, 3),
'batch_size': 32,
'n_classes': 10,
'shuffle': True
}
class_map_file = bu.class_mapping(input_json=val_json,
output_csv=labels_path +
'class_mapping.csv')
# Datasets
val_partition, val_labels = bu.get_ids_labels(val_labels, class_map_file)
tr_partition, tr_labels = bu.get_ids_labels(train_labels, class_map_file)
# Generators
training_generator = mt.DataGenerator(tr_partition[:500000], tr_labels,
**params)
validation_generator = mt.DataGenerator(val_partition[:100000], val_labels,
**params)
print(len(training_generator))
for m in models:
weight_file = mt.weight_file_name(m,
'bdd100k_cl0-500k',
epochs,
data_augmentation=False)
weight_file = h5_path + weight_file
print("Building: " + weight_file)
if m in ('mobilenet', 'mobilenetv2', 'nasnet'):
###
model = mt.model_struct(m, (224, 224, 3),
params['n_classes'],
weights='imagenet',
include_top=False)
new_model = mt.model_struct(m,
params['dim'],
params['n_classes'],
weights=None,
include_top=False)
print("Loading weights...")
for new_layer, layer in zip(new_model.layers[1:],
model.layers[1:]):
new_layer.set_weights(layer.get_weights())
base_model = new_model
###
else:
base_model = mt.model_struct(m,
params['dim'],
params['n_classes'],
weights='imagenet',
include_top=False)
print("Configuring top layers")
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(10, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.summary()
# for layer in base_model.layers:
# layer.trainable = False
model.compile('adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
checkpoint = ModelCheckpoint(weight_file.rstrip('.h5') +
'_ep{epoch:02d}_vl{val_loss:.2f}.hdf5',
monitor='val_acc',
verbose=0,
save_best_only=True,
save_weights_only=False,
mode='auto')
# Train model on dataset
model.fit_generator(generator=training_generator,
validation_data=validation_generator,
verbose=1,
epochs=epochs,
use_multiprocessing=True,
workers=6,
callbacks=[checkpoint])