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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/05/03 05:44:32 UTC
[GitHub] zhreshold closed pull request #8582: Yolo2 operator
zhreshold closed pull request #8582: Yolo2 operator
URL: https://github.com/apache/incubator-mxnet/pull/8582
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diff --git a/src/operator/contrib/yolo_output-inl.h b/src/operator/contrib/yolo_output-inl.h
new file mode 100644
index 00000000000..9f95bcacdca
--- /dev/null
+++ b/src/operator/contrib/yolo_output-inl.h
@@ -0,0 +1,735 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * \file yolo_output-inl.h
+ * \brief yolo-v2 output layer, v1 to be added
+ * \author Joshua Zhang
+*/
+#ifndef MXNET_OPERATOR_CONTRIB_YOLO_OUTPUT_INL_H_
+#define MXNET_OPERATOR_CONTRIB_YOLO_OUTPUT_INL_H_
+#include <dmlc/logging.h>
+#include <dmlc/parameter.h>
+#include <mxnet/operator.h>
+#include <mxnet/base.h>
+#include <nnvm/tuple.h>
+#include <map>
+#include <vector>
+#include <string>
+#include <algorithm>
+#include <utility>
+#include "../operator_common.h"
+#include "../mshadow_op.h"
+#include "../mxnet_op.h"
+#include "../tensor/sort_op.h"
+
+namespace mxnet {
+namespace op {
+namespace yolo2out_enum {
+enum Yolo2OutputOpInputs {kData, kLabel};
+enum Yolo2OutputOpOutputs {kOut, kTemp, kCopy};
+enum Yolo2OutputOpAuxiliary {kCounter};
+enum Yolo2OutputOpResource {kTempSpace};
+} // namespace yolo2out_enum
+
+struct Yolo2OutputParam : public dmlc::Parameter<Yolo2OutputParam> {
+ int num_class;
+ int num_anchor;
+ float overlap_thresh;
+ float object_grad_scale;
+ float background_grad_scale;
+ float class_grad_scale;
+ float coord_grad_scale;
+ nnvm::Tuple<float> anchors;
+ int warmup_samples;
+ float warmup_grad_scale;
+ float nms_threshold;
+ int nms_topk;
+ bool force_suppress;
+ DMLC_DECLARE_PARAMETER(Yolo2OutputParam) {
+ DMLC_DECLARE_FIELD(num_class).set_lower_bound(1)
+ .describe("Number of object classes.");
+ DMLC_DECLARE_FIELD(num_anchor).set_default(5)
+ .set_lower_bound(1)
+ .describe("Number of anchors.");
+ DMLC_DECLARE_FIELD(overlap_thresh).set_default(0.6)
+ .describe("Positive overlap threshold.");
+ DMLC_DECLARE_FIELD(object_grad_scale).set_default(1.0)
+ .describe("Gradient scale for positive objects.");
+ DMLC_DECLARE_FIELD(background_grad_scale).set_default(1.0)
+ .describe("Gradient scale for background.");
+ DMLC_DECLARE_FIELD(class_grad_scale).set_default(1.0)
+ .describe("Gradient scale for positive objects.");
+ DMLC_DECLARE_FIELD(coord_grad_scale).set_default(1.0)
+ .describe("Gradient scale for box offsets.");
+ DMLC_DECLARE_FIELD(anchors)
+ .set_default({1.08f, 1.19f, 3.42f, 4.41f, 6.63f, 11.38f, 9.42f, 5.11f, 16.62f, 10.52f})
+ .describe("Predefined anchor box widths and heights.");
+ DMLC_DECLARE_FIELD(warmup_samples).set_default(12800)
+ .describe("Number of images to warm up towards averaging position for box "
+ "predictions when starting a new training. ");
+ DMLC_DECLARE_FIELD(warmup_grad_scale).set_default(0.01)
+ .describe("Gradient scale for non-critical anchors during warm-up stage.");
+ DMLC_DECLARE_FIELD(nms_threshold).set_default(0.5f)
+ .describe("Non-maximum suppression threshold.");
+ DMLC_DECLARE_FIELD(force_suppress).set_default(false)
+ .describe("Suppress all detections regardless of class_id.");
+ DMLC_DECLARE_FIELD(nms_topk).set_default(-1)
+ .describe("Keep maximum top k detections before nms, -1 for no limit.");
+ }
+}; // struct Yolo2OutputParam
+
+/*!
+ * \brief 1-D Intersection between two sections, either along x or y direction
+ *
+ * \param l1 left/top coordinate of box 1
+ * \param r1 right/bottom coordinate of box 1
+ * \param l2 left/top coordinate of box 2
+ * \param r2 right/bottom coordinate of box 2
+ * \tparam DType the data type
+ */
+template<typename DType>
+MSHADOW_XINLINE DType Intersect(DType l1, DType r1, DType l2, DType r2) {
+ DType left = l1 > l2 ? l1 : l2;
+ DType right = r1 < r2 ? r1 : r2;
+ DType w = right - left;
+ return w > 0 ? w : DType(0);
+}
+
+/*!
