add dataloader test scripts

dygraph/dataloader_test.sh

+#!/bin/bash
+mkdir dataloader_test_log
+
+# single card
+python ./mnist/train.py > dataloader_test_log/mnist 2>&1
+python ./resnet/train.py > dataloader_test_log/resnet 2>&1
+python ./se_resnet/train.py > dataloader_test_log/se_resnet 2>&1
+python ./transformer/train.py > dataloader_test_log/transformer 2>&1
+
+python ./mnist/train_sp.py > dataloader_test_log/mnist_sp 2>&1
+python ./resnet/train_sp.py > dataloader_test_log/resnet_sp 2>&1
+python ./se_resnet/train_sp.py > dataloader_test_log/se_resnet_sp 2>&1
+python ./transformer/train_sp.py > dataloader_test_log/transformer_sp 2>&1
+
+python ./mnist/train_mp.py > dataloader_test_log/mnist_mp 2>&1
+python ./resnet/train_mp.py > dataloader_test_log/resnet_mp 2>&1
+python ./se_resnet/train_mp.py > dataloader_test_log/se_resnet_mp 2>&1
+python ./transformer/train_mp.py > dataloader_test_log/transformer_mp 2>&1
+
+# multiple card
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/mnist_8 ./mnist/train.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/resnet_8 ./resnet/train.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/se_resnet_8 ./se_resnet/train.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/transformer_8 ./transformer/train.py --use_data_parallel 1
+
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/mnist_8_sp ./mnist/train_sp.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/resnet_8_sp ./resnet/train_sp.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/se_resnet_8_sp ./se_resnet/train_sp.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/transformer_8_sp ./transformer/train_sp.py --use_data_parallel 1
+
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/mnist_8_mp ./mnist/train_mp.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/resnet_8_mp ./resnet/train_mp.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/se_resnet_8_mp ./se_resnet/train_mp.py --use_data_parallel 1
+python -m paddle.distributed.launch --log_dir ./dataloader_test_log/transformer_8_mp ./transformer/train_mp.py --use_data_parallel 1
\ No newline at end of file

dygraph/mnist/train.py

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import argparse
 import ast
+import time
 import numpy as np
 from PIL import Image
 import os
@@ -33,7 +34,7 @@ def parse_args():
         default=False,
         help="The flag indicating whether to use data parallel mode to train the model."
     )
-    parser.add_argument("-e", "--epoch", default=5, type=int, help="set epoch")
+    parser.add_argument("-e", "--epoch", default=1, type=int, help="set epoch")
     parser.add_argument("--ce", action="store_true", help="run ce")
     args = parser.parse_args()
     return args
@@ -200,7 +201,9 @@ def train_mnist(args):
         test_reader = paddle.batch(
             paddle.dataset.mnist.test(), batch_size=BATCH_SIZE, drop_last=True)
 
+        total_train_time = 0
         for epoch in range(epoch_num):
+            stime = time.time()
             for batch_id, data in enumerate(train_reader()):
                 dy_x_data = np.array([x[0].reshape(1, 28, 28)
                                       for x in data]).astype('float32')
@@ -229,6 +232,7 @@ def train_mnist(args):
                 if batch_id % 100 == 0:
                     print("Loss at epoch {} step {}: {:}".format(
                         epoch, batch_id, avg_loss.numpy()))
+            total_train_time += (time.time() - stime)
 
             mnist.eval()
             test_cost, test_acc = test_mnist(test_reader, mnist, BATCH_SIZE)
@@ -249,6 +253,7 @@ def train_mnist(args):
 
             inference_mnist()
 
+        print("total train time: {} s".format(total_train_time))
 
 if __name__ == '__main__':
     args = parse_args()

dygraph/mnist/train_mp.py

+# Copyright (c) 2018 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.
+
+from __future__ import print_function
+import argparse
+import ast
+import time
+import numpy as np
+from PIL import Image
+import os
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import AdamOptimizer
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
+from paddle.fluid.dygraph.base import to_variable
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Training for Mnist.")
+    parser.add_argument(
+        "--use_data_parallel",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to use data parallel mode to train the model."
+    )
+    parser.add_argument("-e", "--epoch", default=1, type=int, help="set epoch")
+    parser.add_argument("--ce", action="store_true", help="run ce")
+    args = parser.parse_args()
+    return args
+
+
+class SimpleImgConvPool(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 pool_size,
+                 pool_stride,
+                 pool_padding=0,
+                 pool_type='max',
+                 global_pooling=False,
+                 conv_stride=1,
+                 conv_padding=0,
+                 conv_dilation=1,
+                 conv_groups=1,
+                 act=None,
+                 use_cudnn=False,
+                 param_attr=None,
+                 bias_attr=None):
+        super(SimpleImgConvPool, self).__init__()
+
+        self._conv2d = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=conv_stride,
+            padding=conv_padding,
+            dilation=conv_dilation,
+            groups=conv_groups,
+            param_attr=None,
+            bias_attr=None,
+            act=act,
+            use_cudnn=use_cudnn)
+
+        self._pool2d = Pool2D(
+            pool_size=pool_size,
+            pool_type=pool_type,
+            pool_stride=pool_stride,
+            pool_padding=pool_padding,
+            global_pooling=global_pooling,
+            use_cudnn=use_cudnn)
+
+    def forward(self, inputs):
+        x = self._conv2d(inputs)
+        x = self._pool2d(x)
+        return x
+
+
+class MNIST(fluid.dygraph.Layer):
+    def __init__(self):
+        super(MNIST, self).__init__()
+
+        self._simple_img_conv_pool_1 = SimpleImgConvPool(
+            1, 20, 5, 2, 2, act="relu")
+
+        self._simple_img_conv_pool_2 = SimpleImgConvPool(
+            20, 50, 5, 2, 2, act="relu")
+
+        self.pool_2_shape = 50 * 4 * 4
+        SIZE = 10
+        scale = (2.0 / (self.pool_2_shape**2 * SIZE))**0.5
+        self._fc = Linear(self.pool_2_shape, 10,
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.NormalInitializer(
+                              loc=0.0, scale=scale)),
+                      act="softmax")
+
+    def forward(self, inputs, label=None):
+        x = self._simple_img_conv_pool_1(inputs)
+        x = self._simple_img_conv_pool_2(x)
+        x = fluid.layers.reshape(x, shape=[-1, self.pool_2_shape])
+        x = self._fc(x)
+        if label is not None:
+            acc = fluid.layers.accuracy(input=x, label=label)
+            return x, acc
+        else:
+            return x
+
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(1, 28, 28)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
+    return __reader__
+
+def test_mnist(reader, model, batch_size):
+    acc_set = []
+    avg_loss_set = []
+    for batch_id, data in enumerate(reader()):
+        img, label = data
+        label.stop_gradient = True
+        prediction, acc = model(img, label)
+        loss = fluid.layers.cross_entropy(input=prediction, label=label)
+        avg_loss = fluid.layers.mean(loss)
+        acc_set.append(float(acc.numpy()))
+        avg_loss_set.append(float(avg_loss.numpy()))
+
+        # get test acc and loss
+    acc_val_mean = np.array(acc_set).mean()
+    avg_loss_val_mean = np.array(avg_loss_set).mean()
+
+    return avg_loss_val_mean, acc_val_mean
+
+
+def inference_mnist():
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        mnist_infer = MNIST()
+        # load checkpoint
+        model_dict, _ = fluid.load_dygraph("save_temp")
+        mnist_infer.set_dict(model_dict)
+        print("checkpoint loaded")
+
+        # start evaluate mode
+        mnist_infer.eval()
+
+        def load_image(file):
+            im = Image.open(file).convert('L')
+            im = im.resize((28, 28), Image.ANTIALIAS)
+            im = np.array(im).reshape(1, 1, 28, 28).astype(np.float32)
+            im = im / 255.0 * 2.0 - 1.0
+            return im
+
+        cur_dir = os.path.dirname(os.path.realpath(__file__))
+        tensor_img = load_image(cur_dir + '/image/infer_3.png')
+
+        results = mnist_infer(to_variable(tensor_img))
+        lab = np.argsort(results.numpy())
+        print("Inference result of image/infer_3.png is: %d" % lab[0][-1])
+
+
+def train_mnist(args):
+    epoch_num = args.epoch
+    BATCH_SIZE = 64
+
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.ce:
+            print("ce mode")
+            seed = 33
+            np.random.seed(seed)
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+        mnist = MNIST()
+        adam = AdamOptimizer(learning_rate=0.001, parameter_list=mnist.parameters())
+        if args.use_data_parallel:
+            mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)
+
+        train_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.mnist.train()), 
+                batch_size=BATCH_SIZE,
+                drop_last=True)
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+
+        test_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.mnist.test()), 
+                batch_size=BATCH_SIZE,
+                drop_last=True)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=10, use_multiprocess=True)
+        train_loader.set_sample_list_generator(train_reader, places=place)
+
+        test_loader = fluid.io.DataLoader.from_generator(capacity=10, use_multiprocess=True)
+        test_loader.set_sample_list_generator(test_reader, places=place)
+
+        total_train_time = 0
+        for epoch in range(epoch_num):
+            stime = time.time()
+            for batch_id, data in enumerate(train_loader()):
+                img, label = data
+                label.stop_gradient = True
+
+                cost, acc = mnist(img, label)
+
+                loss = fluid.layers.cross_entropy(cost, label)
+                avg_loss = fluid.layers.mean(loss)
+
+                if args.use_data_parallel:
+                    avg_loss = mnist.scale_loss(avg_loss)
+                    avg_loss.backward()
+                    mnist.apply_collective_grads()
+                else:
+                    avg_loss.backward()
+
+                adam.minimize(avg_loss)
+                # save checkpoint
+                mnist.clear_gradients()
+                if batch_id % 100 == 0:
+                    print("Loss at epoch {} step {}: {:}".format(
+                        epoch, batch_id, avg_loss.numpy()))
+            total_train_time += (time.time() - stime)
+
+            mnist.eval()
+            test_cost, test_acc = test_mnist(test_loader, mnist, BATCH_SIZE)
+            mnist.train()
+            if args.ce:
+                print("kpis\ttest_acc\t%s" % test_acc)
+                print("kpis\ttest_cost\t%s" % test_cost)
+            print("Loss at epoch {} , Test avg_loss is: {}, acc is: {}".format(
+                epoch, test_cost, test_acc))
+
+        save_parameters = (not args.use_data_parallel) or (
+            args.use_data_parallel and
+            fluid.dygraph.parallel.Env().local_rank == 0)
+        if save_parameters:
+            fluid.save_dygraph(mnist.state_dict(), "save_temp")
+            
+            print("checkpoint saved")
+
+            inference_mnist()
+
+        print("total train time: {} s".format(total_train_time))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    train_mnist(args)

