Python test.is_gpu_available方法代码示例

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testDynamicOutputSizeWithRateOneValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      num_filters = 32
      input_size = [5, 3, 9, 11]
      expected_size = [None, num_filters, None, None]
      expected_size_dynamic = [5, num_filters, 7, 9]

      with self.session(use_gpu=True):
        images = array_ops.placeholder(np.float32,
                                       [None, input_size[1], None, None])
        output = layers_lib.convolution2d(
            images,
            num_filters, [3, 3],
            rate=1,
            padding='VALID',
            data_format='NCHW')
        variables_lib.global_variables_initializer().run()
        self.assertEqual(output.op.name, 'Conv/Relu')
        self.assertListEqual(output.get_shape().as_list(), expected_size)
        eval_output = output.eval({images: np.zeros(input_size, np.float32)})
        self.assertListEqual(list(eval_output.shape), expected_size_dynamic) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWithStrideOneSamePaddingNCHW(self):
    # `NCHW` data format is only supported for `GPU` device.
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 32
        input_size = [5, 3, 10, 12]
        expected_size = [5, num_filters, 10, 12]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [3, 3],
            stride=1,
            padding='SAME',
            data_format='NCHW')
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')

        sess.run(variables_lib.global_variables_initializer())
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWithStrideOneValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 32
        input_size = [5, 3, 10, 12]
        expected_size = [5, num_filters, 12, 14]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [3, 3],
            stride=1,
            padding='VALID',
            data_format='NCHW')
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')

        sess.run(variables_lib.global_variables_initializer())
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWithStrideTwoValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 32
        input_size = [5, 3, 9, 11]
        expected_size = [5, num_filters, 19, 23]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [3, 3],
            stride=[2, 2],
            padding='VALID',
            data_format='NCHW')
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.get_shape().as_list()), expected_size)

        sess.run(variables_lib.global_variables_initializer())
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWith1x1StrideTwoSamePaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 1, 1]
        expected_size = [1, num_filters, 2, 2]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 2],
            stride=[2, 2],
            padding='SAME',
            data_format='NCHW')
        self.assertListEqual(list(output.get_shape().as_list()), expected_size)

        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWith2x2StrideTwoSamePaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 2, 2]
        expected_size = [1, num_filters, 4, 4]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 2],
            stride=[2, 2],
            padding='SAME',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWith2x2StrideTwoValidPaddingNCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 2, 2]
        expected_size = [1, num_filters, 4, 4]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 2],
            stride=[2, 2],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWithStride2x1NCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 3, 2]
        expected_size = [1, num_filters, 6, 5]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 4],
            stride=[2, 1],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWithStride2x4NCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 3, 2]
        expected_size = [1, num_filters, 6, 8]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 4],
            stride=[2, 4],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testOutputSizeWithStride2x5NCHW(self):
    if test.is_gpu_available(cuda_only=True):
      with self.session(use_gpu=True) as sess:
        num_filters = 1
        input_size = [1, 1, 3, 2]
        expected_size = [1, num_filters, 6, 10]

        images = random_ops.random_uniform(input_size, seed=1)
        output = layers_lib.conv2d_transpose(
            images,
            num_filters, [2, 4],
            stride=[2, 5],
            padding='VALID',
            data_format='NCHW')
        sess.run(variables_lib.global_variables_initializer())
        self.assertEqual(output.op.name, 'Conv2d_transpose/Relu')
        self.assertListEqual(list(output.eval().shape), expected_size) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testCompareBilinear(self):
    if test.is_gpu_available():
      input_shape = [1, 5, 6, 3]
      target_height = 8
      target_width = 12
      for nptype in [np.float32, np.float64]:
        for align_corners in [True, False]:
          img_np = np.arange(
              0, np.prod(input_shape), dtype=nptype).reshape(input_shape)
          value = {}
          for use_gpu in [True, False]:
            with self.test_session(use_gpu=use_gpu):
              image = constant_op.constant(img_np, shape=input_shape)
              new_size = constant_op.constant([target_height, target_width])
              out_op = image_ops.resize_images(
                  image, new_size,
                  image_ops.ResizeMethod.BILINEAR,
                  align_corners=align_corners)
              value[use_gpu] = out_op.eval()
          self.assertAllClose(value[True], value[False], rtol=1e-5, atol=1e-5) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def _check_has_gpu():
  if not test.is_gpu_available(cuda_only=True):
    raise ValueError(
        """You have asked to run part or all of this on GPU, but it appears
        that no GPU is available. If your machine has GPUs it is possible you
        do not have a version of TensorFlow with GPU support. To build with GPU
        support, add --config=cuda to the build flags.\n """) 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def gpu_availability():
    """
    Detect gpu on user system

    :return: Whether at least a CUDA compatible GPU is detected and usable
    :rtype: bool
    :History: 2018-Apr-25 - Written - Henry Leung (University of Toronto)
    """
    # assume if using tensorflow-gpu, then Nvidia GPU is available
    if is_built_with_cuda():
        return is_gpu_available()
    else:
        return is_built_with_cuda() 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def setUpClass(cls):
    if test.is_gpu_available():
      cls._expected_partition_graph_count = 2
      cls._expected_num_devices = 2
      cls._main_device = "/job:localhost/replica:0/task:0/gpu:0"
    else:
      cls._expected_partition_graph_count = 1
      cls._expected_num_devices = 1
      cls._main_device = "/job:localhost/replica:0/task:0/cpu:0" 

# 需要导入模块: from tensorflow.python.platform import test [as 别名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 别名]
def testMaxPoolingWithArgmax(self):
        # MaxPoolWithArgMax is implemented only on CUDA.
        if not test.is_gpu_available(cuda_only=True):
            return
        '''[[[[  1.   2.]
              [  3.   4.]
              [  5.   6.]]
             [[  7.   8.]
              [  9.  10.]
              [ 11.  12.]]
             [[ 13.  14.]
              [ 15.  16.]
              [ 17.  18.]]]]'''
        tensor_input = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0,
                        10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0]
        with self.test_session(use_gpu=True) as sess:
            t = constant_op.constant(tensor_input, shape=[1, 3, 3, 2])
            out_op, argmax_op = segnet_vgg.max_pool_with_argmax(t)
            out, argmax = sess.run([out_op, argmax_op])
            self.assertShapeEqual(out, out_op)
            self.assertShapeEqual(argmax, argmax_op)
            '''[[[9, 10]
                 [11, 12]]
                [[15, 16]
                 [17, 18]]]'''
            self.assertAllClose(out.ravel(), [9., 10., 11., 12., 15., 16., 17., 18.])
            self.assertAllEqual(argmax.ravel(), [8, 9, 10, 11, 14, 15, 16, 17])