Update deconvolution -> convolution_backwards and Dynamic Shape Support (#1801)

Renames deconvolution -> convolution_backwards to be more consistent with the literature
Note: this is not the cross-correlation operator (which is the adjoint of convolution). This is technically a standard convolution operator combined with an upsampling operator rather than a downsampling operator.
Adds unit tests for the padding, strides, dilations, and other op attributes.
Throws on auto_pad attribute since it has not been implemented
Previously it read the attribute and set it but then did nothing with it
Extended for dynamic shapes
Does not support using asymmetric padding (padding_L != padding_R) and output_shape with dynamic shapes.

src/CMakeLists.txt

@@ -129,9 +129,9 @@ register_migraphx_ops(
     contiguous
     convert
     convolution
+    convolution_backwards
     cosh
     cos
-    deconvolution
     dequantizelinear
     div
     dot

src/include/migraphx/op/convolution.hpp

@@ -79,17 +79,17 @@ struct convolution
         check_shapes{inputs, *this, true}.has(2).same_type().same_ndims().min_ndims(3);
         check_attribute_size();
         // num of dims of input and attribute should match
-        const auto input_size   = inputs[0].max_lens().size();
+        const auto input_ndim   = inputs[0].ndim();
         const auto padding_size = padding.size();
 
-        if(input_size != padding_size / 2 + 2 && input_size != padding_size + 2)
+        if(input_ndim != padding_size / 2 + 2 && input_ndim != padding_size + 2)
         {
             MIGRAPHX_THROW("CONVOLUTION: input and attribute size mismatch!");
         }
 
         const shape& x_shape          = inputs.at(0);
         const shape& w_shape          = inputs.at(1);
-        const size_t num_spatial_dims = input_size - 2;
+        const size_t num_spatial_dims = input_ndim - 2;
         if(num_spatial_dims != this->kdims())
         {
             MIGRAPHX_THROW("CONVOLUTION: input k-dims does not match attribute size");
@@ -105,7 +105,7 @@ struct convolution
         }
         else
         {
-            return fixed_compute_shape(x_shape, w_shape);
+            return static_compute_shape(x_shape, w_shape);
         }
     }
 
@@ -143,23 +143,10 @@ struct convolution
     shape dynamic_compute_shape(shape x_shape, shape w_shape) const
     {
         std::vector<shape::dynamic_dimension> output_dyn_dims = {};
+        output_dyn_dims.push_back(x_shape.to_dynamic().dyn_dims().at(0));
+        output_dyn_dims.push_back(w_shape.to_dynamic().dyn_dims().at(0));
 
-        auto dynamic_shape_push_back = [&](const shape& input_shape) {
-            if(input_shape.dynamic())
-            {
-                output_dyn_dims.push_back(input_shape.dyn_dims().at(0));
-            }
-            else
-            {
-                auto l = input_shape.lens().at(0);
-                output_dyn_dims.push_back({l, l});
-            }
-        };
-
-        dynamic_shape_push_back(x_shape);
-        dynamic_shape_push_back(w_shape);
-
-        const size_t num_spatial_dims = x_shape.max_lens().size() - 2;
+        const size_t num_spatial_dims = x_shape.ndim() - 2;
         if(padding_mode != default_)
         {
             for(std::size_t i = 0; i < num_spatial_dims; ++i)
@@ -198,7 +185,7 @@ struct convolution
         return shape{x_shape.type(), output_dyn_dims};
     }
 
-    shape fixed_compute_shape(shape x_shape, shape w_shape) const
+    shape static_compute_shape(shape x_shape, shape w_shape) const
     {
         std::vector<size_t> output_lens{x_shape.lens()[0], w_shape.lens()[0]};
         auto spatial_lens = calc_conv_lens(x_shape.lens(), w_shape.lens());

src/include/migraphx/op/deconvolution.hpp → src/include/migraphx/op/convolution_backwards.hpp

