Merge branch 'develop' of http://gitlab.baidu.com/PaddlePaddle/PaddleSlim into refine_nas

demo/auto_prune/train.py

+import os
+import sys
+import logging
+import paddle
+import argparse
+import functools
+import math
+import time
+import numpy as np
+import paddle.fluid as fluid
+from paddleslim.prune import AutoPruner
+from paddleslim.common import get_logger
+from paddleslim.analysis import flops
+sys.path.append(sys.path[0] + "/../")
+import models
+from utility import add_arguments, print_arguments
+
+_logger = get_logger(__name__, level=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('model',            str,  "MobileNet",                "The target model.")
+add_arg('pretrained_model', str,  "../pretrained_model/MobileNetV1_pretained",                "Whether to use pretrained model.")
+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('total_images',     int,  1281167,               "The number of total training images.")
+parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step")
+add_arg('config_file',      str, None,                 "The config file for compression with yaml format.")
+add_arg('data',             str, "mnist",                 "Which data to use. 'mnist' or 'imagenet'")
+add_arg('log_period',       int, 10,                 "Log period in batches.")
+add_arg('test_period',      int, 10,                 "Test period in epoches.")
+# 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):
+
+    train_reader = None
+    test_reader = None
+    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)
+    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    # 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)
+    val_program = fluid.default_main_program().clone(for_test=True)
+    opt = create_optimizer(args)
+    opt.minimize(avg_cost)
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    if args.pretrained_model:
+
+        def if_exist(var):
+            return os.path.exists(
+                os.path.join(args.pretrained_model, var.name))
+
+        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
+
+    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    train_reader = paddle.batch(
+        train_reader, batch_size=args.batch_size, drop_last=True)
+
+    train_feeder = feeder = fluid.DataFeeder([image, label], place)
+    val_feeder = feeder = fluid.DataFeeder(
+        [image, label], place, program=val_program)
+
+    def test(epoch, program):
+        batch_id = 0
+        acc_top1_ns = []
+        acc_top5_ns = []
+        for data in val_reader():
+            start_time = time.time()
+            acc_top1_n, acc_top5_n = exe.run(
+                program,
+                feed=train_feeder.feed(data),
+                fetch_list=[acc_top1.name, acc_top5.name])
+            end_time = time.time()
+            if batch_id % args.log_period == 0:
+                _logger.info(
+                    "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".
+                    format(epoch, batch_id,
+                           np.mean(acc_top1_n),
+                           np.mean(acc_top5_n), end_time - start_time))
+            acc_top1_ns.append(np.mean(acc_top1_n))
+            acc_top5_ns.append(np.mean(acc_top5_n))
+            batch_id += 1
+
+        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".
+                     format(epoch,
+                            np.mean(np.array(acc_top1_ns)),
+                            np.mean(np.array(acc_top5_ns))))
+        return np.mean(np.array(acc_top1_ns))
+
+    def train(epoch, program):
+
+        build_strategy = fluid.BuildStrategy()
+        exec_strategy = fluid.ExecutionStrategy()
+        train_program = fluid.compiler.CompiledProgram(
+            program).with_data_parallel(
+                loss_name=avg_cost.name,
+                build_strategy=build_strategy,
+                exec_strategy=exec_strategy)
+
+        batch_id = 0
+        for data in train_reader():
+            start_time = time.time()
+            loss_n, acc_top1_n, acc_top5_n = exe.run(
+                train_program,
+                feed=train_feeder.feed(data),
+                fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name])
+            end_time = time.time()
+            loss_n = np.mean(loss_n)
+            acc_top1_n = np.mean(acc_top1_n)
+            acc_top5_n = np.mean(acc_top5_n)
+            if batch_id % args.log_period == 0:
+                _logger.info(
+                    "epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {}; time: {}".
+                    format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n,
+                           end_time - start_time))
+            batch_id += 1
+
+    params = []
+    for param in fluid.default_main_program().global_block().all_parameters():
+        if "_sep_weights" in param.name:
+            params.append(param.name)
+
+    pruner = AutoPruner(
+        val_program,
+        fluid.global_scope(),
+        place,
+        params=params,
+        init_ratios=[0.33] * len(params),
+        pruned_flops=0.5,
+        pruned_latency=None,
+        server_addr=("", 0),
+        init_temperature=100,
+        reduce_rate=0.85,
+        max_try_number=300,
+        max_client_num=10,
+        search_steps=100,
+        max_ratios=0.9,
+        min_ratios=0.,
+        key="auto_pruner")
+
+    while True:
+        pruned_program, pruned_val_program = pruner.prune(
+            fluid.default_main_program(), val_program)
+        for i in range(1):
+            train(i, pruned_program)
+        score = test(0, pruned_val_program)
+        pruner.reward(score)
+
+
+def main():
+    args = parser.parse_args()
+    print_arguments(args)
+    compress(args)
+
+
+if __name__ == '__main__':
+    main()

demo/imagenet_reader.py

+import os
+import math
+import random
+import functools
+import numpy as np
+import paddle
+from PIL import Image, ImageEnhance
+
+random.seed(0)
+np.random.seed(0)
+
+DATA_DIM = 224
+
+THREAD = 16
+BUF_SIZE = 10240
+
+#DATA_DIR = './data/ILSVRC2012/'
+DATA_DIR = './data/'
+DATA_DIR = os.path.join(os.path.split(os.path.realpath(__file__))[0], DATA_DIR)
+
+img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
+img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))
+
+
+def resize_short(img, target_size):
+    percent = float(target_size) / min(img.size[0], img.size[1])
+    resized_width = int(round(img.size[0] * percent))
+    resized_height = int(round(img.size[1] * percent))
+    img = img.resize((resized_width, resized_height), Image.LANCZOS)
+    return img
+
+
+def crop_image(img, target_size, center):
+    width, height = img.size
+    size = target_size
+    if center == True:
+        w_start = (width - size) / 2
+        h_start = (height - size) / 2
+    else:
+        w_start = np.random.randint(0, width - size + 1)
+        h_start = np.random.randint(0, height - size + 1)
+    w_end = w_start + size
+    h_end = h_start + size
+    img = img.crop((w_start, h_start, w_end, h_end))
+    return img
+
+
+def random_crop(img, size, scale=[0.08, 1.0], ratio=[3. / 4., 4. / 3.]):
+    aspect_ratio = math.sqrt(np.random.uniform(*ratio))
+    w = 1. * aspect_ratio
+    h = 1. / aspect_ratio
+
+    bound = min((float(img.size[0]) / img.size[1]) / (w**2),
+                (float(img.size[1]) / img.size[0]) / (h**2))
+    scale_max = min(scale[1], bound)
+    scale_min = min(scale[0], bound)
+
+    target_area = img.size[0] * img.size[1] * np.random.uniform(scale_min,
+                                                                scale_max)
+    target_size = math.sqrt(target_area)
+    w = int(target_size * w)
+    h = int(target_size * h)
+
+    i = np.random.randint(0, img.size[0] - w + 1)
+    j = np.random.randint(0, img.size[1] - h + 1)
+
+    img = img.crop((i, j, i + w, j + h))
+    img = img.resize((size, size), Image.LANCZOS)
+    return img
+
+
+def rotate_image(img):
+    angle = np.random.randint(-10, 11)
+    img = img.rotate(angle)
+    return img
+
+
+def distort_color(img):
+    def random_brightness(img, lower=0.5, upper=1.5):
+        e = np.random.uniform(lower, upper)
+        return ImageEnhance.Brightness(img).enhance(e)
+
+    def random_contrast(img, lower=0.5, upper=1.5):
+        e = np.random.uniform(lower, upper)
+        return ImageEnhance.Contrast(img).enhance(e)
+
+    def random_color(img, lower=0.5, upper=1.5):
+        e = np.random.uniform(lower, upper)
+        return ImageEnhance.Color(img).enhance(e)
+
+    ops = [random_brightness, random_contrast, random_color]
+    np.random.shuffle(ops)
+
+    img = ops[0](img)
+    img = ops[1](img)
+    img = ops[2](img)
+
+    return img
+
+
+def process_image(sample, mode, color_jitter, rotate):
+    img_path = sample[0]
+
+    img = Image.open(img_path)
+    if mode == 'train':
+        if rotate: img = rotate_image(img)
+        img = random_crop(img, DATA_DIM)
+    else:
+        img = resize_short(img, target_size=256)
+        img = crop_image(img, target_size=DATA_DIM, center=True)
+    if mode == 'train':
+        if color_jitter:
+            img = distort_color(img)
+        if np.random.randint(0, 2) == 1:
+            img = img.transpose(Image.FLIP_LEFT_RIGHT)
+
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+
+    img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
+    img -= img_mean
+    img /= img_std
+
+    if mode == 'train' or mode == 'val':
+        return img, sample[1]
+    elif mode == 'test':
+        return [img]
+
+
+def _reader_creator(file_list,
+                    mode,
+                    shuffle=False,
+                    color_jitter=False,
+                    rotate=False,
+                    data_dir=DATA_DIR,
+                    batch_size=1):
+    def reader():
+        try:
+            with open(file_list) as flist:
+                full_lines = [line.strip() for line in flist]
+                if shuffle:
+                    np.random.shuffle(full_lines)
+                if mode == 'train' and os.getenv('PADDLE_TRAINING_ROLE'):
+                    # distributed mode if the env var `PADDLE_TRAINING_ROLE` exits
+                    trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+                    trainer_count = int(os.getenv("PADDLE_TRAINERS", "1"))
+                    per_node_lines = len(full_lines) // trainer_count
+                    lines = full_lines[trainer_id * per_node_lines:(
+                        trainer_id + 1) * per_node_lines]
+                    print(
+                        "read images from %d, length: %d, lines length: %d, total: %d"
+                        % (trainer_id * per_node_lines, per_node_lines,
+                           len(lines), len(full_lines)))
+                else:
+                    lines = full_lines
+
+                for line in lines:
+                    if mode == 'train' or mode == 'val':
+                        img_path, label = line.split()
+                        img_path = os.path.join(data_dir + "/" + mode,
+                                                img_path)
+                        yield img_path, int(label)
+                    elif mode == 'test':
+                        img_path = os.path.join(data_dir, line)
+                        yield [img_path]
+        except Exception as e:
+            print("Reader failed!\n{}".format(str(e)))
+            os._exit(1)
+
+    mapper = functools.partial(
+        process_image, mode=mode, color_jitter=color_jitter, rotate=rotate)
+
+    return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE)
+
+
+def train(data_dir=DATA_DIR):
+    file_list = os.path.join(data_dir, 'train_list.txt')
+    return _reader_creator(
+        file_list,
+        'train',
+        shuffle=True,
+        color_jitter=False,
+        rotate=False,
+        data_dir=data_dir)
+
+
+def val(data_dir=DATA_DIR):
+    file_list = os.path.join(data_dir, 'val_list.txt')
+    return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir)
+
+
+def test(data_dir=DATA_DIR):
+    file_list = os.path.join(data_dir, 'test_list.txt')
+    return _reader_creator(file_list, 'test', shuffle=False, data_dir=data_dir)

