add lenet network demo, improve mnist demo.

python/paddle_fl/mpc/examples/lenet_with_mnist/train_lenet.py

@@ -44,23 +44,37 @@ epoch_num = 5
 x = pfl_mpc.data(name='x', shape=[BATCH_SIZE, 1, 28, 28], dtype='int64')
 y = pfl_mpc.data(name='y', shape=[BATCH_SIZE, 10], dtype='int64')
 
-# lenet-3 network
-#conv = pfl_mpc.layers.conv2d(input=x, num_filters=16, filter_size=5, act='relu')
-#pool = pfl_mpc.layers.pool2d(input=conv, pool_size=2, pool_stride=2)
-#fc_1 = pfl_mpc.layers.fc(input=pool, size=100, act='relu')
-
-# lenet-5 network
-conv_1 = pfl_mpc.layers.conv2d(input=x, num_filters=16, filter_size=5, act='relu')
-pool_1 = pfl_mpc.layers.pool2d(input=conv_1, pool_size=2, pool_stride=2)
-conv_2 = pfl_mpc.layers.conv2d(input=pool_1, num_filters=16, filter_size=5, act='relu')
-pool_2 = pfl_mpc.layers.pool2d(input=conv_2, pool_size=2, pool_stride=2)
-fc_1 = pfl_mpc.layers.fc(input=pool_2, size=100, act='relu')
-
-fc_out = pfl_mpc.layers.fc(input=fc_1, size=10)
-cost, softmax = pfl_mpc.layers.softmax_with_cross_entropy(logits=fc_out,
-                                                          label=y,
-                                                          soft_label=True,
-                                                          return_softmax=True)
+
+class Model(object):
+    def __int__(self):
+        pass
+
+    def lenet3(self):
+        conv = pfl_mpc.layers.conv2d(input=x, num_filters=16, filter_size=5, act='relu')
+        pool = pfl_mpc.layers.pool2d(input=conv, pool_size=2, pool_stride=2)
+        fc_1 = pfl_mpc.layers.fc(input=pool, size=100, act='relu')
+        fc_out = pfl_mpc.layers.fc(input=fc_1, size=10)
+        cost, softmax = pfl_mpc.layers.softmax_with_cross_entropy(logits=fc_out,
+                                                                  label=y,
+                                                                  soft_label=True,
+                                                                  return_softmax=True)
+        return cost, softmax
+
+    def lenet5(self):
+        conv_1 = pfl_mpc.layers.conv2d(input=x, num_filters=16, filter_size=5, act='relu')
+        pool_1 = pfl_mpc.layers.pool2d(input=conv_1, pool_size=2, pool_stride=2)
+        conv_2 = pfl_mpc.layers.conv2d(input=pool_1, num_filters=16, filter_size=5, act='relu')
+        pool_2 = pfl_mpc.layers.pool2d(input=conv_2, pool_size=2, pool_stride=2)
+        fc_1 = pfl_mpc.layers.fc(input=pool_2, size=100, act='relu')
+        fc_out = pfl_mpc.layers.fc(input=fc_1, size=10)
+        cost, softmax = pfl_mpc.layers.softmax_with_cross_entropy(logits=fc_out,
+                                                                  label=y,
+                                                                  soft_label=True,
+                                                                  return_softmax=True)
+        return cost, softmax
+
+model = Model()
+cost, softmax = model.lenet5()
 
 infer_program = fluid.default_main_program().clone(for_test=False)
 

python/paddle_fl/mpc/examples/logistic_with_mnist/README.md

@@ -8,7 +8,7 @@ This document introduces how to run MNIST demo based on Paddle-MPC, which has tw
 
