Merge changes Iecc2518d,I69ec0b9d,Ie5622d75

* changes:
  loss_function(*output)
  fix
  update_create_programs

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

@@ -12,87 +12,140 @@ 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')))
+    print('Usage: {} <network building filename> [model_dir]\n'.format(this_name))
+    print('    example: {} {}'.format(this_name, os.path.join(dirname, 'example.py')))
 
+class ModelBuilder:
+    """
+    Attributes:
+        _save_path: Save path of programs
 
+        def _inference():
+            Build inference network(without loss and optimizer)
+            **This function is declared in the network_desc_path file, and will be set in initialize()**
 
-def inference_warpper(filename):
-    """Build inference network(without loss and optimizer)
-    Args:
-        filename: path of file which defined real inference function
             Returns:
                 list<Variable>: inputs
                 and
                 list<Variable>: outputs
+            pass
+
+        def _loss_function(*outputs):
+            **This function is declared in the network_desc_path file, and will be set in initialize()**
+            Args:
+                *outputs: the second result of inference()
+
+            Returns:
+                Variable: loss
+                and
+                list<Variable>: labels
+            pass
     """
 
-    with open(filename, 'r') as f:
-        code = f.read()
-    compiled = compile(code, filename, 'exec')
+    def initialize(self, network_desc_path, save_path=None):
+        """compile the network description module
+        Args:
+            network_desc_path: path
+            save_path: model save path, default is ./model/<network_desc_path without .py>/
+
+        Returns:
+            bool: True if succeed else False
+        """
+        if not isinstance(network_desc_path, str):
+            print('network_desc_path must be str')
+            return False
+
+        if not network_desc_path.endswith('.py'):
+            print('network_desc_path must be end with .py')
+            return False
+
+        if not os.path.exists(network_desc_path):
+            print('file not exists:', network_desc_path)
+            return False
 
         scope = dict()
+        with open(network_desc_path, 'r') as f:
+            code = f.read()
+            compiled = compile(code, network_desc_path, 'exec')
             exec(compiled, scope)
-    return scope['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 not 'inference' in scope:
+            print('inference not defined')
+            return False
 
-    if len(argv) > 2:
-        model_dir = argv[2]
-    else:
-        model_dir = './model'
+        if not 'loss_function' in scope:
+            print('loss_function not defined')
+            return False
+
+        if save_path is None:
+            # example /a/b/c.d -> ./model/c
+            save_path = os.path.join('./model', os.path.splitext(os.path.split(network_desc_path)[1])[0])
+            print('save in the default path:', save_path)
+
+        self._save_path = save_path
 
+        self._inference = scope['inference']
+        self._loss_function = scope['loss_function']
+
+        return True
+
+    def build_and_save(self):
+        """Build programs and save to _save_path
+        """
         main_program = fluid.Program()
         startup_program = fluid.Program()
         with fluid.program_guard(main_program, startup_program):
-        inputs, outputs = inference_warpper(network_build_file)
-
+            inputs, outputs = self._inference()
             test_program = main_program.clone(for_test=True)
+            loss, labels = self._loss_function(*outputs)
 
-        labels = list()
-        losses = list()
-        for output in outputs:
-            label = fluid.layers.data(name='label_' + output.name, shape=output.shape, dtype='float32')
-            loss = fluid.layers.square_error_cost(input=output, label=label)
-            loss = fluid.layers.mean(loss, name='loss_' + output.name)
-
-            labels.append(label)
-            losses.append(loss)
-
-        loss_all = fluid.layers.sum(losses)
             optimizer = fluid.optimizer.SGD(learning_rate=1.0)
-        params_grads = optimizer.backward(loss_all)
+            params_grads = optimizer.backward(loss)
 
-    if not os.path.exists(model_dir):
-        os.mkdir(model_dir)
+        if not os.path.exists(self._save_path):
+            os.makedirs(self._save_path)
 
         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:
+        for name, program in programs.items():
+            with open(os.path.join(self._save_path, name), 'w') as f:
                 f.write(program.desc.serialize_to_string())
 
-    model_desc_path = os.path.join(model_dir, 'model.yaml')
+        model_desc_path = os.path.join(self._save_path, 'model.yaml')
         model_desc = {
             'inputs': [{"name": var.name, "shape": var.shape} for var in inputs],
-        'outputs': [{"name": var.name, "shape": var.shape, "label_name": label.name, "loss_name": loss.name} for var, label, loss in zip(outputs, labels, losses)],
-        'loss_all': loss_all.name,
+            'outputs': [{"name": var.name, "shape": var.shape} for var in outputs],
+            'labels': [{"name": var.name, "shape": var.shape} for var in labels],
+            'loss': loss.name,
         }
 
         with open(model_desc_path, 'w') as f:
             yaml.safe_dump(model_desc, f, encoding='utf-8', allow_unicode=True)
 