+ * \brief Box intersection-over-union ratio calculation
+ *
+ * \param l1 left coordinate of box
+ * \param t1 top coordinate of box
+ * \param r1 right coordinate of box
+ * \param b1 bottom coordinate of box
+ * \tparam DType the data type
+ */
+template<typename DType>
+MSHADOW_XINLINE DType Area(DType l1, DType t1, DType r1, DType b1) {
+ DType width = r1 - l1;
+ DType height = b1 - t1;
+ if (width <= 0 || height <= 0) return DType(0);
+ return width * height;
+}
+
+/*!
+ * \brief Box intersection-over-union ratio calculation
+ *
+ * \param l1 left coordinate of box 1
+ * \param t1 top coordinate of box 1
+ * \param r1 right coordinate of box 1
+ * \param b1 bottom coordinate of box 1
+ * \param l2 left coordinate of box 2
+ * \param t2 top coordinate of box 2
+ * \param r2 right coordinate of box 2
+ * \param b2 bottom coordinate of box 2
+ * \tparam DType the data type
+ */
+template<typename DType>
+MSHADOW_XINLINE DType IOU(DType l1, DType t1, DType r1, DType b1,
+ DType l2, DType t2, DType r2, DType b2) {
+ DType inter_area = Intersect(l1, r1, l2, r2) * Intersect(t1, b1, t2, b2);
+ if (inter_area <= 0) return DType(0);
+ DType area1 = Area(l1, t1, r1, b1);
+ DType area2 = Area(l2, t2, r2, b2);
+ return inter_area / (area1 + area2 - inter_area);
+}
+
+// compute intersection-over-union overlap between two boxes
+struct calc_overlap {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(int i, DType* out,
+ const DType* L1, const DType* T1, const DType* R1, const DType* B1,
+ const DType* L2, const DType* T2, const DType* R2, const DType* B2) {
+ out[i] = IOU(L1[i], T1[i], R1[i], B1[i], L2[i], T2[i], R2[i], B2[i]);
+ }
+};
+
+// clip a DType value to [0., 1.0]
+struct clip_zero_one {
+ template<typename DType>
+ MSHADOW_XINLINE static DType Map(DType a) {
+ if (a < 0.f) return DType(0.f);
+ if (a > 1.f) return DType(1.f);
+ return DType(a);
+ }
+}; // struct clip_zero_one
+
+
+// find best anchor box per ground-truth, and calculate grad
+struct box_grad {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(int i, DType* grad, DType* out_label,
+ const DType* label, const DType* anchor, const DType* pred,
+ const index_t label_width, const index_t label_offset,
+ const index_t pred_width, const index_t pred_offset,
+ const index_t grad_width, const index_t grad_offset,
+ const index_t num_anchor, const index_t num_label,
+ const index_t width, const index_t height,
+ const float box_scale, const float object_scale) {
+ for (int n = 0; n < static_cast<int>(num_label); ++n) {
+ int offset = (i * num_label + n) * label_width;
+ DType class_id = label[offset];
+ if (class_id < 0) return; // padded label
+ offset += label_offset;
+ // ground-truth
+ DType gl = label[offset];
+ DType gt = label[offset + 1];
+ DType gr = label[offset + 2];
+ DType gb = label[offset + 3];
+ DType gx = (gl + gr) / 2;
+ DType gy = (gt + gb) / 2;
+ DType gw = gr - gl;
+ DType gh = gb - gt;
+ if (gx < 0 || gy < 0 || gx > 1 || gy > 1) continue; // invalid gt
+ if (gw <= 0 || gh <= 0 || gw > 1 || gh > 1) continue; // invalid gt
+ // specific block region only where gt center located
+ int col = static_cast<int>(gx * width);
+ int row = static_cast<int>(gy * height);
+ int best_anchor = 0;
+ DType best_ovp = 0;
+ // find best anchor
+ for (int j = 0; j < static_cast<int>(num_anchor); ++j) {
+ DType aw = anchor[j * 2] / width;
+ DType ah = anchor[j * 2 + 1] / height;
+ if (aw < 0 || ah < 0) continue; // invalid param
+ DType minw = gw < aw ? gw : aw;
+ DType minh = gh < ah ? gh : ah;
+ DType ovp = minw * minh;
+ ovp = ovp / (gw * gh + aw * ah - ovp);
+ if (ovp > best_ovp) {
+ best_ovp = ovp;
+ best_anchor = j;
+ }
+ }
+ // box prediction and box grad
+ offset = (i * width * height * num_anchor + row * width * num_anchor +
+ col * num_anchor + best_anchor) * pred_width + pred_offset;
+ DType px = pred[offset];
+ DType py = pred[offset + 1];
+ DType pw = pred[offset + 2];
+ DType ph = pred[offset + 3];
+ int out_offset = (i * width * height * num_anchor + row * width * num_anchor +
+ col * num_anchor + best_anchor) * grad_width + grad_offset;
+ DType aw = anchor[best_anchor * 2];
+ DType ah = anchor[best_anchor * 2 + 1];
+ DType scale = box_scale * (2 - gw * gh);
+ grad[out_offset] = scale * (px - gx * width + col); // x
+ grad[out_offset + 1] = scale * (py - gy * height + row); // y
+ grad[out_offset + 2] = scale * (pw - logf(gw * width / aw)); // w
+ grad[out_offset + 3] = scale * (ph - logf(gh * height / ah)); // h
+
+ // object grad
+ px = (px + col) / width;
+ py = (py + row) / height;
+ pw = expf(pw) * anchor[best_anchor * 2] / width;
+ ph = expf(ph) * anchor[best_anchor * 2 + 1] / height;
+ DType iou = IOU(px - pw / 2, py - ph / 2, px + pw / 2, py + ph / 2, gl, gt, gr, gb);
+ --out_offset; // layout : num_class + 1 + 4
+ --offset;
+ grad[out_offset] = object_scale * (pred[offset] - iou);
+
+ // class target
+ offset = i * width * height * num_anchor + row * width * num_anchor +
+ col * num_anchor + best_anchor;
+ out_label[offset] = class_id;
+ }
+ }
+};
+
+/*!