dygraph/mnist/train_sp.py

+# Copyright (c) 2018 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.
+
+from __future__ import print_function
+import argparse
+import ast
+import time
+import numpy as np
+from PIL import Image
+import os
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.optimizer import AdamOptimizer
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
+from paddle.fluid.dygraph.base import to_variable
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Training for Mnist.")
+    parser.add_argument(
+        "--use_data_parallel",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to use data parallel mode to train the model."
+    )
+    parser.add_argument("-e", "--epoch", default=1, type=int, help="set epoch")
+    parser.add_argument("--ce", action="store_true", help="run ce")
+    args = parser.parse_args()
+    return args
+
+
+class SimpleImgConvPool(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 pool_size,
+                 pool_stride,
+                 pool_padding=0,
+                 pool_type='max',
+                 global_pooling=False,
+                 conv_stride=1,
+                 conv_padding=0,
+                 conv_dilation=1,
+                 conv_groups=1,
+                 act=None,
+                 use_cudnn=False,
+                 param_attr=None,
+                 bias_attr=None):
+        super(SimpleImgConvPool, self).__init__()
+
+        self._conv2d = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=conv_stride,
+            padding=conv_padding,
+            dilation=conv_dilation,
+            groups=conv_groups,
+            param_attr=None,
+            bias_attr=None,
+            act=act,
+            use_cudnn=use_cudnn)
+
+        self._pool2d = Pool2D(
+            pool_size=pool_size,
+            pool_type=pool_type,
+            pool_stride=pool_stride,
+            pool_padding=pool_padding,
+            global_pooling=global_pooling,
+            use_cudnn=use_cudnn)
+
+    def forward(self, inputs):
+        x = self._conv2d(inputs)
+        x = self._pool2d(x)
+        return x
+
+
+class MNIST(fluid.dygraph.Layer):
+    def __init__(self):
+        super(MNIST, self).__init__()
+
+        self._simple_img_conv_pool_1 = SimpleImgConvPool(
+            1, 20, 5, 2, 2, act="relu")
+
+        self._simple_img_conv_pool_2 = SimpleImgConvPool(
+            20, 50, 5, 2, 2, act="relu")
+
+        self.pool_2_shape = 50 * 4 * 4
+        SIZE = 10
+        scale = (2.0 / (self.pool_2_shape**2 * SIZE))**0.5
+        self._fc = Linear(self.pool_2_shape, 10,
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.NormalInitializer(
+                              loc=0.0, scale=scale)),
+                      act="softmax")
+
+    def forward(self, inputs, label=None):
+        x = self._simple_img_conv_pool_1(inputs)
+        x = self._simple_img_conv_pool_2(x)
+        x = fluid.layers.reshape(x, shape=[-1, self.pool_2_shape])
+        x = self._fc(x)
+        if label is not None:
+            acc = fluid.layers.accuracy(input=x, label=label)
+            return x, acc
+        else:
+            return x
+
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(1, 28, 28)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
+    return __reader__
+
+def test_mnist(reader, model, batch_size):
+    acc_set = []
+    avg_loss_set = []
+    for batch_id, data in enumerate(reader()):
+        img, label = data
+        label.stop_gradient = True
+        prediction, acc = model(img, label)
+        loss = fluid.layers.cross_entropy(input=prediction, label=label)
+        avg_loss = fluid.layers.mean(loss)
+        acc_set.append(float(acc.numpy()))
+        avg_loss_set.append(float(avg_loss.numpy()))
+
+        # get test acc and loss
+    acc_val_mean = np.array(acc_set).mean()
+    avg_loss_val_mean = np.array(avg_loss_set).mean()
+
+    return avg_loss_val_mean, acc_val_mean
+
+
+def inference_mnist():
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        mnist_infer = MNIST()
+        # load checkpoint
+        model_dict, _ = fluid.load_dygraph("save_temp")
+        mnist_infer.set_dict(model_dict)
+        print("checkpoint loaded")
+
+        # start evaluate mode
+        mnist_infer.eval()
+
+        def load_image(file):
+            im = Image.open(file).convert('L')
+            im = im.resize((28, 28), Image.ANTIALIAS)
+            im = np.array(im).reshape(1, 1, 28, 28).astype(np.float32)
+            im = im / 255.0 * 2.0 - 1.0
+            return im
+
+        cur_dir = os.path.dirname(os.path.realpath(__file__))
+        tensor_img = load_image(cur_dir + '/image/infer_3.png')
+
+        results = mnist_infer(to_variable(tensor_img))
+        lab = np.argsort(results.numpy())
+        print("Inference result of image/infer_3.png is: %d" % lab[0][-1])
+
+
+def train_mnist(args):
+    epoch_num = args.epoch
+    BATCH_SIZE = 64
+
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.ce:
+            print("ce mode")
+            seed = 33
+            np.random.seed(seed)
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+        mnist = MNIST()
+        adam = AdamOptimizer(learning_rate=0.001, parameter_list=mnist.parameters())
+        if args.use_data_parallel:
+            mnist = fluid.dygraph.parallel.DataParallel(mnist, strategy)
+
+        train_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.mnist.train()), 
+                batch_size=BATCH_SIZE,
+                drop_last=True)
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+
+        test_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.mnist.test()), 
+                batch_size=BATCH_SIZE,
+                drop_last=True)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=10)
+        train_loader.set_sample_list_generator(train_reader, places=place)
+
+        test_loader = fluid.io.DataLoader.from_generator(capacity=10)
+        test_loader.set_sample_list_generator(test_reader, places=place)
+
+        total_train_time = 0
+        for epoch in range(epoch_num):
+            stime = time.time()
+            for batch_id, data in enumerate(train_loader()):
+                img, label = data
+                label.stop_gradient = True
+
+                cost, acc = mnist(img, label)
+
+                loss = fluid.layers.cross_entropy(cost, label)
+                avg_loss = fluid.layers.mean(loss)
+
+                if args.use_data_parallel:
+                    avg_loss = mnist.scale_loss(avg_loss)
+                    avg_loss.backward()
+                    mnist.apply_collective_grads()
+                else:
+                    avg_loss.backward()
+
+                adam.minimize(avg_loss)
+                # save checkpoint
+                mnist.clear_gradients()
+                if batch_id % 100 == 0:
+                    print("Loss at epoch {} step {}: {:}".format(
+                        epoch, batch_id, avg_loss.numpy()))
+            total_train_time += (time.time() - stime)
+
+            mnist.eval()
+            test_cost, test_acc = test_mnist(test_loader, mnist, BATCH_SIZE)
+            mnist.train()
+            if args.ce:
+                print("kpis\ttest_acc\t%s" % test_acc)
+                print("kpis\ttest_cost\t%s" % test_cost)
+            print("Loss at epoch {} , Test avg_loss is: {}, acc is: {}".format(
+                epoch, test_cost, test_acc))
+
+        save_parameters = (not args.use_data_parallel) or (
+            args.use_data_parallel and
+            fluid.dygraph.parallel.Env().local_rank == 0)
+        if save_parameters:
+            fluid.save_dygraph(mnist.state_dict(), "save_temp")
+            
+            print("checkpoint saved")
+
+            inference_mnist()
+
+        print("total train time: {} s".format(total_train_time))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    train_mnist(args)

