compatible with paddlepaddle lower than 1.8.4

paddlex/__init__.py

@@ -43,11 +43,6 @@ except:
         "[WARNING] pycocotools install: https://paddlex.readthedocs.io/zh_CN/develop/install.html#pycocotools"
     )
 
-import paddle
-if paddle.__version__ == '0.0.0':
-    print("[NOTICE]\tYou are using paddlepaddle with version= 0.0.0")
-elif paddle.__version__ < '1.8.4':
-    raise Exception("[ERROR] paddlepaddle-gpu or paddlepaddle >= 1.8.4 is required")
 import paddlehub as hub
 if hub.version.hub_version < '1.8.2':
     raise Exception("[ERROR] paddlehub >= 1.8.2 is required")

paddlex/cv/models/ppyolo.py

@@ -18,6 +18,7 @@ import tqdm
 import os.path as osp
 import numpy as np
 from multiprocessing.pool import ThreadPool
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
 from paddle.fluid.optimizer import ExponentialMovingAverage
@@ -123,6 +124,9 @@ class PPYOLO(BaseAPI):
         self.use_ema = False
         self.with_dcn_v2 = with_dcn_v2
 
+        if paddle.__version__ < '1.8.4' and paddle.__version__ != '0.0.0':
+            raise Exception("PPYOLO requires paddlepaddle or paddlepaddle-gpu >= 1.8.4")
+
     def _get_backbone(self, backbone_name):
         if backbone_name.startswith('ResNet50_vd'):
             backbone = paddlex.cv.nets.ResNet(

paddlex/cv/nets/detection/loss/yolo_loss.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import paddle
 from paddle import fluid
 try:
     from collections.abc import Sequence
@@ -67,6 +68,20 @@ class YOLOv3Loss(object):
                 scale_x_y = self.scale_x_y if not isinstance(
                     self.scale_x_y, Sequence) else self.scale_x_y[i]
                 anchor_mask = anchor_masks[i]
+                if paddle.__version__ < '1.8.4' and paddle.__version__ != '0.0.0':
+                    loss = fluid.layers.yolov3_loss(
+                        x=output,
+                        gt_box=gt_box,
+                        gt_label=gt_label,
+                        gt_score=gt_score,
+                        anchors=anchors,
+                        anchor_mask=anchor_mask,
+                        class_num=num_classes,
+                        ignore_thresh=self._ignore_thresh,
+                        downsample_ratio=self.downsample[i],
+                        use_label_smooth=self._label_smooth,
+                        name=prefix_name + "yolo_loss" + str(i))
+                else:
                     loss = fluid.layers.yolov3_loss(
                         x=output,
                         gt_box=gt_box,
@@ -81,6 +96,7 @@ class YOLOv3Loss(object):
                         scale_x_y=scale_x_y,
                         name=prefix_name + "yolo_loss" + str(i))
 
+
                 losses.append(fluid.layers.reduce_mean(loss))
 
             return {'loss': sum(losses)}

paddlex/cv/nets/detection/yolo_v3.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import paddle
 from paddle import fluid
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
@@ -407,6 +408,17 @@ class YOLOv3:
             scale_x_y = self.scale_x_y if not isinstance(
                 self.scale_x_y, Sequence) else self.scale_x_y[i]
 
+            if paddle.__version__ < '1.8.4' and paddle.__version__ != '0.0.0':
+                box, score = fluid.layers.yolo_box(
+                    x=input,
+                    img_size=im_size,
+                    anchors=self.mask_anchors[i],
+                    class_num=self.num_classes,
+                    conf_thresh=self.nms.score_threshold,
+                    downsample_ratio=self.downsample[i],
+                    name=self.prefix_name + 'yolo_box' + str(i),
+                    clip_bbox=self.clip_bbox)
+            else:
                 box, score = fluid.layers.yolo_box(
                     x=input,
                     img_size=im_size,
@@ -417,6 +429,7 @@ class YOLOv3:
                     name=self.prefix_name + 'yolo_box' + str(i),
                     clip_bbox=self.clip_bbox,
                     scale_x_y=self.scale_x_y)
+
             boxes.append(box)
             scores.append(fluid.layers.transpose(score, perm=[0, 2, 1]))