demo/models/__init__.py

+from .mobilenet import MobileNet
+from .resnet import ResNet34, ResNet50
+from .mobilenet_v2 import MobileNetV2
+
+__all__ = ['MobileNet', 'ResNet34', 'ResNet50', 'MobileNetV2']

demo/models/mobilenet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle.fluid as fluid
+from paddle.fluid.initializer import MSRA
+from paddle.fluid.param_attr import ParamAttr
+
+__all__ = ['MobileNet']
+
+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": "piecewise_decay",
+        "batch_size": 256,
+        "epochs": [10, 16, 30],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    }
+}
+
+
+class MobileNet():
+    def __init__(self):
+        self.params = train_parameters
+
+    def net(self, input, class_dim=1000, scale=1.0):
+        # conv1: 112x112
+        input = self.conv_bn_layer(
+            input,
+            filter_size=3,
+            channels=3,
+            num_filters=int(32 * scale),
+            stride=2,
+            padding=1,
+            name="conv1")
+
+        # 56x56
+        input = self.depthwise_separable(
+            input,
+            num_filters1=32,
+            num_filters2=64,
+            num_groups=32,
+            stride=1,
+            scale=scale,
+            name="conv2_1")
+
+        input = self.depthwise_separable(
+            input,
+            num_filters1=64,
+            num_filters2=128,
+            num_groups=64,
+            stride=2,
+            scale=scale,
+            name="conv2_2")
+
+        # 28x28
+        input = self.depthwise_separable(
+            input,
+            num_filters1=128,
+            num_filters2=128,
+            num_groups=128,
+            stride=1,
+            scale=scale,
+            name="conv3_1")
+
+        input = self.depthwise_separable(
+            input,
+            num_filters1=128,
+            num_filters2=256,
+            num_groups=128,
+            stride=2,
+            scale=scale,
+            name="conv3_2")
+
+        # 14x14
+        input = self.depthwise_separable(
+            input,
+            num_filters1=256,
+            num_filters2=256,
+            num_groups=256,
+            stride=1,
+            scale=scale,
+            name="conv4_1")
+
+        input = self.depthwise_separable(
+            input,
+            num_filters1=256,
+            num_filters2=512,
+            num_groups=256,
+            stride=2,
+            scale=scale,
+            name="conv4_2")
+
+        # 14x14
+        for i in range(5):
+            input = self.depthwise_separable(
+                input,
+                num_filters1=512,
+                num_filters2=512,
+                num_groups=512,
+                stride=1,
+                scale=scale,
+                name="conv5" + "_" + str(i + 1))
+        # 7x7
+        input = self.depthwise_separable(
+            input,
+            num_filters1=512,
+            num_filters2=1024,
+            num_groups=512,
+            stride=2,
+            scale=scale,
+            name="conv5_6")
+
+        input = self.depthwise_separable(
+            input,
+            num_filters1=1024,
+            num_filters2=1024,
+            num_groups=1024,
+            stride=1,
+            scale=scale,
+            name="conv6")
+
+        input = fluid.layers.pool2d(
+            input=input,
+            pool_size=0,
+            pool_stride=1,
+            pool_type='avg',
+            global_pooling=True)
+
+        output = fluid.layers.fc(input=input,
+                                 size=class_dim,
+                                 act='softmax',
+                                 param_attr=ParamAttr(
+                                     initializer=MSRA(), name="fc7_weights"),
+                                 bias_attr=ParamAttr(name="fc7_offset"))
+
+        return output
+
+    def conv_bn_layer(self,
+                      input,
+                      filter_size,
+                      num_filters,
+                      stride,
+                      padding,
+                      channels=None,
+                      num_groups=1,
+                      act='relu',
+                      use_cudnn=True,
+                      name=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            act=None,
+            use_cudnn=use_cudnn,
+            param_attr=ParamAttr(
+                initializer=MSRA(), name=name + "_weights"),
+            bias_attr=False)
+        bn_name = name + "_bn"
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + "_scale"),
+            bias_attr=ParamAttr(name=bn_name + "_offset"),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def depthwise_separable(self,
+                            input,
+                            num_filters1,
+                            num_filters2,
+                            num_groups,
+                            stride,
+                            scale,
+                            name=None):
+        depthwise_conv = self.conv_bn_layer(
+            input=input,
+            filter_size=3,
+            num_filters=int(num_filters1 * scale),
+            stride=stride,
+            padding=1,
+            num_groups=int(num_groups * scale),
+            use_cudnn=False,
+            name=name + "_dw")
+
+        pointwise_conv = self.conv_bn_layer(
+            input=depthwise_conv,
+            filter_size=1,
+            num_filters=int(num_filters2 * scale),
+            stride=1,
+            padding=0,
+            name=name + "_sep")
+        return pointwise_conv

demo/models/mobilenet_v2.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 paddle.fluid as fluid
+from paddle.fluid.initializer import MSRA
+from paddle.fluid.param_attr import ParamAttr
+
+__all__ = [
+    'MobileNetV2', 'MobileNetV2_x0_25, '
+    'MobileNetV2_x0_5', 'MobileNetV2_x1_0', 'MobileNetV2_x1_5',
+    'MobileNetV2_x2_0', 'MobileNetV2_scale'
+]
+
+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": "piecewise_decay",
+        "batch_size": 256,
+        "epochs": [30, 60, 90],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    }
+}
+
+
+class MobileNetV2():
+    def __init__(self, scale=1.0, change_depth=False):
+        self.params = train_parameters
+        self.scale = scale
+        self.change_depth = change_depth
+
+    def net(self, input, class_dim=1000):
+        scale = self.scale
+        change_depth = self.change_depth
+        #if change_depth is True, the new depth is 1.4 times as deep as before.
+        bottleneck_params_list = [
+            (1, 16, 1, 1),
+            (6, 24, 2, 2),
+            (6, 32, 3, 2),
+            (6, 64, 4, 2),
+            (6, 96, 3, 1),
+            (6, 160, 3, 2),
+            (6, 320, 1, 1),
+        ] if change_depth == False else [
+            (1, 16, 1, 1),
+            (6, 24, 2, 2),
+            (6, 32, 5, 2),
+            (6, 64, 7, 2),
+            (6, 96, 5, 1),
+            (6, 160, 3, 2),
+            (6, 320, 1, 1),
+        ]
+
+        #conv1 
+        input = self.conv_bn_layer(
+            input,
+            num_filters=int(32 * scale),
+            filter_size=3,
+            stride=2,
+            padding=1,
+            if_act=True,
+            name='conv1_1')
+
+        # bottleneck sequences
+        i = 1
+        in_c = int(32 * scale)
+        for layer_setting in bottleneck_params_list:
+            t, c, n, s = layer_setting
+            i += 1
+            input = self.invresi_blocks(
+                input=input,
+                in_c=in_c,
+                t=t,
+                c=int(c * scale),
+                n=n,
+                s=s,
+                name='conv' + str(i))
+            in_c = int(c * scale)
+        #last_conv
+        input = self.conv_bn_layer(
+            input=input,
+            num_filters=int(1280 * scale) if scale > 1.0 else 1280,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            if_act=True,
+            name='conv9')
+
+        input = fluid.layers.pool2d(
+            input=input,
+            pool_size=7,
+            pool_stride=1,
+            pool_type='avg',
+            global_pooling=True)
+
+        output = fluid.layers.fc(input=input,
+                                 size=class_dim,
+                                 act='softmax',
+                                 param_attr=ParamAttr(name='fc10_weights'),
+                                 bias_attr=ParamAttr(name='fc10_offset'))
+        return output
+
+    def conv_bn_layer(self,
+                      input,
+                      filter_size,
+                      num_filters,
+                      stride,
+                      padding,
+                      channels=None,
+                      num_groups=1,
+                      if_act=True,
+                      name=None,
+                      use_cudnn=True):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            act=None,
+            use_cudnn=use_cudnn,
+            param_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+        bn_name = name + '_bn'
+        bn = fluid.layers.batch_norm(
+            input=conv,
+            param_attr=ParamAttr(name=bn_name + "_scale"),
+            bias_attr=ParamAttr(name=bn_name + "_offset"),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+        if if_act:
+            return fluid.layers.relu6(bn)
+        else:
+            return bn
+
+    def shortcut(self, input, data_residual):
+        return fluid.layers.elementwise_add(input, data_residual)
+
+    def inverted_residual_unit(self,
+                               input,
+                               num_in_filter,
+                               num_filters,
+                               ifshortcut,
+                               stride,
+                               filter_size,
+                               padding,
+                               expansion_factor,
+                               name=None):
+        num_expfilter = int(round(num_in_filter * expansion_factor))
+
+        channel_expand = self.conv_bn_layer(
+            input=input,
+            num_filters=num_expfilter,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            num_groups=1,
+            if_act=True,
+            name=name + '_expand')
+
+        bottleneck_conv = self.conv_bn_layer(
+            input=channel_expand,
+            num_filters=num_expfilter,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            num_groups=num_expfilter,
+            if_act=True,
+            name=name + '_dwise',
+            use_cudnn=False)
+
+        linear_out = self.conv_bn_layer(
+            input=bottleneck_conv,
+            num_filters=num_filters,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            num_groups=1,
+            if_act=False,
+            name=name + '_linear')
+        if ifshortcut:
+            out = self.shortcut(input=input, data_residual=linear_out)
+            return out
+        else:
+            return linear_out
+
+    def invresi_blocks(self, input, in_c, t, c, n, s, name=None):
+        first_block = self.inverted_residual_unit(
+            input=input,
+            num_in_filter=in_c,
+            num_filters=c,
+            ifshortcut=False,
+            stride=s,
+            filter_size=3,
+            padding=1,
+            expansion_factor=t,
+            name=name + '_1')
+
+        last_residual_block = first_block
+        last_c = c
+
+        for i in range(1, n):
+            last_residual_block = self.inverted_residual_unit(
+                input=last_residual_block,
+                num_in_filter=last_c,
+                num_filters=c,
+                ifshortcut=True,
+                stride=1,
+                filter_size=3,
+                padding=1,
+                expansion_factor=t,
+                name=name + '_' + str(i + 1))
+        return last_residual_block
+
+
+def MobileNetV2_x0_25():
+    model = MobileNetV2(scale=0.25)
+    return model
+
+
+def MobileNetV2_x0_5():
+    model = MobileNetV2(scale=0.5)
+    return model
+
+
+def MobileNetV2_x1_0():
+    model = MobileNetV2(scale=1.0)
+    return model
+
+
+def MobileNetV2_x1_5():
+    model = MobileNetV2(scale=1.5)
+    return model
+
+
+def MobileNetV2_x2_0():
+    model = MobileNetV2(scale=2.0)
+    return model
+
+
+def MobileNetV2_scale():
+    model = MobileNetV2(scale=1.2, change_depth=True)
+    return model