+ * \brief Implementation of the non-maximum suppression operation
+ *
+ * \param i the launched thread index
+ * \param index sorted index in descending order
+ * \param input the input of nms op
+ * \param k nms topk number
+ * \param ref compare reference position
+ * \param num number of input boxes in each batch
+ * \param stride input stride, usually 6 (id-score-x1-y1-x2-y2)
+ * \param offset_box box offset, usually 2
+ * \param thresh nms threshold
+ * \param force force suppress regardless of class id
+ * \param offset_id class id offset, used when force == false, usually 0
+ * \tparam DType the data type
+ */
+struct nms_impl {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(int i, DType* index, const DType* input,
+ int k, int ref, int num, int stride,
+ int offset_box, int offset_id,
+ float thresh, bool force) {
+ int b = i / k; // batch
+ int pos = i % k + ref + 1; // position
+ if (index[b * num + ref] < 0) return; // reference has been suppressed
+ if (index[b * num + pos] < 0) return; // self been suppressed
+ int ref_offset = static_cast<int>(index[b * num + ref]) * stride + offset_box;
+ int pos_offset = static_cast<int>(index[b * num + pos]) * stride + offset_box;
+ if (!force) {
+ int ref_id = static_cast<int>(input[ref_offset - offset_box + offset_id]);
+ int pos_id = static_cast<int>(input[pos_offset - offset_box + offset_id]);
+ if (ref_id != pos_id) return; // different class
+ }
+ DType refl = input[ref_offset];
+ DType reft = input[ref_offset + 1];
+ DType refr = input[ref_offset + 2];
+ DType refb = input[ref_offset + 3];
+ DType pl = input[pos_offset];
+ DType pt = input[pos_offset + 1];
+ DType pr = input[pos_offset + 2];
+ DType pb = input[pos_offset + 3];
+ DType iou = IOU(refl, reft, refr, refb, pl, pt, pr, pb);
+ if (iou > thresh) {
+ index[b * num + pos] = -1;
+ }
+ }
+};
+
+struct nms_assign {
+ template<typename DType>
+ MSHADOW_XINLINE static void Map(int i, DType* out, const DType* input,
+ const DType *index, int k, int num, int stride) {
+ int count = 0;
+ for (int j = 0; j < k; ++j) {
+ int location = static_cast<int>(index[i * num + j]);
+ if (location >= 0) {
+ // copy to output
+ int out_location = (i * num + count) * stride;
+ int in_location = location * stride;
+ for (int s = 0; s < stride; ++s) {
+ out[out_location + s] = input[in_location + s];
+ }
+ ++count;
+ }
+ }
+ }
+};
+
+template<typename xpu, typename DType>
+class Yolo2OutputOp : public Operator {
+ public:
+ explicit Yolo2OutputOp(Yolo2OutputParam param) {
+ this->param_ = param;
+ }
+
+ virtual void Forward(const OpContext &ctx,
+ const std::vector<TBlob> &in_data,
+ const std::vector<OpReqType> &req,
+ const std::vector<TBlob> &out_data,
+ const std::vector<TBlob> &aux_args) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ using namespace mxnet_op;
+ CHECK_EQ(in_data.size(), 2U) << "Yolo2Output Input: [data, label]";
+ CHECK_EQ(out_data.size(), 3U) << "Yolo2Output Output: [output, temp, copy]";
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+ Tensor<xpu, 4, DType> data = in_data[yolo2out_enum::kData]
+ .get<xpu, 4, DType>(s);
+ Tensor<xpu, 3, DType> out = out_data[yolo2out_enum::kOut]
+ .get<xpu, 3, DType>(s);
+ Tensor<xpu, 3, DType> temp = out_data[yolo2out_enum::kTemp]
+ .get<xpu, 3, DType>(s);
+ Tensor<xpu, 3, DType> out_copy = out_data[yolo2out_enum::kCopy]
+ .get<xpu, 3, DType>(s);
+ Shape<3> tshape = temp.shape_;
+ int nc = param_.num_class;
+ Shape<3> softmax_shape = Shape3(tshape[0], nc, tshape[1]);
+ Shape<1> bias_shape = Shape1(param_.num_anchor * 2);
+ Shape<3> anchors_shape = Shape3(tshape[0], tshape[1], 2);
