/
testNet_pano_full.lua
232 lines (191 loc) · 9.3 KB
/
testNet_pano_full.lua
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require 'nn'
require 'torch'
torch.setdefaulttensortype('torch.FloatTensor')
require 'nngraph'
local model_utils=require 'model_utils'
local matio = require 'matio'
require 'cunn'
require 'image'
require 'cudnn'
require 'sys'
-- make model
model = {}
model.criterion = nn.BCECriterion():cuda()
model.criterion_3 = nn.MSECriterion():cuda()
-- encoder
local input_x = nn.Identity()()
local conv1 = nn.SpatialConvolution(6,32,3,3,1,1,1,1)(input_x)
local conv1_relu = nn.ReLU(true)(conv1)
local pool1 = nn.SpatialMaxPooling(2,2,2,2)(conv1_relu)
local conv2 = nn.SpatialConvolution(32,64,3,3,1,1,1,1)(pool1)
local conv2_relu = nn.ReLU(true)(conv2)
local pool2 = nn.SpatialMaxPooling(2,2,2,2)(conv2_relu)
local conv3 = nn.SpatialConvolution(64,128,3,3,1,1,1,1)(pool2)
local conv3_relu = nn.ReLU(true)(conv3)
local pool3 = nn.SpatialMaxPooling(2,2,2,2)(conv3_relu)
local conv4 = nn.SpatialConvolution(128,256,3,3,1,1,1,1)(pool3)
local conv4_relu = nn.ReLU(true)(conv4)
local pool4 = nn.SpatialMaxPooling(2,2,2,2)(conv4_relu)
local conv5 = nn.SpatialConvolution(256,512,3,3,1,1,1,1)(pool4)
local conv5_relu = nn.ReLU(true)(conv5)
local pool5 = nn.SpatialMaxPooling(2,2,2,2)(conv5_relu)
local conv6 = nn.SpatialConvolution(512,1024,3,3,1,1,1,1)(pool5)
local conv6_relu = nn.ReLU(true)(conv6)
local pool6 = nn.SpatialMaxPooling(2,2,2,2)(conv6_relu)
local conv7 = nn.SpatialConvolution(1024,2048,3,3,1,1,1,1)(pool6)
local conv7_relu = nn.ReLU(true)(conv7)
local pool7 = nn.SpatialMaxPooling(2,2,2,2)(conv7_relu)
local unpool00 = nn.SpatialUpSamplingNearest(2)(pool7)
local deconv00 = nn.SpatialConvolution(2048,1024,3,3,1,1,1,1)(unpool00)
local deconv00_relu = nn.ReLU(true)(deconv00)
local unpool0_ = nn.JoinTable(2)({deconv00_relu, pool6})
local unpool0 = nn.SpatialUpSamplingNearest(2)(unpool0_)
local deconv0 = nn.SpatialConvolution(1024*2,512,3,3,1,1,1,1)(unpool0)
local deconv0_relu = nn.ReLU(true)(deconv0)
local unpool1_ = nn.JoinTable(2)({deconv0_relu, pool5})
local unpool1 = nn.SpatialUpSamplingNearest(2)(unpool1_)
local deconv1 = nn.SpatialConvolution(512*2,256,3,3,1,1,1,1)(unpool1)
local deconv1_relu = nn.ReLU(true)(deconv1)
local unpool2_ = nn.JoinTable(2)({deconv1_relu, pool4})
local unpool2 = nn.SpatialUpSamplingNearest(2)(unpool2_)
local deconv2 = nn.SpatialConvolution(256*2,128,3,3,1,1,1,1)(unpool2)
local deconv2_relu = nn.ReLU(true)(deconv2)
local unpool3_ = nn.JoinTable(2)({deconv2_relu, pool3})
local unpool3 = nn.SpatialUpSamplingNearest(2)(unpool3_)
local deconv3 = nn.SpatialConvolution(128*2,64,3,3,1,1,1,1)(unpool3)
local deconv3_relu = nn.ReLU(true)(deconv3)
local unpool4_ = nn.JoinTable(2)({deconv3_relu, pool2})
local unpool4 = nn.SpatialUpSamplingNearest(2)(unpool4_)
local deconv4 = nn.SpatialConvolution(64*2,32,3,3,1,1,1,1)(unpool4)
local deconv4_relu = nn.ReLU(true)(deconv4)
local unpool5_ = nn.JoinTable(2)({deconv4_relu, pool1})
local unpool5 = nn.SpatialUpSamplingNearest(2)(unpool5_)
local deconv5 = nn.SpatialConvolution(32*2,3,3,3,1,1,1,1)(unpool5)
