Merge remote-tracking branch 'upstream/develop' into develop

demo/distillation/train.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import sys
+import math
+import logging
+import paddle
+import argparse
+import functools
+import numpy as np
+import paddle.fluid as fluid
+sys.path.append(sys.path[0] + "/../")
+import models
+import imagenet_reader as reader
+from utility import add_arguments, print_arguments
+from paddleslim.dist import merge, l2_loss, soft_label_loss, fsp_loss
+
+logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
+_logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
+
+parser = argparse.ArgumentParser(description=__doc__)
+add_arg = functools.partial(add_arguments, argparser=parser)
+# yapf: disable
+add_arg('batch_size',       int,  64*4,                 "Minibatch size.")
+add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
+add_arg('total_images',     int,  1281167,              "Training image number.")
+add_arg('image_shape',      str,  "3,224,224",         "Input image size")
+add_arg('lr',               float,  0.1,               "The learning rate used to fine-tune pruned model.")
+add_arg('lr_strategy',      str,  "piecewise_decay",   "The learning rate decay strategy.")
+add_arg('l2_decay',         float,  3e-5,               "The l2_decay parameter.")
+add_arg('momentum_rate',    float,  0.9,               "The value of momentum_rate.")
+add_arg('num_epochs',       int,  120,               "The number of total epochs.")
+add_arg('data',             str, "mnist",                 "Which data to use. 'mnist' or 'imagenet'")
+add_arg('log_period',       int, 20,                 "Log period in batches.")
+add_arg('model',            str,  "MobileNet",          "Set the network to use.")
+add_arg('pretrained_model', str,  None,                "Whether to use pretrained model.")
+add_arg('teacher_model',    str,  "ResNet50",          "Set the teacher network to use.")
+add_arg('teacher_pretrained_model', str,  "../pretrain/ResNet50_pretrained",                "Whether to use pretrained model.")
+parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step")
+# yapf: enable
+
+model_list = [m for m in dir(models) if "__" not in m]
+
+
+def piecewise_decay(args):
+    step = int(math.ceil(float(args.total_images) / args.batch_size))
+    bd = [step * e for e in args.step_epochs]
+    lr = [args.lr * (0.1**i) for i in range(len(bd) + 1)]
+    learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=learning_rate,
+        momentum=args.momentum_rate,
+        regularization=fluid.regularizer.L2Decay(args.l2_decay))
+    return optimizer
+
+
+def cosine_decay(args):
+    step = int(math.ceil(float(args.total_images) / args.batch_size))
+    learning_rate = fluid.layers.cosine_decay(
+        learning_rate=args.lr, step_each_epoch=step, epochs=args.num_epochs)
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=learning_rate,
+        momentum=args.momentum_rate,
+        regularization=fluid.regularizer.L2Decay(args.l2_decay))
+    return optimizer
+
+
+def create_optimizer(args):
+    if args.lr_strategy == "piecewise_decay":
+        return piecewise_decay(args)
+    elif args.lr_strategy == "cosine_decay":
+        return cosine_decay(args)
+
+
+def compress(args):
+    if args.data == "mnist":
+        import paddle.dataset.mnist as reader
+        train_reader = reader.train()
+        val_reader = reader.test()
+        class_dim = 10
+        image_shape = "1,28,28"
+    elif args.data == "imagenet":
+        import imagenet_reader as reader
+        train_reader = reader.train()
+        val_reader = reader.val()
+        class_dim = 1000
+        image_shape = "3,224,224"
+    else:
+        raise ValueError("{} is not supported.".format(args.data))
+    image_shape = [int(m) for m in image_shape.split(",")]
+
+    assert args.model in model_list, "{} is not in lists: {}".format(
+        args.model, model_list)
+    student_program = fluid.Program()
+    s_startup = fluid.Program()
+
+    with fluid.program_guard(student_program, s_startup):
+        with fluid.unique_name.guard():
+            image = fluid.layers.data(
+                name='image', shape=image_shape, dtype='float32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            train_loader = fluid.io.DataLoader.from_generator(
+                feed_list=[image, label],
+                capacity=64,
+                use_double_buffer=True,
+                iterable=True)
+            valid_loader = fluid.io.DataLoader.from_generator(
+                feed_list=[image, label],
+                capacity=64,
+                use_double_buffer=True,
+                iterable=True)
+            # model definition
+            model = models.__dict__[args.model]()
+            out = model.net(input=image, class_dim=class_dim)
+            cost = fluid.layers.cross_entropy(input=out, label=label)
+            avg_cost = fluid.layers.mean(x=cost)
+            acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+            acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+    #print("="*50+"student_model_params"+"="*50)
+    #for v in student_program.list_vars():
+    #    print(v.name, v.shape)
+
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    train_reader = paddle.batch(
+        train_reader, batch_size=args.batch_size, drop_last=True)
+    val_reader = paddle.batch(
+        val_reader, batch_size=args.batch_size, drop_last=True)
+    val_program = student_program.clone(for_test=True)
+
+    places = fluid.cuda_places()
+    train_loader.set_sample_list_generator(train_reader, places)
+    valid_loader.set_sample_list_generator(val_reader, place)
+
+    teacher_model = models.__dict__[args.teacher_model]()
+    # define teacher program
+    teacher_program = fluid.Program()
+    t_startup = fluid.Program()
+    teacher_scope = fluid.Scope()
+    with fluid.scope_guard(teacher_scope):
+        with fluid.program_guard(teacher_program, t_startup):
+            with fluid.unique_name.guard():
+                image = fluid.layers.data(
+                    name='image', shape=image_shape, dtype='float32')
+                predict = teacher_model.net(image, class_dim=class_dim)
+
+            #print("="*50+"teacher_model_params"+"="*50)
+            #for v in teacher_program.list_vars():
+            #    print(v.name, v.shape)
+
+        exe.run(t_startup)
+        assert args.teacher_pretrained_model and os.path.exists(
+            args.teacher_pretrained_model
+        ), "teacher_pretrained_model should be set when teacher_model is not None."
+
+        def if_exist(var):
+            return os.path.exists(
+                os.path.join(args.teacher_pretrained_model, var.name)
+            ) and var.name != 'conv1_weights' and var.name != 'fc_0.w_0' and var.name != 'fc_0.b_0'
+
+        fluid.io.load_vars(
+            exe,
+            args.teacher_pretrained_model,
+            main_program=teacher_program,
+            predicate=if_exist)
+
+    data_name_map = {'image': 'image'}
+    main = merge(
+        teacher_program,
+        student_program,
+        data_name_map,
+        place,
+        teacher_scope=teacher_scope)
+
+    #print("="*50+"teacher_vars"+"="*50)
+    #for v in teacher_program.list_vars():
+    #    if '_generated_var' not in v.name and 'fetch' not in v.name and 'feed' not in v.name:
+    #        print(v.name, v.shape)
+    #return
+
+    with fluid.program_guard(main, s_startup):
+        l2_loss_v = l2_loss("teacher_fc_0.tmp_0", "fc_0.tmp_0", main)
+        fsp_loss_v = fsp_loss("teacher_res2a_branch2a.conv2d.output.1.tmp_0",
+                              "teacher_res3a_branch2a.conv2d.output.1.tmp_0",
+                              "depthwise_conv2d_1.tmp_0", "conv2d_3.tmp_0",
+                              main)
+        loss = avg_cost + l2_loss_v + fsp_loss_v
+        opt = create_optimizer(args)
+        opt.minimize(loss)
+    exe.run(s_startup)
+    build_strategy = fluid.BuildStrategy()
+    build_strategy.fuse_all_reduce_ops = False
+    parallel_main = fluid.CompiledProgram(main).with_data_parallel(
+        loss_name=loss.name, build_strategy=build_strategy)
+
+    for epoch_id in range(args.num_epochs):
+        for step_id, data in enumerate(train_loader):
+            loss_1, loss_2, loss_3, loss_4 = exe.run(
+                parallel_main,
+                feed=data,
+                fetch_list=[
+                    loss.name, avg_cost.name, l2_loss_v.name, fsp_loss_v.name
+                ])
+            if step_id % args.log_period == 0:
+                _logger.info(
+                    "train_epoch {} step {} loss {:.6f}, class loss {:.6f}, l2 loss {:.6f}, fsp loss {:.6f}".
+                    format(epoch_id, step_id, loss_1[0], loss_2[0], loss_3[0],
+                           loss_4[0]))
+        val_acc1s = []
+        val_acc5s = []
+        for step_id, data in enumerate(valid_loader):
+            val_loss, val_acc1, val_acc5 = exe.run(
+                val_program,
+                data,
+                fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name])
+            val_acc1s.append(val_acc1)
+            val_acc5s.append(val_acc5)
+            if step_id % args.log_period == 0:
+                _logger.info(
+                    "valid_epoch {} step {} loss {:.6f}, top1 {:.6f}, top5 {:.6f}".
+                    format(epoch_id, step_id, val_loss[0], val_acc1[0],
+                           val_acc5[0]))
+        _logger.info("epoch {} top1 {:.6f}, top5 {:.6f}".format(
+            epoch_id, np.mean(val_acc1s), np.mean(val_acc5s)))
+
+
+def main():
+    args = parser.parse_args()
+    print_arguments(args)
+    compress(args)
+
+
+if __name__ == '__main__':
+    main()

