Refine Image classification (#2974)

* Refine Image classification

PaddleCV/image_classification/build_model.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.
+import paddle
+import paddle.fluid as fluid
+import utils.utility as utility
+
+
+def _calc_label_smoothing_loss(softmax_out, label, class_dim, epsilon):
+    """Calculate label smoothing loss
+
+    Returns:
+        label smoothing loss
+        
+    """
+
+    label_one_hot = fluid.layers.one_hot(input=label, depth=class_dim)
+    smooth_label = fluid.layers.label_smooth(
+        label=label_one_hot, epsilon=epsilon, dtype="float32")
+    loss = fluid.layers.cross_entropy(
+        input=softmax_out, label=smooth_label, soft_label=True)
+    return loss
+
+
+def _basic_model(data, model, args, is_train):
+    image = data[0]
+    label = data[1]
+
+    net_out = model.net(input=image, class_dim=args.class_dim)
+    softmax_out = fluid.layers.softmax(net_out, use_cudnn=False)
+
+    if is_train and args.use_label_smoothing:
+        cost = _calc_label_smoothing_loss(softmax_out, label, args.class_dim,
+                                          args.epsilon)
+
+    else:
+        cost = fluid.layers.cross_entropy(input=softmax_out, label=label)
+
+    avg_cost = fluid.layers.mean(cost)
+    acc_top1 = fluid.layers.accuracy(input=softmax_out, label=label, k=1)
+    acc_top5 = fluid.layers.accuracy(input=softmax_out, label=label, k=5)
+    return [avg_cost, acc_top1, acc_top5]
+
+
+def _googlenet_model(data, model, args, is_train):
+    """GoogLeNet model output, include avg_cost, acc_top1 and acc_top5
+        
+    Returns:
+         GoogLeNet model output
+
+    """
+    image = data[0]
+    label = data[1]
+
+    out0, out1, out2 = model.net(input=image, class_dim=args.class_dim)
+    cost0 = fluid.layers.cross_entropy(input=out0, label=label)
+    cost1 = fluid.layers.cross_entropy(input=out1, label=label)
+    cost2 = fluid.layers.cross_entropy(input=out2, label=label)
+
+    avg_cost0 = fluid.layers.mean(x=cost0)
+    avg_cost1 = fluid.layers.mean(x=cost1)
+    avg_cost2 = fluid.layers.mean(x=cost2)
+
+    avg_cost = avg_cost0 + 0.3 * avg_cost1 + 0.3 * avg_cost2
+    acc_top1 = fluid.layers.accuracy(input=out0, label=label, k=1)
+    acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
+
+    return [avg_cost, acc_top1, acc_top5]
+
+
+def _mixup_model(data, model, args, is_train):
+    """output of Mixup processing network, include avg_cost
+    """
+    image = data[0]
+    y_a = data[1]
+    y_b = data[2]
+    lam = data[3]
+
+    net_out = model.net(input=image, class_dim=args.class_dim)
+    softmax_out = fluid.layers.softmax(net_out, use_cudnn=False)
+    if not args.use_label_smoothing:
+        loss_a = fluid.layers.cross_entropy(input=softmax_out, label=y_a)
+        loss_b = fluid.layers.cross_entropy(input=softmax_out, label=y_b)
+    else:
+        loss_a = _calc_label_smoothing_loss(softmax_out, y_a, args.class_dim,
+                                            args.epsilon)
+        loss_b = _calc_label_smoothing_loss(softmax_out, y_b, args.class_dim,
+                                            args.epsilon)
+
+    loss_a_mean = fluid.layers.mean(x=loss_a)
+    loss_b_mean = fluid.layers.mean(x=loss_b)
+    cost = lam * loss_a_mean + (1 - lam) * loss_b_mean
+    avg_cost = fluid.layers.mean(x=cost)
+    return [avg_cost]
+
+
+def create_model(model, args, is_train):
+    """Create model, include basic model, googlenet model and mixup model
+    """
+    py_reader, data = utility.create_pyreader(is_train, args)
+
+    if args.model == "GoogLeNet":
+        loss_out = _googlenet_model(data, model, args, is_train)
+    else:
+        if args.use_mixup and is_train:
+            loss_out = _mixup_model(data, model, args, is_train)
+        else:
+            loss_out = _basic_model(data, model, args, is_train)
+    return py_reader, loss_out

PaddleCV/image_classification/eval.py

@@ -26,43 +26,48 @@ import functools
 
 import paddle
 import paddle.fluid as fluid
-import reader_cv2 as reader
+import reader
 import models
-from utils.learning_rate import cosine_decay
-from utils.utility import add_arguments, print_arguments, check_gpu
+from utils import *
 
 parser = argparse.ArgumentParser(description=__doc__)
 add_arg = functools.partial(add_arguments, argparser=parser)
 # yapf: disable
-add_arg('batch_size',       int,  256,                 "Minibatch size.")
-add_arg('use_gpu',          bool, True,                "Whether to use GPU or not.")
-add_arg('class_dim',        int,  1000,                "Class number.")
-add_arg('image_shape',      str,  "3,224,224",         "Input image size")
-add_arg('pretrained_model', str,  None,                "Whether to use pretrained model.")
-add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.")
-add_arg('resize_short_size', int, 256,                "Set resize short size")
+add_arg('data_dir',         str,  "./data/ILSVRC2012/", "The ImageNet datset")
+add_arg('batch_size',       int,  256,                  "Minibatch size.")
+add_arg('use_gpu',          bool, True,                 "Whether to use GPU or not.")
+add_arg('class_dim',        int,  1000,                 "Class number.")
+add_arg('image_shape',      str,  "3,224,224",          "Input image size")
+parser.add_argument("--pretrained_model", default=None, required=True, type=str, help="The path to load pretrained model")
+add_arg('model',            str,  "ResNet50", "Set the network to use.")
+add_arg('resize_short_size', int, 256,                  "Set resize short size")
+add_arg('reader_thread',    int,  8,                    "The number of multi thread reader")
+add_arg('reader_buf_size',  int,  2048,                 "The buf size of multi thread reader")
+parser.add_argument('--image_mean', nargs='+', type=float, default=[0.485, 0.456, 0.406], help="The mean of input image data")
+parser.add_argument('--image_std', nargs='+', type=float, default=[0.229, 0.224, 0.225], help="The std of input image data")
+add_arg('crop_size',        int,  224,                  "The value of crop size")
 # yapf: enable
 
 
 def eval(args):
-    # parameters from arguments
-    class_dim = args.class_dim
-    model_name = args.model
-    pretrained_model = args.pretrained_model
     image_shape = [int(m) for m in args.image_shape.split(",")]
 
     model_list = [m for m in dir(models) if "__" not in m]
-    assert model_name in model_list, "{} is not in lists: {}".format(args.model,
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
+    assert os.path.isdir(
+        args.pretrained_model
+    ), "{} doesn't exist, please load right pretrained model path for eval".format(
+        args.pretrained_model)
 
     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__[model_name]()
+    model = models.__dict__[args.model]()
 
-    if model_name == "GoogleNet":
-        out0, out1, out2 = model.net(input=image, class_dim=class_dim)
+    if args.model == "GoogLeNet":
+        out0, out1, out2 = model.net(input=image, class_dim=args.class_dim)
         cost0 = fluid.layers.cross_entropy(input=out0, label=label)
         cost1 = fluid.layers.cross_entropy(input=out1, label=label)
         cost2 = fluid.layers.cross_entropy(input=out2, label=label)
@@ -74,7 +79,8 @@ def eval(args):
         acc_top1 = fluid.layers.accuracy(input=out0, label=label, k=1)
         acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
     else:
-        out = model.net(input=image, class_dim=class_dim)
+        out = model.net(input=image, class_dim=args.class_dim)
+
         cost, pred = fluid.layers.softmax_with_cross_entropy(
             out, label, return_softmax=True)
         avg_cost = fluid.layers.mean(x=cost)
@@ -89,9 +95,10 @@ def eval(args):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-    fluid.io.load_persistables(exe, pretrained_model)
+    fluid.io.load_persistables(exe, args.pretrained_model)
 
-    val_reader = reader.val(settings=args, batch_size=args.batch_size)
+    val_reader = paddle.batch(
+        reader.val(settings=args), batch_size=args.batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
 
     test_info = [[], [], []]
@@ -129,7 +136,7 @@ def eval(args):
 def main():
     args = parser.parse_args()
     print_arguments(args)
-    check_gpu(args.use_gpu)
+    check_gpu()
     eval(args)
 
 

PaddleCV/image_classification/infer.py

@@ -26,43 +26,44 @@ import functools
 
 import paddle
 import paddle.fluid as fluid
-import reader_cv2 as reader
+import reader
 import models
-import utils
-from utils.utility import add_arguments, print_arguments, check_gpu
+from utils import *
 
 parser = argparse.ArgumentParser(description=__doc__)
 # yapf: disable
 add_arg = functools.partial(add_arguments, argparser=parser)
+add_arg('data_dir',         str,  "./data/ILSVRC2012/", "The ImageNet data")
 add_arg('use_gpu',          bool, True,                 "Whether to use GPU or not.")
 add_arg('class_dim',        int,  1000,                 "Class number.")
 add_arg('image_shape',      str,  "3,224,224",          "Input image size")
-add_arg('pretrained_model', str,  None,                 "Whether to use pretrained model.")
-add_arg('model',            str,  "SE_ResNeXt50_32x4d", "Set the network to use.")
-add_arg('save_inference',   bool, False,                 "Whether to save inference model or not")
-add_arg('resize_short_size', int, 256,                  "Set resize short size")
+parser.add_argument("--pretrained_model", default=None, required=True, type=str, help="The path to load pretrained model")
+add_arg('model',            str,  "ResNet50",            "Set the network to use.")
+add_arg('save_inference',   bool, False,                "Whether to save inference model or not")
+add_arg('resize_short_size',int,  256,                  "Set resize short size")
+add_arg('reader_thread',    int,  1,                    "The number of multi thread reader")
+add_arg('reader_buf_size',  int,  2048,                 "The buf size of multi thread reader")
+parser.add_argument('--image_mean', nargs='+', type=float, default=[0.485, 0.456, 0.406], help="The mean of input image data")
+parser.add_argument('--image_std', nargs='+', type=float, default=[0.229, 0.224, 0.225], help="The std of input image data")
+add_arg('crop_size',        int,  224,                  "The value of crop size")
+add_arg('topk',             int,  1,                    "topk")
+add_arg('label_path',            str,  "./utils/tools/readable_label.txt", "readable label filepath")
 # yapf: enable
 
 
 def infer(args):
-    # parameters from arguments
-    class_dim = args.class_dim
-    model_name = args.model
-    save_inference = args.save_inference
-    pretrained_model = args.pretrained_model
     image_shape = [int(m) for m in args.image_shape.split(",")]
     model_list = [m for m in dir(models) if "__" not in m]
-    assert model_name in model_list, "{} is not in lists: {}".format(args.model,
+    assert args.model in model_list, "{} is not in lists: {}".format(args.model,
                                                                      model_list)
-
+    assert os.path.isdir(args.pretrained_model
+                         ), "please load right pretrained model path for infer"
     image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
-
-    # model definition
-    model = models.__dict__[model_name]()
-    if model_name == "GoogleNet":
-        out, _, _ = model.net(input=image, class_dim=class_dim)
+    model = models.__dict__[args.model]()
+    if args.model == "GoogLeNet":
+        out, _, _ = model.net(input=image, class_dim=args.class_dim)
     else:
-        out = model.net(input=image, class_dim=class_dim)
+        out = model.net(input=image, class_dim=args.class_dim)
         out = fluid.layers.softmax(out)
 
     test_program = fluid.default_main_program().clone(for_test=True)
@@ -73,39 +74,51 @@ def infer(args):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
-    fluid.io.load_persistables(exe, pretrained_model)
-    if save_inference:
+    fluid.io.load_persistables(exe, args.pretrained_model)
+    if args.save_inference:
         fluid.io.save_inference_model(
-            dirname=model_name,
+            dirname=args.model,
             feeded_var_names=['image'],
             main_program=test_program,
             target_vars=out,
             executor=exe,
             model_filename='model',
             params_filename='params')
-        print("model: ", model_name, " is already saved")
+        print("model: ", args.model, " is already saved")
         exit(0)
-    test_batch_size = 1
 
-    test_reader = reader.test(settings=args, batch_size=test_batch_size)
+    test_batch_size = 1
+    test_reader = paddle.batch(
+        reader.test(settings=args), batch_size=test_batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image])
 
-    TOPK = 1
+    TOPK = args.topk
+    assert os.path.exists(args.label_path), "Index file doesn't exist!"
+    f = open(args.label_path)
+    label_dict = {}
+    for item in f.readlines():
+        key = item.split(" ")[0]
+        value = [l.replace("\n", "") for l in item.split(" ")[1:]]
+        label_dict[key] = value
+
     for batch_id, data in enumerate(test_reader()):
         result = exe.run(test_program,
                          fetch_list=fetch_list,
                          feed=feeder.feed(data))
         result = result[0][0]
         pred_label = np.argsort(result)[::-1][:TOPK]
-        print("Test-{0}-score: {1}, class {2}"
-              .format(batch_id, result[pred_label], pred_label))
+        readable_pred_label = []
+        for label in pred_label:
+            readable_pred_label.append(label_dict[str(label)])
+        print("Test-{0}-score: {1}, class{2} {3}".format(batch_id, result[
+            pred_label], pred_label, readable_pred_label))
         sys.stdout.flush()
 
 
 def main():
     args = parser.parse_args()
     print_arguments(args)
-    check_gpu(args.use_gpu)
+    check_gpu()
     infer(args)
 
 

PaddleCV/image_classification/legacy/README.md

@@ -8,3 +8,9 @@ For historical reasons, We keep "no name" models here, which are different from
 |- |:-: |:-:|
 |[ResNet152](http://paddle-imagenet-models.bj.bcebos.com/ResNet152_pretrained.zip) | 78.18%/93.93% | 78.11%/94.04% |
 |[SE_ResNeXt50_32x4d](http://paddle-imagenet-models.bj.bcebos.com/se_resnext_50_model.tar) | 78.32%/93.96% | 77.58%/93.73% |
+
+---
+
+2019/08/08
+We move the dist_train and fp16 part to PaddlePaddle Fleet now.
+and dist_train folder is temporary stored here.