+ index_t temp_space_size = 2 * softmax_shape.Size() + bias_shape.Size()
+ + anchors_shape.Size();
+ Tensor<xpu, 1, DType> temp_buffer = ctx.requested[yolo2out_enum::kTempSpace]
+ .get_space_typed<xpu, 1, DType>(Shape1(temp_space_size), s);
+ CHECK_EQ(temp_buffer.CheckContiguous(), true);
+ Tensor<xpu, 3, DType> softmax_in(temp_buffer.dptr_, softmax_shape, s);
+ Tensor<xpu, 3, DType> softmax_out(softmax_in.dptr_ + softmax_in.MSize(),
+ softmax_shape, s);
+ Tensor<xpu, 1, DType> xpu_bias(softmax_out.dptr_ + softmax_out.MSize(),
+ bias_shape, s);
+ Tensor<xpu, 3, DType> temp_anchors(xpu_bias.dptr_ + xpu_bias.MSize(),
+ anchors_shape, s);
+ ScalarExp<DType> in_w = ScalarExp<DType>(1.0 / data.shape_[3]);
+ ScalarExp<DType> in_h = ScalarExp<DType>(1.0 / data.shape_[2]);
+
+ // change the order of dimensions;
+ temp = reshape(transpose(data, Shape4(0, 2, 3, 1)), temp.shape_);
+ softmax_in = transpose(slice<2>(temp, 0, nc), Shape3(0, 2, 1));
+ Softmax(softmax_out, softmax_in);
+ slice<2>(temp, 0, nc) = transpose(softmax_out, Shape3(0, 2, 1));
+ // class id to output
+ slice<2>(out, 0, 1) = transpose(reduce_keepdim<red::maximum, true>(
+ softmax_out, 1), Shape3(0, 2, 1));
+ // apply logistic to score, x, y
+ slice<2>(temp, nc, nc + 3) = F<mshadow_op::sigmoid>(slice<2>(temp, nc, nc + 3));
+ // scores to output
+ slice<2>(out, 1, 2) = F<mshadow_op::identity>(slice<2>(
+ temp, nc, nc + 1));
+ // x = (logistic(pred[0]) + i) / in_w
+ tshape[2] = 1;
+ slice<2>(out, 2, 3) = in_w * (slice<2>(temp, nc + 1, nc + 2) +
+ reshape(broadcast_with_axis(repmat(range<DType>(
+ 0, data.shape_[3], 1, param_.num_anchor), data.shape_[2]), -1,
+ data.shape_[0]), tshape));
+ // y = (logistic(pred[1]) + j) / in_h
+ slice<2>(out, 3, 4) = in_h * (slice<2>(temp, nc + 2, nc + 3) +
+ reshape(broadcast_with_axis(range<DType>(0,
+ data.shape_[2], 1, data.shape_[3] * param_.num_anchor), -1, data.shape_[0]),
+ tshape));
+ // anchors
+ nnvm::Tuple<DType> anchors(param_.anchors.begin(), param_.anchors.end());
+ Tensor<cpu, 1, DType> cpu_bias(anchors.begin(), Shape1(anchors.ndim()));
+ Copy(xpu_bias, cpu_bias, s);
+ temp_anchors = reshape(repmat(xpu_bias, data.shape_[0] * data.shape_[2] * data.shape_[3]),
+ temp_anchors.shape_);
+ // w = exp(pred[2]) * anchor[w] / in_w
+ slice<2>(out, 4, 5) = in_w * F<mshadow_op::exp>(slice<2>(temp, nc + 3, nc + 4)) *
+ slice<2>(temp_anchors, 0, 1);
+ // h = exp(pred[3]) * anchor[y] / in_h
+ slice<2>(out, 5, 6) = in_h * F<mshadow_op::exp>(slice<2>(temp, nc + 4, nc + 5)) *
+ slice<2>(temp_anchors, 1, 2);
+
+ // convert output from x, y, w, h to xmin, ymin, xmax, ymax format
+ slice<2>(out, 2, 3) -= ScalarExp<DType>(0.5) * slice<2>(out, 4, 5);
+ slice<2>(out, 3, 4) -= ScalarExp<DType>(0.5) * slice<2>(out, 5, 6);
+ slice<2>(out, 4, 5) += slice<2>(out, 2, 3);
+ slice<2>(out, 5, 6) += slice<2>(out, 3, 4);
+
+ // make copy for backward
+ out_copy = F<mshadow_op::identity>(out);
+
+ // clip to boundaries
+ slice<2>(out, 2, 6) = F<clip_zero_one>(slice<2>(out, 2, 6));
+
+ // apply nms
+ if (param_.nms_threshold > 0 && param_.nms_threshold < 1) {
+ int keep = param_.nms_topk < 0 ? out.shape_[1] : param_.nms_topk;
+ keep = keep > static_cast<int>(out.shape_[1]) ? out.shape_[1] : keep;
+ if (keep > 0) {
+ // descend sort by score
+ int num_batch = out.shape_[0];
+ int num_elem = out.shape_[1];
+ Shape<1> sort_index_shape = Shape1(num_batch * num_elem);
+ Shape<3> buffer_shape = Shape3(num_batch, num_elem, out.shape_[2]);
+ index_t nms_ws_size = 3 * sort_index_shape.Size() + buffer_shape.Size();
