def test_loss_op_for_warmup(setup_y_true_tensor, setup_y_pred_tensor):
# 1. build loss function
batch_size = 1
warmup_bs = 100
yolo_loss = YoloLoss(grid_size=9, nb_class=4)
custom_loss = yolo_loss.custom_loss(batch_size, warmup_bs)
# 2. placeholder : (y_true, y_pred)
y_true, y_true_value = setup_y_true_tensor
y_pred, y_pred_value = setup_y_pred_tensor
# 3. loss operation
loss_op = custom_loss(y_true, y_pred)
# 4. setup feed values for each placeholders (true_boxes, y_true, y_pred
true_boxes_value = np.zeros((1, 1, 1, 1, 10, 4))
true_boxes_value[0, 0, 0, 0, 0, :] = [3.46875, 4.78125, 1, 5.625]
true_boxes_value[0, 0, 0, 0, 1, :] = [4.484375, 4.875, 1.15625, 5.625]
# 5. run loss_op in session
# y_true, y_pred�� ���� value�� insert
loss_value = run_op(loss_op,
feed_dict={
y_true: y_true_value,
y_pred: y_pred_value
})
assert np.allclose(loss_value, 3.1930623)
def test_loss_op(setup_y_true_tensor, setup_y_pred_tensor,
setup_true_boxes_tensor):
# 1. build loss function
batch_size = 1
warmup_bs = 0
yolo_loss = YoloLoss(grid_size=9, nb_class=4)
custom_loss = yolo_loss.custom_loss(batch_size, warmup_bs)
# 2. placeholder : (y_true, y_pred)
y_true, y_true_value = setup_y_true_tensor
y_pred, y_pred_value = setup_y_pred_tensor
# 3. loss operation
loss_op = custom_loss(y_true, y_pred)
# 4. setup feed values for each placeholders (true_boxes, y_true, y_pred
_, true_boxes_value = setup_true_boxes_tensor
# 5. run loss_op in session
# y_true, y_pred�� ���� value�� insert
loss_value = run_op(loss_op,
feed_dict={
y_true: y_true_value,
y_pred: y_pred_value
})
assert np.allclose(loss_value, 5.47542)
def create_yolo(architecture,
labels,
input_size=(224, 224),
anchors=[
0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434,
7.88282, 3.52778, 9.77052, 9.16828
],
coord_scale=1.0,
class_scale=1.0,
object_scale=5.0,
no_object_scale=1.0,
weights=None,
strip_size=32):
n_classes = len(labels)
n_boxes = int(len(anchors) / 2)
yolo_network = create_yolo_network(architecture,
input_size,
n_classes,
n_boxes,
weights,
strip_size=strip_size)
yolo_loss = YoloLoss(yolo_network.get_grid_size(), n_classes, anchors,
coord_scale, class_scale, object_scale,
no_object_scale)
yolo_decoder = YoloDecoder(anchors)
yolo = YOLO(yolo_network, yolo_loss, yolo_decoder, labels, input_size)
return yolo
def test_loss_op(setup_y_true_tensor, setup_y_pred_tensor, setup_true_boxes_tensor):
# 1. build loss function
batch_size = 1
warmup_bs = 0
yolo_loss = YoloLoss(grid_size=9, nb_class=4)
custom_loss = yolo_loss.custom_loss(batch_size, warmup_bs)
# 2. placeholder : (y_true, y_pred)
y_true, y_true_value = setup_y_true_tensor
y_pred, y_pred_value = setup_y_pred_tensor
# 3. loss operation
loss_op = custom_loss(y_true, y_pred)
# 4. setup feed values for each placeholders (true_boxes, y_true, y_pred
_, true_boxes_value = setup_true_boxes_tensor
# 5. run loss_op in session
# y_true, y_pred�� ���� value�� insert
loss_value = run_op(loss_op, feed_dict={y_true: y_true_value,
y_pred: y_pred_value})
assert np.allclose(loss_value, 5.47542)
def test_loss_op_for_warmup(setup_y_true_tensor, setup_y_pred_tensor):
# 1. build loss function
batch_size = 1
warmup_bs = 100
yolo_loss = YoloLoss(grid_size=9, nb_class=4)
custom_loss = yolo_loss.custom_loss(batch_size, warmup_bs)
# 2. placeholder : (y_true, y_pred)
y_true, y_true_value = setup_y_true_tensor
y_pred, y_pred_value = setup_y_pred_tensor
# 3. loss operation
loss_op = custom_loss(y_true, y_pred)
# 4. setup feed values for each placeholders (true_boxes, y_true, y_pred
true_boxes_value = np.zeros((1,1,1,1,10,4))
true_boxes_value[0,0,0,0,0,:] = [3.46875, 4.78125, 1, 5.625]
true_boxes_value[0,0,0,0,1,:] = [4.484375, 4.875, 1.15625, 5.625]
# 5. run loss_op in session
# y_true, y_pred�� ���� value�� insert
loss_value = run_op(loss_op, feed_dict={y_true: y_true_value,
y_pred: y_pred_value})
assert np.allclose(loss_value, 3.1930623)
def create_yolo(architecture,
labels,
input_size=416,
anchors=[
0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434,
7.88282, 3.52778, 9.77052, 9.16828
],
feature_weights_path=None):
n_classes = len(labels)
n_boxes = int(len(anchors) / 2)
yolo_network = create_yolo_network(architecture, input_size, n_classes,
n_boxes, feature_weights_path)
yolo_loss = YoloLoss(yolo_network.get_grid_size(), n_classes, anchors)
yolo_decoder = YoloDecoder(anchors)
yolo = YOLO(yolo_network, yolo_loss, yolo_decoder, labels, input_size)
return yolo