PaddleCV/image_classification/dist_train/README.md → PaddleCV/image_classification/legacy/dist_train/README.md

@@ -112,7 +112,7 @@ Speed-ups of Multiple-GPU Training of Resnet50 on Imagenet
 
 #### Environment
 
-  - GPU: NVIDIA® Tesla® V100 
+  - GPU: NVIDIA® Tesla® V100
   - Machine number * Card number: 4 * 4
   - System: Centos 6u3
   - Cuda/Cudnn: 9.0/7.1
@@ -127,5 +127,3 @@ Speed-ups of Multiple-GPU Training of Resnet50 on Imagenet
 <img src="../images/resnet_dgc.png" width=528> <br />
 Performance using DGC for resnet-fp32 under different bandwidth
 </p>
-
-

PaddleCV/image_classification/dist_train/batch_merge.py → PaddleCV/image_classification/legacy/dist_train/batch_merge.py

@@ -15,6 +15,7 @@
 import paddle.fluid as fluid
 import numpy as np
 
+
 def copyback_repeat_bn_params(main_prog):
     repeat_vars = set()
     for op in main_prog.global_block().ops:
@@ -22,9 +23,11 @@ def copyback_repeat_bn_params(main_prog):
             repeat_vars.add(op.input("Mean")[0])
             repeat_vars.add(op.input("Variance")[0])
     for vname in repeat_vars:
-        real_var = fluid.global_scope().find_var("%s.repeat.0" % vname).get_tensor()
+        real_var = fluid.global_scope().find_var("%s.repeat.0" %
+                                                 vname).get_tensor()
         orig_var = fluid.global_scope().find_var(vname).get_tensor()
-        orig_var.set(np.array(real_var), fluid.CUDAPlace(0)) # test on GPU0
+        orig_var.set(np.array(real_var), fluid.CUDAPlace(0))  # test on GPU0
+
 
 def append_bn_repeat_init_op(main_prog, startup_prog, num_repeats):
     repeat_vars = set()
@@ -32,7 +35,7 @@ def append_bn_repeat_init_op(main_prog, startup_prog, num_repeats):
         if op.type == "batch_norm":
             repeat_vars.add(op.input("Mean")[0])
             repeat_vars.add(op.input("Variance")[0])
-    
+
     for i in range(num_repeats):
         for op in startup_prog.global_block().ops:
             if op.type == "fill_constant":
@@ -45,13 +48,10 @@ def append_bn_repeat_init_op(main_prog, startup_prog, num_repeats):
                             type=var.type,
                             dtype=var.dtype,
                             shape=var.shape,
-                            persistable=var.persistable
-                        )
+                            persistable=var.persistable)
                         main_prog.global_block()._clone_variable(repeat_var)
                         startup_prog.global_block().append_op(
                             type="fill_constant",
                             inputs={},
                             outputs={"Out": repeat_var},
-                            attrs=op.all_attrs()
-                        )
-
+                            attrs=op.all_attrs())

PaddleCV/image_classification/dist_train/env.py → PaddleCV/image_classification/legacy/dist_train/env.py

@@ -23,7 +23,7 @@ def dist_env():
     trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
     num_trainers = 1
     training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
-    assert(training_role == "PSERVER" or training_role == "TRAINER")
+    assert (training_role == "PSERVER" or training_role == "TRAINER")
 
     # - PADDLE_TRAINER_ENDPOINTS means nccl2 mode.
     # - PADDLE_PSERVER_ENDPOINTS means pserver mode.
@@ -36,7 +36,7 @@ def dist_env():
         num_trainers = len(trainer_endpoints)
     elif pserver_endpoints:
         num_trainers = int(os.getenv("PADDLE_TRAINERS_NUM"))
-    
+
     return {
         "trainer_id": trainer_id,
         "num_trainers": num_trainers,

PaddleCV/image_classification/reader_cv2.py → PaddleCV/image_classification/legacy/reader_pil.py

@@ -17,11 +17,9 @@ import math
 import random
 import functools
 import numpy as np
-import cv2
-import io
+from PIL import Image, ImageEnhance
 
 import paddle
-import paddle.fluid as fluid
 
 random.seed(0)
 np.random.seed(0)
@@ -31,202 +29,133 @@ DATA_DIM = 224
 THREAD = 8
 BUF_SIZE = 2048
 
-DATA_DIR = './data/ILSVRC2012'
+DATA_DIR = 'data/ILSVRC2012'
 
 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 rotate_image(img):
-    """ rotate_image """
-    (h, w) = img.shape[:2]
-    center = (w / 2, h / 2)
-    angle = np.random.randint(-10, 11)
-    M = cv2.getRotationMatrix2D(center, angle, 1.0)
-    rotated = cv2.warpAffine(img, M, (w, h))
-    return rotated
+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, settings, scale=None, ratio=None):
-    """ random_crop """
-    lower_scale = settings.lower_scale
-    lower_ratio = settings.lower_ratio
-    upper_ratio = settings.upper_ratio
-    scale = [lower_scale, 1.0] if scale is None else scale
-    ratio = [lower_ratio, upper_ratio] if ratio is None else ratio
 
+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.shape[0]) / img.shape[1]) / (h**2),
-                (float(img.shape[1]) / img.shape[0]) / (w**2))
-
+    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.shape[0] * img.shape[1] * np.random.uniform(scale_min,
-                                                                  scale_max)
+    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.shape[0] - h + 1)
-    j = np.random.randint(0, img.shape[1] - w + 1)
 
-    img = img[i:i + h, j:j + w, :]
+    i = np.random.randint(0, img.size[0] - w + 1)
+    j = np.random.randint(0, img.size[1] - h + 1)
 
-    resized = cv2.resize(
-        img,
-        (size, size)
-        #, interpolation=cv2.INTER_LANCZOS4
-    )
-    return resized
+    img = img.crop((i, j, i + w, j + h))
+    img = img.resize((size, size), Image.LANCZOS)
+    return img
 
 
-def distort_color(img):
+def rotate_image(img):
+    angle = np.random.randint(-10, 11)
+    img = img.rotate(angle)
     return img
 
 
-def resize_short(img, target_size):
-    """ resize_short """
-    percent = float(target_size) / min(img.shape[0], img.shape[1])
-    resized_width = int(round(img.shape[1] * percent))
-    resized_height = int(round(img.shape[0] * percent))
-    resized = cv2.resize(
-        img,
-        (resized_width, resized_height),
-        #interpolation=cv2.INTER_LANCZOS4
-    )
-    return resized
+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 crop_image(img, target_size, center):
-    """ crop_image """
-    height, width = img.shape[:2]
-    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[h_start:h_end, w_start:w_end, :]
-    return img
+    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 create_mixup_reader(settings, rd):
-    class context:
-        tmp_mix = []
-        tmp_l1 = []
-        tmp_l2 = []
-        tmp_lam = []
-
-    batch_size = settings.batch_size
-    alpha = settings.mixup_alpha
-
-    def fetch_data():
-
-        data_list = []
-        for i, item in enumerate(rd()):
-            data_list.append(item)
-            if i % batch_size == batch_size - 1:
-                yield data_list
-                data_list = []
-
-    def mixup_data():
-
-        for data_list in fetch_data():
-            if alpha > 0.:
-                lam = np.random.beta(alpha, alpha)
-            else:
-                lam = 1.
-            l1 = np.array(data_list)
-            l2 = np.random.permutation(l1)
-            mixed_l = [
-                l1[i][0] * lam + (1 - lam) * l2[i][0] for i in range(len(l1))
-            ]
-            yield mixed_l, l1, l2, lam
-
-    def mixup_reader():
-
-        for context.tmp_mix, context.tmp_l1, context.tmp_l2, context.tmp_lam in mixup_data(
-        ):
-            for i in range(len(context.tmp_mix)):
-                mixed_l = context.tmp_mix[i]
-                l1 = context.tmp_l1[i]
-                l2 = context.tmp_l2[i]
-                lam = context.tmp_lam
-                yield mixed_l, l1[1], l2[1], lam
-
-    return mixup_reader
-
-
-def process_image(sample,
-                  settings,
-                  mode,
-                  color_jitter,
-                  rotate,
-                  crop_size=224,
-                  mean=None,
-                  std=None):
-    """ process_image """
-
-    mean = [0.485, 0.456, 0.406] if mean is None else mean
-    std = [0.229, 0.224, 0.225] if std is None else std
 
+def process_image(sample, mode, color_jitter, rotate):
     img_path = sample[0]
-    img = cv2.imread(img_path)
 
+    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 rotate:
-            img = rotate_image(img)
-        if crop_size > 0:
-            img = random_crop(img, crop_size, settings)
         if color_jitter:
             img = distort_color(img)
         if np.random.randint(0, 2) == 1:
-            img = img[:, ::-1, :]
-    else:
-        if crop_size > 0:
-            target_size = settings.resize_short_size
-            img = resize_short(img, target_size)
-            img = crop_image(img, target_size=crop_size, center=True)
-
-    img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
-    img_mean = np.array(mean).reshape((3, 1, 1))
-    img_std = np.array(std).reshape((3, 1, 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])
+        return img, sample[1]
     elif mode == 'test':
-        return (img, )
+        return [img]
 
 
-def process_batch_data(input_data, settings, mode, color_jitter, rotate):
+def process_batch_data(input_data, mode, color_jitter, rotate):
     batch_data = []
-    crop_size = int(settings.image_shape.split(',')[-1])
     for sample in input_data:
-        if os.path.isfile(sample[0]):
-            batch_data.append(
-                process_image(sample, settings, mode, color_jitter, rotate, crop_size))
-        else:
-            print("File not exist : %s" % sample[0])
+        batch_data.append(process_image(sample, mode, color_jitter, rotate))
     return batch_data
 
 
-def _reader_creator(settings,
-                    file_list,
+def _reader_creator(file_list,
                     batch_size,
                     mode,
                     shuffle=False,
                     color_jitter=False,
                     rotate=False,
                     data_dir=DATA_DIR,
-                    shuffle_seed=0):
+                    shuffle_seed=0,
+                    infinite=False):
     def reader():
         def read_file_list():
             with open(file_list) as flist:
@@ -241,9 +170,10 @@ def _reader_creator(settings,
                 img_path = os.path.join(data_dir, img_path)
                 batch_data.append([img_path, int(label)])
                 if len(batch_data) == batch_size:
-                    if mode == 'train' or mode == 'val' or mode == 'test':
+                    if mode == 'train' or mode == 'val':
                         yield batch_data
-
+                    elif mode == 'test':
+                        yield [sample[0] for sample in batch_data]
                     batch_data = []
 
         return read_file_list
@@ -258,20 +188,14 @@ def _reader_creator(settings,
         data_reader = fluid.contrib.reader.distributed_batch_reader(data_reader)
 
     mapper = functools.partial(
-        process_batch_data,
-        settings=settings,
-        mode=mode,
-        color_jitter=color_jitter,
-        rotate=rotate)
+        process_batch_data, mode=mode, color_jitter=color_jitter, rotate=rotate)
 
-    return paddle.reader.xmap_readers(
-        mapper, data_reader, THREAD, BUF_SIZE, order=False)
+    return paddle.reader.xmap_readers(mapper, data_reader, THREAD, BUF_SIZE)
 
 
-def train(settings, batch_size, data_dir=DATA_DIR, shuffle_seed=0):
+def train(batch_size, data_dir=DATA_DIR, shuffle_seed=0, infinite=False):
     file_list = os.path.join(data_dir, 'train_list.txt')
-    reader = _reader_creator(
-        settings,
+    return _reader_creator(
         file_list,
         batch_size,
         'train',
@@ -279,29 +203,17 @@ def train(settings, batch_size, data_dir=DATA_DIR, shuffle_seed=0):
         color_jitter=False,
         rotate=False,
         data_dir=data_dir,
-        shuffle_seed=shuffle_seed)
-    if settings.use_mixup == True:
-        reader = create_mixup_reader(settings, reader)
-    return reader
+        shuffle_seed=shuffle_seed,
+        infinite=infinite)
 
 
-def val(settings, batch_size, data_dir=DATA_DIR):
+def val(batch_size, data_dir=DATA_DIR):
     file_list = os.path.join(data_dir, 'val_list.txt')
     return _reader_creator(
-        settings,
-        file_list,
-        batch_size,
-        'val',
-        shuffle=False,
-        data_dir=data_dir)
+        file_list, batch_size, 'val', shuffle=False, data_dir=data_dir)
 
 
-def test(settings, batch_size, data_dir=DATA_DIR):
+def test(batch_size, data_dir=DATA_DIR):
     file_list = os.path.join(data_dir, 'val_list.txt')
     return _reader_creator(
-        settings,
-        file_list,
-        batch_size,
-        'test',
-        shuffle=False,
-        data_dir=data_dir)
+        file_list, batch_size, 'test', shuffle=False, data_dir=data_dir)

PaddleCV/image_classification/models/__init__.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 .alexnet import AlexNet
-from .mobilenet import MobileNet
-from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x1_0, MobileNetV2_x1_5, MobileNetV2_x2_0, MobileNetV2_scale
-from .googlenet import GoogleNet
+from .mobilenet import MobileNet, MobileNetV1
+from .mobilenet_v2 import MobileNetV2, MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x1_0, MobileNetV2_x1_5, MobileNetV2_x2_0, MobileNetV2_scale
+from .googlenet import GoogLeNet
 from .vgg import VGG11, VGG13, VGG16, VGG19
 from .resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
 from .resnet_vc import ResNet50_vc, ResNet101_vc, ResNet152_vc
@@ -11,14 +25,13 @@ from .resnext_vd import ResNeXt50_vd_64x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_6
 from .resnet_dist import DistResNet
 from .inception_v4 import InceptionV4
 from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_32x4d
-from .se_resnext_vd import SE_ResNeXt50_32x4d_vd, SE_ResNeXt101_32x4d_vd, SENet154_vd
+from .se_resnext_vd import SE_ResNeXt50_32x4d_vd, SE_ResNeXt101_32x4d_vd, SE_154_vd
 from .dpn import DPN68, DPN92, DPN98, DPN107, DPN131
-from .shufflenet_v2_swish import ShuffleNetV2, ShuffleNetV2_x0_5_swish, ShuffleNetV2_x1_0_swish, ShuffleNetV2_x1_5_swish, ShuffleNetV2_x2_0_swish
-from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0
+from .shufflenet_v2_swish import ShuffleNetV2_swish, ShuffleNetV2_x0_5_swish, ShuffleNetV2_x1_0_swish, ShuffleNetV2_x1_5_swish, ShuffleNetV2_x2_0_swish
+from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2
 from .fast_imagenet import FastImageNet
 from .xception import Xception_41, Xception_65, Xception_71
 from .densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
 from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
 from .darknet import DarkNet53
 from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl
-