demo/models/resnet.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import paddle
+import paddle.fluid as fluid
+import math
+from paddle.fluid.param_attr import ParamAttr
+
+__all__ = ["ResNet", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
+
+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": "piecewise_decay",
+        "batch_size": 256,
+        "epochs": [10, 16, 30],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    }
+}
+
+
+class ResNet():
+    def __init__(self, layers=50, prefix_name=''):
+        self.params = train_parameters
+        self.layers = layers
+        self.prefix_name = prefix_name
+
+    def net(self, input, class_dim=1000, conv1_name='conv1', fc_name=None):
+        layers = self.layers
+        prefix_name = self.prefix_name if self.prefix_name is '' else self.prefix_name + '_'
+        supported_layers = [34, 50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_filters = [64, 128, 256, 512]
+
+        # TODO(wanghaoshuang@baidu.com):
+        # fix name("conv1") conflict between student and teacher in distillation.
+        conv = self.conv_bn_layer(
+            input=input,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu',
+            name=prefix_name + conv1_name)
+        conv = fluid.layers.pool2d(
+            input=conv,
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
+
+        if layers >= 50:
+            for block in range(len(depth)):
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    conv_name = prefix_name + conv_name
+                    conv = self.bottleneck_block(
+                        input=conv,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        name=conv_name)
+
+            pool = fluid.layers.pool2d(
+                input=conv, pool_size=7, pool_type='avg', global_pooling=True)
+            stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
+            fc_name = fc_name if fc_name is None else prefix_name + fc_name
+            out = fluid.layers.fc(input=pool,
+                                  size=class_dim,
+                                  act='softmax',
+                                  name=fc_name,
+                                  param_attr=fluid.param_attr.ParamAttr(
+                                      initializer=fluid.initializer.Uniform(
+                                          -stdv, stdv)))
+        else:
+            for block in range(len(depth)):
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    conv_name = prefix_name + conv_name
+                    conv = self.basic_block(
+                        input=conv,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        is_first=block == i == 0,
+                        name=conv_name)
+
+            pool = fluid.layers.pool2d(
+                input=conv, pool_type='avg', global_pooling=True)
+            stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
+            fc_name = fc_name if fc_name is None else prefix_name + fc_name
+            out = fluid.layers.fc(
+                input=pool,
+                size=class_dim,
+                act='softmax',
+                name=fc_name,
+                param_attr=fluid.param_attr.ParamAttr(
+                    initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
+        return out
+
+    def conv_bn_layer(self,
+                      input,
+                      num_filters,
+                      filter_size,
+                      stride=1,
+                      groups=1,
+                      act=None,
+                      name=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False,
+            name=name + '.conv2d.output.1')
+        if self.prefix_name == '':
+            if name == "conv1":
+                bn_name = "bn_" + name
+            else:
+                bn_name = "bn" + name[3:]
+        else:
+            if name.split("_")[1] == "conv1":
+                bn_name = name.split("_", 1)[0] + "_bn_" + name.split("_",
+                                                                      1)[1]
+            else:
+                bn_name = name.split("_", 1)[0] + "_bn" + name.split("_",
+                                                                     1)[1][3:]
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            name=bn_name + '.output.1',
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance', )
+
+    def shortcut(self, input, ch_out, stride, is_first, name):
+        ch_in = input.shape[1]
+        if ch_in != ch_out or stride != 1 or is_first == True:
+            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
+        else:
+            return input
+
+    def bottleneck_block(self, input, num_filters, stride, name):
+        conv0 = self.conv_bn_layer(
+            input=input,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        conv1 = self.conv_bn_layer(
+            input=conv0,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+        conv2 = self.conv_bn_layer(
+            input=conv1,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        short = self.shortcut(
+            input,
+            num_filters * 4,
+            stride,
+            is_first=False,
+            name=name + "_branch1")
+
+        return fluid.layers.elementwise_add(
+            x=short, y=conv2, act='relu', name=name + ".add.output.5")
+
+    def basic_block(self, input, num_filters, stride, is_first, name):
+        conv0 = self.conv_bn_layer(
+            input=input,
+            num_filters=num_filters,
+            filter_size=3,
+            act='relu',
+            stride=stride,
+            name=name + "_branch2a")
+        conv1 = self.conv_bn_layer(
+            input=conv0,
+            num_filters=num_filters,
+            filter_size=3,
+            act=None,
+            name=name + "_branch2b")
+        short = self.shortcut(
+            input, num_filters, stride, is_first, name=name + "_branch1")
+        return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
+
+
+def ResNet34(prefix_name=''):
+    model = ResNet(layers=34, prefix_name=prefix_name)
+    return model
+
+
+def ResNet50(prefix_name=''):
+    model = ResNet(layers=50, prefix_name=prefix_name)
+    return model
+
+
+def ResNet101():
+    model = ResNet(layers=101)
+    return model
+
+
+def ResNet152():
+    model = ResNet(layers=152)
+    return model

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, max_flops=base_flops, search_steps=search_steps)
+
+        ### 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/prune/train.py

+import os
+import sys
+import logging
+import paddle
+import argparse
+import functools
+import math
+import time
+import numpy as np
+import paddle.fluid as fluid
+from paddleslim.prune import Pruner
+from paddleslim.common import get_logger
+from paddleslim.analysis import flops
+sys.path.append(sys.path[0] + "/../")
+import models
+from utility import add_arguments, print_arguments
+
+_logger = get_logger(__name__, level=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('model',            str,  "MobileNet",                "The target model.")
+add_arg('pretrained_model', str,  "../pretrained_model/MobileNetV1_pretained",                "Whether to use pretrained model.")
+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('total_images',     int,  1281167,               "The number of total training images.")
+parser.add_argument('--step_epochs', nargs='+', type=int, default=[30, 60, 90], help="piecewise decay step")
+add_arg('config_file',      str, None,                 "The config file for compression with yaml format.")
+add_arg('data',             str, "mnist",                 "Which data to use. 'mnist' or 'imagenet'")
+add_arg('log_period',       int, 10,                 "Log period in batches.")
+add_arg('test_period',      int, 10,                 "Test period in epoches.")
+# 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):
+    train_reader = None
+    test_reader = None
+    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)
+    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    # 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)
+    val_program = fluid.default_main_program().clone(for_test=True)
+    opt = create_optimizer(args)
+    opt.minimize(avg_cost)
+    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(fluid.default_startup_program())
+
+    if args.pretrained_model:
+
+        def if_exist(var):
+            return os.path.exists(
+                os.path.join(args.pretrained_model, var.name))
+
+        fluid.io.load_vars(exe, args.pretrained_model, predicate=if_exist)
+
+    val_reader = paddle.batch(val_reader, batch_size=args.batch_size)
+    train_reader = paddle.batch(
+        train_reader, batch_size=args.batch_size, drop_last=True)
+
+    train_feeder = feeder = fluid.DataFeeder([image, label], place)
+    val_feeder = feeder = fluid.DataFeeder(
+        [image, label], place, program=val_program)
+
+    def test(epoch, program):
+        batch_id = 0
+        acc_top1_ns = []
+        acc_top5_ns = []
+        for data in val_reader():
+            start_time = time.time()
+            acc_top1_n, acc_top5_n = exe.run(
+                program,
+                feed=train_feeder.feed(data),
+                fetch_list=[acc_top1.name, acc_top5.name])
+            end_time = time.time()
+            if batch_id % args.log_period == 0:
+                _logger.info(
+                    "Eval epoch[{}] batch[{}] - acc_top1: {}; acc_top5: {}; time: {}".
+                    format(epoch, batch_id,
+                           np.mean(acc_top1_n),
+                           np.mean(acc_top5_n), end_time - start_time))
+            acc_top1_ns.append(np.mean(acc_top1_n))
+            acc_top5_ns.append(np.mean(acc_top5_n))
+            batch_id += 1
+
+        _logger.info("Final eval epoch[{}] - acc_top1: {}; acc_top5: {}".
+                     format(epoch,
+                            np.mean(np.array(acc_top1_ns)),
+                            np.mean(np.array(acc_top5_ns))))
+
+    def train(epoch, program):
+
+        build_strategy = fluid.BuildStrategy()
+        exec_strategy = fluid.ExecutionStrategy()
+        train_program = fluid.compiler.CompiledProgram(
+            program).with_data_parallel(
+                loss_name=avg_cost.name,
+                build_strategy=build_strategy,
+                exec_strategy=exec_strategy)
+
+        batch_id = 0
+        for data in train_reader():
+            start_time = time.time()
+            loss_n, acc_top1_n, acc_top5_n = exe.run(
+                train_program,
+                feed=train_feeder.feed(data),
+                fetch_list=[avg_cost.name, acc_top1.name, acc_top5.name])
+            end_time = time.time()
+            loss_n = np.mean(loss_n)
+            acc_top1_n = np.mean(acc_top1_n)
+            acc_top5_n = np.mean(acc_top5_n)
+            if batch_id % args.log_period == 0:
+                _logger.info(
+                    "epoch[{}]-batch[{}] - loss: {}; acc_top1: {}; acc_top5: {}; time: {}".
+                    format(epoch, batch_id, loss_n, acc_top1_n, acc_top5_n,
+                           end_time - start_time))
+            batch_id += 1
+
+    params = []
+    for param in fluid.default_main_program().global_block().all_parameters():
+        if "_sep_weights" in param.name:
+            params.append(param.name)
+    _logger.info("fops before pruning: {}".format(
+        flops(fluid.default_main_program())))
+    pruner = Pruner()
+    pruned_val_program = pruner.prune(
+        val_program,
+        fluid.global_scope(),
+        params=params,
+        ratios=[0.33] * len(params),
+        place=place,
+        only_graph=True)
+
+    pruned_program = pruner.prune(
+        fluid.default_main_program(),
+        fluid.global_scope(),
+        params=params,
+        ratios=[0.33] * len(params),
+        place=place)
+
+    _logger.info("fops after pruning: {}".format(flops(pruned_program)))
+
+    for i in range(args.num_epochs):
+        train(i, pruned_program)
+        if i % args.test_period == 0:
+            test(i, pruned_val_program)
+
+
+def main():
+    args = parser.parse_args()
+    print_arguments(args)
+    compress(args)
+
+
+if __name__ == '__main__':
+    main()

