diff --git a/paddlex/cv/nets/detection/loss/yolo_loss.py b/paddlex/cv/nets/detection/loss/yolo_loss.py index 31e68ecd8311e89d4378761cfc19df8fc15b83e6..a2c9bc3b94475cd22c499765e8a7305ec48e14fb 100644 --- a/paddlex/cv/nets/detection/loss/yolo_loss.py +++ b/paddlex/cv/nets/detection/loss/yolo_loss.py @@ -19,6 +19,7 @@ from __future__ import print_function from paddle import fluid from .iou_loss import IouLoss from .iou_aware_loss import IouAwareLoss +import paddlex class YOLOv3Loss(object): @@ -157,7 +158,7 @@ class YOLOv3Loss(object): loss_cls = fluid.layers.sigmoid_cross_entropy_with_logits(cls, tcls) loss_cls = fluid.layers.elementwise_mul(loss_cls, tobj, axis=0) - loss_cls = fluid.layers.reduce_sum(loss_cls) + loss_cls = fluid.layers.reduce_sum(loss_cls, dim=[1, 2, 3, 4]) loss_xys.append(fluid.layers.reduce_mean(loss_x + loss_y)) loss_whs.append(fluid.layers.reduce_mean(loss_w + loss_h)) @@ -270,6 +271,7 @@ class YOLOv3Loss(object): # 1. get pred bbox, which is same with YOLOv3 infer mode, use yolo_box here # NOTE: img_size is set as 1.0 to get noramlized pred bbox + batch_size = int(batch_size / paddlex.env_info['num']) bbox, prob = fluid.layers.yolo_box( x=output, img_size=fluid.layers.ones( diff --git a/paddlex/cv/nets/detection/yolo_v3.py b/paddlex/cv/nets/detection/yolo_v3.py index 9a18e42610380e1fa74a72a6ae3c0738900d0efe..0262546e79adda39e0b795aea6e230ffc8e296f1 100644 --- a/paddlex/cv/nets/detection/yolo_v3.py +++ b/paddlex/cv/nets/detection/yolo_v3.py @@ -205,7 +205,7 @@ class YOLOv3: p = fluid.layers.expand_as(gamma, input) input_shape_tmp = fluid.layers.cast(input_shape, dtype="int64") random_matrix = fluid.layers.uniform_random( - input_shape_tmp, dtype='float32', min=0.0, max=1.0, seed=1) + input_shape_tmp, dtype='float32', min=0.0, max=1.0) one_zero_m = fluid.layers.less_than(random_matrix, p) one_zero_m.stop_gradient = True one_zero_m = fluid.layers.cast(one_zero_m, dtype="float32") diff --git a/paddlex/cv/transforms/det_transforms.py b/paddlex/cv/transforms/det_transforms.py index 0f944e2a0617f05e929dc8430fd5d70d9f48754d..6d7a118dc8c1eb90859700d3742a3522c88982cf 100644 --- a/paddlex/cv/transforms/det_transforms.py +++ b/paddlex/cv/transforms/det_transforms.py @@ -1299,7 +1299,6 @@ class BatchRandomShape(DetTransform): im = np.swapaxes(im, 1, 0) data_list[0] = im batch_data[data_id] = tuple(data_list) - np.save('im.npy', im) return batch_data