PaddleCV/image_classification/models/alexnet.py

@@ -23,22 +23,10 @@ import paddle.fluid as fluid
 
 __all__ = ['AlexNet']
 
-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": [40, 70, 100],
-        "steps": [0.01, 0.001, 0.0001, 0.00001]
-    }
-}
-
 
 class AlexNet():
     def __init__(self):
-        self.params = train_parameters
+        pass
 
     def net(self, input, class_dim=1000):
         stdv = 1.0 / math.sqrt(input.shape[1] * 11 * 11)

PaddleCV/image_classification/models/darknet.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#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
+#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
+#    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.
+#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.param_attr import ParamAttr
 import math
 __all__ = ["DarkNet53"]
 
-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 DarkNet53():
     def __init__(self):
-        self.params = train_parameters
+
+        pass
 
     def net(self, input, class_dim=1000):
         DarkNet_cfg = {53: ([1, 2, 8, 8, 4], self.basicblock)}
@@ -45,17 +38,11 @@ class DarkNet53():
             padding=1,
             name="yolo_input")
         conv = self.downsample(
-            conv1,
-            ch_out=conv1.shape[1] * 2,
-            name="yolo_input.downsample")
-        
+            conv1, ch_out=conv1.shape[1] * 2, name="yolo_input.downsample")
+
         for i, stage in enumerate(stages):
             conv = self.layer_warp(
-                block_func,
-                conv,
-                32 * (2**i),
-                stage,
-                name="stage.{}".format(i))
+                block_func, conv, 32 * (2**i), stage, name="stage.{}".format(i))
             if i < len(stages) - 1:  # do not downsaple in the last stage
                 conv = self.downsample(
                     conv,
@@ -64,18 +51,22 @@ class DarkNet53():
         pool = fluid.layers.pool2d(
             input=conv, pool_type='avg', global_pooling=True)
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(input=pool,
-                              size=class_dim,
-                              param_attr=ParamAttr(
-                              initializer=fluid.initializer.Uniform(-stdv, stdv),name='fc_weights'),
-                              bias_attr=ParamAttr(name='fc_offset'))
+        out = fluid.layers.fc(
+            input=pool,
+            size=class_dim,
+            param_attr=ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name='fc_weights'),
+            bias_attr=ParamAttr(name='fc_offset'))
         return out
-        
-        
 
-
-
-    def conv_bn_layer(self, input, ch_out, filter_size, stride, padding, name=None):
+    def conv_bn_layer(self,
+                      input,
+                      ch_out,
+                      filter_size,
+                      stride,
+                      padding,
+                      name=None):
         conv = fluid.layers.conv2d(
             input=input,
             num_filters=ch_out,
@@ -96,9 +87,13 @@ class DarkNet53():
             moving_variance_name=bn_name + '.var')
         return out
 
-
-
-    def downsample(self, input, ch_out, filter_size=3, stride=2, padding=1, name=None):
+    def downsample(self,
+                   input,
+                   ch_out,
+                   filter_size=3,
+                   stride=2,
+                   padding=1,
+                   name=None):
         return self.conv_bn_layer(
             input,
             ch_out=ch_out,
@@ -107,22 +102,14 @@ class DarkNet53():
             padding=padding,
             name=name)
 
-
     def basicblock(self, input, ch_out, name=None):
-        conv1 = self.conv_bn_layer(
-            input, ch_out, 1, 1, 0, name=name + ".0")
-        conv2 = self.conv_bn_layer(
-            conv1, ch_out * 2, 3, 1, 1, name=name + ".1")
+        conv1 = self.conv_bn_layer(input, ch_out, 1, 1, 0, name=name + ".0")
+        conv2 = self.conv_bn_layer(conv1, ch_out * 2, 3, 1, 1, name=name + ".1")
         out = fluid.layers.elementwise_add(x=input, y=conv2, act=None)
         return out
 
-
     def layer_warp(self, block_func, input, ch_out, count, name=None):
-        res_out = block_func(
-            input, ch_out, name='{}.0'.format(name))
+        res_out = block_func(input, ch_out, name='{}.0'.format(name))
         for j in range(1, count):
-            res_out = block_func(
-                res_out, ch_out, name='{}.{}'.format(name, j))
+            res_out = block_func(res_out, ch_out, name='{}.{}'.format(name, j))
         return res_out
-
-

PaddleCV/image_classification/models/densenet.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#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
+#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
+#    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.
+#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
 import paddle.fluid as fluid
 import math
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ["DenseNet", "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264"]
+__all__ = [
+    "DenseNet", "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201",
+    "DenseNet264"
+]
 
-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 DenseNet():
     def __init__(self, layers=121):
-        self.params = train_parameters
         self.layers = layers
-        
 
     def net(self, input, bn_size=4, dropout=0, class_dim=1000):
         layers = self.layers
         supported_layers = [121, 161, 169, 201, 264]
         assert layers in supported_layers, \
             "supported layers are {} but input layer is {}".format(supported_layers, layers)
-        densenet_spec = {121: (64, 32, [6, 12, 24, 16]),
-                         161: (96, 48, [6, 12, 36, 24]),
-                         169: (64, 32, [6, 12, 32, 32]),
-                         201: (64, 32, [6, 12, 48, 32]),
-                         264: (64, 32, [6, 12, 64, 48])}
-        
-        
+        densenet_spec = {
+            121: (64, 32, [6, 12, 24, 16]),
+            161: (96, 48, [6, 12, 36, 24]),
+            169: (64, 32, [6, 12, 32, 32]),
+            201: (64, 32, [6, 12, 48, 32]),
+            264: (64, 32, [6, 12, 64, 48])
+        }
+
         num_init_features, growth_rate, block_config = densenet_spec[layers]
         conv = fluid.layers.conv2d(
             input=input,
@@ -58,46 +53,61 @@ class DenseNet():
             act=None,
             param_attr=ParamAttr(name="conv1_weights"),
             bias_attr=False)
-        conv = fluid.layers.batch_norm(input=conv, 
-                                       act='relu',
-                                       param_attr=ParamAttr(name='conv1_bn_scale'),
-                                       bias_attr=ParamAttr(name='conv1_bn_offset'),
-                                       moving_mean_name='conv1_bn_mean',
-                                       moving_variance_name='conv1_bn_variance') 
-        conv = fluid.layers.pool2d(input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+        conv = fluid.layers.batch_norm(
+            input=conv,
+            act='relu',
+            param_attr=ParamAttr(name='conv1_bn_scale'),
+            bias_attr=ParamAttr(name='conv1_bn_offset'),
+            moving_mean_name='conv1_bn_mean',
+            moving_variance_name='conv1_bn_variance')
+        conv = fluid.layers.pool2d(
+            input=conv,
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
         num_features = num_init_features
         for i, num_layers in enumerate(block_config):
-            conv = self.make_dense_block(conv, num_layers, bn_size, growth_rate, dropout, name='conv'+str(i+2))
+            conv = self.make_dense_block(
+                conv,
+                num_layers,
+                bn_size,
+                growth_rate,
+                dropout,
+                name='conv' + str(i + 2))
             num_features = num_features + num_layers * growth_rate
             if i != len(block_config) - 1:
-                conv = self.make_transition(conv, num_features // 2, name='conv'+str(i+2)+'_blk')
+                conv = self.make_transition(
+                    conv, num_features // 2, name='conv' + str(i + 2) + '_blk')
                 num_features = num_features // 2
-        conv = fluid.layers.batch_norm(input=conv, 
-                                       act='relu',
-                                       param_attr=ParamAttr(name='conv5_blk_bn_scale'),
-                                       bias_attr=ParamAttr(name='conv5_blk_bn_offset'),
-                                       moving_mean_name='conv5_blk_bn_mean',
-                                       moving_variance_name='conv5_blk_bn_variance') 
-        conv = fluid.layers.pool2d(input=conv, pool_type='avg', global_pooling=True)
+        conv = fluid.layers.batch_norm(
+            input=conv,
+            act='relu',
+            param_attr=ParamAttr(name='conv5_blk_bn_scale'),
+            bias_attr=ParamAttr(name='conv5_blk_bn_offset'),
+            moving_mean_name='conv5_blk_bn_mean',
+            moving_variance_name='conv5_blk_bn_variance')
+        conv = fluid.layers.pool2d(
+            input=conv, pool_type='avg', global_pooling=True)
         stdv = 1.0 / math.sqrt(conv.shape[1] * 1.0)
-        out = fluid.layers.fc(input=conv,
-                              size=class_dim,
-                              param_attr=fluid.param_attr.ParamAttr(
-                                  initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_weights"),
-                              bias_attr=ParamAttr(name='fc_offset'))
+        out = fluid.layers.fc(
+            input=conv,
+            size=class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name="fc_weights"),
+            bias_attr=ParamAttr(name='fc_offset'))
         return out
- 
-
 
     def make_transition(self, input, num_output_features, name=None):
-        bn_ac = fluid.layers.batch_norm(input, 
-                                        act='relu',                                      
-                                        param_attr=ParamAttr(name=name + '_bn_scale'),
-                                        bias_attr=ParamAttr(name + '_bn_offset'),
-                                        moving_mean_name=name + '_bn_mean',
-                                        moving_variance_name=name + '_bn_variance'
-                                       )
-        
+        bn_ac = fluid.layers.batch_norm(
+            input,
+            act='relu',
+            param_attr=ParamAttr(name=name + '_bn_scale'),
+            bias_attr=ParamAttr(name + '_bn_offset'),
+            moving_mean_name=name + '_bn_mean',
+            moving_variance_name=name + '_bn_variance')
+
         bn_ac_conv = fluid.layers.conv2d(
             input=bn_ac,
             num_filters=num_output_features,
@@ -105,25 +115,36 @@ class DenseNet():
             stride=1,
             act=None,
             bias_attr=False,
-            param_attr=ParamAttr(name=name + "_weights")
-        )
-        pool = fluid.layers.pool2d(input=bn_ac_conv, pool_size=2, pool_stride=2, pool_type='avg')
+            param_attr=ParamAttr(name=name + "_weights"))
+        pool = fluid.layers.pool2d(
+            input=bn_ac_conv, pool_size=2, pool_stride=2, pool_type='avg')
         return pool
-        
-    def make_dense_block(self, input, num_layers, bn_size, growth_rate, dropout, name=None):
+
+    def make_dense_block(self,
+                         input,
+                         num_layers,
+                         bn_size,
+                         growth_rate,
+                         dropout,
+                         name=None):
         conv = input
         for layer in range(num_layers):
-            conv = self.make_dense_layer(conv, growth_rate, bn_size, dropout, name=name + '_' + str(layer+1))
+            conv = self.make_dense_layer(
+                conv,
+                growth_rate,
+                bn_size,
+                dropout,
+                name=name + '_' + str(layer + 1))
         return conv
-        
-        
+
     def make_dense_layer(self, input, growth_rate, bn_size, dropout, name=None):
-        bn_ac = fluid.layers.batch_norm(input, 
-                                        act='relu',                                      
-                                        param_attr=ParamAttr(name=name + '_x1_bn_scale'),
-                                        bias_attr=ParamAttr(name + '_x1_bn_offset'),
-                                        moving_mean_name=name + '_x1_bn_mean',
-                                        moving_variance_name=name + '_x1_bn_variance')
+        bn_ac = fluid.layers.batch_norm(
+            input,
+            act='relu',
+            param_attr=ParamAttr(name=name + '_x1_bn_scale'),
+            bias_attr=ParamAttr(name + '_x1_bn_offset'),
+            moving_mean_name=name + '_x1_bn_mean',
+            moving_variance_name=name + '_x1_bn_variance')
         bn_ac_conv = fluid.layers.conv2d(
             input=bn_ac,
             num_filters=bn_size * growth_rate,
@@ -132,12 +153,13 @@ class DenseNet():
             act=None,
             bias_attr=False,
             param_attr=ParamAttr(name=name + "_x1_weights"))
-        bn_ac = fluid.layers.batch_norm(bn_ac_conv, 
-                                        act='relu',                                      
-                                        param_attr=ParamAttr(name=name + '_x2_bn_scale'),
-                                        bias_attr=ParamAttr(name + '_x2_bn_offset'),
-                                        moving_mean_name=name + '_x2_bn_mean',
-                                        moving_variance_name=name + '_x2_bn_variance')
+        bn_ac = fluid.layers.batch_norm(
+            bn_ac_conv,
+            act='relu',
+            param_attr=ParamAttr(name=name + '_x2_bn_scale'),
+            bias_attr=ParamAttr(name + '_x2_bn_offset'),
+            moving_mean_name=name + '_x2_bn_mean',
+            moving_variance_name=name + '_x2_bn_variance')
         bn_ac_conv = fluid.layers.conv2d(
             input=bn_ac,
             num_filters=growth_rate,
@@ -148,33 +170,32 @@ class DenseNet():
             bias_attr=False,
             param_attr=ParamAttr(name=name + "_x2_weights"))
         if dropout:
-            bn_ac_conv = fluid.layers.dropout(x=bn_ac_conv, dropout_prob=dropout)
+            bn_ac_conv = fluid.layers.dropout(
+                x=bn_ac_conv, dropout_prob=dropout)
         bn_ac_conv = fluid.layers.concat([input, bn_ac_conv], axis=1)
         return bn_ac_conv
-    
-        
+
+
 def DenseNet121():
-    model=DenseNet(layers=121)
+    model = DenseNet(layers=121)
     return model
 
