[Enhancement] Simplify Config and Builder (#2897)

* Simplify Config and Builder

paddleseg/cvlibs/__init__.py

@@ -15,3 +15,4 @@
 from . import manager
 from . import param_init
 from .config import Config
+from .builder import Builder, SegBuilder

paddleseg/cvlibs/builder.py

@@ -13,22 +13,31 @@
 # limitations under the License.
 
 import copy
+from typing import Any, Optional
 
+import yaml
+import paddle
 
-class ComponentBuilder(object):
+from paddleseg.cvlibs import manager, Config
+from paddleseg.utils import utils, logger
+from paddleseg.utils.utils import CachedProperty as cached_property
+
+
+class Builder(object):
     """
-    This class is responsible for building components. All component classes must be available 
-        in the list of maintained components.
+    The base class for building components. 
 
     Args:
-        com_list (list): A list of component classes.
+        config (Config): A Config class.
+        comp_list (list, optional): A list of component classes. Default: None
     """
 
-    def __init__(self, com_list):
+    def __init__(self, config: Config, comp_list: Optional[list]=None):
         super().__init__()
-        self.com_list = com_list
+        self.config = config
+        self.comp_list = comp_list
 
-    def create_object(self, cfg):
+    def build_component(self, cfg):
         """
         Create Python object, such as model, loss, dataset, etc.
         """
@@ -44,17 +53,17 @@ def create_object(self, cfg):
         params = {}
         for key, val in cfg.items():
             if self.is_meta_type(val):
-                params[key] = self.create_object(val)
+                params[key] = self.build_component(val)
             elif isinstance(val, list):
                 params[key] = [
-                    self.create_object(item)
+                    self.build_component(item)
                     if self.is_meta_type(item) else item for item in val
                 ]
             else:
                 params[key] = val
 
         try:
-            obj = self.create_object_impl(com_class, **params)
+            obj = self.build_component_impl(com_class, **params)
         except Exception as e:
             if hasattr(com_class, '__name__'):
                 com_name = com_class.__name__
@@ -64,28 +73,16 @@ def create_object(self, cfg):
                 f"Tried to create a {com_name} object, but the operation has failed. "
                 "Please double check the arguments used to create the object.\n"
                 f"The error message is: \n{str(e)}")
-        return obj
-
-    def create_object_impl(self, component_class, *args, **kwargs):
-        raise NotImplementedError
-
-    def load_component_class(self, cfg):
-        raise NotImplementedError
-
-    @classmethod
-    def is_meta_type(cls, obj):
-        raise NotImplementedError
 
+        return obj
 
-class DefaultComponentBuilder(ComponentBuilder):
-    def create_object_impl(self, component_class, *args, **kwargs):
+    def build_component_impl(self, component_class, *args, **kwargs):
         return component_class(*args, **kwargs)
 
     def load_component_class(self, class_type):
-        for com in self.com_list:
+        for com in self.comp_list:
             if class_type in com.components_dict:
                 return com[class_type]
-
         raise RuntimeError("The specified component ({}) was not found.".format(
             class_type))
 
