| /external/oboe/samples/RhythmGame/third_party/glm/detail/ |
| D | glm.cpp | 19 template struct tvec1<float32, lowp>;
30 template struct tvec1<float32, mediump>;
41 template struct tvec1<float32, highp>;
53 template struct tvec2<float32, lowp>;
64 template struct tvec2<float32, mediump>;
75 template struct tvec2<float32, highp>;
87 template struct tvec3<float32, lowp>;
98 template struct tvec3<float32, mediump>;
109 template struct tvec3<float32, highp>;
121 template struct tvec4<float32, lowp>;
[all …]
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | nary_ops_test.py | 58 [np.array([[1, 2, 3]], dtype=np.float32)],
59 expected=np.array([[1, 2, 3]], dtype=np.float32))
62 [np.array([1, 2], dtype=np.float32),
63 np.array([10, 20], dtype=np.float32)],
64 expected=np.array([11, 22], dtype=np.float32))
66 [np.array([-4], dtype=np.float32),
67 np.array([10], dtype=np.float32),
68 np.array([42], dtype=np.float32)],
69 expected=np.array([48], dtype=np.float32))
94 [np.array([[1, 2, 3]], dtype=np.float32)],
[all …]
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| D | tensor_list_ops_test.py | 41 element_dtype=dtypes.float32,
52 element_dtype=dtypes.float32,
58 l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
59 _, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
65 val = array_ops.placeholder(dtype=dtypes.float32)
68 element_dtype=dtypes.float32,
77 l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
78 _, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
86 element_dtype=dtypes.float32,
91 _, e11 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
[all …]
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| D | stack_ops_test.py | 37 v = array_ops.placeholder(dtypes.float32)
40 h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
43 c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
52 x = array_ops.placeholder(dtypes.float32)
55 h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
58 return gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
64 v = array_ops.placeholder(dtypes.float32)
67 h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo")
70 c1 = gen_data_flow_ops.stack_pop_v2(h1, dtypes.float32)
71 h2 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="bar")
[all …]
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| D | function_test.py | 39 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
40 bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32)
45 @function.Defun(dtypes.float32, dtypes.float32)
65 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
66 bval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
71 @function.Defun(dtypes.float32, dtypes.float32)
89 aval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32)
90 bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32)
95 @function.Defun(dtypes.float32, dtypes.float32)
110 @function.Defun(dtypes.float32, dtypes.int32, dtypes.int32)
[all …]
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| D | fused_batchnorm_test.py | 94 x_val = np.random.random_sample(x_shape).astype(np.float32)
95 scale_val = np.random.random_sample(scale_shape).astype(np.float32)
96 offset_val = np.random.random_sample(scale_shape).astype(np.float32)
112 np.float32, shape=x_val_converted.shape, name="x")
113 scale = array_ops.placeholder(np.float32, shape=scale_shape, name="scale")
115 np.float32, shape=scale_shape, name="offset")
138 x_val = np.random.random_sample(x_shape).astype(np.float32)
139 scale_val = np.random.random_sample(scale_shape).astype(np.float32)
140 offset_val = np.random.random_sample(scale_shape).astype(np.float32)
141 mean_val = np.random.random_sample(scale_shape).astype(np.float32)
[all …]
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| /external/tensorflow/tensorflow/lite/testing/nnapi_tflite_zip_tests/ |
| D | not_supported.txt | 9 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
10 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[1,1,1,3],is_arg_max=True,output_type=tf…
13 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
