/external/tensorflow/tensorflow/python/keras/_impl/keras/applications/ |
D | imagenet_utils.py | 248 def _obtain_input_shape(input_shape, argument 274 if weights != 'imagenet' and input_shape and len(input_shape) == 3: 276 if input_shape[0] not in {1, 3}: 279 str(input_shape[0]) + ' input channels.') 280 default_shape = (input_shape[0], default_size, default_size) 282 if input_shape[-1] not in {1, 3}: 285 str(input_shape[-1]) + ' input channels.') 286 default_shape = (default_size, default_size, input_shape[-1]) 293 if input_shape is not None: 294 if input_shape != default_shape: [all …]
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D | mobilenet_test.py | 63 input_shape = (None, None, 1) 66 input_shape=input_shape) 69 input_shape = (None, None, 4) 72 input_shape=input_shape) 80 input_shape = (size, size, 3) 81 model = keras.applications.MobileNet(input_shape=input_shape, 84 self.assertEqual(model.input_shape, (None,) + input_shape) 87 input_shape = (3, size, size) 88 model = keras.applications.MobileNet(input_shape=input_shape, 91 self.assertEqual(model.input_shape, (None,) + input_shape) [all …]
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D | imagenet_utils_test.py | 95 input_shape=(224, 224, 3), 107 input_shape = shape + (3,) 109 input_shape = (3,) + shape 112 input_shape=input_shape, 121 input_shape = shape + (3,) 123 input_shape = (3,) + shape 126 input_shape=input_shape, 135 input_shape = shape + (5,) 137 input_shape = (5,) + shape 140 input_shape=input_shape, [all …]
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D | mobilenet.py | 218 def build(self, input_shape): argument 219 if len(input_shape) < 4: 221 'Received input shape:', str(input_shape)) 226 if input_shape[channel_axis] is None: 230 input_dim = int(input_shape[channel_axis]) 272 def compute_output_shape(self, input_shape): argument 274 rows = input_shape[2] 275 cols = input_shape[3] 276 out_filters = input_shape[1] * self.depth_multiplier 278 rows = input_shape[1] [all …]
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/external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
D | convolutional.py | 1048 def compute_output_shape(self, input_shape): argument 1049 input_shape = tensor_shape.TensorShape(input_shape).as_list() 1050 size = self.size * input_shape[1] if input_shape[1] is not None else None 1051 return tensor_shape.TensorShape([input_shape[0], size, input_shape[2]]) 1105 def compute_output_shape(self, input_shape): argument 1106 input_shape = tensor_shape.TensorShape(input_shape).as_list() 1108 height = self.size[0] * input_shape[ 1109 2] if input_shape[2] is not None else None 1110 width = self.size[1] * input_shape[ 1111 3] if input_shape[3] is not None else None [all …]
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D | core.py | 85 def compute_output_shape(self, input_shape): argument 86 return input_shape 173 input_shape = K.shape(inputs) 174 noise_shape = (input_shape[0], 1, input_shape[2]) 225 input_shape = K.shape(inputs) 227 return (input_shape[0], input_shape[1], 1, 1) 229 return (input_shape[0], 1, 1, input_shape[3]) 278 input_shape = K.shape(inputs) 280 return (input_shape[0], input_shape[1], 1, 1, 1) 282 return (input_shape[0], 1, 1, 1, input_shape[4]) [all …]
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D | pooling_test.py | 31 input_shape=(3, 4, 5)) 33 keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5)) 40 input_shape=(3, 4, 5, 6)) 44 input_shape=(3, 5, 6, 4)) 48 input_shape=(3, 4, 5, 6)) 52 input_shape=(3, 5, 6, 4)) 59 input_shape=(3, 4, 3, 4, 3)) 63 input_shape=(3, 4, 3, 4, 3)) 67 input_shape=(3, 4, 3, 4, 3)) 71 input_shape=(3, 4, 3, 4, 3)) [all …]
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D | merge.py | 84 def build(self, input_shape): argument 86 if not isinstance(input_shape, list): 88 if len(input_shape) < 2: 91 'Got ' + str(len(input_shape)) + ' inputs.') 92 batch_sizes = [s[0] for s in input_shape if s is not None] 98 'batch sizes. Got tensors with shapes : ' + str(input_shape)) 99 if input_shape[0] is None: 102 output_shape = input_shape[0][1:] 103 for i in range(1, len(input_shape)): 104 if input_shape[i] is None: [all …]