demo/nas/sa_nas_mobilenetv2.py

+import sys
+sys.path.append('..')
+import numpy as np
+import argparse
+import ast
+import time
+import argparse
+import ast
+import logging
+import paddle
+import paddle.fluid as fluid
+from paddleslim.nas.search_space.search_space_factory import SearchSpaceFactory
+from paddleslim.analysis import flops
+from paddleslim.nas import SANAS
+from paddleslim.common import get_logger
+from optimizer import create_optimizer
+import imagenet_reader
+
+_logger = get_logger(__name__, level=logging.INFO)
+
+
+def create_data_loader(image_shape):
+    data_shape = [-1] + image_shape
+    data = fluid.data(name='data', shape=data_shape, dtype='float32')
+    label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
+    data_loader = fluid.io.DataLoader.from_generator(
+        feed_list=[data, label],
+        capacity=1024,
+        use_double_buffer=True,
+        iterable=True)
+    return data_loader, data, label
+
+
+def build_program(main_program,
+                  startup_program,
+                  image_shape,
+                  archs,
+                  args,
+                  is_test=False):
+    with fluid.program_guard(main_program, startup_program):
+        data_loader, data, label = create_data_loader(image_shape)
+        output = archs(data)
+
+        softmax_out = fluid.layers.softmax(input=output, use_cudnn=False)
+        cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
+        avg_cost = fluid.layers.mean(cost)
+        acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
+        acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
+
+        if is_test == False:
+            optimizer = create_optimizer(args)
+            optimizer.minimize(avg_cost)
+    return data_loader, avg_cost, acc_top1, acc_top5
+
+
+def search_mobilenetv2(config, args, image_size, is_server=True):
+    factory = SearchSpaceFactory()
+    space = factory.get_search_space(config)
+    if is_server:
+        ### start a server and a client
+        sa_nas = SANAS(
+            config,
+            server_addr=("", 8883),
+            init_temperature=args.init_temperature,
+            reduce_rate=args.reduce_rate,
+            search_steps=args.search_steps,
+            is_server=True)
+    else:
+        ### start a client
+        sa_nas = SANAS(
+            config,
+            server_addr=("10.255.125.38", 8883),
+            init_temperature=args.init_temperature,
+            reduce_rate=args.reduce_rate,
+            search_steps=args.search_steps,
+            is_server=False)
+
+    image_shape = [3, image_size, image_size]
+    for step in range(args.search_steps):
+        archs = sa_nas.next_archs()[0]
+
+        train_program = fluid.Program()
+        test_program = fluid.Program()
+        startup_program = fluid.Program()
+        train_loader, avg_cost, acc_top1, acc_top5 = build_program(
+            train_program, startup_program, image_shape, archs, args)
+
+        current_flops = flops(train_program)
+        print('step: {}, current_flops: {}'.format(step, current_flops))
+        if current_flops > args.max_flops:
+            continue
+
+        test_loader, test_avg_cost, test_acc_top1, test_acc_top5 = build_program(
+            test_program,
+            startup_program,
+            image_shape,
+            archs,
+            args,
+            is_test=True)
+        test_program = test_program.clone(for_test=True)
+
+        place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_program)
+
+        if args.data == 'cifar10':
+            train_reader = paddle.batch(
+                paddle.reader.shuffle(
+                    paddle.dataset.cifar.train10(cycle=False), buf_size=1024),
+                batch_size=args.batch_size,
+                drop_last=True)
+
+            test_reader = paddle.batch(
+                paddle.dataset.cifar.test10(cycle=False),
+                batch_size=args.batch_size,
+                drop_last=False)
+        elif args.data == 'imagenet':
+            train_reader = paddle.batch(
+                imagenet_reader.train(),
+                batch_size=args.batch_size,
+                drop_last=True)
+            test_reader = paddle.batch(
+                imagenet_reader.val(),
+                batch_size=args.batch_size,
+                drop_last=False)
+
+        #test_loader, _, _ = create_data_loader(image_shape)
+        train_loader.set_sample_list_generator(
+            train_reader,
+            places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
+        test_loader.set_sample_list_generator(test_reader, places=place)
+
+        build_strategy = fluid.BuildStrategy()
+        train_compiled_program = fluid.CompiledProgram(
+            train_program).with_data_parallel(
+                loss_name=avg_cost.name, build_strategy=build_strategy)
+        for epoch_id in range(args.retain_epoch):
+            for batch_id, data in enumerate(train_loader()):
+                fetches = [avg_cost.name]
+                s_time = time.time()
+                outs = exe.run(train_compiled_program,
+                               feed=data,
+                               fetch_list=fetches)[0]
+                batch_time = time.time() - s_time
+                if batch_id % 10 == 0:
+                    _logger.info(
+                        'TRAIN: steps: {}, epoch: {}, batch: {}, cost: {}, batch_time: {}ms'.
+                        format(step, epoch_id, batch_id, outs[0], batch_time))
+
+        reward = []
+        for batch_id, data in enumerate(test_loader()):
+            test_fetches = [
+                test_avg_cost.name, test_acc_top1.name, test_acc_top5.name
+            ]
+            batch_reward = exe.run(test_program,
+                                   feed=data,
+                                   fetch_list=test_fetches)
+            reward_avg = np.mean(np.array(batch_reward), axis=1)
+            reward.append(reward_avg)
+
+            _logger.info(
+                'TEST: step: {}, batch: {}, avg_cost: {}, acc_top1: {}, acc_top5: {}'.
+                format(step, batch_id, batch_reward[0], batch_reward[1],
+                       batch_reward[2]))
+
+        finally_reward = np.mean(np.array(reward), axis=0)
+        _logger.info(
+            'FINAL TEST: avg_cost: {}, acc_top1: {}, acc_top5: {}'.format(
+                finally_reward[0], finally_reward[1], finally_reward[2]))
+
+        sa_nas.reward(float(finally_reward[1]))
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser(
+        description='SA NAS MobileNetV2 cifar10 argparase')
+    parser.add_argument(
+        '--use_gpu',
+        type=ast.literal_eval,
+        default=True,
+        help='Whether to use GPU in train/test model.')
+    parser.add_argument(
+        '--batch_size', type=int, default=256, help='batch size.')
+    parser.add_argument(
+        '--data',
+        type=str,
+        default='cifar10',
+        choices=['cifar10', 'imagenet'],
+        help='server address.')
+    # controller
+    parser.add_argument(
+        '--reduce_rate', type=float, default=0.85, help='reduce rate.')
+    parser.add_argument(
+        '--init_temperature',
+        type=float,
+        default=10.24,
+        help='init temperature.')
+    parser.add_argument(
+        '--is_server',
+        type=ast.literal_eval,
+        default=True,
+        help='Whether to start a server.')
+    # nas args
+    parser.add_argument(
+        '--max_flops', type=int, default=592948064, help='reduce rate.')
+    parser.add_argument(
+        '--retain_epoch', type=int, default=5, help='train epoch before val.')
+    parser.add_argument(
+        '--end_epoch', type=int, default=500, help='end epoch present client.')
+    parser.add_argument(
+        '--search_steps',
+        type=int,
+        default=100,
+        help='controller server number.')
+    parser.add_argument(
+        '--server_address', type=str, default=None, help='server address.')
+    # optimizer args
+    parser.add_argument(
+        '--lr_strategy',
+        type=str,
+        default='piecewise_decay',
+        help='learning rate decay strategy.')
+    parser.add_argument('--lr', type=float, default=0.1, help='learning rate.')
+    parser.add_argument(
+        '--l2_decay', type=float, default=1e-4, help='learning rate decay.')
+    parser.add_argument(
+        '--step_epochs',
+        nargs='+',
+        type=int,
+        default=[30, 60, 90],
+        help="piecewise decay step")
+    parser.add_argument(
+        '--momentum_rate',
+        type=float,
+        default=0.9,
+        help='learning rate decay.')
+    parser.add_argument(
+        '--warm_up_epochs',
+        type=float,
+        default=5.0,
+        help='learning rate decay.')
+    parser.add_argument(
+        '--num_epochs', type=int, default=120, help='learning rate decay.')
+    parser.add_argument(
+        '--decay_epochs', type=float, default=2.4, help='learning rate decay.')
+    parser.add_argument(
+        '--decay_rate', type=float, default=0.97, help='learning rate decay.')
+    parser.add_argument(
+        '--total_images',
+        type=int,
+        default=1281167,
+        help='learning rate decay.')
+    args = parser.parse_args()
+    print(args)
+
+    if args.data == 'cifar10':
+        image_size = 32
+        block_num = 3
+    elif args.data == 'imagenet':
+        image_size = 224
+        block_num = 6
+    else:
+        raise NotImplemented(
+            'data must in [cifar10, imagenet], but received: {}'.format(
+                args.data))
+
+    config_info = {
+        'input_size': image_size,
+        'output_size': 1,
+        'block_num': block_num,
+        'block_mask': None
+    }
+    config = [('MobileNetV2Space', config_info)]
+
+    search_mobilenetv2(config, args, image_size, is_server=args.is_server)