+
 def DenseNet161():
-    model=DenseNet(layers=161)
+    model = DenseNet(layers=161)
     return model
 
+
 def DenseNet169():
-    model=DenseNet(layers=169)
+    model = DenseNet(layers=169)
     return model
 
+
 def DenseNet201():
-    model=DenseNet(layers=201)
+    model = DenseNet(layers=201)
     return model
 
+
 def DenseNet264():
-    model=DenseNet(layers=264)
+    model = DenseNet(layers=264)
     return model
-
-        
-        
-        
-  
-        

PaddleCV/image_classification/models/dpn.py

@@ -27,22 +27,9 @@ from paddle.fluid.param_attr import ParamAttr
 
 __all__ = ["DPN", "DPN68", "DPN92", "DPN98", "DPN107", "DPN131"]
 
-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 DPN(object):
     def __init__(self, layers=68):
-        self.params = train_parameters
         self.layers = layers
 
     def net(self, input, class_dim=1000):

PaddleCV/image_classification/models/googlenet.py

@@ -20,24 +20,13 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ['GoogleNet']
-
-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, 70, 100],
-        "steps": [0.1, 0.01, 0.001, 0.0001]
-    }
-}
-
-
-class GoogleNet():
+__all__ = ['GoogLeNet']
+
+
+class GoogLeNet():
     def __init__(self):
-        self.params = train_parameters
+
+        pass
 
     def conv_layer(self,
                    input,

PaddleCV/image_classification/models/inception_v4.py

@@ -24,22 +24,11 @@ from paddle.fluid.param_attr import ParamAttr
 
 __all__ = ['InceptionV4']
 
-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 InceptionV4():
     def __init__(self):
-        self.params = train_parameters
+
+        pass
 
     def net(self, input, class_dim=1000):
         x = self.inception_stem(input)

PaddleCV/image_classification/models/mobilenet.py

@@ -20,24 +20,12 @@ 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": [30, 60, 90],
-        "steps": [0.1, 0.01, 0.001, 0.0001]
-    }
-}
+__all__ = ['MobileNet', 'MobileNetV1']
 
 
 class MobileNet():
     def __init__(self):
-        self.params = train_parameters
+        pass
 
     def net(self, input, class_dim=1000, scale=1.0):
         # conv1: 112x112
@@ -208,3 +196,8 @@ class MobileNet():
             padding=0,
             name=name + "_sep")
         return pointwise_conv
+
+
+def MobileNetV1():
+    model = MobileNet()
+    return model

PaddleCV/image_classification/models/mobilenet_v2.py

@@ -19,28 +19,17 @@ 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]
-    }
-}
+__all__ = [
+    'MobileNetV2', 'MobileNetV2_x0_25, '
+    'MobileNetV2_x0_5', 'MobileNetV2_x1_0', 'MobileNetV2_x1_5',
+    'MobileNetV2_x2_0', 'MobileNetV2_scale'
+]
 
 
 class MobileNetV2():
     def __init__(self, scale=1.0, change_depth=False):
-        self.params = train_parameters
         self.scale = scale
-        self.change_depth=change_depth
-        
+        self.change_depth = change_depth
 
     def net(self, input, class_dim=1000):
         scale = self.scale
@@ -55,13 +44,13 @@ class MobileNetV2():
             (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), 
+            (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 
@@ -224,29 +213,33 @@ class MobileNetV2():
                 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
\ No newline at end of file
+    return model

PaddleCV/image_classification/models/resnet.py

@@ -22,24 +22,13 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ["ResNet", "ResNet18", "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": [30, 60, 90],
-        "steps": [0.1, 0.01, 0.001, 0.0001]
-    }
-}
+__all__ = [
+    "ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"
+]
 
 
 class ResNet():
     def __init__(self, layers=50):
-        self.params = train_parameters
         self.layers = layers
 
     def net(self, input, class_dim=1000):
@@ -59,7 +48,12 @@ class ResNet():
         num_filters = [64, 128, 256, 512]
 
         conv = self.conv_bn_layer(
-            input=input, num_filters=64, filter_size=7, stride=2, act='relu',name="conv1")
+            input=input,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu',
+            name="conv1")
         conv = fluid.layers.pool2d(
             input=conv,
             pool_size=3,
@@ -71,41 +65,44 @@ class ResNet():
                 for i in range(depth[block]):
                     if layers in [101, 152] and block == 2:
                         if i == 0:
-                            conv_name="res"+str(block+2)+"a"
+                            conv_name = "res" + str(block + 2) + "a"
                         else:
-                            conv_name="res"+str(block+2)+"b"+str(i)
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
                     else:
-                        conv_name="res"+str(block+2)+chr(97+i)
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
                     conv = self.bottleneck_block(
                         input=conv,
                         num_filters=num_filters[block],
-                        stride=2 if i == 0 and block != 0 else 1, name=conv_name)
+                        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)
-            out = fluid.layers.fc(input=pool,
-                                  size=class_dim,
-                                  param_attr=fluid.param_attr.ParamAttr(
-                                      initializer=fluid.initializer.Uniform(-stdv, stdv)))
+            out = fluid.layers.fc(
+                input=pool,
+                size=class_dim,
+                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 = "res" + str(block + 2) + chr(97 + i)
                     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,
+                        is_first=block == i == 0,
                         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)
-            out = fluid.layers.fc(input=pool,
-                                  size=class_dim,
-                                  param_attr=fluid.param_attr.ParamAttr(
-                                      initializer=fluid.initializer.Uniform(-stdv, stdv)))
+            out = fluid.layers.fc(
+                input=pool,
+                size=class_dim,
+                param_attr=fluid.param_attr.ParamAttr(
+                    initializer=fluid.initializer.Uniform(-stdv, stdv)))
         return out
 
     def conv_bn_layer(self,
@@ -127,18 +124,19 @@ class ResNet():
             param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False,
             name=name + '.conv2d.output.1')
-        
+
         if name == "conv1":
             bn_name = "bn_" + name
         else:
-            bn_name = "bn" + name[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',)
+            bn_name = "bn" + name[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]
@@ -149,29 +147,53 @@ class ResNet():
 
     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")
+            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")
+            name=name + "_branch2b")
         conv2 = self.conv_bn_layer(
-            input=conv1, num_filters=num_filters * 4, filter_size=1, act=None, name=name+"_branch2c")
+            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")
 
-        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")
 
-        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")
+        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 ResNet18():
     model = ResNet(layers=18)

PaddleCV/image_classification/models/resnet_vd.py

@@ -22,26 +22,16 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ["ResNet", "ResNet50_vd","ResNet101_vd", "ResNet152_vd", "ResNet200_vd"]
-
-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]
-    }
-}
+__all__ = [
+    "ResNet", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd", "ResNet200_vd"
+]
 
 
 class ResNet():
-    def __init__(self, layers=50, is_3x3 = False):
-        self.params = train_parameters
+    def __init__(self, layers=50, is_3x3=False):
         self.layers = layers
         self.is_3x3 = is_3x3
+
     def net(self, input, class_dim=1000):
         is_3x3 = self.is_3x3
         layers = self.layers
@@ -60,14 +50,33 @@ class ResNet():
         num_filters = [64, 128, 256, 512]
         if is_3x3 == False:
             conv = self.conv_bn_layer(
-                input=input, num_filters=64, filter_size=7, stride=2, act='relu')
+                input=input,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
         else:
             conv = self.conv_bn_layer(
-                input=input, num_filters=32, filter_size=3, stride=2, act='relu', name='conv1_1')
+                input=input,
+                num_filters=32,
+                filter_size=3,
+                stride=2,
+                act='relu',
+                name='conv1_1')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=32, filter_size=3, stride=1, act='relu', name='conv1_2')
+                input=conv,
+                num_filters=32,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_2')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_3')
+                input=conv,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_3')
 
         conv = fluid.layers.pool2d(
             input=conv,
@@ -80,32 +89,29 @@ class ResNet():
             for i in range(depth[block]):
                 if layers in [101, 152, 200] and block == 2:
                     if i == 0:
-                        conv_name="res"+str(block+2)+"a"
+                        conv_name = "res" + str(block + 2) + "a"
                     else:
-                        conv_name="res"+str(block+2)+"b"+str(i)
+                        conv_name = "res" + str(block + 2) + "b" + str(i)
                 else:
-                    conv_name="res"+str(block+2)+chr(97+i)
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
                 conv = self.bottleneck_block(
                     input=conv,
                     num_filters=num_filters[block],
                     stride=2 if i == 0 and block != 0 else 1,
-                    if_first=block==0, 
+                    if_first=block == 0,
                     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)
 
-        out = fluid.layers.fc(input=pool,
-                              size=class_dim,
-                              param_attr=fluid.param_attr.ParamAttr(
-                                  initializer=fluid.initializer.Uniform(-stdv, stdv)))
-            
-        
+        out = fluid.layers.fc(
+            input=pool,
+            size=class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv)))
+
         return out
-    
-    
-        
 
     def conv_bn_layer(self,
                       input,
@@ -128,29 +134,30 @@ class ResNet():
         if name == "conv1":
             bn_name = "bn_" + name
         else:
-            bn_name = "bn" + name[3:] 
-        return fluid.layers.batch_norm(input=conv, 
-                                       act=act,
-                                       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')
-    
-    
+            bn_name = "bn" + name[3:]
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            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 conv_bn_layer_new(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        pool = fluid.layers.pool2d(input=input,
+                          input,
+                          num_filters,
+                          filter_size,
+                          stride=1,
+                          groups=1,
+                          act=None,
+                          name=None):
+        pool = fluid.layers.pool2d(
+            input=input,
             pool_size=2,
             pool_stride=2,
             pool_padding=0,
             pool_type='avg')
-        
+
         conv = fluid.layers.conv2d(
             input=pool,
             num_filters=num_filters,
@@ -165,14 +172,13 @@ class ResNet():
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(input=conv, 
-                                       act=act,
-                                       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')
-    
-    
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            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, name, if_first=False):
         ch_in = input.shape[1]
@@ -180,43 +186,57 @@ class ResNet():
             if if_first:
                 return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
             else:
-                return self.conv_bn_layer_new(input, ch_out, 1, stride, name=name)
+                return self.conv_bn_layer_new(
+                    input, ch_out, 1, stride, name=name)
         else:
             return input
 
     def bottleneck_block(self, input, num_filters, stride, name, if_first):
         conv0 = self.conv_bn_layer(
-            input=input, num_filters=num_filters, filter_size=1, act='relu', name=name+"_branch2a")
+            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")
+            name=name + "_branch2b")
         conv2 = self.conv_bn_layer(
-            input=conv1, num_filters=num_filters * 4, filter_size=1, act=None, name=name+"_branch2c")
+            input=conv1,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None,
+            name=name + "_branch2c")
 
-        short = self.shortcut(input, num_filters * 4, stride, if_first=if_first, name=name + "_branch1")
+        short = self.shortcut(
+            input,
+            num_filters * 4,
+            stride,
+            if_first=if_first,
+            name=name + "_branch1")
 
         return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
-    
-
 
 
 def ResNet50_vd():
-    model = ResNet(layers=50, is_3x3 = True)
+    model = ResNet(layers=50, is_3x3=True)
     return model
 
+
 def ResNet101_vd():
-    model = ResNet(layers=101, is_3x3 = True)
+    model = ResNet(layers=101, is_3x3=True)
     return model
 
+
 def ResNet152_vd():
-    model = ResNet(layers=152, is_3x3 = True)
+    model = ResNet(layers=152, is_3x3=True)
     return model
 
+
 def ResNet200_vd():
-    model = ResNet(layers=200, is_3x3 = True)
+    model = ResNet(layers=200, is_3x3=True)
     return model
-

PaddleCV/image_classification/models/resnext.py

@@ -22,25 +22,14 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ["ResNeXt", "ResNeXt50_64x4d", "ResNeXt101_64x4d", "ResNeXt152_64x4d",  "ResNeXt50_32x4d", "ResNeXt101_32x4d",
-           "ResNeXt152_32x4d"]
-
-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]
-    }
-}
+__all__ = [
+    "ResNeXt", "ResNeXt50_64x4d", "ResNeXt101_64x4d", "ResNeXt152_64x4d",
+    "ResNeXt50_32x4d", "ResNeXt101_32x4d", "ResNeXt152_32x4d"
+]
 
 
 class ResNeXt():
     def __init__(self, layers=50, cardinality=64):
-        self.params = train_parameters
         self.layers = layers
         self.cardinality = cardinality
 
@@ -57,7 +46,7 @@ class ResNeXt():
             depth = [3, 4, 23, 3]
         elif layers == 152:
             depth = [3, 8, 36, 3]
-            
+
         num_filters1 = [256, 512, 1024, 2048]
         num_filters2 = [128, 256, 512, 1024]
 
@@ -67,7 +56,7 @@ class ResNeXt():
             filter_size=7,
             stride=2,
             act='relu',
-            name="res_conv1") #debug
+            name="res_conv1")  #debug
         conv = fluid.layers.pool2d(
             input=conv,
             pool_size=3,
@@ -86,7 +75,8 @@ class ResNeXt():
                     conv_name = "res" + str(block + 2) + chr(97 + i)
                 conv = self.bottleneck_block(
                     input=conv,
-                    num_filters=num_filters1[block] if cardinality == 64 else num_filters2[block],
+                    num_filters=num_filters1[block]
+                    if cardinality == 64 else num_filters2[block],
                     stride=2 if i == 0 and block != 0 else 1,
                     cardinality=cardinality,
                     name=conv_name)
@@ -94,11 +84,13 @@ class ResNeXt():
         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)
-        out = fluid.layers.fc(input=pool,
-                              size=class_dim,
-                              param_attr=fluid.param_attr.ParamAttr(
-                                  initializer=fluid.initializer.Uniform(-stdv, stdv),name='fc_weights'),
-                              bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
+        out = fluid.layers.fc(
+            input=pool,
+            size=class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name='fc_weights'),
+            bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
         return out
 
     def conv_bn_layer(self,
@@ -158,13 +150,16 @@ class ResNeXt():
             name=name + "_branch2b")
         conv2 = self.conv_bn_layer(
             input=conv1,
-            num_filters=num_filters if cardinality == 64 else num_filters*2,
+            num_filters=num_filters if cardinality == 64 else num_filters * 2,
             filter_size=1,
             act=None,
             name=name + "_branch2c")
 
         short = self.shortcut(
-            input, num_filters if cardinality == 64 else num_filters*2, stride, name=name + "_branch1")
+            input,
+            num_filters if cardinality == 64 else num_filters * 2,
+            stride,
+            name=name + "_branch1")
 
         return fluid.layers.elementwise_add(
             x=short, y=conv2, act='relu', name=name + ".add.output.5")
@@ -174,6 +169,7 @@ def ResNeXt50_64x4d():
     model = ResNeXt(layers=50, cardinality=64)
     return model
 
