MemoryError: Unable to allocate 137. MiB for an array with shape (3000, 4000, 3) and data type float32

Question

我在运行包含 10 个正数和 10 个负数训练数据的 model 时遇到了这个问题。

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MemoryError: Unable to allocate 137. MiB for an array with shape (3000, 4000, 3) and data type float32

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我的代码是这样的：

# Define a single function that can extract features using hog sub-sampling and make predictions
def find_cars(img, ystart, ystop, scale, svc,orient, 
              pix_per_cell, cell_per_block,show_all_rectangles=False):
    
    # array of rectangles where cars were detected
    rectangles = []
    
    img = img.astype(np.float32)/255
    
    img_tosearch = img[ystart:ystop,:,:]

    # apply color conversion if other than 'RGB'
    ctrans_tosearch = cv2.cvtColor(img_tosearch, cv2.COLOR_RGB2YUV)
    
    # rescale image if other than 1.0 scale
    imshape = ctrans_tosearch.shape
    ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
    
    ch1 = ctrans_tosearch[:,:,0]
    ch2 = ctrans_tosearch[:,:,1]
    ch3 = ctrans_tosearch[:,:,2]
    
    # Define blocks and steps as above
    nxblocks = (ch1.shape[1] // pix_per_cell)+1  #-1
    nyblocks = (ch1.shape[0] // pix_per_cell)+1  #-1 
    
    nfeat_per_block = orient*(cell_per_block**2)
    
    # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
    window = 64
    nblocks_per_window = (window // pix_per_cell)-1 
    cells_per_step = 2  # Instead of overlap, define how many cells to step
    nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
    nysteps = (nyblocks - nblocks_per_window) // cells_per_step
    
    # Compute individual channel HOG features for the entire image
    hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)   
    hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
    hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
    
    for xb in range(nxsteps):
        for yb in range(nysteps):
            ypos = yb*cells_per_step
            xpos = xb*cells_per_step
            
            # Extract HOG for this patch
            hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
            hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
            xleft = xpos*pix_per_cell
            ytop = ypos*pix_per_cell
                                  
            test_prediction = svc.predict(hog_features.reshape(1,-1))
            
            if test_prediction == 1 or show_all_rectangles:
                xbox_left = np.int(xleft*scale)
                ytop_draw = np.int(ytop*scale)
                win_draw = np.int(window*scale)
                rectangles.append(((xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart)))
                
    return rectangles

test_img = mpimg.imread('C:/Users/shrey/Desktop/Mod1-IITR/DJI_0314.JPG')

ystart = 20
ystop = 50
scale = 1
orient = 11
pix_per_cell = 16
cell_per_block = 2

rectangles = find_cars(test_img, ystart, ystop, scale, svc,orient, pix_per_cell, cell_per_block)

print(len(rectangles), 'rectangles found in image')

我正在使用 Windows-Jupyter 笔记本。 如何更正此代码问题？

Answer 1

错误正如它所说，它无法分配memory，因为没有足够的。

但是，您的系统上可用的 memory 可能比 137MiB 多得多，jupyter 只是限制了您的系统可以使用多少 memory，因为它必须在您开始运行代码之前保留一些 ZCD69B4957F06CD818D7BF3D61 请参阅此处了解如何在 jupyter 中增加 memory 限制