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ImageDetect.lua
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ImageDetect.lua
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--[[----------------------------------------------------------------------------
Copyright (c) 2016-present, Facebook, Inc. All rights reserved.
This source code is licensed under the BSD-style license found in the
LICENSE file in the root directory of this source tree. An additional grant
of patent rights can be found in the PATENTS file in the same directory.
------------------------------------------------------------------------------]]
local utils = paths.dofile'utils.lua'
local mu = paths.dofile'myutils.lua'
local ImageDetect = torch.class('fbcoco.ImageDetect')
function ImageDetect:__init(model, transformer, scale, max_size)
assert(model, 'must provide model!')
assert(transformer, 'must provide transformer!')
self.model = model
self.image_transformer = transformer
self.scale = scale or {600}
self.max_size = max_size or 1000
self.sm = nn.SoftMax():cuda()
end
local function getImages(self,images,im)
local num_scales = #self.scale
local imgs = {}
local im_sizes = {}
local im_scales = {}
im = self.image_transformer:forward(im)
local channel = im:size(1)
local im_size = im[1]:size()
local im_size_min = math.min(im_size[1],im_size[2])
local im_size_max = math.max(im_size[1],im_size[2])
for i=1,num_scales do
local im_scale = self.scale[i]/im_size_min
if torch.round(im_scale*im_size_max) > self.max_size then
im_scale = self.max_size/im_size_max
end
local im_s = {im_size[1]*im_scale,im_size[2]*im_scale}
table.insert(imgs,image.scale(im,im_s[2],im_s[1]))
table.insert(im_sizes,im_s)
table.insert(im_scales,im_scale)
end
-- create single tensor with all images, padding with zero for different sizes
im_sizes = torch.IntTensor(im_sizes)
local max_shape = im_sizes:max(1)[1]
images:resize(num_scales,channel,max_shape[1],max_shape[2]):zero()
for i=1,num_scales do
images[i][{{},{1,imgs[i]:size(2)},{1,imgs[i]:size(3)}}]:copy(imgs[i])
end
return im_scales
end
local function project_im_rois(im_rois,scales)
local levels
local rois = torch.FloatTensor()
if #scales > 1 then
assert(false, 'Multiple-scale mode is not properly implemented.')
local scales = torch.FloatTensor(scales)
local widths = im_rois[{{},3}] - im_rois[{{},1}] + 1
local heights = im_rois[{{},4}] - im_rois[{{}, 2}] + 1
local areas = widths * heights
local scaled_areas = areas:view(-1,1) * torch.pow(scales:view(1,-1),2)
local diff_areas = torch.abs(scaled_areas - 224 * 224)
levels = select(2, diff_areas:min(2))
else
levels = torch.FloatTensor()
rois:resize(im_rois:size(1),5)
rois[{{},1}]:fill(1)
rois[{{},{2,5}}]:copy(im_rois):add(-1):mul(scales[1]):add(1)
end
return rois
end
local function recursiveSplit(x, bs, dim)
if type(x) == 'table' then
local res = {}
for k,v in pairs(x) do
local tmp = v:split(bs,dim)
for i=1,#tmp do
if not res[i] then res[i] = {} end
res[i][k] = tmp[i]
end
end
return res
else
return x:split(bs, dim)
end
end
function ImageDetect:memoryEfficientForward(model, input, bs, recompute_features)
local images = input[1]
local rois = input[2]
local recompute_features = recompute_features == nil and true or recompute_features
assert(model.output[1]:numel() > 0)
local rest = nn.Sequential()
for i=2,#model.modules do rest:add(model:get(i)) end
local final = model:get(#model.modules)
-- assuming the net has bbox regression part
self.output = self.output or {torch.CudaTensor(), torch.CudaTensor()}
local num_classes = self.model.output[1]:size(2)
self.output[1]:resize(rois:size(1), num_classes)
self.output[2]:resize(rois:size(1), num_classes * 4)
if recompute_features then
model:get(1):forward{images,rois}