+
 def ResNeXt50_32x4d():
     model = ResNeXt(layers=50, cardinality=32)
     return model
@@ -183,6 +179,7 @@ def ResNeXt101_64x4d():
     model = ResNeXt(layers=101, cardinality=64)
     return model
 
+
 def ResNeXt101_32x4d():
     model = ResNeXt(layers=101, cardinality=32)
     return model
@@ -192,6 +189,7 @@ def ResNeXt152_64x4d():
     model = ResNeXt(layers=152, cardinality=64)
     return model
 
+
 def ResNeXt152_32x4d():
     model = ResNeXt(layers=152, cardinality=32)
     return model

PaddleCV/image_classification/models/resnext101_wsl.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#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
+#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
+#    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.
+#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
@@ -19,24 +19,14 @@ import paddle.fluid as fluid
 import math
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ["ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl", "ResNeXt101_32x48d_wsl", "Fix_ResNeXt101_32x48d_wsl"]
-
-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]
-    }
-}
+__all__ = [
+    "ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl",
+    "ResNeXt101_32x48d_wsl", "Fix_ResNeXt101_32x48d_wsl"
+]
 
 
 class ResNeXt101_wsl():
     def __init__(self, layers=101, cardinality=32, width=48):
-        self.params = train_parameters
         self.layers = layers
         self.cardinality = cardinality
         self.width = width
@@ -49,7 +39,6 @@ class ResNeXt101_wsl():
         depth = [3, 4, 23, 3]
         base_width = cardinality * width
         num_filters = [base_width * i for i in [1, 2, 4, 8]]
-        
 
         conv = self.conv_bn_layer(
             input=input,
@@ -57,7 +46,7 @@ class ResNeXt101_wsl():
             filter_size=7,
             stride=2,
             act='relu',
-            name="conv1") #debug
+            name="conv1")  #debug
         conv = fluid.layers.pool2d(
             input=conv,
             pool_size=3,
@@ -67,7 +56,7 @@ class ResNeXt101_wsl():
 
         for block in range(len(depth)):
             for i in range(depth[block]):
-                conv_name = 'layer' + str(block+1) + "." + str(i)
+                conv_name = 'layer' + str(block + 1) + "." + str(i)
                 conv = self.bottleneck_block(
                     input=conv,
                     num_filters=num_filters[block],
@@ -78,11 +67,13 @@ class ResNeXt101_wsl():
         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)
-        out = fluid.layers.fc(input=pool,
-                              size=class_dim,
-                              param_attr=fluid.param_attr.ParamAttr(
-                                  initializer=fluid.initializer.Uniform(-stdv, stdv),name='fc.weight'),
-                              bias_attr=fluid.param_attr.ParamAttr(name='fc.bias'))
+        out = fluid.layers.fc(
+            input=pool,
+            size=class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name='fc.weight'),
+            bias_attr=fluid.param_attr.ParamAttr(name='fc.bias'))
         return out
 
     def conv_bn_layer(self,
@@ -113,7 +104,8 @@ class ResNeXt101_wsl():
             if "conv1" == name:
                 bn_name = 'bn' + name[-1]
             else:
-                bn_name = (name[:10] if name[7:9].isdigit() else name[:9]) + 'bn' + name[-1]
+                bn_name = (name[:10] if name[7:9].isdigit() else name[:9]
+                           ) + 'bn' + name[-1]
         return fluid.layers.batch_norm(
             input=conv,
             act=act,
@@ -148,32 +140,35 @@ class ResNeXt101_wsl():
             name=name + ".conv2")
         conv2 = self.conv_bn_layer(
             input=conv1,
-            num_filters=num_filters//(width//8),
+            num_filters=num_filters // (width // 8),
             filter_size=1,
             act=None,
             name=name + ".conv3")
 
         short = self.shortcut(
-            input, num_filters//(width//8), stride, name=name + ".downsample")
-
-        return fluid.layers.elementwise_add(
-            x=short, y=conv2, act='relu')
+            input,
+            num_filters // (width // 8),
+            stride,
+            name=name + ".downsample")
 
+        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
 
 
-    
 def ResNeXt101_32x8d_wsl():
     model = ResNeXt101_wsl(cardinality=32, width=8)
     return model
-    
+
+
 def ResNeXt101_32x16d_wsl():
     model = ResNeXt101_wsl(cardinality=32, width=16)
     return model
 
+
 def ResNeXt101_32x32d_wsl():
     model = ResNeXt101_wsl(cardinality=32, width=32)
     return model
-    
+
+
 def ResNeXt101_32x48d_wsl():
     model = ResNeXt101_wsl(cardinality=32, width=48)
     return model

PaddleCV/image_classification/models/resnext_vd.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#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
+#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
+#    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.
+#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 paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 import math
 
-__all__ = ["ResNeXt","ResNeXt50_vd_64x4d","ResNeXt101_vd_64x4d","ResNeXt152_vd_64x4d","ResNeXt50_vd_32x4d","ResNeXt101_vd_32x4d", "ResNeXt152_vd_32x4d"]
-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]
-    }
-}
+__all__ = [
+    "ResNeXt", "ResNeXt50_vd_64x4d", "ResNeXt101_vd_64x4d",
+    "ResNeXt152_vd_64x4d", "ResNeXt50_vd_32x4d", "ResNeXt101_vd_32x4d",
+    "ResNeXt152_vd_32x4d"
+]
 
 
 class ResNeXt():
-    def __init__(self, layers=50, is_3x3 = False, cardinality=64):
-        self.params = train_parameters
+    def __init__(self, layers=50, is_3x3=False, cardinality=64):
         self.layers = layers
         self.is_3x3 = is_3x3
         self.cardinality = cardinality
+
     def net(self, input, class_dim=1000):
         is_3x3 = self.is_3x3
         layers = self.layers
@@ -52,17 +45,36 @@ class ResNeXt():
             depth = [3, 8, 36, 3]
         num_filters1 = [256, 512, 1024, 2048]
         num_filters2 = [128, 256, 512, 1024]
-        
+
         if is_3x3 == False:
             conv = self.conv_bn_layer(
-                input=input, num_filters=64, filter_size=7, stride=2, act='relu')
+                input=input,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
         else:
             conv = self.conv_bn_layer(
-                input=input, num_filters=32, filter_size=3, stride=2, act='relu', name='conv1_1')
+                input=input,
+                num_filters=32,
+                filter_size=3,
+                stride=2,
+                act='relu',
+                name='conv1_1')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=32, filter_size=3, stride=1, act='relu', name='conv1_2')
+                input=conv,
+                num_filters=32,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_2')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_3')
+                input=conv,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_3')
 
         conv = fluid.layers.pool2d(
             input=conv,
@@ -75,32 +87,32 @@ class ResNeXt():
             for i in range(depth[block]):
                 if layers in [101, 152, 200] and block == 2:
                     if i == 0:
-                        conv_name="res"+str(block+2)+"a"
+                        conv_name = "res" + str(block + 2) + "a"
                     else:
-                        conv_name="res"+str(block+2)+"b"+str(i)
+                        conv_name = "res" + str(block + 2) + "b" + str(i)
                 else:
-                    conv_name="res"+str(block+2)+chr(97+i)
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
                 conv = self.bottleneck_block(
                     input=conv,
-                    num_filters=num_filters1[block] if cardinality == 64 else num_filters2[block],
+                    num_filters=num_filters1[block]
+                    if cardinality == 64 else num_filters2[block],
                     stride=2 if i == 0 and block != 0 else 1,
                     cardinality=cardinality,
-                    if_first=block==0, 
+                    if_first=block == 0,
                     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)
-        out = fluid.layers.fc(input=pool,
-                              size=class_dim,
-                              param_attr=fluid.param_attr.ParamAttr(
-                                  initializer=fluid.initializer.Uniform(-stdv, stdv),name='fc_weights'),
-                              bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
-        
+        out = fluid.layers.fc(
+            input=pool,
+            size=class_dim,
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name='fc_weights'),
+            bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
+
         return out
-    
-    
-        
 
     def conv_bn_layer(self,
                       input,
@@ -118,34 +130,36 @@ class ResNeXt():
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
+            use_cudnn=False,
             param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
         else:
-            bn_name = "bn" + name[3:] 
-        return fluid.layers.batch_norm(input=conv, 
-                                       act=act,
-                                       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')
-    
-    
+            bn_name = "bn" + name[3:]
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            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 conv_bn_layer_new(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        pool = fluid.layers.pool2d(input=input,
+                          input,
+                          num_filters,
+                          filter_size,
+                          stride=1,
+                          groups=1,
+                          act=None,
+                          name=None):
+        pool = fluid.layers.pool2d(
+            input=input,
             pool_size=2,
             pool_stride=2,
             pool_padding=0,
             pool_type='avg')
-        
+
         conv = fluid.layers.conv2d(
             input=pool,
             num_filters=num_filters,
@@ -154,20 +168,20 @@ class ResNeXt():
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
+            use_cudnn=False,
             param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(input=conv, 
-                                       act=act,
-                                       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')
-    
-    
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            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, name, if_first=False):
         ch_in = input.shape[1]
@@ -175,13 +189,19 @@ class ResNeXt():
             if if_first:
                 return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
             else:
-                return self.conv_bn_layer_new(input, ch_out, 1, stride, name=name)
+                return self.conv_bn_layer_new(
+                    input, ch_out, 1, stride, name=name)
         else:
             return input
 
-    def bottleneck_block(self, input, num_filters, stride, cardinality, name, if_first):
+    def bottleneck_block(self, input, num_filters, stride, cardinality, name,
+                         if_first):
         conv0 = self.conv_bn_layer(
-            input=input, num_filters=num_filters, filter_size=1, act='relu', name=name+"_branch2a")
+            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,
@@ -189,36 +209,49 @@ class ResNeXt():
             stride=stride,
             act='relu',
             groups=cardinality,
-            name=name+"_branch2b")
+            name=name + "_branch2b")
         conv2 = self.conv_bn_layer(
-            input=conv1, num_filters=num_filters if cardinality == 64 else num_filters*2, filter_size=1, act=None, name=name+"_branch2c")
+            input=conv1,
+            num_filters=num_filters if cardinality == 64 else num_filters * 2,
+            filter_size=1,
+            act=None,
+            name=name + "_branch2c")
 
-        short = self.shortcut(input, num_filters if cardinality == 64 else num_filters*2, stride, if_first=if_first, name=name + "_branch1")
+        short = self.shortcut(
+            input,
+            num_filters if cardinality == 64 else num_filters * 2,
+            stride,
+            if_first=if_first,
+            name=name + "_branch1")
 
         return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
-    
+
 
 def ResNeXt50_vd_64x4d():
-    model = ResNeXt(layers=50, is_3x3 = True)
+    model = ResNeXt(layers=50, is_3x3=True)
     return model
 
+
 def ResNeXt50_vd_32x4d():
-    model = ResNeXt(layers=50, cardinality=32, is_3x3 = True)
+    model = ResNeXt(layers=50, cardinality=32, is_3x3=True)
     return model
 
+
 def ResNeXt101_vd_64x4d():
-    model = ResNeXt(layers=101, is_3x3 = True)
+    model = ResNeXt(layers=101, is_3x3=True)
     return model
 
+
 def ResNeXt101_vd_32x4d():
-    model = ResNeXt(layers=101, cardinality=32, is_3x3 = True)
+    model = ResNeXt(layers=101, cardinality=32, is_3x3=True)
     return model
 
+
 def ResNeXt152_vd_64x4d():
-    model = ResNeXt(layers=152, is_3x3 = True)
+    model = ResNeXt(layers=152, is_3x3=True)
     return model
 
+
 def ResNeXt152_vd_32x4d():
-    model = ResNeXt(layers=152, cardinality=32, is_3x3 = True)
+    model = ResNeXt(layers=152, cardinality=32, is_3x3=True)
     return model
-

PaddleCV/image_classification/models/se_resnext.py

@@ -27,23 +27,9 @@ __all__ = [
     "SE_ResNeXt152_32x4d"
 ]
 
-train_parameters = {
-    "input_size": [3, 224, 224],
-    "input_mean": [0.485, 0.456, 0.406],
-    "input_std": [0.229, 0.224, 0.225],
-    "dropout_seed": None,
-    "learning_strategy": {
-        "name": "piecewise_decay",
-        "batch_size": 256,
-        "epochs": [40, 80, 100],
-        "steps": [0.1, 0.01, 0.001, 0.0001]
-    }
-}
-
 
 class SE_ResNeXt():
     def __init__(self, layers=50):
-        self.params = train_parameters
         self.layers = layers
 
     def net(self, input, class_dim=1000):
@@ -139,8 +125,7 @@ class SE_ResNeXt():
             pool_type='avg',
             global_pooling=True,
             use_cudnn=False)
-        drop = fluid.layers.dropout(
-            x=pool, dropout_prob=0.5, seed=self.params['dropout_seed'])
+        drop = fluid.layers.dropout(x=pool, dropout_prob=0.5)
         stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
         out = fluid.layers.fc(
             input=drop,

PaddleCV/image_classification/models/se_resnext_vd.py

@@ -23,26 +23,12 @@ import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 
 __all__ = [
-    "SE_ResNeXt", "SE_ResNeXt50_32x4d_vd", "SE_ResNeXt101_32x4d_vd",
-    "SENet154_vd"
+    "SE_ResNeXt", "SE_ResNeXt50_32x4d_vd", "SE_ResNeXt101_32x4d_vd", "SE154_vd"
 ]
 
