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LocalizationLayer.lua
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LocalizationLayer.lua
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require 'torch'
require 'nn'
require 'densecap.modules.OurCrossEntropyCriterion'
require 'densecap.modules.BilinearRoiPooling'
require 'densecap.modules.ReshapeBoxFeatures'
require 'densecap.modules.ApplyBoxTransform'
require 'densecap.modules.InvertBoxTransform'
require 'densecap.modules.BoxSamplerHelper'
require 'densecap.modules.RegularizeLayer'
require 'densecap.modules.MakeAnchors'
-- local net_utils = require 'net_utils'
local box_utils = require 'densecap.box_utils'
local utils = require 'densecap.utils'
--[[
A LocalizationLayer wraps up all of the complexities of detection regions and
using a spatial transformer to attend to their features. Used on its own, it can
be used for learnable region proposals; it can also be plugged into larger modules
to do region proposal + classification (detection) or region proposal + captioning\
(dense captioning).
Input:
- cnn_features: 1 x C x H x W array of CNN features
Returns: List of:
- roi_features: (pos + neg) x D x HH x WW array of features for RoIs;
roi_features[{{1, pos}}] gives the features for the positive RoIs
and the rest are negatives.
- roi_boxes: (pos + neg) x 4 array of RoI box coordinates (xc, yc, w, h);
roi_boxes[{{1, pos}}] gives the coordinates for the positive boxes
and the rest are negatives.
- gt_boxes_sample: pos x 4 array of ground-truth region boxes corresponding to
sampled positives. This will be an empty Tensor at test-time.
- gt_labels_sample: pos x L array of ground-truth labels corresponding to sampled
positives. This will be an empty Tensor at test-time.
Before each forward pass, you need to call the setImageSize method to set the size
of the underlying image for that forward pass. During training, you also need to call
the setGroundTruth method to set the ground-truth boxes and sequnces:
- gt_boxes: 1 x B1 x 4 array of ground-truth region boxes
- gt_labels: 1 x B1 x L array of ground-truth labels for regions
After each forward pass, the instance variable stats will be populated with useful
information; in particular stats.losses has all the losses.
If you set the instance variable timing to true, then stats.times will contain
times for all forward and backward passes.
--]]
local layer, parent = torch.class('nn.LocalizationLayer', 'nn.Module')
-- Forward declaration; defined below
local build_rpn
function layer:__init(opt)
parent.__init(self)
opt = opt or {}
opt.input_dim = utils.getopt(opt, 'input_dim')
opt.output_height = utils.getopt(opt, 'output_height')
opt.output_width = utils.getopt(opt, 'output_width')
-- list x0, y0, sx, sy
opt.field_centers = utils.getopt(opt, 'field_centers')
opt.backend = utils.getopt(opt, 'backend', 'cudnn')
opt.rpn_filter_size = utils.getopt(opt, 'rpn_filter_size', 3)
opt.rpn_num_filters = utils.getopt(opt, 'rpn_num_filters', 256)
opt.zero_box_conv = utils.getopt(opt, 'zero_box_conv', true)
opt.std = utils.getopt(opt, 'std', 0.01)
opt.anchor_scale = utils.getopt(opt, 'anchor_scale', 1.0)
opt.sampler_batch_size = utils.getopt(opt, 'sampler_batch_size', 256)
opt.sampler_high_thresh = utils.getopt(opt, 'sampler_high_thresh', 0.7)
opt.sampler_low_thresh = utils.getopt(opt, 'sampler_low_thresh', 0.5)
opt.train_remove_outbounds_boxes = utils.getopt(opt, 'train_remove_outbounds_boxes', 1)
utils.ensureopt(opt, 'mid_box_reg_weight')
utils.ensureopt(opt, 'mid_objectness_weight')
opt.box_reg_decay = utils.getopt(opt, 'box_reg_decay', 0)
self.opt = opt
self.losses = {}
self.nets = {}
-- Computes region proposals from conv features
self.nets.rpn = build_rpn(opt)
-- Performs positive / negative sampling of region proposals
self.nets.box_sampler_helper = nn.BoxSamplerHelper{
batch_size=opt.sampler_batch_size,
