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diff --git a/nn/runtime/test/specs/V1_3/transpose_conv2d_quant8_signed.mod.py b/nn/runtime/test/specs/V1_3/transpose_conv2d_quant8_signed.mod.py
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+++ b/nn/runtime/test/specs/V1_3/transpose_conv2d_quant8_signed.mod.py
@@ -0,0 +1,317 @@
+#
+# Copyright (C) 2019 The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+layout = BoolScalar("layout", False) # NHWC
+
+# TRANSPOSE_CONV2D_LARGE, pad = same, stride = 32
+i1 = Input("op1", "TENSOR_FLOAT32", "{25, 1, 1, 1}") # input 0
+w1 = Parameter("op2", "TENSOR_FLOAT32", "{16, 1, 1, 1}", [1] * 16) # weight
+b1 = Parameter("op3", "TENSOR_FLOAT32", "{16}", [0] * 16) # bias
+s1 = Int32Vector("shape", [25, 32, 32, 16]) # output shape
+act = Int32Scalar("act", 0) # act = none
+o1 = Output("op4", "TENSOR_FLOAT32", "{25, 32, 32, 16}") # output
+Model().Operation("TRANSPOSE_CONV_2D", i1, w1, b1, s1, 1, 32, 32, act, layout).To(o1)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128),
+ w1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128),
+ b1: ("TENSOR_INT32", 0.25, 0),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128)
+})
+
+# Per-channel quantization
+channelQuant8_signed = DataTypeConverter().Identify({
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -28),
+ w1: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.5] * 16)),
+ b1: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.125] * 16, hide=True)),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -48)
+})
+
+Example({
+ i1: [1] * 25,
+ o1: ([1] * 16 + [0] * (32 * 32 - 1) * 16) * 25
+}).AddVariations(quant8_signed, channelQuant8_signed, includeDefault=False)
+
+#######################################################
+
+layout = BoolScalar("layout", False) # NHWC
+
+# TRANSPOSE_CONV2D, pad = valid, stride = 2
+i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # input 0
+w1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 3, 3, 1}", [1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, 8, 10, 12, 14, 16, 18]) # weight
+b1 = Parameter("op3", "TENSOR_FLOAT32", "{2}", [-1.5, -2]) # bias
+s1 = Int32Vector("shape", [1, 5, 5, 2]) # output shape
+act = Int32Scalar("act", 0) # act = none
+o1 = Output("op4", "TENSOR_FLOAT32", "{1, 5, 5, 2}") # output
+Model().Operation("TRANSPOSE_CONV_2D", i1, w1, b1, s1, 2, 2, 2, act, layout).To(o1)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128),
+ w1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128),
+ b1: ("TENSOR_INT32", 0.25, 0),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -128)
+})
+
+quant8_signed_mult_gt_1 = DataTypeConverter().Identify({
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -28),
+ w1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, 0),
+ b1: ("TENSOR_INT32", 0.25, 0),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, -48)
+})
+
+# Per-channel quantization
+channelQuant8_signed = DataTypeConverter().Identify({
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -28),
+ w1: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.5])),
+ b1: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.0625, 0.125], hide=True)),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -48)
+})
+
+channelQuant8_signed_mult_gt_1 = DataTypeConverter().Identify({
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -28),
+ w1: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.5])),
+ b1: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.0625, 0.125], hide=True)),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, -48)
+})
+
+Example({
+ i1: [1, 2, 3, 4],
+ o1: [-0.5, 0, 1.5, 2, 5.5, 8, 4.5, 6, 8.5, 10,
+ 5.5, 6, 7.5, 8, 23.5, 26, 16.5, 18, 20.5, 22,
+ 14.5, 18, 22.5, 26, 60.5, 70, 40.5, 46, 52.5, 58,
+ 19.5, 22, 25.5, 28, 59.5, 66, 34.5, 38, 42.5, 46,
+ 37.5, 40, 43.5, 46, 101.5, 108, 58.5, 62, 66.5, 70]
+}).AddNchw(i1, o1, s1, layout).AddAllActivations(o1, act).AddVariations(quant8_signed, quant8_signed_mult_gt_1, channelQuant8_signed, channelQuant8_signed_mult_gt_1, includeDefault=False)
+
+#######################################################
+
+# TRANSPOSE_CONV2D_LARGE, pad = same, stride = 3, act = relu
+i2 = Input("op1", "TENSOR_FLOAT32", "{1, 1, 2, 1}") # input 0
+w2 = Parameter("op2", "TENSOR_FLOAT32", "{1, 3, 3, 1}", [9, 5, 6, 9, 8, 5, 3, 1, 4]) # weight
+b2 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [-1000]) # bias
+s2 = Int32Vector("shape", [1, 3, 4, 1]) # output shape
+o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 4, 1}") # output
+Model().Operation("TRANSPOSE_CONV_2D", i2, w2, b2, s2, 1, 3, 3, 1, layout).To(o2)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+ i2: ("TENSOR_QUANT8_ASYMM_SIGNED", 2.0, -128),
+ w2: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, 0),
+ b2: ("TENSOR_INT32", 0.5, 0),
+ o2: ("TENSOR_QUANT8_ASYMM_SIGNED", 20.0, -78)
+})
+
+# Per-channel quantization
+channelQuant8_signed = DataTypeConverter().Identify({
+ i2: ("TENSOR_QUANT8_ASYMM_SIGNED", 2.0, -128),
+ w2: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25])),
+ b2: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.5], hide=True)),
+ o2: ("TENSOR_QUANT8_ASYMM_SIGNED", 20.0, -78)
+})
+
+Example({
+ i2: [300, 500],
+ o2: [500., 800., 3500., 1500.,
+ 1400., 500., 3500., 3000.,
+ 0., 200., 500., 0.]
