├── .gitignore
├── README.md
├── filter_rootfile.py
├── main.py
├── plot_training.py
├── resnet_example.py
├── setup.sh
├── template.py
├── train_dataloader.cfg
├── updater_training_plot.py
├── valid_dataloader.cfg
└── view_data.ipynb


/.gitignore:
--------------------------------------------------------------------------------
1 | *~
2 | *.pyc


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # PyTorch 5-particle Classifier Example
 2 | 
 3 | Example of training using on 5-particle practice sample
 4 | 
 5 | ### Training
 6 | 
 7 | To run the training, first setup the requirements (below) and then run
 8 | 
 9 |     python main.py
10 | 
11 | ### Plotting
12 | 
13 | Plotting training and results coming in future scripts.
14 | 
15 | ### Requirements
16 | 
17 | * pytorch, of course
18 | * ROOT6
19 | * LArCV2
20 | * pytorch interface, [LArCVDataset](https://github.com/DeepLearnPhysics/larcvdataset)
21 | 
22 | Also, download the training and validation sets from the [open data webpage](http://deeplearnphysics.org/DataChallenge/)
23 | 
24 | * [Training](http://www.stanford.edu/~kterao/public_data/v0.1.0/2d/classification/five_particles/practice_train_5k.root)
25 | * [Validation](http://www.stanford.edu/~kterao/public_data/v0.1.0/2d/classification/five_particles/practice_test_5k.root)
26 | 
27 | 
28 | Note: as it stands, network learns, but overtrains.  Working on setting proper meta-parameters and/or adding data-augmentation.
29 | 
30 | Also, you might need to set the GPU device ID in the shell. For example, to set to device `1`,
31 | 
32 |     export CUDA_VISIBLE_DEVICES=1
33 | 
34 | 
35 | ### Sources
36 | 
37 | * `main.py` derives from the pytorch examples [repo](https://github.com/pytorch/examples/blob/master/imagenet/main.py)
38 | * `resnet_example.py` is modified from the pytorch torchvision models resnet module
39 | 
40 | 
41 | 
42 | 


--------------------------------------------------------------------------------
/filter_rootfile.py:
--------------------------------------------------------------------------------
 1 | from __future__ import print_function
 2 | import os,sys
 3 | import ROOT as rt
 4 | from larcv import larcv
 5 | 
 6 | infile = "/home/phy68/data/practice_test_5k.root"
 7 | outfile = "filtered.root"
 8 | 
 9 | io = larcv.IOManager(larcv.IOManager.kBOTH)
10 | io.add_in_file(infile)
11 | io.set_out_file(outfile)
12 | io.initialize()
13 | 
14 | nentries = io.get_n_entries()
15 | print("Number of entries: ",nentries)
16 | 
17 | nsaved = 0
18 | for ientry in range(nentries):
19 |     io.read_entry(ientry)
20 | 
21 |     print("------------------------------")
22 |     print("entry ",ientry)
23 | 
24 |     selectme = False
25 | 
26 |     event_truth_data = io.get_data("particle","mctruth")
27 |     particle_v = event_truth_data.as_vector()
28 |     
29 |     nparticles = particle_v.size()
30 |     print("particle list:")
31 |     for iparticle in range(nparticles):
32 |         part = particle_v.at(iparticle)
33 |         # part is instance of class Particle. for definition: ../larcv2/larcv/core/DataFormat/Particle.h
34 |         # PDG codes: https://pdg.lbl.gov/2007/reviews/montecarlorpp.pdf
35 |         print("  [%d] PDG:%d  E:%.1f GeV"%(iparticle,part.pdg_code(),part.energy_init()))
36 |         if part.pdg_code()==11:
37 |             # example, electron selection
38 |             selectme = True
39 |     
40 |     if selectme:
41 |         print("SAVED")
42 |         nsaved += 1
43 |         io.save_entry()
44 | 
45 | io.finalize()
46 | print("Number saved: ",nsaved)
47 | print("done")
48 | 


--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
  1 | from __future__ import print_function
  2 | import os,sys
  3 | import shutil
  4 | import time
  5 | import traceback
  6 | import numpy as np
  7 | 
  8 | import torch
  9 | import torch.nn as nn
 10 | import torch.nn.parallel
 11 | import torch.backends.cudnn as cudnn
 12 | import torch.distributed as dist
 13 | import torch.optim
 14 | import torch.utils.data
 15 | import torch.utils.data.distributed
 16 | import torchvision.transforms as transforms
 17 | import torchvision.datasets as datasets
 18 | import torchvision.models as models
 19 | 
 20 | from larcvdataset import LArCVDataset
 21 | import resnet_example
 22 | 
 23 | best_prec1 = 0.0
 24 | 
 25 | torch.cuda.device( 1 )
 26 | 
 27 | def padandcrop(npimg2d):
 28 |     imgpad  = np.zeros( (264,264), dtype=np.float32 )
 29 |     imgpad[4:256+4,4:256+4] = npimg2d[:,:]
 30 |     randx = np.random.randint(0,8)
 31 |     randy = np.random.randint(0,8)
 32 |     return imgpad[randx:randx+256,randy:randy+256]
 33 | 
 34 | def padandcropandflip(npimg2d):
 35 |     imgpad  = np.zeros( (264,264), dtype=np.float32 )
 36 |     imgpad[4:256+4,4:256+4] = npimg2d[:,:]
 37 |     if np.random.rand()>0.5:
 38 |         imgpad = np.flip( imgpad, 0 )
 39 |     if np.random.rand()>0.5:
 40 |         imgpad = np.flip( imgpad, 1 )
 41 |     randx = np.random.randint(0,8)
 42 |     randy = np.random.randint(0,8)
 43 |     return imgpad[randx:randx+256,randy:randy+256]
 44 | 
 45 | 
 46 | def main():
 47 | 
 48 |     global best_prec1
 49 | 
 50 |     # create model: loading resnet18 as defined in torchvision module
 51 |     #model = resnet_example.resnet18(pretrained=False, num_classes=5, input_channels=1)
 52 |     model = resnet_example.resnet14(pretrained=False, num_classes=5, input_channels=1)
 53 |     model.cuda()
 54 | 
 55 |     print("Loaded model: ",model)
 56 | 
 57 | 
 58 |     # define loss function (criterion) and optimizer
 59 |     criterion = nn.CrossEntropyLoss().cuda()
 60 | 
 61 |     # training parameters
 62 |     lr = 1.0e-2
 63 |     momentum = 0.9
 64 |     weight_decay = 1.0e-4
 65 |     batchsize = 64
 66 |     batchsize_valid = 64
 67 |     start_epoch = 0
 68 |     epochs      = 50000
 69 |     nbatches_per_epoch = int(epochs/batchsize)
 70 |     nbatches_per_valid = int(epochs/batchsize_valid)
 71 | 
 72 |     optimizer = torch.optim.SGD(model.parameters(), lr,
 73 |                                 momentum=momentum,
 74 |                                 weight_decay=weight_decay)
 75 | 
 76 |     cudnn.benchmark = True
 77 | 
 78 |     # dataset
 79 |     iotrain = LArCVDataset("train_dataloader.cfg", "ThreadProcessor", loadallinmem=True)
 80 |     iovalid = LArCVDataset("valid_dataloader.cfg", "ThreadProcessorTest",loadallinmem=False)
 81 |     
 82 |     iotrain.start(batchsize)
 83 |     iovalid.start(batchsize_valid)
 84 | 
 85 |     # Resume training option
 86 |     if False:
 87 |         checkpoint = torch.load( "checkpoint.pth.p01.tar" )
 88 |         best_prec1 = checkpoint["best_prec1"]
 89 |         model.load_state_dict(checkpoint["state_dict"])
 90 |         optimizer.load_state_dict(checkpoint['optimizer'])
 91 |     
 92 |     if False:
 93 |         data = iotrain[0]
 94 |         img = data["image"]
 95 |         lbl = data["label"]
 96 |         img_np = np.zeros( (img.shape[0], 1, 256, 256), dtype=np.float32 )
 97 |         for j in range(img.shape[0]):
 98 |             imgtemp = img[j].reshape( (256,256) )
 99 |             print(imgtemp.shape)
100 |             img_np[j,0,:,:] = padandcrop(imgtemp)
101 |             lbl_np[j] = np.argmax(lbl[j])
102 |         print("Train label")
103 |         print(lbl_np)
104 | 
105 |         datatest = iovalid[0]
106 |         imgtest = data["image"]
107 |         print("Test image shape")
108 |         print(imgtest.shape)
109 | 
110 |         iotrain.stop()
111 |         iovalid.stop()
112 |         
113 |         return
114 | 
115 |     for epoch in range(start_epoch, epochs):
116 | 
117 |         adjust_learning_rate(optimizer, epoch, lr)
118 |         #print("Epoch [%d]: "%(epoch),)
119 |         #for param_group in optimizer.param_groups:
120 |         #    print("lr=%.3e"%(param_group['lr']),)
121 |         #print()
122 | 
123 |         # train for one epoch
124 |         try:
125 |             train_ave_loss, train_ave_acc = train(iotrain, model, criterion, optimizer, nbatches_per_epoch, epoch, 50)
126 |         except Exception as e:
127 |             print("Error in training routine!")
128 |             print(e.message)
129 |             print(e.__class__.__name__)
130 |             traceback.print_exc(e)
131 |             break
132 |         print("Epoch [%d] train aveloss=%.3f aveacc=%.3f"%(epoch,train_ave_loss,train_ave_acc))
133 | 
134 |         # evaluate on validation set
135 |         try:
136 |             prec1 = validate(iovalid, model, criterion, nbatches_per_valid, 1)
137 |         except Exception as e:
138 |             print("Error in validation routine!")
