Cherry pick yaml constructor (#5917)

* yaml support no constructor

* fix yaml constructor in eval & export

* fix typo

* revert config

ppdet/core/workspace.py

@@ -210,9 +210,17 @@ def create(cls_or_name, **kwargs):
     assert type(cls_or_name) in [type, str
                                  ], "should be a class or name of a class"
     name = type(cls_or_name) == str and cls_or_name or cls_or_name.__name__
-    assert name in global_config and \
-        isinstance(global_config[name], SchemaDict), \
-        "the module {} is not registered".format(name)
+    if name in global_config:
+        if isinstance(global_config[name], SchemaDict):
+            pass
+        elif hasattr(global_config[name], "__dict__"):
+            # support instance return directly
+            return global_config[name]
+        else:
+            raise ValueError("The module {} is not registered".format(name))
+    else:
+        raise ValueError("The module {} is not registered".format(name))
+
     config = global_config[name]
     cls = getattr(config.pymodule, name)
     cls_kwargs = {}

ppdet/data/source/__init__.py

@@ -27,3 +27,4 @@ from .category import *
 from .keypoint_coco import *
 from .mot import *
 from .sniper_coco import SniperCOCODataSet
+from .dataset import ImageFolder

ppdet/data/source/dataset.py

@@ -23,6 +23,7 @@ from paddle.io import Dataset
 from ppdet.core.workspace import register, serializable
 from ppdet.utils.download import get_dataset_path
 import copy
+import ppdet.data.source as source
 
 
 @serializable
@@ -60,6 +61,9 @@ class DetDataset(Dataset):
     def __len__(self, ):
         return len(self.roidbs)
 
+    def __call__(self, *args, **kwargs):
+        return self
+
     def __getitem__(self, idx):
         # data batch
         roidb = copy.deepcopy(self.roidbs[idx])
@@ -198,3 +202,40 @@ class ImageFolder(DetDataset):
     def set_images(self, images):
         self.image_dir = images
         self.roidbs = self._load_images()
+
+
+@register
+class CommonDataset(object):
+    def __init__(self, **dataset_args):
+        super(CommonDataset, self).__init__()
+        dataset_args = copy.deepcopy(dataset_args)
+        type = dataset_args.pop("name")
+        self.dataset = getattr(source, type)(**dataset_args)
+
+    def __call__(self):
+        return self.dataset
+
+
+@register
+class TrainDataset(CommonDataset):
+    pass
+
+
+@register
+class EvalMOTDataset(CommonDataset):
+    pass
+
+
+@register
+class TestMOTDataset(CommonDataset):
+    pass
+
+
+@register
+class EvalDataset(CommonDataset):
+    pass
+
+
+@register
+class TestDataset(CommonDataset):
+    pass

ppdet/engine/trainer.py

@@ -67,10 +67,13 @@ class Trainer(object):
         self.is_loaded_weights = False
 
         # build data loader
+        capital_mode = self.mode.capitalize()
         if cfg.architecture in MOT_ARCH and self.mode in ['eval', 'test']:
-            self.dataset = cfg['{}MOTDataset'.format(self.mode.capitalize())]
+            self.dataset = self.cfg['{}MOTDataset'.format(
+                capital_mode)] = create('{}MOTDataset'.format(capital_mode))()
         else:
-            self.dataset = cfg['{}Dataset'.format(self.mode.capitalize())]
+            self.dataset = self.cfg['{}Dataset'.format(capital_mode)] = create(
+                '{}Dataset'.format(capital_mode))()
 
         if cfg.architecture == 'DeepSORT' and self.mode == 'train':
             logger.error('DeepSORT has no need of training on mot dataset.')
@@ -81,7 +84,7 @@ class Trainer(object):
             self.dataset.set_images(images)
 
         if self.mode == 'train':
-            self.loader = create('{}Reader'.format(self.mode.capitalize()))(
+            self.loader = create('{}Reader'.format(capital_mode))(
                 self.dataset, cfg.worker_num)
 
         if cfg.architecture == 'JDE' and self.mode == 'train':
@@ -370,6 +373,8 @@ class Trainer(object):
     def train(self, validate=False):
         assert self.mode == 'train', "Model not in 'train' mode"
         Init_mark = False
+        if validate:
+            self.cfg.EvalDataset = create("EvalDataset")()
 
         sync_bn = (getattr(self.cfg, 'norm_type', None) == 'sync_bn' and
                    self.cfg.use_gpu and self._nranks > 1)

ppdet/optimizer.py

@@ -16,6 +16,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import sys
 import math
 import weakref
 import paddle
@@ -25,6 +26,7 @@ import paddle.optimizer as optimizer
 import paddle.regularizer as regularizer
 
 from ppdet.core.workspace import register, serializable
+import copy
 
 __all__ = ['LearningRate', 'OptimizerBuilder']
 
@@ -252,7 +254,18 @@ class LearningRate(object):
                  schedulers=[PiecewiseDecay(), LinearWarmup()]):
         super(LearningRate, self).__init__()
         self.base_lr = base_lr
-        self.schedulers = schedulers
+        self.schedulers = []
+
+        schedulers = copy.deepcopy(schedulers)
+        for sched in schedulers:
+            if isinstance(sched, dict):
+                # support dict sched instantiate
+                module = sys.modules[__name__]
+                type = sched.pop("name")
+                scheduler = getattr(module, type)(**sched)
+                self.schedulers.append(scheduler)
+            else:
+                self.schedulers.append(sched)
 
     def __call__(self, step_per_epoch):
         assert len(self.schedulers) >= 1