demo/utility.py

+"""Contains common utility functions."""
+#  Copyright (c) 2018 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 distutils.util
+import os
+import numpy as np
+import six
+import logging
+import paddle.fluid as fluid
+import paddle.compat as cpt
+from paddle.fluid import core
+from paddle.fluid.framework import Program
+
+logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
+_logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
+
+
+def print_arguments(args):
+    """Print argparse's arguments.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        parser.add_argument("name", default="Jonh", type=str, help="User name.")
+        args = parser.parse_args()
+        print_arguments(args)
+
+    :param args: Input argparse.Namespace for printing.
+    :type args: argparse.Namespace
+    """
+    print("-----------  Configuration Arguments -----------")
+    for arg, value in sorted(six.iteritems(vars(args))):
+        print("%s: %s" % (arg, value))
+    print("------------------------------------------------")
+
+
+def add_arguments(argname, type, default, help, argparser, **kwargs):
+    """Add argparse's argument.
+
+    Usage:
+
+    .. code-block:: python
+
+        parser = argparse.ArgumentParser()
+        add_argument("name", str, "Jonh", "User name.", parser)
+        args = parser.parse_args()
+    """
+    type = distutils.util.strtobool if type == bool else type
+    argparser.add_argument(
+        "--" + argname,
+        default=default,
+        type=type,
+        help=help + ' Default: %(default)s.',
+        **kwargs)
+
+
+def save_persistable_nodes(executor, dirname, graph):
+    """
+    Save persistable nodes to the given directory by the executor.
+
+    Args:
+        executor(Executor): The executor to run for saving node values.
+        dirname(str): The directory path.
+        graph(IrGraph): All the required persistable nodes in the graph will be saved.
+    """
+    persistable_node_names = set()
+    persistable_nodes = []
+    all_persistable_nodes = graph.all_persistable_nodes()
+    for node in all_persistable_nodes:
+        name = cpt.to_text(node.name())
+        if name not in persistable_node_names:
+            persistable_node_names.add(name)
+            persistable_nodes.append(node)
+    program = Program()
+    var_list = []
+    for node in persistable_nodes:
+        var_desc = node.var()
+        if var_desc.type() == core.VarDesc.VarType.RAW or \
+                var_desc.type() == core.VarDesc.VarType.READER:
+            continue
+        var = program.global_block().create_var(
+            name=var_desc.name(),
+            shape=var_desc.shape(),
+            dtype=var_desc.dtype(),
+            type=var_desc.type(),
+            lod_level=var_desc.lod_level(),
+            persistable=var_desc.persistable())
+        var_list.append(var)
+    fluid.io.save_vars(executor=executor, dirname=dirname, vars=var_list)
+
+
+def load_persistable_nodes(executor, dirname, graph):
+    """
+    Load persistable node values from the given directory by the executor.
+
+    Args:
+        executor(Executor): The executor to run for loading node values.
+        dirname(str): The directory path.
+        graph(IrGraph): All the required persistable nodes in the graph will be loaded.
+    """
+    persistable_node_names = set()
+    persistable_nodes = []
+    all_persistable_nodes = graph.all_persistable_nodes()
+    for node in all_persistable_nodes:
+        name = cpt.to_text(node.name())
+        if name not in persistable_node_names:
+            persistable_node_names.add(name)
+            persistable_nodes.append(node)
+    program = Program()
+    var_list = []
+
+    def _exist(var):
+        return os.path.exists(os.path.join(dirname, var.name))
+
+    def _load_var(name, scope):
+        return np.array(scope.find_var(name).get_tensor())
+
+    def _store_var(name, array, scope, place):
+        tensor = scope.find_var(name).get_tensor()
+        tensor.set(array, place)
+
+    for node in persistable_nodes:
+        var_desc = node.var()
+        if var_desc.type() == core.VarDesc.VarType.RAW or \
+                var_desc.type() == core.VarDesc.VarType.READER:
+            continue
+        var = program.global_block().create_var(
+            name=var_desc.name(),
+            shape=var_desc.shape(),
+            dtype=var_desc.dtype(),
+            type=var_desc.type(),
+            lod_level=var_desc.lod_level(),
+            persistable=var_desc.persistable())
+        if _exist(var):
+            var_list.append(var)
+        else:
+            _logger.info("Cannot find the var %s!!!" % (node.name()))
+    fluid.io.load_vars(executor=executor, dirname=dirname, vars=var_list)

paddleslim/analysis/__init__.py

@@ -15,6 +15,9 @@ import flops as flops_module
 from flops import *
 import model_size as model_size_module
 from model_size import *
+import sensitive
+from sensitive import *
 __all__ = []
 __all__ += flops_module.__all__
 __all__ += model_size_module.__all__
+__all__ += sensitive.__all__

paddleslim/analysis/sensitive.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 sys
+import os
+import logging
+import pickle
+import numpy as np
+from ..core import GraphWrapper
+from ..common import get_logger
+from ..prune import Pruner
+
+_logger = get_logger(__name__, level=logging.INFO)
+
+__all__ = ["sensitivity"]
+
+
+def sensitivity(program,
+                scope,
+                place,
+                param_names,
+                eval_func,
+                sensitivities_file=None,
+                step_size=0.2):
+
+    graph = GraphWrapper(program)
+    sensitivities = _load_sensitivities(sensitivities_file)
+
+    for name in param_names:
+        if name not in sensitivities:
+            size = graph.var(name).shape()[0]
+            sensitivities[name] = {
+                'pruned_percent': [],
+                'loss': [],
+                'size': size
+            }
+    baseline = None
+    for name in sensitivities:
+        ratio = step_size
+        while ratio < 1:
+            ratio = round(ratio, 2)
+            if ratio in sensitivities[name]['pruned_percent']:
+                _logger.debug('{}, {} has computed.'.format(name, ratio))
+                ratio += step_size
+                continue
+            if baseline is None:
+                baseline = eval_func(graph.program, scope)
+
+            param_backup = {}
+            pruner = Pruner()
+            pruned_program = pruner.prune(
+                program=graph.program,
+                scope=scope,
+                params=[name],
+                ratios=[ratio],
+                place=place,
+                lazy=True,
+                only_graph=False,
+                param_backup=param_backup)
+            pruned_metric = eval_func(pruned_program, scope)
+            loss = (baseline - pruned_metric) / baseline
+            _logger.info("pruned param: {}; {}; loss={}".format(name, ratio,
+                                                                loss))
+            sensitivities[name]['pruned_percent'].append(ratio)
+            sensitivities[name]['loss'].append(loss)
+            _save_sensitivities(sensitivities, sensitivities_file)
+
+            # restore pruned parameters
+            for param_name in param_backup.keys():
+                param_t = scope.find_var(param_name).get_tensor()
+                param_t.set(param_backup[param_name], place)
+            ratio += step_size
+        return sensitivities
+
+
+def _load_sensitivities(sensitivities_file):
+    """
+    Load sensitivities from file.
+    """
+    sensitivities = {}
+    if sensitivities_file and os.path.exists(sensitivities_file):
+        with open(sensitivities_file, 'rb') as f:
+            if sys.version_info < (3, 0):
+                sensitivities = pickle.load(f)
+            else:
+                sensitivities = pickle.load(f, encoding='bytes')
+
+    for param in sensitivities:
+        sensitivities[param]['pruned_percent'] = [
+            round(p, 2) for p in sensitivities[param]['pruned_percent']
+        ]
+    return sensitivities
+
+
+def _save_sensitivities(sensitivities, sensitivities_file):
+    """
+        Save sensitivities into file.
+        """
+    with open(sensitivities_file, 'wb') as f:
+        pickle.dump(sensitivities, f)

paddleslim/common/controller_client.py

@@ -50,9 +50,11 @@ class ControllerClient(object):
         tokens = ",".join([str(token) for token in tokens])
         socket_client.send("{}\t{}\t{}".format(self._key, tokens, reward)
                            .encode())
-        tokens = socket_client.recv(1024).decode()
-        tokens = [int(token) for token in tokens.strip("\n").split(",")]
-        return tokens
+        response = socket_client.recv(1024).decode()
+        if response.strip('\n').split("\t") == "ok":
+            return True
+        else:
+            return False
 
     def next_tokens(self):
         """

paddleslim/common/controller_server.py

@@ -117,9 +117,8 @@ class ControllerServer(object):
                     reward = messages[2]
                     tokens = [int(token) for token in tokens.split(",")]
                     self._controller.update(tokens, float(reward))
-                    tokens = self._controller.next_tokens()
-                    tokens = ",".join([str(token) for token in tokens])
-                    conn.send(tokens.encode())
+                    response = "ok"
+                    conn.send(response.encode())
                     _logger.debug("send message to {}: [{}]".format(addr,
                                                                     tokens))
                 conn.close()

paddleslim/common/log_helper.py

@@ -19,7 +19,7 @@ import logging
 __all__ = ['get_logger']
 
