From dc8c2b831ac78a7c79894949322c6df1dfaac14f Mon Sep 17 00:00:00 2001
From: wogong <wogong38@gmail.com>
Date: Wed, 6 Nov 2019 22:19:38 +0800
Subject: [PATCH] update test function, using sum batch size as total num
 instead of len(dataloader)

---
 core/test.py | 55 ++++++++++++----------------------------------------
 1 file changed, 12 insertions(+), 43 deletions(-)

diff --git a/core/test.py b/core/test.py
index 2935762..799e745 100644
--- a/core/test.py
+++ b/core/test.py
@@ -1,48 +1,16 @@
 import torch.utils.data
 import torch.nn as nn
 
-
-def test_from_save(model, saved_model, data_loader, device):
-    """Evaluate classifier for source domain."""
-    # set eval state for Dropout and BN layers
-    classifier = model.load_state_dict(torch.load(saved_model))
-    classifier.eval()
-
-    # init loss and accuracy
-    loss = 0.0
-    acc = 0.0
-
-    # set loss function
-    criterion = nn.NLLLoss()
-
-    # evaluate network
-    for (images, labels) in data_loader:
-        images = images.to(device)
-        labels = labels.to(device)  #labels = labels.squeeze(1)
-        preds = classifier(images)
-
-        criterion(preds, labels)
-
-        loss += criterion(preds, labels).data.item()
-
-        pred_cls = preds.data.max(1)[1]
-        acc += pred_cls.eq(labels.data).cpu().sum()
-
-    loss /= len(data_loader)
-    acc /= len(data_loader.dataset)
-
-    print("Avg Loss = {}, Avg Accuracy = {:.2%}".format(loss, acc))
-
-
 def test(model, data_loader, device, flag):
     """Evaluate model for dataset."""
     # set eval state for Dropout and BN layers
     model.eval()
 
     # init loss and accuracy
-    loss = 0.0
-    acc = 0.0
-    acc_domain = 0.0
+    loss_ = 0.0
+    acc_ = 0.0
+    acc_domain_ = 0.0
+    n_total = 0
 
     # set loss function
     criterion = nn.CrossEntropyLoss()
@@ -59,17 +27,18 @@ def test(model, data_loader, device, flag):
 
         preds, domain = model(images, alpha=0)
 
-        loss += criterion(preds, labels).data.item()
+        loss_ += criterion(preds, labels).item()
 
         pred_cls = preds.data.max(1)[1]
         pred_domain = domain.data.max(1)[1]
-        acc += pred_cls.eq(labels.data).sum().item()
-        acc_domain += pred_domain.eq(labels_domain.data).sum().item()
+        acc_ += pred_cls.eq(labels.data).sum().item()
+        acc_domain_ += pred_domain.eq(labels_domain.data).sum().item()
+        n_total += size
 
-    loss /= len(data_loader)
-    acc /= len(data_loader.dataset)
-    acc_domain /= len(data_loader.dataset)
+    loss = loss_ / n_total
+    acc = acc_ / n_total
+    acc_domain = acc_domain_ / n_total
 
-    #print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_domain))
+    print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, {}/{}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_, n_total, acc_domain))
 
     return loss, acc, acc_domain