local deconv6_sf = nn.Sigmoid()(deconv5)
-- joint part
local deconv00_c = nn.SpatialConvolution(2048,1024,3,3,1,1,1,1)(unpool00)
local deconv00_relu_c = nn.ReLU(true)(deconv00_c)
local unpool0_c = nn.JoinTable(2)({deconv00_relu_c, unpool0_})
local unpool0_c = nn.SpatialUpSamplingNearest(2)(unpool0_c)
local deconv0_c = nn.SpatialConvolution(1024*3,512,3,3,1,1,1,1)(unpool0_c)
local deconv0_relu_c = nn.ReLU(true)(deconv0_c)
local unpool1_c = nn.JoinTable(2)({deconv0_relu_c, unpool1_})
local unpool1_c = nn.SpatialUpSamplingNearest(2)(unpool1_c)
local deconv1_c = nn.SpatialConvolution(512*3,256,3,3,1,1,1,1)(unpool1_c)
local deconv1_relu_c = nn.ReLU(true)(deconv1_c)
local unpool2_c = nn.JoinTable(2)({deconv1_relu_c, unpool2_})
local unpool2_c = nn.SpatialUpSamplingNearest(2)(unpool2_c)
local deconv2_c = nn.SpatialConvolution(256*3,128,3,3,1,1,1,1)(unpool2_c)
local deconv2_relu_c = nn.ReLU(true)(deconv2_c)
local unpool3_c = nn.JoinTable(2)({deconv2_relu_c, unpool3_})
local unpool3_c = nn.SpatialUpSamplingNearest(2)(unpool3_c)
local deconv3_c = nn.SpatialConvolution(128*3,64,3,3,1,1,1,1)(unpool3_c)
local deconv3_relu_c = nn.ReLU(true)(deconv3_c)
local unpool4_c = nn.JoinTable(2)({deconv3_relu_c, unpool4_})
local unpool4_c = nn.SpatialUpSamplingNearest(2)(unpool4_c)
local deconv4_c = nn.SpatialConvolution(64*3,32,3,3,1,1,1,1)(unpool4_c)
local deconv4_relu_c = nn.ReLU(true)(deconv4_c)
local unpool5_c = nn.JoinTable(2)({deconv4_relu_c, unpool5_})
local unpool5_c = nn.SpatialUpSamplingNearest(2)(unpool5_c)
local deconv5_c = nn.SpatialConvolution(32*3,1,3,3,1,1,1,1)(unpool5_c)
local deconv6_sf_c = nn.Sigmoid()(deconv5_c)
-- box
local deconv6_comb = nn.JoinTable(2)({deconv6_sf, deconv6_sf_c})
local conv_r1 = nn.SpatialConvolution(4,8,3,3,1,1,1,1)(deconv6_comb)
local conv_r1_relu = nn.ReLU(true)(conv_r1)
local pool_r1 = nn.SpatialMaxPooling(2,2,2,2)(conv_r1_relu)
local conv_r2 = nn.SpatialConvolution(8,16,3,3,1,1,1,1)(pool_r1)
local conv_r2_relu = nn.ReLU(true)(conv_r2)
local pool_r2 = nn.SpatialMaxPooling(2,2,2,2)(conv_r2_relu)
local conv_r3 = nn.SpatialConvolution(16,32,3,3,1,1,1,1)(pool_r2)
local conv_r3_relu = nn.ReLU(true)(conv_r3)
local pool_r3 = nn.SpatialMaxPooling(2,2,2,2)(conv_r3_relu)
local conv_r4 = nn.SpatialConvolution(32,64,3,3,1,1,1,1)(pool_r3)
local conv_r4_relu = nn.ReLU(true)(conv_r4)
local pool_r4 = nn.SpatialMaxPooling(2,2,2,2)(conv_r4_relu)
local conv_r5 = nn.SpatialConvolution(64,128,3,3,1,1,1,1)(pool_r4)
local conv_r5_relu = nn.ReLU(true)(conv_r5)
local pool_r5 = nn.SpatialMaxPooling(2,2,2,2)(conv_r5_relu)
local conv_r6 = nn.SpatialConvolution(128,256,3,3,1,1,1,1)(pool_r5)
local conv_r6_relu = nn.ReLU(true)(conv_r6)
local pool_r6 = nn.SpatialMaxPooling(2,2,2,2)(conv_r6_relu)
local conv_r7 = nn.SpatialConvolution(256,512,3,3,1,1,1,1)(pool_r6)
local conv_r7_relu = nn.ReLU(true)(conv_r7)
local pool_r7 = nn.SpatialMaxPooling(2,2,2,2)(conv_r7_relu)
local ref0 = nn.Reshape(512*4*8)(pool_r7)
local ref1 = nn.Linear(512*4*8, 1024)(ref0)
local ref1_relu = nn.ReLU(true)(ref1)
local ref2 = nn.Linear(1024, 256)(ref1_relu)