dygraph/parse_dataloader_test_result.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.
+
+from __future__ import print_function
+
+import os
+import sys
+import logging
+import paddle.compat as cpt
+
+TEST_LOG_DIR = './dataloader_test_log'
+
+def parse_test_result(dirname):
+    if not os.path.exists(dirname):
+        logging.warning("log dir is not exist.")
+        return
+
+    for parent, _, filenames in os.walk(dirname):
+        for filename in filenames:
+          filepath = os.path.join(parent, filename)
+          if not os.path.isfile(filepath):
+              continue
+          with open(filepath, 'rb') as f:
+              for line in f.readlines():
+                  if line.startswith(b'total train time:'):
+                      print("%s - %s" % (filepath, cpt.to_text(line)[:-1]))
+
+if __name__ == "__main__":
+    parse_test_result(TEST_LOG_DIR)
\ No newline at end of file

dygraph/resnet/train.py

@@ -25,6 +25,7 @@ from paddle.fluid import framework
 
 import math
 import sys
+import time
 
 IMAGENET1000 = 1281167
 base_lr = 0.1
@@ -41,7 +42,7 @@ def parse_args():
         help="The flag indicating whether to use data parallel mode to train the model."
     )
     parser.add_argument(
-        "-e", "--epoch", default=120, type=int, help="set epoch")
+        "-e", "--epoch", default=1, type=int, help="set epoch")
     parser.add_argument(
         "-b", "--batch_size", default=32, type=int, help="set epoch")
     parser.add_argument("--ce", action="store_true", help="run ce")
@@ -309,7 +310,8 @@ def train_resnet():
 
         #file_name = './model/epoch_0.npz'
         #model_data = np.load( file_name )
-
+        
+        total_train_time = 0
         for eop in range(epoch):
 
             resnet.train()
@@ -324,6 +326,7 @@ def train_resnet():
 
             print("load finished")
 
+            stime = time.time()
             for batch_id, data in enumerate(train_reader()):
                 dy_x_data = np.array(
                     [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
@@ -365,6 +368,7 @@ def train_resnet():
                     print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
                            ( eop, batch_id, total_loss / total_sample, \
                              total_acc1 / total_sample, total_acc5 / total_sample))
+            total_train_time += (time.time() - stime)
 
             if args.ce:
                 print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
@@ -383,7 +387,8 @@ def train_resnet():
                 fluid.save_dygraph(resnet.state_dict(),
                                                 'resnet_params')
 
+            print("total train time: {} s".format(total_train_time))
 
-if __name__ == '__main__':
 
+if __name__ == '__main__':
     train_resnet()

dygraph/resnet/train_mp.py

+# Copyright (c) 2019 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.
+
+import numpy as np
+import argparse
+import ast
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+
+from paddle.fluid import framework
+
+import math
+import sys
+import time
+
+IMAGENET1000 = 1281167
+base_lr = 0.1
+momentum_rate = 0.9
+l2_decay = 1e-4
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Training for Resnet.")
+    parser.add_argument(
+        "--use_data_parallel",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to use data parallel mode to train the model."
+    )
+    parser.add_argument(
+        "-e", "--epoch", default=1, type=int, help="set epoch")
+    parser.add_argument(
+        "-b", "--batch_size", default=32, type=int, help="set epoch")
+    parser.add_argument("--ce", action="store_true", help="run ce")
+    args = parser.parse_args()
+    return args
+
+
+args = parse_args()
+batch_size = args.batch_size
+
+
+def optimizer_setting(parameter_list=None):
+
+    total_images = IMAGENET1000
+
+    step = int(math.ceil(float(total_images) / batch_size))
+
+    epochs = [30, 60, 90]
+    bd = [step * e for e in epochs]
+
+    lr = []
+    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
+    if fluid.in_dygraph_mode():
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=fluid.layers.piecewise_decay(
+                boundaries=bd, values=lr),
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay),
+            parameter_list=parameter_list)
+    else:
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=fluid.layers.piecewise_decay(
+                boundaries=bd, values=lr),
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay))
+        
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu')
+        self.pool2d_max = Pool2D(
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
+
+        self.bottleneck_block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block]
+                        if i == 0 else num_filters[block] * 4,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        shortcut=shortcut))
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1
+
+        import math
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = Linear(self.pool2d_avg_output,
+                      class_dim,
+                      act='softmax',
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = self.out(y)
+        return y
+
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
+    return __reader__
+
+def eval(model, data):
+
+    model.eval()
+    total_loss = 0.0
+    total_acc1 = 0.0
+    total_acc5 = 0.0
+    total_sample = 0
+    for batch_id, data in enumerate(data()):
+        img = data[0]
+        label = data[1]
+        label.stop_gradient = True
+
+        out = model(img)
+        #loss = fluid.layers.cross_entropy(input=out, label=label)
+        #avg_loss = fluid.layers.mean(x=loss)
+
+        acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+
+        #dy_out = avg_loss.numpy()
+
+        #total_loss += dy_out
+        total_acc1 += acc_top1.numpy()
+        total_acc5 += acc_top5.numpy()
+        total_sample += 1
+
+        # print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
+        if batch_id % 10 == 0:
+            print("test | batch step %d, acc1 %0.3f acc5 %0.3f" % \
+                  ( batch_id, total_acc1 / total_sample, total_acc5 / total_sample))
+    if args.ce:
+        print("kpis\ttest_acc1\t%0.3f" % (total_acc1 / total_sample))
+        print("kpis\ttest_acc5\t%0.3f" % (total_acc5 / total_sample))
+    print("final eval acc1 %0.3f acc5 %0.3f" % \
+          (total_acc1 / total_sample, total_acc5 / total_sample))
+
+
+def train_resnet():
+    epoch = args.epoch
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.ce:
+            print("ce mode")
+            seed = 33
+            np.random.seed(seed)
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+
+        resnet = ResNet()
+        optimizer = optimizer_setting(parameter_list=resnet.parameters())
+
+        if args.use_data_parallel:
+            resnet = fluid.dygraph.parallel.DataParallel(resnet, strategy)
+
+        train_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.train(use_xmap=False)), 
+                batch_size=batch_size,
+                drop_last=True)
+        
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+
+        test_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.test(use_xmap=False)), 
+                batch_size=batch_size,
+                drop_last=True)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=10, use_multiprocess=True)
+        train_loader.set_sample_list_generator(train_reader, places=place)
+
+        test_loader = fluid.io.DataLoader.from_generator(capacity=10, use_multiprocess=True)
+        test_loader.set_sample_list_generator(test_reader, places=place)
+
+        #file_name = './model/epoch_0.npz'
+        #model_data = np.load( file_name )
+        total_train_time = 0
+        for eop in range(epoch):
+
+            resnet.train()
+            total_loss = 0.0
+            total_acc1 = 0.0
+            total_acc5 = 0.0
+            total_sample = 0
+
+            #dict_state = resnet.state_dict()
+
+            #resnet.load_dict( model_data )
+
+            print("load finished")
+
+            stime = time.time()
+            for batch_id, data in enumerate(train_loader()):
+                img = data[0]
+                label = data[1]
+                label.stop_gradient = True
+
+                out = resnet(img)
+                loss = fluid.layers.cross_entropy(input=out, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+
+                acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+                acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+
+                dy_out = avg_loss.numpy()
+
+                if args.use_data_parallel:
+                    avg_loss = resnet.scale_loss(avg_loss)
+                    avg_loss.backward()
+                    resnet.apply_collective_grads()
+                else:
+                    avg_loss.backward()
+
+                optimizer.minimize(avg_loss)
+                resnet.clear_gradients()
+
+                total_loss += dy_out
+                total_acc1 += acc_top1.numpy()
+                total_acc5 += acc_top5.numpy()
+                total_sample += 1
+                #print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
+                if batch_id % 10 == 0:
+                    print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                           ( eop, batch_id, total_loss / total_sample, \
+                             total_acc1 / total_sample, total_acc5 / total_sample))
+            total_train_time += (time.time() - stime)
+
+            if args.ce:
+                print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
+                print("kpis\ttrain_acc5\t%0.3f" % (total_acc5 / total_sample))
+                print("kpis\ttrain_loss\t%0.3f" % (total_loss / total_sample))
+            print("epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  (eop, batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+            resnet.eval()
+            eval(resnet, test_loader)
+
+            save_parameters = (not args.use_data_parallel) or (
+                args.use_data_parallel and
+                fluid.dygraph.parallel.Env().local_rank == 0)
+            if save_parameters:
+                fluid.save_dygraph(resnet.state_dict(),
+                                                'resnet_params')
+
+            print("total train time: {} s".format(total_train_time))
+
+if __name__ == '__main__':
+    train_resnet()