else
model:get(1).output[2] = rois
end
local features = model:get(1).output
assert(features[2]:size(1) == rois:size(1))
local roi_split = features[2]:split(bs,1)
local output1_split = self.output[1]:split(bs,1)
local output2_split = self.output[2]:split(bs,1)
for i,v in ipairs(roi_split) do
local out = rest:forward({features[1], v})
output1_split[i]:copy(out[1])
output2_split[i]:copy(out[2])
end
local function test()
local output_full = model:forward({images,rois})
local output_split = self.output
assert((output_full[1] - output_split[1]):abs():max() == 0)
assert((output_full[2] - output_split[2]):abs():max() == 0)
end
--test()
return self.output
end
function ImageDetect:computeRawOutputs(im, boxes, min_images, recompute_features)
self.model:evaluate()
local inputs = {torch.FloatTensor(),torch.FloatTensor()}
local im_scales = getImages(self,inputs[1],im)
inputs[2] = project_im_rois(boxes,im_scales)
if min_images then
assert(inputs[1]:size(1) == 1)
inputs[1] = inputs[1]:expand(min_images, inputs[1]:size(2), inputs[1]:size(3), inputs[1]:size(4))
end
self.inputs_cuda = self.inputs_cuda or {torch.CudaTensor(),torch.CudaTensor()}
self.inputs_cuda[1]:resize(inputs[1]:size()):copy(inputs[1])
self.inputs_cuda[2]:resize(inputs[2]:size()):copy(inputs[2])
return self.model:forward(self.inputs_cuda)
end
-- supposes boxes is in [x1,y1,x2,y2] format
function ImageDetect:detect(im, boxes, min_images, recompute_features)
self.model:evaluate()
local inputs = {torch.FloatTensor(),torch.FloatTensor()}
local im_scales = getImages(self,inputs[1],im)
inputs[2] = project_im_rois(boxes,im_scales)
if min_images then
assert(inputs[1]:size(1) == 1)
inputs[1] = inputs[1]:expand(min_images, inputs[1]:size(2), inputs[1]:size(3), inputs[1]:size(4))
end
self.inputs_cuda = self.inputs_cuda or {torch.CudaTensor(),torch.CudaTensor()}
self.inputs_cuda[1]:resize(inputs[1]:size()):copy(inputs[1])
self.inputs_cuda[2]:resize(inputs[2]:size()):copy(inputs[2])
local output0 = self:memoryEfficientForward(self.model, self.inputs_cuda, 500, recompute_features)
--local output0 = self.model:forward(self.inputs_cuda)
local class_values, bbox_values
if torch.type(output0) == 'table' then
class_values= output0[1]
bbox_values = output0[2]:float()
for i,v in ipairs(bbox_values:split(4,2)) do
utils.convertFrom(v,boxes,v)
end
else
class_values = output0
end
if not self.model.noSoftMax then
class_values = self.sm:forward(class_values)
end
return class_values:float(), bbox_values
end
---------------------------------------------
function ImageDetect:detect_VID_LONGMEM(im, boxes, det_T, min_images, recompute_features)
self.model:evaluate()
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
assert(T == #boxes, '#im does not equate to #boxes.')
assert(det_T%2==1, 'det_T must be odd number.')
-- Assume we can hold all conv map in memory
local conv_stack = self.model.conv_stack
local conv_maps = {}
local B = math.ceil(T / det_T)
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + det_T - 1, T)
local batch_im = im[{{start_ptr, end_ptr}, {}, {}, {}}]
for tmpidx = 1, batch_im:size(1) do
local tmp_im = batch_im[{tmpidx, {}, {}, {}}]
tmp_im:copy(self.image_transformer:forward(tmp_im))
end
batch_im = batch_im:cuda()
local output = conv_stack:forward(batch_im):clone()
table.insert(conv_maps, output)
start_ptr = end_ptr + 1
end
conv_maps = torch.cat(conv_maps, 1)
-- set STMM N and T
local default_N, default_T = utils.set_NT(self.model.STMM_stack, 1, det_T)
-- forward, do left->right and right->left individually
local B = math.ceil(T / det_T)
local start_ptr, inv_start_ptr = 1, T
local STMMs = self.model.STMM_stack:findModules('nn.STMM')
assert(#STMMs == 2, 'You sure have only one layer?')