low_thresh=opt.sampler_low_thresh,
high_thresh=opt.sampler_high_thresh,
}
-- Interpolates conv features for each RoI
self.nets.roi_pooling = nn.BilinearRoiPooling(opt.output_height, opt.output_width)
-- Used to compute box regression targets from GT boxes
self.nets.invert_box_transform = nn.InvertBoxTransform()
-- Construct criterions
self.nets.obj_crit_pos = nn.OurCrossEntropyCriterion() -- for objectness
self.nets.obj_crit_neg = nn.OurCrossEntropyCriterion() -- for objectness
self.nets.box_reg_crit = nn.SmoothL1Criterion() -- for RPN box regression
-- Intermediates computed during forward pass
-- Output of RPN
self.rpn_out = nil
self.rpn_boxes = nil
self.rpn_anchors = nil
self.rpn_trans = nil
self.rpn_scores = nil
-- Outputs of sampler
self.pos_data = nil
self.pos_boxes = nil
self.pos_anchors = nil
self.pos_trans = nil
self.pos_target_data = nil
self.pos_target_boxes = nil
self.pos_target_labels = nil
self.neg_data = nil
self.neg_scores = nil
self.roi_boxes = torch.Tensor()
-- Used as targets for pos / neg objectness crits
self.pos_labels = torch.Tensor()
self.neg_labels = torch.Tensor()
-- Used as targets for bounding box regression
self.pos_trans_targets = torch.Tensor()
-- Used to track image size; must call setImageSize before each forward pass
self.image_width = nil
self.image_height = nil
self._called_forward_size = false
self._called_backward_size = false
-- Other instance variables
self.timer = torch.Timer()
self.timing = false -- Set to true to enable timing
self.dump_vars = false -- Set to true to dump vars
self:reset_stats()
self:setTestArgs()
self:training()
end
function layer:parameters()
-- The only part of the DetectionModule that has parameters is the RPN,
-- so just forward the call
return self.nets.rpn:parameters()
end
-- This needs to be called before each forward pass
function layer:setImageSize(image_height, image_width)
self.image_height = image_height
self.image_width = image_width
self._called_forward_size = false
self._called_backward_size = false
end
--[[
This needs to be called before every training-time forward pass.
Inputs:
- gt_boxes: 1 x B1 x 4 array of ground-truth region boxes
- gt_labels: 1 x B1 x L array of ground-truth labels for regions
--]]
function layer:setGroundTruth(gt_boxes, gt_labels)
self.gt_boxes = gt_boxes
self.gt_labels = gt_labels
self._called_forward_gt = false
self._called_backward_gt = false
end
function layer:reset_stats()
self.stats = {}
self.stats.losses = {}
self.stats.times = {}
self.stats.vars = {}
end
function layer:clearState()
self.timer = nil
self.rpn_out = nil
self.rpn_boxes = nil
self.rpn_anchors = nil
self.rpn_trans = nil
self.rpn_scores = nil
self.pos_data = nil
self.pos_boxes = nil
self.pos_anchors = nil
self.pos_trans = nil
self.pos_target_data = nil
self.pos_target_boxes = nil
self.pos_target_labels = nil
self.neg_data = nil
self.neg_scores = nil
self.roi_boxes:set()
self.nets.rpn:clearState()
self.nets.roi_pooling:clearState()
end
function layer:timeit(name, f)
self.timer = self.timer or torch.Timer()
if self.timing then
cutorch.synchronize()
self.timer:reset()
f()
cutorch.synchronize()
self.stats.times[name] = self.timer:time().real
else
f()
end
end
function layer:setTestArgs(args)
args = args or {}
self.test_clip_boxes = utils.getopt(args, 'clip_boxes', true)
self.test_nms_thresh = utils.getopt(args, 'nms_thresh', 0.7)
self.test_max_proposals = utils.getopt(args, 'max_proposals', 300)
end
function layer:updateOutput(input)
if self.train then
return self:_forward_train(input)
else
return self:_forward_test(input)
end
end
function layer:_forward_test(input)
local cnn_features = input
local arg = {
clip_boxes = self.test_clip_boxes,
nms_thresh = self.test_nms_thresh,
max_proposals = self.test_max_proposals
}
-- Make sure that setImageSize has been called
assert(self.image_height and self.image_width and not self._called_forward_size,