139 |             print(e.message)
140 |             print(e.__class__.__name__)
141 |             traceback.print_exc(e)
142 |             break
143 | 
144 |         # remember best prec@1 and save checkpoint
145 |         is_best = prec1 > best_prec1
146 |         best_prec1 = max(prec1, best_prec1)
147 |         save_checkpoint({
148 |             'epoch': epoch + 1,
149 |             'state_dict': model.state_dict(),
150 |             'best_prec1': best_prec1,
151 |             'optimizer' : optimizer.state_dict(),
152 |         }, is_best, -1)
153 |         if epoch==5*50:
154 |             save_checkpoint({
155 |                 'epoch': epoch + 1,
156 |                 'state_dict': model.state_dict(),
157 |                 'best_prec1': best_prec1,
158 |                 'optimizer' : optimizer.state_dict(),
159 |             }, False, epoch)
160 |             
161 | 
162 |     iotrain.stop()
163 |     iovalid.stop()
164 | 
165 | 
166 | 
167 | def train(train_loader, model, criterion, optimizer, nbatches, epoch, print_freq):
168 |     batch_time = AverageMeter()
169 |     data_time = AverageMeter()
170 |     format_time = AverageMeter()
171 |     train_time = AverageMeter()
172 |     losses = AverageMeter()
173 |     top1 = AverageMeter()
174 | 
175 |     # switch to train mode
176 |     model.train()
177 | 
178 |     for i in range(0,nbatches):
179 |         #print("epoch ",epoch," batch ",i," of ",nbatches)
180 |         optimizer.zero_grad()
181 |         
182 |         batchstart = time.time()
183 |     
184 |         end = time.time()        
185 |         data = train_loader[i]
186 |         # measure data loading time
187 |         data_time.update(time.time() - end)
188 | 
189 |         end = time.time()
190 |         img = data["image"]
191 |         lbl = data["label"]
192 |         img_np = np.zeros( (img.shape[0], 1, 256, 256), dtype=np.float32 )
193 |         lbl_np = np.zeros( (lbl.shape[0] ), dtype=np.int )
194 |         # batch loop
195 |         for j in range(img.shape[0]):
196 |             imgtmp = img[j].reshape( (256,256) )
197 |             img_np[j,0,:,:] = padandcropandflip(imgtmp) # data augmentation
198 |             lbl_np[j] = np.argmax(lbl[j])
199 |             #print(lbl[j]," ",lbl_np[j])
200 |         input_var  = torch.from_numpy(img_np).cuda()
201 |         target_var = torch.from_numpy(lbl_np).cuda()
202 |         #print("target: ",target_var,target_var.shape)
203 | 
204 |         # measure data formatting time
205 |         format_time.update(time.time() - end)        
206 | 
207 |         # compute output
208 |         end = time.time()
209 |         # forward
210 |         output = model(input_var)
211 |         # loss calculation
212 |         loss = criterion(output, target_var)
213 |         # compute gradient and do SGD step
214 |         loss.backward()
215 |         optimizer.step()
216 | 
217 |         # measure accuracy and record loss
218 |         with torch.no_grad():
219 |             prec1 = accuracy(output.detach(), target_var, topk=(1,))        
220 |             losses.update(loss.detach().cpu().item(), input_var.size(0))
221 |             top1.update(prec1[0], input_var.size(0))
222 | 
223 |         train_time.update(time.time()-end)
224 | 
225 |         # measure elapsed time
226 |         batch_time.update(time.time() - batchstart)
227 | 
228 | 
229 |         if i % print_freq == 0:
230 |             status = (epoch,i,nbatches,
231 |                       batch_time.val,batch_time.avg,
232 |                       data_time.val,data_time.avg,
233 |                       format_time.val,format_time.avg,
234 |                       train_time.val,train_time.avg,
235 |                       losses.val,losses.avg,
236 |                       top1.val,top1.avg)
237 |             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)
238 |             #print('Epoch: [{0}][{1}/{2}]\t'
239 |             #      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
240 |             #      'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
241 |             #      'Loss {losses.val:.4f} ({losses.avg:.4f})\t'
242 |             #      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
243 |             #          epoch, i, len(train_loader), batch_time=batch_time,
244 |             #          data_time=data_time, losses=losses, top1=top1 ))
245 |     return losses.avg,top1.avg
246 | 
247 | 
248 | def validate(val_loader, model, criterion, nbatches, print_freq):
249 |     batch_time = AverageMeter()
250 |     losses = AverageMeter()
251 |     top1 = AverageMeter()
252 | 
253 |     # switch to evaluate mode
254 |     model.eval()
255 | 
256 |     end = time.time()
257 |     for i in range(0,nbatches):
258 |         data = val_loader[i]
259 |         img = data["imagetest"]
260 |         lbl = data["labeltest"]
261 |         img_np = np.zeros( (img.shape[0], 1, 256, 256), dtype=np.float32 )
262 |         lbl_np = np.zeros( (lbl.shape[0] ), dtype=np.int )
263 |         for j in range(img.shape[0]):
264 |             img_np[j,0,:,:] = img[j].reshape( (256,256) )
265 |             lbl_np[j] = np.argmax(lbl[j])
266 |         inimg_var  = torch.from_numpy(img_np).cuda()
267 |         target = torch.from_numpy(lbl_np).cuda()
268 |         
269 |         # compute output
270 |         with torch.no_grad():
271 |             output = model(inimg_var)
272 |             loss = criterion(output, target)
273 | 
274 |             # measure accuracy and record loss
275 |             prec1 = accuracy(output.detach(), target, topk=(1,))
276 |             losses.update(loss.detach().cpu().item(), inimg_var.size(0))
277 |             top1.update(prec1[0], inimg_var.size(0))
278 | 
279 |         # measure elapsed time
280 |         batch_time.update(time.time() - end)
281 |         end = time.time()
282 | 
283 |         if i % print_freq == 0:
284 |             status = (i,nbatches,batch_time.val,batch_time.avg,losses.val,losses.avg,top1.val,top1.avg)
285 |             #print("Test: [%d/%d]\tTime %.3f (%.3f)\tLoss %.3f (%.3f)\tPrec@1 %.3f (%.3f)"%status)
286 |             #print('Test: [{0}/{1}]\t'
287 |             #      'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
288 |             #      'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
289 |             #      'Prec@1 {top1.val:.3f} ({top1.avg:.3f})'.format(
290 |             #        i, len(val_loader), batch_time=batch_time, loss=losses,
291 |             #        top1=top1))
292 | 
293 |     #print(' * Prec@1 {top1.avg:.3f}'
294 |     #      .format(top1=top1))
295 |     print("Test:Result* Prec@1 %.3f\tLoss %.3f"%(top1.avg,losses.avg))
296 | 
297 |     return float(top1.avg)
298 | 
299 | 
300 | def save_checkpoint(state, is_best, p, filename='checkpoint.pth.tar'):
301 |     if p>0:
302 |         filename = "checkpoint.%dth.tar"%(p)
303 |     torch.save(state, filename)
304 |     if is_best:
305 |         shutil.copyfile(filename, 'model_best.pth.tar')
306 | 
307 | 
308 | class AverageMeter(object):
309 |     """Computes and stores the average and current value"""
310 |     def __init__(self):
311 |         self.reset()
312 | 
313 |     def reset(self):
314 |         self.val = 0
315 |         self.avg = 0
316 |         self.sum = 0
317 |         self.count = 0
318 | 
319 |     def update(self, val, n=1):
320 |         self.val = val
321 |         self.sum += val * n
322 |         self.count += n
323 |         self.avg = self.sum / self.count
324 | 
325 | 
326 | def adjust_learning_rate(optimizer, epoch, lr):
327 |     """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
328 |     #lr = lr * (0.5 ** (epoch // 300))
329 |     lr = lr
330 |     #lr = lr*0.992
331 |     #print "adjust learning rate to ",lr
332 |     for param_group in optimizer.param_groups:
333 |         param_group['lr'] = lr
334 | 
335 | 
336 | def accuracy(output, target, topk=(1,)):
337 |     """Computes the precision@k for the specified values of k"""
338 |     maxk = max(topk)
339 |     batch_size = target.size(0)
340 | 
341 |     _, pred = output.topk(maxk, 1, True, True)
342 |     pred = pred.t()
343 |     correct = pred.eq(target.view(1, -1).expand_as(pred))
344 | 
345 |     res = []
346 |     for k in topk:
347 |         correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
348 |         res.append(correct_k.mul_(100.0 / batch_size))
349 |     return res
350 | 
351 | def dump_lr_schedule( startlr, numepochs ):
352 |     for epoch in range(0,numepochs):
353 |         lr = startlr*(0.5**(epoch//300))
354 |         if epoch%10==0:
355 |             print("Epoch [%d] lr=%.3e"%(epoch,lr))
356 |     print("Epoch [%d] lr=%.3e"%(epoch,lr))
357 |     return
358 | 
359 | if __name__ == '__main__':
360 |     #dump_lr_schedule(1.0e-2, 4000)
361 |     main()
362 | 


--------------------------------------------------------------------------------
/plot_training.py:
--------------------------------------------------------------------------------
  1 | import os,sys,re
  2 | 
  3 | 
  4 | def make_training_plot( logfile, outputpath ):
  5 | 
  6 |     loglines = open(logfile,'r').readlines()
  7 | 
  8 |     # store tuples (epoch,loss,acc)
  9 |     test_pts  = []
 10 |     train_pts = []
 11 |     lr_pts    = []
 12 |     lr_max = 0
 13 |     lr_min = 1.0e6
 14 | 
 15 |     epoch_scale = 0.2
 16 |     
 17 |     current_epoch = 0
 18 |     for l in loglines:
 19 |         l = l.strip()
 20 |         data = l.split()
 21 |         if "train aveloss" in l:
 22 |             pt = ( int(filter(str.isdigit,data[1])), float(re.findall("\d+\.\d+",data[3])[0]), float(re.findall("\d+\.\d+",data[4])[0]) )
 23 |             current_epoch = pt[0]
 24 |             train_pts.append(pt)
 25 |         if "Test:Result*" in l:
 26 |             pt = ( current_epoch, float(data[4]), float(data[2]) )
 27 |             test_pts.append(pt)