 
-def get_logger(name, level, fmt=None):
+def get_logger(name, level, fmt='%(asctime)s-%(levelname)s: %(message)s'):
     """
     Get logger from logging with given name, level and format without
     setting logging basicConfig. For setting basicConfig in paddle
@@ -39,10 +39,10 @@ def get_logger(name, level, fmt=None):
     logger = logging.getLogger(name)
     logger.setLevel(level)
     handler = logging.StreamHandler()
-
     if fmt:
         formatter = logging.Formatter(fmt=fmt)
         handler.setFormatter(formatter)
 
     logger.addHandler(handler)
+    logger.propagate = 0
     return logger

paddleslim/nas/sa_nas.py

@@ -111,6 +111,8 @@ class SANAS(object):
         Return reward of current searched network.
         Args:
             score(float): The score of current searched network.
+        Returns:
+            bool: True means updating successfully while false means failure.
         """
-        self._controller_client.update(self._current_tokens, score)
         self._iter += 1
+        return self._controller_client.update(self._current_tokens, score)

paddleslim/nas/search_space/__init__.py

@@ -14,6 +14,8 @@
 
 import mobilenetv2
 from .mobilenetv2 import *
+import mobilenetv1
+from .mobilenetv1 import *
 import resnet
 from .resnet import *
 import search_space_registry
@@ -28,4 +30,3 @@ __all__ += mobilenetv2.__all__
 __all__ += search_space_registry.__all__
 __all__ += search_space_factory.__all__
 __all__ += search_space_base.__all__
-

paddleslim/nas/search_space/base_layer.py

@@ -20,7 +20,7 @@ def conv_bn_layer(input,
                   filter_size,
                   num_filters,
                   stride,
-                  padding,
+                  padding='SAME',
                   num_groups=1,
                   act=None,
                   name=None,
@@ -51,15 +51,10 @@ def conv_bn_layer(input,
         param_attr=ParamAttr(name=name + '_weights'),
         bias_attr=False)
     bn_name = name + '_bn'
-    bn = fluid.layers.batch_norm(
-        input=conv,
-        param_attr=ParamAttr(name=bn_name + '_scale'),
-        bias_attr=ParamAttr(name=bn_name + '_offset'),
-        moving_mean_name=bn_name + '_mean',
-        moving_variance_name=bn_name + '_variance')
-    if act == 'relu6':
-        return fluid.layers.relu6(bn)
-    elif act == 'sigmoid':
-        return fluid.layers.sigmoid(bn)
-    else:
-        return bn
+    return fluid.layers.batch_norm(
+               input=conv,
+               act = act,
+               param_attr=ParamAttr(name=bn_name + '_scale'),
+               bias_attr=ParamAttr(name=bn_name + '_offset'),
+               moving_mean_name=bn_name + '_mean',
+               moving_variance_name=bn_name + '_variance')

paddleslim/nas/search_space/combine_search_space.py

@@ -25,12 +25,14 @@ from .base_layer import conv_bn_layer
 
 __all__ = ["CombineSearchSpace"]
 
+
 class CombineSearchSpace(object):
     """
     Combine Search Space.
     Args:
         configs(list<tuple>): multi config.
     """
+
     def __init__(self, config_lists):
         self.lens = len(config_lists)
         self.spaces = []
@@ -50,11 +52,10 @@ class CombineSearchSpace(object):
         """
         cls = SEARCHSPACE.get(key)
         space = cls(config['input_size'], config['output_size'],
-                    config['block_num'])
+                    config['block_num'], config['block_mask'])
 
         return space
 
-        
     def init_tokens(self):
         """
         Combine init tokens.
@@ -96,4 +97,3 @@ class CombineSearchSpace(object):
             model_archs.append(space.token2arch(token))
 
         return model_archs
-

paddleslim/nas/search_space/mobilenetv1.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 absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+from .search_space_base import SearchSpaceBase
+from .base_layer import conv_bn_layer
+from .search_space_registry import SEARCHSPACE
+
+__all__ = ["MobileNetV1Space"]
+
+
+@SEARCHSPACE.register
+class MobileNetV1Space(SearchSpaceBase):
+    def __init__(self,
+                 input_size,
+                 output_size,
+                 block_num,
+                 scale=1.0,
+                 class_dim=1000):
+        super(MobileNetV1Space, self).__init__(input_size, output_size,
+                                               block_num)
+        self.scale = scale
+        self.class_dim = class_dim
+        # self.head_num means the channel of first convolution
+        self.head_num = np.array([3, 4, 8, 12, 16, 24, 32])  # 7
+        # self.filter_num1 ~ self.filtet_num9 means channel of the following convolution
+        self.filter_num1 = np.array([3, 4, 8, 12, 16, 24, 32, 48])  # 8
+        self.filter_num2 = np.array([8, 12, 16, 24, 32, 48, 64, 80])  # 8
+        self.filter_num3 = np.array(
+            [16, 24, 32, 48, 64, 80, 96, 128, 144, 160])  #10
+        self.filter_num4 = np.array(
+            [24, 32, 48, 64, 80, 96, 128, 144, 160, 192])  #10
+        self.filter_num5 = np.array(
+            [32, 48, 64, 80, 96, 128, 144, 160, 192, 224, 256, 320])  #12
+        self.filter_num6 = np.array(
+            [64, 80, 96, 128, 144, 160, 192, 224, 256, 320, 384])  #11
+        self.filter_num7 = np.array([
+            64, 80, 96, 128, 144, 160, 192, 224, 256, 320, 384, 512, 1024, 1048
+        ])  #14
+        self.filter_num8 = np.array(
+            [128, 144, 160, 192, 224, 256, 320, 384, 512, 576, 640, 704,
+             768])  #13
+        self.filter_num9 = np.array(
+            [160, 192, 224, 256, 320, 384, 512, 640, 768, 832, 1024,
+             1048])  #12
+        # self.k_size means kernel size
+        self.k_size = np.array([3, 5])  #2
+        # self.repeat means repeat_num in forth downsample 
+        self.repeat = np.array([1, 2, 3, 4, 5, 6])  #6
+
+        assert self.block_num < 6, 'MobileNetV1: block number must less than 6, but receive block number is {}'.format(
+            self.block_num)
+
+    def init_tokens(self):
+        """
+        The initial token.
+        The first one is the index of the first layers' channel in self.head_num,
+        each line in the following represent the index of the [filter_num1, filter_num2, kernel_size]
+        and depth means repeat times for forth downsample
+        """
+        # yapf: disable
+        base_init_tokens = [6,  # 32
+            6, 6, 0,  # 32, 64, 3
+            6, 7, 0,  # 64, 128, 3
+            7, 6, 0,  # 128, 128, 3
+            6, 10, 0,  # 128, 256, 3
+            10, 8, 0,  # 256, 256, 3
+            8, 11, 0,  # 256, 512, 3
+            4,  # depth 5
+            11, 8, 0,  # 512, 512, 3
+            8, 10, 0,  # 512, 1024, 3
+            10, 10, 0]  # 1024, 1024, 3
+        # yapf: enable
+        if self.block_num < 5:
+            self.token_len = 1 + (self.block_num * 2 - 1) * 3
+        else:
+            self.token_len = 2 + (self.block_num * 2 - 1) * 3
+        return base_init_tokens[:self.token_len]
+
+    def range_table(self):
+        """
+        Get range table of current search space, constrains the range of tokens.
+        """
+        # yapf: disable
+        base_range_table = [len(self.head_num),
+            len(self.filter_num1), len(self.filter_num2), len(self.k_size),
+            len(self.filter_num2), len(self.filter_num3), len(self.k_size),
+            len(self.filter_num3), len(self.filter_num4), len(self.k_size),
+            len(self.filter_num4), len(self.filter_num5), len(self.k_size),
+            len(self.filter_num5), len(self.filter_num6), len(self.k_size),
+            len(self.filter_num6), len(self.filter_num7), len(self.k_size),
+            len(self.repeat),
+            len(self.filter_num7), len(self.filter_num8), len(self.k_size),
+            len(self.filter_num8), len(self.filter_num9), len(self.k_size),
+            len(self.filter_num9), len(self.filter_num9), len(self.k_size)]
+        # yapf: enable
+        return base_range_table[:self.token_len]
+
+    def token2arch(self, tokens=None):
+
+        if tokens is None:
+            tokens = self.tokens()
+
+        bottleneck_param_list = []
+
+        if self.block_num >= 1:
+            # tokens[0] = 32
+            # 32, 64
+            bottleneck_param_list.append(
+                (self.filter_num1[tokens[1]], self.filter_num2[tokens[2]], 1,
+                 self.k_size[tokens[3]]))
+        if self.block_num >= 2:
+            # 64 128 128 128
+            bottleneck_param_list.append(
+                (self.filter_num2[tokens[4]], self.filter_num3[tokens[5]], 2,
+                 self.k_size[tokens[6]]))
+            bottleneck_param_list.append(
+                (self.filter_num3[tokens[7]], self.filter_num4[tokens[8]], 1,
+                 self.k_size[tokens[9]]))
+        if self.block_num >= 3:
+            # 128 256 256 256
+            bottleneck_param_list.append(
+                (self.filter_num4[tokens[10]], self.filter_num5[tokens[11]], 2,
+                 self.k_size[tokens[12]]))
+            bottleneck_param_list.append(
+                (self.filter_num5[tokens[13]], self.filter_num6[tokens[14]], 1,
+                 self.k_size[tokens[15]]))
+        if self.block_num >= 4:
+            # 256 512 (512 512) *  5
+            bottleneck_param_list.append(
+                (self.filter_num6[tokens[16]], self.filter_num7[tokens[17]], 2,
+                 self.k_size[tokens[18]]))
+            for i in range(self.repeat[tokens[19]]):
+                bottleneck_param_list.append(
+                    (self.filter_num7[tokens[20]],
+                     self.filter_num8[tokens[21]], 1, self.k_size[tokens[22]]))
+        if self.block_num >= 5:
+            # 512 1024 1024 1024
+            bottleneck_param_list.append(
+                (self.filter_num8[tokens[23]], self.filter_num9[tokens[24]], 2,
+                 self.k_size[tokens[25]]))
+            bottleneck_param_list.append(
+                (self.filter_num9[tokens[26]], self.filter_num9[tokens[27]], 1,
+                 self.k_size[tokens[28]]))
+
+        def net_arch(input):
+            input = conv_bn_layer(
+                input=input,
+                filter_size=3,
+                num_filters=self.head_num[tokens[0]],
+                stride=2,
+                name='mobilenetv1')
+
+            for i, layer_setting in enumerate(bottleneck_param_list):
+                filter_num1, filter_num2, stride, kernel_size = layer_setting
+                input = self._depthwise_separable(
+                    input=input,
+                    num_filters1=filter_num1,
+                    num_filters2=filter_num2,
+                    num_groups=filter_num1,
+                    stride=stride,
+                    scale=self.scale,
+                    kernel_size=kernel_size,
+                    name='mobilenetv1_{}'.format(str(i + 1)))
+
+            if self.output_size == 1:
+                print('NOTE: if output_size is 1, add fc layer in the end!!!')
+                input = fluid.layers.fc(
+                    input=input,
+                    size=self.class_dim,
+                    param_attr=ParamAttr(name='mobilenetv2_fc_weights'),
+                    bias_attr=ParamAttr(name='mobilenetv2_fc_offset'))
+            else:
+                assert self.output_size == input.shape[2], \
+                          ("output_size must EQUAL to input_size / (2^block_num)."
+                          "But receive input_size={}, output_size={}, block_num={}".format(
+                          self.input_size, self.output_size, self.block_num))
+
+            return input
+
+        return net_arch
+
+    def _depthwise_separable(self,
+                             input,
+                             num_filters1,
+                             num_filters2,
+                             num_groups,
+                             stride,
+                             scale,
+                             kernel_size,
+                             name=None):
+        depthwise_conv = conv_bn_layer(
+            input=input,
+            filter_size=kernel_size,
+            num_filters=int(num_filters1 * scale),
+            stride=stride,
+            num_groups=int(num_groups * scale),
+            use_cudnn=False,
+            name=name + '_dw')
+        pointwise_conv = conv_bn_layer(
+            input=depthwise_conv,
+            filter_size=1,
+            num_filters=int(num_filters2 * scale),
+            stride=1,
+            name=name + '_sep')
+
+        return pointwise_conv

