Merge pull request #30 from koba-jon/develop/v2.0.1

Develop/v2.0.1

Author: koba-jon

Anomaly_Detection/AnoGAN2d/README.md

@@ -98,12 +98,14 @@ If you want to view specific examples of command line arguments, please view "sr
 #!/bin/bash
 
 DATA='MVTecAD'
+HEATMAP_MAX=0.1
 
 ./AnoGAN2d \
     --test true \
     --dataset ${DATA} \
     --test_dir "test_anomaly" \
     --test_result_dir "test_result_anomaly" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3
@@ -113,6 +115,7 @@ DATA='MVTecAD'
     --dataset ${DATA} \
     --test_dir "test_normal" \
     --test_result_dir "test_result_normal" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3

Anomaly_Detection/AnoGAN2d/scripts/test.sh

@@ -1,12 +1,14 @@
 #!/bin/bash
 
 DATA='MVTecAD'
+HEATMAP_MAX=0.1
 
 ./AnoGAN2d \
     --test true \
     --dataset ${DATA} \
     --test_dir "test_anomaly" \
     --test_result_dir "test_result_anomaly" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3
@@ -16,6 +18,7 @@ DATA='MVTecAD'
     --dataset ${DATA} \
     --test_dir "test_normal" \
     --test_result_dir "test_result_normal" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3

Anomaly_Detection/AnoGAN2d/src/main.cpp

@@ -69,6 +69,7 @@ po::options_description parse_arguments(){
         ("test_result_dir", po::value<std::string>()->default_value("test_result"), "test result directory : ./<test_result_dir>")
         ("test_search_epoch", po::value<size_t>()->default_value(100), "epoch to search latent variable in test")
         ("test_Lambda", po::value<float>()->default_value(0.1), "anomaly score rate between reconstruction and feature matching in test")
+        ("heatmap_max", po::value<float>()->default_value(1.0), "heatmap maximum-value in test")
 
         // (5) Define for Anomaly Detection
         ("AD", po::value<bool>()->default_value(false), "anomaly detection mode on/off")

Anomaly_Detection/AnoGAN2d/src/test.cpp

@@ -35,13 +35,13 @@ void test(po::variables_map &vm, torch::Device &device, GAN_Generator &gen, GAN_
     size_t search_epoch;
     float ave_anomaly_score, ave_res_loss, ave_dis_loss;
     double seconds, ave_time;
-    std::string path, result_dir, fname;
+    std::string path, result_dir, output_dir, heatmap_dir, fname;
     std::string dataroot;
     std::ofstream ofs, ofs_score;
     std::chrono::system_clock::time_point start, end;
     std::tuple<torch::Tensor, std::vector<std::string>> data;
     std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> anomaly_score_with_alpha;
-    torch::Tensor image, z, output;
+    torch::Tensor image, z, output, heatmap;
     torch::Tensor loss, anomaly_score, res_loss, dis_loss;
     datasets::ImageFolderWithPaths dataset;
     DataLoader::ImageFolderWithPaths dataloader;
@@ -73,6 +73,8 @@ void test(po::variables_map &vm, torch::Device &device, GAN_Generator &gen, GAN_
     gen->eval();
     dis->eval();
     result_dir = vm["test_result_dir"].as<std::string>();  fs::create_directories(result_dir);
+    output_dir = result_dir + "/output";  fs::create_directories(output_dir);
+    heatmap_dir = result_dir + "/heatmap";  fs::create_directories(heatmap_dir);
     ofs.open(result_dir + "/loss.txt", std::ios::out);
     ofs_score.open(result_dir + "/anomaly_score.txt", std::ios::out);
     while (dataloader(data)){
@@ -113,9 +115,13 @@ void test(po::variables_map &vm, torch::Device &device, GAN_Generator &gen, GAN_
         ofs << '<' << std::get<1>(data).at(0) << "> anomaly_score:" << anomaly_score.item<float>() << " res:" << res_loss.item<float>() << " dis:" << dis_loss.item<float>() << " (time:" << ave_time << ')' << std::endl;
         ofs_score << anomaly_score.item<float>() << std::endl;
 
-        fname = result_dir + '/' + std::get<1>(data).at(0);
+        fname = output_dir + '/' + std::get<1>(data).at(0);
         visualizer::save_image(output.detach(), fname, /*range=*/output_range, /*cols=*/1, /*padding=*/0);
 
+        fname = heatmap_dir + '/' + std::get<1>(data).at(0);
+        heatmap = visualizer::create_heatmap(torch::abs(image - output).mean(/*dim=*/1, /*keepdim=*/true), /*range=*/{0, (output_range.second - output_range.first) * vm["heatmap_max"].as<float>()});
+        visualizer::save_image(heatmap.detach(), fname, /*range=*/{0.0, 1.0}, /*cols=*/1, /*padding=*/0);
+
     }
 
     // (6) Calculate Average

Anomaly_Detection/DAGMM2d/README.md

@@ -98,12 +98,14 @@ If you want to view specific examples of command line arguments, please view "sr
 #!/bin/bash
 
 DATA='MVTecAD'
+HEATMAP_MAX=0.1
 
 ./DAGMM2d \
     --test true \
     --dataset ${DATA} \
     --test_dir "test_anomaly" \
     --test_result_dir "test_result_anomaly" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3
@@ -113,6 +115,7 @@ DATA='MVTecAD'
     --dataset ${DATA} \
     --test_dir "test_normal" \
     --test_result_dir "test_result_normal" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3

