add multiscale-training & refine train+eval (#2911)

ppdet/data/transform/operators.py

@@ -127,18 +127,24 @@ class ResizeImage(BaseOperator):
                  use_cv2=True):
         """
         Args:
-            target_size (int): the taregt size of image's short side
+            target_size (int|list): the target size of image's short side, 
+                multi-scale training is adopted when type is list.
             max_size (int): the max size of image
             interp (int): the interpolation method
-            use_cv2 (bool): use the cv2 interpolation method or use PIL interpolation method
+            use_cv2 (bool): use the cv2 interpolation method or use PIL 
+                interpolation method
         """
         super(ResizeImage, self).__init__()
-        self.target_size = int(target_size)
         self.max_size = int(max_size)
         self.interp = int(interp)
         self.use_cv2 = use_cv2
-        if not (isinstance(self.target_size, int) and isinstance(
-                self.max_size, int) and isinstance(self.interp, int)):
+        if not (isinstance(target_size, int) or isinstance(target_size, list)):
+            raise TypeError(
+                "Type of target_size is invalid. Must be Integer or List, now is {}".
+                format(type(target_size)))
+        self.target_size = target_size
+        if not (isinstance(self.max_size, int) and isinstance(self.interp,
+                                                              int)):
             raise TypeError("{}: input type is invalid.".format(self))
 
     def __call__(self, sample, context=None):
@@ -152,10 +158,15 @@ class ResizeImage(BaseOperator):
         im_shape = im.shape
         im_size_min = np.min(im_shape[0:2])
         im_size_max = np.max(im_shape[0:2])
+        if isinstance(self.target_size, list):
+            # Case for multi-scale training
+            selected_size = random.choice(self.target_size)
+        else:
+            selected_size = self.target_size
         if float(im_size_min) == 0:
             raise ZeroDivisionError('{}: min size of image is 0'.format(self))
         if self.max_size != 0:
-            im_scale = float(self.target_size) / float(im_size_min)
+            im_scale = float(selected_size) / float(im_size_min)
             # Prevent the biggest axis from being more than max_size
             if np.round(im_scale * im_size_max) > self.max_size:
                 im_scale = float(self.max_size) / float(im_size_max)
@@ -168,8 +179,8 @@ class ResizeImage(BaseOperator):
                 ],
                 dtype=np.float32)
         else:
-            im_scale_x = float(self.target_size) / float(im_shape[1])
-            im_scale_y = float(self.target_size) / float(im_shape[0])
+            im_scale_x = float(selected_size) / float(im_shape[1])
+            im_scale_y = float(selected_size) / float(im_shape[0])
         if self.use_cv2:
             im = cv2.resize(
                 im,
@@ -180,7 +191,9 @@ class ResizeImage(BaseOperator):
                 interpolation=self.interp)
         else:
             im = Image.fromarray(im)
-            im = im.resize((self.target_size, self.target_size), self.interp)
+            resize_w = selected_size * im_scale_x
+            resize_h = selected_size * im_scale_y
+            im = im.resize((resize_w, resize_h), self.interp)
             im = np.array(im)
 
         sample['image'] = im

tools/train.py

@@ -171,8 +171,9 @@ def main():
             it, np.mean(outs[-1]), logs, end_time - start_time)
         logger.info(strs)
 
-        if it > 0 and it % cfg.snapshot_iter == 0:
-            checkpoint.save(exe, train_prog, os.path.join(save_dir, str(it)))
+        if it > 0 and it % cfg.snapshot_iter == 0 or it == cfg.max_iters - 1:
+            save_name = str(it) if it != cfg.max_iters - 1 else "model_final"
+            checkpoint.save(exe, train_prog, os.path.join(save_dir, save_name))
 
             if FLAGS.eval:
                 # evaluation
@@ -184,7 +185,6 @@ def main():
                 eval_results(results, eval_feed, cfg.metric, resolution,
                              FLAGS.output_file)
 
-    checkpoint.save(exe, train_prog, os.path.join(save_dir, "model_final"))
     train_pyreader.reset()