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Results 1 - 10 of 51 for conv2 (0.06 sec)
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tensorflow/compiler/mlir/lite/tests/prepare-quantize-signed.mlir
%b2 = arith.constant dense<[1.0e-2, 2.1473647e1, -2.1473647e2]> : tensor<3xf32> %conv = "tfl.conv_2d"(%0, %w, %b) { dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32 } : (tensor<1x5x5x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<1x5x5x3xf32> %conv2 = "tfl.conv_2d"(%0, %w, %b2) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_lifting.mlir
// CHECK: %[[CONV2D:.*]] = "tf.Conv2D"(%arg0, %[[CONST]]) <{data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true}> : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 33.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/add_dump_tensor_op.mlir
%1 = "tf.PartitionedCall"(%arg0, %cst, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_conv2d_with_bias_and_relu6_fn_1} : (tensor<1x2x2x3xf32>, tensor<2x2x3x2xf32>, tensor<2xf32>) -> tensor<*xf32> loc(callsite("test@conv"("Conv2D_1") at "QuantizationUnit(\12\08Conv2D_1\1a\04conv)"))
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 22 22:55:22 UTC 2024 - 37.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_drq.mlir
%conv = "tf.Conv2D"(%arg0, %arg1) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 2, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32> return %conv : tensor<*xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel_4bit.pbtxt
key: "narrow_range" value { b: true } } attr { key: "num_bits" value { i: 4 } } } node { name: "BoxPredictor_4/ClassPredictor/Conv2D" op: "Conv2D" input: "input" input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel" attr { key: "T" value { type: DT_FLOAT } } attr {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.1K bytes - Viewed (0) -
platforms/software/dependency-management/src/test/groovy/org/gradle/internal/component/local/model/LocalComponentGraphResolveStateFactoryTest.groovy
def file3 = new File("artifact-3.zip") def conf1 = dependencyScope("conf1") def conf2 = dependencyScope("conf2") def child1 = consumable("child1", [conf1, conf2]) consumable("child2", [conf1]) addArtifact(conf1, artifact1, file1) addArtifact(conf2, artifact2, file2) addArtifact(child1, artifact3, file3) when:
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Wed May 22 19:04:04 UTC 2024 - 15.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tpu_space_to_depth_pass.cc
} } // Handle Conv2D input, stride and filter. HandleConv2DInput(conv2d, block_size); HandleConv2DStride(conv2d); HandleConv2DFilter(conv2d, block_size); // Book keeping new filter shape for backprop filter rewrite. // Filter shape is defined in HandleConv2DFilter, thus it is RankedTensorType. filter_shape = mlir::cast<RankedTensorType>(conv2d.getFilter().getType()).getShape();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 29.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/dilated_conv.h
// // // SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BatchToSpaceND -> BiasAdd // // SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> Pad -> BatchToSpaceND -> // BiasAdd // // SpaceToBatchND -> Expand -> Conv2D -> Squeeze -> BiasAdd -> BatchToSpaceND // // SpaceToBatchND -> Conv2D -> Pad -> BatchToSpaceND -> BiasAdd // // SpaceToBatchND -> Conv2D -> BatchToSpaceND -> BiasAdd // //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/bridge/optimize.mlir
%zp_offset: tensor<?x2x2x1xi32>, %bias: tensor<1xi32> ) -> tensor<?x2x2x1xi32> { // CHECK-DAG: %[[conv:.*]] = mhlo.convolution // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]] // CHECK-DAG: %[[result:.*]] = chlo.broadcast_add %[[conv]], %[[combined]] // CHECK: return %[[result]] %0 = mhlo.convolution(%lhs, %rhs) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 24 02:26:47 UTC 2024 - 10.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range.mlir
%b = arith.constant dense<-1.23697901> : tensor<64xf32> %conv = "tfl.conv_2d"(%arg0, %w, %b) {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<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32> func.return %conv : tensor<1x112x112x64xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 23 21:09:00 UTC 2024 - 23.2K bytes - Viewed (0)