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tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range.mlir
%b = arith.constant dense<0.0> : tensor<16xf32> %conv_3d = "tfl.conv_3d"(%arg0, %w, %b) {dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x32x32x32x8xf32>, tensor<1x1x1x8x16xf32>, tensor<16xf32>) -> tensor<1x32x32x32x16xf32> func.return %conv_3d : tensor<1x32x32x32x16xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 23 21:09:00 UTC 2024 - 23.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/decompose-hybrid-quantization.mlir
// CHECK: %[[VAL2:.+]] = "tfl.dequantize"(%[[VAL1]]) : (tensor<1x1x1x8x16x!quant.uniform<{{.+}}>>) -> tensor<1x1x1x8x16xf32> // CHECK: %[[VAL3:.+]] = "tfl.conv_3d"(%arg0, %[[VAL2]], %[[VAL0]]) <{dilation_d_factor = 1 : i32, dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_d = 1 : i32, stride_h = 1 : i32, stride_w = 1 : i32}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.1K 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/lite/tests/ops.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 06 19:09:08 UTC 2024 - 189.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema_v3b.fbs
SELECT_V2 = 123, DENSIFY = 124, SEGMENT_SUM = 125, BATCH_MATMUL = 126, PLACEHOLDER_FOR_GREATER_OP_CODES = 127, CUMSUM = 128, CALL_ONCE = 129, BROADCAST_TO = 130, RFFT2D = 131, CONV_3D = 132, IMAG=133, REAL=134, COMPLEX_ABS=135, HASHTABLE = 136, HASHTABLE_FIND = 137, HASHTABLE_IMPORT = 138, HASHTABLE_SIZE = 139, REDUCE_ALL = 140, CONV_3D_TRANSPOSE = 141,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 14:28:27 UTC 2024 - 30K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/schema/schema.fbs
fused_activation_function:ActivationFunctionType; dilation_w_factor:int = 1; dilation_h_factor:int = 1; // Parameters for Conv2D version 8 or above. // When set, quantized_bias_type defines the dtype for both bias and accumulator. quantized_bias_type: TensorType; } // Options for both Conv3D and Conv3DTranspose. table Conv3DOptions { padding:Padding; stride_d:int; stride_w:int; stride_h:int;
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 18:01:23 UTC 2024 - 41.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/lift_quantizable_spots_as_functions.mlir
%0 = "tf.Conv3D"(%arg0, %cst) { data_format = "NDHWC", device = "", dilations = [1, 1, 1, 1, 1], padding = "SAME", strides = [1, 1, 2, 1, 1] } : (tensor<1x3x4x3x3xf32>, tensor<2x3x3x3x2xf32>) -> tensor<1x3x2x3x2xf32> %1 = "tf.Relu"(%0) {device = ""} : (tensor<1x3x2x3x2xf32>) -> tensor<1x3x2x3x2xf32> %2 = "tf.Conv3D"(%arg0, %cst) {
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/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-tf.mlir
// CHECK: %1 = "tfl.conv_2d"(%arg0, %0, %[[CONSTANT]]) <{dilation_h_factor = 2 : i32, dilation_w_factor = 3 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 4 : i32, stride_w = 5 : i32}> : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x8x7x16xf32> // CHECK: %2 = "tf.Conv2D"
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 29 07:26:59 UTC 2024 - 59.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/lift_quantizable_spots_as_functions.cc
} else if (function_name.contains("conv2d")) { // For Conv2D, the channel dimension must be static to calculate the // feature group count. if (!HasStaticShapeAtDims(call_op->getOperand(0), /*dims=*/3)) { return absl::InternalError( "The channel dimension of Conv2D is required to be static."); } } else if (function_name.contains("conv3d")) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 16.4K bytes - Viewed (0)