paddleslim/nas/search_space/mobilenetv2.py

@@ -32,11 +32,15 @@ class MobileNetV2Space(SearchSpaceBase):
                  input_size,
                  output_size,
                  block_num,
+                 block_mask=None,
                  scale=1.0,
                  class_dim=1000):
         super(MobileNetV2Space, self).__init__(input_size, output_size,
-                                               block_num)
+                                               block_num, block_mask)
+        assert self.block_mask == None, 'MobileNetV2Space will use origin MobileNetV2 as seach space, so use input_size, output_size and block_num to search'
+        # self.head_num means the first convolution channel
         self.head_num = np.array([3, 4, 8, 12, 16, 24, 32])  #7
+        # self.filter_num1 ~ self.filter_num6 means following convlution channel
         self.filter_num1 = np.array([3, 4, 8, 12, 16, 24, 32, 48])  #8
         self.filter_num2 = np.array([8, 12, 16, 24, 32, 48, 64, 80])  #8
         self.filter_num3 = np.array([16, 24, 32, 48, 64, 80, 96, 128])  #8
@@ -46,15 +50,21 @@ class MobileNetV2Space(SearchSpaceBase):
             [32, 48, 64, 80, 96, 128, 144, 160, 192, 224])  #10
         self.filter_num6 = np.array(
             [64, 80, 96, 128, 144, 160, 192, 224, 256, 320, 384, 512])  #12
+        # self.k_size means kernel size
         self.k_size = np.array([3, 5])  #2
+        # self.multiply means expansion_factor of each _inverted_residual_unit
         self.multiply = np.array([1, 2, 3, 4, 6])  #5
+        # self.repeat means repeat_num _inverted_residual_unit in each _invresi_blocks 
         self.repeat = np.array([1, 2, 3, 4, 5, 6])  #6
         self.scale = scale
         self.class_dim = class_dim
 
+        assert self.block_num < 7, 'MobileNetV2: block number must less than 7, but receive block number is {}'.format(
+            self.block_num)
+
     def init_tokens(self):
         """
-        The initial token send to controller.
+        The initial token.
         The first one is the index of the first layers' channel in self.head_num,
         each line in the following represent the index of the [expansion_factor, filter_num, repeat_num, kernel_size]
         """
@@ -80,18 +90,18 @@ class MobileNetV2Space(SearchSpaceBase):
 
     def range_table(self):
         """
-        get range table of current search space 
+        Get range table of current search space, constrains the range of tokens. 
         """
         # head_num + 7 * [multiple(expansion_factor), filter_num, repeat, kernel_size]
         # yapf: disable
-        range_table_base =  [7,
-                5, 8, 6, 2,
-                5, 8, 6, 2,
-                5, 8, 6, 2,
-                5, 8, 6, 2,
-                5, 10, 6, 2,
-                5, 10, 6, 2,
-                5, 12, 6, 2]
+        range_table_base =  [len(self.head_num),
+                len(self.multiply), len(self.filter_num1), len(self.repeat), len(self.k_size),
+                len(self.multiply), len(self.filter_num1), len(self.repeat), len(self.k_size),
+                len(self.multiply), len(self.filter_num2), len(self.repeat), len(self.k_size),
+                len(self.multiply), len(self.filter_num3), len(self.repeat), len(self.k_size),
+                len(self.multiply), len(self.filter_num4), len(self.repeat), len(self.k_size),
+                len(self.multiply), len(self.filter_num5), len(self.repeat), len(self.k_size),
+                len(self.multiply), len(self.filter_num6), len(self.repeat), len(self.k_size)]
         range_table_base = list(np.array(range_table_base) - 1)
         # yapf: enable
         return range_table_base[:self.token_len]
@@ -101,11 +111,9 @@ class MobileNetV2Space(SearchSpaceBase):
         return net_arch function
         """
 
-        assert self.block_num < 7, 'block number must less than 7, but receive block number is {}'.format(
-            self.block_num)
-
         if tokens is None:
             tokens = self.init_tokens()
+        print(tokens)
 
         bottleneck_params_list = []
         if self.block_num >= 1:
@@ -128,7 +136,7 @@ class MobileNetV2Space(SearchSpaceBase):
                 (self.multiply[tokens[13]], self.filter_num3[tokens[14]],
                  self.repeat[tokens[15]], 2, self.k_size[tokens[16]]))
             bottleneck_params_list.append(
-                (self.multiply[tokens[17]], self.filter_num3[tokens[18]],
+                (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(

paddleslim/nas/search_space/resnet.py

@@ -32,31 +32,144 @@ class ResNetSpace(SearchSpaceBase):
                  input_size,
                  output_size,
                  block_num,
-                 scale=1.0,
+                 block_mask=None,
+                 extract_feature=False,
                  class_dim=1000):
-        super(ResNetSpace, self).__init__(input_size, output_size, block_num)
-        pass
+        super(ResNetSpace, self).__init__(input_size, output_size, block_num,
+                                          block_mask)
+        assert self.block_mask == None, 'ResNetSpace will use origin ResNet as seach space, so use input_size, output_size and block_num to search'
+        # self.filter_num1 ~ self.filter_num4 means convolution channel
+        self.filter_num1 = np.array([48, 64, 96, 128, 160, 192, 224])  #7 
+        self.filter_num2 = np.array([64, 96, 128, 160, 192, 256, 320])  #7
+        self.filter_num3 = np.array([128, 160, 192, 256, 320, 384])  #6
+        self.filter_num4 = np.array([192, 256, 384, 512, 640])  #5
+        # self.repeat1 ~ self.repeat4 means depth of network
+        self.repeat1 = [2, 3, 4, 5, 6]  #5
+        self.repeat2 = [2, 3, 4, 5, 6, 7]  #6
+        self.repeat3 = [2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24]  #13
+        self.repeat4 = [2, 3, 4, 5, 6, 7]  #6
+        self.class_dim = class_dim
+        self.extract_feature = extract_feature
+        assert self.block_num < 5, 'ResNet: block number must less than 5, but receive block number is {}'.format(
+            self.block_num)
 
     def init_tokens(self):
-        return [0, 0, 0, 0, 0, 0]
+        """
+        The initial token.
+        return 2 * self.block_num, 2 means depth and num_filter
+        """
+        init_token_base = [0, 0, 0, 0, 0, 0, 0, 0]
+        self.token_len = self.block_num * 2
+        return init_token_base[:self.token_len]
 
     def range_table(self):
-        return [2, 2, 2, 2, 2, 2]
+        """
+        Get range table of current search space, constrains the range of tokens.
+        """
+        #2 * self.block_num, 2 means depth and num_filter
+        range_table_base = [
+            len(self.filter_num1), len(self.repeat1), len(self.filter_num2),
+            len(self.repeat2), len(self.filter_num3), len(self.repeat3),
+            len(self.filter_num4), len(self.repeat4)
+        ]
+        return range_table_base[:self.token_len]
 
     def token2arch(self, tokens=None):
+        """
+        return net_arch function
+        """
         if tokens is None:
-            self.init_tokens()
+            tokens = self.init_tokens()
+
+        depth = []
+        num_filters = []
+        if self.block_num >= 1:
+            filter1 = self.filter_num1[tokens[0]]
+            repeat1 = self.repeat1[tokens[1]]
+            num_filters.append(filter1)
+            depth.append(repeat1)
+        if self.block_num >= 2:
+            filter2 = self.filter_num2[tokens[2]]
+            repeat2 = self.repeat2[tokens[3]]
+            num_filters.append(filter2)
+            depth.append(repeat2)
+        if self.block_num >= 3:
+            filter3 = self.filter_num3[tokens[4]]
+            repeat3 = self.repeat3[tokens[5]]
+            num_filters.append(filter3)
+            depth.append(repeat3)
+        if self.block_num >= 4:
+            filter4 = self.filter_num4[tokens[6]]
+            repeat4 = self.repeat4[tokens[7]]
+            num_filters.append(filter4)
+            depth.append(repeat4)
 
         def net_arch(input):
-            input = conv_bn_layer(
-                input,
-                num_filters=32,
-                filter_size=3,
+            conv = conv_bn_layer(
+                input=input,
+                filter_size=5,
+                num_filters=filter1,
                 stride=2,
-                padding='SAME',
-                act='sigmoid',
-                name='resnet_conv1_1')
+                act='relu',
+                name='resnet_conv0')
+            for block in range(len(depth)):
+                for i in range(depth[block]):
+                    conv = self._bottleneck_block(
+                        input=conv,
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        name='resnet_depth{}_block{}'.format(i, block))
 
