Comments (5)
Solving compile problem with help of this post
For Future Reference:
This is how the makefile.sh
was:
# Makefile
TF_INC = `python -c "import tensorflow; print(tensorflow.sysconfig.get_include())"`
TF_LIB = `python -c "import tensorflow; print(tensorflow.sysconfig.get_lib())"`
ifndef CUDA_HOME
CUDA_HOME := /usr/local/cuda
endif
CUDA_HOME := /usr/local/cuda-9.0
CC = gcc -O2 -pthread
CXX = g++
GPUCC = nvcc --expt-relaxed-constexpr
CFLAGS = -std=c++11 -I$(TF_INC) -I"$(CUDA_HOME)/.." -DGOOGLE_CUDA=1
GPUCFLAGS = -c
LFLAGS = -pthread -shared -fPIC
GPULFLAGS = -x cu -Xcompiler -fPIC
CGPUFLAGS = -L$(CUDA_HOME)/lib -L$(CUDA_HOME)/lib64 -lcudart -L$(TF_LIB) -ltensorflow_framework
OUT_DIR = src/ops/build
PREPROCESSING_SRC = "src/ops/preprocessing/preprocessing.cc" "src/ops/preprocessing/kernels/flow_augmentation.cc" "src/ops/preprocessing/kernels/augmentation_base.cc" "src/ops/preprocessing/kernels/data_augmentation.cc"
GPU_SRC_DATA_AUG = src/ops/preprocessing/kernels/data_augmentation.cu.cc
GPU_SRC_FLOW = src/ops/preprocessing/kernels/flow_augmentation_gpu.cu.cc
GPU_PROD_DATA_AUG = $(OUT_DIR)/data_augmentation.o
GPU_PROD_FLOW = $(OUT_DIR)/flow_augmentation_gpu.o
PREPROCESSING_PROD = $(OUT_DIR)/preprocessing.so
DOWNSAMPLE_SRC = "src/ops/downsample/downsample_kernel.cc" "src/ops/downsample/downsample_op.cc"
GPU_SRC_DOWNSAMPLE = src/ops/downsample/downsample_kernel_gpu.cu.cc
GPU_PROD_DOWNSAMPLE = $(OUT_DIR)/downsample_kernel_gpu.o
DOWNSAMPLE_PROD = $(OUT_DIR)/downsample.so
CORRELATION_SRC = "src/ops/correlation/correlation_kernel.cc" "src/ops/correlation/correlation_grad_kernel.cc" "src/ops/correlation/correlation_op.cc"
GPU_SRC_CORRELATION = src/ops/correlation/correlation_kernel.cu.cc
GPU_SRC_CORRELATION_GRAD = src/ops/correlation/correlation_grad_kernel.cu.cc
GPU_SRC_PAD = src/ops/correlation/pad.cu.cc
GPU_PROD_CORRELATION = $(OUT_DIR)/correlation_kernel_gpu.o
GPU_PROD_CORRELATION_GRAD = $(OUT_DIR)/correlation_grad_kernel_gpu.o
GPU_PROD_PAD = $(OUT_DIR)/correlation_pad_gpu.o
CORRELATION_PROD = $(OUT_DIR)/correlation.so
FLOWWARP_SRC = "src/ops/flow_warp/flow_warp_op.cc" "src/ops/flow_warp/flow_warp.cc" "src/ops/flow_warp/flow_warp_grad.cc"
GPU_SRC_FLOWWARP = "src/ops/flow_warp/flow_warp.cu.cc"
GPU_SRC_FLOWWARP_GRAD = "src/ops/flow_warp/flow_warp_grad.cu.cc"
GPU_PROD_FLOWWARP = "$(OUT_DIR)/flow_warp_gpu.o"
GPU_PROD_FLOWWARP_GRAD = "$(OUT_DIR)/flow_warp_grad_gpu.o"
FLOWWARP_PROD = "$(OUT_DIR)/flow_warp.so"
ifeq ($(OS),Windows_NT)
detected_OS := Windows
else
detected_OS := $(shell sh -c 'uname -s 2>/dev/null || echo not')
endif
ifeq ($(detected_OS),Darwin) # Mac OS X
CGPUFLAGS += -undefined dynamic_lookup
endif
ifeq ($(detected_OS),Linux)
CFLAGS += -D_MWAITXINTRIN_H_INCLUDED -D_FORCE_INLINES -D__STRICT_ANSI__ -D_GLIBCXX_USE_CXX11_ABI=0
endif
all: correlation flowwarp
#preprocessing:
# $(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_DATA_AUG) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_DATA_AUG)
# $(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_FLOW) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_FLOW)
# $(CXX) -g $(CFLAGS) $(PREPROCESSING_SRC) $(GPU_PROD_DATA_AUG) $(GPU_PROD_FLOW) $(LFLAGS) $(CGPUFLAGS) -o $(PREPROCESSING_PROD)
#downsample:
# $(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_DOWNSAMPLE) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_DOWNSAMPLE)
# $(CXX) -g $(CFLAGS) $(DOWNSAMPLE_SRC) $(GPU_PROD_DOWNSAMPLE) $(LFLAGS) $(CGPUFLAGS) -o $(DOWNSAMPLE_PROD)
correlation:
$(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_CORRELATION) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_CORRELATION)
$(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_CORRELATION_GRAD) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_CORRELATION_GRAD)
$(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_PAD) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_PAD)
$(CXX) -g $(CFLAGS) $(CORRELATION_SRC) $(GPU_PROD_CORRELATION) $(GPU_PROD_CORRELATION_GRAD) $(GPU_PROD_PAD) $(LFLAGS) $(CGPUFLAGS) -o $(CORRELATION_PROD)
flowwarp:
$(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_FLOWWARP) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_FLOWWARP)
$(GPUCC) -g $(CFLAGS) $(GPUCFLAGS) $(GPU_SRC_FLOWWARP_GRAD) $(GPULFLAGS) $(GPUDEF) -o $(GPU_PROD_FLOWWARP_GRAD)
$(CXX) -g $(CFLAGS) $(FLOWWARP_SRC) $(GPU_PROD_FLOWWARP) $(GPU_PROD_FLOWWARP_GRAD) $(LFLAGS) $(CGPUFLAGS) -o $(FLOWWARP_PROD)
clean:
rm -f $(PREPROCESSING_PROD) $(GPU_PROD_FLOW) $(GPU_PROD_DATA_AUG) $(DOWNSAMPLE_PROD) $(GPU_PROD_DOWNSAMPLE)
from mastersthesis.
There is a problem using Keras that it freezes on the validation process.
A solution was devised using this post.
from mastersthesis.
Warp
function showed issues for training. Turns out the Warp
function warps right image using flow but not disparity.
The disparity can be a special form of flow, one which only has one channel. The flow seems to have to channels.
By modifying the output from (?, 512, 512, 1)
to (?, 512, 512, 2)
the network seems to start to train properly.
from mastersthesis.
Correction should be done on DisparityNet_SD.
This network is similar to S type but I created it to be like C type.
from mastersthesis.
brightness_error
and up/down sampling
should be added to current structures.
A closer imitation of FlowNet is needed.
from mastersthesis.
Related Issues (20)
- Image Warping HOT 2
- A plugin for disparity rendering HOT 1
- Look for texture and simulate some data. HOT 2
- Search for Datasets HOT 2
- Converting Disparity Map to Mesh HOT 1
- EndoAbS Dataset HOT 2
- Needed Textures List HOT 1
- Minimum and Maximum Disparities HOT 1
- Test a dataset: SlidingThings3D. HOT 4
- Purchasing a Human Anatomy Model HOT 3
- Human Anatomy Model into Blender and Render Test HOT 1
- Create new Data Generator for SlidingOrgans HOT 1
- Learning Rate Scheduler HOT 3
- Optimizing the network's Hyper Parameters
- Transfer Learning
- A better loss function to train with HOT 1
- Train Networks For Presentation Day
- Combine Output Directories
- Create a Real World Test Dataset HOT 2
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from mastersthesis.