+ Tensor<xpu, 1, DType> nms_workspace = ctx.requested[yolo2out_enum::kTempSpace]
+ .get_space_typed<xpu, 1, DType>(Shape1(nms_ws_size), s);
+ CHECK_EQ(nms_workspace.CheckContiguous(), true);
+ Tensor<xpu, 1, DType> sorted_index(nms_workspace.dptr_, sort_index_shape, s);
+ Tensor<xpu, 1, DType> scores(sorted_index.dptr_ + sorted_index.MSize(),
+ sort_index_shape, s);
+ Tensor<xpu, 1, DType> batch_id(scores.dptr_ + scores.MSize(),
+ sort_index_shape, s);
+ Tensor<xpu, 3, DType> buffer(batch_id.dptr_ + batch_id.MSize(),
+ buffer_shape, s);
+
+ // copy score to buffer, sort accordingly to get sorted index
+ scores = reshape(slice<2>(out, 1, 2), scores.shape_);
+ sorted_index = range<DType>(0, num_batch * num_elem);
+ mxnet::op::SortByKey(scores, sorted_index, false);
+ batch_id = F<mshadow_op::floor>(sorted_index / ScalarExp<DType>(num_elem));
+ mxnet::op::SortByKey(batch_id, scores, true);
+ batch_id = F<mshadow_op::floor>(sorted_index / ScalarExp<DType>(num_elem));
+ mxnet::op::SortByKey(batch_id, sorted_index, true);
+
+ // go through each box as reference, suppress if overlap > threshold
+ // sorted_index with -1 is marked as suppressed
+ for (int ref = 0; ref < keep; ++ref) {
+ int num_worker = keep - ref - 1;
+ if (num_worker < 1) continue;
+ Kernel<nms_impl, xpu>::Launch(s, num_batch * num_worker, sorted_index.dptr_,
+ out.dptr_, num_worker, ref, num_elem, out.shape_[2], 2, 0,
+ param_.nms_threshold, param_.force_suppress);
+ }
+
+ // store the result
+ buffer = F<mshadow_op::identity>(out);
+ out = -1;
+ Kernel<nms_assign, xpu>::Launch(s, num_batch, out.dptr_,
+ buffer.dptr_, sorted_index.dptr_, keep, num_elem, out.shape_[2]);
+ }
+ }
+ }
+
+ virtual void Backward(const OpContext &ctx,
+ const std::vector<TBlob> &out_grad,
+ const std::vector<TBlob> &in_data,
+ const std::vector<TBlob> &out_data,
+ const std::vector<OpReqType> &req,
+ const std::vector<TBlob> &in_grad,
+ const std::vector<TBlob> &aux_states) {
+ using namespace mshadow;
+ using namespace mshadow::expr;
+ CHECK_EQ(in_data.size(), 2U) << "Yolo2Output Input: [data, label]";
+ CHECK_EQ(out_data.size(), 3U) << "Yolo2Output Output: [output, temp, copy]";
+ Stream<xpu> *s = ctx.get_stream<xpu>();
+ Tensor<xpu, 3, DType> label = in_data[yolo2out_enum::kLabel].get<xpu, 3, DType>(s);
+ Tensor<xpu, 4, DType> grad = in_grad[yolo2out_enum::kData].get<xpu, 4, DType>(s);
+ Tensor<xpu, 3, DType> temp_out = out_data[yolo2out_enum::kTemp].get<xpu, 3, DType>(s);
+ Tensor<xpu, 3, DType> out_copy = out_data[yolo2out_enum::kCopy].get<xpu, 3, DType>(s);
+ Tensor<xpu, 1> counter = aux_states[yolo2out_enum::kCounter].get<xpu, 1, real_t>(s);
+ index_t num_batch = label.shape_[0];
+ index_t num_label = label.shape_[1];
+ index_t label_width = label.shape_[2];
+ index_t pred_width = temp_out.shape_[2];
+ index_t num_box = temp_out.shape_[1];
+ index_t in_width = grad.shape_[3];
+ index_t in_height = grad.shape_[2];
+ index_t nc = param_.num_class;
+ index_t grad_width = nc + 5;
+ index_t num_anchor = param_.num_anchor;
+ const DType ignore_label = static_cast<DType>(-1);
+
+ // temp space
+ Shape<2> label_shape = Shape2(num_batch, num_box);
+ Shape<3> softmax_shape = Shape3(num_batch, nc, num_box);
+ Shape<4> overlaps_shape = Shape4(9, num_batch, num_box, num_label);
+ Shape<3> grad_shape = Shape3(num_batch, num_box, grad_width);
+ Shape<1> anchor_shape = Shape1(num_anchor * 2);
+ // Shape<4> label_index_shape = Shape4(2, num_batch, num_label, 1);
+ // Shape<3> temp_index_mask_shape = Shape3(num_batch, num_label, num_box);
+ size_t temp_size_total = label_shape.Size() + 2 * softmax_shape.Size() +