 28 |         if "lr=" in l:
 29 |             pt = ( int(filter(str.isdigit,data[1])), float( data[-1].split("=")[-1] ) )
 30 |             if pt[1]>lr_max:
 31 |                 lr_max = pt[1]
 32 |             if pt[1]<lr_min:
 33 |                 lr_min = pt[1]
 34 |             lr_pts.append( pt )
 35 | 
 36 | 
 37 |     sys.argv.append("-b")
 38 |     import ROOT as rt
 39 |     rt.gStyle.SetOptStat(0)
 40 | 
 41 |     graphs = {}
 42 |     graphs["trainacc"]  = rt.TGraph( len(train_pts) )
 43 |     graphs["trainloss"] = rt.TGraph( len(train_pts) )
 44 |     graphs["testacc"]   = rt.TGraph( len(test_pts) )
 45 |     graphs["testloss"]  = rt.TGraph( len(test_pts) )
 46 |     graphs["lr"]        = rt.TGraph( len(lr_pts) )
 47 | 
 48 |     accmax = 0
 49 |     accmin = 1.0e6
 50 |     lossmax = 0
 51 |     lossmin = 1.0e6
 52 |     for ipt,pt in enumerate(train_pts):
 53 |         graphs["trainacc"].SetPoint( ipt, pt[0]*epoch_scale, pt[2] )
 54 |         graphs["trainloss"].SetPoint( ipt, pt[0]*epoch_scale, pt[1] )
 55 |         if accmax<pt[2]:
 56 |             accmax = pt[2]
 57 |         if accmin>pt[2]:
 58 |             accmin = pt[2]
 59 |         if lossmax<pt[1]:
 60 |             lossmax = pt[1]
 61 |         if lossmin>pt[1]:
 62 |             lossmin = pt[1]
 63 | 
 64 |     for ipt,pt in enumerate(test_pts):
 65 |         graphs["testacc"].SetPoint( ipt, pt[0]*epoch_scale, pt[2] )
 66 |         graphs["testloss"].SetPoint( ipt, pt[0]*epoch_scale, pt[1] )
 67 |         if accmax<pt[2]:
 68 |             accmax = pt[2]
 69 |         if accmin>pt[2]:
 70 |             accmin = pt[2]
 71 |         if lossmax<pt[1]:
 72 |             lossmax = pt[1]
 73 |         if lossmin>pt[1]:
 74 |             lossmin = pt[1]
 75 | 
 76 | 
 77 |     c = rt.TCanvas("c","",1400,600)
 78 |     c.Divide(2,1)
 79 | 
 80 |     # hitogram to set scales
 81 |     hloss = rt.TH1D("hloss",";epoch;loss",100, 0,train_pts[-1][0]*epoch_scale*1.1)
 82 |     hloss.SetMinimum( 0.5*lossmin )
 83 |     hloss.SetMaximum( 5.0*lossmax )
 84 | 
 85 |     hacc = rt.TH1D("hacc",";epoch;accuracy (percent)",100, 0,train_pts[-1][0]*epoch_scale*1.1)
 86 |     hacc.SetMinimum( 0.0 )
 87 |     hacc.SetMaximum( 100.0 )
 88 |     
 89 |     # Loss
 90 |     c.cd(1).SetLogy(1)
 91 |     c.cd(1).SetGridx(1)
 92 |     c.cd(1).SetGridy(1)
 93 |     hloss.Draw()
 94 |     graphs["trainloss"].SetLineColor(rt.kBlack)
 95 |     graphs["testloss"].SetLineColor(rt.kBlue)
 96 |     graphs["lr"].SetLineColor(rt.kRed)
 97 |     graphs["trainloss"].Draw("LP")
 98 |     graphs["testloss"].Draw("LP")
 99 | 
100 |     # superimpose lr graph
101 |     rightmax = 1.1*lr_max
102 |     rightmin = 0.9*lr_min
103 |     scale    = rt.gPad.GetUymax()/rightmax
104 |     for ipt,pt in enumerate(lr_pts):
105 |         graphs["lr"].SetPoint( ipt, pt[0]*epoch_scale, pt[1]*scale )    
106 |     graphs["lr"].Draw("LPsame")
107 |     lraxis = rt.TGaxis( rt.gPad.GetUxmax(), rt.gPad.GetUymin(), rt.gPad.GetUxmax(), rt.gPad.GetUymax(), rightmin, rightmax, 510, "+LG" )
108 |     lraxis.SetLineColor(rt.kRed)
109 |     lraxis.SetLabelColor(rt.kRed)
110 |     lraxis.Draw()
111 | 
112 |     # Accuracy
113 |     c.cd(2).SetLogy(0)
114 |     c.cd(2).SetGridx(1)
115 |     c.cd(2).SetGridy(1)
116 |     hacc.Draw()
117 |     graphs["trainacc"].SetLineColor(rt.kBlack)
118 |     graphs["testacc"].SetLineColor(rt.kBlue)
119 |     graphs["trainacc"].Draw("LP")
120 |     graphs["testacc"].Draw("LP")
121 |     
122 |     c.Update()
123 |     c.Draw()
124 | 
125 |     c.SaveAs(outputpath)
126 | 
127 | 
128 |     
129 | if __name__=="__main__":
130 |     logfile = sys.argv[1]
131 |     make_training_plot( logfile, "training.png" )
132 | 


--------------------------------------------------------------------------------
/resnet_example.py:
--------------------------------------------------------------------------------
  1 | import torch.nn as nn
  2 | import math
  3 | import torch.utils.model_zoo as model_zoo
  4 | 
  5 | 
  6 | __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
  7 |            'resnet152']
  8 | 
  9 | 
 10 | model_urls = {
 11 |     'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
 12 |     'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
 13 |     'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
 14 |     'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
 15 |     'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
 16 | }
 17 | 
 18 | 
 19 | def conv3x3(in_planes, out_planes, stride=1):
 20 |     """3x3 convolution with padding"""
 21 |     return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
 22 |                      padding=1, bias=False)
 23 | 
 24 | 
 25 | class BasicBlock(nn.Module):
 26 |     expansion = 1
 27 | 
 28 |     def __init__(self, inplanes, planes, stride=1, downsample=None):
 29 |         super(BasicBlock, self).__init__()
 30 |         self.conv1 = conv3x3(inplanes, planes, stride)
 31 |         self.bn1 = nn.BatchNorm2d(planes)
 32 |         self.relu = nn.ReLU(inplace=True)
 33 |         self.conv2 = conv3x3(planes, planes)
 34 |         self.bn2 = nn.BatchNorm2d(planes)
 35 |         self.downsample = downsample
 36 |         self.stride = stride
 37 | 
 38 |     def forward(self, x):
 39 |         residual = x
 40 | 
 41 |         out = self.conv1(x)
 42 |         out = self.bn1(out)
 43 |         out = self.relu(out)
 44 | 
 45 |         out = self.conv2(out)
 46 |         out = self.bn2(out)
 47 | 
 48 |         if self.downsample is not None:
 49 |             residual = self.downsample(x)
 50 | 
 51 |         out += residual
 52 |         out = self.relu(out)
 53 | 
 54 |         return out
 55 | 
 56 | 
 57 | class Bottleneck(nn.Module):
 58 |     expansion = 4
 59 | 
 60 |     def __init__(self, inplanes, planes, stride=1, downsample=None):
 61 |         super(Bottleneck, self).__init__()
 62 |         self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
 63 |         self.bn1 = nn.BatchNorm2d(planes)
 64 |         self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
 65 |                                padding=1, bias=False)
 66 |         self.bn2 = nn.BatchNorm2d(planes)
 67 |         self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
 68 |         self.bn3 = nn.BatchNorm2d(planes * 4)
 69 |         self.relu = nn.ReLU(inplace=True)
 70 |         self.downsample = downsample
 71 |         self.stride = stride
 72 | 
 73 |     def forward(self, x):
 74 |         residual = x
 75 | 
 76 |         out = self.conv1(x)
 77 |         out = self.bn1(out)
 78 |         out = self.relu(out)
 79 | 
 80 |         out = self.conv2(out)
 81 |         out = self.bn2(out)
 82 |         out = self.relu(out)
 83 | 
 84 |         out = self.conv3(out)
 85 |         out = self.bn3(out)
 86 | 
 87 |         if self.downsample is not None:
 88 |             residual = self.downsample(x)
 89 | 
 90 |         out += residual
 91 |         out = self.relu(out)
 92 | 
 93 |         return out
 94 | 
 95 | 
 96 | class ResNet(nn.Module):
 97 | 
 98 |     def __init__(self, block, layers, num_classes=1000, input_channels=3):
 99 |         self.inplanes = 64
100 |         super(ResNet, self).__init__()
101 |         self.conv1 = nn.Conv2d(input_channels, 64, kernel_size=7, stride=2, padding=3,
102 |                                bias=False)
103 |         self.bn1 = nn.BatchNorm2d(64)
104 |         self.relu = nn.ReLU(inplace=True)
105 |         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
106 |         self.layer1 = self._make_layer(block, 64, layers[0])
107 |         self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
108 |         self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
109 |         self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
110 |         self.avgpool = nn.AvgPool2d(7, stride=2)
111 | 
112 |         self.dropout = nn.Dropout2d(p=0.5,inplace=True)
113 | 
114 |         #print "block.expansion=",block.expansion
115 |         self.fc = nn.Linear(512 * block.expansion, num_classes)
116 | 
117 |         for m in self.modules():
118 |             if isinstance(m, nn.Conv2d):
119 |                 n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
120 |                 m.weight.data.normal_(0, math.sqrt(2. / n))
121 |             elif isinstance(m, nn.BatchNorm2d):
122 |                 m.weight.data.fill_(1)
123 |                 m.bias.data.zero_()
124 | 
125 |     def _make_layer(self, block, planes, blocks, stride=1):
126 |         downsample = None
127 |         if stride != 1 or self.inplanes != planes * block.expansion:
128 |             downsample = nn.Sequential(
129 |                 nn.Conv2d(self.inplanes, planes * block.expansion,
130 |                           kernel_size=1, stride=stride, bias=False),
131 |                 nn.BatchNorm2d(planes * block.expansion),
132 |             )
133 | 
134 |         layers = []
135 |         layers.append(block(self.inplanes, planes, stride, downsample))
136 |         self.inplanes = planes * block.expansion
137 |         for i in range(1, blocks):
138 |             layers.append(block(self.inplanes, planes))
139 | 
140 |         return nn.Sequential(*layers)
141 | 
142 |     def forward(self, x):
143 | 
144 |         x = self.conv1(x)
145 |         x = self.bn1(x)
146 |         x = self.relu(x)
147 |         x = self.maxpool(x)
148 | 
149 |         x = self.layer1(x)
150 |         x = self.layer2(x)
151 |         x = self.layer3(x)
152 |         x = self.layer4(x)
153 | 
154 |         x = self.avgpool(x)
155 |         x = self.dropout(x)
156 |         #print "avepool: ",x.data.shape
157 |         x = x.view(x.size(0), -1)
158 |         #print "view: ",x.data.shape
159 |         x = self.fc(x)
160 | 
161 |         return x
162 | 
163 | 
164 | def resnet14(pretrained=False, **kwargs):
165 |     """Constructs a ResNet-18 model.