demo/nas/sa_nas_mobilenetv2_cifar10.py

-import sys
-sys.path.append('..')
-import numpy as np
-import argparse
-import ast
-import paddle
-import paddle.fluid as fluid
-from paddleslim.nas.search_space.search_space_factory import SearchSpaceFactory
-from paddleslim.analysis import flops
-from paddleslim.nas import SANAS
-
-
-def create_data_loader():
-    data = fluid.data(name='data', shape=[-1, 3, 32, 32], dtype='float32')
-    label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
-    data_loader = fluid.io.DataLoader.from_generator(
-        feed_list=[data, label],
-        capacity=1024,
-        use_double_buffer=True,
-        iterable=True)
-    return data_loader, data, label
-
-
-def init_sa_nas(config):
-    factory = SearchSpaceFactory()
-    space = factory.get_search_space(config)
-    model_arch = space.token2arch()[0]
-    main_program = fluid.Program()
-    startup_program = fluid.Program()
-
-    with fluid.program_guard(main_program, startup_program):
-        data_loader, data, label = create_data_loader()
-        output = model_arch(data)
-        cost = fluid.layers.mean(
-            fluid.layers.softmax_with_cross_entropy(
-                logits=output, label=label))
-
-        base_flops = flops(main_program)
-        search_steps = 10000000
-
-        ### start a server and a client
-        sa_nas = SANAS(config, search_steps=search_steps, is_server=True)
-
-        ### start a client, server_addr is server address
-        #sa_nas = SANAS(config, max_flops = base_flops, server_addr=("10.255.125.38", 18607), search_steps = search_steps, is_server=False)
-
-    return sa_nas, search_steps
-
-
-def search_mobilenetv2_cifar10(config, args):
-    sa_nas, search_steps = init_sa_nas(config)
-    for i in range(search_steps):
-        print('search step: ', i)
-        archs = sa_nas.next_archs()[0]
-        train_program = fluid.Program()
-        test_program = fluid.Program()
-        startup_program = fluid.Program()
-        with fluid.program_guard(train_program, startup_program):
-            train_loader, data, label = create_data_loader()
-            output = archs(data)
-            cost = fluid.layers.mean(
-                fluid.layers.softmax_with_cross_entropy(
-                    logits=output, label=label))[0]
-            test_program = train_program.clone(for_test=True)
-
-            optimizer = fluid.optimizer.Momentum(
-                learning_rate=0.1,
-                momentum=0.9,
-                regularization=fluid.regularizer.L2Decay(1e-4))
-            optimizer.minimize(cost)
-
-        place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
-        exe = fluid.Executor(place)
-        exe.run(startup_program)
-        train_reader = paddle.reader.shuffle(
-            paddle.dataset.cifar.train10(cycle=False), buf_size=1024)
-        train_loader.set_sample_generator(
-            train_reader,
-            batch_size=512,
-            places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
-
-        test_loader, _, _ = create_data_loader()
-        test_reader = paddle.dataset.cifar.test10(cycle=False)
-        test_loader.set_sample_generator(
-            test_reader,
-            batch_size=256,
-            drop_last=False,
-            places=fluid.cuda_places() if args.use_gpu else fluid.cpu_places())
-
-        for epoch_id in range(10):
-            for batch_id, data in enumerate(train_loader()):
-                loss = exe.run(train_program,
-                               feed=data,
-                               fetch_list=[cost.name])[0]
-                if batch_id % 5 == 0:
-                    print('epoch: {}, batch: {}, loss: {}'.format(
-                        epoch_id, batch_id, loss[0]))
-
-        for data in test_loader():
-            reward = exe.run(test_program, feed=data,
-                             fetch_list=[cost.name])[0]
-
-        print('reward:', reward)
-        sa_nas.reward(float(reward))
-
-
-if __name__ == '__main__':
-
-    parser = argparse.ArgumentParser(
-        description='SA NAS MobileNetV2 cifar10 argparase')
-    parser.add_argument(
-        '--use_gpu',
-        type=ast.literal_eval,
-        default=True,
-        help='Whether to use GPU in train/test model.')
-    args = parser.parse_args()
-    print(args)
-
-    config_info = {'input_size': 32, 'output_size': 1, 'block_num': 5}
-    config = [('MobileNetV2Space', config_info)]
-
-    search_mobilenetv2_cifar10(config, args)

