def edit_feature(self, feature):
height, width = feature["image"].shape[0], feature["image"].shape[1]
feature["x1"], feature["y1"], feature["x2"], feature["y2"] = feature["x1"]/width, feature["y1"]/height, feature["x2"]/width, feature["y2"]/height
feature["image"] = cv2.resize(feature["image"], (128, 64)) #cv2 requires (width, height)
anchorbox = get_fpn_anchor_box(input_shape=feature["image"].shape)
target_cls, target_loc = get_target(anchorbox, feature["label"], feature["x1"], feature["y1"], feature["x2"], feature["y2"], num_classes=10)
feature["target_cls"], feature["target_loc"] = target_cls, target_loc
return feature
def __init__(self):
self.model = RetinaNet(input_shape=(64, 128, 3), num_classes=10)
self.optimizer = tf.optimizers.Adam(learning_rate=0.0001)
self.loss = MyLoss()
self.anchorbox = tf.convert_to_tensor(
get_fpn_anchor_box(input_shape=(64, 128, 3)))
self.anchor_w_h = tf.tile(self.anchorbox[:, 2:], [1, 2]) - tf.tile(
self.anchorbox[:, :2], [1, 2])
def __init__(self, num_classes, input_shape, pred_key, gt_key, mode="eval", output_name=("mAP", "AP50", "AP75")):
super().__init__(outputs=output_name, mode=mode)
self.pred_key = pred_key
self.gt_key = gt_key
self.output_name = output_name
assert len(self.output_name) == 3, 'MeanAvgPrecision trace adds 3 fields mAP AP50 AP75 to state '
self.iou_thres = np.linspace(.5, 0.95, np.round((0.95 - .5) / .05) + 1, endpoint=True)
self.rec_thres = np.linspace(.0, 1.00, np.round((1.00 - .0) / .01) + 1, endpoint=True)
self.categories = [n + 1 for n in range(num_classes)] # MSCOCO style class label starts from 1
self.maxdets = 100
self.image_ids = []
self.anch_box = get_fpn_anchor_box(input_shape=input_shape)[0]
def __init__(self, inputs=None, outputs=None, mode=None):
super().__init__(inputs=inputs, outputs=outputs, mode=mode)
self.anchorbox = get_fpn_anchor_box(input_shape=(64, 128, 3))
def __init__(self, inputs=None, outputs=None, mode=None):
super().__init__(inputs=inputs, outputs=outputs, mode=mode)
self.anchorbox = tf.convert_to_tensor(
get_fpn_anchor_box(input_shape=(64, 128, 3)))
self.anchor_w_h = tf.tile(self.anchorbox[:, 2:], [1, 2]) - tf.tile(
self.anchorbox[:, :2], [1, 2])
