Add CenterTrack based on dla34.
The implemented network refers to the nuScenes_3Dtracking.pth model. Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
This commit is contained in:
Davide Sapienza
@@ -109,6 +109,9 @@ target_link_libraries(test_resnet101_cnet3d tkDNN)
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add_executable(test_dla34_cnet3d tests/centernet/dla34_cnet3d/dla34_cnet3d.cpp)
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target_link_libraries(test_dla34_cnet3d tkDNN)
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add_executable(test_dla34_cnet3d_track tests/centernet/dla34_cnet3d_track/dla34_cnet3d_track.cpp)
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target_link_libraries(test_dla34_cnet3d_track tkDNN)
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# DEMOS
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add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
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target_link_libraries(test_rtinference tkDNN)
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+20
-19
@@ -72,27 +72,28 @@ do
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./test_imuodom &>> $out_file
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print_output $? imuodom
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test_net yolo4
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test_net yolo4x
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test_net yolo4_berkeley
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test_net yolo4tiny
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test_net yolo3
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test_net yolo3_berkeley
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test_net yolo3_coco4
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test_net yolo3_flir
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test_net yolo3_512
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test_net yolo3tiny
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test_net yolo3tiny_512
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test_net yolo2
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test_net yolo2_voc
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# test_net yolo4
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# test_net yolo4x
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# test_net yolo4_berkeley
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# test_net yolo4tiny
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# test_net yolo3
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# test_net yolo3_berkeley
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# test_net yolo3_coco4
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# test_net yolo3_flir
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# test_net yolo3_512
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# test_net yolo3tiny
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# test_net yolo3tiny_512
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# test_net yolo2
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# test_net yolo2_voc
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#test_net yolo2tiny
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test_net csresnext50-panet-spp
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# test_net csresnext50-panet-spp
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#test_net csresnext50-panet-spp_berkeley
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test_net resnet101_cnet
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test_net dla34_cnet
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test_net mobilenetv2ssd
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test_net mobilenetv2ssd512
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test_net bdd-mobilenetv2ssd
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# test_net resnet101_cnet
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# test_net dla34_cnet
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# test_net mobilenetv2ssd
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# test_net mobilenetv2ssd512
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# test_net bdd-mobilenetv2ssd
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test_net dla34_cnet3d_track
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done
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echo "If errors occured, check logfile $out_file"
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@@ -0,0 +1,632 @@
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#include <iostream>
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#include "tkdnn.h"
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const char *input_bin = "dla34_cnet3d_track/debug/input_base-level0-0.bin";
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// const char *input_bin = "dla34_cnet3d_track/debug/input.bin";
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// const char *pre_img_bin = "dla34_cnet3d_track/debug/pre_imgages.bin";
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// const char *pre_hm_bin = "dla34_cnet3d_track/debug/pre_hms.bin";
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// //pre
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// const char *pre_img_conv1_bin = "dla34_cnet3d_track/layers/base-pre_img_layer-0.bin";
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// const char *pre_hm_conv1_bin = "dla34_cnet3d_track/layers/base-pre_hm_layer-0.bin";
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// const char *conv1_bin = "dla34_cnet3d_track/layers/base-base_layer-0.bin";
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const char *conv2_bin = "dla34_cnet3d_track/layers/base-level0-0.bin";
