update yolov3 use new API. test=develop

yolov3/darknet.py

@@ -12,14 +12,14 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
 
+from paddle.static import InputSpec
 from paddle.fluid.dygraph.nn import Conv2D, BatchNorm
-
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.download import get_weights_path_from_url
+from paddle.utils.download import get_weights_path_from_url
 
 __all__ = ['DarkNet', 'darknet53']
 
@@ -131,7 +131,7 @@ class LayerWarp(fluid.dygraph.Layer):
 DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}
 
 
-class DarkNet(Model):
+class DarkNet(fluid.dygraph.Layer):
     """DarkNet model from
     `"YOLOv3: An Incremental Improvement" <https://arxiv.org/abs/1804.02767>`_
 
@@ -190,7 +190,8 @@ def _darknet(num_layers=53, input_channels=3, pretrained=True):
         weight_path = get_weights_path_from_url(*(pretrain_infos[num_layers]))
         assert weight_path.endswith('.pdparams'), \
                 "suffix of weight must be .pdparams"
-        model.load(weight_path[:-9])
+        weight_dict, _ = fluid.load_dygraph(weight_path[:-9])
+        model.set_dict(weight_dict)
     return model
 
 

yolov3/infer.py

@@ -20,12 +20,11 @@ import argparse
 import numpy as np
 from PIL import Image
 
+import paddle
 from paddle import fluid
 from paddle.fluid.optimizer import Momentum
 from paddle.io import DataLoader
 
-from paddle.incubate.hapi.model import Model, Input, set_device
-
 from modeling import yolov3_darknet53, YoloLoss
 from transforms import *
 from utils import print_arguments
@@ -36,6 +35,7 @@ logger = logging.getLogger(__name__)
 
 IMAGE_MEAN = [0.485, 0.456, 0.406]
 IMAGE_STD = [0.229, 0.224, 0.225]
+NUM_MAX_BOXES = 50
 
 
 def get_save_image_name(output_dir, image_path):
@@ -62,24 +62,18 @@ def load_labels(label_list, with_background=True):
 
 
 def main():
-    device = set_device(FLAGS.device)
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
-
-    inputs = [
-        Input(
-            [None, 1], 'int64', name='img_id'), Input(
-                [None, 2], 'int32', name='img_shape'), Input(
-                    [None, 3, None, None], 'float32', name='image')
-    ]
+    device = paddle.set_device(FLAGS.device)
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     cat2name = load_labels(FLAGS.label_list, with_background=False)
 
     model = yolov3_darknet53(
         num_classes=len(cat2name),
+        num_max_boxes=NUM_MAX_BOXES,
         model_mode='test',
         pretrained=FLAGS.weights is None)
 
-    model.prepare(inputs=inputs, device=FLAGS.device)
+    model.prepare()
 
     if FLAGS.weights is not None:
         model.load(FLAGS.weights, reset_optimizer=True)

yolov3/main.py

@@ -21,13 +21,11 @@ import os
 
 import numpy as np
 
+import paddle
 from paddle import fluid
 from paddle.fluid.optimizer import Momentum
-from paddle.io import DataLoader
-
-from paddle.incubate.hapi.model import Model, Input, set_device
-from paddle.incubate.hapi.distributed import DistributedBatchSampler
-from paddle.incubate.hapi.vision.transforms import Compose, BatchCompose
+from paddle.io import DataLoader, DistributedBatchSampler
+from paddle.vision.transforms import Compose, BatchCompose
 
 from modeling import yolov3_darknet53, YoloLoss
 from coco import COCODataset
@@ -61,22 +59,8 @@ def make_optimizer(step_per_epoch, parameter_list=None):
 
 
 def main():
-    device = set_device(FLAGS.device)
-    fluid.enable_dygraph(device) if FLAGS.dynamic else None
-
-    inputs = [
-        Input(
-            [None, 1], 'int64', name='img_id'), Input(
-                [None, 2], 'int32', name='img_shape'), Input(
-                    [None, 3, None, None], 'float32', name='image')
-    ]
-
-    labels = [
-        Input(
-            [None, NUM_MAX_BOXES, 4], 'float32', name='gt_bbox'), Input(
-                [None, NUM_MAX_BOXES], 'int32', name='gt_label'), Input(
-                    [None, NUM_MAX_BOXES], 'float32', name='gt_score')
-    ]
+    device = paddle.set_device(FLAGS.device)
+    paddle.disable_static(device) if FLAGS.dynamic else None
 
     if not FLAGS.eval_only:  # training mode
         train_transform = Compose([
@@ -129,6 +113,7 @@ def main():
     pretrained = FLAGS.eval_only and FLAGS.weights is None
     model = yolov3_darknet53(
         num_classes=dataset.num_classes,
+        num_max_boxes=NUM_MAX_BOXES,
         model_mode='eval' if FLAGS.eval_only else 'train',
         pretrained=pretrained)
 
@@ -140,11 +125,7 @@ def main():
         len(batch_sampler), parameter_list=model.parameters())
 
     model.prepare(
-        optim,
-        YoloLoss(num_classes=dataset.num_classes),
-        inputs=inputs,
-        labels=labels,
-        device=FLAGS.device)
+        optimizer=optim, loss=YoloLoss(num_classes=dataset.num_classes))
 
