Merge pull request #183 from ROCmSoftwarePlatform/tf_pb

tensorflow protobuf support

test/tf/biasadd_test.pb

+
+;
+
0Placeholder*
+shape:


*
+dtype0

+/
+
1Placeholder*
+dtype0
*
+shape:
+:
+	bias_add1BiasAdd
0
1*
+
T0
*
+data_formatNHWC"
\ No newline at end of file

test/tf/identity_test.pb

+
+:
+
0Placeholder*
+shape:
*
+dtype0

+ 
+identityIdentity
0*
+
T0
"
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test/tf/pooling_test.pb

+
+:
+
0Placeholder*
+dtype0
*
+shape:

+u
+avg_poolingAvgPool
0*
+ksize
+

*
+paddingVALID*
+
T0
*
+data_formatNHWC*
+strides
+


+u
+max_poolingMaxPool
0*
+data_formatNHWC*
+strides
+

*
+ksize
+

*
+paddingVALID*
+
T0
"
\ No newline at end of file

test/tf/relu_test.pb

+
+:
+
0Placeholder*
+dtype0
*
+shape:

+
+reluRelu
0*
+
T0
"
\ No newline at end of file

test/tf/softmax_test.pb

+
+2
+
0Placeholder*
+dtype0
*
+shape
+:

