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Merged
neil-tan merged 16 commits into from
Nov 19, 2017
Merged

F/52 #54

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838dae1
1. tensor have the name to perform lookup
Knight-X Nov 18, 2017
4c6de65
modifying context class to use TName
neil-tan Nov 18, 2017
23c7ffa
merged
neil-tan Nov 18, 2017
7041775
1. implement lookup for reference count
Knight-X Nov 18, 2017
1dba94b
1. make array pass test for name lookup optimization
Knight-X Nov 18, 2017
1bf0026
1. make nntest pass for name lookup optimization
Knight-X Nov 18, 2017
cb39597
1. make tensor transform pass for name lookup
Knight-X Nov 18, 2017
3d310f3
porting MathTests.hpp; added ctx.get() and ctx.gc(); WIP
neil-tan Nov 18, 2017
e12a69a
1. pass mlp test for name lookup
Knight-X Nov 19, 2017
860cf9f
Merge branch 'F/52' of https://github.com/neil-tan/uTensor into F/52
Knight-X Nov 19, 2017
13071ba
Math, Matrix, Context passed
neil-tan Nov 19, 2017
b7530f5
MathTests.hpp: make variable naming more consistent
neil-tan Nov 19, 2017
13872d7
1. pass run deep mlp demo for name lookup
Knight-X Nov 19, 2017
eacb822
Merge branch 'F/52' of https://github.com/neil-tan/uTensor into F/52
Knight-X Nov 19, 2017
1e58e90
updated readme
neil-tan Nov 19, 2017
147f990
release 0.1.0
neil-tan Nov 19, 2017
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70 changes: 24 additions & 46 deletions ArrayTests.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -15,38 +15,25 @@ class ArrayOpsTest : public Test {
testStart("quantize_v2");

//reference inputs /Users/neitan01/Documents/mbed/uTensor.git/TESTS/scripts/PRE-GEN/qA
TENSOR b = ctx.add(t_import.float_import ("/fs/testData/qB/in/Cast_1_0.idx"));
TENSOR b_min = ctx.add(t_import.float_import("/fs/testData/qB/in/Min_1_0.idx"));
TENSOR b_max = ctx.add(t_import.float_import("/fs/testData/qB/in/Max_1_0.idx"));
S_TENSOR b_q_ref = ctx.add(t_import.float_import ("/fs/testData/qB/in/Cast_1_0.idx", "b_q_ref"));
S_TENSOR b_min_q_ref = ctx.add(t_import.float_import("/fs/testData/qB/in/Min_1_0.idx", "b_min_q_ref"));
S_TENSOR b_max_q_ref = ctx.add(t_import.float_import("/fs/testData/qB/in/Max_1_0.idx", "b_max_q_ref"));

//reference outputs
TENSOR b_q_ref = ctx.add(t_import.ubyte_import("/fs/testData/qB/out/qB_0.idx"));
TENSOR b_min_q_ref = ctx.add(t_import.float_import("/fs/testData/qB/out/qB_1.idx"));
TENSOR b_max_q_ref = ctx.add(t_import.float_import("/fs/testData/qB/out/qb_2.idx"));

TENSOR b_q = ctx.add(new RamTensor<unsigned char>(b_q_ref.lock()->getShape()));
TENSOR b_min_q = ctx.add(new RamTensor<float>(b_min_q_ref.lock()->getShape()));
TENSOR b_max_q = ctx.add(new RamTensor<float>(b_max_q_ref.lock()->getShape()));

TList inputs = {b, b_min, b_max};
TList outputs = {b_q, b_min_q, b_max_q};
S_TENSOR out_b_q = b_q.lock();
S_TENSOR out_b_min_q = b_min_q.lock();
S_TENSOR out_b_max_q = b_max_q.lock();
S_TENSOR ref_b_q = b_q_ref.lock();
S_TENSOR ref_b_min_q = b_min_q_ref.lock();
S_TENSOR ref_b_max_q = b_max_q_ref.lock();
S_TENSOR ref_b_q = ctx.add(t_import.ubyte_import("/fs/testData/qB/out/qB_0.idx", "ref_b_q"));
S_TENSOR ref_b_min_q = ctx.add(t_import.float_import("/fs/testData/qB/out/qB_1.idx", "ref_b_min_q"));
S_TENSOR ref_b_max_q = ctx.add(t_import.float_import("/fs/testData/qB/out/qb_2.idx", "ref_b_max_q"));

S_TENSOR out_b_q = ctx.add(new RamTensor<unsigned char>(b_q_ref->getShape(), "b_q"));
S_TENSOR out_b_min_q = ctx.add(new RamTensor<float>(b_min_q_ref->getShape(), "b_min_q"));
S_TENSOR out_b_max_q = ctx.add(new RamTensor<float>(b_max_q_ref->getShape(), "b_max_q"));