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const char *conv3_bin = "dla34_cnet3d_track/layers/base-level1-0.bin";
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// s - stage, t - tree
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const char *s1_t1_conv1_bin = "dla34_cnet3d_track/layers/base-level2-tree1-conv1.bin";
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const char *s1_t1_conv2_bin = "dla34_cnet3d_track/layers/base-level2-tree1-conv2.bin";
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const char *s1_t1_project = "dla34_cnet3d_track/layers/base-level2-project-0.bin";
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const char *s1_t2_conv1_bin = "dla34_cnet3d_track/layers/base-level2-tree2-conv1.bin";
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const char *s1_t2_conv2_bin = "dla34_cnet3d_track/layers/base-level2-tree2-conv2.bin";
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const char *s1_root_conv1_bin = "dla34_cnet3d_track/layers/base-level2-root-conv.bin";
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const char *s2_t1_t1_conv1_bin = "dla34_cnet3d_track/layers/base-level3-tree1-tree1-conv1.bin";
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const char *s2_t1_t1_conv2_bin = "dla34_cnet3d_track/layers/base-level3-tree1-tree1-conv2.bin";
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const char *s2_t1_t1_project = "dla34_cnet3d_track/layers/base-level3-tree1-project-0.bin";
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const char *s2_t1_t2_conv1_bin = "dla34_cnet3d_track/layers/base-level3-tree1-tree2-conv1.bin";
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const char *s2_t1_t2_conv2_bin = "dla34_cnet3d_track/layers/base-level3-tree1-tree2-conv2.bin";
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const char *s2_t1_root_conv1_bin = "dla34_cnet3d_track/layers/base-level3-tree1-root-conv.bin";
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const char *s2_t2_t1_conv1_bin = "dla34_cnet3d_track/layers/base-level3-tree2-tree1-conv1.bin";
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const char *s2_t2_t1_conv2_bin = "dla34_cnet3d_track/layers/base-level3-tree2-tree1-conv2.bin";
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const char *s2_t2_t2_conv1_bin = "dla34_cnet3d_track/layers/base-level3-tree2-tree2-conv1.bin";
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const char *s2_t2_t2_conv2_bin = "dla34_cnet3d_track/layers/base-level3-tree2-tree2-conv2.bin";
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const char *s2_t2_root_conv1_bin = "dla34_cnet3d_track/layers/base-level3-tree2-root-conv.bin";
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const char *s3_t1_t1_conv1_bin = "dla34_cnet3d_track/layers/base-level4-tree1-tree1-conv1.bin";
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const char *s3_t1_t1_conv2_bin = "dla34_cnet3d_track/layers/base-level4-tree1-tree1-conv2.bin";
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const char *s3_t1_t1_project = "dla34_cnet3d_track/layers/base-level4-tree1-project-0.bin";
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const char *s3_t1_t2_conv1_bin = "dla34_cnet3d_track/layers/base-level4-tree1-tree2-conv1.bin";
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const char *s3_t1_t2_conv2_bin = "dla34_cnet3d_track/layers/base-level4-tree1-tree2-conv2.bin";
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const char *s3_t1_root_conv1_bin = "dla34_cnet3d_track/layers/base-level4-tree1-root-conv.bin";
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const char *s3_t2_t1_conv1_bin = "dla34_cnet3d_track/layers/base-level4-tree2-tree1-conv1.bin";
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const char *s3_t2_t1_conv2_bin = "dla34_cnet3d_track/layers/base-level4-tree2-tree1-conv2.bin";
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const char *s3_t2_t2_conv1_bin = "dla34_cnet3d_track/layers/base-level4-tree2-tree2-conv1.bin";
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const char *s3_t2_t2_conv2_bin = "dla34_cnet3d_track/layers/base-level4-tree2-tree2-conv2.bin";
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const char *s3_t2_root_conv1_bin = "dla34_cnet3d_track/layers/base-level4-tree2-root-conv.bin";
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const char *s4_t1_conv1_bin = "dla34_cnet3d_track/layers/base-level5-tree1-conv1.bin";
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const char *s4_t1_conv2_bin = "dla34_cnet3d_track/layers/base-level5-tree1-conv2.bin";
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const char *s4_t1_project = "dla34_cnet3d_track/layers/base-level5-project-0.bin";
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const char *s4_t2_conv1_bin = "dla34_cnet3d_track/layers/base-level5-tree2-conv1.bin";
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const char *s4_t2_conv2_bin = "dla34_cnet3d_track/layers/base-level5-tree2-conv2.bin";
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const char *s4_root_conv1_bin = "dla34_cnet3d_track/layers/base-level5-root-conv.bin";
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//final
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// const char *fc_bin = "dla34_cnet3d_track/layers/output.bin";
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const char *ida_0_p_1_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_0-proj_1-conv.bin";
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const char *ida_0_p_1_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_0-proj_1-conv-conv_offset_mask.bin";
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const char *ida_0_up_1_deconv_bin = "dla34_cnet3d_track/layers/dla_up-ida_0-up_1.bin";