-            return input
+            if self.output_size == 1:
+                conv = fluid.layers.fc(
+                    input=conv,
+                    size=self.class_dim,
+                    act=None,
+                    param_attr=fluid.param_attr.ParamAttr(
+                        initializer=fluid.initializer.NormalInitializer(0.0,
+                                                                        0.01)),
+                    bias_attr=fluid.param_attr.ParamAttr(
+                        initializer=fluid.initializer.ConstantInitializer(0)))
+
+            return conv
 
         return net_arch
+
+    def _shortcut(self, input, ch_out, stride, name=None):
+        ch_in = input.shape[1]
+        if ch_in != ch_out or stride != 1:
+            return conv_bn_layer(
+                input=input,
+                filter_size=1,
+                num_filters=ch_out,
+                stride=stride,
+                name=name + '_conv')
+        else:
+            return input
+
+    def _bottleneck_block(self, input, num_filters, stride, name=None):
+        conv0 = conv_bn_layer(
+            input=input,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name=name + '_bottleneck_conv0')
+        conv1 = conv_bn_layer(
+            input=conv0,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu',
+            name=name + '_bottleneck_conv1')
+        conv2 = conv_bn_layer(
+            input=conv1,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None,
+            name=name + '_bottleneck_conv2')
+
+        short = self._shortcut(
+            input, num_filters * 4, stride, name=name + '_shortcut')
+
+        return fluid.layers.elementwise_add(
+            x=short, y=conv2, act='relu', name=name + '_bottleneck_add')

paddleslim/nas/search_space/search_space_base.py

@@ -19,10 +19,11 @@ class SearchSpaceBase(object):
     """Controller for Neural Architecture Search.
     """
 
-    def __init__(self, input_size, output_size, block_num, *argss):
+    def __init__(self, input_size, output_size, block_num, block_mask, *argss):
         self.input_size = input_size
         self.output_size = output_size
         self.block_num = block_num
+        self.block_mask = block_mask
 
     def init_tokens(self):
         """Get init tokens in search space.

paddleslim/prune/auto_pruner.py

@@ -96,8 +96,10 @@ class AutoPruner(object):
         self._pruner = Pruner()
         if self._pruned_flops:
             self._base_flops = flops(program)
-            _logger.info("AutoPruner - base flops: {};".format(
-                self._base_flops))
+            self._max_flops = self._base_flops * (1 - self._pruned_flops)
+            _logger.info(
+                "AutoPruner - base flops: {}; pruned_flops: {}; max_flops: {}".
+                format(self._base_flops, self._pruned_flops, self._max_flops))
         if self._pruned_latency:
             self._base_latency = latency(program)
 
@@ -106,7 +108,7 @@ class AutoPruner(object):
                 self, _program, self._params, self._pruned_flops,
                 self._pruned_latency)
         init_tokens = self._ratios2tokens(self._init_ratios)
-
+        _logger.info("range table: {}".format(self._range_table))
         controller = SAController(self._range_table, self._reduce_rate,
                                   self._init_temperature, self._max_try_number,
                                   init_tokens, self._constrain_func)
@@ -143,10 +145,10 @@ class AutoPruner(object):
     def _get_range_table(self, min_ratios, max_ratios):
         assert isinstance(min_ratios, list) or isinstance(min_ratios, float)
         assert isinstance(max_ratios, list) or isinstance(max_ratios, float)
-        min_ratios = min_ratios if isinstance(min_ratios,
-                                              list) else [min_ratios]
-        max_ratios = max_ratios if isinstance(max_ratios,
-                                              list) else [max_ratios]
+        min_ratios = min_ratios if isinstance(
+            min_ratios, list) else [min_ratios] * len(self._params)
+        max_ratios = max_ratios if isinstance(
+            max_ratios, list) else [max_ratios] * len(self._params)
         min_tokens = self._ratios2tokens(min_ratios)
         max_tokens = self._ratios2tokens(max_ratios)
         return (min_tokens, max_tokens)
@@ -160,10 +162,17 @@ class AutoPruner(object):
             ratios,
             place=self._place,
             only_graph=True)
-        return flops(pruned_program) < self._base_flops * (
-            1 - self._pruned_flops)
-
-    def prune(self, program):
+        current_flops = flops(pruned_program)
+        result = current_flops < self._max_flops
+        if not result:
+            _logger.info("Failed try ratios: {}; flops: {}; max_flops: {}".
+                         format(ratios, current_flops, self._max_flops))
+        else:
+            _logger.info("Success try ratios: {}; flops: {}; max_flops: {}".
+                         format(ratios, current_flops, self._max_flops))
+        return result
+
+    def prune(self, program, eval_program=None):
         """
         Prune program with latest tokens generated by controller.
         Args:
@@ -178,10 +187,21 @@ class AutoPruner(object):
             self._params,
             self._current_ratios,
             place=self._place,
+            only_graph=False,
             param_backup=self._param_backup)
+        pruned_val_program = None
+        if eval_program is not None:
+            pruned_val_program = self._pruner.prune(
+                program,
+                self._scope,
+                self._params,
+                self._current_ratios,
+                place=self._place,
+                only_graph=True)
+
         _logger.info("AutoPruner - pruned ratios: {}".format(
             self._current_ratios))
-        return pruned_program
+        return pruned_program, pruned_val_program
 
     def reward(self, score):
         """

paddleslim/prune/pruner.py

@@ -12,13 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import logging
 import numpy as np
 import paddle.fluid as fluid
 import copy
 from ..core import VarWrapper, OpWrapper, GraphWrapper
+from ..common import get_logger
 
 __all__ = ["Pruner"]
 
+_logger = get_logger(__name__, level=logging.INFO)
+
 
 class Pruner():
     def __init__(self, criterion="l1_norm"):
@@ -69,6 +73,10 @@ class Pruner():
             only_graph=only_graph,
             param_backup=param_backup,
             param_shape_backup=param_shape_backup)
+        for op in graph.ops():
+            if op.type() == 'depthwise_conv2d' or op.type(
+            ) == 'depthwise_conv2d_grad':
+                op.set_attr('groups', op.inputs('Filter')[0].shape()[0])
         return graph.program
 
     def _prune_filters_by_ratio(self,
@@ -94,27 +102,49 @@ class Pruner():
         """
         if params[0].name() in self.pruned_list[0]:
             return
-        param_t = scope.find_var(params[0].name()).get_tensor()
-        pruned_idx = self._cal_pruned_idx(
-            params[0].name(), np.array(param_t), ratio, axis=0)
-        for param in params:
-            assert isinstance(param, VarWrapper)
-            param_t = scope.find_var(param.name()).get_tensor()
-            if param_backup is not None and (param.name() not in param_backup):
-                param_backup[param.name()] = copy.deepcopy(np.array(param_t))
-            pruned_param = self._prune_tensor(
-                np.array(param_t), pruned_idx, pruned_axis=0, lazy=lazy)
-            if not only_graph:
+
+        if only_graph:
+            pruned_num = int(round(params[0].shape()[0] * ratio))
+            for param in params:
+                ori_shape = param.shape()
+                if param_backup is not None and (
+                        param.name() not in param_backup):
+                    param_backup[param.name()] = copy.deepcopy(ori_shape)
+                new_shape = list(ori_shape)
+                new_shape[0] -= pruned_num
+                param.set_shape(new_shape)
+                _logger.debug("prune [{}] from {} to {}".format(param.name(
+                ), ori_shape, new_shape))
+                self.pruned_list[0].append(param.name())
+            return range(pruned_num)
+
+        else:
+
+            param_t = scope.find_var(params[0].name()).get_tensor()
+            pruned_idx = self._cal_pruned_idx(
+                params[0].name(), np.array(param_t), ratio, axis=0)
+            for param in params:
+                assert isinstance(param, VarWrapper)
+                param_t = scope.find_var(param.name()).get_tensor()
+                if param_backup is not None and (
+                        param.name() not in param_backup):
+                    param_backup[param.name()] = copy.deepcopy(
+                        np.array(param_t))
+                pruned_param = self._prune_tensor(
+                    np.array(param_t), pruned_idx, pruned_axis=0, lazy=lazy)
                 param_t.set(pruned_param, place)
-            ori_shape = param.shape()
-            if param_shape_backup is not None and (
-                    param.name() not in param_shape_backup):
-                param_shape_backup[param.name()] = copy.deepcopy(param.shape())
-            new_shape = list(param.shape())
-            new_shape[0] = pruned_param.shape[0]
-            param.set_shape(new_shape)
-            self.pruned_list[0].append(param.name())
-        return pruned_idx
+                ori_shape = param.shape()
+                if param_shape_backup is not None and (
+                        param.name() not in param_shape_backup):
+                    param_shape_backup[param.name()] = copy.deepcopy(
+                        param.shape())
+                new_shape = list(param.shape())
+                new_shape[0] = pruned_param.shape[0]
+                param.set_shape(new_shape)
+                _logger.debug("prune [{}] from {} to {}".format(param.name(
+                ), ori_shape, new_shape))
+                self.pruned_list[0].append(param.name())
+            return pruned_idx
 