local left2right = STMMs[1]
local right2left = STMMs[2]
local left2right_mem, right2left_mem, left2right_coll, right2left_coll = nil, nil, {}, {}
for bidx = 1, B do
collectgarbage()
-- left -> right
local end_ptr = math.min(start_ptr + det_T - 1, T)
local len = end_ptr - start_ptr + 1
local cur_conv_maps = conv_maps[{{start_ptr, end_ptr}, {}, {}, {}}]
utils.set_NT(left2right, 1, len)
local left2right_output
if left2right_mem then
left2right_output = left2right:forward({cur_conv_maps, left2right_mem}):clone()
else
left2right_output = left2right:forward(cur_conv_maps):clone()
end
left2right_mem = left2right_output[{{len}, {}, {}, {}}]
table.insert(left2right_coll, left2right_output)
-- right -> left
local inv_end_ptr = math.max(inv_start_ptr - det_T + 1, 1)
local inv_len = inv_start_ptr - inv_end_ptr + 1
local inv_seq = torch.range(inv_start_ptr, inv_end_ptr, -1):long()
local cur_inv_conv_maps = conv_maps:index(1, inv_seq)
utils.set_NT(right2left, 1, inv_len)
local right2left_output
if right2left_mem then
right2left_output = right2left:forward({cur_inv_conv_maps, right2left_mem}):clone()
else
right2left_output = right2left:forward(cur_inv_conv_maps):clone()
end
right2left_mem = right2left_output[{{inv_len}, {}, {}, {}}]
table.insert(right2left_coll, right2left_output)
-- track counter
start_ptr = end_ptr + 1
inv_start_ptr = inv_end_ptr - 1
end
left2right_coll = torch.cat(left2right_coll, 1)
right2left_coll = torch.cat(right2left_coll, 1)
-- revert back right2left_coll
right2left_coll = right2left_coll:index(1, torch.range(T,1,-1):long())
-- merge
local stmm_maps = torch.cat({left2right_coll, right2left_coll}, 2)
-- clear model state and collectgarbage
conv_maps = nil
collectgarbage()
-- set back N and T
utils.set_NT(self.model.STMM_stack, default_N, default_T)
start_ptr = 1
for bidx = 1, B do
local end_ptr = math.min(start_ptr + det_T - 1, T)
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local cur_stmm_maps = stmm_maps[{{start_ptr, end_ptr}, {}, {}, {}}]
local score, bbox = self:conv2pred(cur_stmm_maps, cur_boxes, self.model.post_stack)
collectgarbage()
for tmpidx = 1, #cur_boxes do
local glbidx = tmpidx + start_ptr - 1
score_coll[glbidx] = score[tmpidx]
bbox_coll[glbidx] = bbox[tmpidx]
end
start_ptr = end_ptr + 1
end
return score_coll, bbox_coll
end
---------------------------------------------
function ImageDetect:detect_VID_MULWINSIZE(im, boxes, det_T, min_images, recompute_features)
self.model:evaluate()
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
assert(T == #boxes, '#im does not equate to #boxes.')
if T <= det_T then
score_coll, bbox_coll = self:detect_VID(im, boxes, min_images, recompute_features)
else
-- NOTE A set of window size
local det_T_seq = {det_T, det_T-4, det_T-8}
-- Assume we can hold all conv map in memory
local conv_stack = self.model.conv_stack
local conv_maps = {}
local B = math.ceil(T / det_T)
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + det_T - 1, T)
local batch_im = im[{{start_ptr, end_ptr}, {}, {}, {}}]
for tmpidx = 1, batch_im:size(1) do
local tmp_im = batch_im[{tmpidx, {}, {}, {}}]
tmp_im:copy(self.image_transformer:forward(tmp_im))
end
batch_im = batch_im:cuda()
local output = conv_stack:forward(batch_im):clone()
--output = output:float()
table.insert(conv_maps, output)
start_ptr = end_ptr + 1
end
conv_maps = torch.cat(conv_maps, 1)
-- Fetch network
self.top = self.top or nn.Sequential()
:add(nn.ParallelTable()
:add(self.model.STMM_stack)
:add(nn.Identity()))
:add(self.model.post_stack)
local default_N, default_T = utils.set_NT(self.top, 1, 1)
-- Compute multiple window size
for tIdx, t in ipairs(det_T_seq) do
collectgarbage()
-- Init container
score_coll[tIdx] = {}
bbox_coll[tIdx] = {}
assert(t%2==1, 'det_T must be odd number.')
local center_idx = (t - 1) / 2 + 1
-- Set NT
utils.set_NT(self.top, 1, t)
-- Compute STMM output
for start_ptr = 1, T - t + 1 do
local end_ptr = math.min(start_ptr + t - 1, T)
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local cur_conv_maps = conv_maps[{{start_ptr, end_ptr}, {}, {}, {}}]
local score, bbox = self:conv2pred(cur_conv_maps, cur_boxes, self.top)
collectgarbage()
if start_ptr == 1 then
for tmpidx = 1, center_idx do
local glbidx = tmpidx + start_ptr - 1
score_coll[tIdx][glbidx] = score[tmpidx]
bbox_coll[tIdx][glbidx] = bbox[tmpidx]
end
elseif start_ptr == T-t+1 then
for tmpidx = center_idx, t do
local glbidx = tmpidx + start_ptr - 1
score_coll[tIdx][glbidx] = score[tmpidx]
bbox_coll[tIdx][glbidx] = bbox[tmpidx]
end
else
local glbidx = center_idx + start_ptr - 1
score_coll[tIdx][glbidx] = score[center_idx]
bbox_coll[tIdx][glbidx] = bbox[center_idx]
end
end
end
-- Merge different win size
local score_coll_flat, bbox_coll_flat = {}, {}
for frame_idx = 1, T do
local score_tmp, bbox_tmp = {}, {}
for tIdx = 1, #det_T_seq do
score_tmp[tIdx] = score_coll[tIdx][frame_idx]
bbox_tmp[tIdx] = bbox_coll[tIdx][frame_idx]
end
score_coll_flat[frame_idx] = torch.cat(score_tmp, 1)
bbox_coll_flat[frame_idx] = torch.cat(bbox_tmp, 1)
end
score_coll = score_coll_flat
bbox_coll = bbox_coll_flat
-- Set NT
utils.set_NT(self.top, default_N, default_T)
end
return score_coll, bbox_coll
end
function ImageDetect:detect_VID_LAST(im, boxes, det_T, min_images, recompute_features)
self.model:evaluate()
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
assert(T == #boxes, '#im does not equate to #boxes.')