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const char *ida_0_n_1_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_0-node_1-conv.bin";
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const char *ida_0_n_1_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_0-node_1-conv-conv_offset_mask.bin";
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const char *ida_1_p_1_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-proj_1-conv.bin";
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const char *ida_1_p_1_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-proj_1-conv-conv_offset_mask.bin";
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const char *ida_1_up_1_deconv_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-up_1.bin";
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const char *ida_1_n_1_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-node_1-conv.bin";
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const char *ida_1_n_1_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-node_1-conv-conv_offset_mask.bin";
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const char *ida_1_p_2_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-proj_2-conv.bin";
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const char *ida_1_p_2_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-proj_2-conv-conv_offset_mask.bin";
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const char *ida_1_up_2_deconv_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-up_2.bin";
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const char *ida_1_n_2_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-node_2-conv.bin";
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const char *ida_1_n_2_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_1-node_2-conv-conv_offset_mask.bin";
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const char *ida_2_p_1_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-proj_1-conv.bin";
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const char *ida_2_p_1_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-proj_1-conv-conv_offset_mask.bin";
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const char *ida_2_up_1_deconv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-up_1.bin";
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const char *ida_2_n_1_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-node_1-conv.bin";
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const char *ida_2_n_1_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-node_1-conv-conv_offset_mask.bin";
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const char *ida_2_p_2_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-proj_2-conv.bin";
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const char *ida_2_p_2_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-proj_2-conv-conv_offset_mask.bin";
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const char *ida_2_up_2_deconv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-up_2.bin";
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const char *ida_2_n_2_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-node_2-conv.bin";
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const char *ida_2_n_2_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-node_2-conv-conv_offset_mask.bin";
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const char *ida_2_p_3_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-proj_3-conv.bin";
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const char *ida_2_p_3_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-proj_3-conv-conv_offset_mask.bin";
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const char *ida_2_up_3_deconv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-up_3.bin";
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const char *ida_2_n_3_dcn_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-node_3-conv.bin";
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const char *ida_2_n_3_conv_bin = "dla34_cnet3d_track/layers/dla_up-ida_2-node_3-conv-conv_offset_mask.bin";
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const char *ida_up_p_1_dcn_bin = "dla34_cnet3d_track/layers/ida_up-proj_1-conv.bin";
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const char *ida_up_p_1_conv_bin = "dla34_cnet3d_track/layers/ida_up-proj_1-conv-conv_offset_mask.bin";
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const char *ida_up_up_1_deconv_bin = "dla34_cnet3d_track/layers/ida_up-up_1.bin";
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const char *ida_up_n_1_dcn_bin = "dla34_cnet3d_track/layers/ida_up-node_1-conv.bin";
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const char *ida_up_n_1_conv_bin = "dla34_cnet3d_track/layers/ida_up-node_1-conv-conv_offset_mask.bin";
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const char *ida_up_p_2_dcn_bin = "dla34_cnet3d_track/layers/ida_up-proj_2-conv.bin";
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const char *ida_up_p_2_conv_bin = "dla34_cnet3d_track/layers/ida_up-proj_2-conv-conv_offset_mask.bin";
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const char *ida_up_up_2_deconv_bin = "dla34_cnet3d_track/layers/ida_up-up_2.bin";
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const char *ida_up_n_2_dcn_bin = "dla34_cnet3d_track/layers/ida_up-node_2-conv.bin";
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const char *ida_up_n_2_conv_bin = "dla34_cnet3d_track/layers/ida_up-node_2-conv-conv_offset_mask.bin";
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const char *hm_conv1_bin = "dla34_cnet3d_track/layers/hm-0.bin";
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const char *hm_conv2_bin = "dla34_cnet3d_track/layers/hm-2.bin";
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const char *wh_conv1_bin = "dla34_cnet3d_track/layers/wh-0.bin";
