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Results 1 - 7 of 7 for 1x256x16xf32 (0.17 sec)
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tensorflow/compiler/mlir/lite/tests/prepare-quantize.mlir
^bb0(%arg0: tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>, %arg1: tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>): %0 = "tfl.dequantize"(%arg0) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>) -> tensor<1x6x6x16xf32> %1 = "tfl.dequantize"(%arg1) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>) -> tensor<1x6x6x16xf32> %2 = "tfl.minimum"(%0, %1) : (tensor<1x6x6x16xf32>, tensor<1x6x6x16xf32>) -> tensor<1x6x6x16xf32>
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/quantize.mlir
%1 = "tfl.softmax"(%0) {beta = 1.000000e+00 : f32} : (tensor<1x6x6x16xf32>) -> tensor<1x6x6x16xf32> func.return %1 : tensor<1x6x6x16xf32> // CHECK: %[[sm:.*]] = "tfl.softmax"(%arg0) // CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[sm]]) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 3.906250e-03>>) -> tensor<1x6x6x16xf32> // CHECK: return %[[dq]] : tensor<1x6x6x16xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 39.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/optimize.mlir
%cst_6 = arith.constant dense<[1, 0]> : tensor<2xi32> %2057 = "tfl.transpose"(%arg0, %cst_10) : (tensor<1x16x256xf32>, tensor<3xi32>) -> tensor<1x256x16xf32> %2058 = "tfl.reshape"(%2057, %cst_3) : (tensor<1x256x16xf32>, tensor<2xi32>) -> tensor<256x16xf32> %2059 = "tfl.transpose"(%2058, %cst_6) : (tensor<256x16xf32>, tensor<2xi32>) -> tensor<16x256xf32> return %2059: tensor<16x256xf32> // CHECK-DAG: %cst = arith.constant dense<[16, 256]> : tensor<2xi32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 16 20:31:41 UTC 2024 - 284.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/flatbuffer2mlir/many_attribute_op.mlir
// Confirm a wide array of attribute survives the round-trip func.func @main(tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> { ^bb0(%arg0: tensor<1x6x6x16xf32>): // CHECK: "tfl.average_pool_2d"(%{{.*}}) <{filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 824 bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/decompose-hybrid-quantization.mlir
func.return %2 : tensor<4x256x36xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/legalize-tf.mlir
// CHECK-NEXT: return %[[reshape]] : tensor<*xf32> } func.func @avgPool2D(%arg0: tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> { // OK %0 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> // Unsupported ksize
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 05 01:54:33 UTC 2024 - 153.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/legalize_hlo.mlir
// CHECK: %[[VAL_2:.*]] = "tf.Conv2D"(%[[VAL_0]], %[[VAL_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<1x8x8x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x6x6x16xf32> // CHECK: return %[[VAL_2]] : tensor<1x6x6x16xf32> // CHECK: }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 340.2K bytes - Viewed (0)