added parse onnx for imagescaler

src/include/migraph/operators.hpp

@@ -306,10 +306,6 @@ struct contiguous
         check_shapes{inputs, *this}.has(1);
         auto lens = inputs.at(0).lens();
         auto t    = inputs.at(0).type();
-        // if(lens.size() < 2)
-        // {
-        //     MIGRAPH_THROW("Number of dimensions should exceed 1");
-        // }
         return {t, lens};
     }
 };

src/onnx/onnx.cpp

@@ -56,7 +56,7 @@ struct onnx_parser
         add_generic_op("Sub", op::sub{});
         add_generic_op("Sum", op::add{});
 
-        // add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler);
+        add_mem_op("ImageScaler", &onnx_parser::parse_imagescaler);
         add_mem_op("LeakyRelu", &onnx_parser::parse_leaky_relu);
         add_mem_op("Constant", &onnx_parser::parse_constant);
         add_mem_op("Conv", &onnx_parser::parse_conv);
@@ -325,10 +325,33 @@ struct onnx_parser
         return prog.add_instruction(op, args.front());
     }
 
-    // instruction_ref parse_imagescaler(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
-    // {
+    instruction_ref parse_imagescaler(const std::string&,
+                                      attribute_map attributes,
+                                      std::vector<instruction_ref> args)
+    {
+        float scale = 1.0;
+        std::vector<float> bias{};
+        if(contains(attributes, "scale"))
+        {
+            scale = parse_value(attributes.at("scale")).at<float>();
+        }
+
+        if(contains(attributes, "bias"))
+        {
+            auto&& bias_floats = attributes["bias"].floats();
+            bias               = std::vector<float>(bias_floats.begin(), bias_floats.end());
+        }
+        auto input_shape = args.front()->get_shape();
 
-    // }
+        auto scale_val = prog.add_literal(scale);
+        auto bias_vals = prog.add_literal(
+            migraph::literal{migraph::shape{migraph::shape::float_type, {bias.size()}}, bias});
+
+        auto scale_tensor = prog.add_instruction(migraph::op::scalar{input_shape}, scale_val);
+        auto img_scaled   = prog.add_instruction(migraph::op::mul{}, args.front(), scale_tensor);
+        auto bias_bcast   = prog.add_instruction(migraph::op::broadcast{1, input_shape}, bias_vals);
+        return prog.add_instruction(migraph::op::add{}, img_scaled, bias_bcast);
+    }
 
     void parse_from(std::istream& is)
     {

src/targets/gpu/device/mul.cpp

@@ -5,9 +5,18 @@ namespace migraph {
 namespace gpu {
 namespace device {
 
-void mul(const argument& result, const argument& arg1, const argument& arg2)
+void mul(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2)
 {
-    nary(result, arg1, arg2)([](auto x, auto y) { return x * y; });
+    nary(stream, result, arg1, arg2)([](auto x, auto y) { return x * y; });
+}
+
+void mul(hipStream_t stream,
+         const argument& result,
+         const argument& arg1,
+         const argument& arg2,
+         const argument& arg3)
+{
+    nary(stream, result, arg1, arg2, arg3)([](auto x, auto y, auto z) { return x * y * z; });
 }
 
 } // namespace device

src/targets/gpu/include/migraph/gpu/device/mul.hpp

@@ -3,12 +3,19 @@
 #define MIGRAPH_GUARD_RTGLIB_DEVICE_MUL_HPP
 
 #include <migraph/argument.hpp>
+#include <hip/hip_runtime_api.h>
 
 namespace migraph {
 namespace gpu {
 namespace device {
 
-void mul(const argument& result, const argument& arg1, const argument& arg2);
+void mul(hipStream_t stream, const argument& result, const argument& arg1, const argument& arg2);
+
+void mul(hipStream_t stream,
+         const argument& result,
+         const argument& arg1,
+         const argument& arg2,
+         const argument& arg3);
 
 } // namespace device
 } // namespace gpu

src/targets/gpu/mul.cpp

@@ -14,9 +14,9 @@ shape hip_mul::compute_shape(const std::vector<shape>& inputs) const
     return inputs.at(0);
 }
 
-argument hip_mul::compute(context&, const shape&, const std::vector<argument>& args) const
+argument hip_mul::compute(context& ctx, const shape&, const std::vector<argument>& args) const
 {
-    device::mul(args[2], args[0], args[1]);
+    device::mul(ctx.get_stream().get(), args[2], args[0], args[1]);
     return args[2];
 }
 

test/onnx/onnx_test.cpp

@@ -100,6 +100,23 @@ void leaky_relu_test()
     EXPECT(p == prog);
 }
 
+void imagescaler_test()
+{
+    migraph::program p;
+    migraph::shape s{migraph::shape::float_type, {1, 3, 16, 16}};
+    auto l0 = p.add_parameter("0", s);
+    auto scale_val = p.add_literal(0.5f);
+    auto bias_vals = p.add_literal(migraph::literal{migraph::shape{migraph::shape::float_type, {3}}, {0.01, 0.02, 0.03}});
+    auto scaled_tensor = p.add_instruction(migraph::op::scalar{s}, scale_val);
+    auto img_scaled = p.add_instruction(migraph::op::mul{}, l0, scaled_tensor);
+    auto bias_bcast = p.add_instruction(migraph::op::broadcast{1, s}, bias_vals);
+    p.add_instruction(migraph::op::add{}, img_scaled, bias_bcast);
+
+    auto prog = migraph::parse_onnx("imagescaler_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 int main()
 {
     pytorch_conv_bias_test();
@@ -107,4 +124,5 @@ int main()
     pytorch_conv_bn_relu_maxpool();
     pytorch_conv_relu_maxpool_x2();
     leaky_relu_test();
+    imagescaler_test();
 }

test/op_shape_test.cpp

@@ -93,7 +93,7 @@ void contiguous_shape()
     throws_shape(migraph::op::contiguous{}, input, input);
 
     migraph::shape single{migraph::shape::float_type, {2}};
-    throws_shape(migraph::op::contiguous{}, single);
+    expect_shape(single, migraph::op::contiguous{}, single);
 }
 
 void reshape_shape()