dygraph/resnet/train_sp.py

+# Copyright (c) 2019 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.
+
+import numpy as np
+import argparse
+import ast
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+
+from paddle.fluid import framework
+
+import math
+import sys
+import time
+
+IMAGENET1000 = 1281167
+base_lr = 0.1
+momentum_rate = 0.9
+l2_decay = 1e-4
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Training for Resnet.")
+    parser.add_argument(
+        "--use_data_parallel",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to use data parallel mode to train the model."
+    )
+    parser.add_argument(
+        "-e", "--epoch", default=1, type=int, help="set epoch")
+    parser.add_argument(
+        "-b", "--batch_size", default=32, type=int, help="set epoch")
+    parser.add_argument("--ce", action="store_true", help="run ce")
+    args = parser.parse_args()
+    return args
+
+
+args = parse_args()
+batch_size = args.batch_size
+
+
+def optimizer_setting(parameter_list=None):
+
+    total_images = IMAGENET1000
+
+    step = int(math.ceil(float(total_images) / batch_size))
+
+    epochs = [30, 60, 90]
+    bd = [step * e for e in epochs]
+
+    lr = []
+    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
+    if fluid.in_dygraph_mode():
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=fluid.layers.piecewise_decay(
+                boundaries=bd, values=lr),
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay),
+            parameter_list=parameter_list)
+    else:
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=fluid.layers.piecewise_decay(
+                boundaries=bd, values=lr),
+            momentum=momentum_rate,
+            regularization=fluid.regularizer.L2Decay(l2_decay))
+        
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv = ConvBNLayer(
+            num_channels=3,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu')
+        self.pool2d_max = Pool2D(
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
+
+        self.bottleneck_block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block]
+                        if i == 0 else num_filters[block] * 4,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        shortcut=shortcut))
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1
+
+        import math
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = Linear(self.pool2d_avg_output,
+                      class_dim,
+                      act='softmax',
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = self.out(y)
+        return y
+
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
+    return __reader__
+
+def eval(model, data):
+
+    model.eval()
+    total_loss = 0.0
+    total_acc1 = 0.0
+    total_acc5 = 0.0
+    total_sample = 0
+    for batch_id, data in enumerate(data()):
+        img = data[0]
+        label = data[1]
+        label.stop_gradient = True
+
+        out = model(img)
+        #loss = fluid.layers.cross_entropy(input=out, label=label)
+        #avg_loss = fluid.layers.mean(x=loss)
+
+        acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+
+        #dy_out = avg_loss.numpy()
+
+        #total_loss += dy_out
+        total_acc1 += acc_top1.numpy()
+        total_acc5 += acc_top5.numpy()
+        total_sample += 1
+
+        # print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
+        if batch_id % 10 == 0:
+            print("test | batch step %d, acc1 %0.3f acc5 %0.3f" % \
+                  ( batch_id, total_acc1 / total_sample, total_acc5 / total_sample))
+    if args.ce:
+        print("kpis\ttest_acc1\t%0.3f" % (total_acc1 / total_sample))
+        print("kpis\ttest_acc5\t%0.3f" % (total_acc5 / total_sample))
+    print("final eval acc1 %0.3f acc5 %0.3f" % \
+          (total_acc1 / total_sample, total_acc5 / total_sample))
+
+
+def train_resnet():
+    epoch = args.epoch
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.ce:
+            print("ce mode")
+            seed = 33
+            np.random.seed(seed)
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+
+        resnet = ResNet()
+        optimizer = optimizer_setting(parameter_list=resnet.parameters())
+
+        if args.use_data_parallel:
+            resnet = fluid.dygraph.parallel.DataParallel(resnet, strategy)
+
+        train_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.train(use_xmap=False)), 
+                batch_size=batch_size,
+                drop_last=True)
+        
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+
+        test_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.test(use_xmap=False)), 
+                batch_size=batch_size,
+                drop_last=True)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=10)
+        train_loader.set_sample_list_generator(train_reader, places=place)
+
+        test_loader = fluid.io.DataLoader.from_generator(capacity=10)
+        test_loader.set_sample_list_generator(test_reader, places=place)
+
+        #file_name = './model/epoch_0.npz'
+        #model_data = np.load( file_name )
+        total_train_time = 0
+        for eop in range(epoch):
+
+            resnet.train()
+            total_loss = 0.0
+            total_acc1 = 0.0
+            total_acc5 = 0.0
+            total_sample = 0
+
+            #dict_state = resnet.state_dict()
+
+            #resnet.load_dict( model_data )
+
+            print("load finished")
+
+            stime = time.time()
+            for batch_id, data in enumerate(train_loader()):
+                img = data[0]
+                label = data[1]
+                label.stop_gradient = True
+
+                out = resnet(img)
+                loss = fluid.layers.cross_entropy(input=out, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+
+                acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+                acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+
+                dy_out = avg_loss.numpy()
+
+                if args.use_data_parallel:
+                    avg_loss = resnet.scale_loss(avg_loss)
+                    avg_loss.backward()
+                    resnet.apply_collective_grads()
+                else:
+                    avg_loss.backward()
+
+                optimizer.minimize(avg_loss)
+                resnet.clear_gradients()
+
+                total_loss += dy_out
+                total_acc1 += acc_top1.numpy()
+                total_acc5 += acc_top5.numpy()
+                total_sample += 1
+                #print("epoch id: %d, batch step: %d, loss: %f" % (eop, batch_id, dy_out))
+                if batch_id % 10 == 0:
+                    print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                           ( eop, batch_id, total_loss / total_sample, \
+                             total_acc1 / total_sample, total_acc5 / total_sample))
+            total_train_time += (time.time() - stime)
+
+            if args.ce:
+                print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
+                print("kpis\ttrain_acc5\t%0.3f" % (total_acc5 / total_sample))
+                print("kpis\ttrain_loss\t%0.3f" % (total_loss / total_sample))
+            print("epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  (eop, batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+            resnet.eval()
+            eval(resnet, test_loader)
+
+            save_parameters = (not args.use_data_parallel) or (
+                args.use_data_parallel and
+                fluid.dygraph.parallel.Env().local_rank == 0)
+            if save_parameters:
+                fluid.save_dygraph(resnet.state_dict(),
+                                                'resnet_params')
+
+            print("total train time: {} s".format(total_train_time))
+
+if __name__ == '__main__':
+    train_resnet()

dygraph/se_resnet/train.py

@@ -16,6 +16,7 @@ import contextlib
 import unittest
 import numpy as np
 import six
+import time
 
 import paddle
 import paddle.fluid as fluid
@@ -29,7 +30,7 @@ import argparse
 import ast
 
 parser = argparse.ArgumentParser("Training for Se-ResNeXt.")
-parser.add_argument("-e", "--epoch", default=200, type=int, help="set epoch")
+parser.add_argument("-e", "--epoch", default=1, type=int, help="set epoch")
 parser.add_argument("--ce", action="store_true", help="run ce")
 parser.add_argument(
     "--use_data_parallel",
@@ -52,7 +53,7 @@ train_parameters = {
     "batch_size": batch_size,
     "lr": 0.0125,
     "total_images": 6149,
-    "num_epochs": 200
+    "num_epochs": 1
 }
 
 momentum_rate = 0.9
@@ -99,8 +100,7 @@ class ConvBNLayer(fluid.dygraph.Layer):
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
-            bias_attr=False,
-            param_attr=fluid.ParamAttr(name="weights"))
+            bias_attr=False)
 
         self._batch_norm = BatchNorm(num_filters, act=act)
 
@@ -399,11 +399,13 @@ def train():
         test_reader = paddle.batch(
             paddle.dataset.flowers.test(use_xmap=False), batch_size=32)
 