demo/optimizer.py

+#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+#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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle.fluid as fluid
+import paddle.fluid.layers.ops as ops
+from paddle.fluid.initializer import init_on_cpu
+from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
+
+
+def cosine_decay(learning_rate, step_each_epoch, epochs=120):
+    """Applies cosine decay to the learning rate.
+    lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
+    """
+    global_step = _decay_step_counter()
+
+    with init_on_cpu():
+        epoch = ops.floor(global_step / step_each_epoch)
+        decayed_lr = learning_rate * \
+                     (ops.cos(epoch * (math.pi / epochs)) + 1)/2
+    return decayed_lr
+
+
+def cosine_decay_with_warmup(learning_rate, step_each_epoch, epochs=120):
+    """Applies cosine decay to the learning rate.
+    lr = 0.05 * (math.cos(epoch * (math.pi / 120)) + 1)
+    decrease lr for every mini-batch and start with warmup.
+    """
+    global_step = _decay_step_counter()
+    lr = fluid.layers.tensor.create_global_var(
+        shape=[1],
+        value=0.0,
+        dtype='float32',
+        persistable=True,
+        name="learning_rate")
+
+    warmup_epoch = fluid.layers.fill_constant(
+        shape=[1], dtype='float32', value=float(5), force_cpu=True)
+
+    with init_on_cpu():
+        epoch = ops.floor(global_step / step_each_epoch)
+        with fluid.layers.control_flow.Switch() as switch:
+            with switch.case(epoch < warmup_epoch):
+                decayed_lr = learning_rate * (global_step /
+                                              (step_each_epoch * warmup_epoch))
+                fluid.layers.tensor.assign(input=decayed_lr, output=lr)
+            with switch.default():
+                decayed_lr = learning_rate * \
+                    (ops.cos((global_step - warmup_epoch * step_each_epoch) * (math.pi / (epochs * step_each_epoch))) + 1)/2
+                fluid.layers.tensor.assign(input=decayed_lr, output=lr)
+    return lr
+
+
+def exponential_decay_with_warmup(learning_rate,
+                                  step_each_epoch,
+                                  decay_epochs,
+                                  decay_rate=0.97,
+                                  warm_up_epoch=5.0):
+    """Applies exponential decay to the learning rate.
+    """
+    global_step = _decay_step_counter()
+    lr = fluid.layers.tensor.create_global_var(
+        shape=[1],
+        value=0.0,
+        dtype='float32',
+        persistable=True,
+        name="learning_rate")
+
+    warmup_epoch = fluid.layers.fill_constant(
+        shape=[1], dtype='float32', value=float(warm_up_epoch), force_cpu=True)
+
+    with init_on_cpu():
+        epoch = ops.floor(global_step / step_each_epoch)
+        with fluid.layers.control_flow.Switch() as switch:
+            with switch.case(epoch < warmup_epoch):
+                decayed_lr = learning_rate * (global_step /
+                                              (step_each_epoch * warmup_epoch))
+                fluid.layers.assign(input=decayed_lr, output=lr)
+            with switch.default():
+                div_res = (global_step - warmup_epoch * step_each_epoch
+                           ) / decay_epochs
+                div_res = ops.floor(div_res)
+                decayed_lr = learning_rate * (decay_rate**div_res)
+                fluid.layers.assign(input=decayed_lr, output=lr)
+
+    return lr
+
+
+def lr_warmup(learning_rate, warmup_steps, start_lr, end_lr):
+    """ Applies linear learning rate warmup for distributed training
+        Argument learning_rate can be float or a Variable
+        lr = lr + (warmup_rate * step / warmup_steps)
+    """
+    assert (isinstance(end_lr, float))
+    assert (isinstance(start_lr, float))
+    linear_step = end_lr - start_lr
+    with fluid.default_main_program()._lr_schedule_guard():
+        lr = fluid.layers.tensor.create_global_var(
+            shape=[1],
+            value=0.0,
+            dtype='float32',
+            persistable=True,
+            name="learning_rate_warmup")
+
+        global_step = fluid.layers.learning_rate_scheduler._decay_step_counter(
+        )
+
+        with fluid.layers.control_flow.Switch() as switch:
+            with switch.case(global_step < warmup_steps):
+                decayed_lr = start_lr + linear_step * (global_step /
+                                                       warmup_steps)
+                fluid.layers.tensor.assign(decayed_lr, lr)
+            with switch.default():
+                fluid.layers.tensor.assign(learning_rate, lr)
+
+        return lr
+
+
+class Optimizer(object):
+    """A class used to represent several optimizer methods
+
+    Attributes:
+        batch_size: batch size on all devices.
+        lr: learning rate.
+        lr_strategy: learning rate decay strategy.
+        l2_decay: l2_decay parameter.
