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Results 1 - 10 of 152 for conv3 (0.04 sec)
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test/typeparam/issue49027.dir/a.go
package a func Conv(v interface{}) string { return conv[string](v) } func conv[T any](v interface{}) T { return v.(T) } func Conv2(v interface{}) (string, bool) { return conv2[string](v) } func conv2[T any](v interface{}) (T, bool) { x, ok := v.(T) return x, ok } func Conv3(v interface{}) string { return conv3[string](v) }
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Tue Oct 19 22:47:48 UTC 2021 - 871 bytes - Viewed (0) -
test/typeparam/issue49027.dir/main.go
"./a" "fmt" ) func main() { s := "foo" x := a.Conv(s) if x != s { panic(fmt.Sprintf("got %s wanted %s", x, s)) } y, ok := a.Conv2(s) if !ok { panic("conversion failed") } if y != s { panic(fmt.Sprintf("got %s wanted %s", y, s)) } z := a.Conv3(s) if z != s { panic(fmt.Sprintf("got %s wanted %s", z, s)) } w := a.Conv4(a.Mystring(s)) if w != a.Mystring(s) {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Mar 24 02:14:15 UTC 2022 - 617 bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/layout_optimization_to_nhwc.mlir
%arg2: tensor<256xf32>, // batch_norm args %arg3: tensor<7x7x3x64xf32>, // conv filter #0 %arg4: tensor<1x1x64x256xf32> // conv filter #1 ) -> tensor<?x256xf32> { // This is a simplified ResNet layer that gets input in NHWC format, converts
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 7.3K bytes - Viewed (0) -
test/inline.go
} // Ensure OCONVNOP is zero cost. func Conv(v uint64) uint64 { // ERROR "can inline Conv" return conv2(conv2(conv2(v))) // ERROR "inlining call to (conv1|conv2)" } func conv2(v uint64) uint64 { // ERROR "can inline conv2" return conv1(conv1(conv1(conv1(v)))) // ERROR "inlining call to conv1" } func conv1(v uint64) uint64 { // ERROR "can inline conv1"
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Oct 19 23:33:25 UTC 2023 - 11.7K bytes - Viewed (0) -
test/newinline.go
} // Ensure OCONVNOP is zero cost. func Conv(v uint64) uint64 { // ERROR "can inline Conv" return conv2(conv2(conv2(v))) // ERROR "inlining call to (conv1|conv2)" } func conv2(v uint64) uint64 { // ERROR "can inline conv2" return conv1(conv1(conv1(conv1(v)))) // ERROR "inlining call to conv1" } func conv1(v uint64) uint64 { // ERROR "can inline conv1"
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Nov 16 20:15:25 UTC 2023 - 11.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfr/examples/mnist/mnist_train.py
# output shape: [-1, 28, 28, 32] conv1 = gen_mnist_ops.new_conv2d(x, self.weights['f1'], self.biases['b1'], 1, 1, 1, 1, 'SAME', 'RELU') # Max pooling. The kernel size spec {ksize} also follows the layout of # the data. Here we have a pooling window of 2, and a stride of 2. # output shape: [-1, 14, 14, 32] max_pool1 = gen_mnist_ops.new_max_pool(conv1, 2, 2, 2, 2, 'SAME')
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Oct 20 03:05:18 UTC 2021 - 6.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/dilated-conv.mlir
// CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x128x128x8xf32> // CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[CONV]], [[BIAS]]) : (tensor<1x128x128x8xf32>, tensor<8xf32>) -> tensor<1x128x128x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 44.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/g3doc/space_to_depth.md
```python conv0 = tf.compat.v1.layers.Conv2D( filters=filters, kernel_size=kernel_size, strides=2, padding=('SAME' if strides == 1 else 'VALID'), use_bias=False, kernel_initializer=tf.variance_scaling_initializer(), data_format=data_format) # Use the image size without space-to-depth transform as the input of conv0.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Oct 24 02:51:43 UTC 2020 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/prepare-quantize-dynamic-range.mlir
// CHECK: %[[conv3d:.*]] = "tfl.conv_3d"(%arg0, %[[w]], %[[const]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<?x28x28x28x8xf32>, tensor<3x3x3x8x16xf32>, none) -> tensor<?x26x26x26x16xf32> // CHECK: %2 = "tfl.shape"(%[[conv3d]]) : (tensor<?x26x26x26x16xf32>) -> tensor<5xi64>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 38.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_lifting.td
[(HasRankOf<1> $add_rhs_value), (HasEqualElementSize<[-1], [0]> $conv_out, $add_rhs)], [], (addBenefit -1)>; // Convert conv+sub+mul pattern to conv+mul+add. // (conv - sub) * mul -> conv * mul + (-sub) * mul // // This is needed to support Conv+BatchNorm pattern from Jax models converted // using jax2tf w/o native serialization. Note that Jax2tf patterns always
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 8.4K bytes - Viewed (0)