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D | core_test.py | 36 keras.layers.Masking, kwargs={}, input_shape=(3, 2, 3)) 41 keras.layers.Dropout, kwargs={'rate': 0.5}, input_shape=(3, 2)) 48 input_shape=(3, 2)) 59 input_shape=(2, 3, 4)) 65 input_shape=(2, 3, 4, 5)) 71 input_shape=(2, 3, 4, 5)) 77 input_shape=(2, 3, 4, 4, 5)) 83 input_shape=(2, 3, 4, 4, 5)) 91 input_shape=(3, 2)) 98 input_shape=(3, 2)) [all …]
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/external/tensorflow/tensorflow/python/kernel_tests/ |
D | pool_test.py | 147 def _test(self, input_shape, **kwargs): argument 151 np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1 161 input_shape=[1, 1, 10, 1], 172 for input_shape in [[2, 9, 2], [2, 10, 2]]: 177 input_shape=input_shape, 187 input_shape=input_shape, 198 for input_shape in [[2, 9, 10, 2], [2, 10, 9, 2]]: 203 input_shape=input_shape, 213 input_shape=input_shape, 224 for input_shape in [[2, 9, 10, 11, 2], [2, 10, 9, 11, 2]]: [all …]
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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
D | split_op.cc | 38 const TensorShape input_shape = ctx->InputShape(1); in Compile() local 57 int32 split_dim = split_dim_orig < 0 ? split_dim_orig + input_shape.dims() in Compile() 59 OP_REQUIRES(ctx, 0 <= split_dim && split_dim < input_shape.dims(), in Compile() 60 errors::InvalidArgument("-input rank(-", input_shape.dims(), in Compile() 62 input_shape.dims(), "), but got ", in Compile() 71 ctx, input_shape.dim_size(split_dim) % num_split == 0, in Compile() 75 split_dim_orig, " (size = ", input_shape.dim_size(split_dim), ") ", in Compile() 80 const int32 slice_size = input_shape.dim_size(split_dim) / num_split; in Compile() 84 std::vector<int64> begin(input_shape.dims(), 0); in Compile() 85 std::vector<int64> limits(input_shape.dims()); in Compile() [all …]
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D | shape_op.cc | 36 const TensorShape input_shape = ctx->InputShape(0); in Compile() local 37 Tensor shape_constant(out_dtype_, TensorShape({input_shape.dims()})); in Compile() 38 OP_REQUIRES_OK(ctx, TensorShapeToConstant(input_shape, &shape_constant)); in Compile() 56 const TensorShape input_shape = ctx->InputShape(i); in Compile() local 57 Tensor shape_constant(out_dtype_, TensorShape({input_shape.dims()})); in Compile() 58 OP_REQUIRES_OK(ctx, TensorShapeToConstant(input_shape, &shape_constant)); in Compile() 75 const TensorShape input_shape = ctx->InputShape(0); in Compile() local 76 const int rank = input_shape.dims(); in Compile() 91 const TensorShape input_shape = ctx->InputShape(0); in Compile() local 92 const int64 size = input_shape.num_elements(); in Compile() [all …]
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D | unpack_op.cc | 47 const TensorShape input_shape = ctx->InputShape(0); in Compile() local 50 if (axis < 0) axis += input_shape.dims(); in Compile() 52 OP_REQUIRES(ctx, 0 <= axis && axis < input_shape.dims(), in Compile() 54 -input_shape.dims(), ", ", in Compile() 55 input_shape.dims(), ")")); in Compile() 58 ctx, input_shape.dims() > 0 && input_shape.dim_size(axis) == num, in Compile() 60 ", got shape ", input_shape.DebugString())); in Compile() 62 auto output_shape = input_shape; in Compile() 67 std::vector<int64> start_indices(input_shape.dims(), 0); in Compile() 68 std::vector<int64> limit_indices(input_shape.dims()); in Compile() [all …]
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D | spacetodepth_op.cc | 50 const gtl::InlinedVector<int64, 4> input_shape = in Compile() local 68 OP_REQUIRES(ctx, input_shape[1 + i] % block_size_ == 0, in Compile() 70 "input shape[", 1 + i, "]=", input_shape[1 + i], in Compile() 75 reshaped_shape.push_back(input_shape[0]); in Compile() 77 reshaped_shape.push_back(input_shape[1 + i] / block_size_); in Compile() 80 reshaped_shape.push_back(input_shape[feature_dim]); in Compile() 91 output_shape.push_back(input_shape[0]); in Compile() 93 output_shape.push_back(input_shape[1 + i] / block_size_); in Compile() 95 output_shape.push_back(input_shape[feature_dim] * block_elems); in Compile() 100 OP_REQUIRES(ctx, input_shape[2 + i] % block_size_ == 0, in Compile() [all …]