@@ -21,9 +21,11 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_OPERATORS_DECONVOLUTION_HPP
-#define MIGRAPHX_GUARD_OPERATORS_DECONVOLUTION_HPP
+#ifndef MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_BACKWARDS_HPP
+#define MIGRAPHX_GUARD_OPERATORS_CONVOLUTION_BACKWARDS_HPP
 
+#include <cmath>
+#include <utility>
 #include <migraphx/op/common.hpp>
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/config.hpp>
@@ -31,14 +33,13 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/par_dfor.hpp>
 #include <migraphx/shape_for_each.hpp>
-#include <cmath>
-#include <utility>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
-struct deconvolution
+struct convolution_backwards
 {
     std::vector<std::size_t> padding  = {0, 0};
     std::vector<std::size_t> stride   = {1, 1};
@@ -57,45 +58,91 @@ struct deconvolution
                     f(self.group, "group"));
     }
 
-    std::string name() const { return "deconvolution"; }
+    std::string name() const { return "convolution_backwards"; }
 
     void check_attribute_size() const
     {
-        if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
-           stride.size() != dilation.size())
+        if(padding.size() != stride.size() or stride.size() != dilation.size())
         {
-            MIGRAPHX_THROW("deconvolution: inconsistent attribute sizes");
+            MIGRAPHX_THROW("CONVOLUTION_BACKWARDS: inconsistent attribute sizes");
         }
     }
 
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(2).same_type().same_ndims().min_ndims(3);
+        check_shapes{inputs, *this, true}.has(2).same_type().same_ndims().min_ndims(3);
+
+        const shape& x_shape = inputs.at(0);
+        const shape& w_shape = inputs.at(1);
+        if(x_shape.ndim() - 2 != this->kdims())
+        {
+            MIGRAPHX_THROW("CONVOLUTION_BACKWARDS: input k-dims does not match attribute size");
+        }
 
-        const shape& input   = inputs.at(0);
-        const shape& weights = inputs.at(1);
-        size_t kdims         = input.lens().size() - 2;
-        if(kdims != this->kdims())
+        if(not x_shape.dynamic() and not w_shape.dynamic() and
+           x_shape.lens().at(1) != (w_shape.lens().at(0) * group))
         {
-            MIGRAPHX_THROW("deconvolution: input k-dims does not match attribute size");
+            MIGRAPHX_THROW("CONVOLUTION_BACKWARDS: mismatched channel numbers");
         }
 
-        std::vector<size_t> output_lens{input.lens()[0], weights.lens()[1]};
+        if(x_shape.dynamic() or w_shape.dynamic())
+        {
+            return dynamic_compute_shape(x_shape, w_shape);
+        }
+        else
+        {
+            return static_compute_shape(x_shape, w_shape);
+        }
+    }
 
-        for(size_t i = 0; i < kdims; i++)
+    std::vector<std::size_t> calc_spatial_lens(std::vector<std::size_t> x_lens,
+                                               std::vector<std::size_t> w_lens) const
     {
-            output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(
+        std::vector<size_t> spatial_lens(x_lens.size() - 2);
+
+        // stride * (input - 1) + output_padding + ((kernel - 1) * dilation + 1) - padding_L -
+        // padding_R. This assumes padding_L = padding_R and output_padding handled in parser.
+        for(size_t i = 0; i < spatial_lens.size(); i++)
+        {
+            spatial_lens.at(i) = (std::size_t(std::max<std::ptrdiff_t>(
                 1,
-                stride[i] * (input.lens()[i + 2] - 1) +
-                    ((weights.lens()[i + 2] - 1) * dilation[i] + 1) - 2 * padding[i])));
+                stride[i] * (x_lens[i + 2] - 1) + ((w_lens[i + 2] - 1) * dilation[i] + 1) -
+                    2 * padding[i])));
+        }
+        return spatial_lens;
     }
-        return inputs[0].with_lens(output_lens);
+
+    shape dynamic_compute_shape(shape x_shape, shape w_shape) const
+    {
+        std::vector<shape::dynamic_dimension> output_dyn_dims = {};
+        output_dyn_dims.push_back(x_shape.to_dynamic().dyn_dims().at(0));
+        output_dyn_dims.push_back(w_shape.to_dynamic().dyn_dims().at(1));
+        const std::size_t num_spatial_dims = x_shape.ndim() - 2;
+        // Does not compute for optimals
+        auto min_spatial_dims = calc_spatial_lens(x_shape.min_lens(), w_shape.min_lens());
+        auto max_spatial_dims = calc_spatial_lens(x_shape.max_lens(), w_shape.max_lens());
+        for(size_t i = 0; i < num_spatial_dims; ++i)
+        {
+            output_dyn_dims.push_back(
+                shape::dynamic_dimension{min_spatial_dims[i], max_spatial_dims[i], {}});
+        }
+        return shape{x_shape.type(), output_dyn_dims};
+    }
+
+    shape static_compute_shape(shape x_shape, shape w_shape) const
+    {
+        std::vector<size_t> output_lens{x_shape.lens()[0], w_shape.lens()[1]};
+        auto spatial_lens = calc_spatial_lens(x_shape.lens(), w_shape.lens());
+        std::for_each(spatial_lens.begin(), spatial_lens.end(), [&output_lens](auto x) {
+            output_lens.push_back(x);
+        });
+        return x_shape.with_lens(output_lens);
     }
 