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const char *wh_conv2_bin = "dla34_cnet3d_track/layers/wh-2.bin";
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const char *reg_conv1_bin = "dla34_cnet3d_track/layers/reg-0.bin";
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const char *reg_conv2_bin = "dla34_cnet3d_track/layers/reg-2.bin";
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const char *track_conv1_bin = "dla34_cnet3d_track/layers/tracking-0.bin";
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const char *track_conv2_bin = "dla34_cnet3d_track/layers/tracking-2.bin";
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const char *dep_conv1_bin = "dla34_cnet3d_track/layers/dep-0.bin";
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const char *dep_conv2_bin = "dla34_cnet3d_track/layers/dep-2.bin";
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const char *rot_conv1_bin = "dla34_cnet3d_track/layers/rot-0.bin";
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const char *rot_conv2_bin = "dla34_cnet3d_track/layers/rot-2.bin";
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const char *dim_conv1_bin = "dla34_cnet3d_track/layers/dim-0.bin";
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const char *dim_conv2_bin = "dla34_cnet3d_track/layers/dim-2.bin";
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const char *a_off_conv1_bin = "dla34_cnet3d_track/layers/amodel_offset-0.bin";
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const char *a_off_conv2_bin = "dla34_cnet3d_track/layers/amodel_offset-2.bin";
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const char *output_bin[]={
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"dla34_cnet3d_track/debug/hm.bin",
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"dla34_cnet3d_track/debug/wh.bin",
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"dla34_cnet3d_track/debug/reg.bin",
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"dla34_cnet3d_track/debug/tracking.bin",
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"dla34_cnet3d_track/debug/dep.bin",
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"dla34_cnet3d_track/debug/rot.bin",
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"dla34_cnet3d_track/debug/dim.bin",
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"dla34_cnet3d_track/debug/amodel_offset.bin"};
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// const char *output_bin = "dla34_cnet3d_track/debug/base-level0-2.bin";
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int main()
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{
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downloadWeightsifDoNotExist("dla34_cnet3d_track/debug/input.bin", "dla34_cnet3d_track", "https://cloud.hipert.unimore.it/s/rjNfgGL9FtAXLHp/download");
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// Network layout
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// tk::dnn::dataDim_t dim_in0(1, 3, 512, 512, 1);
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// tk::dnn::dataDim_t dim_in1(1, 1, 512, 512, 1);
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tk::dnn::dataDim_t dim_in0(1, 16, 512, 512, 1);
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// dnnType *i0_d, *i1_d, *i2_d;
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// dnnType *i0_h, *i1_h, *i2_h;
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// checkCuda( cudaMalloc(&i0_d, dim_in0.tot()*sizeof(dnnType)) );
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// checkCuda( cudaMalloc(&i1_d, dim_in1.tot()*sizeof(dnnType)) );
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// checkCuda( cudaMalloc(&i2_d, dim_in0.tot()*sizeof(dnnType)) );
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tk::dnn::Network net(dim_in0);
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tk::dnn::Layer *last1, *last2, *last3, *last4;
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tk::dnn::Layer *base1, *base2, *base3, *base4, *base5, *base6, *ida1, *ida2_1, *ida2_2, *ida3_1, *ida3_2, *ida3_3, *idaup_1, *idaup_2;
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// tk::dnn::Layer *pre_img, *pre_hm;
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// //pre-img
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// readBinaryFile(pre_img_bin, dim_in0.tot(), &i0_h, &i0_d);
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// tk::dnn::Input *in_pre_img = new tk::dnn::Input(&net, dim_in0, i0_d);
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// tk::dnn::Conv2d pre_img_conv1(&net, 16, 7, 7, 1, 1, 3, 3, pre_img_conv1_bin, true);
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// tk::dnn::Activation pre_img_relu(&net, CUDNN_ACTIVATION_RELU);
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// pre_img = &pre_img_relu;
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// //pre-hm
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// readBinaryFile(pre_hm_bin, dim_in1.tot(), &i1_h, &i1_d);
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// tk::dnn::Input *in_pre_hm = new tk::dnn::Input(&net, dim_in1, i1_d);
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// tk::dnn::Conv2d pre_hm_conv1(&net, 16, 7, 7, 1, 1, 3, 3, pre_hm_conv1_bin, true);
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// tk::dnn::Activation pre_hm_relu(&net, CUDNN_ACTIVATION_RELU);
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// pre_hm = &pre_hm_relu;
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// // image input
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// readBinaryFile(input_bin, dim_in0.tot(), &i2_h, &i2_d);
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// tk::dnn::Input *input_image = new tk::dnn::Input(&net, dim_in0, i2_d);
