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Results 1 - 10 of 12 for input_shapes_ (0.2 sec)
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tensorflow/compiler/mlir/quantization/tensorflow/python/integration_test/quantize_model_test.py
target_opset: quant_opts_pb2.OpSet, ): lhs_batch_size, rhs_batch_size = batch_sizes input_shape = (*lhs_batch_size, 1, 1024) filter_shape = (*rhs_batch_size, 1024, 3) static_input_shape = [dim if dim is not None else 2 for dim in input_shape] model = self._create_matmul_model( input_shape, filter_shape, self._input_saved_model_path, has_bias,
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/tests/prepare-composite-functions-tf.mlir
tensor<?x8x8xf32>, %arg1: tensor<8x10xf32>, %arg2: tensor<8x10xf32>, %arg3: tensor<8x40xf32>, %arg4: tensor<10x40xf32>, %arg5: tensor<40xf32>) -> (tensor<8x10xf32>, tensor<?x8x10xf32>, tensor<8x10xf32>, tensor<8x10xf32>, tensor<f32>) attributes {tf._input_shapes = ["tfshape$dim { size: -1 } dim { size: 8 } dim { size: 8 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$unknown_rank: true", "tfshape$unknown_rank: false", "tfshape$unknown_rank: false"],...
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 122.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/shape_inference.cc
// on `xla_call_module_context_` for details. std::vector<xla::Shape> input_shapes; input_shapes.reserve(op.getArgs().size()); for (mlir::Type type : op.getArgs().getTypes()) { input_shapes.push_back(xla::TypeToShape(type)); } absl::Status status = loader->RefineDynamicShapes(input_shapes); if (!status.ok()) { // Do not return false here. //
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Jun 08 07:28:49 UTC 2024 - 134.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_n_z.cc
assert(input_shape.size() == stride.size()); for (int i = 0, e = input_shape.size(); i < e; ++i) { if (ShapedType::isDynamic(input_shape[i])) continue; int64_t dim_i = input_shape[i]; int64_t begin_i = begin[i]; int64_t end_i = end[i]; int64_t stride_i = stride[i]; // [0]: mask for begin, [1]: mask for end int64_t masks[] = {begin_mask & (1 << i), end_mask & (1 << i)};
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 170.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/optimize.cc
// Ex. input_shape = [1 x 1 x 1 x 1 x 2 x 1] => 4 // returns 0 if the input shape is not static. int GetNumLeadingOnes(ShapedType input_type) { if (!input_type.hasStaticShape()) return 0; auto input_shape = input_type.getShape(); int num_leading_broadcast_dims = 0; for (int i = 0; i < input_shape.size(); ++i) { if (input_shape[i] == 1) { ++num_leading_broadcast_dims;
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/uniform_quantized_stablehlo_to_tfl_pass.cc
auto dim_attr = DenseIntElementsAttr::get(dim_type, dims); auto dim = rewriter.create<arith::ConstantOp>(op.getLoc(), dim_attr); input_shape.insert(input_shape.begin() + dim_to_expand, 1); TensorType expanded_type = input_type.clone(input_shape); input = rewriter.create<TFL::ExpandDimsOp>(op.getLoc(), expanded_type, input, dim);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 09:00:19 UTC 2024 - 99.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/ir/tfl_ops.cc
Value input_permutation) { auto input_shape = input_value.getType().dyn_cast<ShapedType>().getShape(); absl::flat_hash_map<int32_t, int32_t> permutation_map; for (size_t before_dim_idx = 0, after_dim_idx = 0; before_dim_idx < input_shape.size(); ++before_dim_idx) { if (input_shape[before_dim_idx] == 1) { continue; }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 169.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
LogicalResult BatchToSpaceOp::verify() { BatchToSpaceOp op = *this; // Op already has a constraint that block_size >= 2. int64_t block_size = op.getBlockSize(); llvm::SmallVector<int64_t, 4> input_shape(4, ShapedType::kDynamic); auto input_type = mlir::cast<TensorType>(op.getInput().getType()); if (input_type.hasRank()) { if (input_type.getRank() != 4) return op.emitOpError()
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo.cc
// Set dimension sizes specified by broadcast_dimensions. ArrayRef<int64_t> input_shape = mlir::cast<ShapedType>(input.getType()).getShape(); for (auto x : llvm::enumerate(broadcast_dimensions)) { expanded_shape[x.value().getSExtValue()] = rewriter.getI64IntegerAttr(input_shape[x.index()]); } // Create the expanded type wrapped in a arith::ConstantOp. auto attr_type =
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/translate/import_model.cc
specs_.unconditionally_use_set_output_shapes; for (const auto& name_and_value : func_def->attr()) { if (name_and_value.first == "_input_shapes") { auto& list = name_and_value.second.list(); auto& signature = func_def->signature(); // Some models have "_input_shapes" attribute, but with its value empty if (list.shape_size() > 0 && list.shape_size() != signature.input_arg_size()) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 01 11:17:36 UTC 2024 - 183.2K bytes - Viewed (0)