+}).AddNchw(i2, o2, s2, layout).AddVariations(quant8_signed, channelQuant8_signed, includeDefault=False)
+
+#######################################################
+# TRANSPOSE_CONV2D_SAME, outputShape = [1, 4, 4, 1], pad = same, stride = 1, act = none
+i3 = Input("op1", "TENSOR_FLOAT32", "{1, 4, 4, 2}") # input 0
+w3 = Parameter("op2", "TENSOR_FLOAT32", "{1, 3, 3, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]) # weight
+b3 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) # bias
+s3 = Int32Vector("shape", [1, 4, 4, 1]) # output shape
+o3 = Output("op4", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # output
+Model().Operation("TRANSPOSE_CONV_2D", i3, w3, b3, s3, 1, 1, 1, 0, layout).To(o3)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+ i3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -28),
+ w3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, 0),
+ b3: ("TENSOR_INT32", 0.25, 0),
+ o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 16.0, -128)
+})
+
+Example({
+ i3: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+ 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32],
+ o3: [184, 412, 568, 528,
+ 678, 1347, 1689, 1434,
+ 1494, 2715, 3057, 2442,
+ 1968, 3352, 3652, 2760]
+}).AddNchw(i3, o3, s3, layout).AddVariations(quant8_signed, includeDefault=False)
+
+#######################################################
+# TRANSPOSE_CONV2D_VALID, outputShape = [1, 6, 6, 1], pad = valid, stride = 1, act = none
+i4 = Input("op1", "TENSOR_FLOAT32", "{1, 4, 4, 2}") # input 0
+w4 = Parameter("op2", "TENSOR_FLOAT32", "{1, 3, 3, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]) # weight
+b4 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) # bias
+s4 = Int32Vector("shape", [1, 6, 6, 1]) # output shape
+o4 = Output("op4", "TENSOR_FLOAT32", "{1, 6, 6, 1}") # output
+Model().Operation("TRANSPOSE_CONV_2D", i4, w4, b4, s4, 2, 1, 1, 0, layout).To(o4)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+ i4: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, -118),
+ w4: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, 0),
+ b4: ("TENSOR_INT32", 0.125, 0),
+ o4: ("TENSOR_QUANT8_ASYMM_SIGNED", 32.0, -48)
+})
+
+Example({
+ i4: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+ 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32],
+ o4: [5, 22, 59, 101, 114, 83,
+ 52, 184, 412, 568, 528, 344,
+ 237, 678, 1347, 1689, 1434, 879,
+ 597, 1494, 2715, 3057, 2442, 1431,
+ 856, 1968, 3352, 3652, 2760, 1548,
+ 689, 1534, 2543, 2729, 2010, 1103]
+}).AddNchw(i4, o4, s4, layout).AddVariations(quant8_signed, includeDefault=False)
+
+#######################################################
+# TRANSPOSE_CONV2D_EXPLICIT, pad = [1, 2, 2, 1], stride = 1, act = none
+i5 = Input("op1", "TENSOR_FLOAT32", "{1, 4, 4, 2}") # input 0
+w5 = Parameter("op2", "TENSOR_FLOAT32", "{1, 3, 3, 2}", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]) # weight
+b5 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) # bias
+o5 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 3, 1}") # output
+Model().Operation("TRANSPOSE_CONV_2D", i5, w5, b5, 1, 2, 2, 1, 1, 1, 0, layout).To(o5)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+ i5: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -28),
+ w5: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.25, 0),
+ b5: ("TENSOR_INT32", 0.125, 0),
+ o5: ("TENSOR_QUANT8_ASYMM_SIGNED", 20.0, -78)
+})
+
+Example({
+ i5: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+ 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32],
+ o5: [678, 1347, 1689,
+ 1494, 2715, 3057,
+ 1968, 3352, 3652]
+}).AddNchw(i5, o5, layout).AddVariations(quant8_signed, includeDefault=False)
+
+#######################################################
+# zero-sized input, implicit padding
+
+# Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates.