Anomaly_Detection/DAGMM2d/scripts/test.sh

@@ -1,12 +1,14 @@
 #!/bin/bash
 
 DATA='MVTecAD'
+HEATMAP_MAX=0.1
 
 ./DAGMM2d \
     --test true \
     --dataset ${DATA} \
     --test_dir "test_anomaly" \
     --test_result_dir "test_result_anomaly" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3
@@ -16,6 +18,7 @@ DATA='MVTecAD'
     --dataset ${DATA} \
     --test_dir "test_normal" \
     --test_result_dir "test_result_normal" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3

Anomaly_Detection/DAGMM2d/src/main.cpp

@@ -71,6 +71,7 @@ po::options_description parse_arguments(){
         ("test_dir", po::value<std::string>()->default_value("test"), "test image directory : ./datasets/<dataset>/<test_dir>/<image files>")
         ("test_load_epoch", po::value<std::string>()->default_value("latest"), "training epoch used for testing")
         ("test_result_dir", po::value<std::string>()->default_value("test_result"), "test result directory : ./<test_result_dir>")
+        ("heatmap_max", po::value<float>()->default_value(1.0), "heatmap maximum-value in test")
 
         // (5) Define for Anomaly Detection
         ("AD", po::value<bool>()->default_value(false), "anomaly detection mode on/off")

Anomaly_Detection/DAGMM2d/src/test.cpp

@@ -31,12 +31,12 @@ void test(po::variables_map &vm, torch::Device &device, Encoder &enc, Decoder &d
     // (0) Initialization and Declaration
     float ave_loss, ave_anomaly_score, score;
     double seconds, ave_time;
-    std::string path, result_dir, fname;
+    std::string path, result_dir, output_dir, heatmap_dir, fname;
     std::string dataroot;
     std::ofstream ofs, ofs_loss, ofs_score;
     std::chrono::system_clock::time_point start, end;
     std::tuple<torch::Tensor, std::vector<std::string>> data;
-    torch::Tensor image, output;
+    torch::Tensor image, output, heatmap;
     torch::Tensor z, z_c, z_r, z_r1, z_r2;
     torch::Tensor mu, sigma, phi;
     torch::Tensor loss, anomaly_score;
@@ -72,6 +72,8 @@ void test(po::variables_map &vm, torch::Device &device, Encoder &enc, Decoder &d
     dec->eval();
     est->eval();
     result_dir = vm["test_result_dir"].as<std::string>();  fs::create_directories(result_dir);
+    output_dir = result_dir + "/output";  fs::create_directories(output_dir);
+    heatmap_dir = result_dir + "/heatmap";  fs::create_directories(heatmap_dir);
     ofs.open(result_dir + "/loss.txt", std::ios::out);
     ofs_loss.open(result_dir + "/reconstruction_error.txt", std::ios::out);
     ofs_score.open(result_dir + "/anomaly_score.txt", std::ios::out);
@@ -118,9 +120,13 @@ void test(po::variables_map &vm, torch::Device &device, Encoder &enc, Decoder &d
         ofs_loss << loss.item<float>() << std::endl;
         ofs_score << score << std::endl;
 
-        fname = result_dir + '/' + std::get<1>(data).at(0);
+        fname = output_dir + '/' + std::get<1>(data).at(0);
         visualizer::save_image(output.detach(), fname, /*range=*/output_range, /*cols=*/1, /*padding=*/0);
 
+        fname = heatmap_dir + '/' + std::get<1>(data).at(0);
+        heatmap = visualizer::create_heatmap(torch::abs(image - output).mean(/*dim=*/1, /*keepdim=*/true), /*range=*/{0, (output_range.second - output_range.first) * vm["heatmap_max"].as<float>()});
+        visualizer::save_image(heatmap.detach(), fname, /*range=*/{0.0, 1.0}, /*cols=*/1, /*padding=*/0);
+
     }
 
     // (6) Calculate Average

Anomaly_Detection/EGBAD2d/README.md

@@ -98,12 +98,14 @@ If you want to view specific examples of command line arguments, please view "sr
 #!/bin/bash
 
 DATA='MVTecAD'
+HEATMAP_MAX=0.1
 
 ./EGBAD2d \
     --test true \
     --dataset ${DATA} \
     --test_dir "test_anomaly" \
     --test_result_dir "test_result_anomaly" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3
@@ -113,6 +115,7 @@ DATA='MVTecAD'
     --dataset ${DATA} \
     --test_dir "test_normal" \
     --test_result_dir "test_result_normal" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3

Anomaly_Detection/EGBAD2d/scripts/test.sh

@@ -1,12 +1,14 @@
 #!/bin/bash
 
 DATA='MVTecAD'
+HEATMAP_MAX=0.1
 
 ./EGBAD2d \
     --test true \
     --dataset ${DATA} \
     --test_dir "test_anomaly" \
     --test_result_dir "test_result_anomaly" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3
@@ -16,6 +18,7 @@ DATA='MVTecAD'
     --dataset ${DATA} \
     --test_dir "test_normal" \
     --test_result_dir "test_result_normal" \
+    --heatmap_max ${HEATMAP_MAX} \
     --size 256 \
     --gpu_id 0 \
     --nc 3