assert(det_T%2==1, 'det_T must be odd number.')
if T <= det_T then
score_coll, bbox_coll = self:detect_VID(im, boxes, min_images, recompute_features)
else
-- Assume we can hold all conv map in memory
local conv_stack = self.model.conv_stack
local conv_maps = {}
local B = math.ceil(T / det_T)
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + det_T - 1, T)
local batch_im = im[{{start_ptr, end_ptr}, {}, {}, {}}]
for tmpidx = 1, batch_im:size(1) do
local tmp_im = batch_im[{tmpidx, {}, {}, {}}]
tmp_im:copy(self.image_transformer:forward(tmp_im))
end
batch_im = batch_im:cuda()
local output = conv_stack:forward(batch_im):clone()
--output = output:float()
table.insert(conv_maps, output)
start_ptr = end_ptr + 1
end
conv_maps = torch.cat(conv_maps, 1)
-- Compute STMM output
self.top = self.top or nn.Sequential()
:add(nn.ParallelTable()
:add(self.model.STMM_stack)
:add(nn.Identity()))
:add(self.model.post_stack)
for start_ptr = 1, T - det_T + 1 do
-- clear state of the model
--self.top:clearState()
local end_ptr = math.min(start_ptr + det_T - 1, T)
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local cur_conv_maps = conv_maps[{{start_ptr, end_ptr}, {}, {}, {}}]
local score, bbox = self:conv2pred(cur_conv_maps, cur_boxes, self.top)
collectgarbage()
if start_ptr == 1 then
for tmpidx = 1, det_T do
local glbidx = tmpidx + start_ptr - 1
score_coll[glbidx] = score[tmpidx]
bbox_coll[glbidx] = bbox[tmpidx]
end
else
local glbidx = det_T + start_ptr - 1
score_coll[glbidx] = score[det_T]
bbox_coll[glbidx] = bbox[det_T]
end
end
end
return score_coll, bbox_coll
end
---------------------------------------------
function ImageDetect:detect_VID_NO_OVERLAP(im, boxes, det_T, min_images, recompute_features)
self.model:evaluate()
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
assert(T == #boxes, '#im does not equate to #boxes.')
self.model:clearState()
local CONV_FORWARD_T = 4
local conv_stack = self.model.conv_stack
local conv_maps = {}
local B = math.ceil(T / CONV_FORWARD_T)
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + CONV_FORWARD_T - 1, T)
local batch_im = im[{{start_ptr, end_ptr}, {}, {}, {}}]
for tmpidx = 1, batch_im:size(1) do
local tmp_im = batch_im[{tmpidx, {}, {}, {}}]
tmp_im:copy(self.image_transformer:forward(tmp_im))
end
batch_im = batch_im:cuda()
local output = conv_stack:forward(batch_im)
output = output:float()
table.insert(conv_maps, output)
start_ptr = end_ptr + 1
end
conv_maps = torch.cat(conv_maps, 1)
if T > det_T then
local B = math.ceil(T / det_T)
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + det_T - 1, T)
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local cur_conv_maps = conv_maps[{{start_ptr, end_ptr}, {}, {}, {}}]
cur_conv_maps = cur_conv_maps:cuda()
local only_center = false
local score, bbox = self:conv2pred(cur_conv_maps, cur_boxes,
self.model.STMM_stack, self.model.post_stack, only_center)
collectgarbage()
for tmpidx = 1, T do
local glbidx = tmpidx + start_ptr - 1
score_coll[glbidx] = score[tmpidx]
bbox_coll[glbidx] = bbox[tmpidx]
end
start_ptr = end_ptr + 1
end
else
conv_maps = conv_maps:cuda()
local score, bbox = self:conv2pred(conv_maps, boxes,
self.model.STMM_stack, self.model.post_stack, false)
collectgarbage()
for tmpidx = 1, T do
score_coll[tmpidx] = score[tmpidx]
bbox_coll[tmpidx] = bbox[tmpidx]
end
end
return score_coll, bbox_coll
end
function ImageDetect:detect_VID_CENTER(im, boxes, det_T, min_images, recompute_features)
self.model:evaluate()
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
assert(T == #boxes, '#im does not equate to #boxes.')