+p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores
+p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi
+o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
+o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
+tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out
+tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out
+model = Model("zero_sized").Operation("BOX_WITH_NMS_LIMIT", p1, p2, [0], 0.3, -1, 0, 0.4, 1.0, 0.3).To(o1, tmp1, o2, tmp2)
+
+# Use ROI_ALIGN op to convert into zero-sized feature map.
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 1}")
+zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 1}")
+model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)
+
+# TRANSPOSE_CONV_2D op with numBatches = 0.
+w = Parameter("weights", "TENSOR_FLOAT32", "{2, 3, 3, 1}", [1, 3, 5, 7, 9, 11, 9, 7, 5, 2, 4, 6, 8, 10, 12, 10, 8, 6]) # weight
+b = Parameter("bias", "TENSOR_FLOAT32", "{2}", [-1.5, -2]) # bias
+s = Int32Vector("shape", [0, 5, 5, 2]) # output shape
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 5, 5, 2}") # out
+model = model.Operation("TRANSPOSE_CONV_2D", zero_sized, w, b, s, 2, 2, 2, 0, layout).To(o3)
+
+quant8_signed = DataTypeConverter().Identify({
+ p1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ zero_sized: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ w: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ b: ("TENSOR_INT32", 0.01, 0),
+ o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0)
+})
+
+Example({
+ i1: [1],
+ o1: [],
+ o2: [],
+ o3: [],
+}).AddNchw(i1, zero_sized, o3, s, layout).AddVariations(quant8_signed, includeDefault=False)
+
+#######################################################
+# zero-sized input, explicit padding
+
+# Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates.
+p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores
+p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi
+o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
+o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
+tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out
+tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out
+model = Model("zero_sized").Operation("BOX_WITH_NMS_LIMIT", p1, p2, [0], 0.3, -1, 0, 0.4, 1.0, 0.3).To(o1, tmp1, o2, tmp2)
+
+# Use ROI_ALIGN op to convert into zero-sized feature map.
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 1}")
+zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 4, 4, 1}")
+model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 4, 4, 2.0, 2.0, 4, 4, layout).To(zero_sized)
+
+# TRANSPOSE_CONV_2D op with numBatches = 0.
+w = Parameter("weights", "TENSOR_FLOAT32", "{1, 3, 3, 1}", [1, 3, 5, 7, 9, 11, 9, 7, 5]) # weight
+b = Parameter("bias", "TENSOR_FLOAT32", "{1}", [-1.5]) # bias
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 3, 3, 1}") # out
+model = model.Operation("TRANSPOSE_CONV_2D", zero_sized, w, b, 1, 2, 2, 1, 1, 1, 0, layout).To(o3)
+
+quant8_signed = DataTypeConverter().Identify({
+ p1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+ o1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+ i1: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ zero_sized: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ w: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0),
+ b: ("TENSOR_INT32", 0.01, 0),
+ o3: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.1, 0)
+})
+
+Example({
+ i1: [1],
+ o1: [],
+ o2: [],
+ o3: [],
+}).AddNchw(i1, zero_sized, o3, layout).AddVariations(quant8_signed, includeDefault=False)
+
+#######################################################
+# TRANSPOSE_CONV2D_SAME, outputShape = [1, 4, 4, 1], pad = same, stride = 2, act = none
+i8 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}") # input 0
+w8 = Parameter("op2", "TENSOR_FLOAT32", "{1, 1, 1, 1}", [2]) # weight
+b8 = Parameter("op3", "TENSOR_FLOAT32", "{1}", [0]) # bias
+s8 = Int32Vector("shape", [1, 4, 4, 1]) # output shape
+o8 = Output("op4", "TENSOR_FLOAT32", "{1, 4, 4, 1}") # output
+Model().Operation("TRANSPOSE_CONV_2D", i8, w8, b8, s8, 1, 2, 2, 0, layout).To(o8)
+
+# Additional data type
+quant8_signed = DataTypeConverter().Identify({
+ i8: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, -28),
+ w8: ("TENSOR_QUANT8_ASYMM_SIGNED", 0.5, 0),
+ b8: ("TENSOR_INT32", 0.25, 0),
+ o8: ("TENSOR_QUANT8_ASYMM_SIGNED", 16.0, -128)
+})
+
+Example({
+ i8: [1, 2, 3, 4],
+ o8: [2, 0, 4, 0, 0, 0, 0, 0, 6, 0, 8, 0, 0, 0, 0, 0]
+}).AddNchw(i8, o8, s8, layout).AddVariations(quant8_signed, includeDefault=False)
generated by cgit v1.2.3 (git 2.25.1) at 2024-07-22 21:32:36 +0000