runtime/test/specs/V1_2/max_pool_v1_2.mod.py

#
# Copyright (C) 2018 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

# TEST 1: MAX_POOL_2D_NCHW_1, pad = 0, stride = 1, filter = 1, act = none
i1 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
o1 = Output("op4", "TENSOR_FLOAT32", "{1, 2, 2, 1}")
Model().Operation("MAX_POOL_2D", i1, 0, 0, 0, 0, 1, 1, 1, 1, 0, layout).To(o1)

# Additional data type
quant8 = DataTypeConverter().Identify({
    i1: ("TENSOR_QUANT8_ASYMM", 0.5, 0),
    o1: ("TENSOR_QUANT8_ASYMM", 0.5, 0)
})

# Instantiate an example
example = Example({
    i1: [1.0, 2.0, 3.0, 4.0],
    o1: [1.0, 2.0, 3.0, 4.0]
}).AddNchw(i1, o1, layout).AddVariations("relaxed", quant8, "float16")


# TEST 2: MAX_POOL_2D_NCHW_2, act = none
bat = 5
row = 50
col = 70
chn = 3
std = 20
flt = 20
pad = 0
output_row = (row + 2 * pad - flt + std) // std
output_col = (col + 2 * pad - flt + std) // std

i2 = Input("op1", ("TENSOR_FLOAT32", [bat, row, col, chn]))
o2 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
Model().Operation("MAX_POOL_2D", i2, pad, pad, pad, pad, std, std, flt, flt, 0, layout).To(o2)

# Additional data type
quant8 = DataTypeConverter().Identify({
    i2: ("TENSOR_QUANT8_ASYMM", 0.5, 0),
    o2: ("TENSOR_QUANT8_ASYMM", 0.5, 0)
})

# Instantiate an example
example = Example({
    i2: [x % std + 1 for x in range(bat * row * col * chn)],
    o2: [std for _ in range(bat * output_row * output_col * chn)]
}).AddNchw(i2, o2, layout).AddVariations("relaxed", quant8, "float16")


# TEST 3: MAX_POOL_2D_NCHW_3, act = relu6
bat = 5
row = 50
col = 70
chn = 3
std = 20
flt = 20
pad = 0
output_row = (row + 2 * pad - flt + std) // std
output_col = (col + 2 * pad - flt + std) // std

i3 = Input("op1", ("TENSOR_FLOAT32", [bat, row, col, chn]))
o3 = Output("op4", ("TENSOR_FLOAT32", [bat, output_row, output_col, chn]))
Model().Operation("MAX_POOL_2D", i3, pad, pad, pad, pad, std, std, flt, flt, 3, layout).To(o3)

# Additional data type
quant8 = DataTypeConverter().Identify({
    i3: ("TENSOR_QUANT8_ASYMM", 0.25, 0),
    o3: ("TENSOR_QUANT8_ASYMM", 0.25, 0)
})

# Instantiate an example
example = Example({
    i3: [x % std + 1 for x in range(bat * row * col * chn)],
    o3: [6 for _ in range(bat * output_row * output_col * chn)]
}).AddNchw(i3, o3, layout).AddVariations("relaxed", quant8, "float16")


# TEST 4: MAX_POOL_2D_NCHW_4, pad = same, stride = 2, filter = 2, act = none
i4 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 4, 1}")
o4 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 2, 1}")
Model().Operation("MAX_POOL_2D", i4, 1, 2, 2, 2, 2, 0, layout).To(o4)

# Additional data type
quant8 = DataTypeConverter().Identify({
    i4: ("TENSOR_QUANT8_ASYMM", 0.25, 0),
    o4: ("TENSOR_QUANT8_ASYMM", 0.25, 0)
})

# Instantiate an example
example = Example({
    i4: [0, 6, 2, 4, 3, 2, 10, 7],
    o4: [6, 10]
}).AddNchw(i4, o4, layout).AddVariations("relaxed", quant8, "float16")


# TEST 5: 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, 2, 2, 1}")
model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)

# MAX_POOL_2D op with numBatches = 0.
o3 = Output("out", "TENSOR_FLOAT32", "{0, 1, 1, 1}") # out
model = model.Operation("MAX_POOL_2D", zero_sized, 0, 0, 0, 0, 1, 1, 2, 2, 0, layout).To(o3)

quant8 = DataTypeConverter().Identify({
    p1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
    o1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
    i1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    zero_sized: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    o3: ("TENSOR_QUANT8_ASYMM", 0.1, 128)
})

# Create test case with dummy values.
Example({
    i1: [1],
    o1: [0],
    o2: [0],
    o3: [0],
}).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", quant8, "float16")


# TEST 6: 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)

# MAX_POOL_2D op with numBatches = 0.
o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out
model = model.Operation("MAX_POOL_2D", zero_sized, 1, 1, 1, 2, 2, 0, layout).To(o3)

quant8 = DataTypeConverter().Identify({
    p1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
    o1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
    i1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    zero_sized: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
    o3: ("TENSOR_QUANT8_ASYMM", 0.1, 128)
})

# Create test case with dummy values.
Example({
    i1: [1],
    o1: [0],
    o2: [0],
    o3: [0],
}).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", quant8, "float16")