'Must call setImageSize before each forward pass')
self._called_forward_size = true
local rpn_out
self:timeit('rpn:forward_test', function()
rpn_out = self.nets.rpn:forward(cnn_features)
end)
local rpn_boxes, rpn_anchors = rpn_out[1], rpn_out[2]
local rpn_trans, rpn_scores = rpn_out[3], rpn_out[4]
local num_boxes = rpn_boxes:size(2)
-- Maybe clip boxes to image boundary
local valid
if arg.clip_boxes then
local bounds = {
x_min=1, y_min=1,
x_max=self.image_width,
y_max=self.image_height
}
rpn_boxes, valid = box_utils.clip_boxes(rpn_boxes, bounds, 'xcycwh')
--print(string.format('%d/%d boxes are predicted valid',
-- torch.sum(valid), valid:nElement()))
-- Clamp parallel arrays only to valid boxes (not oob of the image)
local function clamp_data(data)
-- data should be 1 x kHW x D
-- valid is byte of shape kHW
assert(data:size(1) == 1, 'must have 1 image per batch')
assert(data:dim() == 3)
local mask = valid:view(1, -1, 1):expandAs(data)
return data[mask]:view(1, -1, data:size(3))
end
rpn_boxes = clamp_data(rpn_boxes)
rpn_anchors = clamp_data(rpn_anchors)
rpn_trans = clamp_data(rpn_trans)
rpn_scores = clamp_data(rpn_scores)
num_boxes = rpn_boxes:size(2)
end
-- Convert rpn boxes from (xc, yc, w, h) format to (x1, y1, x2, y2)
local rpn_boxes_x1y1x2y2 = box_utils.xcycwh_to_x1y1x2y2(rpn_boxes)
-- Convert objectness positive / negative scores to probabilities
local rpn_scores_exp = torch.exp(rpn_scores)
local pos_exp = rpn_scores_exp[{1, {}, 1}]
local neg_exp = rpn_scores_exp[{1, {}, 2}]
local scores = (pos_exp + neg_exp):pow(-1):cmul(pos_exp)
local verbose = false
if verbose then
print('in LocalizationLayer forward_test')
print(string.format('Before NMS there are %d boxes', num_boxes))
print(string.format('Using NMS threshold %f', arg.nms_thresh))
end
-- Run NMS and sort by objectness score
local boxes_scores = scores.new(num_boxes, 5)
boxes_scores[{{}, {1, 4}}] = rpn_boxes_x1y1x2y2
boxes_scores[{{}, 5}] = scores
local idx
self:timeit('nms', function()
if arg.max_proposals == -1 then
idx = box_utils.nms(boxes_scores, arg.nms_thresh)
else
idx = box_utils.nms(boxes_scores, arg.nms_thresh, arg.max_proposals)
end
end)
-- Use NMS indices to pull out corresponding data from RPN
-- All these are being converted from (1, B2, D) to (B3, D)
-- where B2 are the number of boxes after boundary clipping and B3
-- is the number of boxes after NMS
local rpn_boxes_nms = rpn_boxes:index(2, idx)[1]
local rpn_anchors_nms = rpn_anchors:index(2, idx)[1]
local rpn_trans_nms = rpn_trans:index(2, idx)[1]
local rpn_scores_nms = rpn_scores:index(2, idx)[1]
local scores_nms = scores:index(1, idx)
if verbose then
print(string.format('After NMS there are %d boxes', rpn_boxes_nms:size(1)))
end
-- Use roi pooling to get features for boxes
local roi_features
self:timeit('roi_pooling:forward_test', function()
self.nets.roi_pooling:setImageSize(self.image_height, self.image_width)
roi_features = self.nets.roi_pooling:forward{cnn_features[1], rpn_boxes_nms}
end)
if self.dump_vars then
local vars = self.stats.vars or {}
vars.test_rpn_boxes_nms = rpn_boxes_nms
vars.test_rpn_anchors_nms = rpn_anchors_nms
vars.test_rpn_scores_nms = scores:index(1, idx)
self.stats.vars = vars
end
local empty = roi_features.new()
self.output = {roi_features, rpn_boxes_nms, empty, empty}
return self.output
-- return roi_features, rpn_boxes_nms, scores_nms
end
--[[
Input: List of:
- cnn_features: N x C x H x W array of CNN features
- gt_boxes: N x B1 x 4 array of ground-truth region boxes
- gt_labels: N x B1 x L array of ground-truth labels for regions
Returns: List of:
- roi_features: B2 x D x HH x WW array of features for RoIs sampled as positives
- roi_boxes: B2 x 4 array of boxes for sampled RoIs (xc, yc, w, h)
- gt_boxes_sample: B2 x 4 array of ground-truth region boxes corresponding to
sampled positives.