-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, 20],
-        "steps": [0.01, 0.001, 0.0001, 0.00001]
-    }
-}
-
 
 class SE_ResNeXt():
     def __init__(self, layers=50):
-        self.params = train_parameters
         self.layers = layers
 
     def net(self, input, class_dim=1000):
@@ -57,11 +43,26 @@ class SE_ResNeXt():
             num_filters = [128, 256, 512, 1024]
 
             conv = self.conv_bn_layer(
-                input=input, num_filters=64, filter_size=3, stride=2, act='relu', name='conv1_1')
+                input=input,
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                act='relu',
+                name='conv1_1')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_2')
+                input=conv,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_2')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=128, filter_size=3, stride=1, act='relu', name='conv1_3')
+                input=conv,
+                num_filters=128,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_3')
             conv = fluid.layers.pool2d(
                 input=conv,
                 pool_size=3,
@@ -75,11 +76,26 @@ class SE_ResNeXt():
             num_filters = [128, 256, 512, 1024]
 
             conv = self.conv_bn_layer(
-                input=input, num_filters=64, filter_size=3, stride=2, act='relu', name='conv1_1')
+                input=input,
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                act='relu',
+                name='conv1_1')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_2')
+                input=conv,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_2')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=128, filter_size=3, stride=1, act='relu', name='conv1_3')
+                input=conv,
+                num_filters=128,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_3')
             conv = fluid.layers.pool2d(
                 input=conv,
                 pool_size=3,
@@ -100,7 +116,12 @@ class SE_ResNeXt():
                 act='relu',
                 name='conv1_1')
             conv = self.conv_bn_layer(
-                input=conv, num_filters=64, filter_size=3, stride=1, act='relu',name='conv1_2')
+                input=conv,
+                num_filters=64,
+                filter_size=3,
+                stride=1,
+                act='relu',
+                name='conv1_2')
             conv = self.conv_bn_layer(
                 input=conv,
                 num_filters=128,
@@ -121,20 +142,22 @@ class SE_ResNeXt():
                     stride=2 if i == 0 and block != 0 else 1,
                     cardinality=cardinality,
                     reduction_ratio=reduction_ratio,
-                    if_first=block==0, 
-                    name=str(n)+'_'+str(i+1))
+                    if_first=block == 0,
+                    name=str(n) + '_' + str(i + 1))
 
         pool = fluid.layers.pool2d(
             input=conv, pool_size=7, pool_type='avg', global_pooling=True)
         if layers == 152:
             pool = fluid.layers.dropout(x=pool, dropout_prob=0.2)
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(input=pool,
-                              size=class_dim,
-                              param_attr=ParamAttr(
-                              initializer=fluid.initializer.Uniform(-stdv, stdv),name='fc6_weights'),
-                              bias_attr=ParamAttr(name='fc6_offset'))
-        
+        out = fluid.layers.fc(
+            input=pool,
+            size=class_dim,
+            param_attr=ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name='fc6_weights'),
+            bias_attr=ParamAttr(name='fc6_offset'))
+
         return out
 
     def shortcut(self, input, ch_out, stride, name, if_first=False):
@@ -142,17 +165,36 @@ class SE_ResNeXt():
         if ch_in != ch_out or stride != 1:
             filter_size = 1
             if if_first:
-                return self.conv_bn_layer(input, ch_out, filter_size, stride, name='conv'+name+'_prj')
+                return self.conv_bn_layer(
+                    input,
+                    ch_out,
+                    filter_size,
+                    stride,
+                    name='conv' + name + '_prj')
             else:
-                return self.conv_bn_layer_new(input, ch_out, filter_size, stride, name='conv'+name+'_prj')
+                return self.conv_bn_layer_new(
+                    input,
+                    ch_out,
+                    filter_size,
+                    stride,
+                    name='conv' + name + '_prj')
         else:
             return input
-        
 
-    def bottleneck_block(self, input, num_filters, stride, cardinality,
-                         reduction_ratio,if_first, name=None):
+    def bottleneck_block(self,
+                         input,
+                         num_filters,
+                         stride,
+                         cardinality,
+                         reduction_ratio,
+                         if_first,
+                         name=None):
         conv0 = self.conv_bn_layer(
-            input=input, num_filters=num_filters, filter_size=1, act='relu',name='conv'+name+'_x1')
+            input=input,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name='conv' + name + '_x1')
         conv1 = self.conv_bn_layer(
             input=conv0,
             num_filters=num_filters,
@@ -160,18 +202,23 @@ class SE_ResNeXt():
             stride=stride,
             groups=cardinality,
             act='relu',
-            name='conv'+name+'_x2')
+            name='conv' + name + '_x2')
         if cardinality == 64:
             num_filters = num_filters // 2
         conv2 = self.conv_bn_layer(
-            input=conv1, num_filters=num_filters * 2, filter_size=1, act=None, name='conv'+name+'_x3')
+            input=conv1,
+            num_filters=num_filters * 2,
+            filter_size=1,
+            act=None,
+            name='conv' + name + '_x3')
         scale = self.squeeze_excitation(
             input=conv2,
             num_channels=num_filters * 2,
             reduction_ratio=reduction_ratio,
-            name='fc'+name)
+            name='fc' + name)
 
-        short = self.shortcut(input, num_filters * 2, stride, if_first=if_first, name=name)
+        short = self.shortcut(
+            input, num_filters * 2, stride, if_first=if_first, name=name)
 
         return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
 
@@ -192,29 +239,31 @@ class SE_ResNeXt():
             groups=groups,
             act=None,
             bias_attr=False,
-            param_attr=ParamAttr(name=name + '_weights'),
-            )
+            param_attr=ParamAttr(name=name + '_weights'), )
         bn_name = name + "_bn"
-        return fluid.layers.batch_norm(input=conv, act=act, 
-                                       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')
-    
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            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 conv_bn_layer_new(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        pool = fluid.layers.pool2d(input=input,
+                          input,
+                          num_filters,
+                          filter_size,
+                          stride=1,
+                          groups=1,
+                          act=None,
+                          name=None):
+        pool = fluid.layers.pool2d(
+            input=input,
             pool_size=2,
             pool_stride=2,
             pool_padding=0,
             pool_type='avg')
-        
+
         conv = fluid.layers.conv2d(
             input=pool,
             num_filters=num_filters,
@@ -226,33 +275,39 @@ class SE_ResNeXt():
             param_attr=ParamAttr(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(bn_name + '_offset'),
-                                       moving_mean_name=bn_name + '_mean',
-                                       moving_variance_name=bn_name + '_variance')
-    
+        return fluid.layers.batch_norm(
+            input=conv,
+            act=act,
+            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 squeeze_excitation(self, input, num_channels, reduction_ratio, name=None):
+    def squeeze_excitation(self,
+                           input,
+                           num_channels,
+                           reduction_ratio,
+                           name=None):
         pool = fluid.layers.pool2d(
             input=input, pool_size=0, pool_type='avg', global_pooling=True)
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        squeeze = fluid.layers.fc(input=pool,
-                                  size=num_channels // reduction_ratio,
-                                  act='relu',
-                                  param_attr=fluid.param_attr.ParamAttr(
-                                      initializer=fluid.initializer.Uniform(
-                                          -stdv, stdv),name=name+'_sqz_weights'),
-                                 bias_attr=ParamAttr(name=name+'_sqz_offset'))
+        squeeze = fluid.layers.fc(
+            input=pool,
+            size=num_channels // reduction_ratio,
+            act='relu',
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name=name + '_sqz_weights'),
+            bias_attr=ParamAttr(name=name + '_sqz_offset'))
         stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
-        excitation = fluid.layers.fc(input=squeeze,
-                                     size=num_channels,
-                                     act='sigmoid',
-                                     param_attr=fluid.param_attr.ParamAttr(
-                                         initializer=fluid.initializer.Uniform(
-                                             -stdv, stdv),name=name+'_exc_weights'),
-                                    bias_attr=ParamAttr(name=name+'_exc_offset'))
+        excitation = fluid.layers.fc(
+            input=squeeze,
+            size=num_channels,
+            act='sigmoid',
+            param_attr=fluid.param_attr.ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name=name + '_exc_weights'),
+            bias_attr=ParamAttr(name=name + '_exc_offset'))
         scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
         return scale
 
@@ -267,6 +322,6 @@ def SE_ResNeXt101_32x4d_vd():
     return model
 
 
-def SENet154_vd(): 
+def SE_154_vd():
     model = SE_ResNeXt(layers=152)
     return model

PaddleCV/image_classification/models/shufflenet_v2.py

@@ -22,36 +22,27 @@ import paddle.fluid as fluid
 from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ['ShuffleNetV2_x0_25', 'ShuffleNetV2_x0_33', 'ShuffleNetV2_x0_5', 'ShuffleNetV2_x1_0', 'ShuffleNetV2_x1_5', 'ShuffleNetV2_x2_0']
-
-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]
-    }
-}
+__all__ = [
+    'ShuffleNetV2_x0_25', 'ShuffleNetV2_x0_33', 'ShuffleNetV2_x0_5',
+    'ShuffleNetV2_x1_0', 'ShuffleNetV2_x1_5', 'ShuffleNetV2_x2_0',
+    'ShuffleNetV2'
+]
 
 
 class ShuffleNetV2():
     def __init__(self, scale=1.0):
-        self.params = train_parameters
         self.scale = scale
 
     def net(self, input, class_dim=1000):
-        scale = self.scale 
+        scale = self.scale
         stage_repeats = [4, 8, 4]
-        
+
         if scale == 0.25:
-            stage_out_channels = [-1, 24,  24,  48, 96, 512]
+            stage_out_channels = [-1, 24, 24, 48, 96, 512]
         elif scale == 0.33:
-            stage_out_channels = [-1, 24,  32,  64, 128, 512]
+            stage_out_channels = [-1, 24, 32, 64, 128, 512]
         elif scale == 0.5:
-            stage_out_channels = [-1, 24,  48,  96, 192, 1024]
+            stage_out_channels = [-1, 24, 48, 96, 192, 1024]
         elif scale == 1.0:
             stage_out_channels = [-1, 24, 116, 232, 464, 1024]
         elif scale == 1.5:
@@ -59,50 +50,77 @@ class ShuffleNetV2():
         elif scale == 2.0:
             stage_out_channels = [-1, 24, 224, 488, 976, 2048]
         else:
-            raise ValueError(
-                """{} groups is not supported for
+            raise ValueError("""{} groups is not supported for
                        1x1 Grouped Convolutions""".format(num_groups))
 
         #conv1
-        
+
         input_channel = stage_out_channels[1]
-        conv1 = self.conv_bn_layer(input=input, filter_size=3, num_filters=input_channel, padding=1, stride=2,name='stage1_conv')    
-        pool1 = fluid.layers.pool2d(input=conv1, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+        conv1 = self.conv_bn_layer(
+            input=input,
+            filter_size=3,
+            num_filters=input_channel,
+            padding=1,
+            stride=2,
+            name='stage1_conv')
+        pool1 = fluid.layers.pool2d(
+            input=conv1,
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
         conv = pool1
         # bottleneck sequences
         for idxstage in range(len(stage_repeats)):
             numrepeat = stage_repeats[idxstage]
-            output_channel = stage_out_channels[idxstage+2]
+            output_channel = stage_out_channels[idxstage + 2]
             for i in range(numrepeat):
                 if i == 0:
-                    conv = self.inverted_residual_unit(input=conv, num_filters=output_channel, stride=2, 
-                                                       benchmodel=2,name=str(idxstage+2)+'_'+str(i+1))
+                    conv = self.inverted_residual_unit(
+                        input=conv,
+                        num_filters=output_channel,
+                        stride=2,
+                        benchmodel=2,
+                        name=str(idxstage + 2) + '_' + str(i + 1))
                 else:
-                    conv = self.inverted_residual_unit(input=conv, num_filters=output_channel, stride=1, 
-                                                       benchmodel=1,name=str(idxstage+2)+'_'+str(i+1))
-                
-        conv_last = self.conv_bn_layer(input=conv, filter_size=1, num_filters=stage_out_channels[-1], 
-                                       padding=0, stride=1, name='conv5')
-        pool_last = fluid.layers.pool2d(input=conv_last, pool_size=7, pool_stride=1, pool_padding=0, pool_type='avg')
+                    conv = self.inverted_residual_unit(
+                        input=conv,
+                        num_filters=output_channel,
+                        stride=1,
+                        benchmodel=1,
+                        name=str(idxstage + 2) + '_' + str(i + 1))
 
+        conv_last = self.conv_bn_layer(
+            input=conv,
+            filter_size=1,
+            num_filters=stage_out_channels[-1],
+            padding=0,
+            stride=1,
+            name='conv5')
+        pool_last = fluid.layers.pool2d(
+            input=conv_last,
+            pool_size=7,
+            pool_stride=1,
+            pool_padding=0,
+            pool_type='avg')
 
         output = fluid.layers.fc(input=pool_last,
                                  size=class_dim,
-                                 param_attr=ParamAttr(initializer=MSRA(),name='fc6_weights'),
+                                 param_attr=ParamAttr(
+                                     initializer=MSRA(), name='fc6_weights'),
                                  bias_attr=ParamAttr(name='fc6_offset'))
         return output
 
-    
     def conv_bn_layer(self,
-                  input,
-                  filter_size,
-                  num_filters,
-                  stride,
-                  padding,
-                  num_groups=1,
-                  use_cudnn=True,
-                  if_act=True,
-                  name=None):
+                      input,
+                      filter_size,
+                      num_filters,
+                      stride,
+                      padding,
+                      num_groups=1,
+                      use_cudnn=True,
+                      if_act=True,
+                      name=None):
         conv = fluid.layers.conv2d(
             input=input,
             num_filters=num_filters,
@@ -112,162 +130,179 @@ class ShuffleNetV2():
             groups=num_groups,
             act=None,
             use_cudnn=use_cudnn,
-            param_attr=ParamAttr(initializer=MSRA(),name=name+'_weights'),
+            param_attr=ParamAttr(
+                initializer=MSRA(), name=name + '_weights'),
             bias_attr=False)
-        out = int((input.shape[2] - 1)/float(stride) + 1)
+        out = int((input.shape[2] - 1) / float(stride) + 1)
         bn_name = name + '_bn'
         if if_act:
-            return fluid.layers.batch_norm(input=conv, act='relu',
-                                           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')
+            return fluid.layers.batch_norm(
+                input=conv,
+                act='relu',
+                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')
         else:
-            return 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')
+            return 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')
 
