def __get_input_template(self):
scales = np.linspace(self.default_boxes_config["min_scale"],
self.default_boxes_config["max_scale"],
len(self.default_boxes_config["layers"]))
mbox_conf_layers = []
mbox_loc_layers = []
mbox_default_boxes_layers = []
for i, layer in enumerate(self.default_boxes_config["layers"]):
layer_default_boxes = ssd_utils.generate_default_boxes_for_feature_map(
feature_map_size=layer["size"],
image_size=self.input_size,
offset=layer["offset"],
scale=scales[i],
next_scale=scales[i + 1] if
i + 1 <= len(self.default_boxes_config["layers"]) - 1 else 1,
aspect_ratios=layer["aspect_ratios"],
variances=self.default_boxes_config["variances"],
extra_box_for_ar_1=self.extra_box_for_ar_1)
layer_default_boxes = np.reshape(layer_default_boxes, (-1, 8))
layer_conf = np.zeros(
(layer_default_boxes.shape[0], self.num_classes))
layer_conf[:, 0] = 1 # all classes are background by default
mbox_conf_layers.append(layer_conf)
mbox_loc_layers.append(np.zeros(
(layer_default_boxes.shape[0], 12)))
mbox_default_boxes_layers.append(layer_default_boxes)
mbox_conf = np.concatenate(mbox_conf_layers, axis=0)
mbox_loc = np.concatenate(mbox_loc_layers, axis=0)
mbox_default_boxes = np.concatenate(mbox_default_boxes_layers, axis=0)
template = np.concatenate([mbox_conf, mbox_loc, mbox_default_boxes],
axis=-1)
template = np.expand_dims(template, axis=0)
return np.tile(template, (self.batch_size, 1, 1))
def call(self, inputs):
default_boxes = generate_default_boxes_for_feature_map(
feature_map_size=self.feature_map_size,
image_size=self.image_size,
offset=self.offset,
scale=self.scale,
next_scale=self.next_scale,
aspect_ratios=self.aspect_ratios,
variances=self.variances,
extra_box_for_ar_1=self.extra_box_for_ar_1)
default_boxes = np.expand_dims(default_boxes, axis=0)
default_boxes = tf.constant(default_boxes, dtype='float32')
default_boxes = tf.tile(default_boxes,
(tf.shape(inputs)[0], 1, 1, 1, 1))
return default_boxes
print("generating default boxes")
scales = np.linspace(default_boxes_config["min_scale"],
default_boxes_config["max_scale"],
len(default_boxes_config["layers"]))
mbox_conf_layers = []
mbox_loc_layers = []
mbox_default_boxes_layers = []
for i, layer in enumerate(default_boxes_config["layers"]):
temp_image = image.copy()
print(f"displaying default boxes for layer: {layer['name']}")
layer_default_boxes = ssd_utils.generate_default_boxes_for_feature_map(
feature_map_size=layer["size"],
image_size=input_size,
offset=layer["offset"],
scale=scales[i],
next_scale=scales[i + 1]
if i + 1 <= len(default_boxes_config["layers"]) - 1 else 1,
aspect_ratios=layer["aspect_ratios"],
variances=default_boxes_config["variances"],
extra_box_for_ar_1=extra_box_for_ar_1)
grid_size = input_size / layer["size"]
offset = layer["offset"]
offset_x, offset_y = offset
cx = np.linspace(offset_x * grid_size, input_size - (offset_x * grid_size),
layer["size"])
cy = np.linspace(offset_y * grid_size, input_size - (offset_y * grid_size),
layer["size"])
for n in range(len(cx)):