assert(det_T%2==1, 'det_T must be odd number.')
local center_idx = (det_T - 1) / 2 + 1
self.model:clearState()
if false then
--if T <= det_T then
score_coll, bbox_coll = self:detect_VID(
im, boxes, min_images, recompute_features)
else
local CONV_FORWARD_T = 1
local conv_stack = self.model.conv_stack
local conv_maps = {}
local B = math.ceil(T / CONV_FORWARD_T)
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + CONV_FORWARD_T - 1, T)
local batch_im = im[{{start_ptr, end_ptr}, {}, {}, {}}]
for tmpidx = 1, batch_im:size(1) do
local tmp_im = batch_im[{tmpidx, {}, {}, {}}]
tmp_im:copy(self.image_transformer:forward(tmp_im))
end
batch_im = batch_im:cuda()
local output = conv_stack:forward(batch_im)
output = output:float()
table.insert(conv_maps, output)
start_ptr = end_ptr + 1
end
conv_maps = torch.cat(conv_maps, 1)
--print('Start compute detection.')
if T > det_T then
for start_ptr = 1, T - det_T + 1 do
local end_ptr = math.min(start_ptr + det_T - 1, T)
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local cur_conv_maps = conv_maps[{{start_ptr, end_ptr}, {}, {}, {}}]
cur_conv_maps = cur_conv_maps:cuda()
local only_center = true
if start_ptr == 1 or start_ptr == T - det_T + 1 then
only_center = false
end
local score, bbox = self:conv2pred(cur_conv_maps, cur_boxes,
self.model.STMM_stack, self.model.post_stack, only_center)
collectgarbage()
if start_ptr == 1 then
for tmpidx = 1, center_idx do
local glbidx = tmpidx + start_ptr - 1
score_coll[glbidx] = score[tmpidx]
bbox_coll[glbidx] = bbox[tmpidx]
end
elseif start_ptr == T-det_T+1 then
for tmpidx = center_idx, det_T do
local glbidx = tmpidx + start_ptr - 1
score_coll[glbidx] = score[tmpidx]
bbox_coll[glbidx] = bbox[tmpidx]
end
else
local glbidx = center_idx + start_ptr - 1
score_coll[glbidx] = score[center_idx]
bbox_coll[glbidx] = bbox[center_idx]
end
end
else
conv_maps = conv_maps:cuda()
local score, bbox = self:conv2pred(conv_maps, boxes,
self.model.STMM_stack, self.model.post_stack, false)
collectgarbage()
for tmpidx = 1, T do
score_coll[tmpidx] = score[tmpidx]
bbox_coll[tmpidx] = bbox[tmpidx]
end
end
--print('Done compute detection.')
end
return score_coll, bbox_coll
end
function ImageDetect:detect_VID_CENTER_OVERLAP(im, boxes, det_T, min_images, recompute_features)
self.model:evaluate()
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
assert(T == #boxes, '#im does not equate to #boxes.')
assert(det_T%2==1, 'det_T must be odd number.')