+        total_train_time = 0
         for epoch_id in range(epoch_num):
             total_loss = 0.0
             total_acc1 = 0.0
             total_acc5 = 0.0
             total_sample = 0
+            stime = time.time()
             for batch_id, data in enumerate(train_reader()):
 
                 dy_x_data = np.array([x[0].reshape(3, 224, 224)
@@ -446,6 +448,7 @@ def train():
                     print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f lr %0.5f" % \
                            ( epoch_id, batch_id, total_loss / total_sample, \
                              total_acc1 / total_sample, total_acc5 / total_sample, lr))
+            total_train_time += (time.time() - stime)
 
             if args.ce:
                 print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
@@ -458,6 +461,8 @@ def train():
             eval(se_resnext, test_reader)
             se_resnext.train()
 
+        print("total train time: {} s".format(total_train_time))
+
 
 if __name__ == '__main__':
     train()

dygraph/se_resnet/train_mp.py

+# Copyright (c) 2019 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.
+
+import contextlib
+import unittest
+import numpy as np
+import six
+import time
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+import sys
+import math
+import argparse
+import ast
+
+parser = argparse.ArgumentParser("Training for Se-ResNeXt.")
+parser.add_argument("-e", "--epoch", default=1, type=int, help="set epoch")
+parser.add_argument("--ce", action="store_true", help="run ce")
+parser.add_argument(
+    "--use_data_parallel",
+    type=ast.literal_eval,
+    default=False,
+    help="The flag indicating whether to use data parallel mode to train the model."
+)
+args = parser.parse_args()
+batch_size = 64
+train_parameters = {
+    "input_size": [3, 224, 224],
+    "input_mean": [0.485, 0.456, 0.406],
+    "input_std": [0.229, 0.224, 0.225],
+    "learning_strategy": {
+        "name": "cosine_decay",
+        "batch_size": batch_size,
+        "epochs": [40, 80, 100],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    },
+    "batch_size": batch_size,
+    "lr": 0.0125,
+    "total_images": 6149,
+    "num_epochs": 1
+}
+
+momentum_rate = 0.9
+l2_decay = 1.2e-4
+
+
+def optimizer_setting(params, parameter_list):
+    ls = params["learning_strategy"]
+    if "total_images" not in params:
+        total_images = 6149
+    else:
+        total_images = params["total_images"]
+
+    batch_size = ls["batch_size"]
+    step = int(math.ceil(float(total_images) / batch_size))
+    bd = [step * e for e in ls["epochs"]]
+    lr = params["lr"]
+    num_epochs = params["num_epochs"]
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=fluid.layers.cosine_decay(
+            learning_rate=lr, step_each_epoch=step, epochs=num_epochs),
+        momentum=momentum_rate,
+        regularization=fluid.regularizer.L2Decay(l2_decay),
+        parameter_list=parameter_list)
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class SqueezeExcitation(fluid.dygraph.Layer):
+    def __init__(self, num_channels, reduction_ratio):
+
+        super(SqueezeExcitation, self).__init__()
+        self._num_channels = num_channels
+        self._pool = Pool2D(pool_size=0, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(num_channels * 1.0)
+        self._fc = Linear(
+            num_channels,
+            num_channels // reduction_ratio,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)),
+            act='relu')
+        stdv = 1.0 / math.sqrt(num_channels / 16.0 * 1.0)
+        self._excitation = Linear(
+            num_channels // reduction_ratio,
+            num_channels,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)),
+            act='sigmoid')
+
+    def forward(self, input):
+        y = self._pool(input)
+        y = fluid.layers.reshape(y, shape=[-1, self._num_channels])
+        y = self._fc(y)
+        y = self._excitation(y)
+        y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 cardinality,
+                 reduction_ratio,
+                 shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act="relu")
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            groups=cardinality,
+            act="relu")
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 2,
+            filter_size=1,
+            act=None)
+
+        self.scale = SqueezeExcitation(
+            num_channels=num_filters * 2,
+            reduction_ratio=reduction_ratio)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 2,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 2
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+        scale = self.scale(conv2)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=scale, act='relu')
+        return y
+
+
+class SeResNeXt(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(SeResNeXt, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 6, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+        elif layers == 101:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 23, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+        elif layers == 152:
+            cardinality = 64
+            reduction_ratio = 16
+            depth = [3, 8, 36, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                act='relu')
+            self.conv1 = ConvBNLayer(
+                num_channels=64,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            self.conv2 = ConvBNLayer(
+                num_channels=64,
+                num_filters=128,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+
+        self.bottleneck_block_list = []
+        num_channels = 64
+        if layers == 152:
+            num_channels = 128
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        cardinality=cardinality,
+                        reduction_ratio=reduction_ratio,
+                        shortcut=shortcut))
+                num_channels = bottleneck_block._num_channels_out
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 2 * 1 * 1
+
+        self.out = Linear(self.pool2d_avg_output,
+                      class_dim,
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        if self.layers == 50 or self.layers == 101:
+            y = self.conv0(inputs)
+            y = self.pool(y)
+        elif self.layers == 152:
+            y = self.conv0(inputs)
+            y = self.conv1(y)
+            y = self.conv2(y)
+            y = self.pool(y)
+
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.dropout(y, dropout_prob=0.5, seed=100)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = self.out(y)
+        return y
+
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
+    return __reader__
+
+def eval(model, data):
+
+    model.eval()
+    batch_size = 32
+    total_loss = 0.0
+    total_acc1 = 0.0
+    total_acc5 = 0.0
+    total_sample = 0
+    for batch_id, data in enumerate(data()):
+        img, label = data
+        label.stop_gradient = True
+        out = model(img)
+
+        softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+        loss = fluid.layers.cross_entropy(input=softmax_out, label=label)
+        avg_loss = fluid.layers.mean(x=loss)
+        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
+        dy_out = avg_loss.numpy()
+
+        total_loss += dy_out
+        total_acc1 += acc_top1.numpy()
+        total_acc5 += acc_top5.numpy()
+        total_sample += 1
+        if batch_id % 10 == 0:
+            print("test | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  ( batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+
+    if args.ce:
+        print("kpis\ttest_acc1\t%0.3f" % (total_acc1 / total_sample))
+        print("kpis\ttest_acc5\t%0.3f" % (total_acc5 / total_sample))
+        print("kpis\ttest_loss\t%0.3f" % (total_loss / total_sample))
+    print("final eval loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+          (total_loss / total_sample, \
+           total_acc1 / total_sample, total_acc5 / total_sample))
+
+
+def train():
+
+    epoch_num = train_parameters["num_epochs"]
+    if args.ce:
+        epoch_num = args.epoch
+    batch_size = train_parameters["batch_size"]
+
+    trainer_count = fluid.dygraph.parallel.Env().nranks
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.ce:
+            print("ce mode")
+            seed = 90
+            np.random.seed(seed)
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+        se_resnext = SeResNeXt()
+        optimizer = optimizer_setting(train_parameters, se_resnext.parameters())
+        if args.use_data_parallel:
+            se_resnext = fluid.dygraph.parallel.DataParallel(se_resnext,
+                                                             strategy)
+        train_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.train(use_xmap=False)), 
+                batch_size=batch_size,
+                drop_last=True)
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+        test_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.test(use_xmap=False)), 
+                batch_size=32)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=10, use_multiprocess=True)
+        train_loader.set_sample_list_generator(train_reader, places=place)
+
+        test_loader = fluid.io.DataLoader.from_generator(capacity=10, use_multiprocess=True)
+        test_loader.set_sample_list_generator(test_reader, places=place)
+
+        total_train_time = 0
+        for epoch_id in range(epoch_num):
+            total_loss = 0.0
+            total_acc1 = 0.0
+            total_acc5 = 0.0
+            total_sample = 0
+            stime = time.time()
+            for batch_id, data in enumerate(train_loader()):
+                img, label = data
+                label.stop_gradient = True
+
+                out = se_resnext(img)
+                softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+                loss = fluid.layers.cross_entropy(
+                    input=softmax_out, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+
+                acc_top1 = fluid.layers.accuracy(
+                    input=softmax_out, label=label, k=1)
+                acc_top5 = fluid.layers.accuracy(
+                    input=softmax_out, label=label, k=5)
+
+                dy_out = avg_loss.numpy()
+                if args.use_data_parallel:
+                    avg_loss = se_resnext.scale_loss(avg_loss)
+                    avg_loss.backward()
+                    se_resnext.apply_collective_grads()
+                else:
+                    avg_loss.backward()
+
+                optimizer.minimize(avg_loss)
+                se_resnext.clear_gradients()
+
+                lr = optimizer._global_learning_rate().numpy()
+                total_loss += dy_out
+                total_acc1 += acc_top1.numpy()
+                total_acc5 += acc_top5.numpy()
+                total_sample += 1
+                if batch_id % 10 == 0:
+                    print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f lr %0.5f" % \
+                           ( epoch_id, batch_id, total_loss / total_sample, \
+                             total_acc1 / total_sample, total_acc5 / total_sample, lr))
+            total_train_time += (time.time() - stime)
+
+            if args.ce:
+                print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
+                print("kpis\ttrain_acc5\t%0.3f" % (total_acc5 / total_sample))
+                print("kpis\ttrain_loss\t%0.3f" % (total_loss / total_sample))
+            print("epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  (epoch_id, batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+            se_resnext.eval()
+            eval(se_resnext, test_loader)
+            se_resnext.train()
+
+        print("total train time: {} s".format(total_train_time))
+
+
+if __name__ == '__main__':
+    train()