-      
     def channel_shuffle(self, x, groups):
-        batchsize, num_channels, height, width = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
+        batchsize, num_channels, height, width = x.shape[0], x.shape[
+            1], x.shape[2], x.shape[3]
         channels_per_group = num_channels // groups
-    
+
         # reshape
-        x = fluid.layers.reshape(x=x, shape=[batchsize, groups, channels_per_group, height, width])
+        x = fluid.layers.reshape(
+            x=x, shape=[batchsize, groups, channels_per_group, height, width])
 
-        x = fluid.layers.transpose(x=x, perm=[0,2,1,3,4])
+        x = fluid.layers.transpose(x=x, perm=[0, 2, 1, 3, 4])
 
         # flatten
-        x = fluid.layers.reshape(x=x, shape=[batchsize, num_channels, height, width])
+        x = fluid.layers.reshape(
+            x=x, shape=[batchsize, num_channels, height, width])
 
         return x
 
-    
-    def inverted_residual_unit(self, input, num_filters, stride, benchmodel, name=None):
+    def inverted_residual_unit(self,
+                               input,
+                               num_filters,
+                               stride,
+                               benchmodel,
+                               name=None):
         assert stride in [1, 2], \
             "supported stride are {} but your stride is {}".format([1,2], stride)
-            
-        oup_inc = num_filters//2
+
+        oup_inc = num_filters // 2
         inp = input.shape[1]
-        
+
         if benchmodel == 1:
             x1, x2 = fluid.layers.split(
-                input, num_or_sections=[input.shape[1]//2, input.shape[1]//2], dim=1)            
-            
+                input,
+                num_or_sections=[input.shape[1] // 2, input.shape[1] // 2],
+                dim=1)
+
             conv_pw = self.conv_bn_layer(
-                input=x2, 
-                num_filters=oup_inc, 
-                filter_size=1, 
+                input=x2,
+                num_filters=oup_inc,
+                filter_size=1,
                 stride=1,
                 padding=0,
                 num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv1')
+                name='stage_' + name + '_conv1')
 
             conv_dw = self.conv_bn_layer(
-                input=conv_pw, 
-                num_filters=oup_inc, 
-                filter_size=3, 
-                stride=stride, 
+                input=conv_pw,
+                num_filters=oup_inc,
+                filter_size=3,
+                stride=stride,
                 padding=1,
-                num_groups=oup_inc, 
+                num_groups=oup_inc,
                 if_act=False,
                 use_cudnn=False,
-                name='stage_'+name+'_conv2')
+                name='stage_' + name + '_conv2')
 
             conv_linear = self.conv_bn_layer(
-                input=conv_dw, 
-                num_filters=oup_inc, 
-                filter_size=1, 
-                stride=1, 
+                input=conv_dw,
+                num_filters=oup_inc,
+                filter_size=1,
+                stride=1,
                 padding=0,
-                num_groups=1, 
+                num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv3')
-            
+                name='stage_' + name + '_conv3')
+
             out = fluid.layers.concat([x1, conv_linear], axis=1)
 
-            
         else:
             #branch1
             conv_dw_1 = self.conv_bn_layer(
-                input=input, 
-                num_filters=inp, 
-                filter_size=3, 
+                input=input,
+                num_filters=inp,
+                filter_size=3,
                 stride=stride,
                 padding=1,
                 num_groups=inp,
                 if_act=False,
                 use_cudnn=False,
-                name='stage_'+name+'_conv4')
-            
+                name='stage_' + name + '_conv4')
+
             conv_linear_1 = self.conv_bn_layer(
-                input=conv_dw_1, 
-                num_filters=oup_inc, 
-                filter_size=1, 
+                input=conv_dw_1,
+                num_filters=oup_inc,
+                filter_size=1,
                 stride=1,
                 padding=0,
                 num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv5')
-            
+                name='stage_' + name + '_conv5')
+
             #branch2
             conv_pw_2 = self.conv_bn_layer(
-                input=input, 
-                num_filters=oup_inc, 
-                filter_size=1, 
+                input=input,
+                num_filters=oup_inc,
+                filter_size=1,
                 stride=1,
                 padding=0,
                 num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv1')
+                name='stage_' + name + '_conv1')
 
             conv_dw_2 = self.conv_bn_layer(
-                input=conv_pw_2, 
-                num_filters=oup_inc, 
-                filter_size=3, 
-                stride=stride, 
+                input=conv_pw_2,
+                num_filters=oup_inc,
+                filter_size=3,
+                stride=stride,
                 padding=1,
-                num_groups=oup_inc, 
+                num_groups=oup_inc,
                 if_act=False,
                 use_cudnn=False,
-                name='stage_'+name+'_conv2')
+                name='stage_' + name + '_conv2')
 
             conv_linear_2 = self.conv_bn_layer(
-                input=conv_dw_2, 
-                num_filters=oup_inc, 
-                filter_size=1, 
-                stride=1, 
+                input=conv_dw_2,
+                num_filters=oup_inc,
+                filter_size=1,
+                stride=1,
                 padding=0,
-                num_groups=1, 
+                num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv3')
+                name='stage_' + name + '_conv3')
             out = fluid.layers.concat([conv_linear_1, conv_linear_2], axis=1)
-            
+
         return self.channel_shuffle(out, 2)
 
+
 def ShuffleNetV2_x0_25():
     model = ShuffleNetV2(scale=0.25)
     return model
 
+
 def ShuffleNetV2_x0_33():
     model = ShuffleNetV2(scale=0.33)
     return model
- 
+
+
 def ShuffleNetV2_x0_5():
     model = ShuffleNetV2(scale=0.5)
     return model
 
+
 def ShuffleNetV2_x1_0():
     model = ShuffleNetV2(scale=1.0)
     return model
 
+
 def ShuffleNetV2_x1_5():
     model = ShuffleNetV2(scale=1.5)
     return model
 
+
 def ShuffleNetV2_x2_0():
     model = ShuffleNetV2(scale=2.0)
     return model

PaddleCV/image_classification/models/shufflenet_v2_swish.py

@@ -22,32 +22,22 @@ import paddle.fluid as fluid
 from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr
 
-__all__ = ['ShuffleNetV2_x0_5_swish', 'ShuffleNetV2_x1_0_swish', 'ShuffleNetV2_x1_5_swish', 'ShuffleNetV2_x2_0_swish']
-
-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 ShuffleNetV2():
+__all__ = [
+    'ShuffleNetV2_x0_5_swish', 'ShuffleNetV2_x1_0_swish',
+    'ShuffleNetV2_x1_5_swish', 'ShuffleNetV2_x2_0_swish', 'ShuffleNetV2_swish'
+]
+
+
+class ShuffleNetV2_swish():
     def __init__(self, scale=1.0):
-        self.params = train_parameters
         self.scale = scale
 
     def net(self, input, class_dim=1000):
-        scale = self.scale 
+        scale = self.scale
         stage_repeats = [4, 8, 4]
-        
+
         if scale == 0.5:
-            stage_out_channels = [-1, 24,  48,  96, 192, 1024]
+            stage_out_channels = [-1, 24, 48, 96, 192, 1024]
         elif scale == 1.0:
             stage_out_channels = [-1, 24, 116, 232, 464, 1024]
         elif scale == 1.5:
@@ -55,50 +45,77 @@ class ShuffleNetV2():
         elif scale == 2.0:
             stage_out_channels = [-1, 24, 224, 488, 976, 2048]
         else:
-            raise ValueError(
-                """{} groups is not supported for
+            raise ValueError("""{} groups is not supported for
                        1x1 Grouped Convolutions""".format(num_groups))
 
         #conv1
-        
+
         input_channel = stage_out_channels[1]
-        conv1 = self.conv_bn_layer(input=input, filter_size=3, num_filters=input_channel, padding=1, stride=2,name='stage1_conv')    
-        pool1 = fluid.layers.pool2d(input=conv1, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+        conv1 = self.conv_bn_layer(
+            input=input,
+            filter_size=3,
+            num_filters=input_channel,
+            padding=1,
+            stride=2,
+            name='stage1_conv')
+        pool1 = fluid.layers.pool2d(
+            input=conv1,
+            pool_size=3,
+            pool_stride=2,
+            pool_padding=1,
+            pool_type='max')
         conv = pool1
         # bottleneck sequences
         for idxstage in range(len(stage_repeats)):
             numrepeat = stage_repeats[idxstage]
-            output_channel = stage_out_channels[idxstage+2]
+            output_channel = stage_out_channels[idxstage + 2]
             for i in range(numrepeat):
                 if i == 0:
-                    conv = self.inverted_residual_unit(input=conv, num_filters=output_channel, stride=2, 
-                                                       benchmodel=2,name=str(idxstage+2)+'_'+str(i+1))
+                    conv = self.inverted_residual_unit(
+                        input=conv,
+                        num_filters=output_channel,
+                        stride=2,
+                        benchmodel=2,
+                        name=str(idxstage + 2) + '_' + str(i + 1))
                 else:
-                    conv = self.inverted_residual_unit(input=conv, num_filters=output_channel, stride=1, 
-                                                       benchmodel=1,name=str(idxstage+2)+'_'+str(i+1))
-                
-        conv_last = self.conv_bn_layer(input=conv, filter_size=1, num_filters=stage_out_channels[-1], 
-                                       padding=0, stride=1, name='conv5')
-        pool_last = fluid.layers.pool2d(input=conv_last, pool_size=7, pool_stride=1, pool_padding=0, pool_type='avg')
+                    conv = self.inverted_residual_unit(
+                        input=conv,
+                        num_filters=output_channel,
+                        stride=1,
+                        benchmodel=1,
+                        name=str(idxstage + 2) + '_' + str(i + 1))
 
+        conv_last = self.conv_bn_layer(
+            input=conv,
+            filter_size=1,
+            num_filters=stage_out_channels[-1],
+            padding=0,
+            stride=1,
+            name='conv5')
+        pool_last = fluid.layers.pool2d(
+            input=conv_last,
+            pool_size=7,
+            pool_stride=1,
+            pool_padding=0,
+            pool_type='avg')
 
         output = fluid.layers.fc(input=pool_last,
                                  size=class_dim,
-                                 param_attr=ParamAttr(initializer=MSRA(),name='fc6_weights'),
+                                 param_attr=ParamAttr(
+                                     initializer=MSRA(), name='fc6_weights'),
                                  bias_attr=ParamAttr(name='fc6_offset'))
         return output
 
-    
     def conv_bn_layer(self,
-                  input,
-                  filter_size,
-                  num_filters,
-                  stride,
-                  padding,
-                  num_groups=1,
-                  use_cudnn=True,
-                  if_act=True,
-                  name=None):
+                      input,
+                      filter_size,
+                      num_filters,
+                      stride,
+                      padding,
+                      num_groups=1,
+                      use_cudnn=True,
+                      if_act=True,
+                      name=None):
         conv = fluid.layers.conv2d(
             input=input,
             num_filters=num_filters,
@@ -108,154 +125,169 @@ class ShuffleNetV2():
             groups=num_groups,
             act=None,
             use_cudnn=use_cudnn,
-            param_attr=ParamAttr(initializer=MSRA(),name=name+'_weights'),
+            param_attr=ParamAttr(
+                initializer=MSRA(), name=name + '_weights'),
             bias_attr=False)
-        out = int((input.shape[2] - 1)/float(stride) + 1)
+        out = int((input.shape[2] - 1) / float(stride) + 1)
         bn_name = name + '_bn'
         if if_act:
-            return fluid.layers.batch_norm(input=conv, act='swish',
-                                           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')
+            return fluid.layers.batch_norm(
+                input=conv,
+                act='swish',
+                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')
         else:
-            return 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')
+            return 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')
 
-      
     def channel_shuffle(self, x, groups):
-        batchsize, num_channels, height, width = x.shape[0], x.shape[1], x.shape[2], x.shape[3]
+        batchsize, num_channels, height, width = x.shape[0], x.shape[
+            1], x.shape[2], x.shape[3]
         channels_per_group = num_channels // groups
-    
+
         # reshape
-        x = fluid.layers.reshape(x=x, shape=[batchsize, groups, channels_per_group, height, width])
+        x = fluid.layers.reshape(
+            x=x, shape=[batchsize, groups, channels_per_group, height, width])
 
-        x = fluid.layers.transpose(x=x, perm=[0,2,1,3,4])
+        x = fluid.layers.transpose(x=x, perm=[0, 2, 1, 3, 4])
 
         # flatten
-        x = fluid.layers.reshape(x=x, shape=[batchsize, num_channels, height, width])
+        x = fluid.layers.reshape(
+            x=x, shape=[batchsize, num_channels, height, width])
 
         return x
 
-    
-    def inverted_residual_unit(self, input, num_filters, stride, benchmodel, name=None):
+    def inverted_residual_unit(self,
+                               input,
+                               num_filters,
+                               stride,
+                               benchmodel,
+                               name=None):
         assert stride in [1, 2], \
             "supported stride are {} but your stride is {}".format([1,2], stride)
-            
-        oup_inc = num_filters//2
+
+        oup_inc = num_filters // 2
         inp = input.shape[1]
-        
+
         if benchmodel == 1:
             x1, x2 = fluid.layers.split(
-                input, num_or_sections=[input.shape[1]//2, input.shape[1]//2], dim=1)            
-            
+                input,
+                num_or_sections=[input.shape[1] // 2, input.shape[1] // 2],
+                dim=1)
+
             conv_pw = self.conv_bn_layer(
-                input=x2, 
-                num_filters=oup_inc, 
-                filter_size=1, 
+                input=x2,
+                num_filters=oup_inc,
+                filter_size=1,
                 stride=1,
                 padding=0,
                 num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv1')
+                name='stage_' + name + '_conv1')
 
             conv_dw = self.conv_bn_layer(
-                input=conv_pw, 
-                num_filters=oup_inc, 
-                filter_size=3, 
-                stride=stride, 
+                input=conv_pw,
+                num_filters=oup_inc,
+                filter_size=3,
+                stride=stride,
                 padding=1,
-                num_groups=oup_inc, 
+                num_groups=oup_inc,
                 if_act=False,
                 use_cudnn=False,
-                name='stage_'+name+'_conv2')
+                name='stage_' + name + '_conv2')
 
             conv_linear = self.conv_bn_layer(
-                input=conv_dw, 
-                num_filters=oup_inc, 
-                filter_size=1, 
-                stride=1, 
+                input=conv_dw,
+                num_filters=oup_inc,
+                filter_size=1,
+                stride=1,
                 padding=0,
-                num_groups=1, 
+                num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv3')
-            
+                name='stage_' + name + '_conv3')
+
             out = fluid.layers.concat([x1, conv_linear], axis=1)
 
