| # |
| # 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: L2_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("L2_POOL_2D", i1, 0, 0, 0, 0, 1, 1, 1, 1, 0, layout).To(o1) |
| |
| # 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).AddRelaxed().AddVariations("float16") |
| |
| |
| # TEST 2: L2_POOL_2D_NCHW_2, pad = same, stride = 2, filter = 2, act = none |
| i2 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 4, 1}") |
| o2 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 2, 1}") |
| Model().Operation("L2_POOL_2D", i2, 1, 2, 2, 2, 2, 0, layout).To(o2) |
| |
| # Instantiate an example |
| example = Example({ |
| i2: [0, 6, 2, 4, 3, 2, 10, 7], |
| o2: [3.5, 6.5] |
| }).AddNchw(i2, o2, layout).AddRelaxed().AddVariations("float16") |
| |
| |
| # TEST 3: L2_POOL_2D_NCHW_LARGE, pad = 0, stride = 1, filter = 2, act = none |
| i3 = Input("op1", "TENSOR_FLOAT32", "{1, 2, 2, 3}") |
| o3 = Output("op4", "TENSOR_FLOAT32", "{1, 1, 1, 3}") |
| Model("large").Operation("L2_POOL_2D", i3, 0, 0, 0, 0, 1, 1, 2, 2, 0, layout).To(o3) |
| |
| # Instantiate an example |
| example = Example({ |
| i3: [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0], |
| o3: [6.442049503326416, 7.3143692016601562, 8.2158384323120117] |
| }).AddNchw(i3, o3, layout).AddRelaxed().AddVariations("float16") |
| |
| |
| # TEST 4: 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) |
| |
| # L2_POOL_2D op with numBatches = 0. |
| o3 = Output("out", "TENSOR_FLOAT32", "{0, 1, 1, 1}") # out |
| model = model.Operation("L2_POOL_2D", zero_sized, 0, 0, 0, 0, 1, 1, 2, 2, 0, layout).To(o3) |
| |
| # Create test case with dummy values. |
| Example({ |
| i1: [1], |
| o1: [0], |
| o2: [0], |
| o3: [0], |
| }).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", "float16") |
| |
| |
| # TEST 5: 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) |
| |
| # L2_POOL_2D op with numBatches = 0. |
| o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 1}") # out |
| model = model.Operation("L2_POOL_2D", zero_sized, 1, 1, 1, 2, 2, 0, layout).To(o3) |
| |
| # Create test case with dummy values. |
| Example({ |
| i1: [1], |
| o1: [0], |
| o2: [0], |
| o3: [0], |
| }).AddNchw(i1, zero_sized, o3, layout).AddVariations("relaxed", "float16") |