我在尝试使用Keras从VGG19网络(运行在CPU上)提取图像特征时遇到内存错误。步幅的值似乎高得令人难以置信,我不确定它们是什么意思,可能有关联吗?上传的图像最初是736 x 491,但在传输到网络之前调整为224 x 224。在
RuntimeError: CorrMM failed to allocate working memory of 576 x 50176
Apply node that caused the error: CorrMM{half, (1, 1)} (Elemwise{Composite{(i0 * (Abs((i1 + i2)) + i1 + i2))}}[(0, 1)].0, Subtensor{::, ::, ::int64, ::int64}.0)
Toposort index: 77
Inputs types: [TensorType(float32, 4D), TensorType(float32, 4D)]
Inputs shapes: [(1, 64, 224, 224), (64, 64, 3, 3)]
Inputs strides: [(12845056, 200704, 896, 4), (4, 256, -49152, -16384)]
Inputs values: ['not shown', 'not shown']
Outputs clients: [[Elemwise{Composite{(i0 * (Abs((i1 + i2)) + i1 + i2))}}[(0, 1)](TensorConstant{(1, 1, 1, 1) of 0.5}, CorrMM{half, (1, 1)}.0, InplaceDimShuffle{0,3,1,2}.0)]]
我运行的代码:
^{pr2}$形状和模型摘要
x shape (1, 3, 224, 224)
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 3, 224, 224) 0
____________________________________________________________________________________________________
block1_conv1 (Convolution2D) (None, 64, 224, 224) 1792 input_1[0][0]
____________________________________________________________________________________________________
block1_conv2 (Convolution2D) (None, 64, 224, 224) 36928 block1_conv1[0][0]
____________________________________________________________________________________________________
block1_pool (MaxPooling2D) (None, 64, 112, 112) 0 block1_conv2[0][0]
____________________________________________________________________________________________________
block2_conv1 (Convolution2D) (None, 128, 112, 112) 73856 block1_pool[0][0]
____________________________________________________________________________________________________
block2_conv2 (Convolution2D) (None, 128, 112, 112) 147584 block2_conv1[0][0]
____________________________________________________________________________________________________
block2_pool (MaxPooling2D) (None, 128, 56, 56) 0 block2_conv2[0][0]
____________________________________________________________________________________________________
block3_conv1 (Convolution2D) (None, 256, 56, 56) 295168 block2_pool[0][0]
____________________________________________________________________________________________________
block3_conv2 (Convolution2D) (None, 256, 56, 56) 590080 block3_conv1[0][0]
____________________________________________________________________________________________________
block3_conv3 (Convolution2D) (None, 256, 56, 56) 590080 block3_conv2[0][0]
____________________________________________________________________________________________________
block3_conv4 (Convolution2D) (None, 256, 56, 56) 590080 block3_conv3[0][0]
____________________________________________________________________________________________________
block3_pool (MaxPooling2D) (None, 256, 28, 28) 0 block3_conv4[0][0]
____________________________________________________________________________________________________
block4_conv1 (Convolution2D) (None, 512, 28, 28) 1180160 block3_pool[0][0]
____________________________________________________________________________________________________
block4_conv2 (Convolution2D) (None, 512, 28, 28) 2359808 block4_conv1[0][0]
____________________________________________________________________________________________________
block4_conv3 (Convolution2D) (None, 512, 28, 28) 2359808 block4_conv2[0][0]
____________________________________________________________________________________________________
block4_conv4 (Convolution2D) (None, 512, 28, 28) 2359808 block4_conv3[0][0]
____________________________________________________________________________________________________
block4_pool (MaxPooling2D) (None, 512, 14, 14) 0 block4_conv4[0][0]
____________________________________________________________________________________________________
block5_conv1 (Convolution2D) (None, 512, 14, 14) 2359808 block4_pool[0][0]
____________________________________________________________________________________________________
block5_conv2 (Convolution2D) (None, 512, 14, 14) 2359808 block5_conv1[0][0]
____________________________________________________________________________________________________
block5_conv3 (Convolution2D) (None, 512, 14, 14) 2359808 block5_conv2[0][0]
____________________________________________________________________________________________________
block5_conv4 (Convolution2D) (None, 512, 14, 14) 2359808 block5_conv3[0][0]
____________________________________________________________________________________________________
block5_pool (MaxPooling2D) (None, 512, 7, 7) 0 block5_conv4[0][0]
____________________________________________________________________________________________________
flatten (Flatten) (None, 25088) 0 block5_pool[0][0]
____________________________________________________________________________________________________
fc1 (Dense) (None, 4096) 102764544 flatten[0][0]
____________________________________________________________________________________________________
fc2 (Dense) (None, 4096) 16781312 fc1[0][0]
====================================================================================================
Total params: 139,570,240
Trainable params: 139,570,240
Non-trainable params: 0
问题在于
VGG19
体系结构在推理阶段每个样本大约需要250MB
。一个batch_size=32
so模型的默认值试图分配超过8GB
的内存,这比OPs机器拥有的内存要多得多。在相关问题 更多 >
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