前端智能化漫谈 - pix2code实战篇

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前端智能化漫谈 (2) - pix2code实战篇

将pix2code跑起来

先来干货介绍将pix2code跑起来的步骤:

1.下载pix2code源代码

git clone https://github.com/tonybeltramelli/pix2code

网速慢的话需要等一等,.git就有700兆左右。图像数据也有450兆左右。

2.解压数据

cd datasets
zip -F pix2code_datasets.zip --out datasets.zip
unzip datasets.zip

3.创建训练集和测试集

cd ../model

./build_datasets.py ../datasets/ios/all_data
./build_datasets.py ../datasets/android/all_data
./build_datasets.py ../datasets/web/all_data

4.安装python库

pip install opencv-python
pip install tensorflow
pip install keras

5.图像转数组

./convert_imgs_to_arrays.py ../datasets/ios/training_set ../datasets/ios/training_features
./convert_imgs_to_arrays.py ../datasets/android/training_set ../datasets/android/training_features
./convert_imgs_to_arrays.py ../datasets/web/training_set ../datasets/web/training_features

6.训练

以Android为例:
第一次训练的话:

mkdir bin
cd model
./train.py ../datasets/android/training_features ../bin 1

如果不是第一次训练:

cd model
./train.py ../datasets/android/training_features ../bin 1 ../bin/pix2code.h5

7. 推理

找张Android或iOS或Web图片,来试验一下效果吧:

python ./sample.py ../bin pix2code ../datasets/android/eval_set/0B6A8CB3-E640-4B5E-8121-61B15CA9038A.png ../code greedy

生成的DSL如下:

Result greedy: <START>stack
row
btn,btn,switch,switch

row
label,slider,label

row
radio


footer
btn-dashboard,btn-search

<END>

对比一下源文件:

stack 
row 
btn, btn, switch, switch

row 
label, slider, label

row 
radio


footer 
btn-dashboard, btn-notifications, btn-notifications

上面的结果是完全一致的,很厉害。下面的footer部分识别出了点问题。

8. 生成html/Android/iOS源文件

cd compiler

# compile .gui file to Android XML UI
./android-compiler.py <input file path>.gui

# compile .gui file to iOS Storyboard
./ios-compiler.py <input file path>.gui

# compile .gui file to HTML/CSS (Bootstrap style)
./web-compiler.py <input file path>.gui

pix2code过程解说

数据

为了避免github规定每个包最大50MB的规定(现在有git-lfs了应该不用了吧),所以训练数据分成了10个压缩包。

首先我们把这些压缩包整理成一个。作者是采用了zip -F这个修复命令,生成一个新的datasets.zip包,大小为466,387,966字节,然后将其解压:

# reassemble and unzip the data
cd datasets
zip -F pix2code_datasets.zip --out datasets.zip
unzip datasets.zip

解压后会生成android,web和ios三个目录。

三个目录只有一层子目录,即all_data目当,下面分别有1750张png图片,以1750个对应的gui文件。

android图片的统一大小为6881070,3通道RGB,144ppi。
ios图片大小为760
1340,3通道RGB,144ppi。
web图片为2400*1380,3通道RGB,144ppi。

下面我们将all_data数据分为训练集和验证集:

# split training set and evaluation set while ensuring no training example in the evaluation set
# usage: build_datasets.py <input path> <distribution (default: 6)>
./build_datasets.py ../datasets/ios/all_data
./build_datasets.py ../datasets/android/all_data
./build_datasets.py ../datasets/web/all_data

build_datasets.py会将all_data数据分为training_set和eval_set两部分。

TRAINING_SET_NAME = "training_set"
EVALUATION_SET_NAME = "eval_set"

默认情况下,1750个图像和DSL会被分为训练集1500个和测试集250个。我们以ios为例:

Splitting datasets, training samples: 1500, evaluation samples: 250
Training dataset: ../datasets/ios/training_set
Evaluation dataset: ../datasets/ios/eval_set

训练之前,我们将图像正则化一下,有助于提升训练效果。
这时候需要opencv-python库去做图片的resize,需要先安装一下。
RGB数据是0到255的整数,我们通过除以255将其转换成0到1之间的浮点数。
代码如下:

