Add GoogLeNetLoss

ppcls/configs/ImageNet/Inception/GoogLeNet.yaml

@@ -22,11 +22,10 @@ Arch:
 # loss function config for traing/eval process
 Loss:
   Train:
-    - CELoss:
+    - GoogLeNetLoss:
         weight: 1.0
-        epsilon: 0.1
   Eval:
-    - CELoss:
+    - GoogLeNetLoss:
         weight: 1.0
 
 
@@ -36,6 +35,7 @@ Optimizer:
   lr:
     name: Cosine
     learning_rate: 0.01
+    warmup_epoch: 5
   regularizer:
     name: 'L2'
     coeff: 0.0001
@@ -77,6 +77,9 @@ DataLoader:
       image_root: ./dataset/ILSVRC2012/
       cls_label_path: ./dataset/ILSVRC2012/val_list.txt
       transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
         - ResizeImage:
             resize_short: 256
         - CropImage:

ppcls/loss/__init__.py

@@ -5,6 +5,7 @@ import paddle.nn as nn
 from ppcls.utils import logger
 
 from .celoss import CELoss
+from .googlenetloss import GoogLeNetLoss
 from .centerloss import CenterLoss
 from .emlloss import EmlLoss
 from .msmloss import MSMLoss

ppcls/loss/googlenetloss.py

+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class GoogLeNetLoss(nn.Layer):
+    """
+    Cross entropy loss used after googlenet
+    """
+    def __init__(self, epsilon=None):
+        super().__init__()
+        assert (epsilon is None or epsilon <= 0 or epsilon >= 1), "googlenet is not support label_smooth"
+            
+
+    def forward(self, inputs, label):
+        input0, input1, input2 = inputs
+        if isinstance(input0, dict):
+            input0 = input0["logits"]
+        if isinstance(input1, dict):
+            input1 = input1["logits"]
+        if isinstance(input2, dict):
+            input2 = input2["logits"]
+
+        loss0 = F.cross_entropy(input0, label=label, soft_label=False)
+        loss1 = F.cross_entropy(input1, label=label, soft_label=False)
+        loss2 = F.cross_entropy(input2, label=label, soft_label=False)
+        loss = loss0 + 0.3 * loss1 + 0.3 * loss2
+        loss = loss.mean()
+        return {"GooleNetLoss": loss}

ppcls/metric/metrics.py

@@ -25,6 +25,8 @@ class TopkAcc(nn.Layer):
         self.topk = topk
 
     def forward(self, x, label):
+        if isinstance(x, list):
+            x = x[0]
         if isinstance(x, dict):
             x = x["logits"]