+        momentum_rate: momentum rate when using Momentum optimizer.
+        step_epochs: piecewise decay steps.
+        num_epochs: number of total epochs.
+
+        total_images: total images.
+        step: total steps in the an epoch.
+        
+    """
+
+    def __init__(self, args):
+        self.batch_size = args.batch_size
+        self.lr = args.lr
+        self.lr_strategy = args.lr_strategy
+        self.l2_decay = args.l2_decay
+        self.momentum_rate = args.momentum_rate
+        self.step_epochs = args.step_epochs
+        self.num_epochs = args.num_epochs
+        self.warm_up_epochs = args.warm_up_epochs
+        self.decay_epochs = args.decay_epochs
+        self.decay_rate = args.decay_rate
+        self.total_images = args.total_images
+
+        self.step = int(math.ceil(float(self.total_images) / self.batch_size))
+
+    def piecewise_decay(self):
+        """piecewise decay with Momentum optimizer
+
+            Returns:
+            a piecewise_decay optimizer
+        """
+        bd = [self.step * e for e in self.step_epochs]
+        lr = [self.lr * (0.1**i) for i in range(len(bd) + 1)]
+        learning_rate = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=learning_rate,
+            momentum=self.momentum_rate,
+            regularization=fluid.regularizer.L2Decay(self.l2_decay))
+        return optimizer
+
+    def cosine_decay(self):
+        """cosine decay with Momentum optimizer
+
+        Returns:
+            a cosine_decay optimizer
+        """
+
+        learning_rate = fluid.layers.cosine_decay(
+            learning_rate=self.lr,
+            step_each_epoch=self.step,
+            epochs=self.num_epochs)
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=learning_rate,
+            momentum=self.momentum_rate,
+            regularization=fluid.regularizer.L2Decay(self.l2_decay))
+        return optimizer
+
+    def cosine_decay_warmup(self):
+        """cosine decay with warmup
+
+        Returns:
+            a cosine_decay_with_warmup optimizer
+        """
+
+        learning_rate = cosine_decay_with_warmup(
+            learning_rate=self.lr,
+            step_each_epoch=self.step,
+            epochs=self.num_epochs)
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=learning_rate,
+            momentum=self.momentum_rate,
+            regularization=fluid.regularizer.L2Decay(self.l2_decay))
+        return optimizer
+
+    def exponential_decay_warmup(self):
+        """exponential decay with warmup
+
+        Returns:
+            a exponential_decay_with_warmup optimizer
+        """
+
+        learning_rate = exponential_decay_with_warmup(
+            learning_rate=self.lr,
+            step_each_epoch=self.step,
+            decay_epochs=self.step * self.decay_epochs,
+            decay_rate=self.decay_rate,
+            warm_up_epoch=self.warm_up_epochs)
+        optimizer = fluid.optimizer.RMSProp(
+            learning_rate=learning_rate,
+            regularization=fluid.regularizer.L2Decay(self.l2_decay),
+            momentum=self.momentum_rate,
+            rho=0.9,
+            epsilon=0.001)
+        return optimizer
+
+    def linear_decay(self):
+        """linear decay with Momentum optimizer
+
+        Returns:
+            a linear_decay optimizer
+        """
+
+        end_lr = 0
+        learning_rate = fluid.layers.polynomial_decay(
+            self.lr, self.step, end_lr, power=1)
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=learning_rate,
+            momentum=self.momentum_rate,
+            regularization=fluid.regularizer.L2Decay(self.l2_decay))
+
+        return optimizer
+
+    def adam_decay(self):
+        """Adam optimizer
+
+        Returns: 
+            an adam_decay optimizer
+        """
+
+        return fluid.optimizer.Adam(learning_rate=self.lr)
+
+    def cosine_decay_RMSProp(self):
+        """cosine decay with RMSProp optimizer
+
+        Returns: 
+            an cosine_decay_RMSProp optimizer
+        """
+
+        learning_rate = fluid.layers.cosine_decay(
+            learning_rate=self.lr,
+            step_each_epoch=self.step,
+            epochs=self.num_epochs)
+        optimizer = fluid.optimizer.RMSProp(
+            learning_rate=learning_rate,
+            momentum=self.momentum_rate,
+            regularization=fluid.regularizer.L2Decay(self.l2_decay),
+            # Apply epsilon=1 on ImageNet dataset.
+            epsilon=1)
+        return optimizer
+
+    def default_decay(self):
+        """default decay
+
+        Returns:
+            default decay optimizer
+        """
+
+        optimizer = fluid.optimizer.Momentum(
+            learning_rate=self.lr,
+            momentum=self.momentum_rate,
+            regularization=fluid.regularizer.L2Decay(self.l2_decay))
+        return optimizer
+
+
+def create_optimizer(args):
+    Opt = Optimizer(args)
+    optimizer = getattr(Opt, args.lr_strategy)()
+
+    return optimizer