     # NOTE: we implement COCO metric of YOLOv3 model here, separately
     # from 'prepare' and 'fit' framework for follwing reason:

yolov3/modeling.py

@@ -15,14 +15,15 @@
 from __future__ import division
 from __future__ import print_function
 
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.nn import Conv2D, BatchNorm
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
 
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.loss import Loss
-from paddle.incubate.hapi.download import get_weights_path_from_url
+from paddle.static import InputSpec
+from paddle.utils.download import get_weights_path_from_url
+
 from darknet import darknet53
 
 __all__ = ['YoloLoss', 'YOLOv3', 'yolov3_darknet53']
@@ -125,7 +126,7 @@ class YoloDetectionBlock(fluid.dygraph.Layer):
         return route, tip
 
 
-class YOLOv3(Model):
+class YOLOv3(fluid.dygraph.Layer):
     """YOLOv3 model from
     `"YOLOv3: An Incremental Improvement" <https://arxiv.org/abs/1804.02767>`_
 
@@ -194,25 +195,13 @@ class YOLOv3(Model):
                         act='leaky_relu'))
                 self.route_blocks.append(route)
 
-    def extract_feats(self, inputs):
-        out = self.backbone.conv0(inputs)
-        out = self.backbone.downsample0(out)
-        blocks = []
-        for i, conv_block_i in enumerate(
-                self.backbone.darknet53_conv_block_list):
-            out = conv_block_i(out)
-            blocks.append(out)
-            if i < len(self.backbone.stages) - 1:
-                out = self.backbone.downsample_list[i](out)
-        return blocks[-1:-4:-1]
-
     def forward(self, img_id, img_shape, inputs):
         outputs = []
         boxes = []
         scores = []
         downsample = 32
 
-        feats = self.extract_feats(inputs)
+        feats = self.backbone(inputs)
         route = None
         for idx, feat in enumerate(feats):
             if idx > 0:
@@ -267,7 +256,7 @@ class YOLOv3(Model):
         return outputs + preds
 
 
-class YoloLoss(Loss):
+class YoloLoss(fluid.dygraph.Layer):
     def __init__(self, num_classes=80, num_max_boxes=50):
         super(YoloLoss, self).__init__()
         self.num_classes = num_classes
@@ -279,11 +268,16 @@ class YoloLoss(Loss):
         ]
         self.anchor_masks = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
 
-    def forward(self, outputs, labels):
+    def forward(self, *inputs):
         downsample = 32
-        gt_box, gt_label, gt_score = labels
         losses = []
 
+        if len(inputs) == 6:
+            output1, output2, output3, gt_box, gt_label, gt_score = inputs
+        elif len(inputs) == 8:
+            output1, output2, output3, img_id, bbox, gt_box, gt_label, gt_score = inputs
+
+        outputs = [output1, output2, output3]
         for idx, out in enumerate(outputs):
             if idx == 3: break  # debug
             anchor_mask = self.anchor_masks[idx]
@@ -306,9 +300,23 @@ class YoloLoss(Loss):
 
 def _yolov3_darknet(num_layers=53,
                     num_classes=80,
+                    num_max_boxes=50,
                     model_mode='train',
                     pretrained=True):
-    model = YOLOv3(num_classes, model_mode)
+    inputs = [
+        InputSpec(
+            [None, 1], 'int64', name='img_id'), InputSpec(
+                [None, 2], 'int32', name='img_shape'), InputSpec(
+                    [None, 3, None, None], 'float32', name='image')
+    ]
+    labels = [
+        InputSpec(
+            [None, num_max_boxes, 4], 'float32', name='gt_bbox'), InputSpec(
+                [None, num_max_boxes], 'int32', name='gt_label'), InputSpec(
+                    [None, num_max_boxes], 'float32', name='gt_score')
+    ]
+    net = YOLOv3(num_classes, model_mode)
+    model = paddle.Model(net, inputs, labels)
     if pretrained:
         assert num_layers in pretrain_infos.keys(), \
                 "YOLOv3-DarkNet{} do not have pretrained weights now, " \
@@ -320,11 +328,15 @@ def _yolov3_darknet(num_layers=53,
     return model
 
 
-def yolov3_darknet53(num_classes=80, model_mode='train', pretrained=True):
+def yolov3_darknet53(num_classes=80,
+                     num_max_boxes=50,
+                     model_mode='train',
+                     pretrained=True):
     """YOLOv3 model with 53-layer DarkNet as backbone
     
     Args:
         num_classes (int): class number, default 80.
+        num_classes (int): max bbox number in a image, default 50.
         model_mode (str): 'train', 'eval', 'test' mode, network structure
             will be diffrent in the output layer and data, in 'train' mode,
             no output layer append, in 'eval' and 'test', output feature
@@ -334,4 +346,5 @@ def yolov3_darknet53(num_classes=80, model_mode='train', pretrained=True):
         pretrained (bool): If True, returns a model with pre-trained model
             on COCO, default True
     """
-    return _yolov3_darknet(53, num_classes, model_mode, pretrained)
+    return _yolov3_darknet(53, num_classes, num_max_boxes, model_mode,
+                           pretrained)