add command line parser.

Author: lcy-seso

generate_chinese_poetry/generate.py

@@ -1,11 +1,9 @@
-#!/usr/bin/env python
-#coding=utf-8
-
 import os
 import sys
 import gzip
 import logging
 import numpy as np
+import click
 
 import reader
 import paddle.v2 as paddle
@@ -36,19 +34,45 @@ def infer_a_batch(inferer, test_batch, beam_size, id_to_text, fout):
  fout.flush
 
 
+@click.command("generate")
+@click.option(
+ "--model_path",
+ default="",
+ help="The path of the trained model for generation.")
+@click.option(
+ "--word_dict_path", required=True, help="The path of word dictionary.")
+@click.option(
+ "--test_data_path",
+ required=True,
+ help="The path of input data for generation.")
+@click.option(
+ "--batch_size",
+ default=1,
+ help="The number of training examples in one forward pass in generation.")
+@click.option(
+ "--beam_size", default=5, help="The beam expansion in beam search.")
+@click.option(
+ "--save_file",
+ required=True,
+ help="The file path to save the generated results.")
+@click.option(
+ "--use_gpu", default=False, help="Whether to use GPU in generation.")
 def generate(model_path, word_dict_path, test_data_path, batch_size, beam_size,
  save_file, use_gpu):
- assert os.path.exists(model_path), "trained model does not exist."
+ assert os.path.exists(model_path), "The given model does not exist."
+ assert os.path.exists(test_data_path), "The given test data does not exist."
 
- paddle.init(use_gpu=use_gpu, trainer_count=1)
  with gzip.open(model_path, "r") as f:
  parameters = paddle.parameters.Parameters.from_tar(f)
 
  id_to_text = {}
+ assert os.path.exists(
+ word_dict_path), "The given word dictionary path does not exist."
  with open(word_dict_path, "r") as f:
  for i, line in enumerate(f):
  id_to_text[i] = line.strip().split("\t")[0]
 
+ paddle.init(use_gpu=use_gpu, trainer_count=1)
  beam_gen = encoder_decoder_network(
  word_count=len(id_to_text),
  emb_dim=512,
@@ -78,11 +102,4 @@ def generate(model_path, word_dict_path, test_data_path, batch_size, beam_size,
 
 
 if __name__ == "__main__":
- generate(
- model_path="models/pass_00025.tar.gz",
- word_dict_path="data/word_dict.txt",
- test_data_path="data/input.txt",
- save_file="gen_result.txt",
- batch_size=4,
- beam_size=5,
- use_gpu=True)
+ generate()

generate_chinese_poetry/network_conf.py

@@ -1,6 +1,3 @@
-#!/usr/bin/env python
-#coding=utf-8
-
 import paddle.v2 as paddle
 from paddle.v2.layer import parse_network
 

generate_chinese_poetry/reader.py

@@ -1,6 +1,3 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
 from utils import load_dict
 
 

generate_chinese_poetry/train.py

@@ -1,9 +1,7 @@
-#!/usr/bin/env python
-#coding=utf-8
-
-import gzip
 import os
+import gzip
 import logging
+import click
 
 import paddle.v2 as paddle
 import reader
@@ -24,24 +22,59 @@ def load_initial_model(model_path, parameters):
  parameters.init_from_tar(f)
 
 
-def main(num_passes,
- batch_size,
- use_gpu,
- trainer_count,
- save_dir_path,
- encoder_depth,
- decoder_depth,
- word_dict_path,
- train_data_path,
- init_model_path=""):
+@click.command("train")
+@click.option(
+ "--num_passes", default=10, help="Number of passes for the training task.")
+@click.option(
+ "--batch_size",
+ default=16,
+ help="The number of training examples in one forward/backward pass.")
+@click.option(
+ "--use_gpu", default=False, help="Whether to use gpu to train the model.")
+@click.option(
+ "--trainer_count", default=1, help="The thread number used in training.")
+@click.option(
+ "--save_dir_path",
+ default="models",
+ help="The path to saved the trained models.")
+@click.option(
+ "--encoder_depth",
+ default=3,
+ help="The number of stacked LSTM layers in encoder.")
+@click.option(
+ "--decoder_depth",
+ default=3,
+ help="The number of stacked LSTM layers in encoder.")
+@click.option(
+ "--train_data_path", required=True, help="The path of trainning data.")
+@click.option(
+ "--word_dict_path", required=True, help="The path of word dictionary.")
+@click.option(
+ "--init_model_path",
+ default="",
+ help=("The path of a trained model used to initialized all "
+ "the model parameters."))
+def train(num_passes,
+ batch_size,
+ use_gpu,
+ trainer_count,
+ save_dir_path,
+ encoder_depth,
+ decoder_depth,
+ train_data_path,
+ word_dict_path,
+ init_model_path=""):
  if not os.path.exists(save_dir_path):
  os.mkdir(save_dir_path)
+ assert os.path.exists(
+ word_dict_path), "The given word dictionary does not exist."
+ assert os.path.exists(
+ train_data_path), "The given training data does not exist."
 
  # initialize PaddlePaddle
- paddle.init(use_gpu=use_gpu, trainer_count=trainer_count, parallel_nn=1)
+ paddle.init(use_gpu=use_gpu, trainer_count=trainer_count)
 
  # define optimization method and the trainer instance
- # optimizer = paddle.optimizer.Adam(
  optimizer = paddle.optimizer.AdaDelta(
  learning_rate=1e-3,
  gradient_clipping_threshold=25.0,
@@ -74,7 +107,7 @@ def main(num_passes,
  # define the event_handler callback
  def event_handler(event):
  if isinstance(event, paddle.event.EndIteration):
- if (not event.batch_id % 2000) and event.batch_id:
+ if (not event.batch_id % 1000) and event.batch_id:
  save_path = os.path.join(save_dir_path,
  "pass_%05d_batch_%05d.tar.gz" %
  (event.pass_id, event.batch_id))
@@ -94,15 +127,5 @@ def event_handler(event):
  reader=train_reader, event_handler=event_handler, num_passes=num_passes)
 
 
-if __name__ == '__main__':
- main(
- num_passes=500,
- batch_size=4 * 500,
- use_gpu=True,
- trainer_count=4,
- encoder_depth=3,
- decoder_depth=3,
- save_dir_path="models",
- word_dict_path="data/word_dict.txt",
- train_data_path="data/song.poet.txt",
- init_model_path="")
+if __name__ == "__main__":
+ train()

generate_chinese_poetry/utils.py

@@ -1,6 +1,3 @@
-#!/usr/bin/env python
-#coding=utf-8
-
 import os
 import sys
 import re
@@ -30,7 +27,3 @@ def find_optiaml_pass(log_file):
  cost_info.iteritems(),
  key=lambda x: sum(x[1]) / (len(x[1])),
  reverse=False)[0][0])
-
-
-if __name__ == '__main__':
- find_optiaml_pass('trained_models/models_first_round/train.log')