166 | 
167 |     Args:
168 |         pretrained (bool): If True, returns a model pre-trained on ImageNet
169 |     """
170 |     model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs)
171 |     if pretrained:
172 |         raise RuntimeError("No pretrained resnet-14.")
173 |     return model
174 | 
175 | def resnet18(pretrained=False, **kwargs):
176 |     """Constructs a ResNet-18 model.
177 | 
178 |     Args:
179 |         pretrained (bool): If True, returns a model pre-trained on ImageNet
180 |     """
181 |     model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
182 |     if pretrained:
183 |         model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
184 |     return model
185 | 
186 | 
187 | def resnet34(pretrained=False, **kwargs):
188 |     """Constructs a ResNet-34 model.
189 | 
190 |     Args:
191 |         pretrained (bool): If True, returns a model pre-trained on ImageNet
192 |     """
193 |     model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
194 |     if pretrained:
195 |         model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
196 |     return model
197 | 
198 | 
199 | def resnet50(pretrained=False, **kwargs):
200 |     """Constructs a ResNet-50 model.
201 | 
202 |     Args:
203 |         pretrained (bool): If True, returns a model pre-trained on ImageNet
204 |     """
205 |     model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
206 |     if pretrained:
207 |         model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
208 |     return model
209 | 
210 | 
211 | def resnet101(pretrained=False, **kwargs):
212 |     """Constructs a ResNet-101 model.
213 | 
214 |     Args:
215 |         pretrained (bool): If True, returns a model pre-trained on ImageNet
216 |     """
217 |     model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
218 |     if pretrained:
219 |         model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
220 |     return model
221 | 
222 | 
223 | def resnet152(pretrained=False, **kwargs):
224 |     """Constructs a ResNet-152 model.
225 | 
226 |     Args:
227 |         pretrained (bool): If True, returns a model pre-trained on ImageNet
228 |     """
229 |     model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
230 |     if pretrained:
231 |         model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
232 |     return model
233 | 


--------------------------------------------------------------------------------
/setup.sh:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | 
 3 | home=$PWD
 4 | source /usr/local/root/6.16.00_python3/bin/thisroot.sh
 5 | 
 6 | cd ../larcv2
 7 | source configure.sh
 8 | 
 9 | cd ../larcvdataset
10 | source setenv.sh
11 | 
12 | cd $home
13 | 
14 | export CUDA_VISIBLE_DEVICES=1
15 | 


--------------------------------------------------------------------------------
/template.py:
--------------------------------------------------------------------------------
  1 | import argparse
  2 | import os
  3 | import shutil
  4 | import time
  5 | 
  6 | import torch
  7 | import torch.nn as nn
  8 | import torch.nn.parallel
  9 | import torch.backends.cudnn as cudnn
 10 | import torch.distributed as dist
 11 | import torch.optim
 12 | import torch.utils.data
 13 | import torch.utils.data.distributed
 14 | import torchvision.transforms as transforms
 15 | import torchvision.datasets as datasets
 16 | import torchvision.models as models
 17 | 
 18 | model_names = sorted(name for name in models.__dict__
 19 |     if name.islower() and not name.startswith("__")
 20 |     and callable(models.__dict__[name]))
 21 | 
 22 | parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
 23 | parser.add_argument('data', metavar='DIR',
 24 |                     help='path to dataset')
 25 | parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18',
 26 |                     choices=model_names,
 27 |                     help='model architecture: ' +
 28 |                         ' | '.join(model_names) +
 29 |                         ' (default: resnet18)')
 30 | parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
 31 |                     help='number of data loading workers (default: 4)')
 32 | parser.add_argument('--epochs', default=90, type=int, metavar='N',
 33 |                     help='number of total epochs to run')
 34 | parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
 35 |                     help='manual epoch number (useful on restarts)')
 36 | parser.add_argument('-b', '--batch-size', default=256, type=int,
 37 |                     metavar='N', help='mini-batch size (default: 256)')
 38 | parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
 39 |                     metavar='LR', help='initial learning rate')
 40 | parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
 41 |                     help='momentum')
 42 | parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
 43 |                     metavar='W', help='weight decay (default: 1e-4)')
 44 | parser.add_argument('--print-freq', '-p', default=10, type=int,
 45 |                     metavar='N', help='print frequency (default: 10)')
 46 | parser.add_argument('--resume', default='', type=str, metavar='PATH',
 47 |                     help='path to latest checkpoint (default: none)')
 48 | parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
 49 |                     help='evaluate model on validation set')
 50 | parser.add_argument('--pretrained', dest='pretrained', action='store_true',
 51 |                     help='use pre-trained model')
 52 | parser.add_argument('--world-size', default=1, type=int,
 53 |                     help='number of distributed processes')
 54 | parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
 55 |                     help='url used to set up distributed training')
 56 | parser.add_argument('--dist-backend', default='gloo', type=str,
 57 |                     help='distributed backend')
 58 | 
 59 | best_prec1 = 0
 60 | 
 61 | 
 62 | def main():
 63 |     global args, best_prec1
 64 |     args = parser.parse_args()
 65 | 
 66 |     args.distributed = args.world_size > 1
 67 | 
 68 |     if args.distributed:
 69 |         dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
 70 |                                 world_size=args.world_size)
 71 | 
 72 |     # create model
 73 |     if args.pretrained:
 74 |         print("=> using pre-trained model '{}'".format(args.arch))
 75 |         model = models.__dict__[args.arch](pretrained=True)
 76 |     else:
 77 |         print("=> creating model '{}'".format(args.arch))
 78 |         model = models.__dict__[args.arch]()
 79 | 
 80 |     if not args.distributed:
 81 |         if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
 82 |             model.features = torch.nn.DataParallel(model.features)
 83 |             model.cuda()
 84 |         else:
 85 |             model = torch.nn.DataParallel(model).cuda()
 86 |     else:
 87 |         model.cuda()
 88 |         model = torch.nn.parallel.DistributedDataParallel(model)
 89 | 
 90 |     # define loss function (criterion) and optimizer
 91 |     criterion = nn.CrossEntropyLoss().cuda()
 92 | 
 93 |     optimizer = torch.optim.SGD(model.parameters(), args.lr,
 94 |                                 momentum=args.momentum,
 95 |                                 weight_decay=args.weight_decay)
 96 | 
 97 |     # optionally resume from a checkpoint
 98 |     if args.resume:
 99 |         if os.path.isfile(args.resume):
100 |             print("=> loading checkpoint '{}'".format(args.resume))
101 |             checkpoint = torch.load(args.resume)
102 |             args.start_epoch = checkpoint['epoch']
103 |             best_prec1 = checkpoint['best_prec1']
104 |             model.load_state_dict(checkpoint['state_dict'])
105 |             optimizer.load_state_dict(checkpoint['optimizer'])
106 |             print("=> loaded checkpoint '{}' (epoch {})"
107 |                   .format(args.resume, checkpoint['epoch']))
108 |         else:
109 |             print("=> no checkpoint found at '{}'".format(args.resume))
