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[debug]debug with profiler #80

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64 changes: 33 additions & 31 deletions fluid/mnist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,37 +141,39 @@ def run_benchmark(model, args):
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=args.batch_size)

for pass_id in range(args.pass_num):
accuracy.reset(exe)
pass_start = time.time()
for batch_id, data in enumerate(train_reader()):
img_data = np.array(
map(lambda x: x[0].reshape([1, 28, 28]), data)).astype(DTYPE)
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([len(y_data), 1])

start = time.time()
outs = exe.run(
fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics
) # The accuracy is the accumulation of batches, but not the current batch.

end = time.time()
loss = np.array(outs[0])
acc = np.array(outs[1])
print("pass=%d, batch=%d, loss=%f, error=%f, elapse=%f" %
(pass_id, batch_id, loss, 1 - acc, (end - start) / 1000))

pass_end = time.time()

train_avg_acc = accuracy.eval(exe)
test_avg_acc = eval_test(exe, accuracy, inference_program)

print("pass=%d, train_avg_acc=%f, test_avg_acc=%f, elapse=%f" %
(pass_id, train_avg_acc, test_avg_acc,
(pass_end - pass_start) / 1000))
with profiler.profiler(args.device, 'total') as prof:
for pass_id in range(args.pass_num):
accuracy.reset(exe)
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@ranqiu92 ranqiu92 Mar 12, 2018

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fluid.evaluator 下无 Accuracy，请针对本文件所有相应地方做相应修改。
以及前面的 paddle.v2.fluid -> paddle.fluid

pass_start = time.time()
for batch_id, data in enumerate(train_reader()):
img_data = np.array(
map(lambda x: x[0].reshape([1, 28, 28]), data)).astype(
DTYPE)
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([len(y_data), 1])

start = time.time()
outs = exe.run(
fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics
) # The accuracy is the accumulation of batches, but not the current batch.

end = time.time()
loss = np.array(outs[0])
acc = np.array(outs[1])
print("pass=%d, batch=%d, loss=%f, error=%f, elapse=%f" %
(pass_id, batch_id, loss, 1 - acc, (end - start) / 1000))
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计时建议带上单位，以及 这里是要以 ks 为单位吗？
（下同）


pass_end = time.time()

train_avg_acc = accuracy.eval(exe)
test_avg_acc = eval_test(exe, accuracy, inference_program)

print("pass=%d, train_avg_acc=%f, test_avg_acc=%f, elapse=%f" %
(pass_id, train_avg_acc, test_avg_acc,
(pass_end - pass_start) / 1000))


if __name__ == '__main__':
Expand Down
11 changes: 6 additions & 5 deletions fluid/resnet50.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,7 +190,7 @@ def run_benchmark(model, args):
opts = optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
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fluid.evaluator 下无 Accuracy，请针对本文件所有相应地方做相应修改。
以及前面的 paddle.v2.fluid -> paddle.fluid


fluid.memory_optimize(fluid.default_main_program())
# fluid.memory_optimize(fluid.default_main_program())

train_reader = paddle.batch(
paddle.reader.shuffle(
Expand Down Expand Up @@ -230,10 +230,11 @@ def run_benchmark(model, args):
data)).astype('float32')
label = np.array(map(lambda x: x[1], data)).astype('int64')
label = label.reshape([-1, 1])
loss, acc = exe.run(fluid.default_main_program(),
feed={'data': image,
'label': label},
fetch_list=[avg_cost] + accuracy.metrics)
with profiler.profiler(args.device, 'total') as prof:
loss, acc = exe.run(fluid.default_main_program(),
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)
Expand Down
39 changes: 22 additions & 17 deletions fluid/vgg16.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,8 @@
import argparse
import functools

import paddle.v2.fluid.profiler as profiler
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paddle.fluid ，前几行也须进行相应修改。


parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
'--batch_size', type=int, default=128, help="Batch size for training.")
Expand All @@ -18,7 +20,7 @@
type=float,
default=1e-3,
help="Learning rate for training.")
parser.add_argument('--num_passes', type=int, default=50, help="No. of passes.")
parser.add_argument('--pass_num', type=int, default=50, help="No. of passes.")
parser.add_argument(
'--device',
type=str,
Expand Down Expand Up @@ -52,6 +54,7 @@ def conv_block(input, num_filter, groups, dropouts):
conv_with_batchnorm=True,
conv_batchnorm_drop_rate=dropouts,
pool_type='max')
# use_cudnn=False)

conv1 = conv_block(input, 64, 2, [0.3, 0])
conv2 = conv_block(conv1, 128, 2, [0.4, 0])
Expand Down Expand Up @@ -94,23 +97,24 @@ def main():
# Evaluator
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
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fluid.evaluator 下无 Accuracy，请针对本文件所有相应地方做相应修改。


# inference program
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
test_target = accuracy.metrics + accuracy.states
inference_program = fluid.io.get_inference_program(test_target)
# # inference program
# inference_program = fluid.default_main_program().clone()
# with fluid.program_guard(inference_program):
# test_target = accuracy.metrics + accuracy.states
# inference_program = fluid.io.get_inference_program(test_target)

# Optimization
optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
opts = optimizer.minimize(avg_cost)

fluid.memory_optimize(fluid.default_main_program())
# fluid.memory_optimize(fluid.default_main_program())

# Initialize executor
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
exe = fluid.Executor(place)

# Parameter initialization
# with profiler.profiler(args.device, 'total') as prof:
exe.run(fluid.default_startup_program())

# data reader
Expand Down Expand Up @@ -141,7 +145,7 @@ def test(exe):
return accuracy.eval(exe)

iters = 0
for pass_id in range(args.num_passes):
for pass_id in range(args.pass_num):
# train
start_time = time.time()
num_samples = 0
Expand All @@ -152,10 +156,11 @@ def test(exe):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([-1, 1])

loss, acc = exe.run(fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics)
with profiler.profiler(args.device, 'total') as prof:
loss, acc = exe.run(fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics)
iters += 1
num_samples += len(data)
print(
Expand All @@ -165,11 +170,11 @@ def test(exe):

pass_elapsed = time.time() - start_time
pass_train_acc = accuracy.eval(exe)
pass_test_acc = test(exe)
print(
"Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
% (pass_id, num_samples / pass_elapsed, pass_train_acc,
pass_test_acc))
# pass_test_acc = test(exe)
# print(
# "Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
# % (pass_id, num_samples / pass_elapsed, pass_train_acc,
# pass_test_acc))


def print_arguments():
Expand Down