1#
2# Copyright (C) 2018 The Android Open Source Project
3#
4# Licensed under the Apache License, Version 2.0 (the "License");
5# you may not use this file except in compliance with the License.
6# You may obtain a copy of the License at
7#
8#      http://www.apache.org/licenses/LICENSE-2.0
9#
10# Unless required by applicable law or agreed to in writing, software
11# distributed under the License is distributed on an "AS IS" BASIS,
12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13# See the License for the specific language governing permissions and
14# limitations under the License.
15#
16
17layout = BoolScalar("layout", False) # NHWC
18
19# TEST 1: GROUPED_CONV2D, pad = 0, stride = 1, numGroups = 2
20i1 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 3, 2}") # input 0
21w1 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 2, 1}", [1, 2, 2, 1, 4, 3, 2, 1]) # weight
22b1 = Parameter("op3", "TENSOR_FLOAT32", "{2}", [10, -33.5]) # bias
23act = Int32Scalar("act", 0) # act = none
24o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 2}") # output 0
25Model().Operation("GROUPED_CONV_2D", i1, w1, b1, 0, 0, 0, 0, 1, 1, 2, act, layout).To(o1)
26
27# Additional data type
28quant8 = DataTypeConverter().Identify({
29    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
30    w1: ("TENSOR_QUANT8_ASYMM", 0.25, 128),
31    b1: ("TENSOR_INT32", 0.0625, 0),
32    o1: ("TENSOR_QUANT8_ASYMM", 0.5, 80)
33})
34
35quant8_mult_gt_1 = DataTypeConverter().Identify({
36    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
37    w1: ("TENSOR_QUANT8_ASYMM", 0.25, 128),
38    b1: ("TENSOR_INT32", 0.0625, 0),
39    o1: ("TENSOR_QUANT8_ASYMM", 0.05, 80)
40})
41
42# Per-channel quantization
43channelQuant8 = DataTypeConverter().Identify({
44    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
45    w1: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.5])),
46    b1: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.0625, 0.125], hide=True)),
47    o1: ("TENSOR_QUANT8_ASYMM", 0.5, 80)
48})
49
50channelQuant8_mult_gt_1 = DataTypeConverter().Identify({
51    i1: ("TENSOR_QUANT8_ASYMM", 0.25, 100),
52    w1: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.5])),
53    b1: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.0625, 0.125], hide=True)),
54    o1: ("TENSOR_QUANT8_ASYMM", 0.1, 80)
55})
56
57example = Example({
58    i1: [1, 2, 3, 4, 5, 6,
59         6, 5, 4, 3, 2, 1,
60         2, 3, 3, 3, 3, 3],
61    o1: [33, -0.5,
62         33,  7.5,
63         31,  4.5,
64         27, -9.5]
65}).AddNchw(i1, o1, layout).AddAllActivations(o1, act).AddVariations("relaxed", quant8, quant8_mult_gt_1, channelQuant8, channelQuant8_mult_gt_1, "float16").AddInput(w1, b1)
66
67
68# TEST 2: GROUPED_CONV2D_LARGE, pad = same, stride = 1, numGroups = 2, act = none
69i2 = Input("op1", "TENSOR_FLOAT32", "{1, 3, 2, 2}") # input 0
70w2 = Parameter("op2", "TENSOR_FLOAT32", "{2, 2, 3, 1}", [100, 20, 1, 200, 10, 2, 200, 30, 1, 100, 20, 3]) # weight
71b2 = Parameter("op3", "TENSOR_FLOAT32", "{2}", [500, -1000]) # bias
72o2 = Output("op4", "TENSOR_FLOAT32", "{1, 3, 2, 2}") # output 0
73Model("large").Operation("GROUPED_CONV_2D", i2, w2, b2, 1, 1, 1, 2, 0, layout).To(o2)
74
75# Additional data type
76quant8 = DataTypeConverter().Identify({
77    i2: ("TENSOR_QUANT8_ASYMM", 0.25, 128),
78    w2: ("TENSOR_QUANT8_ASYMM", 1.0, 0),
79    b2: ("TENSOR_INT32", 0.25, 0),
80    o2: ("TENSOR_QUANT8_ASYMM", 10.0, 100)
81})
82
83# Per-channel quantization
84channelQuant8 = DataTypeConverter().Identify({
85    i2: ("TENSOR_QUANT8_ASYMM", 0.25, 128),
86    w2: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[2.0, 2.5])),
87    b2: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.5, 0.625], hide=True)),
88    o2: ("TENSOR_QUANT8_ASYMM", 10.0, 100)
89})
90
91example = Example({
92    i2: [1, 2, 3, 4,
93         4, 3, 2, 1,
94         2, 3, 3, 3],
95    o2: [567, -873,
96         1480, -160,
97         608, -840,
98         1370, -10,
99         543, -907,
100         760, -310]
101}).AddNchw(i2, o2, layout).AddVariations("relaxed", quant8, channelQuant8, "float16").AddInput(w2, b2)
102
103
104# TEST 3: GROUPED_CONV2D_CHANNEL, pad = same, stride = 1, numGroups = 3, act = none
105i3 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 9}") # input 0
106w3 = Parameter("op2", "TENSOR_FLOAT32", "{6, 1, 1, 3}", [1, 2, 3, 2, 1, 0, 2, 3, 3, 6, 6, 6, 9, 8, 5, 2, 1, 1]) # weight
107b3 = Parameter("op3", "TENSOR_FLOAT32", "{6}", [10, -20, 30, -40, 50, -60]) # bias
108o3 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 6}") # output 0
109Model("channel").Operation("GROUPED_CONV_2D", i3, w3, b3, 1, 1, 1, 3, 0, layout).To(o3)
110
111# Additional data type
112quant8 = DataTypeConverter().Identify({
113    i3: ("TENSOR_QUANT8_ASYMM", 0.5, 0),
114    w3: ("TENSOR_QUANT8_ASYMM", 0.25, 0),
115    b3: ("TENSOR_INT32", 0.125, 0),
116    o3: ("TENSOR_QUANT8_ASYMM", 2.0, 60)
117})
118
119channelQuant8 = DataTypeConverter().Identify({
120    i3: ("TENSOR_QUANT8_ASYMM", 0.5, 0),
121    w3: ("TENSOR_QUANT8_SYMM_PER_CHANNEL", 0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.25, 0.3] * 3)),
122    b3: ("TENSOR_INT32", 0.0, 0, SymmPerChannelQuantParams(channelDim=0, scales=[0.125, 0.15] * 3, hide=True)),
123    o3: ("TENSOR_QUANT8_ASYMM", 2.0, 60)
124})
125
126example = Example({
127    i3: [1, 2, 3, 4, 55, 4, 3, 2, 1,
128         5, 4, 3, 2, 11, 2, 3, 4, 5,
129         2, 3, 2, 3, 22, 3, 2, 3, 2,
130         1, 0, 2, 1, 33, 1, 2, 0, 1],
131    o3: [24, -16, 215, 338,  98, -51,
132         32,  -6,  73,  50, 134, -45,
133         24, -13, 111, 128, 102, -51,
134         17, -18, 134, 170,  73, -55]
135}).AddNchw(i3, o3, layout).AddVariations("relaxed", quant8, channelQuant8, "float16").AddInput(w3, b3)
136