    @staticmethod
    def get_preprocessed_img(img_path, image_size):
        import cv2
        img = cv2.imread(img_path)
        img = cv2.resize(img, (image_size, image_size))
        img = img.astype('float32')
        img /= 255
        return img

加上复制.gui的完整逻辑如下:

print("Converting images to numpy arrays...")

for f in os.listdir(input_path):
    if f.find(".png") != -1:
        img = Utils.get_preprocessed_img("/".format(input_path, f), IMAGE_SIZE)
        file_name = f[:f.find(".png")]

        np.savez_compressed("/".format(output_path, file_name), features=img)
        retrieve = np.load("/.npz".format(output_path, file_name))["features"]

        assert np.array_equal(img, retrieve)

        shutil.copyfile("/.gui".format(input_path, file_name), "/.gui".format(output_path, file_name))

print("Numpy arrays saved in ".format(output_path))

训练

数据准备好之后,我们就可以开始训练了。

训练使用train.py,一共4个参数.
train.py <is memory intensive (default: 0)> <pretrained weights (optional)>
第一个参数是输入路径,第二个是输出训练结果的路径,第三个是内存选项,第四个是加载已经预训练好的权值。
举几个例子,首先是最基本的情况,只有必要的输入和输出目录:

./train.py ../datasets/web/training_features ../bin

最复杂的例子是加载预训练的结果的:

./train.py ../datasets/android/training_features ../bin 1 ../bin/pix2code.h5

我们直接看代码:

if __name__ == "__main__":
    argv = sys.argv[1:]

    if len(argv) < 2:
        print("Error: not enough argument supplied:")
        print("train.py <input path> <output path> <is memory intensive (default: 0)> <pretrained weights (optional)>")
        exit(0)
    else:
        input_path = argv[0]
        output_path = argv[1]
        use_generator = False if len(argv) < 3 else True if int(argv[2]) == 1 else False
        pretrained_weigths = None if len(argv) < 4 else argv[3]

    run(input_path, output_path, is_memory_intensive=use_generator, pretrained_model=pretrained_weigths)

进入到run之后,首先设置Dataset的参数:

def run(input_path, output_path, is_memory_intensive=False, pretrained_model=None):
    np.random.seed(1234)

    dataset = Dataset()
    dataset.load(input_path, generate_binary_sequences=True)
    dataset.save_metadata(output_path)
    dataset.voc.save(output_path)

dataset的load过程中,需要对文本进行一些处理,第一步当然是先查找图片对应到的gui文本:

    def load(self, path, generate_binary_sequences=False):
        print("Loading data...")
        for f in os.listdir(path):
            if f.find(".gui") != -1:
                gui = open("/".format(path, f), 'r')
                file_name = f[:f.find(".gui")]

                if os.path.isfile("/.png".format(path, file_name)):
                    img = Utils.get_preprocessed_img("/.png".format(path, file_name), IMAGE_SIZE)
                    self.append(file_name, gui, img)
                elif os.path.isfile("/.npz".format(path, file_name)):
                    img = np.load("/.npz".format(path, file_name))["features"]
                    self.append(file_name, gui, img)

找到文本之后,将文本转成向量。我们先看下完整的流程,然后再分别看细节:

        print("Generating sparse vectors...")
        self.voc.create_binary_representation()
        self.next_words = self.sparsify_labels(self.next_words, self.voc)
        if generate_binary_sequences:
            self.partial_sequences = self.binarize(self.partial_sequences, self.voc)
        else:
            self.partial_sequences = self.indexify(self.partial_sequences, self.voc)

        self.size = len(self.ids)
        assert self.size == len(self.input_images) == len(self.partial_sequences) == len(self.next_words)
        assert self.voc.size == len(self.voc.vocabulary)

        print("Dataset size: ".format(self.size))
        print("Vocabulary size: ".format(self.voc.size))

        self.input_shape = self.input_images[0].shape
        self.output_size = self.voc.size

        print("Input shape: ".format(self.input_shape))
        print("Output size: ".format(self.output_size))

其中, create_binary_representation是将文本转成稀疏矩阵存储:

    def create_binary_representation(self):
        if sys.version_info >= (3,):
            items = self.vocabulary.items()
        else:
            items = self.vocabulary.iteritems()
        for key, value in items:
            binary = np.zeros(self.size)
            binary[value] = 1
            self.binary_vocabulary[key] = binary

