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Results 1 - 10 of 17 for 256x32x32x3xf32 (0.14 sec)
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tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant_4bit.mlir
// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) // CHECK: return %0 : tensor<8xf32> } // CHECK-LABEL: fakeQuantWithConv2D func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) { ^bb0(%arg: tensor<256x32x32x3xf32>) : %in = arith.constant dense<0.0> : tensor<3x3x3x16xf32> %min = arith.constant dense<0.0> : tensor<f32> %max = arith.constant dense<15.0> : tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant.mlir
// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) // CHECK: return %0 : tensor<8xf32> } // CHECK-LABEL: fakeQuantWithConv2D func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) { ^bb0(%arg: tensor<256x32x32x3xf32>) : %in = arith.constant dense<0.0> : tensor<3x3x3x16xf32> %min = arith.constant dense<0.0> : tensor<f32> %max = arith.constant dense<255.0> : tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/import_json.json
// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32> { "version": 3, "operator_codes": [ { "builtin_code": "CONV_2D" } ], "subgraphs": [ { "tensors": [
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/optimize.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 3.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize-after-quantization.mlir
// CHECK-LABEL: fuseMulIntoPerTensorConv2dWithQDQs func.func @fuseMulIntoPerTensorConv2dWithQDQs(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x8x7x3xf32> { %cst = arith.constant dense<1.5> : tensor<3xf32> %cst_0 = arith.constant dense<[1.0, 2.0, 3.0]> : tensor<3xf32> %w = arith.constant dense<2.0> : tensor<3x3x3x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 1.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/fold-constants-to-subgraph.mlir
} // ----- module { func.func @main(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x30x30x16xf32> { %0 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16x3x3x3xf32>} : () -> tensor<16x3x3x3xf32> %1 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32> %2 = func.call @fold_all_test(%arg0, %0, %1) : (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 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant-4bit.mlir
// CHECK: %[[R2:.*]] = "tf.Reshape"(%[[FQ]], %cst) // CHECK-SAME: tensor<2x1xf32> } // CHECK-LABEL: fakeQuantWithConv2D func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) { ^bb0(%arg: tensor<256x32x32x3xf32>) : %in = arith.constant dense<0.0> : tensor<3x3x3x16xf32> %min = arith.constant dense<0.0> : tensor<f32> %max = arith.constant dense<15.0> : tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 22K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant.mlir
// CHECK: %[[R2:.*]] = "tf.Reshape"(%[[FQ]], %cst) // CHECK-SAME: tensor<2x1xf32> } // CHECK-LABEL: fakeQuantWithConv2D func.func @fakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) { ^bb0(%arg: tensor<256x32x32x3xf32>) : %in = arith.constant dense<0.0> : tensor<3x3x3x16xf32> %min = arith.constant dense<0.0> : tensor<f32> %max = arith.constant dense<255.0> : tensor<f32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-tf.mlir
func.func @conv(tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>, tensor<256x3x32x32xf32>) -> (tensor<256x8x7x16xf32>, tensor<256x16x32x32xf32>, tensor<256x8x6x16xf32>, tensor<256x32x32x16xf32>, tensor<256x32x32x16xf32>) { ^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<3x3x3x16xf32>, %arg2: tensor<256x3x32x32xf32>) : // OK
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/tests/flatbuffer2mlir/optional_input.json
// CHECK: return %[[RES0]] : tensor<256x32x32x16xf32> { "version": 3, "operator_codes": [ { "builtin_code": "CONV_2D" } ], "subgraphs": [ { "tensors": [
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.8K bytes - Viewed (0)