refine trainer (#2412)

* refine trainer

ppdet/data/reader.py

@@ -184,6 +184,7 @@ class BaseDataLoader(object):
                  batch_sampler=None,
                  return_list=False):
         self.dataset = dataset
+        self.dataset.check_or_download_dataset()
         self.dataset.parse_dataset()
         # get data
         self.dataset.set_transform(self._sample_transforms)

ppdet/data/source/__init__.py

@@ -16,7 +16,9 @@ from . import coco
 # TODO add voc and widerface dataset
 from . import voc
 #from . import widerface
+from . import category
 
 from .coco import *
 from .voc import *
 #from .widerface import *
+from .category import *

ppdet/data/source/coco.py

@@ -49,10 +49,10 @@ class COCODataSet(DetDataset):
         records = []
         ct = 0
 
-        catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
-        cname2cid = dict({
+        self.catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
+        self.cname2cid = dict({
             coco.loadCats(catid)[0]['name']: clsid
-            for catid, clsid in catid2clsid.items()
+            for catid, clsid in self.catid2clsid.items()
         })
 
         if 'annotations' not in coco.dataset:
@@ -119,7 +119,7 @@ class COCODataSet(DetDataset):
                 has_segmentation = False
                 for i, box in enumerate(bboxes):
                     catid = box['category_id']
-                    gt_class[i][0] = catid2clsid[catid]
+                    gt_class[i][0] = self.catid2clsid[catid]
                     gt_bbox[i, :] = box['clean_bbox']
                     is_crowd[i][0] = box['iscrowd']
                     # check RLE format 
@@ -163,4 +163,4 @@ class COCODataSet(DetDataset):
                 break
         assert len(records) > 0, 'not found any coco record in %s' % (anno_path)
         logger.debug('{} samples in file {}'.format(ct, anno_path))
-        self.roidbs, self.cname2cid = records, cname2cid
+        self.roidbs = records

ppdet/data/source/dataset.py

@@ -67,6 +67,10 @@ class DetDataset(Dataset):
 
         return self.transform(roidb)
 
+    def check_or_download_dataset(self):
+        self.dataset_dir = get_dataset_path(self.dataset_dir, self.anno_path,
+                                            self.image_dir)
+
     def set_kwargs(self, **kwargs):
         self.mixup_epoch = kwargs.get('mixup_epoch', -1)
         self.cutmix_epoch = kwargs.get('cutmix_epoch', -1)

ppdet/engine/export_utils.py

@@ -20,7 +20,7 @@ import os
 import yaml
 from collections import OrderedDict
 
-from ppdet.metrics import get_categories
+from ppdet.data.source.category import get_categories
 
 from ppdet.utils.logger import setup_logger
 logger = setup_logger('ppdet.engine')

ppdet/engine/trainer.py

@@ -31,7 +31,8 @@ from paddle.static import InputSpec
 from ppdet.core.workspace import create
 from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
 from ppdet.utils.visualizer import visualize_results
-from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, get_categories, get_infer_results
+from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, get_infer_results
+from ppdet.data.source.category import get_categories
 import ppdet.utils.stats as stats
 
 from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval, VisualDLWriter
@@ -116,8 +117,8 @@ class Trainer(object):
             self._callbacks = []
             self._compose_callback = None
 
-    def _init_metrics(self):
-        if self.mode == 'test':
+    def _init_metrics(self, validate=False):
+        if self.mode == 'test' or (self.mode == 'train' and not validate):
             self._metrics = []
             return
         classwise = self.cfg['classwise'] if 'classwise' in self.cfg else False
@@ -126,9 +127,12 @@ class Trainer(object):
             bias = self.cfg['bias'] if 'bias' in self.cfg else 0
             output_eval = self.cfg['output_eval'] \
                 if 'output_eval' in self.cfg else None
+            clsid2catid = {v: k for k, v in self.dataset.catid2clsid.items()} \
+                                if self.mode == 'eval' else None
             self._metrics = [
                 COCOMetric(
                     anno_file=self.dataset.get_anno(),
+                    clsid2catid=clsid2catid,
                     classwise=classwise,
                     output_eval=output_eval,
                     bias=bias)
@@ -186,6 +190,11 @@ class Trainer(object):
     def train(self, validate=False):
         assert self.mode == 'train', "Model not in 'train' mode"
 
+        # if validation in training is enabled, metrics should be re-init
+        if validate:
+            self._init_metrics(validate=validate)
+            self._reset_metrics()
+
         model = self.model
         if self.cfg.fleet:
             model = fleet.distributed_model(model)

ppdet/metrics/__init__.py

@@ -15,8 +15,4 @@
 from . import metrics
 from .metrics import *
 
-from . import category
-from .category import *
-
-__all__ = metrics.__all__ \
-        + category.__all__
+__all__ = metrics.__all__

ppdet/metrics/metrics.py

@@ -22,10 +22,10 @@ import json
 import paddle
 import numpy as np
 
-from .category import get_categories
 from .map_utils import prune_zero_padding, DetectionMAP
 from .coco_utils import get_infer_results, cocoapi_eval
 from .widerface_utils import face_eval_run
+from ppdet.data.source.category import get_categories
 
 from ppdet.utils.logger import setup_logger
 logger = setup_logger(__name__)
@@ -62,7 +62,9 @@ class COCOMetric(Metric):
         assert os.path.isfile(anno_file), \
                 "anno_file {} not a file".format(anno_file)
         self.anno_file = anno_file
-        self.clsid2catid, self.catid2name = get_categories('COCO', anno_file)
+        self.clsid2catid = kwargs.get('clsid2catid', None)
+        if self.clsid2catid is None:
+            self.clsid2catid, _ = get_categories('COCO', anno_file)
         self.classwise = kwargs.get('classwise', False)
         self.output_eval = kwargs.get('output_eval', None)
         # TODO: bias should be unified