110 | 
111 |     cudnn.benchmark = True
112 | 
113 |     # Data loading code
114 |     traindir = os.path.join(args.data, 'train')
115 |     valdir = os.path.join(args.data, 'val')
116 |     normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
117 |                                      std=[0.229, 0.224, 0.225])
118 | 
119 |     train_dataset = datasets.ImageFolder(
120 |         traindir,
121 |         transforms.Compose([
122 |             transforms.RandomResizedCrop(224),
123 |             transforms.RandomHorizontalFlip(),
124 |             transforms.ToTensor(),
125 |             normalize,
126 |         ]))
127 | 
128 |     if args.distributed:
129 |         train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
130 |     else:
131 |         train_sampler = None
132 | 
133 |     train_loader = torch.utils.data.DataLoader(
134 |         train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
135 |         num_workers=args.workers, pin_memory=True, sampler=train_sampler)
136 | 
137 |     val_loader = torch.utils.data.DataLoader(
138 |         datasets.ImageFolder(valdir, transforms.Compose([
139 |             transforms.Resize(256),
140 |             transforms.CenterCrop(224),
141 |             transforms.ToTensor(),
142 |             normalize,
143 |         ])),
144 |         batch_size=args.batch_size, shuffle=False,
145 |         num_workers=args.workers, pin_memory=True)
146 | 
147 |     if args.evaluate:
148 |         validate(val_loader, model, criterion)
149 |         return
150 | 
151 |     for epoch in range(args.start_epoch, args.epochs):
152 |         if args.distributed:
153 |             train_sampler.set_epoch(epoch)
154 |         adjust_learning_rate(optimizer, epoch)
155 | 
156 |         # train for one epoch
157 |         train(train_loader, model, criterion, optimizer, epoch)
158 | 
159 |         # evaluate on validation set
160 |         prec1 = validate(val_loader, model, criterion)
161 | 
162 |         # remember best prec@1 and save checkpoint
163 |         is_best = prec1 > best_prec1
164 |         best_prec1 = max(prec1, best_prec1)
165 |         save_checkpoint({
166 |             'epoch': epoch + 1,
167 |             'arch': args.arch,
168 |             'state_dict': model.state_dict(),
169 |             'best_prec1': best_prec1,
170 |             'optimizer' : optimizer.state_dict(),
171 |         }, is_best)
172 | 
173 | 
174 | def train(train_loader, model, criterion, optimizer, epoch):
175 |     batch_time = AverageMeter()
176 |     data_time = AverageMeter()
177 |     losses = AverageMeter()
178 |     top1 = AverageMeter()
179 |     top5 = AverageMeter()
180 | 
181 |     # switch to train mode
182 |     model.train()
183 | 
184 |     end = time.time()
185 |     for i, (input, target) in enumerate(train_loader):
186 |         # measure data loading time
187 |         data_time.update(time.time() - end)
188 | 
189 |         target = target.cuda(async=True)
190 |         input_var = torch.autograd.Variable(input)
191 |         target_var = torch.autograd.Variable(target)
192 | 
193 |         # compute output
194 |         output = model(input_var)
195 |         loss = criterion(output, target_var)
196 | 
197 |         # measure accuracy and record loss
198 |         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
199 |         losses.update(loss.data[0], input.size(0))
200 |         top1.update(prec1[0], input.size(0))
201 |         top5.update(prec5[0], input.size(0))
202 | 
203 |         # compute gradient and do SGD step
204 |         optimizer.zero_grad()
205 |         loss.backward()
206 |         optimizer.step()
207 | 
208 |         # measure elapsed time
209 |         batch_time.update(time.time() - end)
210 |         end = time.time()
211 | 
212 |         if i % args.print_freq == 0:
213 |             print('Epoch: [{0}][{1}/{2}]\t'
214 |                   'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
215 |                   'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
216 |                   'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
217 |                   'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
218 |                   'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
219 |                    epoch, i, len(train_loader), batch_time=batch_time,
220 |                    data_time=data_time, loss=losses, top1=top1, top5=top5))
221 | 
222 | 
223 | def validate(val_loader, model, criterion):
224 |     batch_time = AverageMeter()
225 |     losses = AverageMeter()
226 |     top1 = AverageMeter()
227 |     top5 = AverageMeter()
228 | 
229 |     # switch to evaluate mode
230 |     model.eval()
231 | 
232 |     end = time.time()
233 |     for i, (input, target) in enumerate(val_loader):
234 |         target = target.cuda(async=True)
235 |         input_var = torch.autograd.Variable(input, volatile=True)
236 |         target_var = torch.autograd.Variable(target, volatile=True)
237 | 
238 |         # compute output
239 |         output = model(input_var)
240 |         loss = criterion(output, target_var)
241 | 
242 |         # measure accuracy and record loss
243 |         prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
244 |         losses.update(loss.data[0], input.size(0))
245 |         top1.update(prec1[0], input.size(0))
246 |         top5.update(prec5[0], input.size(0))
247 | 
248 |         # measure elapsed time
249 |         batch_time.update(time.time() - end)
250 |         end = time.time()
251 | 
252 |         if i % args.print_freq == 0:
253 |             print('Test: [{0}/{1}]\t'
254 |                   'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
255 |                   'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
256 |                   'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
257 |                   'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
258 |                    i, len(val_loader), batch_time=batch_time, loss=losses,
259 |                    top1=top1, top5=top5))
260 | 
261 |     print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
262 |           .format(top1=top1, top5=top5))
263 | 
264 |     return top1.avg
265 | 
266 | 
267 | def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
268 |     torch.save(state, filename)
269 |     if is_best:
270 |         shutil.copyfile(filename, 'model_best.pth.tar')
271 | 
272 | 
273 | class AverageMeter(object):
274 |     """Computes and stores the average and current value"""
275 |     def __init__(self):
276 |         self.reset()
277 | 
278 |     def reset(self):
279 |         self.val = 0
280 |         self.avg = 0
281 |         self.sum = 0
282 |         self.count = 0
283 | 
284 |     def update(self, val, n=1):
285 |         self.val = val
286 |         self.sum += val * n
287 |         self.count += n
288 |         self.avg = self.sum / self.count
289 | 
290 | 
291 | def adjust_learning_rate(optimizer, epoch):
292 |     """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
293 |     lr = args.lr * (0.1 ** (epoch // 30))
294 |     for param_group in optimizer.param_groups:
295 |         param_group['lr'] = lr
296 | 
297 | 
298 | def accuracy(output, target, topk=(1,)):
299 |     """Computes the precision@k for the specified values of k"""
300 |     maxk = max(topk)
301 |     batch_size = target.size(0)
302 | 
303 |     _, pred = output.topk(maxk, 1, True, True)
304 |     pred = pred.t()
305 |     correct = pred.eq(target.view(1, -1).expand_as(pred))
306 | 
307 |     res = []
308 |     for k in topk:
309 |         correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
310 |         res.append(correct_k.mul_(100.0 / batch_size))
311 |     return res
312 | 
313 | 
314 | if __name__ == '__main__':
315 |     main()
316 | 


--------------------------------------------------------------------------------
/train_dataloader.cfg:
--------------------------------------------------------------------------------
 1 | ThreadProcessor: {
 2 |   Verbosity:3
 3 |   NumThreads: 2
 4 |   NumBatchStorage: 2
