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change duration compute methods #49

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Merged
dzhwinter merged 8 commits into from
Jan 19, 2018
Merged

change duration compute methods #49

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26f5b2d
change duration compute methods
Jan 11, 2018
78cce58
modify time calculation method and print mean loss,acc of every pass
Jan 15, 2018
3713d12
modify time calculation method and print mean loss,acc of every pass
Jan 15, 2018
8d5b1aa
delete redundant line and change judge iter
Jan 15, 2018
6e9b36b
Merge branch 'master' of github.com:dzhwinter/benchmark
Jan 15, 2018
0157ed8
Merge branch 'master' of github.com:dzhwinter/benchmark
Jan 17, 2018
c838e11
modify GPUPlace to CUDAPlace
Jan 17, 2018
ba60b28
move line 173 to line 212
Jan 17, 2018
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25 changes: 15 additions & 10 deletions fluid/resnet50.py
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Original file line number Diff line number Diff line change
Expand Up @@ -167,7 +167,6 @@ def run_benchmark(model, args):
if args.use_cprof:
pr = cProfile.Profile()
pr.enable()
start_time = time.time()

if args.data_set == "cifar10":
class_dim = 10
Expand Down Expand Up @@ -209,14 +208,18 @@ def run_benchmark(model, args):
label = np.array(map(lambda x: x[1], data)).astype('int64')
label = label.reshape([-1, 1])

iter = 0
im_num = 0
start_time = time.time()
for pass_id in range(args.pass_num):
every_pass_loss = []
every_pass_acc = []
accuracy.reset(exe)
if iter == args.iterations:
break
iter = 0
for batch_id, data in enumerate(train_reader()):
if iter == args.skip_batch_num:
if iter < args.skip_batch_num:
iter += 1
continue
if pass_id == 0 and iter == args.skip_batch_num:
start_time = time.time()
if iter == args.iterations:
break
Expand All @@ -229,16 +232,18 @@ def run_benchmark(model, args):
feed={'data': image,
'label': label},
fetch_list=[avg_cost] + accuracy.metrics)
every_pass_acc.append(acc)
every_pass_loss.append(loss)
pass_acc = accuracy.eval(exe)
print("Iter: %d, loss: %s, acc: %s, pass_acc: %s" %
(iter, str(loss), str(acc), str(pass_acc)))
print("Pass: %d, Iter: %d, loss: %s, acc: %s, pass_acc: %s" %
(pass_id, iter, str(loss), str(acc), str(pass_acc)))
iter += 1
im_num += label.shape[0]

print("Pass: %d, Loss: %f, Accuray: %f\n" %
(pass_id, np.mean(every_pass_loss), np.mean(every_pass_acc)))
duration = time.time() - start_time
im_num = im_num - args.skip_batch_num * args.batch_size
examples_per_sec = im_num / duration
sec_per_batch = duration / (iter - args.skip_batch_num)
sec_per_batch = duration / (iter - args.skip_batch_num) / args.pass_num

print('\nTotal examples: %d, total time: %.5f' % (im_num, duration))
print('%.5f examples/sec, %.5f sec/batch \n' %
Expand Down