local center_idx = (det_T - 1) / 2 + 1
self.model:clearState()
local CONV_FORWARD_T = 1
local conv_stack = self.model.conv_stack
local conv_maps = {}
local B = math.ceil(T / CONV_FORWARD_T)
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + CONV_FORWARD_T - 1, T)
local batch_im = im[{{start_ptr, end_ptr}, {}, {}, {}}]
for tmpidx = 1, batch_im:size(1) do
local tmp_im = batch_im[{tmpidx, {}, {}, {}}]
tmp_im:copy(self.image_transformer:forward(tmp_im))
end
batch_im = batch_im:cuda()
local output = conv_stack:forward(batch_im)
output = output:float()
table.insert(conv_maps, output)
start_ptr = end_ptr + 1
end
conv_maps = torch.cat(conv_maps, 1)
if T > det_T then
for start_ptr = 1, T - det_T + 1 do
-- clear state of the model
--self.top:clearState()
local end_ptr = math.min(start_ptr + det_T - 1, T)
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local cur_conv_maps = conv_maps[{{start_ptr, end_ptr}, {}, {}, {}}]
cur_conv_maps = cur_conv_maps:cuda()
local only_center = false
-- conv2pred_iter_replace or conv2pred_iter
local score, bbox = self:conv2pred_iter(cur_conv_maps, cur_boxes,
self.model.STMM_stack, self.model.post_stack, only_center)
collectgarbage()
for tmpidx = 1, #score do
local glbidx = tmpidx + start_ptr - 1
if score_coll[glbidx] and score_coll[glbidx]:nElement() > 0 then
score_coll[glbidx] = torch.cat({score[tmpidx], score_coll[glbidx]}, 1)
else
score_coll[glbidx] = score[tmpidx]
end
if bbox_coll[glbidx] and bbox_coll[glbidx]:nElement() > 0 then
bbox_coll[glbidx] = torch.cat({bbox[tmpidx], bbox_coll[glbidx]}, 1)
else
bbox_coll[glbidx] = bbox[tmpidx]
end
end
end
else
local cur_conv_maps = conv_maps:cuda()
-- conv2pred_iter_replace or conv2pred_iter
local score, bbox = self:conv2pred_iter(cur_conv_maps, boxes,
self.model.STMM_stack, self.model.post_stack, false)
collectgarbage()
for tmpidx = 1, T do
score_coll[tmpidx] = score[tmpidx]
bbox_coll[tmpidx] = bbox[tmpidx]
end
end
--print('Done compute detection.')
return score_coll, bbox_coll, conv_maps
end
function ImageDetect:detect_VID_CENTER_OVERLAP_MULTISCALE(im, boxes, det_T, min_images, recompute_features)
self.model:evaluate()
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
assert(T == #boxes, '#im does not equate to #boxes.')
assert(det_T%2==1, 'det_T must be odd number.')
local center_idx = (det_T - 1) / 2 + 1
self.model:clearState()
if false then
--if T <= det_T then
score_coll, bbox_coll = self:detect_VID(
im, boxes, min_images, recompute_features)
else
local scales = {270, 360, 540}
local ratios = {}
local hgt, wid = im:size(3), im:size(4)
local conv_maps = {}
local CONV_FORWARD_T = 3
local conv_stack = self.model.conv_stack
local B = math.ceil(T / CONV_FORWARD_T)
for scale_idx, scale in ipairs(scales) do
local cur_conv_maps = {}
local im_size_min = math.min(hgt, wid)
local im_size_max = math.max(hgt, wid)
ratios[scale_idx] = scale / im_size_min
local cur_hgt, cur_wid = hgt*ratios[scale_idx], wid*ratios[scale_idx]
local cur_im = torch.FloatTensor(im:size(1), 3, cur_hgt, cur_wid)
for idx = 1, im:size(1) do
cur_im[idx]:copy(image.scale(im[idx], cur_wid, cur_hgt))
end
local start_ptr = 1
for bIdx = 1, B do
local end_ptr = math.min(start_ptr + CONV_FORWARD_T - 1, T)
local batch_im = cur_im[{{start_ptr, end_ptr}, {}, {}, {}}]
for tmpidx = 1, batch_im:size(1) do
local tmp_im = batch_im[{tmpidx, {}, {}, {}}]
tmp_im:copy(self.image_transformer:forward(tmp_im))
end
batch_im = batch_im:cuda()
local output = conv_stack:forward(batch_im)
output = output:float()
table.insert(cur_conv_maps, output)
start_ptr = end_ptr + 1
end
cur_conv_maps = torch.cat(cur_conv_maps, 1)
conv_maps[scale_idx] = cur_conv_maps
end
if T > det_T then
for start_ptr = 1, T - det_T + 1 do
-- clear state of the model
--self.top:clearState()
local end_ptr = math.min(start_ptr + det_T - 1, T)
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local cur_conv_maps = {}
for idx = 1, #conv_maps do
cur_conv_maps[idx] = conv_maps[idx][{{start_ptr, end_ptr}, {}, {}, {}}]:cuda()
end
local only_center = false
local score, bbox = self:conv2pred_mulscale(cur_conv_maps, cur_boxes, ratios,
self.model.STMM_stack, self.model.post_stack, only_center)
collectgarbage()
for tmpidx = 1, #score do
local glbidx = tmpidx + start_ptr - 1
if score_coll[glbidx] and score_coll[glbidx]:nElement() > 0 then
score_coll[glbidx] = torch.cat({score[tmpidx], score_coll[glbidx]}, 1)
else
score_coll[glbidx] = score[tmpidx]
end
if bbox_coll[glbidx] and bbox_coll[glbidx]:nElement() > 0 then
bbox_coll[glbidx] = torch.cat({bbox[tmpidx], bbox_coll[glbidx]}, 1)
else
bbox_coll[glbidx] = bbox[tmpidx]
end
end
end
else
for idx = 1, #conv_maps do
conv_maps[idx] = conv_maps[idx]:cuda()
end
local score, bbox = self:conv2pred_mulscale(conv_maps, boxes, ratios,
self.model.STMM_stack, self.model.post_stack, false)
collectgarbage()
for tmpidx = 1, T do
score_coll[tmpidx] = score[tmpidx]
bbox_coll[tmpidx] = bbox[tmpidx]
end
end
--print('Done compute detection.')
