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Results 1 - 7 of 7 for conv_2d (0.4 sec)
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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) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
// CHECK: %1 = "tfl.conv_2d"(%arg0, %0, %[[CONSTANT]]) <{dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x8x7x16xf32> // CHECK: %2 = "tf.Conv2D"
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/lite/experimental/tac/tests/raise-target-subgraphs.mlir
// CHECK: %[[VAL_5:.*]] = "tfl.conv_2d"(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x30x30x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 74.9K 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/lite/quantization/lite/quantize_model_test.cc
const auto& subgraph = model_.subgraphs[0]; auto conv_op = subgraph->operators[0].get(); const int input_tensor_idx = 0; const int weights_tensor_idx = 1; const int bias_tensor_index = 2; const int output_tensor_idx = 0; const auto bias_tensor = subgraph->tensors[conv_op->inputs[bias_tensor_index]].get(); const auto input_tensor = subgraph->tensors[conv_op->inputs[input_tensor_idx]].get();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 73.9K 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/stablehlo/tests/passes/lift_quantizable_spots_as_functions.mlir
// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-lift-quantizable-spots-as-functions | FileCheck %s // CHECK-LABEL: @conv_fn( // CHECK-SAME: %[[ARG_0:.*]]: tensor<1x3x3x4xf32> func.func @conv_fn(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> { %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 49.8K bytes - Viewed (0)