dygraph/se_resnet/train_sp.py

+# Copyright (c) 2019 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.
+
+import contextlib
+import unittest
+import numpy as np
+import six
+import time
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.base import to_variable
+import sys
+import math
+import argparse
+import ast
+
+parser = argparse.ArgumentParser("Training for Se-ResNeXt.")
+parser.add_argument("-e", "--epoch", default=1, type=int, help="set epoch")
+parser.add_argument("--ce", action="store_true", help="run ce")
+parser.add_argument(
+    "--use_data_parallel",
+    type=ast.literal_eval,
+    default=False,
+    help="The flag indicating whether to use data parallel mode to train the model."
+)
+args = parser.parse_args()
+batch_size = 64
+train_parameters = {
+    "input_size": [3, 224, 224],
+    "input_mean": [0.485, 0.456, 0.406],
+    "input_std": [0.229, 0.224, 0.225],
+    "learning_strategy": {
+        "name": "cosine_decay",
+        "batch_size": batch_size,
+        "epochs": [40, 80, 100],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    },
+    "batch_size": batch_size,
+    "lr": 0.0125,
+    "total_images": 6149,
+    "num_epochs": 1
+}
+
+momentum_rate = 0.9
+l2_decay = 1.2e-4
+
+
+def optimizer_setting(params, parameter_list):
+    ls = params["learning_strategy"]
+    if "total_images" not in params:
+        total_images = 6149
+    else:
+        total_images = params["total_images"]
+
+    batch_size = ls["batch_size"]
+    step = int(math.ceil(float(total_images) / batch_size))
+    bd = [step * e for e in ls["epochs"]]
+    lr = params["lr"]
+    num_epochs = params["num_epochs"]
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=fluid.layers.cosine_decay(
+            learning_rate=lr, step_each_epoch=step, epochs=num_epochs),
+        momentum=momentum_rate,
+        regularization=fluid.regularizer.L2Decay(l2_decay),
+        parameter_list=parameter_list)
+
+    return optimizer
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+
+        return y
+
+
+class SqueezeExcitation(fluid.dygraph.Layer):
+    def __init__(self, num_channels, reduction_ratio):
+
+        super(SqueezeExcitation, self).__init__()
+        self._num_channels = num_channels
+        self._pool = Pool2D(pool_size=0, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(num_channels * 1.0)
+        self._fc = Linear(
+            num_channels,
+            num_channels // reduction_ratio,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)),
+            act='relu')
+        stdv = 1.0 / math.sqrt(num_channels / 16.0 * 1.0)
+        self._excitation = Linear(
+            num_channels // reduction_ratio,
+            num_channels,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)),
+            act='sigmoid')
+
+    def forward(self, input):
+        y = self._pool(input)
+        y = fluid.layers.reshape(y, shape=[-1, self._num_channels])
+        y = self._fc(y)
+        y = self._excitation(y)
+        y = fluid.layers.elementwise_mul(x=input, y=y, axis=0)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 cardinality,
+                 reduction_ratio,
+                 shortcut=True):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act="relu")
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            groups=cardinality,
+            act="relu")
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 2,
+            filter_size=1,
+            act=None)
+
+        self.scale = SqueezeExcitation(
+            num_channels=num_filters * 2,
+            reduction_ratio=reduction_ratio)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 2,
+                filter_size=1,
+                stride=stride)
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 2
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+        scale = self.scale(conv2)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=scale, act='relu')
+        return y
+
+
+class SeResNeXt(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=102):
+        super(SeResNeXt, self).__init__()
+
+        self.layers = layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 50:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 6, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+        elif layers == 101:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 23, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+        elif layers == 152:
+            cardinality = 64
+            reduction_ratio = 16
+            depth = [3, 8, 36, 3]
+            num_filters = [128, 256, 512, 1024]
+            self.conv0 = ConvBNLayer(
+                num_channels=3,
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                act='relu')
+            self.conv1 = ConvBNLayer(
+                num_channels=64,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            self.conv2 = ConvBNLayer(
+                num_channels=64,
+                num_filters=128,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            self.pool = Pool2D(
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+
+        self.bottleneck_block_list = []
+        num_channels = 64
+        if layers == 152:
+            num_channels = 128
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        cardinality=cardinality,
+                        reduction_ratio=reduction_ratio,
+                        shortcut=shortcut))
+                num_channels = bottleneck_block._num_channels_out
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 2 * 1 * 1
+
+        self.out = Linear(self.pool2d_avg_output,
+                      class_dim,
+                      param_attr=fluid.param_attr.ParamAttr(
+                          initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+    def forward(self, inputs):
+        if self.layers == 50 or self.layers == 101:
+            y = self.conv0(inputs)
+            y = self.pool(y)
+        elif self.layers == 152:
+            y = self.conv0(inputs)
+            y = self.conv1(y)
+            y = self.conv2(y)
+            y = self.pool(y)
+
+        for bottleneck_block in self.bottleneck_block_list:
+            y = bottleneck_block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.dropout(y, dropout_prob=0.5, seed=100)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = self.out(y)
+        return y
+
+def reader_decorator(reader):
+    def __reader__():
+        for item in reader():
+            img = np.array(item[0]).astype('float32').reshape(3, 224, 224)
+            label = np.array(item[1]).astype('int64').reshape(1)
+            yield img, label
+    return __reader__
+
+def eval(model, data):
+
+    model.eval()
+    batch_size = 32
+    total_loss = 0.0
+    total_acc1 = 0.0
+    total_acc5 = 0.0
+    total_sample = 0
+    for batch_id, data in enumerate(data()):
+        img, label = data
+        label.stop_gradient = True
+        out = model(img)
+
+        softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+        loss = fluid.layers.cross_entropy(input=softmax_out, label=label)
+        avg_loss = fluid.layers.mean(x=loss)
+        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
+        dy_out = avg_loss.numpy()
+
+        total_loss += dy_out
+        total_acc1 += acc_top1.numpy()
+        total_acc5 += acc_top5.numpy()
+        total_sample += 1
+        if batch_id % 10 == 0:
+            print("test | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  ( batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+
+    if args.ce:
+        print("kpis\ttest_acc1\t%0.3f" % (total_acc1 / total_sample))
+        print("kpis\ttest_acc5\t%0.3f" % (total_acc5 / total_sample))
+        print("kpis\ttest_loss\t%0.3f" % (total_loss / total_sample))
+    print("final eval loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+          (total_loss / total_sample, \
+           total_acc1 / total_sample, total_acc5 / total_sample))
+
+
+def train():
+
+    epoch_num = train_parameters["num_epochs"]
+    if args.ce:
+        epoch_num = args.epoch
+    batch_size = train_parameters["batch_size"]
+
+    trainer_count = fluid.dygraph.parallel.Env().nranks
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.ce:
+            print("ce mode")
+            seed = 90
+            np.random.seed(seed)
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+        se_resnext = SeResNeXt()
+        optimizer = optimizer_setting(train_parameters, se_resnext.parameters())
+        if args.use_data_parallel:
+            se_resnext = fluid.dygraph.parallel.DataParallel(se_resnext,
+                                                             strategy)
+        train_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.train(use_xmap=False)), 
+                batch_size=batch_size,
+                drop_last=True)
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+        test_reader = paddle.batch(
+            reader_decorator(
+                paddle.dataset.flowers.test(use_xmap=False)), 
+                batch_size=32)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=10)
+        train_loader.set_sample_list_generator(train_reader, places=place)
+
+        test_loader = fluid.io.DataLoader.from_generator(capacity=10)
+        test_loader.set_sample_list_generator(test_reader, places=place)
+
+        total_train_time = 0
+        for epoch_id in range(epoch_num):
+            total_loss = 0.0
+            total_acc1 = 0.0
+            total_acc5 = 0.0
+            total_sample = 0
+            stime = time.time()
+            for batch_id, data in enumerate(train_loader()):
+                img, label = data
+                label.stop_gradient = True
+
+                out = se_resnext(img)
+                softmax_out = fluid.layers.softmax(out, use_cudnn=False)
+                loss = fluid.layers.cross_entropy(
+                    input=softmax_out, label=label)
+                avg_loss = fluid.layers.mean(x=loss)
+
+                acc_top1 = fluid.layers.accuracy(
+                    input=softmax_out, label=label, k=1)
+                acc_top5 = fluid.layers.accuracy(
+                    input=softmax_out, label=label, k=5)
+
+                dy_out = avg_loss.numpy()
+                if args.use_data_parallel:
+                    avg_loss = se_resnext.scale_loss(avg_loss)
+                    avg_loss.backward()
+                    se_resnext.apply_collective_grads()
+                else:
+                    avg_loss.backward()
+
+                optimizer.minimize(avg_loss)
+                se_resnext.clear_gradients()
+
+                lr = optimizer._global_learning_rate().numpy()
+                total_loss += dy_out
+                total_acc1 += acc_top1.numpy()
+                total_acc5 += acc_top5.numpy()
+                total_sample += 1
+                if batch_id % 10 == 0:
+                    print( "epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f lr %0.5f" % \
+                           ( epoch_id, batch_id, total_loss / total_sample, \
+                             total_acc1 / total_sample, total_acc5 / total_sample, lr))
+            total_train_time += (time.time() - stime)
+
+            if args.ce:
+                print("kpis\ttrain_acc1\t%0.3f" % (total_acc1 / total_sample))
+                print("kpis\ttrain_acc5\t%0.3f" % (total_acc5 / total_sample))
+                print("kpis\ttrain_loss\t%0.3f" % (total_loss / total_sample))
+            print("epoch %d | batch step %d, loss %0.3f acc1 %0.3f acc5 %0.3f" % \
+                  (epoch_id, batch_id, total_loss / total_sample, \
+                   total_acc1 / total_sample, total_acc5 / total_sample))
+            se_resnext.eval()
+            eval(se_resnext, test_loader)
+            se_resnext.train()
+
+        print("total train time: {} s".format(total_train_time))
+
+
+if __name__ == '__main__':
+    train()