14 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,4,5],is_arg_max=True,output_type=tf…
17 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
18 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[2,3,3],is_arg_max=True,output_type=tf.i…
21 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[5,5],is_arg_max=True,output_type=tf.int…
22 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[5,5],is_arg_max=True,output_type=tf.int…
25 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[10],is_arg_max=True,output_type=tf.int32
26 arg_min_max/arg_min_max_input_dtype=tf.float32,input_shape=[10],is_arg_max=True,output_type=tf.int64
[all …]
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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | dense_attention_test.py | 38 scores = np.array([[[1.1]]], dtype=np.float32)
40 v = np.array([[[1.6]]], dtype=np.float32)
47 expected_scores = np.array([[[1.]]], dtype=np.float32)
51 expected = np.array([[[1.6]]], dtype=np.float32)
56 scores = np.array([[[1.1]]], dtype=np.float32)
58 v = np.array([[[1.6]]], dtype=np.float32)
63 expected_scores = np.array([[[1.]]], dtype=np.float32)
67 expected = np.array([[[1.6]]], dtype=np.float32)
72 scores = np.array([[[1., 0., 1.]]], dtype=np.float32)
74 v = np.array([[[1.6], [0.7], [-0.8]]], dtype=np.float32)
[all …]
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| /external/tensorflow/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/ |
| D | structured_input.py | 43 tf.TensorSpec([1], tf.float32),
44 tf.TensorSpec([2], tf.float32)
56 tf.TensorSpec([], tf.float32),
57 tf.TensorSpec([], tf.float32),
70 'x': tf.TensorSpec([1], tf.float32),
71 'y': tf.TensorSpec([2], tf.float32),
84 'y': tf.TensorSpec([2], tf.float32),
85 'x': tf.TensorSpec([1], tf.float32),
101 'x': tf.TensorSpec([4], tf.float32),
102 'y': tf.TensorSpec([5], tf.float32),
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | list_ops_test.py | 56 element_dtype=dtypes.float32,
60 l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
61 l = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
81 element_dtype=dtypes.float32, element_shape=[], max_num_elements=1)
93 element_dtype=dtypes.float32,
98 l = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
103 element_dtype=dtypes.float32, element_shape=[2, 3], num_elements=3)
104 _, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
105 l = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
112 element_dtype=dtypes.float32, element_shape=[None, 3], num_elements=3)
[all …]
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| D | sparse_matmul_op_test.py | 38 64) / 128.0).reshape([rows, cols]).astype(np.float32)
50 x_dtype=dtypes.float32,
51 y_dtype=dtypes.float32):
63 np_x = math_ops.cast(tf_x, dtypes.float32).eval()
64 np_y = math_ops.cast(tf_y, dtypes.float32).eval()
77 x = np.arange(0., 4.).reshape([4, 1]).astype(np.float32)
78 y = np.arange(-1., 1.).reshape([1, 2]).astype(np.float32)
79 for x_dtype in (dtypes.float32, dtypes.bfloat16):
80 for y_dtype in (dtypes.float32, dtypes.bfloat16):
85 x = np.ones((4, 0)).astype(np.float32)
[all …]
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| D | sparse_conditional_accumulator_test.py | 63 dtypes_lib.float32, name="Q")
79 dtypes_lib.float32, name="Q", reduction_type="Invalid")
84 dtypes_lib.float32,
106 dtypes_lib.float32, name="Q")
113 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([1]))
121 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([3, 3]))
125 values=np.array([[0, 0, 1], [3, 0, 4]]).astype(np.float32)))
132 dtypes = [dtypes_lib.float16, dtypes_lib.float32, dtypes_lib.float64]
156 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([2, 2]))
158 dtypes_lib.float32, name="Q", shape=tensor_shape.TensorShape([2, 2]))
[all …]
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| D | stack_ops_test.py | 39 -1, elem_type=dtypes.float32, stack_name="foo")
42 c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
53 x = constant_op.constant(a, dtype=dtypes.float32)
55 -1, elem_type=dtypes.float32, stack_name="foo")
58 c1 = gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
