Update image classification (#1499)

* Add python-cv2 reader for image classification.
* Update README_cn.md

fluid/PaddleCV/image_classification/README.md

@@ -84,7 +84,7 @@ python train.py \
 
 Or can start the training step by running the ```run.sh```.
 
-**data reader introduction:** Data reader is defined in ```reader.py```. In [training stage](#training-a-model), random crop and flipping are used, while center crop is used in [evaluation](#inference) and [inference](#inference) stages. Supported data augmentation includes:
+**data reader introduction:** Data reader is defined in ```reader.py``` and ```reader_cv2.py```, Using CV2 reader can improve the speed of reading. In [training stage](#training-a-model), random crop and flipping are used, while center crop is used in [evaluation](#inference) and [inference](#inference) stages. Supported data augmentation includes:
 * rotation
 * color jitter
 * random crop
@@ -190,19 +190,20 @@ Models consists of two categories: Models with specified parameters names in mod
 
 Models are trained by starting with learning rate ```0.1``` and decaying it by ```0.1``` after each pre-defined epoches, if not special introduced. Available top-1/top-5 validation accuracy on ImageNet 2012 are listed in table. Pretrained models can be downloaded by clicking related model names.
 
+
 - Released models: specify parameter names
 
-|model | top-1/top-5 accuracy
-|- | -:
-|[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.zip) | 56.34%/79.02%
-|[VGG11](http://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretained.zip) | 68.86%/88.64%
-|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.zip) | 70.7%/89.41%
-|[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.zip) | 76.46%/93.04%
-|[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.zip) | 77.65%/93.71%
+|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
+|- |:-: |:-:|
+|[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.zip) | 56.71%/79.18% | 55.88%/78.65% |
+|[VGG11](http://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretained.zip) | 68.92%/88.66% | 68.61%/88.60% |
+|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.zip) | 70.91%/89.54% | 70.51%/89.35% |
+|[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.zip) | 76.35%/92.80% | 76.22%/92.92% |
+|[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.zip) | 77.49%/93.57% | 77.56%/93.64% |
 
 - Released models: not specify parameter names
 
-|model | top-1/top-5 accuracy
-|- | -:
-|[ResNet152](http://paddle-imagenet-models.bj.bcebos.com/ResNet152_pretrained.zip) | 78.29%/94.11%
-|[SE_ResNeXt50_32x4d](http://paddle-imagenet-models.bj.bcebos.com/se_resnext_50_model.tar) | 78.33%/93.96%
+|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
+|- |:-: |:-:|
+|[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% |

fluid/PaddleCV/image_classification/README_cn.md

@@ -58,7 +58,7 @@ python train.py \
        --model=SE_ResNeXt50_32x4d \
        --batch_size=32 \
        --total_images=1281167 \
-       --class_dim=1000
+       --class_dim=1000 \
        --image_shape=3,224,224 \
        --model_save_dir=output/ \
        --with_mem_opt=False \
@@ -79,8 +79,9 @@ python train.py \
 * **lr**: initialized learning rate. Default: 0.1.
 * **pretrained_model**: model path for pretraining. Default: None.
 * **checkpoint**: the checkpoint path to resume. Default: None.
+* **model_category**: the category of models, ("models"|"models_name"). Default:"models".
 