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D | image_ops.cc | 87 const TensorShape input_shape = context->InputShape(0); in Compile() local 88 OP_REQUIRES(context, input_shape.dims() >= 1, in Compile() 90 input_shape.DebugString())); in Compile() 91 int channel_dim = input_shape.dims() - 1; in Compile() 92 int64 channels = input_shape.dim_size(channel_dim); in Compile() 110 TensorShape channel_shape = input_shape; in Compile() 125 const TensorShape input_shape = context->InputShape(0); in Compile() local 126 OP_REQUIRES(context, input_shape.dims() >= 1, in Compile() 128 input_shape.DebugString())); in Compile() 129 int channel_dim = input_shape.dims() - 1; in Compile() [all …]
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D | depthtospace_op.cc | 50 const gtl::InlinedVector<int64, 4> input_shape = in Compile() local 66 reshaped_shape.push_back(input_shape[0]); in Compile() 68 reshaped_shape.push_back(input_shape[1 + i]); in Compile() 75 reshaped_shape.push_back(input_shape[feature_dim] / block_elems); in Compile() 84 output_shape.push_back(input_shape[0]); in Compile() 86 output_shape.push_back(input_shape[1 + i] * block_size_); in Compile() 88 output_shape.push_back(input_shape[feature_dim] / block_elems); in Compile() 91 reshaped_shape.push_back(input_shape[0]); in Compile() 97 reshaped_shape.push_back(input_shape[feature_dim] / block_elems); in Compile() 99 reshaped_shape.push_back(input_shape[2 + i]); in Compile() [all …]
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D | slice_op.cc | 38 const TensorShape input_shape = ctx->InputShape(0); in Compile() local 46 begin_tensor_shape.num_elements() == input_shape.dims() && in Compile() 47 size_tensor_shape.num_elements() == input_shape.dims(), in Compile() 50 input_shape.dims(), ", but got shapes ", in Compile() 54 const int input_dims = input_shape.dims(); in Compile() 64 size[i] = input_shape.dim_size(i) - begin[i]; in Compile() 71 if (input_shape.dim_size(i) == 0) { in Compile() 78 OP_REQUIRES(ctx, 0 <= b && b <= input_shape.dim_size(i), in Compile() 80 input_shape.dim_size(i), in Compile() 82 OP_REQUIRES(ctx, 0 <= s && b + s <= input_shape.dim_size(i), in Compile() [all …]
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/external/tensorflow/tensorflow/python/framework/ |
D | common_shapes.py | 178 input_shape = op.inputs[0].get_shape().with_rank(4) 188 input_shape = [input_shape[0], input_shape[2], input_shape[3], 189 input_shape[1]] 191 batch_size = input_shape[0] 192 in_rows = input_shape[1] 193 in_cols = input_shape[2] 199 input_shape[3].assert_is_compatible_with(filter_shape[2]) 247 input_shape = op.inputs[0].get_shape().with_rank(4) 250 batch_size = input_shape[0] 251 in_rows = input_shape[1] [all …]
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/external/tensorflow/tensorflow/python/layers/ |
D | pooling.py | 93 def compute_output_shape(self, input_shape): argument 94 input_shape = tensor_shape.TensorShape(input_shape).as_list() 95 length = utils.conv_output_length(input_shape[1], self.pool_size[0], 97 return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]]) 285 def compute_output_shape(self, input_shape): argument 286 input_shape = tensor_shape.TensorShape(input_shape).as_list() 288 rows = input_shape[2] 289 cols = input_shape[3] 291 rows = input_shape[1] 292 cols = input_shape[2] [all …]
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D | core.py | 124 def build(self, input_shape): argument 125 input_shape = tensor_shape.TensorShape(input_shape) 126 if input_shape[-1].value is None: 130 axes={-1: input_shape[-1].value}) 132 shape=[input_shape[-1].value, self.units], 169 def compute_output_shape(self, input_shape): argument 170 input_shape = tensor_shape.TensorShape(input_shape) 171 input_shape = input_shape.with_rank_at_least(2) 172 if input_shape[-1].value is None: 175 % input_shape) [all …]
