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Results 21 - 30 of 36 for depthwise_conv2d_native (0.29 sec)
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tensorflow/compiler/mlir/lite/tests/prepare-tf-fake-quant-4bit.mlir
%fq = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<f32>, tensor<f32>) -> tensor<3x3x3x16xf32> %rst = "tf.DepthwiseConv2dNative"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x30x30x16xf32>
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/quantization/tensorflow/passes/quantized_function_library_xla_weight_only.mlir
%identity = "tf.Identity"(%filter) : (tensor<*xi8>) -> tensor<*xi8> %2 = "tf.Cast"(%identity) {Truncate = false} : (tensor<*xi8>) -> tensor<*xf32> %3 = "tf.DepthwiseConv2dNative"(%input, %2) { padding = "VALID", strides = [1, 1, 1, 1], attr_map = "strides:0,padding:1,explicit_paddings:2,dilations:3" } : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 03 15:43:38 UTC 2023 - 7K bytes - Viewed (0) -
tensorflow/cc/gradients/nn_grad_test.cc
namespace tensorflow { namespace { using ops::AvgPool; using ops::AvgPool3D; using ops::BiasAdd; using ops::Conv2D; using ops::Conv2DBackpropInput; using ops::DepthwiseConv2dNative; using ops::Elu; using ops::FractionalAvgPool; using ops::FractionalMaxPool; using ops::FusedBatchNormV3; using ops::L2Loss; using ops::LogSoftmax; using ops::LRN; using ops::MaxPool;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 22 20:45:22 UTC 2022 - 15K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/preprocess_op.cc
if (!function_name.starts_with("composite_")) { return failure(); } if (function_name.contains("depthwise_conv2d")) { // Uniform Quantized op requires weights of tf.DepthwiseConv2dNative to // be transformed from [H,W,C,M] to [H,W,1,CxM] where // H=height,W=width,C=channel,M=multiplier. Therefore, a reshape op is // inserted between the constant op and the function op so that the
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library_tf_drq.mlir
%cast_1_f32 = "tf.Cast"(%1) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32> %cast_3_f32 = "tf.Cast"(%3) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32> %5 = "tf.DepthwiseConv2dNative"(%cast_1_f32, %cast_3_f32) { padding = "VALID", strides = [1, 1, 1, 1], attr_map = "strides:0,padding:1,explicit_paddings:2,dilations:3" } : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Mar 03 15:43:38 UTC 2023 - 12.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/constant-fold.mlir
%2 = "tf.DepthwiseConv2dNative"(%1, %0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1]} : (tensor<1x520x520x1xf32>, tensor<3x3x1x1xf32>) -> tensor<1x520x520x1xf32> func.return %2 : tensor<1x520x520x1xf32> // CHECK: tf.Const // CHECK-NOT: tf.DepthwiseConv2dNative } // CHECK-LABEL: DontFoldNoConstantFold
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 31 23:22:24 UTC 2024 - 36.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_weights.mlir
%cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32> %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<2x3x3x512xf32>} : () -> tensor<2x3x3x512xf32> %0 = "tf.DepthwiseConv2dNative"(%arg0, %cst_1) { attr_map = "0:strides,1:padding,2:explicit_paddings,3:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1]
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 42K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_tf.cc
// Compared to tfl.conv_2d, tfl.depthwise_conv_2d has an additional // 'depth_multiplier' attribute. However, tf.DepthwiseConv2dNative does not // have a corresponding 'depth_multiplier' attribute; the multiplier is the // fourth dimension in the 4-D filter tensor. We query the multiplier from // tf.DepthwiseConv2dNative and set it as the attribute value accordingly. auto multiplier =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 21:49:50 UTC 2024 - 64.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/quantized_function_library.mlir
%cast_1_f32 = "tf.Cast"(%1) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32> %cast_3_f32 = "tf.Cast"(%3) {Truncate = false} : (tensor<*xi32>) -> tensor<*xf32> %5 = "tf.DepthwiseConv2dNative"(%cast_1_f32, %cast_3_f32) { padding = "VALID", strides = [1, 1, 1, 1], attr_map = "strides:0,padding:1,explicit_paddings:2,dilations:3" } : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Jan 08 01:16:10 UTC 2024 - 30.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
self.assertFalse( self._contains_op(output_graphdef, 'DepthwiseConv2dNative') ) else: self.assertTrue(self._contains_quantized_function_call(output_graphdef)) self.assertTrue( self._contains_op( output_graphdef, 'DepthwiseConv2dNative', 'strides', strides_attr ) ) @parameterized.named_parameters(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0)