save for py3

Author: phy68

main.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import os,sys
 import shutil
 import time
@@ -51,22 +52,22 @@ def main():
     model = resnet_example.resnet14(pretrained=False, num_classes=5, input_channels=1)
     model.cuda()
 
-    print "Loaded model: ",model
+    print("Loaded model: ",model)
 
 
     # define loss function (criterion) and optimizer
     criterion = nn.CrossEntropyLoss().cuda()
 
     # training parameters
-    lr = 1.0e-3
+    lr = 1.0e-4
     momentum = 0.9
     weight_decay = 1.0e-3
-    batchsize = 50
-    batchsize_valid = 500
+    batchsize = 20
+    batchsize_valid = 20
     start_epoch = 0
     epochs      = 1500
-    nbatches_per_epoch = 10000/batchsize
-    nbatches_per_valid = 1000/batchsize_valid
+    nbatches_per_epoch = int(100/batchsize)
+    nbatches_per_valid = int(100/batchsize_valid)
 
     optimizer = torch.optim.SGD(model.parameters(), lr,
                                 momentum=momentum,
@@ -75,8 +76,8 @@ def main():
     cudnn.benchmark = True
 
     # dataset
-    iotrain = LArCVDataset("train_dataloader.cfg", "ThreadProcessor", loadallinmem=True)
-    iovalid = LArCVDataset("valid_dataloader.cfg", "ThreadProcessorTest")
+    iotrain = LArCVDataset("train_dataloader.cfg", "ThreadProcessor", loadallinmem=False)
+    iovalid = LArCVDataset("valid_dataloader.cfg", "ThreadProcessorTest",loadallinmem=False)
 
     iotrain.start(batchsize)
     iovalid.start(batchsize_valid)
@@ -93,20 +94,18 @@ def main():
         img = data["image"]
         lbl = data["label"]
         img_np = np.zeros( (img.shape[0], 1, 256, 256), dtype=np.float32 )
-        lbl_np = np.zeros( (lbl.shape[0]), dtype=np.int )
         for j in range(img.shape[0]):
             imgtemp = img[j].reshape( (256,256) )
-            print imgtemp.shape
+            print(imgtemp.shape)
             img_np[j,0,:,:] = padandcrop(imgtemp)
             lbl_np[j] = np.argmax(lbl[j])
-
-        print "Train label"
-        print lbl_np
+        print("Train label")
+        print(lbl_np)
 
         datatest = iovalid[0]
         imgtest = data["image"]
-        print "Test image shape"
-        print imgtest.shape
+        print("Test image shape")
+        print(imgtest.shape)
 
         iotrain.stop()
         iovalid.stop()
@@ -116,29 +115,29 @@ def main():
     for epoch in range(start_epoch, epochs):
 
         adjust_learning_rate(optimizer, epoch, lr)
-        print "Epoch [%d]: "%(epoch),
-        for param_group in optimizer.param_groups:
-            print "lr=%.3e"%(param_group['lr']),
-        print
+        #print("Epoch [%d]: "%(epoch),)
+        #for param_group in optimizer.param_groups:
+        #    print("lr=%.3e"%(param_group['lr']),)
+        #print()
 
         # train for one epoch
         try:
             train_ave_loss, train_ave_acc = train(iotrain, model, criterion, optimizer, nbatches_per_epoch, epoch, 50)
-        except Exception,e:
-            print "Error in training routine!"            
-            print e.message
-            print e.__class__.__name__
+        except Exception as e:
+            print("Error in training routine!")
+            print(e.message)
+            print(e.__class__.__name__)
             traceback.print_exc(e)
             break
-        print "Epoch [%d] train aveloss=%.3f aveacc=%.3f"%(epoch,train_ave_loss,train_ave_acc)
+        print("Epoch [%d] train aveloss=%.3f aveacc=%.3f"%(epoch,train_ave_loss,train_ave_acc))
 
