Fix bug and optimize MNIST config (open-mmlab#98)

* add simple_test to ClsHead

* optimize lenet training config

* recover path setting

configs/mnist/lenet5.py

@@ -15,22 +15,23 @@
     dict(type='Normalize', **img_norm_cfg),
     dict(type='ImageToTensor', keys=['img']),
     dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label']),
 ]
 test_pipeline = [
     dict(type='Resize', size=32),
     dict(type='Normalize', **img_norm_cfg),
     dict(type='ImageToTensor', keys=['img']),
-    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img']),
 ]
 data = dict(
     samples_per_gpu=128,
     workers_per_gpu=2,
     train=dict(
         type=dataset_type, data_prefix='data/mnist', pipeline=train_pipeline),
     val=dict(
-        type=dataset_type, data_prefix='data/mnist', pipeline=test_pipeline),
-    test=dict(
         type=dataset_type, data_prefix='data/mnist', pipeline=test_pipeline))
+evaluation = dict(
+    interval=5, metric='accuracy', metric_options={'topk': (1, )})
 # optimizer
 optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
 optimizer_config = dict(grad_clip=None)
@@ -40,14 +41,14 @@
 checkpoint_config = dict(interval=1)
 # yapf:disable
 log_config = dict(
-    interval=100,
+    interval=150,
     hooks=[
         dict(type='TextLoggerHook'),
         # dict(type='TensorboardLoggerHook')
     ])
 # yapf:enable
 # runtime settings
-runner = dict(type='EpochBasedRunner', max_epochs=20)
+runner = dict(type='EpochBasedRunner', max_epochs=5)
 dist_params = dict(backend='nccl')
 log_level = 'INFO'
 work_dir = './work_dirs/mnist/'

mmcls/models/heads/cls_head.py

@@ -1,3 +1,6 @@
+import torch
+import torch.nn.functional as F
+
 from mmcls.models.losses import Accuracy
 from ..builder import HEADS, build_loss
 from .base_head import BaseHead
@@ -43,3 +46,13 @@ def loss(self, cls_score, gt_label):
     def forward_train(self, cls_score, gt_label):
         losses = self.loss(cls_score, gt_label)
         return losses
+
+    def simple_test(self, cls_score):
+        """Test without augmentation."""
+        if isinstance(cls_score, list):
+            cls_score = sum(cls_score) / float(len(cls_score))
+        pred = F.softmax(cls_score, dim=1) if cls_score is not None else None
+        if torch.onnx.is_in_onnx_export():
+            return pred
+        pred = list(pred.detach().cpu().numpy())
+        return pred