 #### (1). Prepare Data
 
-Generate encrypted training and testing data utilizing `generate_encrypted_data()` and `generate_encrypted_test_data()` in `process_data.py` script. Users can run the script with command `python process_data.py` to generate encrypted feature and label in given directory, e.g., `./mpc_data/`. Users can specify `class_num` (2 or 10) to determine the encrypted data is for `fc_sigmoid`(two classes) or `lenet`(10 classes) network.  Different suffix names are used for these files to indicate the ownership of different computation parties. For instance, a file named `mnist2_feature.part0` means it is a feature file of party 0.
+Generate encrypted training and testing data utilizing `generate_encrypted_data()` and `generate_encrypted_test_data()` in `process_data.py` script. Users can run the script with command `python process_data.py` to generate encrypted feature and label in given directory, e.g., `./mpc_data/`. Users can specify `class_num` (2 or 10) to determine the encrypted data is for `logisticfc_sigmoid`(two classes) or `lenet` and `logistic_fc_softmax`(10 classes) network.  Different suffix names are used for these files to indicate the ownership of different computation parties. For instance, a file named `mnist2_feature.part0` means it is a feature file of party 0.
 
 #### (2). Launch Demo with A Shell Script
 

python/paddle_fl/mpc/examples/logistic_with_mnist/README_CN.md

@@ -8,7 +8,7 @@
 
 #### 1. 准备数据
 
-使用`process_data.py`脚本中的`generate_encrypted_data()`和`generate_encrypted_test_data()`产生加密训练数据和测试数据，用户可以直接运行脚本`python process_data.py`在指定的目录下（比如`./mpc_data/`）产生加密特征和标签。用户可以通过参数`class_num`指定label的类别数目，从而产生适用于`fc_sigmoid`（二分类）和`lenet`（十分类）网络的加密数据。在指定目录下生成对应于3个计算party的feature和label的加密数据文件，以后缀名区分属于不同party的数据。比如，`mnist2_feature.part0`表示属于party0的feature数据。
+使用`process_data.py`脚本中的`generate_encrypted_data()`和`generate_encrypted_test_data()`产生加密训练数据和测试数据，用户可以直接运行脚本`python process_data.py`在指定的目录下（比如`./mpc_data/`）产生加密特征和标签。用户可以通过参数`class_num`指定label的类别数目，从而产生适用于`logistic_fc_sigmoid`（二分类）或`lenet``logistic_fc_softmax`（十分类）网络的加密数据。在指定目录下生成对应于3个计算party的feature和label的加密数据文件，以后缀名区分属于不同party的数据。比如，`mnist2_feature.part0`表示属于party0的feature数据。
 