+ overlaps_shape.Size() + grad_shape.Size() + anchor_shape.Size();
+ Tensor<xpu, 1, DType> temp_space = ctx.requested[yolo2out_enum::kTempSpace]
+ .get_space_typed<xpu, 1, DType>(Shape1(temp_size_total), s);
+ CHECK_EQ(temp_space.CheckContiguous(), true);
+ Tensor<xpu, 2, DType> temp_label(temp_space.dptr_, label_shape, s);
+ Tensor<xpu, 3, DType> temp_softmax(temp_label.dptr_ + temp_label.MSize(),
+ softmax_shape, s);
+ Tensor<xpu, 3, DType> temp_softmax_grad(temp_softmax.dptr_ + temp_softmax.MSize(),
+ softmax_shape, s);
+ // [0]-[7] for x1, y1, w1, h1, x2, y2, w2, h2, [8] for overlap
+ Tensor<xpu, 4, DType> buffer(temp_softmax_grad.dptr_ + temp_softmax_grad.MSize(),
+ overlaps_shape, s);
+ Tensor<xpu, 3, DType> temp_grad(buffer.dptr_ + buffer.MSize(),
+ grad_shape, s);
+ Tensor<xpu, 1, DType> xpu_bias(temp_grad.dptr_ + temp_grad.MSize(),
+ anchor_shape, s);
+
+ Shape<3> tshape = Shape3(num_batch, num_box, num_label);
+ for (int i = 0; i < 4; ++i) {
+ // gt_x1, gt_y1, gt_x2, gt_y2
+ buffer[i] = reshape(broadcast_with_axis(slice<2>(label, i + 1, i + 2), 0,
+ num_box), tshape);
+ // o_x1, o_y1, o_x2, o_y2
+ buffer[i + 4] = reshape(broadcast_with_axis(slice<2>(out_copy, i + 2,
+ i + 3), 1, num_label), tshape);
+ }
+ mxnet_op::Kernel<calc_overlap, xpu>::Launch(s, tshape.Size(), buffer[8].dptr_,
+ buffer[0].dptr_, buffer[1].dptr_, buffer[2].dptr_, buffer[3].dptr_,
+ buffer[4].dptr_, buffer[5].dptr_, buffer[6].dptr_, buffer[7].dptr_);
+
+ // objectness grad
+ temp_grad = DType(0);
+ slice<2>(temp_grad, nc, nc + 1) = ScalarExp<DType>(param_.background_grad_scale) *
+ slice<2>(temp_out, nc, nc + 1);
+ // mask out when iou > thresh
+ slice<2>(temp_grad, nc, nc + 1) *= F<mshadow_op::lt>(
+ reduce_keepdim<red::maximum, false>(buffer[8], 2),
+ ScalarExp<DType>(param_.overlap_thresh));
+
+ // optional warm up for initial training stage
+ Tensor<cpu, 1, real_t> cpu_counter = ctx.requested[yolo2out_enum::kTempSpace]
+ .get_host_space_typed<1, real_t>(Shape1(1));
+ Copy(cpu_counter, counter, s);
+ if (cpu_counter.dptr_[0] < 0) {
+ cpu_counter.dptr_[0] = 0;
+ counter = 0;
+ }
+ if (cpu_counter.dptr_[0] < param_.warmup_samples) {
+ const ScalarExp<DType> init_scale = ScalarExp<DType>(param_.warmup_grad_scale);
+ slice<2>(temp_grad, nc + 1, nc + 2) = init_scale * (
+ slice<2>(temp_out, nc + 1, nc + 2) - ScalarExp<DType>(0.5));
+ slice<2>(temp_grad, nc + 2, nc + 3) = init_scale * (
+ slice<2>(temp_out, nc + 2, nc + 3) - ScalarExp<DType>(0.5));
+ slice<2>(temp_grad, nc + 3, nc + 4) = init_scale *
+ slice<2>(temp_out, nc + 3, nc + 4);
+ slice<2>(temp_grad, nc + 4, nc + 5) = init_scale *
+ slice<2>(temp_out, nc + 4, nc + 5);
+ }
+ if (cpu_counter.dptr_[0] < 3e38) {
+ counter += num_batch;
+ }
+
+ // find best match for each ground-truth, and calculate grad for box pred
+ nnvm::Tuple<DType> anchors(param_.anchors.begin(), param_.anchors.end());
+ Tensor<cpu, 1, DType> cpu_bias(anchors.begin(), Shape1(anchors.ndim()));
+ Copy(xpu_bias, cpu_bias, s);
+ temp_label = ignore_label; // assign default as ignored
+ mxnet_op::Kernel<box_grad, xpu>::Launch(s, num_batch,
+ temp_grad.dptr_, temp_label.dptr_, label.dptr_, xpu_bias.dptr_, temp_out.dptr_,
+ label_width, 1, pred_width, nc + 1, grad_width, nc + 1,
+ num_anchor, num_label, in_width, in_height, param_.coord_grad_scale,
+ param_.object_grad_scale);
+
+ // softmax loss
+ temp_softmax = transpose(slice<2>(temp_out, 0, nc), Shape3(0, 2, 1));
+ SoftmaxGrad(temp_softmax_grad, temp_softmax, temp_label, ignore_label);
+ slice<2>(temp_grad, 0, nc) = transpose(temp_softmax_grad, Shape3(0, 2, 1))