-**数据读取器说明：** 数据读取器定义在```reader.py```中。在[训练阶段](#training-a-model), 默认采用的增广方式是随机裁剪与水平翻转, 而在[评估](#inference)与[推断](#inference)阶段用的默认方式是中心裁剪。当前支持的数据增广方式有：
+**数据读取器说明：** 数据读取器定义在```reader.py```和```reader_cv2.py```中, 一般, CV2 reader可以提高数据读取速度, reader(PIL)可以得到相对更高的精度, 在[训练阶段](#training-a-model), 默认采用的增广方式是随机裁剪与水平翻转, 而在[评估](#inference)与[推断](#inference)阶段用的默认方式是中心裁剪。当前支持的数据增广方式有：
 * 旋转
 * 颜色抖动
 * 随机裁剪
@@ -183,27 +184,24 @@ Test-12-score: [15.040644], class [386]
 ```
 
 ## 已有模型及其性能
+Models包括两种模型：带有参数名字的模型，和不带有参数名字的模型。通过设置 ```model_category = models_name```来训练带有参数名字的模型。
 
 表格中列出了在"models"目录下支持的神经网络种类，并且给出了已完成训练的模型在ImageNet-2012验证集合上的top-1/top-5精度；如无特征说明，训练模型的初始学习率为```0.1```，每隔预定的epochs会下降```0.1```。预训练模型可以通过点击相应模型的名称进行下载。
 
-|model | top-1/top-5 accuracy
-|- | -:
-|[AlexNet](http://paddle-imagenet-models.bj.bcebos.com/alexnet_model.tar) | 57.21%/79.72%
-|VGG11 | -
-|VGG13 | -
-|VGG16 | -
-|VGG19 | -
-|GoogleNet | -
-|InceptionV4 | -
-|MobileNet | -
-|[ResNet50](http://paddle-imagenet-models.bj.bcebos.com/resnet_50_model.tar) | 76.63%/93.10%
-|ResNet101 | -
-|ResNet152 | -
-|[SE_ResNeXt50_32x4d](http://paddle-imagenet-models.bj.bcebos.com/se_resnext_50_model.tar) | 78.33%/93.96%
-|SE_ResNeXt101_32x4d | -
-|SE_ResNeXt152_32x4d | -
-|DPN68 | -
-|DPN92 | -
-|DPN98 | -
-|DPN107 | -
-|DPN131 | -
+
+- Released models: specify parameter names
+
+|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
+|- |:-: |:-:|
+|[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.zip) | 56.71%/79.18% | 55.88%/78.65% |
+|[VGG11](http://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretained.zip) | 68.92%/88.66% | 68.61%/88.60% |
+|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.zip) | 70.91%/89.54% | 70.51%/89.35% |
+|[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.zip) | 76.35%/92.80% | 76.22%/92.92% |
+|[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.zip) | 77.49%/93.57% | 77.56%/93.64% |
+
+- Released models: not specify parameter names
+
+|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
+|- |:-: |:-:|
+|[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% |

fluid/PaddleCV/image_classification/eval.py

@@ -8,7 +8,8 @@ import sys
 import paddle
 import paddle.fluid as fluid
 import models
-import reader
+#import reader_cv2 as reader
+import reader as reader
 import argparse
 import functools
 from models.learning_rate import cosine_decay
@@ -83,7 +84,7 @@ def eval(args):
 
         fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
 
-    val_reader = paddle.batch(reader.val(), batch_size=args.batch_size)
+    val_reader = paddle.batch(reader.val(""), batch_size=args.batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
 
     fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name]

fluid/PaddleCV/image_classification/reader_cv2.py

+import os
+import math
+import random
+import functools
+import numpy as np
+import paddle
+import cv2
+import io
+
+random.seed(0)
+np.random.seed(0)
+
+DATA_DIM = 224
+
+THREAD = 8
+BUF_SIZE = 102400
+
+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 random_crop(img, size, scale=None, ratio=None):
+    """ random_crop """
+    scale = [0.08, 1.0] if scale is None else scale
+    ratio = [3. / 4., 4. / 3.] if ratio is None else ratio
+
+    aspect_ratio = math.sqrt(np.random.uniform(*ratio))
+    w = 1. * aspect_ratio
+    h = 1. / aspect_ratio
+
+    bound = min((float(img.shape[1]) / img.shape[0]) / (w**2),
+                (float(img.shape[0]) / img.shape[1]) / (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_size = math.sqrt(target_area)
+    w = int(target_size * w)
+    h = int(target_size * h)
+
+    i = np.random.randint(0, img.size[0] - w + 1)
+    j = np.random.randint(0, img.size[1] - h + 1)
+
+    img = img[i:i + h, j:j + w, :]
+    resized = cv2.resize(img, (size, size))
+    return resized
+
+
+def distort_color(img):
+    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))
+    return resized
+
+
+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 process_image(sample,
+                  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
+
+    img_path = sample[0]
+    print('&' * 80)
+    print(img_path)
+    img = cv2.imread(img_path)
+
+    if mode == 'train':
+        if rotate:
+            img = rotate_image(img)
+        if crop_size > 0:
+            img = random_crop(img, crop_size)
+        if color_jitter:
+            img = distort_color(img)
+        if np.random.randint(0, 2) == 1:
+            img = img[:, ::-1, :]
+    else:
+        if crop_size > 0:
+            img = resize_short(img, crop_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_mean
+    img /= img_std
+
+    if mode == 'train' or mode == 'val':
+        return (img, sample[1])
+    elif mode == 'test':
+        return (img, )
+
+
+def image_mapper(**kwargs):
+    """ image_mapper """
+    return functools.partial(process_image, **kwargs)
+
+
+def _reader_creator(file_list,
+                    mode,
+                    shuffle=False,
+                    color_jitter=False,
+                    rotate=False,
+                    data_dir=DATA_DIR):
+    def reader():
+        with open(file_list) as flist:
+            full_lines = [line.strip() for line in flist]
+            if shuffle:
+                np.random.shuffle(lines)
+            if mode == 'train' and os.getenv('PADDLE_TRAINING_ROLE'):
+                # distributed mode if the env var `PADDLE_TRAINING_ROLE` exits
+                trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
+                trainer_count = int(os.getenv("PADDLE_TRAINERS", "1"))
+                per_node_lines = len(full_lines) // trainer_count
+                lines = full_lines[trainer_id * per_node_lines:(trainer_id + 1)
+                                   * per_node_lines]
+                print(
+                    "read images from %d, length: %d, lines length: %d, total: %d"
+                    % (trainer_id * per_node_lines, per_node_lines, len(lines),
+                       len(full_lines)))
+            else:
+                lines = full_lines
+            for line in lines:
+                if mode == 'train' or mode == 'val':
+                    img_path, label = line.split()
+                    img_path = img_path.replace("JPEG", "jpeg")
+                    img_path = os.path.join(data_dir, img_path)
+                    yield img_path, int(label)
+                elif mode == 'test':
+                    img_path = os.path.join(DATA_DIR, line)
+                    yield [img_path]
+
+    image_mapper = functools.partial(
+        process_image,
+        mode=mode,
+        color_jitter=color_jitter,
+        rotate=color_jitter,
+        crop_size=224)
+    reader = paddle.reader.xmap_readers(
+        image_mapper, reader, THREAD, BUF_SIZE, order=False)
+    return reader
+
+
+def train(data_dir=DATA_DIR):
+    file_list = os.path.join(data_dir, 'train_list.txt')
+    return _reader_creator(
+        file_list,
+        'train',
+        shuffle=True,
+        color_jitter=False,
+        rotate=False,
+        data_dir=data_dir)
+
+
+def val(data_dir=DATA_DIR):
+    file_list = os.path.join(data_dir, 'val_list.txt')
+    return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir)
+
+
+def test(data_dir=DATA_DIR):
+    file_list = os.path.join(data_dir, 'val_list.txt')
+    return _reader_creator(file_list, 'test', shuffle=False, data_dir=data_dir)