 #### 2. 使用shell脚本启动demo
 

python/paddle_fl/mpc/examples/logistic_with_mnist/train_fc_softmax.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+MNIST CNN Demo (LeNet5)
+"""
+
+import sys
+import os
+import numpy as np
+import time
+import logging
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+import paddle_fl.mpc as pfl_mpc
+import paddle_fl.mpc.data_utils.aby3 as aby3
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+role, server, port = sys.argv[1], sys.argv[2], sys.argv[3]
+# modify host(localhost).
+pfl_mpc.init("aby3", int(role), "localhost", server, int(port))
+role = int(role)
+
+# data preprocessing
+BATCH_SIZE = 128
+epoch_num = 5
+
+x = pfl_mpc.data(name='x', shape=[BATCH_SIZE, 1, 28, 28], dtype='int64')
+y = pfl_mpc.data(name='y', shape=[BATCH_SIZE, 10], dtype='int64')
+
+fc_out = pfl_mpc.layers.fc(input=x, size=10)
+cost, softmax = pfl_mpc.layers.softmax_with_cross_entropy(logits=fc_out,
+                                                          label=y,
+                                                          soft_label=True,
+                                                          return_softmax=True)
+
+infer_program = fluid.default_main_program().clone(for_test=False)
+
+avg_loss = pfl_mpc.layers.mean(cost)
+optimizer = pfl_mpc.optimizer.SGD(learning_rate=0.1)
+optimizer.minimize(avg_loss)
+
+# prepare train and test reader
+mpc_data_dir = "./mpc_data/"
+if not os.path.exists(mpc_data_dir):
+    raise ValueError("mpc_data_dir is not found. Please prepare encrypted data.")
+
+# train_reader
+feature_reader = aby3.load_aby3_shares(mpc_data_dir + "mnist10_feature", id=role, shape=(1, 28, 28))
+label_reader = aby3.load_aby3_shares(mpc_data_dir + "mnist10_label", id=role, shape=(10,))
+batch_feature = aby3.batch(feature_reader, BATCH_SIZE, drop_last=True)
+batch_label = aby3.batch(label_reader, BATCH_SIZE, drop_last=True)
+
+# test_reader
+test_feature_reader = aby3.load_aby3_shares(mpc_data_dir + "mnist10_test_feature", id=role, shape=(1, 28, 28))
+test_label_reader = aby3.load_aby3_shares(mpc_data_dir + "mnist10_test_label", id=role, shape=(10,))
+test_batch_feature = aby3.batch(test_feature_reader, BATCH_SIZE, drop_last=True)
+test_batch_label = aby3.batch(test_label_reader, BATCH_SIZE, drop_last=True)
+
+place = fluid.CPUPlace()
+
+# async data loader
+loader = fluid.io.DataLoader.from_generator(feed_list=[x, y], capacity=BATCH_SIZE)
+batch_sample = paddle.reader.compose(batch_feature, batch_label)
+loader.set_batch_generator(batch_sample, places=place)
+
+test_loader = fluid.io.DataLoader.from_generator(feed_list=[x, y], capacity=BATCH_SIZE)
+test_batch_sample = paddle.reader.compose(test_batch_feature, test_batch_label)
+test_loader.set_batch_generator(test_batch_sample, places=place)
+
+# infer
+def infer():
+    """
+    MPC infer
+    """
+    mpc_infer_data_dir = "./mpc_infer_data/"
+    if not os.path.exists(mpc_infer_data_dir):
+        os.mkdir(mpc_infer_data_dir)
+    prediction_file = mpc_infer_data_dir + "mnist_debug_prediction"
+    prediction_file_part = prediction_file + ".part{}".format(role)
+
+    if os.path.exists(prediction_file_part):
+        os.remove(prediction_file_part)
+
+    step = 0
+    start_time = time.time()
+    for sample in test_loader():
+        step += 1
+        prediction = exe.run(program=infer_program, feed=sample, fetch_list=[softmax])
+        with open(prediction_file_part, 'ab') as f:
+            f.write(np.array(prediction).tostring())
+        if step % 10 == 0:
+            end_time = time.time()
+            logger.info('MPC infer of step={}, cost time in seconds:{}'.format(step, (end_time - start_time)))
+
+    end_time = time.time()
+    logger.info('MPC infer time in seconds:{}'.format((end_time - start_time)))
+
+# train
+exe = fluid.Executor(place)
+exe.run(fluid.default_startup_program())
+
+mpc_model_basedir = "./mpc_model/"
+
+step = 0
+start_time = time.time()
+logger.info('MPC training start...')
+for epoch_id in range(epoch_num):
+    for sample in loader():
+        step += 1
+        results = exe.run(feed=sample, fetch_list=[softmax])
+        if step % 100 == 0:
+            end_time = time.time()
+            logger.info('MPC training of epoch_id={} step={},  cost time in seconds:{}'
+                        .format(epoch_id, step, (end_time - start_time)))
+    
+    # For each epoch: infer or save infer program
+    #infer()
+    mpc_model_dir = mpc_model_basedir + "epoch{}/party{}".format(epoch_id, role)
+    fluid.io.save_inference_model(dirname=mpc_model_dir,
+        feeded_var_names=["x", "y"],
+        target_vars=[softmax],
+        executor=exe,
+        main_program=infer_program,
+        model_filename="__model__")
+
+end_time = time.time()
+logger.info('MPC training of epoch_num={} batch_size={}, cost time in seconds:{}'
+      .format(epoch_num, BATCH_SIZE, (end_time - start_time)))
+
+# infer
+infer()
+