dygraph/transformer/train.py

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import argparse
 import ast
+import time
 
 import numpy as np
 import paddle
@@ -144,10 +145,12 @@ def train(args):
                                   batch_size=TrainTaskConfig.batch_size)
 
         # loop for training iterations
+        total_train_time = 0
         for i in range(TrainTaskConfig.pass_num):
             dy_step = 0
             sum_cost = 0
             transformer.train()
+            stime = time.time()
             for batch in train_reader():
                 enc_inputs, dec_inputs, label, weights = prepare_train_input(
                     batch, ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
@@ -170,6 +173,7 @@ def train(args):
                     print("pass num : {}, batch_id: {}, dy_graph avg loss: {}".
                           format(i, dy_step,
                                  dy_avg_cost.numpy() * trainer_count))
+            total_train_time += (time.time() - stime)
 
             # switch to evaluation mode
             transformer.eval()
@@ -190,6 +194,8 @@ def train(args):
         if fluid.dygraph.parallel.Env().dev_id == 0:
             fluid.save_dygraph(transformer.state_dict(), args.model_file)
 
+        print("total train time: {} s".format(total_train_time))
+
 
 if __name__ == '__main__':
     args = parse_args()

dygraph/transformer/train_mp.py

+# Copyright (c) 2019 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.
+
+from __future__ import print_function
+import argparse
+import ast
+import time
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.dataset.wmt16 as wmt16
+
+from model import TransFormer, NoamDecay
+from config import *
+from data_util import *
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Arguments for Training")
+    parser.add_argument(
+        "--use_data_parallel",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to use multi-GPU.")
+    parser.add_argument(
+        "--model_file",
+        type=str,
+        default="transformer_params",
+        help="Save the model as a file named `model_file.pdparams`.")
+    parser.add_argument(
+        'opts',
+        help='See config.py for all options',
+        default=None,
+        nargs=argparse.REMAINDER)
+    args = parser.parse_args()
+    merge_cfg_from_list(args.opts, [TrainTaskConfig, ModelHyperParams])
+    return args
+
+
+def prepare_train_input_array(insts, src_pad_idx, trg_pad_idx, n_head):
+    """
+    inputs for training
+    """
+    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
+    src_word = src_word.reshape(-1, src_max_len)
+    src_pos = src_pos.reshape(-1, src_max_len)
+    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
+        [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
+    trg_word = trg_word.reshape(-1, trg_max_len)
+    trg_pos = trg_pos.reshape(-1, trg_max_len)
+
+    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
+                                [1, 1, trg_max_len, 1]).astype("float32")
+
+    lbl_word, lbl_weight, num_token = pad_batch_data(
+        [inst[2] for inst in insts],
+        trg_pad_idx,
+        n_head,
+        is_target=False,
+        is_label=True,
+        return_attn_bias=False,
+        return_max_len=False,
+        return_num_token=True)
+
+    return src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+
+def input_data_array_reader(reader, src_pad_idx, trg_pad_idx, n_head):
+    def __reader__():
+        r = reader()
+        for data in r:
+            src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight = \
+                    prepare_train_input_array(data, src_pad_idx, trg_pad_idx, n_head)
+            yield src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+    return __reader__
+
+def group_inputs(var_inputs):
+    enc_inputs = var_inputs[0:len(encoder_data_input_fields)]
+    dec_inputs = var_inputs[len(encoder_data_input_fields
+                                ):len(encoder_data_input_fields) +
+                            len(decoder_data_input_fields[:-1])]
+    label = var_inputs[-2]
+    weights = var_inputs[-1]
+
+    return enc_inputs, dec_inputs, label, weights
+
+def train(args):
+    """
+    train models
+    :return:
+    """
+
+    trainer_count = fluid.dygraph.parallel.Env().nranks
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+
+        # define model
+        transformer = TransFormer(
+            ModelHyperParams.src_vocab_size,
+            ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1,
+            ModelHyperParams.n_layer, ModelHyperParams.n_head,
+            ModelHyperParams.d_key, ModelHyperParams.d_value,
+            ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
+            ModelHyperParams.prepostprocess_dropout,
+            ModelHyperParams.attention_dropout, ModelHyperParams.relu_dropout,
+            ModelHyperParams.preprocess_cmd, ModelHyperParams.postprocess_cmd,
+            ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps)
+        # define optimizer
+        optimizer = fluid.optimizer.Adam(learning_rate=NoamDecay(
+            ModelHyperParams.d_model, TrainTaskConfig.warmup_steps,
+            TrainTaskConfig.learning_rate),
+            parameter_list = transformer.parameters(),
+                                         beta1=TrainTaskConfig.beta1,
+                                         beta2=TrainTaskConfig.beta2,
+                                         epsilon=TrainTaskConfig.eps)
+        #
+        if args.use_data_parallel:
+            transformer = fluid.dygraph.parallel.DataParallel(
+                transformer, strategy)
+
+        # define data generator for training and validation
+        train_reader = input_data_array_reader(
+            paddle.batch(
+                wmt16.train(
+                    ModelHyperParams.src_vocab_size, 
+                    ModelHyperParams.trg_vocab_size),
+                    batch_size=TrainTaskConfig.batch_size),
+                ModelHyperParams.eos_idx, 
+                ModelHyperParams.eos_idx,
+                ModelHyperParams.n_head)
+
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+
+        val_reader = input_data_array_reader(
+            paddle.batch(
+                wmt16.test(
+                    ModelHyperParams.src_vocab_size, 
+                    ModelHyperParams.trg_vocab_size),
+                    batch_size=TrainTaskConfig.batch_size),
+                ModelHyperParams.eos_idx, 
+                ModelHyperParams.eos_idx,
+                ModelHyperParams.n_head)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=200, use_multiprocess=True)
+        train_loader.set_batch_generator(train_reader, places=place)
+
+        val_loader = fluid.io.DataLoader.from_generator(capacity=200, use_multiprocess=True)
+        val_loader.set_batch_generator(val_reader, places=place)
+
+        # loop for training iterations
+        total_train_time = 0
+        for i in range(TrainTaskConfig.pass_num):
+            dy_step = 0
+            sum_cost = 0
+            transformer.train()
+            stime = time.time()
+            for batch in train_loader():
+                src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                    trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight = batch
+
+                enc_inputs, dec_inputs, label, weights = \
+                    group_inputs([src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+                        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight])
+
+                dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = transformer(
+                    enc_inputs, dec_inputs, label, weights)
+
+                if args.use_data_parallel:
+                    dy_avg_cost = transformer.scale_loss(dy_avg_cost)
+                    dy_avg_cost.backward()
+                    transformer.apply_collective_grads()
+                else:
+                    dy_avg_cost.backward()
+                optimizer.minimize(dy_avg_cost)
+                transformer.clear_gradients()
+
+                dy_step = dy_step + 1
+                if dy_step % 10 == 0:
+                    print("pass num : {}, batch_id: {}, dy_graph avg loss: {}".
+                          format(i, dy_step,
+                                 dy_avg_cost.numpy() * trainer_count))
+            total_train_time += (time.time() - stime)
+
+            # switch to evaluation mode
+            transformer.eval()
+            sum_cost = 0
+            token_num = 0
+            for batch in val_loader():
+                src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                    trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight = batch
+
+                enc_inputs, dec_inputs, label, weights = \
+                    group_inputs([src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+                        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight])
+
+                dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = transformer(
+                    enc_inputs, dec_inputs, label, weights)
+                sum_cost += dy_sum_cost.numpy()
+                token_num += dy_token_num.numpy()
+            print("pass : {} finished, validation avg loss: {}".format(
+                i, sum_cost / token_num))
+
+        if fluid.dygraph.parallel.Env().dev_id == 0:
+            fluid.save_dygraph(transformer.state_dict(), args.model_file)
+
+        print("total train time: {} s".format(total_train_time))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    train(args)