-    argument compute(shape output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
-        auto kdims = this->kdims();
+        argument result{dyn_out.computed_shape};
+        auto num_spatial_dims = this->kdims();
         visit_all(result, args[0], args[1])([&](auto output, auto input, auto weights) {
             using type = typename decltype(output)::value_type;
 
@@ -109,22 +156,22 @@ struct deconvolution
             auto wei_n = wei[0];
             auto wei_c = wei[1];
 
-            auto out_lens = output_shape.lens();
+            auto out_lens = dyn_out.computed_shape.lens();
 
             std::vector<std::size_t> win_size{in_c};
             std::copy(in_lens.begin() + 2, in_lens.end(), std::back_inserter(win_size));
             std::copy(wei.begin() + 2, wei.end(), std::back_inserter(win_size));
-            shape win_shape{output_shape.type(), win_size};
+            shape win_shape{dyn_out.computed_shape.type(), win_size};
 
             par_dfor(in_n, wei_c)([&](int o, int k) {
                 shape_for_each(win_shape, [&](auto idx_win) {
                     const int w = idx_win[0];
 
                     auto input_dims_start = idx_win.begin() + 1;
-                    auto wei_dims_start   = idx_win.begin() + kdims + 1;
+                    auto wei_dims_start   = idx_win.begin() + num_spatial_dims + 1;
 
                     std::vector<std::ptrdiff_t> win_start;
-                    for(std::size_t n = 0; n < kdims; ++n)
+                    for(std::size_t n = 0; n < num_spatial_dims; ++n)
                     {
                         win_start.push_back(std::ptrdiff_t(*(input_dims_start + n) * stride[n]) -
                                             std::ptrdiff_t(padding[n]));
@@ -135,7 +182,7 @@ struct deconvolution
 
                     std::vector<std::ptrdiff_t> idx_out{o, in_ch};
 
-                    for(size_t n = 0; n < kdims; n++)
+                    for(size_t n = 0; n < num_spatial_dims; n++)
                     {
                         idx_out.push_back(win_start[n] + *(wei_dims_start + n) * dilation[n]);
                     }

src/include/migraphx/operators.hpp

@@ -45,9 +45,9 @@
 #include <migraphx/op/contiguous.hpp>
 #include <migraphx/op/convert.hpp>
 #include <migraphx/op/convolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 #include <migraphx/op/cosh.hpp>
 #include <migraphx/op/cos.hpp>
-#include <migraphx/op/deconvolution.hpp>
 #include <migraphx/op/div.hpp>
 #include <migraphx/op/dot.hpp>
 #include <migraphx/op/elu.hpp>

src/onnx/parse_deconvolution.cpp → src/onnx/parse_conv_transpose.cpp

@@ -42,7 +42,7 @@ std::vector<int64_t> to_int64_vector(const std::vector<T>& input_vector)
     return output_vector;
 }
 