paddleslim/common/controller_server.py

@@ -107,6 +107,8 @@ class ControllerServer(object):
                     _logger.debug("send message to {}: [{}]".format(addr,
                                                                     tokens))
                 conn.close()
+        except Exception, err:
+            _logger.error(err)
         finally:
             self._socket_server.close()
             self.close()

paddleslim/common/sa_controller.py

@@ -75,7 +75,7 @@ class SAController(EvolutionaryController):
         iter = int(iter)
         if iter > self._iter:
             self._iter = iter
-            temperature = self._init_temperature * self._reduce_rate**self._iter
+        temperature = self._init_temperature * self._reduce_rate**self._iter
         if (reward > self._reward) or (np.random.random() <= math.exp(
             (reward - self._reward) / temperature)):
             self._reward = reward
@@ -98,7 +98,7 @@ class SAController(EvolutionaryController):
         new_tokens = tokens[:]
         index = int(len(self._range_table[0]) * np.random.random())
         new_tokens[index] = np.random.randint(self._range_table[0][index],
-                                              self._range_table[1][index] + 1)
+                                              self._range_table[1][index])
         _logger.debug("change index[{}] from {} to {}".format(index, tokens[
             index], new_tokens[index]))
         if self._constrain_func is None or self._max_try_times is None:

paddleslim/core/graph_wrapper.py

@@ -54,6 +54,9 @@ class VarWrapper(object):
         """
         return self._var.name
 
+    def __repr__(self):
+        return self._var.name
+
     def shape(self):
         """
         Get the shape of the varibale.
@@ -131,6 +134,11 @@ class OpWrapper(object):
         """
         return self._op.type
 
+    def __repr__(self):
+        return "op[id: {}, type: {}; inputs: {}]".format(self.idx(),
+                                                         self.type(),
+                                                         self.all_inputs())
+
     def is_bwd_op(self):
         """
         Whether this operator is backward op.

paddleslim/nas/sa_nas.py

@@ -60,16 +60,17 @@ class SANAS(object):
         self._init_temperature = init_temperature
         self._is_server = is_server
         self._configs = configs
-        self._keys = hashlib.md5(str(self._configs)).hexdigest()
+        self._key = hashlib.md5(str(self._configs)).hexdigest()
 
         server_ip, server_port = server_addr
         if server_ip == None or server_ip == "":
             server_ip = self._get_host_ip()
 
+        factory = SearchSpaceFactory()
+        self._search_space = factory.get_search_space(configs)
+
         # create controller server
         if self._is_server:
-            factory = SearchSpaceFactory()
-            self._search_space = factory.get_search_space(configs)
             init_tokens = self._search_space.init_tokens()
             range_table = self._search_space.range_table()
             range_table = (len(range_table) * [0], range_table)
@@ -90,6 +91,7 @@ class SANAS(object):
                 search_steps=search_steps,
                 key=self._key)
             self._controller_server.start()
+            server_port = self._controller_server.port()
 
         self._controller_client = ControllerClient(
             server_ip, server_port, key=self._key)
@@ -99,6 +101,9 @@ class SANAS(object):
     def _get_host_ip(self):
         return socket.gethostbyname(socket.gethostname())
 
