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Results 1 - 9 of 9 for Conv2D (0.16 sec)
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tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
// OK %0 = "tf.Conv2D"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32> // Unsupported data format
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test_base.py
def body(self, x, w): z = nn_ops.conv2d(x, w, padding='SAME') return z, w @def_function.function( input_signature=[ tensor_spec.TensorSpec( shape=input_shape, dtype=dtypes.float32, name='input_tensor' ) ] ) def main(self, x): x1 = nn_ops.conv2d(x, self.w, padding='SAME')
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Mar 21 08:51:46 UTC 2024 - 51.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composit_functions_debugging.mlir
// TF-DAG: "tf.DumpTensor"(%[[conv0_float]]) <{enabled = true, file_name = "unquantized_tensor_data.pb", func_name = "conv_with_dump", log_dir_path = "/tmp/dumps/composite_conv2d_with_bias_and_relu6_fn_2", node_name = "Conv2D"}> {device = ""}
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Nov 06 01:23:21 UTC 2023 - 80.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
// Only rank size four input will be only available by the tf.Conv2D // operator verification. if (!input_type || input_type.isDynamicDim(3)) { return failure(); } // Check if the given op is based on grouped convolution. // Dim size zero will be verified by the tf.Conv2D operator verification. if (input_type.getDimSize(3) % filter_type.getDimSize(2) != 0) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/python/integration_test/quantize_model_test.py
), ) quantization.quantize_saved_model( self._input_saved_model_path, self._output_saved_model_path, config, ) expected_outputs = model.conv2d(input_data) root = load.load(self._output_saved_model_path) self.assertCountEqual(root.signatures.keys(), {'serving_default'}) new_outputs = root.signatures['serving_default'](
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 51.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/replace_cast_hacks_with_tf_xla_ops.mlir
%2 = "tf.Cast"(%identity) {Truncate = false} : (tensor<2x3x3x2xi8>) -> tensor<2x3x3x2xi32> %3 = "tf.Sub"(%2, %arg6) : (tensor<2x3x3x2xi32>, tensor<i32>) -> tensor<2x3x3x2xi32> %4 = "tf.Conv2D"(%1, %3) {dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xi32>, tensor<2x3x3x2xi32>) -> tensor<1x3x2x2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 81K bytes - Viewed (0) -
tensorflow/compiler/mlir/g3doc/_includes/tf_passes.md
%device_launch = "tf_device.cluster_func"(%input,...) {func = @_func,...) return ... } func @_func(%input: tensor<2x224x224x3xf32>, %filter: tensor<7x7x3x64xf32>) { %6 = "tf.Conv2D"(%input, %filter) {strides = [1, 2, 2, 1]}: (tensor<2x230x230x3xf32>, tensor<7x7x3x64xf32>) -> tensor<2x112x112x64xf32> } } ``` The program will be transformed into: ```mlir module {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Aug 02 02:26:39 UTC 2023 - 96.4K bytes - Viewed (0) -
tensorflow/compiler/jit/mark_for_compilation_pass.cc
"Betainc", "BiasAddV1", "Bincount", "Bucketize", "Case", "CheckNumerics", "Cholesky", "ControlTrigger", "Conv", "Conv2D", "Conv2DBackpropFilter", "Conv2DBackpropInput", "Conv3D", "Conv3DBackpropFilterV2", "Conv3DBackpropInputV2", "Cross", "Cumprod", "Cumsum",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 21 12:19:41 UTC 2024 - 85.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir
%conv2 = "tfl.conv_2d"(%4, %5, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x112x112x32xf32>, tensor<32x3x3x3xf32>, tensor<32xf32>) -> tensor<1x56x56x32xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 67.5K bytes - Viewed (0)