     def _prune_parameter_by_idx(self,
                                 scope,
@@ -141,24 +171,44 @@ class Pruner():
         """
         if params[0].name() in self.pruned_list[pruned_axis]:
             return
-        for param in params:
-            assert isinstance(param, VarWrapper)
-            param_t = scope.find_var(param.name()).get_tensor()
-            if param_backup is not None and (param.name() not in param_backup):
-                param_backup[param.name()] = copy.deepcopy(np.array(param_t))
-            pruned_param = self._prune_tensor(
-                np.array(param_t), pruned_idx, pruned_axis, lazy=lazy)
-            if not only_graph:
+
+        if only_graph:
+            pruned_num = len(pruned_idx)
+            for param in params:
+                ori_shape = param.shape()
+                if param_backup is not None and (
+                        param.name() not in param_backup):
+                    param_backup[param.name()] = copy.deepcopy(ori_shape)
+                new_shape = list(ori_shape)
+                new_shape[pruned_axis] -= pruned_num
+                param.set_shape(new_shape)
+                _logger.debug("prune [{}] from {} to {}".format(param.name(
+                ), ori_shape, new_shape))
+                self.pruned_list[pruned_axis].append(param.name())
+
+        else:
+            for param in params:
+                assert isinstance(param, VarWrapper)
+                param_t = scope.find_var(param.name()).get_tensor()
+                if param_backup is not None and (
+                        param.name() not in param_backup):
+                    param_backup[param.name()] = copy.deepcopy(
+                        np.array(param_t))
+                pruned_param = self._prune_tensor(
+                    np.array(param_t), pruned_idx, pruned_axis, lazy=lazy)
                 param_t.set(pruned_param, place)
-            ori_shape = param.shape()
+                ori_shape = param.shape()
 
-            if param_shape_backup is not None and (
-                    param.name() not in param_shape_backup):
-                param_shape_backup[param.name()] = copy.deepcopy(param.shape())
-            new_shape = list(param.shape())
-            new_shape[pruned_axis] = pruned_param.shape[pruned_axis]
-            param.set_shape(new_shape)
-            self.pruned_list[pruned_axis].append(param.name())
+                if param_shape_backup is not None and (
+                        param.name() not in param_shape_backup):
+                    param_shape_backup[param.name()] = copy.deepcopy(
+                        param.shape())
+                new_shape = list(param.shape())
+                new_shape[pruned_axis] = pruned_param.shape[pruned_axis]
+                param.set_shape(new_shape)
+                _logger.debug("prune [{}] from {} to {}".format(param.name(
+                ), ori_shape, new_shape))
+                self.pruned_list[pruned_axis].append(param.name())
 
     def _forward_search_related_op(self, graph, param):
         """
@@ -488,14 +538,16 @@ class Pruner():
                 visited.append(op.idx())
         while len(stack) > 0:
             top_op = stack.pop()
-            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())
+            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 child in graph.next_ops(top_op):
                 if (child.type() != 'conv2d') and (child.type() != 'fc') and (

paddleslim/quant/quanter.py

@@ -20,11 +20,19 @@ from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
 from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
 from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
 from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
+from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass
 from paddle.fluid import core
 
-WEIGHT_QUANTIZATION_TYPES=['abs_max', 'channel_wise_abs_max', 'range_abs_max', 'moving_average_abs_max']
-ACTIVATION_QUANTIZATION_TYPES=['abs_max','range_abs_max', 'moving_average_abs_max']
+WEIGHT_QUANTIZATION_TYPES = [
+    'abs_max', 'channel_wise_abs_max', 'range_abs_max',
+    'moving_average_abs_max'
+]
+ACTIVATION_QUANTIZATION_TYPES = [
+    'abs_max', 'range_abs_max', 'moving_average_abs_max'
+]
 VALID_DTYPES = ['int8']
+TRANSFORM_PASS_OP_TYPES = ['conv2d', 'depthwise_conv2d', 'mul']
+QUANT_DEQUANT_PASS_OP_TYPES = ['elementwise_add', 'pool2d']
 
 _quant_config_default = {
     # weight quantize type, default is 'abs_max'
@@ -38,7 +46,8 @@ _quant_config_default = {
     # ops of name_scope in not_quant_pattern list, will not be quantized
     'not_quant_pattern': ['skip_quant'],
     # ops of type in quantize_op_types, will be quantized
-    'quantize_op_types': ['conv2d', 'depthwise_conv2d', 'mul'],
+    'quantize_op_types':
+    ['conv2d', 'depthwise_conv2d', 'mul', 'elementwise_add', 'pool2d'],
     # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
     'dtype': 'int8',
     # window size for 'range_abs_max' quantization. defaulf is 10000
@@ -88,6 +97,12 @@ def _parse_configs(user_config):
     assert isinstance(configs['quantize_op_types'], list), \
         "quantize_op_types must be a list"
 
+    for op_type in configs['quantize_op_types']:
+        assert (op_type in QUANT_DEQUANT_PASS_OP_TYPES) or (
+            op_type in TRANSFORM_PASS_OP_TYPES), "{} is not support, \
+                    now support op types are {}".format(
+                op_type, TRANSFORM_PASS_OP_TYPES + QUANT_DEQUANT_PASS_OP_TYPES)
+
     assert isinstance(configs['dtype'], str), \
         "dtype must be a str."
 
@@ -132,19 +147,37 @@ def quant_aware(program, place, config, scope=None, for_test=False):
     config = _parse_configs(config)
     main_graph = IrGraph(core.Graph(program.desc), for_test=for_test)
 
-    transform_pass = QuantizationTransformPass(
-        scope=scope,
-        place=place,
-        weight_bits=config['weight_bits'],
-        activation_bits=config['activation_bits'],
-        activation_quantize_type=config['activation_quantize_type'],
-        weight_quantize_type=config['weight_quantize_type'],
-        window_size=config['window_size'],
-        moving_rate=config['moving_rate'],
-        quantizable_op_type=config['quantize_op_types'],
-        skip_pattern=config['not_quant_pattern'])
-
-    transform_pass.apply(main_graph)
+    transform_pass_ops = []
+    quant_dequant_ops = []
+    for op_type in config['quantize_op_types']:
+        if op_type in TRANSFORM_PASS_OP_TYPES:
+            transform_pass_ops.append(op_type)
+        elif op_type in QUANT_DEQUANT_PASS_OP_TYPES:
+            quant_dequant_ops.append(op_type)
+    if len(transform_pass_ops) > 0:
+        transform_pass = QuantizationTransformPass(
+            scope=scope,
+            place=place,
+            weight_bits=config['weight_bits'],
+            activation_bits=config['activation_bits'],
+            activation_quantize_type=config['activation_quantize_type'],
+            weight_quantize_type=config['weight_quantize_type'],
+            window_size=config['window_size'],
+            moving_rate=config['moving_rate'],
+            quantizable_op_type=transform_pass_ops,
+            skip_pattern=config['not_quant_pattern'])
+
+        transform_pass.apply(main_graph)
+
+    if len(quant_dequant_ops) > 0:
+        quant_dequant_pass = AddQuantDequantPass(
+            scope=scope,
+            place=place,
+            moving_rate=config['moving_rate'],
+            quant_bits=config['activation_bits'],
+            skip_pattern=config['not_quant_pattern'],
+            quantizable_op_type=quant_dequant_ops)
+        quant_dequant_pass.apply(main_graph)
 
     if for_test:
         quant_program = main_graph.to_program()
@@ -168,7 +201,7 @@ def quant_post(program, place, config, scope=None):
     pass
 
 
-def convert(program, scope, place, config, save_int8=False):
+def convert(program, place, config, scope=None, save_int8=False):
     """
     add quantization ops in program. the program returned is not trainable.
     Args:
@@ -183,7 +216,7 @@ def convert(program, scope, place, config, save_int8=False):
         fluid.Program: freezed int8 program which can be used for inference.
                        if save_int8 is False, this value is None.
     """
-
+    scope = fluid.global_scope() if not scope else scope
     test_graph = IrGraph(core.Graph(program.desc), for_test=True)
 
     # Freeze the graph after training by adjusting the quantize

paddleslim/search/__init__.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.
-"""Controllers and controller server"""

tests/test_sensitivity.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 sys
+sys.path.append("../")
+import unittest
+import numpy
+import paddle
+import paddle.fluid as fluid
+from paddleslim.analysis import sensitivity
+from layers import conv_bn_layer
+
+
+class TestSensitivity(unittest.TestCase):
+    def test_sensitivity(self):
+        main_program = fluid.Program()
+        startup_program = fluid.Program()
+        with fluid.program_guard(main_program, startup_program):
+            input = fluid.data(name="image", shape=[None, 1, 28, 28])
+            label = fluid.data(name="label", shape=[None, 1], dtype="int64")
+            conv1 = conv_bn_layer(input, 8, 3, "conv1")
+            conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
+            sum1 = conv1 + conv2
+            conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
+            conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
+            sum2 = conv4 + sum1
+            conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
+            conv6 = conv_bn_layer(conv5, 8, 3, "conv6")
+            out = fluid.layers.fc(conv6, size=10, act='softmax')
+            acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+        eval_program = main_program.clone(for_test=True)
+
+        place = fluid.CUDAPlace(0)
+        exe = fluid.Executor(place)
+        exe.run(startup_program)
+
+        val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
+
+        def eval_func(program, scope):
+            feeder = fluid.DataFeeder(
+                feed_list=['image', 'label'], place=place, program=program)
+            acc_set = []
+            for data in val_reader():
+                acc_np = exe.run(program=program,
+                                 scope=scope,
+                                 feed=feeder.feed(data),
+                                 fetch_list=[acc_top1])
+                acc_set.append(float(acc_np[0]))
+            acc_val_mean = numpy.array(acc_set).mean()
+            print("acc_val_mean: {}".format(acc_val_mean))
+            return acc_val_mean
+
+        sensitivity(eval_program,
+                    fluid.global_scope(), place, ["conv4_weights"], eval_func,
+                    "./sensitivities_file")
+
+
+if __name__ == '__main__':
+    unittest.main()