-struct parse_deconvolution : op_parser<parse_deconvolution>
+struct parse_conv_transpose : op_parser<parse_conv_transpose>
 {
     std::vector<op_desc> operators() const { return {{"ConvTranspose"}}; }
 
@@ -51,17 +51,15 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
                           onnx_parser::node_info info,
                           std::vector<instruction_ref> args) const
     {
-        operation op = make_op("deconvolution");
+        operation op = make_op("convolution_backwards");
         value values = op.to_value();
-        // op::deconvolution op;
         auto l0      = args[0];
         std::vector<std::int64_t> padding;
         bool asym_padding = false;
-        auto in_lens      = l0->get_shape().lens();
-        assert(in_lens.size() > 2);
-        auto kdims = in_lens.size() - 2;
+        assert(l0->get_shape().ndim() > 2);
+        auto kdims = l0->get_shape().ndim() - 2;
 
-        // ensure pads availabe only when auto_pad is "NOT_SET"
+        // ensure pads available only when auto_pad is "NOT_SET"
         check_padding_mode(info, "CONV_TRANSPOSE");
 
         if(contains(info.attributes, "pads"))
@@ -70,9 +68,9 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
 
             asym_padding = is_asym_padding(padding);
 
+            size_t pad_ndims = padding.size() / 2;
             if(not asym_padding)
             {
-                size_t pad_ndims = padding.size() / 2;
                 check_attr_sizes(kdims, pad_ndims, "PARSE_CONV_TRANSPOSE: inconsistent paddings");
                 values["padding"].clear();
                 std::transform(padding.begin(),
@@ -80,7 +78,19 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
                                std::back_inserter(values["padding"]),
                                [](auto pad_val) { return pad_val; });
             }
+            else if(l0->get_shape().dynamic())
+            {
+                MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: asymmetric padding (padding_L != padding_R) "
+                               "not supported with dynamic shapes");
+            }
+            else
+            {
+                // set padding to 0s, asym_padding handled by parser with slice
+                // TODO changing parser and op to do asym padding in op
+                values["padding"] = std::vector<std::size_t>(pad_ndims, 0);
+            }
         }
+
         if(contains(info.attributes, "strides"))
         {
             values["stride"].clear();
@@ -88,6 +98,7 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
             check_attr_sizes(
                 kdims, values["stride"].size(), "PARSE_CONV_TRANSPOSE: inconsistent strides");
         }
+
         if(contains(info.attributes, "dilations"))
         {
             values["dilation"].clear();
@@ -97,21 +108,10 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
         }
 
         // TODO: auto padding needs to be implemented for this parser and operator
-        if(contains(info.attributes, "auto_pad"))
-        {
-            auto s = info.attributes["auto_pad"].s();
-            if(contains(info.attributes, "pads") and to_upper(s) != "NOTSET")
+        if(contains(info.attributes, "auto_pad") and
+           to_upper(info.attributes.at("auto_pad").s()) != "NOTSET")
         {
-                MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: auto_pad and padding cannot be specified "
-                               "simultaneously");
-            }
-
-            if(s.find("SAME") != std::string::npos)
-            {
-                bool is_same_upper     = (s.find("SAME_UPPER") != std::string::npos);
-                values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
-                                                       : to_value(op::padding_mode_t::same_lower);
-            }
+            MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: auto padding not supported");
         }
 
         if(contains(info.attributes, "group"))
@@ -123,10 +123,10 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
 
         op.from_value(values);
         auto l1 = info.add_instruction(op, l0, args[1]);
-        std::vector<int64_t> dims = to_int64_vector(l1->get_shape().lens());
-        std::vector<int64_t> curr_shape(dims.begin() + 2, dims.end());
         if(asym_padding)
         {
+            std::vector<int64_t> dims = to_int64_vector(l1->get_shape().lens());
+            std::vector<int64_t> curr_shape(dims.begin() + 2, dims.end());
             std::vector<int64_t> axes(kdims);
             std::iota(axes.begin(), axes.end(), 2); // ignore first 2 dims
 