数据加载好之后,根据设置的内存属性来加载数据集。
如果内存充足,就直接将数据集转成数组,直接开始训练了:

    if not is_memory_intensive:
        dataset.convert_arrays()

        input_shape = dataset.input_shape
        output_size = dataset.output_size

        print(len(dataset.input_images), len(dataset.partial_sequences), len(dataset.next_words))
        print(dataset.input_images.shape, dataset.partial_sequences.shape, dataset.next_words.shape)

这个convert_arrays()真的是如其名,只负责转array,调用np.array来干活:

    def convert_arrays(self):
        print("Convert arrays...")
        self.input_images = np.array(self.input_images)
        self.partial_sequences = np.array(self.partial_sequences)
        self.next_words = np.array(self.next_words)

如果内存不是那么可以浪费,就分成批次来处理吧. 记得上一讲曾讲过的BATCH_SIZE参数么,这时就派上用场了:

    else:
        gui_paths, img_paths = Dataset.load_paths_only(input_path)

        input_shape = dataset.input_shape
        output_size = dataset.output_size
        steps_per_epoch = dataset.size / BATCH_SIZE

        voc = Vocabulary()
        voc.retrieve(output_path)

        generator = Generator.data_generator(voc, gui_paths, img_paths, batch_size=BATCH_SIZE, generate_binary_sequences=True)

至此数据预处理完毕,可以开始建模了:

    model = pix2code(input_shape, output_size, output_path)

如果有预训练好的模型就加载进来:

    if pretrained_model is not None:
        model.model.load_weights(pretrained_model)

最后,还是根据传入的内存参数来决定是用哪一种fit:

    if not is_memory_intensive:
        model.fit(dataset.input_images, dataset.partial_sequences, dataset.next_words)
    else:
        model.fit_generator(generator, steps_per_epoch=steps_per_epoch)

推理

前面讲了通过sample进行推理的方法。

我们再来一个例子:

python ./sample.py ../bin pix2code ../datasets/android/eval_set/0BBF8B46-163A-4BFE-9FDA-6E275AED160F.png ../code greedy

我们看下结果:

Vocabulary size: 20
Input shape: (256, 256, 3)
Output size: 20
Result greedy: <START>stack
row
label,btn

row
btn,btn,btn

row
radio

row
switch

row
switch


footer
btn-dashboard,btn-notifications

<END>

我们对比下源文件:

stack 
row 
label, btn

row 
switch, btn, btn

row 
radio

row 
btn

row 
switch


footer 
btn-notifications, btn-notifications

图像如下:

有两种错误,第一处是第二行,将第一个switch识别成了btn;第二处是Footbar,将左边的btn-notifications识别成了btn-dashboard。

提升准确率的事情我们会在后文讨论,我们这里先看下sample部分的实现原理。

if len(argv) < 4:
    print("Error: not enough argument supplied:")
    print("sample.py <trained weights path> <trained model name> <input image> <output path> <search method (default: greedy)>")
    exit(0)
else:
    trained_weights_path = argv[0]
    trained_model_name = argv[1]
    input_path = argv[2]
    output_path = argv[3]
    search_method = "greedy" if len(argv) < 5 else argv[4]

meta_dataset = np.load("/meta_dataset.npy".format(trained_weights_path))
input_shape = meta_dataset[0]
output_size = meta_dataset[1]

model = pix2code(input_shape, output_size, trained_weights_path)
model.load(trained_model_name)

sampler = Sampler(trained_weights_path, input_shape, output_size, CONTEXT_LENGTH)

file_name = basename(input_path)[:basename(input_path).find(".")]
evaluation_img = Utils.get_preprocessed_img(input_path, IMAGE_SIZE)

最后一个greedy参数是搜索模式:

if search_method == "greedy":
    result, _ = sampler.predict_greedy(model, np.array([evaluation_img]))
    print("Result greedy: ".format(result))
else:
    beam_width = int(search_method)
    print("Search with beam width: ".format(beam_width))
    result, _ = sampler.predict_beam_search(model, np.array([evaluation_img]), beam_width=beam_width)
    print("Result beam: ".format(result))

with open("/.gui".format(output_path, file_name), 'w') as out_f:
    out_f.write(result.replace(START_TOKEN, "").replace(END_TOKEN, ""))

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