+    def tokens2arch(self, tokens):
+        return self._search_space.token2arch(self.tokens)
+
     def next_archs(self):
         """
         Get next network architectures.

paddleslim/nas/search_space/combine_search_space.py

@@ -39,6 +39,7 @@ class CombineSearchSpace(object):
         for config_list in config_lists:
             key, config = config_list
             self.spaces.append(self._get_single_search_space(key, config))
+        self.init_tokens()
 
     def _get_single_search_space(self, key, config):
         """
@@ -51,9 +52,11 @@ class CombineSearchSpace(object):
             model space(class)
         """
         cls = SEARCHSPACE.get(key)
-        space = cls(config['input_size'], config['output_size'],
-                    config['block_num'], config['block_mask'])
-
+        block_mask = config['block_mask'] if 'block_mask' in config else None
+        space = cls(config['input_size'],
+                    config['output_size'],
+                    config['block_num'],
+                    block_mask=block_mask)
         return space
 
     def init_tokens(self):

paddleslim/nas/search_space/mobilenetv1.py

@@ -32,10 +32,12 @@ class MobileNetV1Space(SearchSpaceBase):
                  input_size,
                  output_size,
                  block_num,
+                 block_mask,
                  scale=1.0,
                  class_dim=1000):
         super(MobileNetV1Space, self).__init__(input_size, output_size,
-                                               block_num)
+                                               block_num, block_mask)
+        assert self.block_mask == None, 'MobileNetV1Space will use origin MobileNetV1 as seach space, so use input_size, output_size and block_num to search'
         self.scale = scale
         self.class_dim = class_dim
         # self.head_num means the channel of first convolution

paddleslim/nas/search_space/mobilenetv2.py

@@ -113,40 +113,69 @@ class MobileNetV2Space(SearchSpaceBase):
 
         if tokens is None:
             tokens = self.init_tokens()
-        print(tokens)
 
-        bottleneck_params_list = []
+        self.bottleneck_params_list = []
         if self.block_num >= 1:
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (1, self.head_num[tokens[0]], 1, 1, 3))
         if self.block_num >= 2:
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (self.multiply[tokens[1]], self.filter_num1[tokens[2]],
                  self.repeat[tokens[3]], 2, self.k_size[tokens[4]]))
         if self.block_num >= 3:
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (self.multiply[tokens[5]], self.filter_num1[tokens[6]],
                  self.repeat[tokens[7]], 2, self.k_size[tokens[8]]))
         if self.block_num >= 4:
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (self.multiply[tokens[9]], self.filter_num2[tokens[10]],
                  self.repeat[tokens[11]], 2, self.k_size[tokens[12]]))
         if self.block_num >= 5:
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (self.multiply[tokens[13]], self.filter_num3[tokens[14]],
                  self.repeat[tokens[15]], 2, self.k_size[tokens[16]]))
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (self.multiply[tokens[17]], self.filter_num4[tokens[18]],
                  self.repeat[tokens[19]], 1, self.k_size[tokens[20]]))
         if self.block_num >= 6:
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (self.multiply[tokens[21]], self.filter_num5[tokens[22]],
                  self.repeat[tokens[23]], 2, self.k_size[tokens[24]]))
-            bottleneck_params_list.append(
+            self.bottleneck_params_list.append(
                 (self.multiply[tokens[25]], self.filter_num6[tokens[26]],
                  self.repeat[tokens[27]], 1, self.k_size[tokens[28]]))
 
-        def net_arch(input):
+        def _modify_bottle_params(output_stride=None):
+            if output_stride is not None and output_stride % 2 != 0:
+                raise Exception("output stride must to be even number")
+            if output_stride is None:
+                return
+            else:
+                stride = 2
+                for i, layer_setting in enumerate(self.bottleneck_params_list):
+                    t, c, n, s, ks = layer_setting
+                    stride = stride * s
+                    if stride > output_stride:
+                        s = 1
+                    self.bottleneck_params_list[i] = (t, c, n, s, ks)
+
+        def net_arch(input,
+                     end_points=None,
+                     decode_points=None,
+                     output_stride=None):
+            _modify_bottle_params(output_stride)
+
+            decode_ends = dict()
+
+            def check_points(count, points):
+                if points is None:
+                    return False
+                else:
+                    if isinstance(points, list):
+                        return (True if count in points else False)
+                    else:
+                        return (True if count == points else False)
+
             #conv1
             # all padding is 'SAME' in the conv2d, can compute the actual padding automatic. 
             input = conv_bn_layer(
@@ -157,14 +186,21 @@ class MobileNetV2Space(SearchSpaceBase):
                 padding='SAME',
                 act='relu6',
                 name='mobilenetv2_conv1_1')
+            layer_count = 1
+            if check_points(layer_count, decode_points):
+                decode_ends[layer_count] = input
+
+            if check_points(layer_count, end_points):
+                return input, decode_ends
 
             # bottleneck sequences
             i = 1
             in_c = int(32 * self.scale)
-            for layer_setting in bottleneck_params_list:
+            for layer_setting in self.bottleneck_params_list:
                 t, c, n, s, k = layer_setting
                 i += 1
-                input = self._invresi_blocks(
+                #print(input)
+                input, depthwise_output = self._invresi_blocks(
                     input=input,
                     in_c=in_c,
                     t=t,
@@ -174,6 +210,33 @@ class MobileNetV2Space(SearchSpaceBase):
                     k=k,
                     name='mobilenetv2_conv' + str(i))
                 in_c = int(c * self.scale)
+                layer_count += 1
+
+                ### decode_points and end_points means block num
+                if check_points(layer_count, decode_points):
+                    decode_ends[layer_count] = depthwise_output
+
+                if check_points(layer_count, end_points):
+                    return input, decode_ends
+
+            # last conv
+            input = conv_bn_layer(
+                input=input,
+                num_filters=int(1280 * self.scale)
+                if self.scale > 1.0 else 1280,
+                filter_size=1,
+                stride=1,
+                padding='SAME',
+                act='relu6',
+                name='mobilenetv2_conv' + str(i + 1))
+
+            input = fluid.layers.pool2d(
+                input=input,
+                pool_size=7,
+                pool_stride=1,
+                pool_type='avg',
+                global_pooling=True,
+                name='mobilenetv2_last_pool')
 