@@ -94,3 +91,212 @@ def is_meta_type(cls, obj):
         # TODO: should we define a protocol (see https://peps.python.org/pep-0544/#defining-a-protocol)
         # to make it more pythonic?
         return isinstance(obj, dict) and 'type' in obj
+
+    @classmethod
+    def show_msg(cls, name, cfg):
+        msg = 'Use the following config to build {}\n'.format(name)
+        msg += str(yaml.dump({name: cfg}, Dumper=utils.NoAliasDumper))
+        logger.info(msg[0:-1])
+
+
+class SegBuilder(Builder):
+    """
+    This class is responsible for building components for semantic segmentation. 
+    """
+
+    def __init__(self, config, comp_list=None):
+        if comp_list is None:
+            comp_list = [
+                manager.MODELS, manager.BACKBONES, manager.DATASETS,
+                manager.TRANSFORMS, manager.LOSSES, manager.OPTIMIZERS
+            ]
+        super().__init__(config, comp_list)
+
+    @cached_property
+    def model(self) -> paddle.nn.Layer:
+        model_cfg = self.config.model_cfg
+        assert model_cfg != {}, \
+            'No model specified in the configuration file.'
+
+        if self.config.train_dataset_cfg['type'] != 'Dataset':
+            # check and synchronize the num_classes in model config and dataset class
+            assert hasattr(self.train_dataset_class, 'NUM_CLASSES'), \
+                'If train_dataset class is not `Dataset`, it must have `NUM_CLASSES` attr.'
+            num_classes = getattr(self.train_dataset_class, 'NUM_CLASSES')
+            if 'num_classes' in model_cfg:
+                assert model_cfg['num_classes'] == num_classes, \
+                    'The num_classes is not consistent for model config ({}) ' \
+                    'and train_dataset class ({}) '.format(model_cfg['num_classes'], num_classes)
+            else:
+                logger.warning(
+                    'Add the `num_classes` in train_dataset class to '
+                    'model config. We suggest you manually set `num_classes` in model config.'
+                )
+                model_cfg['num_classes'] = num_classes
+            # check and synchronize the in_channels in model config and dataset class
+            assert hasattr(self.train_dataset_class, 'IMG_CHANNELS'), \
+                'If train_dataset class is not `Dataset`, it must have `IMG_CHANNELS` attr.'
+            in_channels = getattr(self.train_dataset_class, 'IMG_CHANNELS')
+            x = utils.get_in_channels(model_cfg)
+            if x is not None:
+                assert x == in_channels, \
+                    'The in_channels in model config ({}) and the img_channels in train_dataset ' \
+                    'class ({}) is not consistent'.format(x, in_channels)
+            else:
+                model_cfg = utils.set_in_channels(model_cfg, in_channels)
+                logger.warning(
+                    'Add the `in_channels` in train_dataset class to '
+                    'model config. We suggest you manually set `in_channels` in model config.'
+                )
+
+        self.show_msg('model', model_cfg)
+        return self.build_component(model_cfg)
+
+    @cached_property
+    def optimizer(self) -> paddle.optimizer.Optimizer:
+        opt_cfg = self.config.optimizer_cfg
+        assert opt_cfg != {}, \
+            'No optimizer specified in the configuration file.'
+        # For compatibility
+        if opt_cfg['type'] == 'adam':
+            opt_cfg['type'] = 'Adam'
+        if opt_cfg['type'] == 'sgd':
+            opt_cfg['type'] = 'SGD'
+        if opt_cfg['type'] == 'SGD' and 'momentum' in opt_cfg:
+            opt_cfg['type'] = 'Momentum'
+            logger.info('If the type is SGD and momentum in optimizer config, '
+                        'the type is changed to Momentum.')
+        self.show_msg('optimizer', opt_cfg)
+        opt = self.build_component(opt_cfg)
+        opt = opt(self.model, self.lr_scheduler)
+        return opt
+
+    @cached_property
+    def lr_scheduler(self) -> paddle.optimizer.lr.LRScheduler:
+        lr_cfg = self.config.lr_scheduler_cfg
+        assert lr_cfg != {}, \
+            'No lr_scheduler specified in the configuration file.'
+
+        use_warmup = False
+        if 'warmup_iters' in lr_cfg:
+            use_warmup = True
+            warmup_iters = lr_cfg.pop('warmup_iters')
+            assert 'warmup_start_lr' in lr_cfg, \
+                "When use warmup, please set warmup_start_lr and warmup_iters in lr_scheduler"
+            warmup_start_lr = lr_cfg.pop('warmup_start_lr')
+            end_lr = lr_cfg['learning_rate']
+
+        lr_type = lr_cfg.pop('type')
+        if lr_type == 'PolynomialDecay':
+            iters = self.config.iters - warmup_iters if use_warmup else self.config.iters
+            iters = max(iters, 1)
+            lr_cfg.setdefault('decay_steps', iters)