- gt_labels_sample: B2 x L array of ground-truth labels corresponding to sampled
positives.
Running the forward pass also updates the instance variable losses, which is a
table mapping names of losses to their values.
--]]
function layer:_forward_train(input)
local cnn_features = input
assert(self.gt_boxes and self.gt_labels and not self._called_forward_gt,
'Must call setGroundTruth before training-time forward pass')
local gt_boxes, gt_labels = self.gt_boxes, self.gt_labels
self._called_forward_gt = true
-- Make sure that setImageSize has been called
assert(self.image_height and self.image_width and not self._called_forward_size,
'Must call setImageSize before each forward pass')
self._called_forward_size = true
local N = cnn_features:size(1)
assert(N == 1, 'Only minibatches with N = 1 are supported')
local B1 = gt_boxes:size(2)
assert(gt_boxes:dim() == 3 and gt_boxes:size(1) == N and gt_boxes:size(3) == 4,
'gt_boxes must have shape (N, B1, 4)')
assert(gt_labels:dim() == 3 and gt_labels:size(1) == N and gt_labels:size(2) == B1,
'gt_labels must have shape (N, B1, L)')
self:reset_stats()
-- Run the RPN forward
self:timeit('rpn:forward', function()
self.rpn_out = self.nets.rpn:forward(cnn_features)
self.rpn_boxes = self.rpn_out[1]
self.rpn_anchors = self.rpn_out[2]
self.rpn_trans = self.rpn_out[3]
self.rpn_scores = self.rpn_out[4]
end)
if self.opt.train_remove_outbounds_boxes == 1 then
local image_height, image_width = nil, nil
local bounds = {
x_min=1, y_min=1,
x_max=self.image_width,
y_max=self.image_height
}
self.nets.box_sampler_helper:setBounds(bounds)
end
-- Run the sampler forward
self:timeit('sampler:forward', function()
local sampler_out = self.nets.box_sampler_helper:forward{
self.rpn_out, {gt_boxes, gt_labels}}
-- Unpack pos data
self.pos_data, self.pos_target_data, self.neg_data = unpack(sampler_out)
self.pos_boxes, self.pos_anchors = self.pos_data[1], self.pos_data[2]
self.pos_trans, self.pos_scores = self.pos_data[3], self.pos_data[4]
-- Unpack target data
self.pos_target_boxes, self.pos_target_labels = unpack(self.pos_target_data)
-- Unpack neg data (only scores matter)
self.neg_boxes = self.neg_data[1]
self.neg_scores = self.neg_data[4]
end)
local num_pos, num_neg = self.pos_boxes:size(1), self.neg_scores:size(1)
-- Concatentate pos_boxes and neg_boxes into roi_boxes
self.roi_boxes:resize(num_pos + num_neg, 4)
self.roi_boxes[{{1, num_pos}}]:copy(self.pos_boxes)
self.roi_boxes[{{num_pos + 1, num_pos + num_neg}}]:copy(self.neg_boxes)
-- Run the RoI pooling forward for positive boxes
self:timeit('roi_pooling:forward', function()
self.nets.roi_pooling:setImageSize(self.image_height, self.image_width)
self.roi_features = self.nets.roi_pooling:forward{cnn_features[1], self.roi_boxes}
end)
-- Compute objectness loss
self:timeit('objectness_loss:forward', function()
if self.pos_scores:type() ~= 'torch.CudaTensor' then
-- ClassNLLCriterion expects LongTensor labels for CPU score types,
-- but CudaTensor labels for GPU score types. self.pos_labels and
-- self.neg_labels will be casted by any call to self:type(), so
-- we need to cast them back to LongTensor for CPU tensor types.