@@ -144,9 +144,11 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
                 make_op("slice", {{"axes", axes}, {"starts", starts}, {"ends", ends}}), l1);
         }
 
-        if(contains(info.attributes, "output_padding"))
+        // TODO, should check output_padding < (strides or dilations)
+        if(contains(info.attributes, "output_padding") and
+           not contains(info.attributes, "output_shape"))
         {
-            size_t non_kdims = dims.size() * 2 - kdims;
+            size_t non_kdims = l1->get_shape().ndim() * 2 - kdims;
             std::vector<int64_t> output_padding(non_kdims, 0);
             copy(info.attributes["output_padding"].ints(), std::back_inserter(output_padding));
             check_attr_sizes(kdims,
@@ -155,14 +157,21 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
             l1 = info.add_instruction(make_op("pad", {{"pads", output_padding}}), l1);
         }
 
+        // TODO, doing unnecessary calcuations with this. Could instead
+        // calculate the padding to conv_transpose that would give the output_shape.
         if(contains(info.attributes, "output_shape"))
         {
+            if(l1->get_shape().dynamic())
+            {
+                MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: output_shape attribute and dynamic shapes "
+                               "not supported");
+            }
+            std::vector<int64_t> dims = to_int64_vector(l1->get_shape().lens());
+            std::vector<int64_t> curr_shape(dims.begin() + 2, dims.end());
             std::vector<int64_t> output_shape;
             copy(info.attributes["output_shape"].ints(), std::back_inserter(output_shape));
             check_attr_sizes(
                 kdims, output_shape.size(), "PARSE_CONV_TRANSPOSE: inconsistent output shape");
-            dims = to_int64_vector(l1->get_shape().lens());
-            copy(dims.begin() + 2, dims.end(), curr_shape.begin());
             if(curr_shape != output_shape)
             {
                 std::vector<int64_t> target_padding(dims.size() * 2 - kdims, 0);

src/targets/cpu/deconvolution.cpp

@@ -23,14 +23,14 @@
  */
 #include <migraphx/config.hpp>
 #include <migraphx/cpu/dnnl.hpp>
-#include <migraphx/op/deconvolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace cpu {
 
 struct dnnl_deconvolution
-    : dnnl_extend_op<dnnl_deconvolution, dnnl::deconvolution_forward, op::deconvolution>
+    : dnnl_extend_op<dnnl_deconvolution, dnnl::deconvolution_forward, op::convolution_backwards>
 {
     std::vector<int> arg_map(int) const
     {

src/targets/cpu/lowering.cpp

@@ -27,7 +27,7 @@
 #include <migraphx/dfor.hpp>
 #include <migraphx/op/identity.hpp>
 #include <migraphx/op/convolution.hpp>
-#include <migraphx/op/deconvolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 #include <migraphx/op/quant_convolution.hpp>
 #include <migraphx/op/dot.hpp>
 #include <migraphx/op/quant_dot.hpp>
@@ -345,7 +345,7 @@ struct cpu_apply
         extend_op("contiguous", "dnnl::reorder");
         extend_op("convolution", "dnnl::convolution");
 #ifndef MIGRAPHX_ENABLE_ZENDNN
-        extend_op("deconvolution", "dnnl::deconvolution");
+        extend_op("convolution_backwards", "dnnl::convolution_backwards");
         extend_op("dot", "dnnl::dot");
 #endif
         extend_op("erf", "cpu::erf");

