to be done

Author: 13308350476

retinanet/model.py

@@ -245,11 +245,11 @@ def forward(self, inputs):
         # MYNOTE：4 layer UP
         x1 = self.layer1(x)
         x2 = self.layer2(x1)
-        x3 = self.layer3(x2)
+        x3 = self.layer3(x2) 
         x4 = self.layer4(x3)
         # MYNOTE：3 layer down
         features = self.fpn([x2, x3, x4])  # MYNOTE：features = [P3 P4 P5 P6 P7]
-        # MYNOTE：这两个的cat
+        # MYNOTE：这两个的cat查查 一阶段和二阶段的区别
         regression = torch.cat([self.regressionModel(feature) for feature in features], dim=1)
 
         classification = torch.cat([self.classificationModel(feature) for feature in features], dim=1)

train.py

@@ -21,6 +21,7 @@
 
 
 def main(args=None):
+    #下来了解
     parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.')
 
     parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.')
@@ -72,6 +73,7 @@ def main(args=None):
     dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler)
 
     # MYNOTE： DATASET dataloder for tedt
+    #验证集1和测试机
     if dataset_val is not None:
         sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False)
         dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val)