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// tk::dnn::Conv2d *conv1 = new tk::dnn::Conv2d(&net, 16, 7, 7, 1, 1, 3, 3, conv1_bin, true);
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// tk::dnn::Activation relu1(&net, CUDNN_ACTIVATION_RELU);
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// tk::dnn::Shortcut s0_input(&net, pre_img);
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// tk::dnn::Shortcut s1_input(&net, pre_hm);
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tk::dnn::Conv2d conv2(&net, 16, 3, 3, 1, 1, 1, 1, conv2_bin, true);
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tk::dnn::Activation relu2(&net, CUDNN_ACTIVATION_RELU);
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base1 = &relu2;
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tk::dnn::Conv2d conv3(&net, 32, 3, 3, 2, 2, 1, 1, conv3_bin, true);
|
||||
tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
|
||||
base2 = &relu3;
|
||||
|
||||
// level 2
|
||||
// tree 1
|
||||
tk::dnn::Conv2d s1_t1_conv1(&net, 64, 3, 3, 2, 2, 1, 1, s1_t1_conv1_bin, true);
|
||||
tk::dnn::Activation s1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s1_t1_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t1_conv2_bin, true);
|
||||
last2 = &s1_t1_conv2;
|
||||
|
||||
// get the basicblock input and apply maxpool conv2d and relu
|
||||
tk::dnn::Layer *route_s1_t1_layers[1] = { base2 };
|
||||
tk::dnn::Route route_s1_t1(&net, route_s1_t1_layers, 1);
|
||||
// downsample
|
||||
tk::dnn::Pooling s1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
|
||||
// project
|
||||
tk::dnn::Conv2d s1_t1_residual1_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_t1_project, true);
|
||||
|
||||
tk::dnn::Shortcut s1_t1_s1(&net, last2);
|
||||
tk::dnn::Activation s1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s1_t1_relu;
|
||||
// tree 2
|
||||
tk::dnn::Conv2d s1_t2_conv1(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv1_bin, true);
|
||||
tk::dnn::Activation s1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s1_t2_conv2(&net, 64, 3, 3, 1, 1, 1, 1, s1_t2_conv2_bin, true);
|
||||
|
||||
tk::dnn::Shortcut s1_t2_s1(&net, last1);
|
||||
tk::dnn::Activation s1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
last2 = &s1_t2_relu;
|
||||
|
||||
// root
|
||||
// join last1 and net in single input 128, 56, 56
|
||||
tk::dnn::Layer *route_s1_root_layers[2] = { last2, last1 };
|
||||
tk::dnn::Route route_s1_root(&net, route_s1_root_layers, 2);
|
||||
tk::dnn::Conv2d s1_root_conv1(&net, 64, 1, 1, 1, 1, 0, 0, s1_root_conv1_bin, true);
|
||||
tk::dnn::Activation s1_root_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
base3 = &s1_root_relu;
|
||||
|
||||
// level 3
|
||||
// tree 1
|
||||
// tree 1
|
||||
tk::dnn::Conv2d s2_t1_t1_conv1(&net, 128, 3, 3, 2, 2, 1, 1, s2_t1_t1_conv1_bin, true);
|
||||
tk::dnn::Activation s2_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s2_t1_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t1_conv2_bin, true);
|
||||
last2 = &s2_t1_t1_conv2;
|
||||
|
||||
// get the basicblock input and apply maxpool conv2d and relu
|
||||
tk::dnn::Layer *route_s2_t1_t1_layers[1] = { base3 };
|
||||
tk::dnn::Route route_s2_t1_t1(&net, route_s2_t1_t1_layers, 1);
|
||||
// downsample
|
||||
tk::dnn::Pooling s2_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
|
||||
last4 = &s2_t1_t1_maxpool1;
|
||||
// project
|
||||
tk::dnn::Conv2d s2_t1_t1_residual1_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_t1_project, true);
|
||||
|
||||
tk::dnn::Shortcut s2_t1_t1_s1(&net, last2);
|
||||
tk::dnn::Activation s2_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s2_t1_t1_relu;
|
||||
|
||||
// tree 2
|
||||
tk::dnn::Conv2d s2_t1_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv1_bin, true);
|
||||
tk::dnn::Activation s2_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s2_t1_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t1_t2_conv2_bin, true);
|
||||
|
||||
tk::dnn::Shortcut s2_t1_t2_s1(&net, last1);
|
||||
tk::dnn::Activation s2_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
last2 = &s2_t1_t2_relu;
|
||||
|
||||
// root
|
||||
// join last1 and net in single input 128, 56, 56
|
||||
tk::dnn::Layer *route_s2_t1_root_layers[2] = { last2, last1 };
|
||||
tk::dnn::Route route_s2_t1_root(&net, route_s2_t1_root_layers, 2);
|
||||
tk::dnn::Conv2d s2_t1_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t1_root_conv1_bin, true);
|
||||
tk::dnn::Activation s2_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s2_t1_root_relu;
|
||||
last3 = &s2_t1_root_relu;
|
||||
// tree 2
|
||||
// tree 1
|
||||
tk::dnn::Conv2d s2_t2_t1_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv1_bin, true);
|
||||
tk::dnn::Activation s2_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s2_t2_t1_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t1_conv2_bin, true);
|
||||
tk::dnn::Shortcut s2_t2_t1_s1(&net, last1);
|
||||
tk::dnn::Activation s2_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s2_t2_t1_relu;
|
||||
|
||||
// tree 2
|
||||
tk::dnn::Conv2d s2_t2_t2_conv1(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv1_bin, true);
|
||||
tk::dnn::Activation s2_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s2_t2_t2_conv2(&net, 128, 3, 3, 1, 1, 1, 1, s2_t2_t2_conv2_bin, true);
|
||||
|
||||
tk::dnn::Shortcut s2_t2_t2_s1(&net, last1);
|
||||
tk::dnn::Activation s2_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
last2 = &s2_t2_t2_relu;
|
||||
|
||||
// root
|
||||
// join last1 and net in single input 128, 56, 56
|
||||
tk::dnn::Layer *route_s2_t2_root_layers[4] = { last2, last1, last4, last3};
|
||||
tk::dnn::Route route_s2_t2_root(&net, route_s2_t2_root_layers, 4);
|
||||
tk::dnn::Conv2d s2_t2_root_conv1(&net, 128, 1, 1, 1, 1, 0, 0, s2_t2_root_conv1_bin, true);
|
||||