 5 |   RandomAccess: true
 6 |   InputFiles: ["/home/phy68/data/practice_train_5k.root"]
 7 |   ProcessName: ["image","label"]
 8 |   ProcessType: ["BatchFillerImage2D","BatchFillerPIDLabel"]
 9 |   ProcessList: {
10 |     image: {
11 |       Verbosity:3
12 |       ImageProducer: "data"
13 |       Channels: [2]
14 |       EnableMirror: false
15 |     }
16 |     label: {
17 |       Verbosity:3
18 |       ParticleProducer: "mctruth"
19 |       PdgClassList: [2212,11,211,13,22]
20 |     }
21 |   }
22 | }


--------------------------------------------------------------------------------
/updater_training_plot.py:
--------------------------------------------------------------------------------
 1 | import os,sys,time
 2 | 
 3 | from plot_training import make_training_plot
 4 | 
 5 | logfile = "log_train_5a.txt"
 6 | outputpath = ""
 7 | 
 8 | while True:
 9 | 
10 |     print "Updating %s from %s"%(outputpath, logfile)
11 |     make_training_plot( logfile, outputpath )
12 |     time.sleep(30)
13 | 


--------------------------------------------------------------------------------
/valid_dataloader.cfg:
--------------------------------------------------------------------------------
 1 | ThreadProcessorTest: {
 2 |   Verbosity:3
 3 |   NumThreads: 2
 4 |   NumBatchStorage: 2
 5 |   RandomAccess: true
 6 |   InputFiles: ["/home/phy68/data/practice_train_5k.root"]
 7 |   ProcessName: ["imagetest","labeltest"]
 8 |   ProcessType: ["BatchFillerImage2D","BatchFillerPIDLabel"]
 9 |   ProcessList: {
10 |     imagetest: {
11 |       Verbosity:3
12 |       ImageProducer: "data"
13 |       Channels: [2]
14 |       EnableMirror: false
15 |     }
16 |     labeltest: {
17 |       Verbosity:3
18 |       ParticleProducer: "mctruth"
19 |       PdgClassList: [2212,11,211,13,22]
20 |     }
21 |   }
22 | }


--------------------------------------------------------------------------------
/view_data.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "Welcome to JupyROOT 6.16/00\n"
 13 |      ]
 14 |     }
 15 |    ],
 16 |    "source": [
 17 |     "import ROOT\n",
 18 |     "from larcv import larcv\n",
 19 |     "from larcvdataset import LArCVDataset\n",
 20 |     "import numpy as np\n",
 21 |     "import matplotlib.pyplot as plt"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 2,
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "name": "stdout",
 31 |      "output_type": "stream",
 32 |      "text": [
 33 |       "CONFIGURE\n",
 34 |       "ThreadProcessor : {\n",
 35 |       "  InputFiles : [\"/home/phy68/data/practice_train_5k.root\"]\n",
 36 |       "  NumBatchStorage : 2\n",
 37 |       "  NumThreads : 2\n",
 38 |       "  ProcessName : [\"image\",\"label\"]\n",
 39 |       "  ProcessType : [\"BatchFillerImage2D\",\"BatchFillerPIDLabel\"]\n",
 40 |       "  RandomAccess : true\n",
 41 |       "  Verbosity : 3\n",
 42 |       "  ProcessList : {\n",
 43 |       "    image : {\n",
 44 |       "      Channels : [2]\n",
 45 |       "      EnableMirror : false\n",
 46 |       "      ImageProducer : \"data\"\n",
 47 |       "      Verbosity : 3\n",
 48 |       "    }\n",
 49 |       "\n",
 50 |       "    label : {\n",
 51 |       "      ParticleProducer : \"mctruth\"\n",
 52 |       "      PdgClassList : [2212,11,211,13,22]\n",
 53 |       "      Verbosity : 3\n",
 54 |       "    }\n",
 55 |       "\n",
 56 |       "  }\n",
 57 |       "\n",
 58 |       "}\n",
 59 |       "\n",
 60 |       "\u001b[93m setting verbosity \u001b[00m3\n"
 61 |      ]
 62 |     }
 63 |    ],
 64 |    "source": [
 65 |     "iotrain = LArCVDataset(\"train_dataloader.cfg\", \"ThreadProcessor\", loadallinmem=False)"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "code",
 70 |    "execution_count": 3,
 71 |    "metadata": {},
 72 |    "outputs": [],
 73 |    "source": [
 74 |     "batchsize=20\n",
 75 |     "iotrain.start(batchsize)"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "code",
 80 |    "execution_count": 4,
 81 |    "metadata": {},
 82 |    "outputs": [],
 83 |    "source": [
 84 |     "def padandcropandflip(npimg2d):\n",
 85 |     "    imgpad  = np.zeros( (264,264), dtype=np.float32 )\n",
 86 |     "    imgpad[4:256+4,4:256+4] = npimg2d[:,:]\n",
 87 |     "    if np.random.rand()>0.5:\n",
 88 |     "        imgpad = np.flip( imgpad, 0 )\n",
 89 |     "    if np.random.rand()>0.5:\n",
 90 |     "        imgpad = np.flip( imgpad, 1 )\n",
 91 |     "    randx = np.random.randint(0,8)\n",
 92 |     "    randy = np.random.randint(0,8)\n",
 93 |     "    return imgpad[randx:randx+256,randy:randy+256]"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 6,
 99 |    "metadata": {},
100 |    "outputs": [],
101 |    "source": [
102 |     "labelname = {0:\"proton\",\n",
103 |     "            1:\"electron\",\n",
104 |     "            2:\"pion\",\n",
105 |     "            3:\"muon\",\n",
106 |     "            4:\"photon\"}"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "code",
111 |    "execution_count": 11,
112 |    "metadata": {
113 |     "scrolled": false
114 |    },
115 |    "outputs": [
116 |     {
117 |      "name": "stdout",
118 |      "output_type": "stream",
119 |      "text": [
120 |       "(20, 65536)\n",
121 |       "LABEL[0]:  muon\n",
122 |       "LABEL[1]:  muon\n",
123 |       "LABEL[2]:  pion\n",
124 |       "LABEL[3]:  photon\n",
125 |       "LABEL[4]:  pion\n",
126 |       "LABEL[5]:  photon\n",
127 |       "LABEL[6]:  muon\n",
128 |       "LABEL[7]:  pion\n",
129 |       "LABEL[8]:  proton\n",
130 |       "LABEL[9]:  photon\n",
131 |       "LABEL[10]:  pion\n",
132 |       "LABEL[11]:  electron\n",
133 |       "LABEL[12]:  proton\n",
134 |       "LABEL[13]:  electron\n",
135 |       "LABEL[14]:  pion\n",
136 |       "LABEL[15]:  pion\n",
137 |       "LABEL[16]:  photon\n",
138 |       "LABEL[17]:  proton\n",
139 |       "LABEL[18]:  pion\n",
140 |       "LABEL[19]:  muon\n"
141 |      ]
142 |     },
143 |     {
144 |      "data": {
145 |       "image/png": "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\n",
146 |       "text/plain": [
147 |        "<Figure size 720x720 with 1 Axes>"
148 |       ]
149 |      },
150 |      "metadata": {
151 |       "needs_background": "light"
152 |      },
153 |      "output_type": "display_data"
154 |     },
155 |     {
156 |      "data": {
157 |       "image/png": "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\n",
158 |       "text/plain": [
159 |        "<Figure size 720x720 with 1 Axes>"
160 |       ]
161 |      },
162 |      "metadata": {
163 |       "needs_background": "light"
164 |      },
165 |      "output_type": "display_data"
166 |     },
167 |     {
168 |      "data": {
169 |       "image/png": "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\n",
170 |       "text/plain": [
171 |        "<Figure size 720x720 with 1 Axes>"
172 |       ]
173 |      },
174 |      "metadata": {
175 |       "needs_background": "light"
176 |      },
177 |      "output_type": "display_data"
178 |     },
179 |     {
180 |      "data": {
181 |       "image/png": "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\n",
182 |       "text/plain": [
183 |        "<Figure size 720x720 with 1 Axes>"
184 |       ]
185 |      },
186 |      "metadata": {
187 |       "needs_background": "light"
188 |      },
189 |      "output_type": "display_data"
190 |     },
191 |     {
192 |      "data": {
193 |       "image/png": "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\n",
194 |       "text/plain": [
195 |        "<Figure size 720x720 with 1 Axes>"
196 |       ]
197 |      },
198 |      "metadata": {
199 |       "needs_background": "light"
200 |      },
201 |      "output_type": "display_data"
202 |     },
203 |     {
204 |      "data": {
205 |       "image/png": "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\n",
206 |       "text/plain": [
207 |        "<Figure size 720x720 with 1 Axes>"
208 |       ]
209 |      },
210 |      "metadata": {
211 |       "needs_background": "light"
212 |      },
213 |      "output_type": "display_data"
214 |     },
215 |     {
216 |      "data": {
