debug

ppcls/data/__init__.py

@@ -106,89 +106,91 @@ def build_dataloader(config, *mode, seed=None):
     # build dataset
     if use_dali:
         from ppcls.data.dataloader.dali import dali_dataloader
-        return dali_dataloader(
+        data_loader = dali_dataloader(
             dataloader_config,
             mode[-1],
             paddle.device.get_device(),
             num_threads=num_workers,
             seed=seed,
             enable_fuse=True)
-
-    config_dataset = dataloader_config['dataset']
-    config_dataset = copy.deepcopy(config_dataset)
-    dataset_name = config_dataset.pop('name')
-    if 'batch_transform_ops' in config_dataset:
-        batch_transform = config_dataset['batch_transform_ops']
-    else:
-        batch_transform = None
-
-    dataset = eval(dataset_name)(**config_dataset)
-
-    logger.debug("build dataset({}) success...".format(dataset))
-
-    # build sampler
-    config_sampler = dataloader_config['sampler']
-    if config_sampler and "name" not in config_sampler:
-        batch_sampler = None
-        batch_size = config_sampler["batch_size"]
-        drop_last = config_sampler["drop_last"]
-        shuffle = config_sampler["shuffle"]
-    else:
-        sampler_name = config_sampler.pop("name")
-        sampler_argspec = inspect.getargspec(eval(sampler_name).__init__).args
-        if "total_epochs" in sampler_argspec:
-            config_sampler.update({"total_epochs": epochs})
-        batch_sampler = eval(sampler_name)(dataset, **config_sampler)
-
-    logger.debug("build batch_sampler({}) success...".format(batch_sampler))
-
-    # build batch operator
-    def mix_collate_fn(batch):
-        batch = transform(batch, batch_ops)
-        # batch each field
-        slots = []
-        for items in batch:
-            for i, item in enumerate(items):
-                if len(slots) < len(items):
-                    slots.append([item])
-                else:
-                    slots[i].append(item)
-        return [np.stack(slot, axis=0) for slot in slots]
-
-    if isinstance(batch_transform, list):
-        batch_ops = create_operators(batch_transform, class_num)
-        batch_collate_fn = mix_collate_fn
-    else:
-        batch_collate_fn = None
-
-    init_fn = partial(
-        worker_init_fn,
-        num_workers=num_workers,
-        rank=dist.get_rank(),
-        seed=seed) if seed is not None else None
-
-    if batch_sampler is None:
-        data_loader = DataLoader(
-            dataset=dataset,
-            places=paddle.device.get_device(),
-            num_workers=num_workers,
-            return_list=True,
-            use_shared_memory=use_shared_memory,
-            batch_size=batch_size,
-            shuffle=shuffle,
-            drop_last=drop_last,
-            collate_fn=batch_collate_fn,
-            worker_init_fn=init_fn)
     else:
-        data_loader = DataLoader(
-            dataset=dataset,
-            places=paddle.device.get_device(),
+        config_dataset = dataloader_config['dataset']
+        config_dataset = copy.deepcopy(config_dataset)
+        dataset_name = config_dataset.pop('name')
+        if 'batch_transform_ops' in config_dataset:
+            batch_transform = config_dataset['batch_transform_ops']
+        else:
+            batch_transform = None
+
+        dataset = eval(dataset_name)(**config_dataset)
+
+        logger.debug("build dataset({}) success...".format(dataset))
+
+        # build sampler
+        config_sampler = dataloader_config['sampler']
+        if config_sampler and "name" not in config_sampler:
+            batch_sampler = None
+            batch_size = config_sampler["batch_size"]
+            drop_last = config_sampler["drop_last"]
+            shuffle = config_sampler["shuffle"]
+        else:
+            sampler_name = config_sampler.pop("name")
+            sampler_argspec = inspect.getargspec(eval(sampler_name)
+                                                 .__init__).args
+            if "total_epochs" in sampler_argspec:
+                config_sampler.update({"total_epochs": epochs})
+            batch_sampler = eval(sampler_name)(dataset, **config_sampler)
+
+        logger.debug("build batch_sampler({}) success...".format(
+            batch_sampler))
+
+        # build batch operator
+        def mix_collate_fn(batch):
+            batch = transform(batch, batch_ops)
+            # batch each field
+            slots = []
+            for items in batch:
+                for i, item in enumerate(items):
+                    if len(slots) < len(items):
+                        slots.append([item])
+                    else:
+                        slots[i].append(item)
+            return [np.stack(slot, axis=0) for slot in slots]
+
+        if isinstance(batch_transform, list):
+            batch_ops = create_operators(batch_transform, class_num)
+            batch_collate_fn = mix_collate_fn
+        else:
+            batch_collate_fn = None
+
+        init_fn = partial(
+            worker_init_fn,
             num_workers=num_workers,
-            return_list=True,
-            use_shared_memory=use_shared_memory,
-            batch_sampler=batch_sampler,
-            collate_fn=batch_collate_fn,
-            worker_init_fn=init_fn)
+            rank=dist.get_rank(),
+            seed=seed) if seed is not None else None
+
+        if batch_sampler is None:
+            data_loader = DataLoader(
+                dataset=dataset,
+                places=paddle.device.get_device(),
+                num_workers=num_workers,
+                return_list=True,
+                use_shared_memory=use_shared_memory,
+                batch_size=batch_size,
+                shuffle=shuffle,
+                drop_last=drop_last,
+                collate_fn=batch_collate_fn,
+                worker_init_fn=init_fn)
+        else:
+            data_loader = DataLoader(
+                dataset=dataset,
+                places=paddle.device.get_device(),
+                num_workers=num_workers,
+                return_list=True,
+                use_shared_memory=use_shared_memory,
+                batch_sampler=batch_sampler,
+                collate_fn=batch_collate_fn,
+                worker_init_fn=init_fn)
 
     total_samples = len(
         data_loader.dataset) if not use_dali else data_loader.size

ppcls/metric/__init__.py

@@ -15,6 +15,7 @@
 import copy
 from collections import OrderedDict
 
+from ..utils import logger
 from .avg_metrics import AvgMetrics
 from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
 from .metrics import DistillationTopkAcc