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Change-Id: I5b1a5ed7cde4d65fb499e038c530fec7acb4f2b7

paddle/fluid/train/custom_trainer/feed/scripts/create_programs.py

-#!/usr/bin/env python
-#-*- coding:utf-8 -*-
-
-from __future__ import print_function, division
-import os
-import sys
-import paddle
-from paddle import fluid
-import yaml
-
-def print_help(this_name):
-    """Print help
-    """
-    dirname = os.path.dirname(this_name)
-    print("Usage: {} <network building filename> [model_dir]\n".format(this_name))
-    print("    example: {} {}".format(this_name, os.path.join(dirname, 'example.py')))
-
-def inference(filename):
-    """Build inference network(without loss and optimizer)
-    Args:
-        filename: path of file which defined real inference function
-    Returns:
-        list<Variable>: inputs
-        and
-        Variable: ctr_output
-    """
-    with open(filename, 'r') as f:
-        code = f.read()
-    compiled = compile(code, filename, 'exec')
-    exec(compiled)
-    return inference()
-
-def main(argv):
-    """Create programs
-    Args:
-        argv: arg list, length should be 2
-    """
-    if len(argv) < 2 or not os.path.exists(argv[1]):
-        print_help(argv[0])
-        exit(1)
-    network_build_file = argv[1]
-
-    if len(argv) >= 2:
-        model_dir = argv[2]
-    else:
-        model_dir = './model'
-
-    main_program = fluid.Program()
-    startup_program = fluid.Program()
-    with fluid.program_guard(main_program, startup_program):
-        inputs, ctr_output = inference(network_build_file)
-
-        test_program = main_program.clone(for_test=True)
-
-        label_target = fluid.layers.data(name='label', shape=[1], dtype='float32')
-
-        loss = fluid.layers.square_error_cost(input=ctr_output, label=label_target)
-        loss = fluid.layers.mean(loss, name='loss')
-        
-        optimizer = fluid.optimizer.SGD(learning_rate=1.0)
-        params_grads = optimizer.backward(loss)
-
-    if not os.path.exists(model_dir):
-        os.mkdir(model_dir)
-
-    programs = {
-        'startup_program': startup_program,
-        'main_program': main_program,
-        'test_program': test_program,
-    }
-    for save_path, program in programs.items():
-        with open(os.path.join(model_dir, save_path), 'w') as f:
-            f.write(program.desc.serialize_to_string())
-
-    model_desc_path = os.path.join(model_dir, 'model.yaml')
-    model_desc = dict()
-    model_desc['inputs'] = {var.name: var.shape for var in inputs}
-    model_desc['loss_name'] = loss.name
-    model_desc['label_name'] = label_target.name
-    model_desc['ctr_output_name'] = ctr_output.name
-    
-    with open(model_desc_path, 'w') as f:
-        yaml.safe_dump(model_desc, f, encoding='utf-8', allow_unicode=True)
-
-
-if __name__ == "__main__":
-    main(sys.argv)
+#!/usr/bin/env python
+#-*- coding:utf-8 -*-
+
+from __future__ import print_function, division
+import os
+import sys
+import paddle
+from paddle import fluid
+import yaml
+
+def print_help(this_name):
+    """Print help
+    """
+    dirname = os.path.dirname(this_name)
+    print("Usage: {} <network building filename> [model_dir]\n".format(this_name))
+    print("    example: {} {}".format(this_name, os.path.join(dirname, 'example.py')))
+
+def inference(filename):
+    """Build inference network(without loss and optimizer)
+    Args:
+        filename: path of file which defined real inference function
+    Returns:
+        list<Variable>: inputs
+        and
+        Variable: ctr_output
+    """
+    with open(filename, 'r') as f:
+        code = f.read()
+    compiled = compile(code, filename, 'exec')
+    exec(compiled)
+    return inference()
+
+def main(argv):
+    """Create programs
+    Args:
+        argv: arg list, length should be 2
+    """
+    if len(argv) < 2 or not os.path.exists(argv[1]):
+        print_help(argv[0])
+        exit(1)
+    network_build_file = argv[1]
+
+    if len(argv) >= 2:
+        model_dir = argv[2]
+    else:
+        model_dir = './model'
+
+    main_program = fluid.Program()
+    startup_program = fluid.Program()
+    with fluid.program_guard(main_program, startup_program):
+        inputs, ctr_output = inference(network_build_file)
+
+        test_program = main_program.clone(for_test=True)
+
+        label_target = fluid.layers.data(name='label', shape=[1], dtype='float32')
+
+        loss = fluid.layers.square_error_cost(input=ctr_output, label=label_target)
+        loss = fluid.layers.mean(loss, name='loss')
+        
+        optimizer = fluid.optimizer.SGD(learning_rate=1.0)
+        params_grads = optimizer.backward(loss)
+
+    if not os.path.exists(model_dir):
+        os.mkdir(model_dir)
+
+    programs = {
+        'startup_program': startup_program,
+        'main_program': main_program,
+        'test_program': test_program,
+    }
+    for save_path, program in programs.items():
+        with open(os.path.join(model_dir, save_path), 'w') as f:
+            f.write(program.desc.serialize_to_string())
+
+    model_desc_path = os.path.join(model_dir, 'model.yaml')
+    model_desc = dict()
+    model_desc['inputs'] = {var.name: var.shape for var in inputs}
+    model_desc['loss_name'] = loss.name
+    model_desc['label_name'] = label_target.name
+    model_desc['ctr_output_name'] = ctr_output.name
+    
+    with open(model_desc_path, 'w') as f:
+        yaml.safe_dump(model_desc, f, encoding='utf-8', allow_unicode=True)
+
+
+if __name__ == "__main__":
+    main(sys.argv)