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Python test.is_gpu_available方法代碼示例

本文整理匯總了Python中tensorflow.python.platform.test.is_gpu_available方法的典型用法代碼示例。如果您正苦於以下問題：Python test.is_gpu_available方法的具體用法？Python test.is_gpu_available怎麽用？Python test.is_gpu_available使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow.python.platform.test的用法示例。


在下文中一共展示了test.is_gpu_available方法的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: testDynamicOutputSizeWithRateOneValidPaddingNCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:23，代碼來源:layers_test.py

示例2: testOutputSizeWithStrideOneSamePaddingNCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:21，代碼來源:layers_test.py

示例3: testOutputSizeWithStrideOneValidPaddingNCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:20，代碼來源:layers_test.py

示例4: testOutputSizeWithStrideTwoValidPaddingNCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:21，代碼來源:layers_test.py

示例5: testOutputSizeWith1x1StrideTwoSamePaddingNCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:21，代碼來源:layers_test.py

示例6: testOutputSizeWith2x2StrideTwoSamePaddingNCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:19，代碼來源:layers_test.py

示例7: testOutputSizeWith2x2StrideTwoValidPaddingNCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:19，代碼來源:layers_test.py

示例8: testOutputSizeWithStride2x1NCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:19，代碼來源:layers_test.py

示例9: testOutputSizeWithStride2x4NCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:19，代碼來源:layers_test.py

示例10: testOutputSizeWithStride2x5NCHW

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:google-research，項目名稱:tf-slim，代碼行數:19，代碼來源:layers_test.py

示例11: testCompareBilinear

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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)
開發者ID:tobegit3hub，項目名稱:deep_image_model，代碼行數:22，代碼來源:image_ops_test.py

示例12: _check_has_gpu

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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 """)
開發者ID:tensorflow，項目名稱:benchmarks，代碼行數:9，代碼來源:benchmark_cnn_test.py

示例13: gpu_availability

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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()
開發者ID:henrysky，項目名稱:astroNN，代碼行數:15，代碼來源:nn_tools.py

示例14: setUpClass

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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"
開發者ID:ryfeus，項目名稱:lambda-packs，代碼行數:11，代碼來源:session_debug_testlib.py

示例15: testMaxPoolingWithArgmax

# 需要導入模塊: from tensorflow.python.platform import test [as 別名]
# 或者: from tensorflow.python.platform.test import is_gpu_available [as 別名]
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])
開發者ID:mengli，項目名稱:MachineLearning，代碼行數:29，代碼來源:segnet_vgg_test.py


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