self.pos_labels = self.pos_labels:long()
self.neg_labels = self.neg_labels:long()
end
self.pos_labels:resize(num_pos):fill(1)
self.neg_labels:resize(num_neg):fill(2)
local obj_loss_pos = self.nets.obj_crit_pos:forward(self.pos_scores, self.pos_labels)
local obj_loss_neg = self.nets.obj_crit_neg:forward(self.neg_scores, self.neg_labels)
local obj_weight = self.opt.mid_objectness_weight
self.stats.losses.obj_loss_pos = obj_weight * obj_loss_pos
self.stats.losses.obj_loss_neg = obj_weight * obj_loss_neg
end)
-- Compute targets for RPN bounding box regression
self:timeit('invert_box_transform:forward', function()
self.pos_trans_targets = self.nets.invert_box_transform:forward{
self.pos_anchors, self.pos_target_boxes}
end)
-- DIRTY DIRTY HACK: To prevent the loss from blowing up, replace boxes
-- with huge pos_trans_targets with ground-truth
local max_trans = torch.abs(self.pos_trans_targets):max(2)
local max_trans_mask = torch.gt(max_trans, 10):expandAs(self.pos_trans_targets)
local mask_sum = max_trans_mask:sum() / 4
if mask_sum > 0 then
local msg = 'WARNING: Masking out %d boxes in LocalizationLayer'
print(string.format(msg, mask_sum))
self.pos_trans[max_trans_mask] = 0
self.pos_trans_targets[max_trans_mask] = 0
end
-- Compute RPN box regression loss
self:timeit('box_reg_loss:forward', function()
local crit = self.nets.box_reg_crit
local weight = self.opt.mid_box_reg_weight
local loss = weight * crit:forward(self.pos_trans, self.pos_trans_targets)
self.stats.losses.box_reg_loss = loss
end)
-- Fish out the box regression loss
local reg_mods = self.nets.rpn:findModules('nn.RegularizeLayer')
assert(#reg_mods == 1)
self.stats.losses.box_decay_loss = reg_mods[1].loss
-- Compute total loss
local total_loss = 0
for k, v in pairs(self.stats.losses) do
total_loss = total_loss + v
end
self.stats.losses.total_loss = total_loss
if self.dump_vars then
local vars = self.stats.vars or {}
vars.pred_scores = self.rpn_scores[1]
vars.pred_boxes = self.rpn_boxes[1]
vars.pred_anchors = self.rpn_anchors[1]
vars.aligned_pos_boxes = self.pos_boxes
vars.aligned_pos_scores = self.pos_scores
vars.aligned_target_boxes = self.pos_target_boxes
vars.sampled_neg_boxes = self.neg_boxes
vars.sampled_neg_scores = self.neg_scores
self.stats.vars = vars
end
self.output = {self.roi_features, self.roi_boxes, self.pos_target_boxes, self.pos_target_labels}
return self.output
end
function layer:updateGradInput(input, gradOutput)
assert(self.train, 'can only call updateGradInput in training mode')
assert(self.gt_boxes and self.gt_labels and not self._called_backward_gt,
'Must call setGroundTruth before each forward pass')
self._called_backward_gt = true
assert(self.image_height and self.image_width and not self._called_backward_size,
'Must call setImageSize before each forward pass')
self._called_backward_size = true
local cnn_features = input
local gt_boxes = self.gt_boxes
local gt_labels = self.gt_labels
local grad_roi_features = gradOutput[1]
local grad_roi_boxes = gradOutput[2]:clone()
local num_pos, num_neg = self.pos_boxes:size(1), self.neg_scores:size(1)
local grad_pos_boxes = grad_roi_boxes[{{1, num_pos}}]
local grad_neg_boxes = grad_roi_boxes[{{num_pos + 1, num_pos + num_neg}}]
local grad_cnn_features = self.gradInput
grad_cnn_features:resizeAs(cnn_features):zero()
-- Backprop RPN box regression loss
local grad_pos_trans
self:timeit('box_reg_loss:backward', function()
local crit = self.nets.box_reg_crit
local weight = self.opt.mid_box_reg_weight
grad_pos_trans = crit:backward(self.pos_trans, self.pos_trans_targets)
-- Note that this is a little weird - it modifies a modules gradInput
-- in-place, which could cause trouble if this gradient is reused.