dygraph/transformer/train_sp.py

+# Copyright (c) 2019 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.
+
+from __future__ import print_function
+import argparse
+import ast
+import time
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.dataset.wmt16 as wmt16
+
+from model import TransFormer, NoamDecay
+from config import *
+from data_util import *
+
+
+def parse_args():
+    parser = argparse.ArgumentParser("Arguments for Training")
+    parser.add_argument(
+        "--use_data_parallel",
+        type=ast.literal_eval,
+        default=False,
+        help="The flag indicating whether to use multi-GPU.")
+    parser.add_argument(
+        "--model_file",
+        type=str,
+        default="transformer_params",
+        help="Save the model as a file named `model_file.pdparams`.")
+    parser.add_argument(
+        'opts',
+        help='See config.py for all options',
+        default=None,
+        nargs=argparse.REMAINDER)
+    args = parser.parse_args()
+    merge_cfg_from_list(args.opts, [TrainTaskConfig, ModelHyperParams])
+    return args
+
+
+def prepare_train_input_array(insts, src_pad_idx, trg_pad_idx, n_head):
+    """
+    inputs for training
+    """
+    src_word, src_pos, src_slf_attn_bias, src_max_len = pad_batch_data(
+        [inst[0] for inst in insts], src_pad_idx, n_head, is_target=False)
+    src_word = src_word.reshape(-1, src_max_len)
+    src_pos = src_pos.reshape(-1, src_max_len)
+    trg_word, trg_pos, trg_slf_attn_bias, trg_max_len = pad_batch_data(
+        [inst[1] for inst in insts], trg_pad_idx, n_head, is_target=True)
+    trg_word = trg_word.reshape(-1, trg_max_len)
+    trg_pos = trg_pos.reshape(-1, trg_max_len)
+
+    trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
+                                [1, 1, trg_max_len, 1]).astype("float32")
+
+    lbl_word, lbl_weight, num_token = pad_batch_data(
+        [inst[2] for inst in insts],
+        trg_pad_idx,
+        n_head,
+        is_target=False,
+        is_label=True,
+        return_attn_bias=False,
+        return_max_len=False,
+        return_num_token=True)
+
+    return src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+
+def input_data_array_reader(reader, src_pad_idx, trg_pad_idx, n_head):
+    def __reader__():
+        r = reader()
+        for data in r:
+            src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight = \
+                    prepare_train_input_array(data, src_pad_idx, trg_pad_idx, n_head)
+            yield src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight
+    return __reader__
+
+def group_inputs(var_inputs):
+    enc_inputs = var_inputs[0:len(encoder_data_input_fields)]
+    dec_inputs = var_inputs[len(encoder_data_input_fields
+                                ):len(encoder_data_input_fields) +
+                            len(decoder_data_input_fields[:-1])]
+    label = var_inputs[-2]
+    weights = var_inputs[-1]
+
+    return enc_inputs, dec_inputs, label, weights
+
+def train(args):
+    """
+    train models
+    :return:
+    """
+
+    trainer_count = fluid.dygraph.parallel.Env().nranks
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
+        if args.use_data_parallel else fluid.CUDAPlace(0)
+    with fluid.dygraph.guard(place):
+        if args.use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+
+        # define model
+        transformer = TransFormer(
+            ModelHyperParams.src_vocab_size,
+            ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1,
+            ModelHyperParams.n_layer, ModelHyperParams.n_head,
+            ModelHyperParams.d_key, ModelHyperParams.d_value,
+            ModelHyperParams.d_model, ModelHyperParams.d_inner_hid,
+            ModelHyperParams.prepostprocess_dropout,
+            ModelHyperParams.attention_dropout, ModelHyperParams.relu_dropout,
+            ModelHyperParams.preprocess_cmd, ModelHyperParams.postprocess_cmd,
+            ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps)
+        # define optimizer
+        optimizer = fluid.optimizer.Adam(learning_rate=NoamDecay(
+            ModelHyperParams.d_model, TrainTaskConfig.warmup_steps,
+            TrainTaskConfig.learning_rate),
+            parameter_list = transformer.parameters(),
+                                         beta1=TrainTaskConfig.beta1,
+                                         beta2=TrainTaskConfig.beta2,
+                                         epsilon=TrainTaskConfig.eps)
+        #
+        if args.use_data_parallel:
+            transformer = fluid.dygraph.parallel.DataParallel(
+                transformer, strategy)
+
+        # define data generator for training and validation
+        train_reader = input_data_array_reader(
+            paddle.batch(
+                wmt16.train(
+                    ModelHyperParams.src_vocab_size, 
+                    ModelHyperParams.trg_vocab_size),
+                    batch_size=TrainTaskConfig.batch_size),
+                ModelHyperParams.eos_idx, 
+                ModelHyperParams.eos_idx,
+                ModelHyperParams.n_head)
+
+        if args.use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+
+        val_reader = input_data_array_reader(
+            paddle.batch(
+                wmt16.test(
+                    ModelHyperParams.src_vocab_size, 
+                    ModelHyperParams.trg_vocab_size),
+                    batch_size=TrainTaskConfig.batch_size),
+                ModelHyperParams.eos_idx, 
+                ModelHyperParams.eos_idx,
+                ModelHyperParams.n_head)
+
+        train_loader = fluid.io.DataLoader.from_generator(capacity=200)
+        train_loader.set_batch_generator(train_reader, places=place)
+
+        val_loader = fluid.io.DataLoader.from_generator(capacity=200)
+        val_loader.set_batch_generator(val_reader, places=place)
+
+        # loop for training iterations
+        total_train_time = 0
+        for i in range(TrainTaskConfig.pass_num):
+            dy_step = 0
+            sum_cost = 0
+            transformer.train()
+            stime = time.time()
+            for batch in train_loader():
+                src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                    trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight = batch
+
+                enc_inputs, dec_inputs, label, weights = \
+                    group_inputs([src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+                        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight])
+
+                dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = transformer(
+                    enc_inputs, dec_inputs, label, weights)
+
+                if args.use_data_parallel:
+                    dy_avg_cost = transformer.scale_loss(dy_avg_cost)
+                    dy_avg_cost.backward()
+                    transformer.apply_collective_grads()
+                else:
+                    dy_avg_cost.backward()
+                optimizer.minimize(dy_avg_cost)
+                transformer.clear_gradients()
+
+                dy_step = dy_step + 1
+                if dy_step % 10 == 0:
+                    print("pass num : {}, batch_id: {}, dy_graph avg loss: {}".
+                          format(i, dy_step,
+                                 dy_avg_cost.numpy() * trainer_count))
+            total_train_time += (time.time() - stime)
+
+            # switch to evaluation mode
+            transformer.eval()
+            sum_cost = 0
+            token_num = 0
+            for batch in val_loader():
+                src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos, \
+                    trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight = batch
+
+                enc_inputs, dec_inputs, label, weights = \
+                    group_inputs([src_word, src_pos, src_slf_attn_bias, trg_word, trg_pos,
+                        trg_slf_attn_bias, trg_src_attn_bias, lbl_word, lbl_weight])
+
+                dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = transformer(
+                    enc_inputs, dec_inputs, label, weights)
+                sum_cost += dy_sum_cost.numpy()
+                token_num += dy_token_num.numpy()
+            print("pass : {} finished, validation avg loss: {}".format(
+                i, sum_cost / token_num))
+
+        if fluid.dygraph.parallel.Env().dev_id == 0:
+            fluid.save_dygraph(transformer.state_dict(), args.model_file)
+
+        print("total train time: {} s".format(total_train_time))
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    train(args)