tk::dnn::Activation s2_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
base4 = &s2_t2_root_relu;
|
||||
|
||||
// level 4
|
||||
// tree 1
|
||||
// tree 1
|
||||
tk::dnn::Conv2d s3_t1_t1_conv1(&net, 256, 3, 3, 2, 2, 1, 1, s3_t1_t1_conv1_bin, true);
|
||||
tk::dnn::Activation s3_t1_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s3_t1_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t1_conv2_bin, true);
|
||||
last2 = &s3_t1_t1_conv2;
|
||||
|
||||
// get the basicblock input and apply maxpool conv2d and relu
|
||||
tk::dnn::Layer *route_s3_t1_t1_layers[1] = { base4 };
|
||||
tk::dnn::Route route_s3_t1_t1(&net, route_s3_t1_t1_layers, 1);
|
||||
// downsample
|
||||
tk::dnn::Pooling s3_t1_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
|
||||
last4 = &s3_t1_t1_maxpool1;
|
||||
// project
|
||||
tk::dnn::Conv2d s3_t1_t1_residual1_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_t1_project, true);
|
||||
|
||||
tk::dnn::Shortcut s3_t1_t1_s1(&net, last2);
|
||||
tk::dnn::Activation s3_t1_t1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s3_t1_t1_relu;
|
||||
|
||||
// tree 2
|
||||
tk::dnn::Conv2d s3_t1_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv1_bin, true);
|
||||
tk::dnn::Activation s3_t1_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s3_t1_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t1_t2_conv2_bin, true);
|
||||
|
||||
tk::dnn::Shortcut s3_t1_t2_s1(&net, last1);
|
||||
tk::dnn::Activation s3_t1_t2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
last2 = &s3_t1_t2_relu;
|
||||
|
||||
// root
|
||||
// join last1 and net in single input 256, 56, 56
|
||||
tk::dnn::Layer *route_s3_t1_root_layers[2] = { last2, last1 };
|
||||
tk::dnn::Route route_s3_t1_root(&net, route_s3_t1_root_layers, 2);
|
||||
tk::dnn::Conv2d s3_t1_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t1_root_conv1_bin, true);
|
||||
tk::dnn::Activation s3_t1_root_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s3_t1_root_relu;
|
||||
last3 = &s3_t1_root_relu;
|
||||
// tree 2
|
||||
// tree 1
|
||||
tk::dnn::Conv2d s3_t2_t1_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv1_bin, true);
|
||||
tk::dnn::Activation s3_t2_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s3_t2_t1_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t1_conv2_bin, true);
|
||||
tk::dnn::Shortcut s3_t2_t1_s1(&net, last1);
|
||||
tk::dnn::Activation s3_t2_t1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s3_t2_t1_relu;
|
||||
|
||||
// tree 2
|
||||
tk::dnn::Conv2d s3_t2_t2_conv1(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv1_bin, true);
|
||||
tk::dnn::Activation s3_t2_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s3_t2_t2_conv2(&net, 256, 3, 3, 1, 1, 1, 1, s3_t2_t2_conv2_bin, true);
|
||||
|
||||
tk::dnn::Shortcut s3_t2_t2_s1(&net, last1);
|
||||
tk::dnn::Activation s3_t2_t2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
last2 = &s3_t2_t2_relu;
|
||||
|
||||
// root
|
||||
// join last1 and net in single input 256, 56, 56
|
||||
tk::dnn::Layer *route_s3_t2_root_layers[4] = { last2, last1, last4, last3};
|
||||
tk::dnn::Route route_s3_t2_root(&net, route_s3_t2_root_layers, 4);
|
||||
tk::dnn::Conv2d s3_t2_root_conv1(&net, 256, 1, 1, 1, 1, 0, 0, s3_t2_root_conv1_bin, true);
|
||||
tk::dnn::Activation s3_t2_root_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
base5 = &s3_t2_root_relu;
|
||||
|
||||
// level 5
|
||||
// tree 1
|
||||
tk::dnn::Conv2d s4_t1_conv1(&net, 512, 3, 3, 2, 2, 1, 1, s4_t1_conv1_bin, true);
|
||||
tk::dnn::Activation s4_t1_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s4_t1_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t1_conv2_bin, true);
|
||||
last2 = &s4_t1_conv2;
|
||||
|
||||
// get the basicblock input and apply maxpool conv2d and relu
|
||||
tk::dnn::Layer *route_s4_t1_layers[1] = { base5 };
|
||||
tk::dnn::Route route_s4_t1(&net, route_s4_t1_layers, 1);
|
||||
// downsample
|
||||
tk::dnn::Pooling s4_t1_maxpool1(&net, 2, 2, 2, 2, 0, 0, tk::dnn::POOLING_MAX);
|
||||
last4 = &s4_t1_maxpool1;
|
||||
// project
|
||||
tk::dnn::Conv2d s4_t1_residual1_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_t1_project, true);
|
||||
|
||||
tk::dnn::Shortcut s4_t1_s1(&net, last2);
|
||||
tk::dnn::Activation s4_t1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
last1 = &s4_t1_relu;
|
||||
|
||||
// tree 2
|
||||
tk::dnn::Conv2d s4_t2_conv1(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv1_bin, true);
|
||||
tk::dnn::Activation s4_t2_relu1(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
tk::dnn::Conv2d s4_t2_conv2(&net, 512, 3, 3, 1, 1, 1, 1, s4_t2_conv2_bin, true);
|
||||
|
||||
tk::dnn::Shortcut s4_t2_s1(&net, last1);
|
||||
tk::dnn::Activation s4_t2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
last2 = &s4_t2_relu;
|
||||
|
||||
// root
|
||||
// join last1 and net in single input 128, 56, 56
|
||||
tk::dnn::Layer *route_s4_root_layers[3] = { last2, last1, last4 };
|
||||
tk::dnn::Route route_s4_root(&net, route_s4_root_layers, 3);
|
||||
tk::dnn::Conv2d s4_root_conv1(&net, 512, 1, 1, 1, 1, 0, 0, s4_root_conv1_bin, true);
|
||||
tk::dnn::Activation s4_root_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
|
||||
base6 = &s4_root_relu;
|
||||
|
||||
//final
|
||||
// tk::dnn::Pooling avgpool(&net, 7, 7, 7, 7, 0, 0, tk::dnn::POOLING_AVERAGE);
|
||||
// tk::dnn::Dense fc(&net, 1000, fc_bin);
|
||||
|
||||
//ida 0
|
||||
tk::dnn::Layer *route_ida0[1] = { base6 };
|
||||
tk::dnn::Route route_ida0_0(&net, route_ida0, 1);
|
||||
|
||||
tk::dnn::DeformConv2d ida_0_p_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_p_1_dcn_bin, ida_0_p_1_conv_bin, true);
|
||||
tk::dnn::Activation ida_0_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d ida_0_up_1_deconv(&net, 256, 4, 4, 2, 2, 1, 1, ida_0_up_1_deconv_bin, false, 256);
|
||||