+
+softmaxSoftmax
0*
+
T0
"
\ No newline at end of file

test/tf/tf_test.cpp

+#include <iostream>
+#include <vector>
+#include <migraphx/literal.hpp>
+#include <migraphx/operators.hpp>
+#include <migraphx/program.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/tf.hpp>
+#include "test.hpp"
+
+TEST_CASE(add_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 2, 2, 3}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {1, 2, 2, 3}});
+    p.add_instruction(migraphx::op::add{}, l0, l1);
+    auto prog = migraphx::parse_tf("add_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(add_bcast_test)
+{
+
+    migraphx::program p;
+    migraphx::shape s0{migraphx::shape::float_type, {2, 3}};
+    auto l0 = p.add_parameter("0", s0);
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {2, 1}});
+    auto l2 = p.add_instruction(migraphx::op::multibroadcast{s0.lens()}, l0);
+    auto l3 = p.add_instruction(migraphx::op::multibroadcast{s0.lens()}, l1);
+    p.add_instruction(migraphx::op::add{}, l2, l3);
+    auto prog = migraphx::parse_tf("add_bcast_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(batchnorm_test)
+{
+    float epsilon  = 1.001e-5f;
+    float momentum = 0.9f;
+
+    migraphx::program p;
+    migraphx::op::batch_norm_inference op{
+        epsilon, momentum, migraphx::op::batch_norm_inference::spatial};
+    migraphx::shape s0{migraphx::shape::float_type, {32}};
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 32, 16, 16}});
+    std::vector<float> const_vals(32);
+    std::fill(const_vals.begin(), const_vals.end(), 1.0f);
+
+    auto l2 = p.add_parameter("2", s0);
+    auto l3 = p.add_parameter("3", s0);
+    auto l4 = p.add_parameter("4", s0);
+    auto l1 = p.add_literal(migraphx::literal{s0, const_vals});
+    p.add_instruction(op, l0, l1, l2, l3, l4);
+    auto prog = migraphx::parse_tf("batchnorm_test.pb", true);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(biasadd_test)
+{
+    migraphx::program p;
+    migraphx::shape s0{migraphx::shape::float_type, {1, 500, 1, 1}};
+    uint64_t axis = 1;
+    auto l0       = p.add_parameter("0", s0);
+    auto l1       = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {500}});
+    auto l2       = p.add_instruction(migraphx::op::broadcast{axis, l0->get_shape()}, l1);
+    p.add_instruction(migraphx::op::add{}, l0, l2);
+    auto prog = migraphx::parse_tf("biasadd_test.pb", true);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(concat_test)
+{
+    migraphx::program p;
+
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {4, 7, 3}});
+    auto l1 = p.add_parameter("1", migraphx::shape{migraphx::shape::float_type, {4, 2, 3}});
+
+    int axis = 1;
+    // tf uses axis as the third input, and it is in int32 format
+    // add the literal using a vector in order to set stride to 1 (like in tf parser)
+    p.add_literal(migraphx::shape{migraphx::shape::int32_type, {1}}, std::vector<int>{axis});
+
+    p.add_instruction(migraphx::op::concat{static_cast<std::size_t>(axis)}, l0, l1);
+    auto prog = migraphx::parse_tf("concat_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(const_test)
+{
+    migraphx::program p;
+    p.add_literal(migraphx::shape{migraphx::shape::float_type, {1}}, std::vector<float>{1.0f});
+    auto prog = migraphx::parse_tf("constant_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(conv_test)
+{
+    migraphx::program p;
+
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
+    std::vector<float> weight_data(3 * 3 * 3 * 32);
+    std::fill(weight_data.begin(), weight_data.end(), 1.0f);
+    auto l1 =
+        p.add_literal(migraphx::shape{migraphx::shape::float_type, {3, 3, 3, 32}}, weight_data);
+
+    migraphx::op::convolution op;
+    op.padding_mode = migraphx::op::padding_mode_t::same;
+    op.stride       = {1, 1};
+    op.dilation     = {1, 1};
+    auto l2         = p.add_instruction(migraphx::op::transpose{{0, 3, 1, 2}}, l1);
+    auto l3         = p.add_instruction(migraphx::op::transpose{{1, 3, 0, 2}}, l2);
+    p.add_instruction(op, l0, l3);
+    auto prog = migraphx::parse_tf("conv_test.pb", true);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(identity_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
+    p.add_instruction(migraphx::op::identity{}, l0);
+    auto prog = migraphx::parse_tf("identity_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(pooling_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
+    migraphx::op::pooling avg_pool_op{"average"};
+    migraphx::op::pooling max_pool_op{"max"};
+    avg_pool_op.padding_mode = migraphx::op::padding_mode_t::valid;
+    max_pool_op.padding_mode = migraphx::op::padding_mode_t::valid;
+    avg_pool_op.stride       = {2, 2};
+    max_pool_op.stride       = {2, 2};
+    avg_pool_op.lengths      = {2, 2};
+    max_pool_op.lengths      = {2, 2};
+    p.add_instruction(max_pool_op, l0);
+    p.add_instruction(avg_pool_op, l0);
+    auto prog = migraphx::parse_tf("pooling_test.pb", true);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(relu_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3, 16, 16}});
+    p.add_instruction(migraphx::op::relu{}, l0);
+    auto prog = migraphx::parse_tf("relu_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(reshape_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {16}});
+    migraphx::shape s0{migraphx::shape::int32_type, {4}};
+    // in tf, the second arg is a literal that contains new dimensions
+    p.add_literal(migraphx::literal{s0, {1, 1, 1, 16}});
+    p.add_instruction(migraphx::op::reshape{{1, 1, 1, 16}}, l0);
+    auto prog = migraphx::parse_tf("reshape_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(softmax_test)
+{
+    migraphx::program p;
+    auto l0   = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3}});
+    auto dims = l0->get_shape().lens();
+    auto r    = p.add_instruction(migraphx::op::reshape{{long(dims[0]), long(dims[1]), 1, 1}}, l0);
+    auto s    = p.add_instruction(migraphx::op::softmax{}, r);
+    p.add_instruction(migraphx::op::reshape{{long(dims[0]), long(dims[1])}}, s);
+    auto prog = migraphx::parse_tf("softmax_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
+TEST_CASE(squeeze_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 2, 3, 1}});
+    p.add_instruction(migraphx::op::squeeze{{0, 3}}, l0);
+    auto prog = migraphx::parse_tf("squeeze_test.pb", false);
+
+    EXPECT(p == prog);
+}
+int main(int argc, const char* argv[]) { test::run(argc, argv); }