-            
         else:
             #branch1
             conv_dw_1 = self.conv_bn_layer(
-                input=input, 
-                num_filters=inp, 
-                filter_size=3, 
+                input=input,
+                num_filters=inp,
+                filter_size=3,
                 stride=stride,
                 padding=1,
                 num_groups=inp,
                 if_act=False,
                 use_cudnn=False,
-                name='stage_'+name+'_conv4')
-            
+                name='stage_' + name + '_conv4')
+
             conv_linear_1 = self.conv_bn_layer(
-                input=conv_dw_1, 
-                num_filters=oup_inc, 
-                filter_size=1, 
+                input=conv_dw_1,
+                num_filters=oup_inc,
+                filter_size=1,
                 stride=1,
                 padding=0,
                 num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv5')
-            
+                name='stage_' + name + '_conv5')
+
             #branch2
             conv_pw_2 = self.conv_bn_layer(
-                input=input, 
-                num_filters=oup_inc, 
-                filter_size=1, 
+                input=input,
+                num_filters=oup_inc,
+                filter_size=1,
                 stride=1,
                 padding=0,
                 num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv1')
+                name='stage_' + name + '_conv1')
 
             conv_dw_2 = self.conv_bn_layer(
-                input=conv_pw_2, 
-                num_filters=oup_inc, 
-                filter_size=3, 
-                stride=stride, 
+                input=conv_pw_2,
+                num_filters=oup_inc,
+                filter_size=3,
+                stride=stride,
                 padding=1,
-                num_groups=oup_inc, 
+                num_groups=oup_inc,
                 if_act=False,
                 use_cudnn=False,
-                name='stage_'+name+'_conv2')
+                name='stage_' + name + '_conv2')
 
             conv_linear_2 = self.conv_bn_layer(
-                input=conv_dw_2, 
-                num_filters=oup_inc, 
-                filter_size=1, 
-                stride=1, 
+                input=conv_dw_2,
+                num_filters=oup_inc,
+                filter_size=1,
+                stride=1,
                 padding=0,
-                num_groups=1, 
+                num_groups=1,
                 if_act=True,
-                name='stage_'+name+'_conv3')
+                name='stage_' + name + '_conv3')
             out = fluid.layers.concat([conv_linear_1, conv_linear_2], axis=1)
-            
+
         return self.channel_shuffle(out, 2)
-    
+
+
 def ShuffleNetV2_x0_5_swish():
-    model = ShuffleNetV2(scale=0.5)
+    model = ShuffleNetV2_swish(scale=0.5)
     return model
 
+
 def ShuffleNetV2_x1_0_swish():
-    model = ShuffleNetV2(scale=1.0)
+    model = ShuffleNetV2_swish(scale=1.0)
     return model
 
+
 def ShuffleNetV2_x1_5_swish():
-    model = ShuffleNetV2(scale=1.5)
+    model = ShuffleNetV2_swish(scale=1.5)
     return model
 
+
 def ShuffleNetV2_x2_0_swish():
-    model = ShuffleNetV2(scale=2.0)
+    model = ShuffleNetV2_swish(scale=2.0)
     return model

PaddleCV/image_classification/models/squeezenet.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#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
+#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
+#    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.
+#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
 import paddle.fluid as fluid
 import math
@@ -18,99 +22,111 @@ from paddle.fluid.param_attr import ParamAttr
 
 __all__ = ["SqueezeNet", "SqueezeNet1_0", "SqueezeNet1_1"]
 
-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 SqueezeNet():
     def __init__(self, version='1.0'):
-        self.params = train_parameters
         self.version = version
-        
+
     def net(self, input, class_dim=1000):
         version = self.version
         assert version in ['1.0', '1.1'], \
             "supported version are {} but input version is {}".format(['1.0', '1.1'], version)
         if version == '1.0':
-            conv = fluid.layers.conv2d(input, 
-                                       num_filters=96, 
-                                       filter_size=7, 
-                                       stride=2, 
-                                       act='relu',
-                                       param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
-                                       bias_attr=ParamAttr(name='conv1_offset'))
-            conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2,pool_type='max')
+            conv = fluid.layers.conv2d(
+                input,
+                num_filters=96,
+                filter_size=7,
+                stride=2,
+                act='relu',
+                param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
+                bias_attr=ParamAttr(name='conv1_offset'))
+            conv = fluid.layers.pool2d(
+                conv, pool_size=3, pool_stride=2, pool_type='max')
             conv = self.make_fire(conv, 16, 64, 64, name='fire2')
             conv = self.make_fire(conv, 16, 64, 64, name='fire3')
             conv = self.make_fire(conv, 32, 128, 128, name='fire4')
-            conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
+            conv = fluid.layers.pool2d(
+                conv, pool_size=3, pool_stride=2, pool_type='max')
             conv = self.make_fire(conv, 32, 128, 128, name='fire5')
             conv = self.make_fire(conv, 48, 192, 192, name='fire6')
             conv = self.make_fire(conv, 48, 192, 192, name='fire7')
             conv = self.make_fire(conv, 64, 256, 256, name='fire8')
-            conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
+            conv = fluid.layers.pool2d(
+                conv, pool_size=3, pool_stride=2, pool_type='max')
             conv = self.make_fire(conv, 64, 256, 256, name='fire9')
         else:
-            conv = fluid.layers.conv2d(input, 
-                                       num_filters=64, 
-                                       filter_size=3, 
-                                       stride=2, 
-                                       padding=1, 
-                                       act='relu',
-                                       param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
-                                       bias_attr=ParamAttr(name='conv1_offset'))
-            conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
+            conv = fluid.layers.conv2d(
+                input,
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                padding=1,
+                act='relu',
+                param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
+                bias_attr=ParamAttr(name='conv1_offset'))
+            conv = fluid.layers.pool2d(
+                conv, pool_size=3, pool_stride=2, pool_type='max')
             conv = self.make_fire(conv, 16, 64, 64, name='fire2')
             conv = self.make_fire(conv, 16, 64, 64, name='fire3')
-            conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
+            conv = fluid.layers.pool2d(
+                conv, pool_size=3, pool_stride=2, pool_type='max')
             conv = self.make_fire(conv, 32, 128, 128, name='fire4')
             conv = self.make_fire(conv, 32, 128, 128, name='fire5')
-            conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
+            conv = fluid.layers.pool2d(
+                conv, pool_size=3, pool_stride=2, pool_type='max')
             conv = self.make_fire(conv, 48, 192, 192, name='fire6')
             conv = self.make_fire(conv, 48, 192, 192, name='fire7')
             conv = self.make_fire(conv, 64, 256, 256, name='fire8')
             conv = self.make_fire(conv, 64, 256, 256, name='fire9')
         conv = fluid.layers.dropout(conv, dropout_prob=0.5)
-        conv = fluid.layers.conv2d(conv, 
-                                   num_filters=class_dim, 
-                                   filter_size=1, 
-                                   act='relu', 
-                                   param_attr=fluid.param_attr.ParamAttr(name="conv10_weights"),
-                                   bias_attr=ParamAttr(name='conv10_offset'))
+        conv = fluid.layers.conv2d(
+            conv,
+            num_filters=class_dim,
+            filter_size=1,
+            act='relu',
+            param_attr=fluid.param_attr.ParamAttr(name="conv10_weights"),
+            bias_attr=ParamAttr(name='conv10_offset'))
         conv = fluid.layers.pool2d(conv, pool_type='avg', global_pooling=True)
         out = fluid.layers.flatten(conv)
         return out
-        
 
-    def make_fire_conv(self, input, num_filters, filter_size, padding=0, name=None):
-        conv = fluid.layers.conv2d(input, 
-                                   num_filters=num_filters, 
-                                   filter_size=filter_size, 
-                                   padding=padding, 
-                                   act='relu',
-                                   param_attr=fluid.param_attr.ParamAttr(name=name + "_weights"),
-                                   bias_attr=ParamAttr(name=name + '_offset'))
+    def make_fire_conv(self,
+                       input,
+                       num_filters,
+                       filter_size,
+                       padding=0,
+                       name=None):
+        conv = fluid.layers.conv2d(
+            input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            padding=padding,
+            act='relu',
+            param_attr=fluid.param_attr.ParamAttr(name=name + "_weights"),
+            bias_attr=ParamAttr(name=name + '_offset'))
         return conv
-        
-    def make_fire(self, input, squeeze_channels, expand1x1_channels, expand3x3_channels, name=None):
-        conv = self.make_fire_conv(input, squeeze_channels, 1, name=name+'_squeeze1x1')
-        conv_path1 = self.make_fire_conv(conv, expand1x1_channels, 1, name=name+'_expand1x1')
-        conv_path2 = self.make_fire_conv(conv, expand3x3_channels, 3, 1, name=name+'_expand3x3')
+
+    def make_fire(self,
+                  input,
+                  squeeze_channels,
+                  expand1x1_channels,
+                  expand3x3_channels,
+                  name=None):
+        conv = self.make_fire_conv(
+            input, squeeze_channels, 1, name=name + '_squeeze1x1')
+        conv_path1 = self.make_fire_conv(
+            conv, expand1x1_channels, 1, name=name + '_expand1x1')
+        conv_path2 = self.make_fire_conv(
+            conv, expand3x3_channels, 3, 1, name=name + '_expand3x3')
         out = fluid.layers.concat([conv_path1, conv_path2], axis=1)
         return out
 
+
 def SqueezeNet1_0():
     model = SqueezeNet(version='1.0')
     return model
 
+
 def SqueezeNet1_1():
     model = SqueezeNet(version='1.1')
     return model

PaddleCV/image_classification/models/vgg.py

@@ -21,22 +21,9 @@ import paddle.fluid as fluid
 
 __all__ = ["VGGNet", "VGG11", "VGG13", "VGG16", "VGG19"]
 
-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 VGGNet():
     def __init__(self, layers=16):
-        self.params = train_parameters
         self.layers = layers
 
     def net(self, input, class_dim=1000):
@@ -93,8 +80,7 @@ class VGGNet():
                 act='relu',
                 param_attr=fluid.param_attr.ParamAttr(
                     name=name + str(i + 1) + "_weights"),
-                bias_attr=fluid.param_attr.ParamAttr(
-                    name=name + str(i + 1) + "_offset"))
+                bias_attr=False)
         return fluid.layers.pool2d(
             input=conv, pool_size=2, pool_type='max', pool_stride=2)
 

PaddleCV/image_classification/scripts/train/AlexNet.sh

+#Training details
+#GPU: NVIDIA® Tesla® P40  8cards 120epochs 55h
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export FLAGS_fast_eager_deletion_mode=1
+export FLAGS_eager_delete_tensor_gb=0.0
+export FLAGS_fraction_of_gpu_memory_to_use=0.98
+
+#AlexNet:
+python train.py \
+       --model=AlexNet \
+       --batch_size=256 \
+       --total_images=1281167 \
+       --class_dim=1000 \
+       --image_shape=3,224,224 \
+       --model_save_dir=output/ \
+       --lr_strategy=piecewise_decay \
+       --num_epochs=120 \
+       --lr=0.01 \
+       --l2_decay=1e-4

PaddleCV/image_classification/scripts/train/DarkNet53.sh

+#Training details
+#DarkNet53
+python train.py \
+       --model=DarkNet53 \
+       --batch_size=256 \
+       --total_images=1281167 \
+       --image_shape=3,256,256 \
+       --class_dim=1000 \
+       --lr_strategy=cosine_decay \
+       --lr=0.1 \
+       --num_epochs=200 \
+       --model_save_dir=output/ \
+       --l2_decay=1e-4 \
+       --use_mixup=True \
+       --resize_short_size=256 \
+       --use_label_smoothing=True \
+       --label_smoothing_epsilon=0.1 \

PaddleCV/image_classification/scripts/train/DenseNet121.sh

+#Training details
+#DenseNet121
+python train.py \
+       --model=DenseNet121 \
+       --batch_size=256 \
+       --total_images=1281167 \
+       --image_shape=3,224,224 \
+       --class_dim=1000 \
+       --lr_strategy=piecewise_decay \
+       --lr=0.1 \
+       --num_epochs=120 \
+       --model_save_dir=output/ \
+       --l2_decay=1e-4

PaddleCV/image_classification/scripts/train/DenseNet161.sh

+#Traing details
+#DenseNet161
+python train.py \
+       --model=DenseNet161 \
+       --batch_size=256 \
+       --total_images=1281167 \
+       --image_shape=3,224,224 \
+       --class_dim=1000 \
+       --lr_strategy=piecewise_decay \
+       --lr=0.1 \
+       --num_epochs=120 \
+       --model_save_dir=output/ \
+       --l2_decay=1e-4

PaddleCV/image_classification/scripts/train/DenseNet169.sh

+#Training details
+#DenseNet169
+python train.py \
+      --model=DenseNet169 \
+      --batch_size=256 \
+      --total_images=1281167 \
+      --image_shape=3,224,224 \
+      --class_dim=1000 \
+      --lr_strategy=piecewise_decay \
+      --lr=0.1 \
+      --num_epochs=120 \
+      --model_save_dir=output/ \
+      --l2_decay=1e-4

PaddleCV/image_classification/scripts/train/ResNeXt50_32x4d.sh

+python train.py \
+       --model=ResNeXt50_32x4d \
+       --batch_size=256 \
+       --total_images=1281167 \
+       --image_shape=3,224,224 \
+       --class_dim=1000 \
+       --lr_strategy=piecewise_decay \
+       --lr=0.1 \
+       --num_epochs=120 \
+       --model_save_dir=output/ \
+       --l2_decay=1e-4