         # evaluate on validation set
         try:
             prec1 = validate(iovalid, model, criterion, nbatches_per_valid, 1)
-        except Exception,e:
-            print "Error in validation routine!"            
-            print e.message
-            print e.__class__.__name__
+        except Exception as e:
+            print("Error in validation routine!")
+            print(e.message)
+            print(e.__class__.__name__)
             traceback.print_exc(e)
             break
 
@@ -177,7 +176,9 @@ def train(train_loader, model, criterion, optimizer, nbatches, epoch, print_freq
     model.train()
 
     for i in range(0,nbatches):
-        #print "epoch ",epoch," batch ",i," of ",nbatches
+        #print("epoch ",epoch," batch ",i," of ",nbatches)
+        optimizer.zero_grad()
+        
         batchstart = time.time()
 
         end = time.time()        
@@ -195,28 +196,24 @@ def train(train_loader, model, criterion, optimizer, nbatches, epoch, print_freq
             imgtmp = img[j].reshape( (256,256) )
             img_np[j,0,:,:] = padandcropandflip(imgtmp) # data augmentation
             lbl_np[j] = np.argmax(lbl[j])
-        input  = torch.from_numpy(img_np).cuda()
-        target = torch.from_numpy(lbl_np).cuda()
+            #print(lbl[j]," ",lbl_np[j])
+        input_var  = torch.from_numpy(img_np).cuda()
+        target_var = torch.from_numpy(lbl_np).cuda()
+        #print("target: ",target_var,target_var.shape)
 
         # measure data formatting time
-        format_time.update(time.time() - end)
-        
-
-        input_var = torch.autograd.Variable(input)
-        target_var = torch.autograd.Variable(target)
+        format_time.update(time.time() - end)        
 
         # compute output
         end = time.time()
         output = model(input_var)
         loss = criterion(output, target_var)
-
         # measure accuracy and record loss
-        prec1 = accuracy(output.data, target, topk=(1,))
-        losses.update(loss.data[0], input.size(0))
-        top1.update(prec1[0], input.size(0))
+        prec1 = accuracy(output.detach(), target_var, topk=(1,))        
+        losses.update(loss.detach().cpu().item(), input_var.size(0))
+        top1.update(prec1[0], input_var.size(0))
 
         # compute gradient and do SGD step
-        optimizer.zero_grad()
         loss.backward()
         optimizer.step()
         train_time.update(time.time()-end)
@@ -233,7 +230,7 @@ def train(train_loader, model, criterion, optimizer, nbatches, epoch, print_freq
                       train_time.val,train_time.avg,
                       losses.val,losses.avg,
                       top1.val,top1.avg)
-            print "Epoch: [%d][%d/%d]\tTime %.3f (%.3f)\tData %.3f (%.3f)\tFormat %.3f (%.3f)\tTrain %.3f (%.3f)\tLoss %.3f (%.3f)\tPrec@1 %.3f (%.3f)"%status
+            print("Epoch: [%d][%d/%d]\tTime %.3f (%.3f)\tData %.3f (%.3f)\tFormat %.3f (%.3f)\tTrain %.3f (%.3f)\tLoss %.3f (%.3f)\tPrec@1 %.3f (%.3f)"%status)
             #print('Epoch: [{0}][{1}/{2}]\t'
             #      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
             #      'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
@@ -262,28 +259,26 @@ def validate(val_loader, model, criterion, nbatches, print_freq):
         for j in range(img.shape[0]):
             img_np[j,0,:,:] = img[j].reshape( (256,256) )
             lbl_np[j] = np.argmax(lbl[j])
-        input  = torch.from_numpy(img_np).cuda()
+        inimg_var  = torch.from_numpy(img_np).cuda()
         target = torch.from_numpy(lbl_np).cuda()
 
-        input_var = torch.autograd.Variable(input, volatile=True)
-        target_var = torch.autograd.Variable(target, volatile=True)
-
         # compute output
-        output = model(input_var)
-        loss = criterion(output, target_var)
+        with torch.no_grad():
+            output = model(inimg_var)
+            loss = criterion(output, target)
 
