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不調(diào)整大小的 DataGenerator(Sequence)

不調(diào)整大小的 DataGenerator(Sequence)

森林海 2023-02-15 16:40:15
我創(chuàng)建了一個(gè)DataGenerator(Sequence)定義batch_size,batch_x和 的類batch_y。batch_x是一批圖像(來(lái)自x_set,圖像的文件路徑列表),由 讀入imread,調(diào)整大小resize并除以 255 以獲得 0 到 1 之間的值。batch_y這些圖像的標(biāo)簽來(lái)自y_set,a包含所有標(biāo)簽的列表。class DataGenerator(Sequence):    def __init__(self, x_set, y_set, batch_size):        self.x, self.y = x_set, y_set        self.batch_size = batch_size    def __len__(self):        return math.ceil(len(self.x) / self.batch_size)    def __getitem__(self, idx):        batch_x = self.x[idx*self.batch_size : (idx + 1)*self.batch_size]        batch_x = np.array([resize(imread(file_name), (64, 128)) for file_name in batch_x])        batch_x = batch_x * 1./255        batch_y = self.y[idx*self.batch_size : (idx + 1)*self.batch_size]        batch_y = np.array(batch_y)        return batch_x, batch_y因?yàn)檫@個(gè)生成器可以工作但在 Colab 上需要很長(zhǎng)時(shí)間,所以我之前調(diào)整了圖像的大小。因此,這不再是必需的,我現(xiàn)在想修改DataGenerator并保留該resize功能。這是我的代碼DataGenerator_withoutresize(Sequence):class DataGenerator_withoutresize(Sequence):    def __init__(self, x_set, y_set, batch_size):        self.x, self.y = x_set, y_set        self.batch_size = batch_size    def __len__(self):        return math.ceil(len(self.x) / self.batch_size)    def __getitem__(self, idx):        batch_x = self.x[idx*self.batch_size : (idx + 1)*self.batch_size]        batch_x = np.array([(imread(file_name) for file_name in batch_x])        batch_x = batch_x * 1./255        batch_y = self.y[idx*self.batch_size : (idx + 1)*self.batch_size]        batch_y = np.array(batch_y)        return batch_x, batch_y這段代碼正確嗎?
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1 回答

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楊魅力

TA貢獻(xiàn)1811條經(jīng)驗(yàn) 獲得超6個(gè)贊

最后,我使用了這段代碼,它對(duì)我有用:


class DataGenerator(Sequence):


    def __init__(self, x_set, y_set, batch_size):

        self.x, self.y = x_set, y_set

        self.batch_size = batch_size


    def __len__(self):

        return math.ceil(len(self.x) / self.batch_size)


    def __getitem__(self, idx):

        batch_x = self.x[idx*self.batch_size : (idx + 1)*self.batch_size]

        batch_x = [imread(file_name) for file_name in batch_x]

        batch_x = np.array(batch_x)

        batch_x = batch_x * 1./255

        batch_y = self.y[idx*self.batch_size : (idx + 1)*self.batch_size]

        batch_y = np.array(batch_y)


        return batch_x, batch_y


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