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Results 1 - 6 of 6 for conv4 (0.06 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
input_shape=(1, 3, 4, 3), filter_shape=(2, 3, 3, 2) ) signatures = { 'serving_default': model.conv.get_concrete_function(), } save_opts = save_options.SaveOptions( function_aliases={'conv_func': model.conv} ) saved_model_save.save( model, self._input_saved_model_path, signatures, save_opts )
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 17 03:36:50 UTC 2024 - 235.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/uniform-quantized-stablehlo-to-tfl.mlir
// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG0]], %[[DQ]]) // CHECK{LITERAL}: dim_numbers = [b, 0, 1, f]x[o, 0, 1, i]->[b, 0, 1, f], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} // CHECK-SAME: (tensor<1x3x3x4xf32>, tensor<2x3x3x4xf32>) -> tensor<1x3x3x2xf32> // CHECK: return %[[CONV]] // -----
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 106.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
if (elements_depth == 1) { return true; } // In TFLite Conv2D uses OHWI format for filter, and 1HWO for Depthwise Conv. // For conv: // Check if last dimension in filter equals the first dimension // For depthwise conv: // Check if the first in filter dimension equals the first dimension. if (filter_shape.empty() || (is_depthwise ? filter_shape.back() != elements_depth
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 102.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
(i != out_batch_dim && out_type.isDynamicDim(i))) { return false; } } } // All ones in "lhs_dilation" means this "mhlo.conv" op should be // converted to "tf.Conv2D" or "tf.DepthwiseConv2dNativeOp". auto lhs_dilation = conv_op.getLhsDilation().value(); if (!lhs_dilation.isSplat() || lhs_dilation.getSplatValue<int64_t>() != 1)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 154.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/shape_inference.mlir
// CHECK: %[[SHAPE:.*]] = "tf.Shape" // CHECK: %[[CONV:.*]] = "tf.Conv2DBackpropInput"(%[[SHAPE]] // CHECK-SAME: (tensor<4xi32>, tensor<1x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32> // CHECK: return %[[CONV]] : tensor<1x1x1x1xf32> %0 = "tf.Shape"(%arg0) : (tensor<1x1x1x1xi32>) -> tensor<4xi32> %1 = "tf.Conv2DBackpropInput"(%0, %arg1, %arg1) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 17:24:10 UTC 2024 - 167.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.td
} func @_func(%input: tensor<2x112x112x12xf32>, %filter: tensor<7x7x3x64xf32>) { %filter_transform = "tf.Pad/tf.Transpose/tf.Reshape"(%filter): tensor<7x7x3x64xf32>) -> tensor<4x4x12x64xf32> %conv = "tf.Conv2D"(%input, %filter_transfrom) {strides = [1, 1, 1, 1]}: (tensor<2x112x112x12xf32>, tensor<4x4x12x64xf32>) -> tensor<2x112x112x64xf32> } } ```
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 21:18:05 UTC 2024 - 99.6K bytes - Viewed (0)