217 |       "image/png": "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\n",
218 |       "text/plain": [
219 |        "<Figure size 720x720 with 1 Axes>"
220 |       ]
221 |      },
222 |      "metadata": {
223 |       "needs_background": "light"
224 |      },
225 |      "output_type": "display_data"
226 |     },
227 |     {
228 |      "data": {
229 |       "image/png": "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\n",
230 |       "text/plain": [
231 |        "<Figure size 720x720 with 1 Axes>"
232 |       ]
233 |      },
234 |      "metadata": {
235 |       "needs_background": "light"
236 |      },
237 |      "output_type": "display_data"
238 |     },
239 |     {
240 |      "data": {
241 |       "image/png": "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\n",
242 |       "text/plain": [
243 |        "<Figure size 720x720 with 1 Axes>"
244 |       ]
245 |      },
246 |      "metadata": {
247 |       "needs_background": "light"
248 |      },
249 |      "output_type": "display_data"
250 |     },
251 |     {
252 |      "data": {
253 |       "image/png": "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\n",
254 |       "text/plain": [
255 |        "<Figure size 720x720 with 1 Axes>"
256 |       ]
257 |      },
258 |      "metadata": {
259 |       "needs_background": "light"
260 |      },
261 |      "output_type": "display_data"
262 |     },
263 |     {
264 |      "data": {
265 |       "image/png": "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\n",
266 |       "text/plain": [
267 |        "<Figure size 720x720 with 1 Axes>"
268 |       ]
269 |      },
270 |      "metadata": {
271 |       "needs_background": "light"
272 |      },
273 |      "output_type": "display_data"
274 |     },
275 |     {
276 |      "data": {
277 |       "image/png": "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\n",
278 |       "text/plain": [
279 |        "<Figure size 720x720 with 1 Axes>"
280 |       ]
281 |      },
282 |      "metadata": {
283 |       "needs_background": "light"
284 |      },
285 |      "output_type": "display_data"
286 |     },
287 |     {
288 |      "data": {
289 |       "image/png": "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\n",
290 |       "text/plain": [
291 |        "<Figure size 720x720 with 1 Axes>"
292 |       ]
293 |      },
294 |      "metadata": {
295 |       "needs_background": "light"
296 |      },
297 |      "output_type": "display_data"
298 |     },
299 |     {
300 |      "data": {
301 |       "image/png": "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\n",
302 |       "text/plain": [
303 |        "<Figure size 720x720 with 1 Axes>"
304 |       ]
305 |      },
306 |      "metadata": {
307 |       "needs_background": "light"
308 |      },
309 |      "output_type": "display_data"
310 |     },
311 |     {
312 |      "data": {
313 |       "image/png": "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\n",
314 |       "text/plain": [
315 |        "<Figure size 720x720 with 1 Axes>"
316 |       ]
317 |      },
318 |      "metadata": {
319 |       "needs_background": "light"
320 |      },
321 |      "output_type": "display_data"
322 |     },
323 |     {
324 |      "data": {
325 |       "image/png": "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\n",
326 |       "text/plain": [
327 |        "<Figure size 720x720 with 1 Axes>"
328 |       ]
329 |      },
330 |      "metadata": {
331 |       "needs_background": "light"
332 |      },
333 |      "output_type": "display_data"
334 |     },
335 |     {
336 |      "data": {
337 |       "image/png": "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\n",
338 |       "text/plain": [
339 |        "<Figure size 720x720 with 1 Axes>"
340 |       ]
341 |      },
342 |      "metadata": {
343 |       "needs_background": "light"
344 |      },
345 |      "output_type": "display_data"
346 |     },
347 |     {
348 |      "data": {
349 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAJBCAYAAABMGhHqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUnklEQVR4nO3dT6ild53n8c93KrYBFYw4XWQqNWOQGoa4KaVIB1qG9KLbmE3pJsRFG0QoFwkouIludNmLVkFmOlBiMIJtOqBiFtLdmSA4GzVVEmL+dMaiTUgVZQpxULHAJsm3F/eJfqmuSv2599xzinq94HKe8zvPufd3+eW5eXOe55yq7g4AAFv+07onAACwScQRAMAgjgAABnEEADCIIwCAQRwBAAwri6OquqOqnq+qE1V1/6p+DgDATqpVfM5RVe1J8v+S/GWSk0meSPKR7n52x38YAMAOum5F3/fWJCe6+1+TpKoeTnI4yXnjqKra+T0AYDe9lvyyu//zueOriqN9SV4a908m+bO5Q1UdSXIkSSrJ9SuaCADA+ZxNXjzf+Kri6KK6+2iSo0myp8q/YQIAbIRVnc06lWT/uH/TMgYAsNFWFUdPJDlQVTdX1Z8kuTvJoyv6WQAAO2Ylp9W6+5Wqui/JPyXZk+TB7n5mFT8LAGAnreSt/JdrT1W7IBsA2E1nk+Pdfejcce+gBwAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYrtvOk6vqhSS/TfJqkle6+1BVvSPJPyR5V5IXktzV3f9/e9MEANgdO/HK0V9098HuPrTcvz/J4919IMnjy30AgKvCKk6rHU7y0LL9UJIPreBnAACsxHbjqJP8c1Udr6ojy9je7j69bP8iyd7zPbGqjlTVsao61tucBADATtnWNUdJ3t/dp6rqT5M8VlX/Mh/s7q6q87ZPdx9NcjRJ9lxgHwCA3batV466+9RyeybJd5LcmuTlqroxSZbbM9udJADAbrniOKqqt1TV217fTvJXSZ5O8miSe5bd7kny3e1OEgBgt2zntNreJN+pqte/z9939z9W1RNJHqmqjyd5Mcld258mAMDuqO71X+6zp6qvX/ckAIBrytnk+Pgooj/wCdkAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYLhpHVfVgVZ2pqqfH2Duq6rGq+tlye8MyXlX15ao6UVVPVdX7Vjl5AICddimvHH0tyR3njN2f5PHuPpDk8eV+knwwyYHl60iSB3ZmmgAAu+OicdTdP0jyq3OGDyd5aNl+KMmHxvjXe8sPk7y9qm7cobkCAKzclV5ztLe7Ty/bv0iyd9nel+Slsd/JZew/qKojVXWsqo71FU4CAGCnbfuC7O7uJJfdN919tLsPdfeh2u4kAAB2yJXG0cuvny5bbs8s46eS7B/73bSMAQBcFa40jh5Ncs+yfU+S747xjy7vWrstya/H6TcAgI133cV2qKpvJrk9yTur6mSSzyX5mySPVNXHk7yY5K5l9+8luTPJiSRnk3xsBXMGAFiZ2rpkaL32VPX1654EAHBNOZsc7+5D5477hGwAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgOGicVRVD1bVmap6eox9vqpOVdWTy9ed47HPVNWJqnq+qj6wqokDAKzCpbxy9LUkd5xn/EvdfXD5+l6SVNUtSe5O8p7lOX9XVXt2arIAAKt20Tjq7h8k+dUlfr/DSR7u7t9398+TnEhy6zbmBwCwq7ZzzdF9VfXUctrthmVsX5KXxj4nl7H/oKqOVNWxqjrW25gEAMBOutI4eiDJu5McTHI6yRcu9xt099HuPtTdh+oKJwEAsNOuKI66++XufrW7X0vylfzx1NmpJPvHrjctYwAAV4UriqOqunHc/XCS19/J9miSu6vqzVV1c5IDSX68vSkCAOye6y62Q1V9M8ntSd5ZVSeTfC7J7VV1MEkneSHJJ5Kku5+pqkeSPJvklST3dverK5k5AMAKVPf6L4feU9XXr3sSAMA15WxyvLsPnTvuE7IBAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAIaLxlFV7a+q71fVs1X1TFV9chl/R1U9VlU/W25vWMarqr5cVSeq6qmqet+qfwkAgJ1yKa8cvZLk0919S5LbktxbVbckuT/J4919IMnjy/0k+WCSA8vXkSQP7PisAQBW5KJx1N2nu/sny/ZvkzyXZF+Sw0keWnZ7KMmHlu3DSb7eW36Y5O1VdeNOTxwAYBUu65qjqnpXkvcm+VGSvd19ennoF0n2Ltv7krw0nnZyGTv3ex2pqmNVdawvd9YAACtyyXFUVW9N8q0kn+ru38zHuruTXFbjdPfR7j7U3Yfqcp4IALBClxRHVfWmbIXRN7r728vwy6+fLltuzyzjp5LsH0+/aRkDANh4l/JutUry1STPdfcXx0OPJrln2b4nyXfH+EeXd63dluTX4/QbAMBGq60zYm+wQ9X7k/zfJD9N8toy/NlsXXf0SJL/muTFJHd196+WmPpfSe5IcjbJx7r72Bv9jD1Vff12fgsAgMt0Njne3YfOHb9oHO0GcQQA7LYLxZFPyAYAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADBct+4JAMC6/K77D9tvqVrjTNgkXjkCABjEEQDA4LQaANec3/1o6/Z/OJXGeXjlCABg8MoRANect/zZumfAJvPKEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAMNF46iq9lfV96vq2ap6pqo+uYx/vqpOVdWTy9ed4zmfqaoTVfV8VX1glb8AAMBOuu4S9nklyae7+ydV9bYkx6vqseWxL3X3386dq+qWJHcneU+S/5Lk/1TVf+/uV3dy4gAAq3DRV466+3R3/2TZ/m2S55Lse4OnHE7ycHf/vrt/nuREklt3YrIAAKt2WdccVdW7krw3yY+Wofuq6qmqerCqbljG9iV5aTztZM4TU1V1pKqOVdWxvvx5AwCsxCXHUVW9Ncm3knyqu3+T5IEk705yMMnpJF+4nB/c3Ue7+1B3H6rLeSIAwApdUhxV1ZuyFUbf6O5vJ0l3v9zdr3b3a0m+kj+eOjuVZP94+k3LGADAxruUd6tVkq8mea67vzjGbxy7fTjJ08v2o0nurqo3V9XNSQ4k+fHOTRkAYHUu5d1qf57kr5P8tKqeXMY+m+QjVXUwSSd5IcknkqS7n6mqR5I8m613ut3rnWoAwNWiutd/OfSeqr5+3ZMAAK4pZ5Pj3X3o3HGfkA0AMIgjAIBBHAEADOIIAGAQRwAAgzgCABgu5XOOVu615Jdnk98l+eW658JFvTPW6Wphra4O1unqYJ2uDpe7Tv/tfIMb8TlHSVJVx873WQNsFut09bBWVwfrdHWwTleHnVonp9UAAAZxBAAwbFIcHV33BLgk1unqYa2uDtbp6mCdrg47sk4bc80RAMAm2KRXjgAA1k4cAQAMGxFHVXVHVT1fVSeq6v51z4c/qqoXquqnVfVkVR1bxt5RVY9V1c+W2xvWPc9rTVU9WFVnqurpMXbedaktX16Or6eq6n3rm/m15QLr9PmqOrUcU09W1Z3jsc8s6/R8VX1gPbO+9lTV/qr6flU9W1XPVNUnl3HH1AZ5g3Xa8WNq7XFUVXuS/O8kH0xyS5KPVNUt650V5/iL7j44Pjvi/iSPd/eBJI8v99ldX0tyxzljF1qXDyY5sHwdSfLALs2R869TknxpOaYOdvf3kmT5u3d3kvcsz/m75e8jq/dKkk939y1Jbkty77IejqnNcqF1Snb4mFp7HCW5NcmJ7v7X7v63JA8nObzmOfHGDid5aNl+KMmH1jeVa1N3/yDJr84ZvtC6HE7y9d7ywyRvr6obd2Wi17gLrNOFHE7ycHf/vrt/nuREtv4+smLdfbq7f7Js/zbJc0n2xTG1Ud5gnS7kio+pTYijfUleGvdP5o1/WXZXJ/nnqjpeVUeWsb3dfXrZ/kWSveuZGue40Lo4xjbPfcvpmAfHaWnrtAGq6l1J3pvkR3FMbaxz1inZ4WNqE+KIzfb+7n5ftl5Gvreq/ud8sLc+C8LnQWwY67LRHkjy7iQHk5xO8oW1zoY/qKq3JvlWkk9192/mY46pzXGeddrxY2oT4uhUkv3j/k3LGBugu08tt2eSfCdbL0m+/PpLyMvtmfXNkOFC6+IY2yDd/XJ3v9rdryX5Sv74Mr91WqOqelO2/of7je7+9jLsmNow51unVRxTmxBHTyQ5UFU3V9WfZOviqUfXPCeSVNVbquptr28n+askT2drfe5ZdrsnyXfXM0POcaF1eTTJR5d32NyW5NfjVAG77JxrUz6crWMq2Vqnu6vqzVV1c7Yu9v3xbs/vWlRVleSrSZ7r7i+OhxxTG+RC67SKY+q6nZnylevuV6rqviT/lGRPkge7+5k1T4ste5N8Z+u/x1yX5O+7+x+r6okkj1TVx5O8mOSuNc7xmlRV30xye5J3VtXJJJ9L8jc5/7p8L8md2boY8WySj+36hK9RF1in26vqYLZO0byQ5BNJ0t3PVNUjSZ7N1rty7u3uV9cw7WvRnyf56yQ/raonl7HPxjG1aS60Th/Z6WPKPx8CADBswmk1AICNIY4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAADDvwONPjBCfLe0RQAAAABJRU5ErkJggg==\n",