+ * ScalarExp<DType>(param_.class_grad_scale);
+
+ // apply logistic grad to score, x, y
+ slice<2>(temp_grad, nc, nc + 3) *= F<mshadow_op::sigmoid_grad>(
+ slice<2>(temp_out, nc, nc + 3));
+
+ // transpose grad to data shape
+ grad = transpose(reshape(temp_grad, Shape4(num_batch, in_height,
+ in_width, num_anchor * grad_width)), Shape4(0, 3, 1, 2));
+ }
+
+ private:
+ Yolo2OutputParam param_;
+}; // class Yolo2OutputOp
+
+template<typename xpu>
+Operator *CreateOp(Yolo2OutputParam, int dtype);
+
+#if DMLC_USE_CXX11
+class Yolo2OutputProp : public OperatorProperty {
+ public:
+ void Init(const std::vector<std::pair<std::string, std::string> >& kwargs) override {
+ param_.Init(kwargs);
+ }
+
+ std::map<std::string, std::string> GetParams() const override {
+ return param_.__DICT__();
+ }
+
+ std::vector<std::string> ListArguments() const override {
+ return {"data", "label"};
+ }
+
+ std::vector<std::string> ListOutputs() const override {
+ return {"output", "temp", "copy"};
+ }
+
+ std::vector<std::string> ListAuxiliaryStates() const override {
+ return {"beta"};
+ }
+
+ int NumVisibleOutputs() const override {
+ return 1;
+ }
+
+ bool InferShape(std::vector<TShape> *in_shape,
+ std::vector<TShape> *out_shape,
+ std::vector<TShape> *aux_shape) const override {
+ using namespace mshadow;
+ CHECK_EQ(in_shape->size(), 2U) << "Input:[data, label]";
+ const TShape &dshape = in_shape->at(0);
+ if (dshape.ndim() == 0) return false;
+ if (dshape.ndim() != 4) throw InferShapeError("4-dim data required",
+ yolo2out_enum::kData);
+
+ // data shape
+ CHECK_EQ(param_.anchors.ndim() % 2, 0)
+ << "invalid anchor provided, should be continuous pairs of [w1, h1, w1, h2...]";
+ CHECK_EQ(param_.num_anchor, param_.anchors.ndim() / 2) << "anchor number mismatch";
+ int num_channel = param_.num_anchor * (param_.num_class + 1 + 4);
+ TShape data_shape = Shape4(dshape[0], num_channel, dshape[2], dshape[3]);
+ SHAPE_ASSIGN_CHECK(*in_shape, yolo2out_enum::kData, data_shape);
+ // label shape
+ TShape lshape = in_shape->at(yolo2out_enum::kLabel);
+ if (lshape.ndim() > 0) {
+ CHECK_EQ(lshape.ndim(), 3) << "Label should be [batch-num_labels-(>=5)] tensor";
+ CHECK_GT(lshape[1], 0) << "Padded label should > 0";
+ CHECK_GE(lshape[2], 5) << "Label width must >=5";
+ } else {
+ lshape = Shape3(dshape[0], 2, 5);
+ }
+ SHAPE_ASSIGN_CHECK(*in_shape, yolo2out_enum::kLabel, lshape);
+ // output shape
+ TShape oshape = Shape3(dshape[0], param_.num_anchor * dshape[2] * dshape[3], 6);
+ out_shape->clear();
+ out_shape->push_back(oshape);
+ out_shape->push_back(Shape3(dshape[0], param_.num_anchor * dshape[2] * dshape[3],
+ param_.num_class + 4 + 1));
+ out_shape->push_back(oshape);
+ aux_shape->clear();
+ aux_shape->push_back(Shape1(1));
+ return true;
+ }
+
+ bool InferType(std::vector<int> *in_type,
+ std::vector<int> *out_type,
+ std::vector<int> *aux_type) const override {
+ CHECK_GE(in_type->size(), 1U);
+ int dtype = (*in_type)[0];
+ CHECK_NE(dtype, -1) << "First input must have specified type";
+ for (index_t i = 0; i < in_type->size(); ++i) {
+ if ((*in_type)[i] == -1) {
+ (*in_type)[i] = dtype;
+ } else {
+ CHECK_EQ((*in_type)[i], dtype) << "This layer requires uniform type. "
+ << "Expected " << dtype << " v.s. given "
+ << (*in_type)[i] << " at " << ListArguments()[i];
+ }
+ }
+ aux_type->clear();
+ aux_type->push_back(mshadow::kFloat32);
+ out_type->clear();
+ out_type->push_back(dtype); // out
+ out_type->push_back(dtype); // temp
+ out_type->push_back(dtype); // out copy
+ return true;
+ }
+
+ OperatorProperty* Copy() const override {
+ auto ptr = new Yolo2OutputProp();