+
+        try:
+            lr_sche = getattr(paddle.optimizer.lr, lr_type)(**lr_cfg)
+        except Exception as e:
+            raise RuntimeError(
+                "Create {} has failed. Please check lr_scheduler in config. "
+                "The error message: {}".format(lr_type, e))
+
+        if use_warmup:
+            lr_sche = paddle.optimizer.lr.LinearWarmup(
+                learning_rate=lr_sche,
+                warmup_steps=warmup_iters,
+                start_lr=warmup_start_lr,
+                end_lr=end_lr)
+
+        return lr_sche
+
+    @cached_property
+    def loss(self) -> dict:
+        loss_cfg = self.config.loss_cfg
+        assert loss_cfg != {}, \
+            'No loss specified in the configuration file.'
+        return self._build_loss('loss', loss_cfg)
+
+    @cached_property
+    def distill_loss(self) -> dict:
+        loss_cfg = self.config.distill_loss_cfg
+        assert loss_cfg != {}, \
+            'No distill_loss specified in the configuration file.'
+        return self._build_loss('distill_loss', loss_cfg)
+
+    def _build_loss(self, loss_name, loss_cfg: dict):
+        def _check_helper(loss_cfg, ignore_index):
+            if 'ignore_index' not in loss_cfg:
+                loss_cfg['ignore_index'] = ignore_index
+                logger.warning('Add the `ignore_index` in train_dataset ' \
+                    'class to {} config. We suggest you manually set ' \
+                    '`ignore_index` in {} config.'.format(loss_name, loss_name)
+                )
+            else:
+                assert loss_cfg['ignore_index'] == ignore_index, \
+                    'the ignore_index in loss and train_dataset must be the same. Currently, loss ignore_index = {}, '\
+                    'train_dataset ignore_index = {}'.format(loss_cfg['ignore_index'], ignore_index)
+
+        # check and synchronize the ignore_index in model config and dataset class
+        if self.config.train_dataset_cfg['type'] != 'Dataset':
+            assert hasattr(self.train_dataset_class, 'IGNORE_INDEX'), \
+                'If train_dataset class is not `Dataset`, it must have `IGNORE_INDEX` attr.'
+            ignore_index = getattr(self.train_dataset_class, 'IGNORE_INDEX')
+            for loss_cfg_i in loss_cfg['types']:
+                if loss_cfg_i['type'] == 'MixedLoss':
+                    for loss_cfg_j in loss_cfg_i['losses']:
+                        _check_helper(loss_cfg_j, ignore_index)
+                else:
+                    _check_helper(loss_cfg_i, ignore_index)
+
+        self.show_msg(loss_name, loss_cfg)
+        loss_dict = {'coef': loss_cfg['coef'], "types": []}
+        for item in loss_cfg['types']:
+            loss_dict['types'].append(self.build_component(item))
+        return loss_dict
+
+    @cached_property
+    def train_dataset(self) -> paddle.io.Dataset:
+        dataset_cfg = self.config.train_dataset_cfg
+        assert dataset_cfg != {}, \
+            'No train_dataset specified in the configuration file.'
+        self.show_msg('train_dataset', dataset_cfg)
+        dataset = self.build_component(dataset_cfg)
+        assert len(dataset) != 0, \
+            'The number of samples in train_dataset is 0. Please check whether the dataset is valid.'
+        return dataset
+
+    @cached_property
+    def val_dataset(self) -> paddle.io.Dataset:
+        dataset_cfg = self.config.val_dataset_cfg
+        assert dataset_cfg != {}, \
+            'No val_dataset specified in the configuration file.'
+        self.show_msg('val_dataset', dataset_cfg)
+        dataset = self.build_component(dataset_cfg)
+        assert len(dataset) != 0, \
+            'The number of samples in val_dataset is 0. Please check whether the dataset is valid.'
+        return dataset
+
+    @cached_property
+    def train_dataset_class(self) -> Any:
+        dataset_cfg = self.config.train_dataset_cfg
+        assert dataset_cfg != {}, \
+            'No train_dataset specified in the configuration file.'
+        dataset_type = dataset_cfg.get('type')
+        return self.load_component_class(dataset_type)
+
+    @cached_property
+    def val_dataset_class(self) -> Any:
+        dataset_cfg = self.config.val_dataset_cfg
+        assert dataset_cfg != {}, \
+            'No val_dataset specified in the configuration file.'
+        dataset_type = dataset_cfg.get('type')
+        return self.load_component_class(dataset_type)
+
+    @cached_property
+    def val_transforms(self) -> list:
+        dataset_cfg = self.config.val_dataset_cfg
+        assert dataset_cfg != {}, \
+            'No val_dataset specified in the configuration file.'
+        transforms = []
+        for item in dataset_cfg.get('transforms', []):
+            transforms.append(self.build_component(item))
+        return transforms