70 -1, elem_type=dtypes.float32, stack_name="foo")
77 a = constant_op.constant(np.ones(2000), dtype=dtypes.float32)
84 v = constant_op.constant(np.zeros(2000), dtype=dtypes.float32)
92 ny = y + gen_data_flow_ops.stack_pop_v2(h, dtypes.float32)
107 -1, elem_type=dtypes.float32, stack_name="foo")
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/boosted_trees/ |
| D | training_ops_test.py | 51 feature1_gains = np.array([7.62], dtype=np.float32)
53 feature1_left_node_contribs = np.array([[-4.375]], dtype=np.float32)
54 feature1_right_node_contribs = np.array([[7.143]], dtype=np.float32)
57 feature2_gains = np.array([0.63], dtype=np.float32)
59 feature2_left_node_contribs = np.array([[-0.6]], dtype=np.float32)
60 feature2_right_node_contribs = np.array([[0.24]], dtype=np.float32)
64 feature3_gains = np.array([7.65], dtype=np.float32)
66 feature3_left_node_contribs = np.array([[-4.89]], dtype=np.float32)
67 feature3_right_node_contribs = np.array([[5.3]], dtype=np.float32)
159 group1_gains = np.array([7.62], dtype=np.float32)
[all …]
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| /external/tensorflow/tensorflow/lite/testing/op_tests/ |
| D | binary_op.py | 39 "dtype": [tf.float32, tf.int32],
47 "dtype": [tf.float32],
55 "dtype": [tf.float32, tf.int32, tf.int64],
63 "dtype": [tf.float32, tf.int32],
71 "dtype": [tf.float32],
79 "dtype": [tf.float32],
87 "dtype": [tf.float32],
95 "dtype": [tf.float32],
103 "dtype": [tf.float32],
111 "dtype": [tf.float32],
[all …]
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| D | strided_slice_np_style.py | 34 "dtype": [tf.float32],
43 "dtype": [tf.float32],
49 "dtype": [tf.float32],
55 "dtype": [tf.float32],
63 "dtype": [tf.float32],
69 "dtype": [tf.float32],
81 "dtype": [tf.float32],
92 "dtype": [tf.float32],
118 "dtype": [tf.float32],
124 "dtype": [tf.float32],
[all …]
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| /external/tensorflow/tensorflow/lite/python/optimize/ |
| D | calibrator_test.py | 49 yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
52 dtypes.float32,
53 dtypes.float32,
72 yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
75 dtypes.float32,
76 dtypes.float32,
90 yield [np.ones(shape=(1, 5, 5, 3), dtype=np.float32)]
93 input_gen, dtypes.float32, dtypes.float32, True, 'conv2d_8/BiasAdd')
108 input_gen, dtypes.float32, dtypes.float32, True, 'Identity')
128 yield [np.ones(shape=(1, 8, 8, 3), dtype=np.float32) for _ in range(4)]
[all …]
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| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | normalization_tpu_test.py | 32 "adapt_data": np.array([[1.], [2.], [3.], [4.], [5.]], dtype=np.float32),
34 "test_data": np.array([[1.], [2.], [3.]], np.float32),
35 "expected": np.array([[-1.414214], [-.707107], [0]], np.float32),
38 "adapt_data": np.array([[1.], [2.], [3.], [4.], [5.]], dtype=np.float32),
40 "test_data": np.array([[1.], [2.], [3.]], np.float32),
41 "expected": np.array([[-1.414214], [-.707107], [0]], np.float32),
44 "adapt_data": np.array([[1., 2., 3., 4., 5.]], dtype=np.float32),
46 "test_data": np.array([[1.], [2.], [3.]], np.float32),
47 "expected": np.array([[-1.414214], [-.707107], [0]], np.float32),
52 np.float32),
[all …]
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| D | normalization_distribution_test.py | 33 "adapt_data": np.array([[1.], [2.], [3.], [4.], [5.]], dtype=np.float32),
35 "test_data": np.array([[1.], [2.], [3.]], np.float32),
36 "expected": np.array([[-1.414214], [-.707107], [0]], np.float32),
39 "adapt_data": np.array([[1.], [2.], [3.], [4.], [5.]], dtype=np.float32),
41 "test_data": np.array([[1.], [2.], [3.]], np.float32),
42 "expected": np.array([[-1.414214], [-.707107], [0]], np.float32),
45 "adapt_data": np.array([[1., 2., 3., 4., 5.]], dtype=np.float32),
47 "test_data": np.array([[1.], [2.], [3.]], np.float32),
48 "expected": np.array([[-1.414214], [-.707107], [0]], np.float32),
53 np.float32),
[all …]
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| /external/tensorflow/tensorflow/python/ops/ |