+ ptr->param_ = param_;
+ return ptr;
+ }
+
+ std::string TypeString() const override {
+ return "_contrib_Yolo2Output";
+ }
+
+ std::vector<int> DeclareBackwardDependency(
+ const std::vector<int> &out_grad,
+ const std::vector<int> &in_data,
+ const std::vector<int> &out_data) const override {
+ return {in_data[yolo2out_enum::kLabel], out_data[yolo2out_enum::kTemp],
+ out_data[yolo2out_enum::kCopy]};
+ }
+
+ std::vector<ResourceRequest> ForwardResource(
+ const std::vector<TShape> &in_shape) const override {
+ return {ResourceRequest::kTempSpace};
+ }
+
+ std::vector<ResourceRequest> BackwardResource(
+ const std::vector<TShape> &in_shape) const override {
+ return {ResourceRequest::kTempSpace};
+ }
+
+ Operator* CreateOperator(Context ctx) const override {
+ LOG(FATAL) << "Not Implemented.";
+ return NULL;
+ }
+
+ Operator* CreateOperatorEx(Context ctx, std::vector<TShape> *in_shape,
+ std::vector<int> *in_type) const override;
+
+ private:
+ Yolo2OutputParam param_;
+}; // Yolo2OutputProp
+#endif // DMLC_USE_CXX11
+} // namespace op
+} // namespace mxnet
+#endif // MXNET_OPERATOR_CONTRIB_YOLO_OUTPUT_INL_H_
diff --git a/src/operator/contrib/yolo_output.cc b/src/operator/contrib/yolo_output.cc
new file mode 100644
index 00000000000..2909ac033d9
--- /dev/null
+++ b/src/operator/contrib/yolo_output.cc
@@ -0,0 +1,66 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * \file yolo_output.cc
+ * \brief Yolo2Output op
+ * \author Joshua Zhang
+*/
+#include "./yolo_output-inl.h"
+
+namespace mxnet {
+namespace op {
+template<>
+Operator *CreateOp<cpu>(Yolo2OutputParam param, int dtype) {
+ Operator *op = NULL;
+ MSHADOW_SGL_DBL_TYPE_SWITCH(dtype, DType, {
+ op = new Yolo2OutputOp<cpu, DType>(param);
+ });
+ return op;
+}
+
+Operator *Yolo2OutputProp::CreateOperatorEx(Context ctx, std::vector<TShape> *in_shape,
+ std::vector<int> *in_type) const {
+ std::vector<TShape> out_shape, aux_shape;
+ std::vector<int> out_type, aux_type;
+ CHECK(InferType(in_type, &out_type, &aux_type));
+ CHECK(InferShape(in_shape, &out_shape, &aux_shape));
+ DO_BIND_DISPATCH(CreateOp, param_, (*in_type)[0]);
+}
+
+DMLC_REGISTER_PARAMETER(Yolo2OutputParam);
+
+MXNET_REGISTER_OP_PROPERTY(_contrib_Yolo2Output, Yolo2OutputProp)
+.describe(R"code(Yolo v2 output layer. This is a convolutional version as described in YOLO 9000 paper.
+
+ Examples::
+
+ x = mx.nd.random.uniform(shape=(1, 75, 20, 20))
+ y = mx.nd.zeros((1, 1, 5))
+ beta = mx.nd.zeros((1))
+ output = Yolo2Output(x, y, beta, num_class=10)
+ output.shape = (1, 2000, 5)
+
+)code" ADD_FILELINE)
+.add_argument("data", "NDArray-or-Symbol", "Input data to the Yolo2OutputOp.")
+.add_argument("label", "NDArray-or-Symbol", "Object detection labels.")
+.add_argument("beta", "NDArray-or-Symbol", "Warm up counting buffer.")
+.add_arguments(Yolo2OutputParam::__FIELDS__());
+} // namespace op
+} // namespace mxnet
diff --git a/src/operator/contrib/yolo_output.cu b/src/operator/contrib/yolo_output.cu
new file mode 100644
index 00000000000..0bc788b9fbb
--- /dev/null
+++ b/src/operator/contrib/yolo_output.cu
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * \file yolo_output.cu
+ * \brief yolo2 output layer
+ * \author Joshua Zhang
+ */
+#include "./yolo_output-inl.h"
+
+namespace mxnet {
+namespace op {
+
+template<>
+Operator* CreateOp<gpu>(Yolo2OutputParam param, int dtype) {
+ Operator *op = NULL;
+ MSHADOW_SGL_DBL_TYPE_SWITCH(dtype, DType, {
+ op = new Yolo2OutputOp<gpu, DType>(param);
+ })
+ return op;
+}
+
+} // namespace op
+} // namespace mxnet
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