//Implementation goes here
timer_start();
ctx.push(new QuantizeV2Op(), inputs, outputs);
ctx.push(new QuantizeV2Op(), {"b_q_ref", "b_min_q_ref", "b_max_q_ref"}, {"b_q", "b_min_q", "b_max_q"});
ctx.eval();
timer_stop();

// printf("refMin is : %f \r\n", *(b_min_q_ref.getPointer({0})));
// printf("outMin is : %f \r\n", *(b_min_q.getPointer({0})));
// printf("diff : output(%f), outMin(%f), outMax(%f)\r\n",
// meanPercentErr(b_q_ref, b_q), meanPercentErr(b_min_q_ref, b_min_q), meanPercentErr(b_max_q_ref, b_max_q));

double result = meanPercentErr<unsigned char>(ref_b_q.get(), out_b_q.get()) + meanPercentErr<float>(ref_b_min_q.get(), out_b_min_q.get()) + meanPercentErr<float>(ref_b_max_q.get(), out_b_max_q.get());
//passed(result < 0.0001);
Expand All @@ -57,27 +44,22 @@ class ArrayOpsTest : public Test {
testStart("dequantize");

//reference inputs
TENSOR a = ctx.add(t_import.ubyte_import("/fs/testData/deQ/in/rQ_0.idx"));
TENSOR a_min = ctx.add(t_import.float_import("/fs/testData/deQ/in/rQ_1.idx"));
TENSOR a_max = ctx.add(t_import.float_import("/fs/testData/deQ/in/rQ_2.idx"));
S_TENSOR a = ctx.add(t_import.ubyte_import("/fs/testData/deQ/in/rQ_0.idx", "a"));
S_TENSOR a_min = ctx.add(t_import.float_import("/fs/testData/deQ/in/rQ_1.idx", "a_min"));
S_TENSOR a_max = ctx.add(t_import.float_import("/fs/testData/deQ/in/rQ_2.idx", "a_max"));

//reference outputs
TENSOR out_ref = ctx.add(t_import.float_import("/fs/testData/deQ/out/deQ_0.idx"));
S_TENSOR out_ref = ctx.add(t_import.float_import("/fs/testData/deQ/out/deQ_0.idx", "out_ref"));

//modify the checks below:
TENSOR out = ctx.add(new RamTensor<float>(out_ref.lock()->getShape()));
TList inputs = {a, a_min, a_max};
TList outputs = {out};

S_TENSOR out_val = out.lock();
S_TENSOR ref_out = out_ref.lock();
S_TENSOR out = ctx.add(new RamTensor<float>(out_ref->getShape(), "out"));

timer_start();
ctx.push(new DequantizeOp(), inputs, outputs);
ctx.push(new DequantizeOp(), {"a", "a_min", "a_max"}, {"out"});
ctx.eval();
timer_stop();

double result = meanPercentErr<float>(out_val.get(), ref_out.get());
double result = meanPercentErr<float>(out.get(), out_ref.get());
//passed(result < 0.0001);
passed(result == 0);
}
Expand All @@ -87,26 +69,22 @@ class ArrayOpsTest : public Test {
TensorIdxImporter t_import;

//reference inputs
TENSOR ref_a = ctx.add(t_import.float_import("/fs/testData/ref_reshape/in/Const_0.idx"));
TENSOR ref_dim = ctx.add(t_import.int_import("/fs/testData/ref_reshape/in/Const_1_0.idx"));
S_TENSOR ref_a = ctx.add(t_import.float_import("/fs/testData/ref_reshape/in/Const_0.idx", "ref_a"));
S_TENSOR ref_dim = ctx.add(t_import.int_import("/fs/testData/ref_reshape/in/Const_1_0.idx", "ref_dim"));

//reference outputs
TENSOR out_ref = ctx.add(t_import.float_import("/fs/testData/ref_reshape/out/ref_reshape_0.idx"));
S_TENSOR out_ref_2 = ctx.add(t_import.float_import("/fs/testData/ref_reshape/out/ref_reshape_0.idx", "out_ref_2"));

//modify the checks below:
TENSOR out = ctx.add(new RamTensor<float>(out_ref.lock()->getShape()));
S_TENSOR out_val = out.lock();
S_TENSOR ref_out = out_ref.lock();
S_TENSOR out_2 = ctx.add(new RamTensor<float>(out_ref_2->getShape(), "out_2"));

TList inputs = {ref_a, ref_dim};
TList outputs = {out};

timer_start();
ctx.push(new ReshapeOp(), inputs, outputs);
ctx.push(new ReshapeOp(), {"ref_a", "ref_dim"}, {"out_2"});
ctx.eval();
timer_stop();

double result = meanPercentErr<float>(out_val.get(), ref_out.get());
double result = meanPercentErr<float>(out_2.get(), out_ref_2.get());
//passed(result < 0.0001);
passed(result == 0);
}
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