end
return score_coll, bbox_coll
end
function ImageDetect:detect_VID_SEQ(im, boxes, det_T, min_images, recompute_features)
det_T = 1
local score_coll = {}
local bbox_coll = {}
local T = im:size(1)
-- assert(T % det_T == 0, 'Number of images must be multiple of detector capacity.')
local B = math.ceil(T / det_T)
local start_ptr = 1
for bidx = 1, B do
local end_ptr = math.min(start_ptr + det_T - 1, T)
local cur_im = im[{{start_ptr, end_ptr}, {}, {}, {}}]
local cur_boxes = {table.unpack(boxes, start_ptr, end_ptr)}
local score, bbox = self:detect_VID(cur_im, cur_boxes, min_images, recompute_features)
for tmpidx = 1, #score do
local glbidx = tmpidx + start_ptr - 1
score_coll[glbidx] = score[tmpidx]
bbox_coll[glbidx] = bbox[tmpidx]
end
start_ptr = end_ptr + 1
end
return score_coll, bbox_coll
end
-- supposes boxes is in [x1,y1,x2,y2] format
function ImageDetect:detect_VID(im, boxes, min_images, recompute_features)
assert(#self.model:findModules('nn.BBoxNorm') > 0, 'WARNING: No nn.BBoxNorm is not found in the model.')
assert(#self.scale==1, 'Current implementation only supports testing with single scale.')
self.model:evaluate()
local H, W = im:size(3), im:size(4)
local im_coll, box_coll = {}, {}
local box_count, im_box_start = 0, {}
for img_idx = 1, im:size(1) do
local cur_inputs = {torch.FloatTensor(),torch.FloatTensor()}
local cur_im = im[{img_idx, {}, {}, {}}]
local cur_boxes = boxes[img_idx]
local im_scales = getImages(self,cur_inputs[1],cur_im)
cur_inputs[2] = project_im_rois(cur_boxes,im_scales)
cur_inputs[2][{{}, 1}]:fill(img_idx)
table.insert(im_coll, cur_inputs[1])
table.insert(box_coll, cur_inputs[2])
im_box_start[img_idx] = box_count + 1
local cur_box_count = cur_inputs[2]:size(1)
box_count = box_count + cur_box_count
end
table.insert(im_box_start, box_count + 1)
im_coll = torch.cat(im_coll, 1)
box_coll = torch.cat(box_coll, 1)
local inputs = {}
inputs[1] = im_coll
inputs[2] = box_coll
boxes = torch.cat(boxes, 1)
-- some global vars
local expanded_T = inputs[1]:size(1)
-- expand (pad)
if min_images then
if inputs[1]:size(1) % min_images ~= 0 then
local pad_T = min_images - inputs[1]:size(1) % min_images
inputs[1] = torch.cat({inputs[1], inputs[1].new(pad_T, inputs[1]:size(2),
inputs[1]:size(3), inputs[1]:size(4)):zero()}, 1)
expanded_T = inputs[1]:size(1)
end
end
self.inputs_cuda = self.inputs_cuda or {torch.CudaTensor(),torch.CudaTensor()}
self.inputs_cuda[1]:resize(inputs[1]:size()):copy(inputs[1])
self.inputs_cuda[2]:resize(inputs[2]:size()):copy(inputs[2])
-- set STMM N and T
local default_N, default_T = utils.set_NT(self.model, 1, expanded_T)
-- forward
local output0 = self.model:forward(self.inputs_cuda)
-- set back N and T
utils.set_NT(self.model, default_N, default_T)
local class_values, bbox_values
if torch.type(output0) == 'table' then
class_values= output0[1]
bbox_values = output0[2]:float()
for i,v in ipairs(bbox_values:split(4,2)) do
utils.convertFrom(v,boxes,v)
end
else
class_values = output0
end
if not self.model.noSoftMax then
class_values = self.sm:forward(class_values)
end
class_values = class_values:float()
-- pack for different image
local class_values_coll, bbox_values_coll = {}, {}
for img_idx = 1, im:size(1) do
local start_idx = im_box_start[img_idx]
local end_idx = im_box_start[img_idx+1]-1 or class_values:size(1)
class_values_coll[img_idx] = class_values[{{start_idx, end_idx}, {}}]
bbox_values_coll[img_idx] = bbox_values[{{start_idx, end_idx}, {}}]
end
return class_values_coll, bbox_values_coll
end
function ImageDetect:conv2pred_mulscale(conv_maps, boxes, ratios, STMM_model, post_model, only_center)
assert(#post_model:findModules('nn.BBoxNorm') > 0, 'WARNING: No nn.BBoxNorm is not found in the post stack.')
