add se resnet 152 profile script #84
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chengduoZH
reviewed
Mar 7, 2018
fluid/se_resnet_152/train.py
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| def parse_args(): | ||
| parser = argparse.ArgumentParser('resnet152 parallel profile.') |
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resnet152 ->SE-Resnet-152
fluid/se_resnet_152/train.py
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| feed_dict = feeder.feed(data) | ||
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| for pass_id in range(1): | ||
| with profiler.profiler('All', 'total', '/tmp/profile') as prof: |
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Exchange line 223 and line 224?
fluid/se_resnet_152/train.py
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| exe.run(fluid.default_main_program(), | ||
| feed=feed_dict, | ||
| fetch_list=[], | ||
| use_program_cache=True) |
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I prefer writing the line229~line238 together.
exe.run(fluid.default_main_program(),
feed=feeder.feed(train_reader_iter.next()) if args.use_python_reader else feed_dict,
fetch_list=[],
use_program_cache=True)
fluid/se_resnet_152/train.py
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| use_program_cache=True) | ||
| train_stop = time.time() | ||
| if step_id > args.skip_first_steps: | ||
| train_time += train_stop - train_start |
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Maybe the prof also needs warm up.
This was referenced Mar 8, 2018
chengduoZH
reviewed
Mar 28, 2018
fluid/SE-ResNeXt-152/train.py
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| regularization=fluid.regularizer.L2Decay(1e-4)) | ||
| opts = optimizer.minimize(avg_cost) | ||
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| # fluid.memory_optimize(fluid.default_main_program()) |
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Why do you remove this?
For big batch_size, such as 12, the model will be oom, if without memory_optimize.
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add back, I think it's a option for user to test.
chengduoZH
reviewed
Mar 28, 2018
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| def time_stamp(): | ||
| return int(round(time.time() * 1000)) |
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What is time_stamp used to do?
| regularization=fluid.regularizer.L2Decay(1e-4)) | ||
| opts = optimizer.minimize(avg_cost) | ||
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| fluid.memory_optimize(fluid.default_main_program()) |
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Maybe it is better that adding an argument, such as use_memopt.
chengduoZH
approved these changes
Mar 28, 2018
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