350 |       "text/plain": [
351 |        "<Figure size 720x720 with 1 Axes>"
352 |       ]
353 |      },
354 |      "metadata": {
355 |       "needs_background": "light"
356 |      },
357 |      "output_type": "display_data"
358 |     },
359 |     {
360 |      "data": {
361 |       "image/png": "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\n",
362 |       "text/plain": [
363 |        "<Figure size 720x720 with 1 Axes>"
364 |       ]
365 |      },
366 |      "metadata": {
367 |       "needs_background": "light"
368 |      },
369 |      "output_type": "display_data"
370 |     },
371 |     {
372 |      "data": {
373 |       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkcAAAJBCAYAAABMGhHqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVN0lEQVR4nO3dX6hlZ5nn8d8zFdtAFIw4U6STzBikpiEyUEqRCbQM6YtpY+ai9CbEizYEobxIQMG5iN7o3PXFqCDTHYiYMYJtJqBioKW7M0FwbtRUJMT8mYyFJqRqygSxMZIChyTPXJyVzkN1Verf2WfvU/X5wGGv/e69z36LN6vyZa21d1V3BwCALf9i3RMAANgk4ggAYBBHAACDOAIAGMQRAMAgjgAAhpXFUVXdXFXPVtWRqrp7Ve8DALCdahXfc1RVe5L8nyT/McnRJI8m+Xh3P73tbwYAsI0uW9HvvSHJke7+ZZJU1QNJDiY5ZRxVVTu/BwDspNeT33T3vzx5fFVxdHWSF8b9o0n+/XxCVR1KcihJKsnlK5oIAMCpnEieP9X4quLojLr73iT3JsmeKv+GCQCwEVZ1NutYkmvH/WuWMQCAjbaqOHo0yb6quq6q/ijJbUkeWtF7AQBsm5WcVuvuV6vqriR/n2RPkvu6+6lVvBcAwHZayUf5z9WeqnZBNgCwk04kj3X3gZPHfYIeAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGC47EJeXFXPJfl9kteSvNrdB6rq3Un+R5L3Jnkuya3d/Y8XNk0AgJ2xHUeO/qy793f3geX+3Uke6e59SR5Z7gMA7AqrOK12MMn9y/b9ST66gvcAAFiJC42jTvIPVfVYVR1axvZ29/Fl+9dJ9p7qhVV1qKoOV9XhvsBJAABslwu65ijJh7r7WFX9qyQPV9X/ng92d1fVKdunu+9Ncm+S7DnNcwAAdtoFHTnq7mPL7UtJvpfkhiQvVtVVSbLcvnShkwQA2CnnHUdVdUVVvfON7SR/nuTJJA8luX152u1Jvn+hkwQA2CkXclptb5LvVdUbv+dvuvvvqurRJA9W1SeTPJ/k1gufJgDAzqju9V/us6eqL1/3JACAS8qJ5LHxVUT/xDdkAwAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYDhjHFXVfVX1UlU9OcbeXVUPV9Uvltsrl/Gqqq9W1ZGqeqKqPrjKyQMAbLezOXL0jSQ3nzR2d5JHuntfkkeW+0nykST7lp9DSe7ZnmkCAOyMM8ZRd/8oyW9PGj6Y5P5l+/4kHx3j3+wtP07yrqq6apvmCgCwcud7zdHe7j6+bP86yd5l++okL4znHV3G/pmqOlRVh6vqcJ/nJAAAttsFX5Dd3Z3knPumu+/t7gPdfaAudBIAANvkfOPoxTdOly23Ly3jx5JcO553zTIGALArnG8cPZTk9mX79iTfH+OfWD61dmOS343TbwAAG++yMz2hqr6d5KYk76mqo0m+kOQvkzxYVZ9M8nySW5en/yDJLUmOJDmR5I4VzBkAYGVq65Kh9dpT1ZevexIAwCXlRPJYdx84edw3ZAMADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAMF00c/bvlBwDgQlw0cQQAsB3EEQDAcNm6J7Bdfvx/t26v+OP1zgMA2N0cOQIAGC6aI0d5dt0TAAAuBo4cAQAMF8+Ro1+uewIAwMXAkSMAgOHiOXK0ZN5/We5+YW0TAQB2M0eOAACGi+bI0RV3bN2+0p0k+ZOqJMlt65oQALArOXIEADBcNEeO3nDFcsTo5eX+K/996/Y/3/Hmc+7Z2SkBALuII0cAAEP1co3OOu2p6stX/B6v/Kc3t1/+263bq1b8ngDA5jqRPNbdB04ed+QIAGC46K45Op0r/vbN7Vfu3Lr9x7/aur1y56cDAGwoR44AAAZxBAAwXDKn1aYrltNpP13uv/LNZfwTa5kOALBBHDkCABgumY/yAwBMPsoPAHAWxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGM4YR1V1X1W9VFVPjrEvVtWxqnp8+bllPPa5qjpSVc9W1YdXNXEAgFU4myNH30hy8ynGv9Ld+5efHyRJVV2f5LYk719e89dVtWe7JgsAsGpnjKPu/lGS357l7zuY5IHu/kN3/yrJkSQ3XMD8AAB21IVcc3RXVT2xnHa7chm7OskL4zlHl7F/pqoOVdXhqjrcFzAJAIDtdL5xdE+S9yXZn+R4ki+d6y/o7nu7+0B3H6jznAQAwHY7rzjq7he7+7Xufj3J1/LmqbNjSa4dT71mGQMA2BXOK46q6qpx92NJ3vgk20NJbquqt1fVdUn2JfnphU0RAGDnXHamJ1TVt5PclOQ9VXU0yReS3FRV+5N0kueSfCpJuvupqnowydNJXk1yZ3e/tpKZAwCsQHWv/3LoPVV9+bonAQBcUk4kj3X3gZPHfUM2AMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMBwxjiqqmur6odV9XRVPVVVn17G311VD1fVL5bbK5fxqqqvVtWRqnqiqj646j8EAMB2OZsjR68m+Wx3X5/kxiR3VtX1Se5O8kh370vyyHI/ST6SZN/ycyjJPds+awCAFTljHHX38e7+2bL9+yTPJLk6ycEk9y9Puz/JR5ftg0m+2Vt+nORdVXXVdk8cAGAVzumao6p6b5IPJPlJkr3dfXx56NdJ9i7bVyd5Ybzs6DJ28u86VFWHq+pwn+usAQBW5KzjqKrekeQ7ST7T3S/Px7q7k5xT43T3vd19oLsP1Lm8EABghc4qjqrqbdkKo29193eX4RffOF223L60jB9Lcu14+TXLGADAxjubT6tVkq8neaa7vzweeijJ7cv27Um+P8Y/sXxq7cYkvxun3wAANlptnRF7iydUfSjJ/0ry8ySvL8Ofz9Z1Rw8m+ddJnk9ya3f/domp/5bk5iQnktzR3Yff6j32VPXlF/KnAAA4RyeSx7r7wMnjZ4yjnSCOAICddro48g3ZAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAACDOAIAGMQRAMAgjgAABnEEADCIIwCAQRwBAAziCABgEEcAAIM4AgAYxBEAwCCOAAAGcQQAMIgjAIBBHAEADOIIAGAQRwAAgzgCABjEEQDAII4AAAZxBAAwiCMAgEEcAQAM4ggAYBBHAADDGeOoqq6tqh9W1dNV9VRVfXoZ/2JVHauqx5efW8ZrPldVR6rq2ar68Cr/AAAA2+mys3jOq0k+290/q6p3Jnmsqh5eHvtKd//X+eSquj7JbUnen+SPk/zPqvq33f3adk4cAGAVznjkqLuPd/fPlu3fJ3kmydVv8ZKDSR7o7j9096+SHElyw3ZMFgBg1c7pmqOqem+SDyT5yTJ0V1U9UVX3VdWVy9jVSV4YLzuaU8RUVR2qqsNVdbjPfd4AACtx1nFUVe9I8p0kn+nul5Pck+R9SfYnOZ7kS+fyxt19b3cf6O4DdS4vBABYobOKo6p6W7bC6Fvd/d0k6e4Xu/u17n49ydfy5qmzY0muHS+/ZhkDANh4Z/NptUry9STPdPeXx/hV42kfS/Lksv1Qktuq6u1VdV2SfUl+un1TBgBYnbP5tNqfJvmLJD+vqseXsc8n+XhV7U/SSZ5L8qkk6e6nqurBJE9n65Nud/qkGgCwW1T3+i+H3lPVl697EgDAJeVE8lh3Hzh53DdkAwAM4ggAYBBHAACDOAIAGMQRAMAgjgAAhrP5nqOVez35zYnklSS/WfdcOKP3xDrtFtZqd7BOu4N12h3OdZ3+zakGN+J7jpKkqg6f6rsG2CzWafewVruDddodrNPusF3r5LQaAMAgjgAAhk2Ko3vXPQHOinXaPazV7mCddgfrtDtsyzptzDVHAACbYJOOHAEArJ04AgAYNiKOqurmqnq2qo5U1d3rng9vqqrnqurnVfV4VR1ext5dVQ9X1S+W2yvXPc9LTVXdV1UvVdWTY+yU61JbvrrsX09U1QfXN/NLy2nW6YtVdWzZpx6vqlvGY59b1unZqvrwemZ96amqa6vqh1X1dFU9VVWfXsbtUxvkLdZp2/eptcdRVe1J8ldJPpLk+iQfr6rr1zsrTvJn3b1/fHfE3Uke6e59SR5Z7rOzvpHk5pPGTrcuH0myb/k5lOSeHZojp16nJPnKsk/t7+4fJMny995tSd6/vOavl78fWb1Xk3y2u69PcmOSO5f1sE9tltOtU7LN+9Ta4yjJDUmOdPcvu/v/JXkgycE1z4m3djDJ/cv2/Uk+ur6pXJq6+0dJfnvS8OnW5WCSb/aWHyd5V1VdtSMTvcSdZp1O52CSB7r7D939qyRHsvX3IyvW3ce7+2fL9u+TPJPk6tinNspbrNPpnPc+tQlxdHWSF8b9o3nrPyw7q5P8Q1U9VlWHlrG93X182f51kr3rmRonOd262Mc2z13L6Zj7xmlp67QBquq9ST6Q5CexT22sk9Yp2eZ9ahPiiM32oe7+YLYOI99ZVf9hPthb3wXh+yA2jHXZaPckeV+S/UmOJ/nSWmfDP6mqdyT5TpLPdPfL8zH71OY4xTpt+z61CXF0LMm14/41yxgboLuPLbcvJfletg5JvvjGIeTl9qX1zZDhdOtiH9sg3f1id7/W3a8n+VrePMxvndaoqt6Wrf/hfqu7v7sM26c2zKnWaRX71CbE0aNJ9lXVdVX1R9m6eOqhNc+JJFV1RVW9843tJH+e5Mlsrc/ty9NuT/L99cyQk5xuXR5K8onlEzY3JvndOFXADjvp2pSPZWufSrbW6baqentVXZeti31/utPzuxRVVSX5epJnuvvL4yH71AY53TqtYp+6bHumfP66+9WquivJ3yfZk+S+7n5qzdNiy94k39v67zGXJfmb7v67qno0yYNV9ckkzye5dY1zvCRV1beT3JTkPVV1NMkXkvxlTr0uP0hyS7YuRjyR5I4dn/Al6jTrdFNV7c/WKZrnknwqSbr7qap6MMnT2fpUzp3d/doapn0p+tMkf5Hk51X1+DL2+dinNs3p1unj271P+edDAACGTTitBgCwMcQRAMAgjgAABnEEADCIIwCAQRwBAAziCABg+P9AR1P+25ppZgAAAABJRU5ErkJggg==\n",
374 |       "text/plain": [
375 |        "<Figure size 720x720 with 1 Axes>"
376 |       ]
377 |      },
378 |      "metadata": {
379 |       "needs_background": "light"
380 |      },
381 |      "output_type": "display_data"
382 |     }
383 |    ],
384 |    "source": [
385 |     "data = iotrain[0]\n",
386 |     "imgbatch = data[\"image\"]\n",
387 |     "lbl = data[\"label\"]\n",
388 |     "print(imgbatch.shape)\n",
389 |     "for ib in range(batchsize):\n",
390 |     "    fig, ax = plt.subplots(1,1,figsize=(10, 10))\n",
391 |     "    img = imgbatch[ib,:].reshape(256,256)\n",
392 |     "    imgout = padandcropandflip(img)\n",
393 |     "    print(\"LABEL[%d]: \"%(ib),labelname[np.argmax(lbl[ib])])\n",
394 |     "    ax.imshow(imgout, cmap='hot', interpolation='nearest')\n",
395 |     "    fig.show()\n"
396 |    ]
397 |   },
398 |   {
399 |    "cell_type": "code",
400 |    "execution_count": null,
401 |    "metadata": {},
402 |    "outputs": [],
403 |    "source": []
404 |   }
405 |  ],
406 |  "metadata": {
407 |   "kernelspec": {
408 |    "display_name": "Python 3",
409 |    "language": "python",
410 |    "name": "python3"
411 |   },
412 |   "language_info": {
413 |    "codemirror_mode": {
414 |     "name": "ipython",
415 |     "version": 3
416 |    },
417 |    "file_extension": ".py",
418 |    "mimetype": "text/x-python",
419 |    "name": "python",
420 |    "nbconvert_exporter": "python",
421 |    "pygments_lexer": "ipython3",
422 |    "version": "3.6.9"
423 |   }
424 |  },
425 |  "nbformat": 4,
426 |  "nbformat_minor": 2
427 | }
428 | 


--------------------------------------------------------------------------------