 
+def main(argv):
+    """Create programs
+    Args:
+        argv: arg list, length should be 2
+    """
+    if len(argv) < 2:
+        print_help(argv[0])
+        exit(1)
+    network_desc_path = argv[1]
+
+    if len(argv) > 2:
+        save_path = argv[2]
+    else:
+        save_path = None
+
+    builder = ModelBuilder()
+    if not builder.initialize(network_desc_path, save_path):
+        print_help(argv[0])
+        exit(1)
+    builder.build_and_save()
+
 if __name__ == "__main__":
     main(sys.argv)

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

@@ -21,13 +21,31 @@ def inference():
     cvm_input = fluid.layers.data(name='cvm_input', shape=[4488], dtype='float32')
 
     net = cvm_input
-    net = fluid.layers.fc(net, 512, act='relu')
-    net = fluid.layers.fc(net, 256, act='relu')
-    net = fluid.layers.fc(net, 256, act='relu')
-    net = fluid.layers.fc(net, 128, act='relu')
-    net = fluid.layers.fc(net, 128, act='relu')
-    net = fluid.layers.fc(net, 128, act='relu')
-    net = fluid.layers.fc(net, 128, act='relu')
+    net = fluid.layers.fc(net, 512, act='relu', name='fc_1')
+    net = fluid.layers.fc(net, 256, act='relu', name='fc_2')
+    net = fluid.layers.fc(net, 256, act='relu', name='fc_3')
+    net = fluid.layers.fc(net, 128, act='relu', name='fc_4')
+    net = fluid.layers.fc(net, 128, act='relu', name='fc_5')
+    net = fluid.layers.fc(net, 128, act='relu', name='fc_6')
+    net = fluid.layers.fc(net, 128, act='relu', name='fc_7')
 
     ctr_output = fluid.layers.fc(net, 1, act='sigmoid', name='ctr')
     return [cvm_input], [ctr_output]
+
+def loss_function(ctr_output):
+    """
+    Args:
+        *outputs: the second result of inference()
+
+    Returns:
+        Variable: loss
+        and
+        list<Variable>: labels
+    """
+    # TODO: calc loss here
+
+    label = fluid.layers.data(name='label_ctr', shape=ctr_output.shape, dtype='float32')
+    loss = fluid.layers.square_error_cost(input=ctr_output, label=label)
+    loss = fluid.layers.mean(loss, name='loss_ctr')
+
+    return loss, [label]

paddle/fluid/train/custom_trainer/feed/unit_test/test_create_programs.cc

 #include <iostream>
 #include <fstream>
 #include <gtest/gtest.h>
+#include <cstdlib>
+#include <cmath>
 
 #include "paddle/fluid/train/custom_trainer/feed/executor/executor.h"
 #include "paddle/fluid/framework/tensor_util.h"
@@ -50,6 +52,11 @@ public:
         context_ptr = nullptr;
     }
 
+    float random(float min_x = 0.0, float max_x = 1.0) {
+        float r = static_cast<float>(rand()) / RAND_MAX;
+        return min_x + (max_x - min_x) * r;
+    }
+
     std::shared_ptr<TrainerContext> context_ptr;
 };
 
@@ -62,36 +69,47 @@ TEST_F(CreateProgramsTest, example_network) {
     ASSERT_EQ(0, executor->initialize(config, context_ptr));
 
     std::string input_name = "cvm_input";
+    std::string loss_name = "loss_ctr";
+    std::string label_name = "label_ctr";
+
+    // loss
+    ASSERT_TRUE(model_desc["loss"]);
+    ASSERT_EQ(loss_name, model_desc["loss"].as<std::string>());
+
+    // input
     ASSERT_TRUE(model_desc["inputs"]);
     ASSERT_EQ(1, model_desc["inputs"].size());
     ASSERT_TRUE(model_desc["inputs"][0]["name"]);
     ASSERT_TRUE(model_desc["inputs"][0]["shape"]);
     ASSERT_EQ(input_name, model_desc["inputs"][0]["name"].as<std::string>());
-    std::vector<int> input_shape = model_desc["inputs"][0]["shape"].as<std::vector<int>>(std::vector<int>());
+    auto input_shape = model_desc["inputs"][0]["shape"].as<std::vector<int>>(std::vector<int>());
     ASSERT_EQ(2, input_shape.size());
     ASSERT_EQ(-1, input_shape[0]);
     ASSERT_EQ(4488, input_shape[1]);
 
