Enable multi testing in ClassificationTrainValidate

PyTorch/sparseconvnet/classificationTrainValidate.py

@@ -30,7 +30,7 @@ def updateStats(stats, output, target, loss):
 
 
 def ClassificationTrainValidate(model, dataset, p):
-    criterion = nn.CrossEntropyLoss()
+    criterion = F.cross_entropy
     if 'n_epochs' not in p:
         p['n_epochs'] = 100
     if 'initial_lr' not in p:
@@ -47,7 +47,8 @@ def ClassificationTrainValidate(model, dataset, p):
         p['use_gpu'] = torch.cuda.is_available()
     if p['use_gpu']:
         model.cuda()
-        criterion.cuda()
+    if 'test_reps' not in p:
+        p['test_reps'] = 1
     optimizer = optim.SGD(model.parameters(),
         lr=p['initial_lr'],
         momentum = p['momentum'],
@@ -100,30 +101,57 @@ def ClassificationTrainValidate(model, dataset, p):
         model.eval()
         s.forward_pass_multiplyAdd_count = 0
         s.forward_pass_hidden_states = 0
-        stats = {'top1': 0, 'top5': 0, 'n': 0, 'nll': 0}
         start = time.time()
-        for batch in dataset['val']():
-            if p['use_gpu']:
-                batch['input']=batch['input'].cuda()
-                batch['target'] = batch['target'].cuda()
-            batch['input'].to_variable()
-            batch['target'] = Variable(batch['target'])
-            output = model(batch['input'])
-            loss = criterion(output, batch['target'])
-            updateStats(stats, output.data, batch['target'].data, loss.data[0])
-        print(epoch, 'test:  top1=%.2f%% top5=%.2f%% nll:%.2f time:%.1fs' %
-              (100 *
-               (1 -
-                1.0 * stats['top1'] /
-                   stats['n']), 100 *
-                  (1 -
-                   1.0 * stats['top5'] /
-                   stats['n']), stats['nll'] /
-                  stats['n'], time.time() -
-                  start))
-        print(
-            '%.3e MultiplyAdds/sample %.3e HiddenStates/sample' %
-            (s.forward_pass_multiplyAdd_count /
-             stats['n'],
-                s.forward_pass_hidden_states /
-                stats['n']))
+        if p['test_reps'] ==1:
+            stats = {'top1': 0, 'top5': 0, 'n': 0, 'nll': 0}
+            for batch in dataset['val']():
+                if p['use_gpu']:
+                    batch['input']=batch['input'].cuda()
+                    batch['target'] = batch['target'].cuda()
+                batch['input'].to_variable()
+                batch['target'] = Variable(batch['target'])
+                output = model(batch['input'])
+                loss = criterion(output, batch['target'])
+                updateStats(stats, output.data, batch['target'].data, loss.data[0])
+            print(epoch, 'test:  top1=%.2f%% top5=%.2f%% nll:%.2f time:%.1fs' %(
+                100 * (1 - 1.0 * stats['top1'] / stats['n']),
+                100 * (1 - 1.0 * stats['top5'] / stats['n']),
+                stats['nll'] / stats['n'],
+                time.time() - start),
+                '%.3e MultiplyAdds/sample %.3e HiddenStates/sample' % (
+                    s.forward_pass_multiplyAdd_count / stats['n'],
+                    s.forward_pass_hidden_states / stats['n']))
+        else:
+            for rep in range(1,p['test_reps']+1):
+                pr=[]
+                ta=[]
+                idxs=[]
+                for batch in dataset['val']():
+                    if p['use_gpu']:
+                        batch['input']=batch['input'].cuda()
+                        batch['target'] = batch['target'].cuda()
+                        batch['idx'] = batch['idx'].cuda()
+                    batch['input'].to_variable()
+                    pr.append( model(batch['input']).data )
+                    ta.append( batch['target'] )
+                    idxs.append( batch['idx'] )
+                pr=torch.cat(pr,0)
+                ta=torch.cat(ta,0)
+                idxs=torch.cat(idxs,0)
+                if rep==1:
+                    target=pr.index_select(0,idxs)
+                    ta=ta.index_select(0,idxs)
+                else:
+                    target.index_add_(0,idxs,pr)
+                loss = criterion(pr, ta)
+                stats = {'top1': 0, 'top5': 0, 'n': 0, 'nll': 0}
+                updateStats(stats, pr, ta, loss.data[0])
+                print(epoch, 'test rep ', rep,
+                    ': top1=%.2f%% top5=%.2f%% nll:%.2f time:%.1fs' %(
+                    100 * (1 - 1.0 * stats['top1'] / stats['n']),
+                    100 * (1 - 1.0 * stats['top5'] / stats['n']),
+                    stats['nll'] / stats['n'],
+                    time.time() - start),
+                    '%.3e MultiplyAdds/sample %.3e HiddenStates/sample' % (
+                    s.forward_pass_multiplyAdd_count / stats['n'],
+                    s.forward_pass_hidden_states / stats['n']))