             # if output_size is 1, add fc layer in the end
             if self.output_size == 1:
@@ -248,6 +311,8 @@ class MobileNetV2Space(SearchSpaceBase):
             name=name + '_dwise',
             use_cudnn=False)
 
+        depthwise_output = bottleneck_conv
+
         linear_out = conv_bn_layer(
             input=bottleneck_conv,
             num_filters=num_filters,
@@ -260,7 +325,7 @@ class MobileNetV2Space(SearchSpaceBase):
         out = linear_out
         if ifshortcut:
             out = self._shortcut(input=input, data_residual=out)
-        return out
+        return out, depthwise_output
 
     def _invresi_blocks(self, input, in_c, t, c, n, s, k, name=None):
         """Build inverted residual blocks.
@@ -276,7 +341,7 @@ class MobileNetV2Space(SearchSpaceBase):
         Returns:
             Variable, layers output.
         """
-        first_block = self._inverted_residual_unit(
+        first_block, depthwise_output = self._inverted_residual_unit(
             input=input,
             num_in_filter=in_c,
             num_filters=c,
@@ -290,7 +355,7 @@ class MobileNetV2Space(SearchSpaceBase):
         last_c = c
 
         for i in range(1, n):
-            last_residual_block = self._inverted_residual_unit(
+            last_residual_block, depthwise_output = self._inverted_residual_unit(
                 input=last_residual_block,
                 num_in_filter=last_c,
                 num_filters=c,
@@ -299,4 +364,4 @@ class MobileNetV2Space(SearchSpaceBase):
                 filter_size=k,
                 expansion_factor=t,
                 name=name + '_' + str(i + 1))
-        return last_residual_block
+        return last_residual_block, depthwise_output

paddleslim/nas/search_space/search_space_base.py

@@ -19,11 +19,19 @@ class SearchSpaceBase(object):
     """Controller for Neural Architecture Search.
     """
 
-    def __init__(self, input_size, output_size, block_num, block_mask, *argss):
+    def __init__(self, input_size, output_size, block_num, block_mask, *args):
+        """init model config
+        """
         self.input_size = input_size
         self.output_size = output_size
         self.block_num = block_num
         self.block_mask = block_mask
+        if self.block_mask is not None:
+            assert isinstance(self.block_mask,
+                              list), 'Block_mask must be a list.'
+            print(
+                "If block_mask is NOT None, we will use block_mask as major configs!"
+            )
 
     def init_tokens(self):
         """Get init tokens in search space.

paddleslim/prune/pruner.py

@@ -528,33 +528,41 @@ class Pruner():
         Returns: 
             list<VarWrapper>: A list of operators.
         """
+        _logger.debug("######################search: {}######################".
+                      format(op_node))
         visited = [op_node.idx()]
         stack = []
         brothers = []
         for op in graph.next_ops(op_node):
-            if (op.type() != 'conv2d') and (op.type() != 'fc') and (
-                    not op.is_bwd_op()):
+            if ("conv2d" not in op.type()) and (op.type() != 'fc') and (
+                    not op.is_bwd_op()) and (not op.is_opt_op()):
                 stack.append(op)
                 visited.append(op.idx())
         while len(stack) > 0:
             top_op = stack.pop()
-            if top_op.type().startswith("elementwise_"):
-                for parent in graph.pre_ops(top_op):
-                    if parent.idx() not in visited and (
-                            not parent.is_bwd_op()):
-                        if ((parent.type() == 'conv2d') or
-                            (parent.type() == 'fc')):
-                            brothers.append(parent)
-                        else:
-                            stack.append(parent)
-                        visited.append(parent.idx())
+            for parent in graph.pre_ops(top_op):
+                if parent.idx() not in visited and (
+                        not parent.is_bwd_op()) and (not parent.is_opt_op()):
+                    _logger.debug("----------go back from {} to {}----------".
+                                  format(top_op, parent))
+                    if (('conv2d' in parent.type()) or
+                        (parent.type() == 'fc')):
+                        brothers.append(parent)
+                    else:
+                        stack.append(parent)
+                    visited.append(parent.idx())
 
             for child in graph.next_ops(top_op):
-                if (child.type() != 'conv2d') and (child.type() != 'fc') and (
+                if ('conv2d' not in child.type()
+                    ) and (child.type() != 'fc') and (
                         child.idx() not in visited) and (
-                            not child.is_bwd_op()):
+                            not child.is_bwd_op()) and (not child.is_opt_op()):
                     stack.append(child)
                     visited.append(child.idx())
+        _logger.debug("brothers: {}".format(brothers))
+        _logger.debug(
+            "######################Finish search######################".format(
+                op_node))
         return brothers
 
     def _cal_pruned_idx(self, name, param, ratio, axis):

tests/test_prune.py

@@ -15,7 +15,7 @@ import sys
 sys.path.append("../")
 import unittest
 import paddle.fluid as fluid
-from prune import Pruner
+from paddleslim.prune import Pruner
 from layers import conv_bn_layer
 
 

tests/test_sa_nas.py

@@ -40,7 +40,11 @@ class TestSANAS(unittest.TestCase):
         base_flops = flops(main_program)
 
         search_steps = 3
-        sa_nas = SANAS(configs, search_steps=search_steps, is_server=True)
+        sa_nas = SANAS(
+            configs,
+            search_steps=search_steps,
+            server_addr=("", 0),
+            is_server=True)
 
         for i in range(search_steps):
             archs = sa_nas.next_archs()