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/*
// Copyright (c) 2018 Intel Corporation
//
// 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.
*/
#include "scale_grad_weights_inst.h"
#include "primitive_type_base.h"
#include "error_handler.h"
#include "json_object.h"
namespace cldnn
{
primitive_type_id scale_grad_weights_type_id()
{
static primitive_type_base<scale_grad_weights> instance;
return &instance;
}
layout scale_grad_weights_inst::calc_output_layout(scale_grad_weights_node const& node)
{
//output buffer will not be used in this primitive
auto input_grad_layout_size = node.input().get_output_layout();
return{ input_grad_layout_size.data_type, input_grad_layout_size.format,{ 1, 1, 1, 1 } };
}
std::string scale_grad_weights_inst::to_string(scale_grad_weights_node const& node)
{
auto desc = node.get_primitive();
auto node_info = node.desc_to_json();
auto& input = node.input();
auto& scale_input = node.weights();
auto& input_grad = node.input_grad();
std::stringstream primitive_description;
json_composite scale_grad_weights_info;
scale_grad_weights_info.add("input", input.id());
scale_grad_weights_info.add("scale input", scale_input.id());
scale_grad_weights_info.add("input grad", input_grad.id());
if (node.bias_term())
scale_grad_weights_info.add("bias", node.bias().id());
node_info.add("scale_grad_weights info", scale_grad_weights_info);
node_info.dump(primitive_description);
return primitive_description.str();
}
scale_grad_weights_inst::typed_primitive_inst(network_impl& network, scale_grad_weights_node const& node)
:parent(network, node)
{
auto scale_layout = node.weights().get_output_layout();
auto scale_format = scale_layout.format;
auto scale_sizes = scale_layout.size;
auto scale_feature_size = scale_layout.size.feature[0];
auto input_layout = node.input().get_output_layout();
auto input_feature_size = input_layout.size.feature[0];
CLDNN_ERROR_NOT_EQUAL(node.id(), "Scale feature size", scale_feature_size, "input feature size", input_feature_size, "");
if (scale_sizes.spatial[0] != 1 || scale_sizes.spatial[1] != 1 || scale_sizes.batch[0] != 1) //Remove if support for other scale sizes will be added.
{
CLDNN_ERROR_MESSAGE(node.id(), "All sizes in scale_input except feature should be 1.");
}
if (node.use_momentum())
{
CLDNN_ERROR_LAYOUT_MISMATCH(node.id(), "Scale memory", node.weights().get_output_layout(), "previous scale grad memory", node.prev_scale_grad().get_output_layout(), "");
CLDNN_ERROR_LAYOUT_MISMATCH(node.id(), "Bias memory", node.bias().get_output_layout(), "previous bias grad memory", node.prev_bias_grad().get_output_layout(), "");
}
if (node.bias_term())
{
auto bias_layout = node.bias().get_output_layout();
auto bias_format = bias_layout.format;
auto bias_raw_sizes = bias_layout.size.raw;
CLDNN_ERROR_NOT_PROPER_FORMAT(node.id(), "Scale format", scale_format.value, "bias format", bias_format);
for (size_t i = 0; i < bias_layout.size.raw.size(); ++i)
{
if (scale_layout.size.raw[i] != bias_raw_sizes[i])
CLDNN_ERROR_MESSAGE(node.id(), "Scale input size do not match bias size! Size index:" + std::to_string(i));
}
}
}
}
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