-        # measure accuracy and record loss
-        prec1 = accuracy(output.data, target, topk=(1,))
-        losses.update(loss.data[0], input.size(0))
-        top1.update(prec1[0], input.size(0))
+            # measure accuracy and record loss
+            prec1 = accuracy(output.detach(), target, topk=(1,))
+            losses.update(loss.detach().cpu().item(), inimg_var.size(0))
+            top1.update(prec1[0], inimg_var.size(0))
 
         # measure elapsed time
         batch_time.update(time.time() - end)
         end = time.time()
 
         if i % print_freq == 0:
             status = (i,nbatches,batch_time.val,batch_time.avg,losses.val,losses.avg,top1.val,top1.avg)
-            print "Test: [%d/%d]\tTime %.3f (%.3f)\tLoss %.3f (%.3f)\tPrec@1 %.3f (%.3f)"%status
+            #print("Test: [%d/%d]\tTime %.3f (%.3f)\tLoss %.3f (%.3f)\tPrec@1 %.3f (%.3f)"%status)
             #print('Test: [{0}/{1}]\t'
             #      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
             #      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
@@ -293,7 +288,7 @@ def validate(val_loader, model, criterion, nbatches, print_freq):
 
     #print(' * Prec@1 {top1.avg:.3f}'
     #      .format(top1=top1))
-    print "Test:Result* Prec@1 %.3f\tLoss %.3f"%(top1.avg,losses.avg)
+    print("Test:Result* Prec@1 %.3f\tLoss %.3f"%(top1.avg,losses.avg))
 
     return float(top1.avg)
 
@@ -353,8 +348,8 @@ def dump_lr_schedule( startlr, numepochs ):
     for epoch in range(0,numepochs):
         lr = startlr*(0.5**(epoch//300))
         if epoch%10==0:
-            print "Epoch [%d] lr=%.3e"%(epoch,lr)
-    print "Epoch [%d] lr=%.3e"%(epoch,lr)
+            print("Epoch [%d] lr=%.3e"%(epoch,lr))
+    print("Epoch [%d] lr=%.3e"%(epoch,lr))
     return
 
 if __name__ == '__main__':

setup.sh

@@ -1,12 +1,12 @@
 #!/bin/bash
 
 home=$PWD
-source ~/setup_root6.sh
+source /usr/local/root/6.16.00_python3/bin/thisroot.sh
 
 cd ../larcv2
 source configure.sh
 
-cd ../torchlarcvdataset
+cd ../larcvdataset
 source setenv.sh
 
 cd $home

train_dataloader.cfg

@@ -2,9 +2,8 @@ ThreadProcessor: {
   Verbosity:3
   NumThreads: 2
   NumBatchStorage: 2
-  RandomAccess: false
-  #InputFiles: ["/home/taritree/working/dlphysics/testset/practice_train_5k.root"]
-  InputFiles: ["/home/taritree/working/dlphysics/testset/train_50k.root"]  
+  RandomAccess: true
+  InputFiles: ["/home/phy68/data/practice_train_5k.root"]
   ProcessName: ["image","label"]
   ProcessType: ["BatchFillerImage2D","BatchFillerPIDLabel"]
   ProcessList: {

valid_dataloader.cfg

@@ -3,8 +3,7 @@ ThreadProcessorTest: {
   NumThreads: 2
   NumBatchStorage: 2
   RandomAccess: true
-  #InputFiles: ["/home/taritree/working/dlphysics/testset/practice_test_5k.root"]
-  InputFiles: ["/home/taritree/working/dlphysics/testset/test_40k.root"]
+  InputFiles: ["/home/phy68/data/practice_train_5k.root"]
   ProcessName: ["imagetest","labeltest"]
   ProcessType: ["BatchFillerImage2D","BatchFillerPIDLabel"]
   ProcessList: {