tk::dnn::Shortcut ida_0_shortcut(&net, base5);
|
||||
tk::dnn::DeformConv2d ida_0_n_1_dcn(&net, 256, 1, 3, 3, 1, 1, 1, 1, ida_0_n_1_dcn_bin, ida_0_n_1_conv_bin, true);
|
||||
tk::dnn::Activation ida_0_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
ida1 = &ida_0_n_1_relu;
|
||||
|
||||
//ida1-1
|
||||
tk::dnn::Layer *route_ida1_layers_1[1] = { base5 };
|
||||
tk::dnn::Route route_ida1_1(&net, route_ida1_layers_1, 1);
|
||||
|
||||
tk::dnn::DeformConv2d ida_1_p_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_1_dcn_bin, ida_1_p_1_conv_bin, true);
|
||||
tk::dnn::Activation ida_1_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d ida_1_up_1_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_1_deconv_bin, false, 128);
|
||||
tk::dnn::Shortcut ida_1_shortcut1(&net, base4);
|
||||
tk::dnn::DeformConv2d ida_1_n_1_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_1_dcn_bin, ida_1_n_1_conv_bin, true);
|
||||
tk::dnn::Activation ida_1_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
ida2_1 = &ida_1_n_1_relu;
|
||||
|
||||
//ida1-2
|
||||
tk::dnn::Layer *route_ida1_layers_2[1] = { ida1 };
|
||||
tk::dnn::Route route_ida1_2(&net, route_ida1_layers_2, 1);
|
||||
|
||||
tk::dnn::DeformConv2d ida_1_p_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_p_2_dcn_bin, ida_1_p_2_conv_bin, true);
|
||||
tk::dnn::Activation ida_1_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d ida_1_up_2_deconv(&net, 128, 4, 4, 2, 2, 1, 1, ida_1_up_2_deconv_bin, false, 128);
|
||||
tk::dnn::Shortcut ida_1_shortcut2(&net, ida2_1);
|
||||
tk::dnn::DeformConv2d ida_1_n_2_dcn(&net, 128, 1, 3, 3, 1, 1, 1, 1, ida_1_n_2_dcn_bin, ida_1_n_2_conv_bin, true);
|
||||
tk::dnn::Activation ida_1_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
ida2_2 = &ida_1_n_2_relu;
|
||||
|
||||
//ida2-1
|
||||
tk::dnn::Layer *route_ida2_layers_1[1] = { base4 };
|
||||
tk::dnn::Route route_ida2_1(&net, route_ida2_layers_1, 1);
|
||||
|
||||
tk::dnn::DeformConv2d ida_2_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_1_dcn_bin, ida_2_p_1_conv_bin, true);
|
||||
tk::dnn::Activation ida_2_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d ida_2_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_1_deconv_bin, false, 64);
|
||||
tk::dnn::Shortcut ida_2_shortcut1(&net, base3);
|
||||
tk::dnn::DeformConv2d ida_2_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_1_dcn_bin, ida_2_n_1_conv_bin, true);
|
||||
tk::dnn::Activation ida_2_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
ida3_1 = &ida_2_n_1_relu;
|
||||
|
||||
//ida2-2
|
||||
tk::dnn::Layer *route_ida2_layers_2[1] = { ida2_1 };
|
||||
tk::dnn::Route route_ida2_2(&net, route_ida2_layers_2, 1);
|
||||
|
||||
tk::dnn::DeformConv2d ida_2_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_2_dcn_bin, ida_2_p_2_conv_bin, true);
|
||||
tk::dnn::Activation ida_2_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d ida_2_up_2_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_2_deconv_bin, false, 64);
|
||||
tk::dnn::Shortcut ida_2_shortcut2(&net, ida3_1);
|
||||
tk::dnn::DeformConv2d ida_2_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_2_dcn_bin, ida_2_n_2_conv_bin, true);
|
||||
tk::dnn::Activation ida_2_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
ida3_2 = &ida_2_n_2_relu;
|
||||
|
||||
//ida2-3
|
||||
tk::dnn::Layer *route_ida2_layers_3[1] = { ida2_2 };
|
||||
tk::dnn::Route route_ida2_3(&net, route_ida2_layers_3, 1);
|
||||
|
||||
tk::dnn::DeformConv2d ida_2_p_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_p_3_dcn_bin, ida_2_p_3_conv_bin, true);
|
||||
tk::dnn::Activation ida_2_p_3_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d ida_2_up_3_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_2_up_3_deconv_bin, false, 64);
|
||||
tk::dnn::Shortcut ida_2_shortcut3(&net, ida3_2);
|
||||
tk::dnn::DeformConv2d ida_2_n_3_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_2_n_3_dcn_bin, ida_2_n_3_conv_bin, true);
|
||||
tk::dnn::Activation ida_2_n_3_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
ida3_3 = &ida_2_n_3_relu;
|
||||
|
||||
//idaup-1
|
||||
tk::dnn::Layer *route_idaup_layers_1[1] = { ida2_2 };
|
||||
tk::dnn::Route route_idaup_1(&net, route_idaup_layers_1, 1);
|
||||
|
||||
tk::dnn::DeformConv2d idaup_p_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_1_dcn_bin, ida_up_p_1_conv_bin, true);
|
||||
tk::dnn::Activation idaup_p_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d idaup_up_1_deconv(&net, 64, 4, 4, 2, 2, 1, 1, ida_up_up_1_deconv_bin, false, 64);
|
||||
tk::dnn::Shortcut idaup_shortcut1(&net, ida3_3);
|
||||
tk::dnn::DeformConv2d idaup_n_1_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_1_dcn_bin, ida_up_n_1_conv_bin, true);
|
||||
tk::dnn::Activation idaup_n_1_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
idaup_1 = &idaup_n_1_relu;
|
||||
|
||||
//idaup-2
|
||||
tk::dnn::Layer *route_idaup_layers_2[1] = { ida1 };
|
||||
tk::dnn::Route route_idaup_2(&net, route_idaup_layers_2, 1);
|
||||
|
||||
tk::dnn::DeformConv2d idaup_p_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_p_2_dcn_bin, ida_up_p_2_conv_bin, true);
|
||||
tk::dnn::Activation idaup_p_2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::DeConv2d idaup_up_2_deconv(&net, 64, 8, 8, 4, 4, 2, 2, ida_up_up_2_deconv_bin, false, 64);
|
||||
tk::dnn::Shortcut idaup_shortcut2(&net, idaup_1);
|
||||
tk::dnn::DeformConv2d idaup_n_2_dcn(&net, 64, 1, 3, 3, 1, 1, 1, 1, ida_up_n_2_dcn_bin, ida_up_n_2_conv_bin, true);
|
||||
tk::dnn::Activation idaup_n_2_relu(&net, CUDNN_ACTIVATION_RELU);
|
||||
idaup_2 = &idaup_n_2_relu;
|
||||
|
||||
tk::dnn::Layer *route_1_0_layers[1] = { idaup_2 };
|
||||
|
||||
// hm
|
||||
tk::dnn::Conv2d *hm_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, hm_conv1_bin, false);
|
||||
