def benchmark(config):
model = SiameseNetwork('inference', config['model'])
params = {
'batch_size': config['benchmark']['batch_size'],
'shuffle': False,
'dim': config['model']['input_shape']
}
dataset_path = config['benchmark']['dataset_path']
train_dataset = ImageDataset(dataset_path, 'train')
train_dataset.prepare(config['benchmark']['test_cases'] // 2)
train_generator = DataGenerator(train_dataset, **params)
test_dataset = ImageDataset(dataset_path, 'validation')
test_dataset.prepare(config['benchmark']['test_cases'] // 2)
test_generator = DataGenerator(test_dataset, **params)
preds = np.array([])
gts = np.array([])
for i in tqdm(range(len(train_generator))):
batch = train_generator[i]
pred = model.predict(batch[0])
preds = np.append(preds, pred.flatten())
gts = np.append(gts, batch[1])
# if config['vis_output'] and not i % config['test_cases']//(5*config['batch_size']):
# show_output(batch[0][0], batch[0][1], pred, batch[1])
tr_acc = compute_accuracy(preds, gts)
print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
evaluation_times = []
preds = np.array([])
gts = np.array([])
for i in tqdm(range(len(test_generator))):
batch = test_generator[i]
start_t = time.time()
pred = model.predict(batch[0])
evaluation_times.append((time.time() - start_t) / len(batch))
preds = np.append(preds, pred.flatten())
gts = np.append(gts, batch[1])
if config['benchmark']['vis_output'] and not i % config['benchmark'][
'test_cases'] // (5 * config['benchmark']['batch_size']):
show_output(batch[0][0], batch[0][1], pred, batch[1])
te_acc = compute_accuracy(preds, gts)
print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
print("Average Evaluation Time Per Sample: " +
str(np.mean(evaluation_times)))
print(preds)
print(gts)
import numpy as np
from siamese import SiameseNetwork, create_pairs, compute_accuracy
# Set constants
BATCH_SIZE = 128
N_EPOCHS = 20
CLASS_DIM = 120
# Construct input data
with open('/book/working/data/inter_emb.np', 'rb') as f:
X_train = np.array(np.load(f), dtype=np.float32)
with open('/book/working/data/labels.np', 'rb') as f:
y_train = np.array(np.load(f), dtype=np.int8)
digit_indices = [np.where(y_train == i)[0] for i in range(CLASS_DIM)]
tr_pairs, tr_y = create_pairs(X_train, digit_indices, CLASS_DIM)
# Construct Siamese network
model, base_network = SiameseNetwork()
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]],
tr_y,
batch_size=BATCH_SIZE,
epochs=N_EPOCHS)
# Compute final accuracy on training set
y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
tr_acc = compute_accuracy(tr_y, y_pred)
print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
# Save
model.save('/book/working/models/siamese.h5')