def get_object_confusion(class1, class2, similarity_model, config):
model_config = config['model']
benchmark_config = config['benchmark']
model_path = model_config['model_filename']
dataset_path = benchmark_config['dataset_path']
params = {
'dim': model_config['input_shape'],
'batch_size': benchmark_config['batch_size'],
'shuffle': False
}
test_dataset = ImageDataset(dataset_path, 'validation')
test_dataset.prepare_specific(benchmark_config['test_cases'] // 2, class1,
class2)
test_generator = DataGenerator(test_dataset, **params)
preds = np.array([])
gts = np.array([])
for i in tqdm(range(len(test_generator))):
batch = test_generator[i]
pred = similarity_model.predict_on_batch(batch[0])
preds = np.append(preds, pred.flatten())
gts = np.append(gts, batch[1])
if benchmark_config['vis_output'] and not i % benchmark_config[
'test_cases'] // (5 * benchmark_config['batch_size']):
show_output(batch[0][0], batch[0][1], pred, batch[1])
te_acc = compute_accuracy(preds, gts)
print("Class 1: " + class1 + ", Class2: " + class2 +
", Distinguishability Score: " + str(te_acc))
return te_acc
def train(self, config):
train_dataset = ImageDataset(config['dataset_path'], 'seen',
config['data_augmentation_suffixes'],
config['allow_different_views'])
train_dataset.prepare(config['num_train_pairs'])
val_dataset = ImageDataset(config['dataset_path'], 'test')
val_dataset.prepare(config['num_val_pairs'])
train_generator = DataGenerator(
train_dataset,
batch_size=config['batch_size'],
dim=self.config['input_shape'],
shuffle=config['shuffle_training_inputs'],
dataset_type=config['dataset_type'])
val_generator = DataGenerator(
val_dataset,
batch_size=config['batch_size'],
dim=self.config['input_shape'],
shuffle=config['shuffle_training_inputs'],
dataset_type=config['dataset_type'])
model_path, _ = os.path.split(self.config['model_filename'])
callbacks = [
keras.callbacks.TensorBoard(log_dir=self.log_dir,
histogram_freq=0,
write_graph=True,
write_images=False),
keras.callbacks.ModelCheckpoint(self.checkpoint_path,
verbose=0,
save_weights_only=True)
]
self.keras_model.compile(
loss=utils.contrastive_loss,
optimizer=Adam(lr=config['learning_rate']),
metrics=[utils.accuracy, utils.auc_roc, 'acc'])
history = self.keras_model.fit_generator(
generator=train_generator,
validation_data=val_generator,
epochs=config['epochs'],
use_multiprocessing=True,
callbacks=callbacks,
workers=multiprocessing.cpu_count())
self.keras_model.save(self.config['model_filename'])
#-------------------------------------------------------
#make new dataset
seen_dataset = ImageDataset(config['dataset_path'], 'seen')
seen = []
#only add imgs that are returned by nearpy
new = []
for i in range(5):
new += [
os.path.join(x, 'view_00000{}'.format(i)) for x in os.listdir(
os.path.join(config['dataset_path'], 'test'))
]
pred_arr = []
dimension = 9984
engine = Engine(dimension, vector_filters=[NearestFilter(5)])
#for i in range(0, iter):
#seen = list(seen_dataset._class_labels)
seen = []
for i in range(5):
seen += [
os.path.join(x, 'view_00000{}'.format(i)) for x in os.listdir(
os.path.join(config['dataset_path'], 'seen'))
]
for class1 in seen:
folder1 = os.path.join(
os.path.join(config['dataset_path'], 'seen'), class1)
for obj in os.listdir(folder1):
im2 = os.path.join(folder1, obj)
image = np.load(im2)
engine.store_vector(image['arr_0'], class1)
for img in new:
#nea rpy stuff
folder1 = os.path.join(
os.path.join(config['dataset_path'], 'test'), img)
im = os.path.join(folder1, os.listdir(folder1)[0])
image = np.load(im)['arr_0']
neighbors = engine.neighbours(image)
for n in neighbors:
folder1 = os.path.join(
os.path.join(config['dataset_path'], 'seen'), n[1])
im = os.path.join(folder1,
os.listdir(folder1)[0]) #make random
neighbor = np.load(im)['arr_0']
prediction = self.predict(
[np.array([image]),
np.array([neighbor])], 1)
pred_arr += [[img[:-12], n[1][:-12], prediction]]
for item in pred_arr:
val_dataset.prepare_specific(1, item[0], item[1])
f = open("ground.txt", "a")
if item[0] == item[1]:
f.write('1 {} {} {} '.format(item[0], item[1], item[2]))
else:
f.write('0 {} {} {} '.format(item[0], item[1], item[2]))
f.close()