tk::dnn::Activation *hm_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *hm = new tk::dnn::Conv2d(&net, 10, 1, 1, 1, 1, 0, 0, hm_conv2_bin, false);
|
||||
hm->setFinal();
|
||||
|
||||
int kernel = 3;
|
||||
int pad = (kernel - 1)/2;
|
||||
tk::dnn::Activation *hm_sig = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_SIGMOID);
|
||||
tk::dnn::Pooling *hmax = new tk::dnn::Pooling(&net, kernel, kernel, 1, 1, pad, pad, tk::dnn::POOLING_MAX);
|
||||
hmax->setFinal();
|
||||
|
||||
// wh
|
||||
tk::dnn::Route *route_1_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
||||
tk::dnn::Conv2d *wh_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, wh_conv1_bin, false);
|
||||
tk::dnn::Activation *wh_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *wh = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, wh_conv2_bin, false);
|
||||
wh->setFinal();
|
||||
|
||||
// reg
|
||||
tk::dnn::Route *route_2_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
||||
tk::dnn::Conv2d *reg_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, reg_conv1_bin, false);
|
||||
tk::dnn::Activation *reg_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *reg = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, reg_conv2_bin, false);
|
||||
reg->setFinal();
|
||||
|
||||
// tracking
|
||||
tk::dnn::Route *route_3_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
||||
tk::dnn::Conv2d *track_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, track_conv1_bin, false);
|
||||
tk::dnn::Activation *track_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *track = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, track_conv2_bin, false);
|
||||
track->setFinal();
|
||||
|
||||
// dep
|
||||
tk::dnn::Route *route_4_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
||||
tk::dnn::Conv2d *dep_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, dep_conv1_bin, false);
|
||||
tk::dnn::Activation *dep_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *dep = new tk::dnn::Conv2d(&net, 1, 1, 1, 1, 1, 0, 0, dep_conv2_bin, false);
|
||||
dep->setFinal();
|
||||
|
||||
// rot
|
||||
tk::dnn::Route *route_5_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
||||
tk::dnn::Conv2d *rot_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, rot_conv1_bin, false);
|
||||
tk::dnn::Activation *rot_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *rot = new tk::dnn::Conv2d(&net, 8, 1, 1, 1, 1, 0, 0, rot_conv2_bin, false);
|
||||
rot->setFinal();
|
||||
|
||||
// dim
|
||||
tk::dnn::Route *route_6_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
||||
tk::dnn::Conv2d *dim_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, dim_conv1_bin, false);
|
||||
tk::dnn::Activation *dim_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *dim_ = new tk::dnn::Conv2d(&net, 3, 1, 1, 1, 1, 0, 0, dim_conv2_bin, false);
|
||||
dim_->setFinal();
|
||||
|
||||
// amodel_offset
|
||||
tk::dnn::Route *route_7_0 = new tk::dnn::Route(&net, route_1_0_layers, 1);
|
||||
tk::dnn::Conv2d *a_off_conv1 = new tk::dnn::Conv2d(&net, 256, 3, 3, 1, 1, 1, 1, a_off_conv1_bin, false);
|
||||
tk::dnn::Activation *a_off_relu1 = new tk::dnn::Activation(&net, CUDNN_ACTIVATION_RELU);
|
||||
tk::dnn::Conv2d *a_off = new tk::dnn::Conv2d(&net, 2, 1, 1, 1, 1, 0, 0, a_off_conv2_bin, false);
|
||||
a_off->setFinal();
|
||||
|
||||
|
||||
// Load input
|
||||
dnnType *data;
|
||||
dnnType *input_h;
|
||||
readBinaryFile(input_bin, dim_in0.tot(), &input_h, &data);
|
||||
//printDeviceVector(64, data, true);
|
||||
|
||||
//print network model
|
||||
net.print();
|
||||
|
||||
//convert network to tensorRT
|
||||
tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("dla34_cnet3d_track"));
|
||||
|
||||
tk::dnn::dataDim_t dim1 = dim_in0; //input dim
|
||||
printCenteredTitle(" CUDNN inference ", '=', 30);
|
||||
{
|
||||
dim1.print();
|
||||
TKDNN_TSTART
|
||||
// tk::dnn::dataDim_t dim_aus;
|
||||
// net.infer(dim_aus, nullptr);
|
||||
net.infer(dim1, data);
|
||||
TKDNN_TSTOP
|
||||
dim1.print();
|
||||
}
|
||||
|
||||
tk::dnn::dataDim_t dim2 = dim_in0;
|
||||
printCenteredTitle(" TENSORRT inference ", '=', 30);
|
||||
{
|
||||
dim2.print();
|
||||
TKDNN_TSTART
|
||||
netRT.infer(dim2, data);
|
||||
TKDNN_TSTOP
|
||||
dim2.print();
|
||||
}
|
||||
// dnnType *out, *out_h;
|
||||
// int odim = net.layers[net.num_layers-1]->output_dim.tot();
|
||||
// readBinaryFile(output_bin, odim, &out_h, &out);
|
||||
// dnnType *cudnn_out;
|
||||
// cudnn_out = net.layers[net.num_layers-1]->dstData;
|
||||
// std::cout<<"CUDNN vs correct";
|
||||
// checkResult(odim, cudnn_out, out);
|
||||
|
||||
|
||||
tk::dnn::Layer *outs[8] = { hm, wh, reg, track, dep, rot, dim_, a_off};
|
||||
int out_count = 1;
|
||||
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
|
||||
for(int i=0; i<8; i++) {
|
||||
printCenteredTitle((std::string(" RESNET CHECK RESULTS ") + std::to_string(i) + " ").c_str(), '=', 30);
|
||||
|
||||
outs[i]->output_dim.print();
|
||||
|
||||
dnnType *out, *out_h;
|
||||
int odim = outs[i]->output_dim.tot();
|
||||
readBinaryFile(output_bin[i], odim, &out_h, &out);
|
||||
|
||||
dnnType *cudnn_out, *rt_out;
|
||||
cudnn_out = outs[i]->dstData;
|
||||
rt_out = (dnnType *)netRT.buffersRT[i+out_count];
|
||||
// there is the maxpool. It isn't an output but it is necessary for the process section
|
||||
if(i==0)
|
||||
out_count ++;
|
||||
|
||||
std::cout<<"CUDNN vs correct";
|
||||
ret_cudnn |= checkResult(odim, cudnn_out, out) == 0 ? 0: ERROR_CUDNN;
|
||||
std::cout<<"TRT vs correct";
|
||||
ret_tensorrt |= checkResult(odim, rt_out, out) == 0 ? 0 : ERROR_TENSORRT;
|
||||
std::cout<<"CUDNN vs TRT ";
|
||||
ret_cudnn_tensorrt |= checkResult(odim, cudnn_out, rt_out) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
|
||||
}
|
||||
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
|
||||
}
|
||||
Reference in New Issue
Block a user