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/external/tensorflow/tensorflow/contrib/fused_conv/python/ops/ |
D | fused_conv2d_bias_activation_benchmark.py | 32 def build_conv_bias_relu_graph(device, input_shape, filter_shape, strides, argument 52 input_shape = [ 53 input_shape[0], input_shape[3], input_shape[1], input_shape[2] 56 inp = variables.Variable(random_ops.truncated_normal(input_shape)) 77 def build_fused_conv_bias_relu_graph(device, input_shape, filter_shape, strides, argument 97 input_shape = [ 98 input_shape[0], input_shape[3], input_shape[1], input_shape[2] 101 inp = variables.Variable(random_ops.truncated_normal(input_shape)) 134 def _run_graph(self, device, input_shape, filter_shape, strides, padding, argument 156 outputs = build_fused_conv_bias_relu_graph(device, input_shape, [all …]
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/external/tensorflow/tensorflow/contrib/lite/toco/graph_transformations/ |
D | propagate_fixed_sizes.cc | 32 void ComputeConvSizes(const Shape& input_shape, int output_depth, int kwidth, in ComputeConvSizes() argument 36 const int input_width = input_shape.dims(2); in ComputeConvSizes() 37 const int input_height = input_shape.dims(1); in ComputeConvSizes() 38 const int batch = input_shape.dims(0); in ComputeConvSizes() 153 const auto& input_shape = input_array.shape(); in ProcessConvOperator() local 154 CHECK_EQ(input_shape.dimensions_count(), 4); in ProcessConvOperator() 168 ComputeConvSizes(input_shape, output_depth, kwidth, kheight, op->stride_width, in ProcessConvOperator() 195 const auto& input_shape = input_array.shape(); in ProcessDepthwiseConvOperator() local 196 CHECK_EQ(input_shape.dimensions_count(), 4); in ProcessDepthwiseConvOperator() 207 const int input_depth = input_shape.dims(3); in ProcessDepthwiseConvOperator() [all …]
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/external/tensorflow/tensorflow/python/ops/ |
D | concat_benchmark.py | 35 def build_graph(device, input_shape, variable, num_inputs, axis, grad): argument 51 inputs = [array_ops.zeros(input_shape) for _ in range(num_inputs)] 56 input_shape[0], 57 random.randint(max(1, input_shape[1] - 5), input_shape[1] + 5) 63 random.randint(max(1, input_shape[0] - 5), input_shape[0] + 5), 64 input_shape[1] 81 def _run_graph(self, device, input_shape, variable, num_inputs, axis, grad, argument 99 outputs = build_graph(device, input_shape, variable, num_inputs, axis, 112 "GB/sec" % (device, input_shape[0], input_shape[1], variable, 114 num_inputs * input_shape[0] * input_shape[1] * 4 * 2 * [all …]
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/external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
D | core_layers.py | 124 def build(self, input_shape): argument 125 input_shape = tensor_shape.TensorShape(input_shape) 127 if input_shape[channel_axis].value is None: 130 input_dim = input_shape[channel_axis].value 213 def compute_output_shape(self, input_shape): argument 214 input_shape = tensor_shape.TensorShape(input_shape).as_list() 216 space = input_shape[1:-1] 226 return tensor_shape.TensorShape([input_shape[0]] + new_space + 229 space = input_shape[2:] 239 return tensor_shape.TensorShape([input_shape[0], self.filters] + [all …]
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/external/tensorflow/tensorflow/core/api_def/base_api/ |
D | api_def_BatchToSpaceND.pbtxt | 6 N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, 23 `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`. 30 input_shape[1], ..., input_shape[N-1]] 35 input_shape[1], block_shape[0], 37 input_shape[M], block_shape[M-1], 39 input_shape[M+1], ..., input_shape[N-1]] 44 input_shape[1] * block_shape[0], 46 input_shape[M] * block_shape[M-1], 48 input_shape[M+1], 50 input_shape[N-1]] [all …]
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