我使用的是tensorflow-gpu (1.2.1)和Theano (0.9.0),2个4G显存Nvidia Quadro M2000 GPU。
1. theano: ValueError: Could not infer context from inputs
THEANO_FLAGS="contexts=dev0->cuda0;dev1->cuda1,gpuarray.preallocate=0.95,mode=FAST_RUN,floatX=float32,on_unused_input=warn" python config.py ERROR (theano.gof.opt): SeqOptimizer applyERROR: SeqOptimizer apply ERROR (theano.gof.opt): Traceback: ERROR: Traceback: ERROR (theano.gof.opt): Traceback (most recent call last): File "/usr/lib/python2.7/site-packages/theano/gof/opt.py", line 235, in apply sub_prof = optimizer.optimize(fgraph) File "/usr/lib/python2.7/site-packages/theano/gof/opt.py", line 87, in optimize ret = self.apply(fgraph, *args, **kwargs) File "/usr/lib/python2.7/site-packages/theano/gpuarray/opt.py", line 322, in apply target = infer_context_name(*fgraph.inputs) File "/usr/lib/python2.7/site-packages/theano/gpuarray/basic_ops.py", line 122, in infer_context_name raise ValueError("Could not infer context from inputs") ValueError: Could not infer context from inputs
theano不能自动支持多GPU,需要自己指定一个,只能在一个上面跑, 需要指定一个设备device=cuda0。
支持多GPU, 需要自己编程,参考http://deeplearning.net/software/theano/tutorial/using_multi_gpu.html#
2. tensorflow: ResourceExhaustedError: OOM when allocating tensor with
theano: MemoryError: Error allocating 1440000000 bytes of device memory (out of memory).
说明GPU内存不够,要调小输入或网络单元。
3. theano切换成新的GPU backend
WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10)
theano 0.9.0从cuda backend切换gpuarray backend,需要安装python2-Cython-0.25+和libgpuarray-0.6.3+, 然后通过gpuarray.preallocate来指定。
补充知识:pytorch网络输入图片通道在前在后(channel_first和channel_last)的问题
刚开始学习pytorch卷积神经网络的时候,网络输入要求是(batch,3,32,32),我们如果想要测试自己电脑上的图片格式为(32,32,3)。即网络要求channel_first,本地图片是channel_last,此时我们只需要使用numpy中的np.transpose()函数调整下通道的顺序即可。
代码如下:
import numpy as np import cv2 path = r"C:\Users\X_man\Desktop\image\cat.jpg" image = cv2.imread(path,0) image = cv2.resize(image,(32,32)) image = cv2.cvtColor(image,cv2.COLOR_GRAY2BGR) print(image.shape)
(32,32,3)
image = np.transpose(image,(2,0,1))
print(image.shape)
(3,32,32)
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