| D | image_grad.py | 105 allowed_types = [dtypes.float32, dtypes.float64]
137 allowed_types = [dtypes.float16, dtypes.float32, dtypes.float64]
199 (reds >= greens), dtypes.float32)
201 (greens >= blues), dtypes.float32)
203 (blues > greens), dtypes.float32)
206 (reds < greens), dtypes.float32)
208 (greens < blues), dtypes.float32)
210 (blues <= greens), dtypes.float32)
225 ds_dr = math_ops.cast(reds > 0, dtypes.float32) * \
231 ds_dg = math_ops.cast(greens > 0, dtypes.float32) * \
[all …]
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| D | nn_grad_test.py | 48 assert x.dtype == dtypes.float32
55 dtype=dtypes.float32)
66 [[-2, -1, 1, 3], [5, 7, 8, 9]], dtype=dtypes.float32)
93 dtype=dtypes.float32, shape=[1, 4, 4, 3], name='input')
95 dtype=dtypes.float32,
104 dtype=dtypes.float32,
108 dtype=dtypes.float32, shape=[2, 2, 3, 2], name='filter')
115 dtype=dtypes.float32, shape=[1, 4, 4, 3], name='input')
117 dtype=dtypes.float32,
144 dtype=dtypes.float32, shape=[1, 4, 4, 3], name='input')
[all …]
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| /external/tensorflow/tensorflow/python/tools/ |
| D | optimize_for_inference_test.py | 81 unused_constant_name, value=0, dtype=dtypes.float32, shape=[])
86 self.set_attr_dtype(unconnected_add_node, "T", dtypes.float32)
89 a_constant_name, value=1, dtype=dtypes.float32, shape=[])
98 b_constant_name, value=1, dtype=dtypes.float32, shape=[])
108 self.set_attr_dtype(add_node, "T", dtypes.float32)
112 self.set_attr_dtype(unused_output_add_node, "T", dtypes.float32)
117 a_constant_name, value=1, dtype=dtypes.float32, shape=[])
120 b_constant_name, value=1, dtype=dtypes.float32, shape=[])
124 self.set_attr_dtype(add_node, "T", dtypes.float32)
128 graph_def, [], [add_name], dtypes.float32.as_datatype_enum)
[all …]
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| /external/tensorflow/tensorflow/python/kernel_tests/random/ |
| D | random_binomial_test.py | 32 _SUPPORTED_DTYPES = (dtypes.float16, dtypes.float32, dtypes.float64,
79 for dt in dtypes.float16, dtypes.float32, dtypes.float64:
85 for dt in dtypes.float16, dtypes.float32, dtypes.float64:
103 rnd = rng.binomial(shape=[10], counts=np.float32(2.), probs=np.float32(0.5))
105 rnd = rng.binomial(shape=[], counts=np.float32(2.), probs=np.float32(0.5))
111 counts=array_ops.ones([10], dtype=np.float32),
112 probs=0.3 * array_ops.ones([10], dtype=np.float32))
116 counts=array_ops.ones([2], dtype=np.float32),
117 probs=0.4 * array_ops.ones([2], dtype=np.float32))
123 counts=np.float32(5.),
[all …]
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| /external/tensorflow/tensorflow/compiler/mlir/tfrt/python_tests/ |
| D | tf_binary_bcast_test.py | 49 arg0 = np.random.uniform(0, 10.0, size=(n, 4)).astype(np.float32)
50 arg1 = np.random.uniform(0, 10.0, size=(4)).astype(np.float32)
51 arg2 = np.random.uniform(0, 10.0, size=(4)).astype(np.float32)
72 lhs0 = np.random.uniform(0, 10.0, size=(1, 1)).astype(np.float32)
73 lhs1 = np.random.uniform(0, 10.0, size=(1, n)).astype(np.float32)
74 lhs2 = np.random.uniform(0, 10.0, size=(m, 1)).astype(np.float32)
75 lhs3 = np.random.uniform(0, 10.0, size=(m, n)).astype(np.float32)
77 rhs0 = np.random.uniform(0, 10.0, size=(1, 1)).astype(np.float32)
78 rhs1 = np.random.uniform(0, 10.0, size=(1, n)).astype(np.float32)
79 rhs2 = np.random.uniform(0, 10.0, size=(m, 1)).astype(np.float32)
[all …]
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_infeed_test.py | 39 tuple_types=[dtypes.float32, dtypes.int32, dtypes.int32])
42 [dtypes.float32, dtypes.int32, dtypes.int32])
60 number_of_tuple_elements=2, tuple_types=[dtypes.float32])
71 i.set_tuple_types([dtypes.float32, dtypes.int32])
72 self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.int32])
73 i.set_tuple_types([dtypes.float32, dtypes.float32])
74 self.assertEqual(i.tuple_types, [dtypes.float32, dtypes.float32])
76 i.set_tuple_types([dtypes.float32])
88 t2 = constant_op.constant(2.0, dtypes.float32, shape=[3, 18])
91 self.assertEqual(i.tuple_types, [dtypes.int32, dtypes.float32])
[all …]
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