local ITER_NUM = 2
local ITER_SCORE_THRESH = 0.05
local T = conv_maps[1]:size(1)
local center_idx = (T + 1) / 2
assert(T == #boxes, 'Number of image should be equal to number of boxes.')
local STMM_maps = {}
-- set STMM N and T
local default_N, default_T = utils.set_NT(STMM_model, 1, T)
for idx = 1, #conv_maps do
STMM_maps[idx] = STMM_model:forward(conv_maps[idx]):clone()
end
-- set back N and T
utils.set_NT(self.model, default_N, default_T)
-- get the roi pooling module
local ROI_GRID = 14
self.roi_pool = self.roi_pool or inn.ROIPooling(ROI_GRID,ROI_GRID,1/16):cuda()
local class_values_coll, bbox_values_coll = {}, {}
for img_idx = 1, T do
if not only_center or img_idx == center_idx then
local cur_boxes = torch.FloatTensor(boxes[img_idx]:size(1), 5)
cur_boxes:narrow(2, 2, 4):copy(boxes[img_idx])
cur_boxes:select(2, 1):fill(1)
local score_coll, box_coll = {}, {}
for iter = 1, ITER_NUM do
if cur_boxes and cur_boxes:nElement() > 0 then
-- max-out
local roi_feat
local roi_num, feat_dim = cur_boxes:size(1), STMM_maps[1]:size(2)
for idx = 1, #STMM_maps do
local cur_scale_box = cur_boxes:clone()
cur_scale_box:narrow(2, 2, 4):mul(ratios[idx])
local cur_STMM_maps = STMM_maps[idx][{{img_idx},{},{},{}}]
local tmp = self.roi_pool:forward({cur_STMM_maps, cur_scale_box:cuda()})
tmp = tmp:view(1, roi_num, feat_dim, ROI_GRID, ROI_GRID)
if idx == 1 then
roi_feat = tmp:clone()
else
roi_feat:copy(torch.max(torch.cat({tmp, roi_feat}, 1), 1))
end
collectgarbage()
end
roi_feat = roi_feat:view(roi_num, feat_dim, ROI_GRID, ROI_GRID)
local output = post_model:forward(roi_feat)
local score, box_coef = output[1]:float(), output[2]:float()
for i,v in ipairs(box_coef:split(4,2)) do
utils.convertFrom(v,cur_boxes:narrow(2, 2, 4),v)
end
if not self.model.noSoftMax then
if self.sm:type() ~= 'torch.FloatTensor' then
self.sm = self.sm:float()
end
score:copy(self.sm:forward(score))
end
local box_coef_flat = box_coef:view(box_coef:size(1), -1, 4)
box_coef_flat = box_coef_flat:narrow(2, 2, box_coef_flat:size(2) - 1):contiguous():view(-1, 4)
local score_flat = score:narrow(2, 2, score:size(2) - 1):contiguous():view(-1)
local idx = score_flat:view(-1):ge(ITER_SCORE_THRESH):nonzero()
if idx:nElement() > 0 then
idx = idx:view(-1)
cur_boxes:resize(idx:nElement(), 5)
cur_boxes:narrow(2, 2, 4):copy(box_coef_flat:index(1, idx))
cur_boxes:select(2, 1):fill(1)
else
cur_boxes = torch.FloatTensor()
end
table.insert(score_coll, score)
table.insert(box_coll, box_coef)
end
end
if #score_coll > 0 and score_coll[1]:nElement() > 0 then
score_coll = torch.cat(score_coll, 1)
box_coll = torch.cat(box_coll, 1)
end
class_values_coll[img_idx] = score_coll
bbox_values_coll[img_idx] = box_coll
end
end
return class_values_coll, bbox_values_coll
end
function ImageDetect:conv2pred_iter(conv_maps, boxes, STMM_model, post_model, only_center)
assert(#post_model:findModules('nn.BBoxNorm') > 0, 'WARNING: No nn.BBoxNorm is not found in the post stack.')
local ITER_NUM = 2
local ITER_SCORE_THRESH = 0.05