grad_pos_trans:mul(weight)
end)
-- Backprop objectness loss
local grad_pos_scores, grad_neg_scores
self:timeit('objectness_loss:backward', function()
grad_pos_scores = self.nets.obj_crit_pos:backward(self.pos_scores, self.pos_labels)
--print('backward: ', self.neg_labels:nElement(), self.neg_labels:sum(), self.neg_scores:sum())
grad_neg_scores = self.nets.obj_crit_neg:backward(self.neg_scores, self.neg_labels)
-- Same problem as above - modifying gradients in-place may be dangerous
grad_pos_scores:mul(self.opt.mid_objectness_weight)
grad_neg_scores:mul(self.opt.mid_objectness_weight)
end)
-- Backprop RoI pooling
--local grad_cnn_features
self:timeit('roi_pooling:backward', function()
local din = self.nets.roi_pooling:backward(
{cnn_features[1], self.roi_boxes},
grad_roi_features)
grad_roi_boxes:add(din[2])
grad_cnn_features:add(din[1]:viewAs(cnn_features))
end)
-- Backprop sampler
local grad_rpn_out
self:timeit('sampler:backward', function()
local grad_pos_data, grad_neg_data = {}, {}
grad_pos_data[1] = grad_pos_boxes
grad_pos_data[3] = grad_pos_trans
grad_pos_data[4] = grad_pos_scores
grad_neg_data[1] = grad_neg_boxes
grad_neg_data[4] = grad_neg_scores
grad_rpn_out = self.nets.box_sampler_helper:backward(
{self.rpn_out, {gt_boxes, gt_labels}},
{grad_pos_data, grad_neg_data})
end)
-- Backprop RPN
self:timeit('rpn:backward', function()
local din = self.nets.rpn:backward(cnn_features, grad_rpn_out)
grad_cnn_features:add(din)
end)
return self.gradInput
end
-- RPN returns {boxes, anchors, transforms, scores}
function build_rpn(opt)
-- Set up anchor sizes
local anchors = opt.anchors
if not anchors then
anchors = torch.Tensor({
{45, 90}, {90, 45}, {64, 64},
{90, 180}, {180, 90}, {128, 128},
{181, 362}, {362, 181}, {256, 256},
{362, 724}, {724, 362}, {512, 512},
}):t():clone()
anchors:mul(opt.anchor_scale)
end
local num_anchors = anchors:size(2)
local rpn = nn.Sequential()
-- Add an extra conv layer and a ReLU
local pad = math.floor(opt.rpn_filter_size / 2)
local conv_layer = nn.SpatialConvolution(
opt.input_dim,
opt.rpn_num_filters,
opt.rpn_filter_size,
opt.rpn_filter_size,
1, 1, pad, pad)
conv_layer.weight:normal(0, opt.std)
conv_layer.bias:zero()
rpn:add(conv_layer)
rpn:add(nn.ReLU(true))
-- Branch to produce box coordinates for each anchor
-- This branch will return {boxes, {anchors, transforms}}
local box_branch = nn.Sequential()
local box_conv_layer = nn.SpatialConvolution(
opt.rpn_num_filters,
4 * num_anchors,
1, 1, 1, 1, 0, 0)
if opt.zero_box_conv then
box_conv_layer.weight:zero()
else
box_conv_layer.weight:normal(0, opt.std)
end
box_conv_layer.bias:zero()
box_branch:add(box_conv_layer)
box_branch:add(nn.RegularizeLayer(opt.box_reg_decay))
local x0, y0, sx, sy = unpack(opt.field_centers)
local seq = nn.Sequential()
seq:add(nn.MakeAnchors(x0, y0, sx, sy, anchors))
seq:add(nn.ReshapeBoxFeatures(num_anchors))
local cat1 = nn.ConcatTable()
cat1:add(seq)
cat1:add(nn.ReshapeBoxFeatures(num_anchors))
box_branch:add(cat1)
local cat2 = nn.ConcatTable()
cat2:add(nn.ApplyBoxTransform())
cat2:add(nn.Identity())
box_branch:add(cat2)
-- Branch to produce box / not box scores for each anchor
local rpn_branch = nn.Sequential()
local rpn_conv_layer = nn.SpatialConvolution(
opt.rpn_num_filters, 2 * num_anchors,
1, 1, 1, 1, 0, 0)
rpn_conv_layer.weight:normal(0, opt.std)
rpn_conv_layer.bias:zero()
rpn_branch:add(rpn_conv_layer)
rpn_branch:add(nn.ReshapeBoxFeatures(num_anchors))
-- Concat and flatten the branches
local concat = nn.ConcatTable()
concat:add(box_branch)
concat:add(rpn_branch)
rpn:add(concat)
rpn:add(nn.FlattenTable())
if opt.backend == 'cudnn' then
require 'cudnn'
cudnn.convert(rpn, cudnn)
end
return rpn
end