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Results 21 - 30 of 101 for conv2 (0.04 sec)
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src/cmd/compile/internal/walk/builtin.go
return mkcall("countrunes", n.Type(), init, typecheck.Conv(n.X.(*ir.ConvExpr).X, types.Types[types.TSTRING])) } if isByteCount(n) { conv := n.X.(*ir.ConvExpr) walkStmtList(conv.Init()) init.Append(ir.TakeInit(conv)...) _, len := backingArrayPtrLen(cheapExpr(conv.X, init)) return len } if isChanLenCap(n) { name := "chanlen" if n.Op() == ir.OCAP {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri Mar 08 22:35:22 UTC 2024 - 31.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions.mlir
%0 = "tf.Conv2D"(%arg0, %arg1) { data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32> %1 = "tf.Relu6"(%0) {device = ""} : (tensor<*xf32>) -> tensor<*xf32> %3 = "tf.Conv2D"(%arg0, %arg1) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 26.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/pipelines/process_nchw_tensor.mlir
// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[TRANSPOSE_0]], %[[CONST]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = {{\[\[}}1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x4x8xf32>, tensor<3x3x8x8xf32>) -> tensor<1x4x4x8xf32> // CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %[[CONV]], dims = [0, 3, 1, 2] : (tensor<1x4x4x8xf32>) -> tensor<1x8x4x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 12.6K bytes - Viewed (0) -
platforms/software/dependency-management/src/main/java/org/gradle/internal/component/external/model/ivy/IvyConfigurationHelper.java
Registered: Wed Jun 12 18:38:38 UTC 2024 - Last Modified: Tue Mar 19 19:13:04 UTC 2024 - 5.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/lift_quantizable_spots_as_functions.mlir
// CHECK: } // CHECK-LABEL: private @composite_conv_with_bias_dynamic_fn_1 // CHECK: %[[CONV:.*]] = stablehlo.convolution(%arg0, %arg1) // CHECK: %[[SHAPE_OF:.*]] = shape.shape_of %[[CONV]] // CHECK: %[[DYNAMIC_BROADCAST_IN_DIM:.*]] = stablehlo.dynamic_broadcast_in_dim %arg2, %[[SHAPE_OF]] // CHECK: %[[ADD:.*]] = stablehlo.add %[[CONV]], %[[DYNAMIC_BROADCAST_IN_DIM]] // CHECK: return %[[ADD]] : tensor<?x28x28x16xf32> // CHECK: }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 49.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.cc
weight_new_shape, &rewriter); // Replace the fc with conv. // The output would be [1, 1, width, output]. auto conv_output_type = RankedTensorType::get({1, 1, width, output_size}, output_type.getElementType()); auto conv = rewriter.create<TFL::Conv2DOp>( fc_op.getLoc(), conv_output_type, reshaped_input, reshaped_weight,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 25.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/fallback_to_flex_ops_default.mlir
%cst_0 = "tf.Const"() {value = dense<-1.000000e+00> : tensor<f32>} : () -> tensor<f32> %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32> %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<1x3x4x3xf32>, tensor<1x1x3x2xf32>) -> tensor<1x3x4x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/fused_kernel_matcher.cc
// attribute which is shared. bool AreFuseCompatible(Conv2DOp conv, BiasAddOp bias_add, PatternRewriter &rewriter) const override { // Verify that the data formats match and are valid for fusion. if (conv.getDataFormat() != bias_add.getDataFormat()) { (void)rewriter.notifyMatchFailure(conv, [&](Diagnostic &diag) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel.pbtxt
key: "narrow_range" value { b: true } } attr { key: "num_bits" value { i: 8 } } } 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) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir
%b = arith.constant dense<-1.23697901> : tensor<64xf32> %conv = "tfl.conv_2d"(%0, %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>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0)