src/targets/gpu/CMakeLists.txt

@@ -176,7 +176,7 @@ register_op(migraphx_gpu
     OPERATORS gpu::rocblas_gemm<op::dot> gpu::rocblas_gemm<op::quant_dot>
     INCLUDES migraphx/gpu/context.hpp)
 register_op(migraphx_gpu HEADER migraphx/gpu/convolution.hpp
-    OPERATORS gpu::miopen_convolution<op::convolution> gpu::miopen_convolution<op::deconvolution> gpu::miopen_convolution<op::quant_convolution>
+    OPERATORS gpu::miopen_convolution<op::convolution> gpu::miopen_convolution<op::convolution_backwards> gpu::miopen_convolution<op::quant_convolution>
     INCLUDES migraphx/gpu/context.hpp)
 rocm_set_soversion(migraphx_gpu ${MIGRAPHX_SO_VERSION})
 rocm_clang_tidy_check(migraphx_gpu)

src/targets/gpu/compile_miopen.cpp

@@ -79,7 +79,7 @@ void compile_miopen::apply(module& m) const
         std::size_t ws = 0;
         try
         {
-            // for the regular convolution and deconvolution, this try would always succeed
+            // for the regular convolution and convolution_backwards, this try would always succeed
             ws = compile(op, ins, int8_x4_format);
         }
         catch(migraphx::exception&)

src/targets/gpu/include/migraphx/gpu/convolution.hpp

@@ -31,7 +31,7 @@
 #include <migraphx/op/identity.hpp>
 #include <migraphx/op/convolution.hpp>
 #include <migraphx/op/quant_convolution.hpp>
-#include <migraphx/op/deconvolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 #include <unordered_map>
 #include <migraphx/reflect.hpp>
 #include <migraphx/gpu/context.hpp>
@@ -146,7 +146,8 @@ struct miopen_convolution
 
     void set_conv_descriptor()
     {
-        cd = (op.name() == "deconvolution") ? make_deconv(op) : make_conv(op);
+        cd =
+            (op.name() == "convolution_backwards") ? make_convolution_backwards(op) : make_conv(op);
     }
 
     value compile(migraphx::context& ctx, const shape& output, const std::vector<shape>& input)

src/targets/gpu/include/migraphx/gpu/miopen.hpp

@@ -170,7 +170,7 @@ inline convolution_descriptor make_conv(const T& op)
 }
 
 template <class T>
-inline convolution_descriptor make_deconv(const T& op)
+inline convolution_descriptor make_convolution_backwards(const T& op)
 {
     auto c = make_obj<convolution_descriptor>(&miopenCreateConvolutionDescriptor);
     miopenConvolutionMode_t c_mode = miopenTranspose;

src/targets/gpu/lowering.cpp

@@ -106,7 +106,7 @@ struct miopen_apply
         add_extend_op("topk");
 
         add_convolution_op("convolution");
-        add_convolution_op("deconvolution");
+        add_convolution_op("convolution_backwards");
         add_convolution_op("quant_convolution");
         add_gemm_op<op::dot>("dot");
         add_gemm_op<op::quant_dot>("quant_dot");

src/targets/ref/lowering.cpp

@@ -27,7 +27,7 @@
 #include <migraphx/dfor.hpp>
 #include <migraphx/op/identity.hpp>
 #include <migraphx/op/convolution.hpp>
-#include <migraphx/op/deconvolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 #include <migraphx/op/quant_convolution.hpp>
 #include <migraphx/op/dot.hpp>
 #include <migraphx/op/quant_dot.hpp>

test/onnx/conv_transpose_auto_pad_test.onnx

+conv_transpose_auto_pad_test:±

+:
+
x
+
w
yconv1"
ConvTranspose*
+auto_pad"
+SAME_UPPER 
conv_transpose_auto_pad_testZ
+
x
+

+

+

+
+Z
+
w
+

+

+

+
+b
+
y
+

+

+

+
+B
\ No newline at end of file

test/onnx/deconv_bias_test.onnx → test/onnx/conv_transpose_bias_test.onnx

-deconv_bias_test:ž

+conv_transpose_bias_test:¦

 "
 
x
 
w
-
b
yconv1"
ConvTransposedeconv_bias_testZ
+
b
yconv1"
ConvTransposeconv_transpose_bias_testZ
 
x
 

 

@@ -24,4 +24,4 @@
 

 

 
-B
+B
\ No newline at end of file