-    ASSERT_TRUE(model_desc["loss_all"]);
-    auto loss_all_name = model_desc["loss_all"].as<std::string>();
+    // label
+    ASSERT_TRUE(model_desc["labels"]);
+    ASSERT_EQ(1, model_desc["labels"].size());
+    ASSERT_TRUE(model_desc["labels"][0]["name"]);
+    ASSERT_TRUE(model_desc["labels"][0]["shape"]);
+    ASSERT_EQ(label_name, model_desc["labels"][0]["name"].as<std::string>());
+    auto label_shape = model_desc["labels"][0]["shape"].as<std::vector<int>>(std::vector<int>());
+    ASSERT_EQ(2, label_shape.size());
+    ASSERT_EQ(-1, label_shape[0]);
+    ASSERT_EQ(1, label_shape[1]);
 
     ASSERT_TRUE(model_desc["outputs"]);
     ASSERT_EQ(1, model_desc["outputs"].size());
     ASSERT_TRUE(model_desc["outputs"][0]["name"]);
     ASSERT_TRUE(model_desc["outputs"][0]["shape"]);
-    ASSERT_TRUE(model_desc["outputs"][0]["label_name"]);
-    ASSERT_TRUE(model_desc["outputs"][0]["loss_name"]);
-
-    auto ctr_output_label_name = model_desc["outputs"][0]["label_name"].as<std::string>();
-    auto ctr_output_loss_name = model_desc["outputs"][0]["loss_name"].as<std::string>();
-    auto ctr_output_name = model_desc["outputs"][0]["name"].as<std::string>();
-    std::vector<int> output_shape = model_desc["outputs"][0]["shape"].as<std::vector<int>>(std::vector<int>());
+    auto output_name = model_desc["outputs"][0]["name"].as<std::string>();
+    auto output_shape = model_desc["outputs"][0]["shape"].as<std::vector<int>>(std::vector<int>());
     ASSERT_EQ(2, output_shape.size());
     ASSERT_EQ(-1, output_shape[0]);
     ASSERT_EQ(1, output_shape[1]);
 
     auto input_var = executor->mutable_var<::paddle::framework::LoDTensor>(input_name);
-    auto label_var = executor->mutable_var<::paddle::framework::LoDTensor>(ctr_output_label_name);
+    auto label_var = executor->mutable_var<::paddle::framework::LoDTensor>(label_name);
     ASSERT_NE(nullptr, input_var);
     ASSERT_NE(nullptr, label_var);
 
@@ -99,28 +117,26 @@ TEST_F(CreateProgramsTest, example_network) {
     auto input_data = input_var->mutable_data<float>(context_ptr->cpu_place);
     ASSERT_NE(nullptr, input_data);
     for (int i = 0; i < input_shape[1]; ++i) {
-        input_data[i] = 0.1;
+        input_data[i] = random();
     }
 
     label_var->Resize({1, 1});
     auto label_data = label_var->mutable_data<float>(context_ptr->cpu_place);
     ASSERT_NE(nullptr, label_data);
-    label_data[0] = 0.5;
+    label_data[0] = random();
 
     ASSERT_EQ(0, executor->run());
 
-    auto loss_var = executor->var<::paddle::framework::LoDTensor>(ctr_output_loss_name);
+    auto loss_var = executor->var<::paddle::framework::LoDTensor>(loss_name);
     auto loss = loss_var.data<float>()[0];
 
-    auto loss_all_var = executor->var<::paddle::framework::LoDTensor>(loss_all_name);
-    auto loss_all = loss_all_var.data<float>()[0];
-
-    auto ctr_output_var = executor->var<::paddle::framework::LoDTensor>(ctr_output_name);
-    auto ctr_output = ctr_output_var.data<float>()[0];
+    auto output_var = executor->var<::paddle::framework::LoDTensor>(output_name);
+    auto output = output_var.data<float>()[0];
 
-    std::cout << "loss: " << loss << std::endl;
-    std::cout << "ctr_output: " << ctr_output << std::endl;
-    ASSERT_NEAR(loss, loss_all, 1e-9);
+    VLOG(3) << "loss: " << loss << std::endl;
+    VLOG(3) << "label: " << label_data[0] << std::endl;
+    VLOG(3) << "output: " << output << std::endl;
+    ASSERT_NEAR(loss, pow(output - label_data[0], 2), 1e-8);
 }
 
 }  // namespace feed