local ref2_relu = nn.ReLU(true)(ref2)
local ref3 = nn.Linear(256, 64)(ref2_relu)
local ref3_relu = nn.ReLU(true)(ref3)
local ref4 = nn.Linear(64, 6)(ref3_relu)
model.core = nn.gModule({input_x},{deconv6_sf, deconv6_sf_c, ref4})
model.core:cuda()
params = torch.load('./model/panofull_joint_box_pretrained.t7')
model_params, grad_params = model_utils.combine_all_parameters(model.core)
model_params = model_params:copy(params)
-- get testing dataset
img_ts = torch.load('./data/panoContext_img_test.t7')
edg_ts = torch.load('./data/panoContext_edge_test.t7')
juc_ts = torch.load('./data/panoContext_cor_test.t7')
lne_ts = torch.load('./data/panoContext_line_test.t7')
ts_size = img_ts:size(1)
print(ts_size)
print('Uploaded testing')
loss_l = 0
loss_c = 0
loss_b = 0
-- test
for i = 1, ts_size do
print(i)
inputMat = img_ts[{{i},{},{},{}}]
inputMat2 = lne_ts[{{i},{},{},{}}]
gtMat = edg_ts[{{i},{},{},{}}]
gt2Mat = juc_ts[{{i},{},{},{}}]
-- flip & average
inputMat_f = image.hflip(torch.reshape(inputMat, 3, 512, 1024))
inputMat2_f = image.hflip(torch.reshape(inputMat2, 3, 512, 1024))
gtMat_f = image.hflip(torch.reshape(gtMat, 3, 512, 1024))
gt2Mat_f = image.hflip(torch.reshape(gt2Mat, 1, 512, 1024))
inputMat_f = torch.reshape(inputMat_f, 1, 3, 512, 1024)
inputMat2_f = torch.reshape(inputMat2_f, 1, 3, 512, 1024)
gtMat_f = torch.reshape(gtMat_f, 1, 3, 512, 1024)
gt2Mat_f = torch.reshape(gt2Mat_f, 1, 1, 512, 1024)
inputMat_a = torch.cat(inputMat, inputMat2, 2)
inputMat_a_f = torch.cat(inputMat_f, inputMat2_f, 2)
inputMat_a = torch.cat(inputMat_a, inputMat_a_f, 1)
gtMat_a = torch.cat(gtMat, gtMat_f, 1)
gt2Mat_a = torch.cat(gt2Mat, gt2Mat_f, 1)
output_a = model.core:forward(inputMat_a:cuda())
loss_l = model.criterion:forward(output_a[1], gtMat_a:cuda()) + loss_l
loss_c = model.criterion:forward(output_a[2], gt2Mat_a:cuda()) + loss_c
-- save img
img = torch.reshape(inputMat, 3, 512, 1024)
image.save( './result/res_panofull_ts_box_joint/img/'..i..'.png', img)
-- save edge
output_l = torch.zeros(2, 3, 512, 1024)
output_l_a = output_a[1]:double()
output_l[{{1},{},{},{}}] = output_l_a[{{1},{},{},{}}]
output_l_f = output_l_a[{{2},{},{},{}}]
output_l_f = torch.reshape(output_l_f, 3, 512, 1024)
output_l_f = image.hflip(output_l_f)
output_l[{{2},{},{},{}}] = output_l_f
output_c = torch.zeros(2, 1, 512, 1024)
output_c_a = output_a[2]:double()
output_c[{{1},{},{},{}}] = output_c_a[{{1},{},{},{}}]
output_c_f = output_c_a[{{2},{},{},{}}]
output_c_f = torch.reshape(output_c_f, 1, 512, 1024)
output_c_f = image.hflip(output_c_f)
output_c[{{2},{},{},{}}] = output_c_f
out_edg = torch.mean(output_l:double(), 1)
out_edg = torch.reshape(out_edg[{{1},{},{},{}}], 3, 512, 1024)
image.save( './result/res_panofull_ts_box_joint/edg/'..i..'.png', out_edg)
out_edg = torch.mean(output_c:double(), 1)
out_edg = torch.reshape(out_edg[{{1},{},{},{}}], 512, 1024)
image.save( './result/res_panofull_ts_box_joint/cor/'..i..'.png', out_edg)
box_param = output_a[3]:double()
matio.save( './result/res_panofull_ts_box_joint/box/'..i..'.mat', box_param)
end
print(loss_l/ts_size)
print(loss_c/ts_size)