├── models ├── correlation_package │ ├── __init__.py │ ├── __init__.pyc │ ├── correlation.pyc │ ├── setup.py │ ├── correlation_cuda_kernel.cuh │ ├── correlation.py │ ├── correlation_cuda.cc │ └── correlation_cuda_kernel.cu ├── __init__.py ├── PWCNet.pyc ├── __init__.pyc └── PWCNet.py ├── data ├── Thumbs.db ├── frame_0010.png ├── frame_0011.png ├── input1_1.jpg ├── input1_2.jpg ├── input2_1.jpg ├── input2_2.jpg ├── input3_1.jpg ├── input3_2.jpg ├── input4_1.jpg ├── input4_2.jpg ├── input5_1.jpg ├── input5_2.jpg ├── input6_1.jpg ├── input6_2.jpg ├── input7_1.png └── input7_2.png ├── .gitignore ├── README.md ├── script_pwc.py ├── LICENSE.md ├── .ipynb_checkpoints └── demo-checkpoint.ipynb └── demo.ipynb /models/correlation_package/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /models/__init__.py: -------------------------------------------------------------------------------- 1 | from .PWCNet import * 2 | -------------------------------------------------------------------------------- /data/Thumbs.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zyong812/pwc-net_Pytorch/HEAD/data/Thumbs.db -------------------------------------------------------------------------------- /data/frame_0010.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zyong812/pwc-net_Pytorch/HEAD/data/frame_0010.png -------------------------------------------------------------------------------- /data/frame_0011.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zyong812/pwc-net_Pytorch/HEAD/data/frame_0011.png -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | *.tar 2 | tmp 3 | models/correlation_package/correlation_cuda.egg-info 4 | models/correlation_package/build 5 | models/correlation_package/dist 6 | .ipynb_checkpoints 7 | 8 | -------------------------------------------------------------------------------- /models/correlation_package/setup.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import os 3 | import torch 4 | 5 | from setuptools import setup, find_packages 6 | from torch.utils.cpp_extension import BuildExtension, CUDAExtension 7 | 8 | cxx_args = ['-std=c++11'] 9 | 10 | nvcc_args = [ 11 | '-gencode', 'arch=compute_50,code=sm_50', 12 | '-gencode', 'arch=compute_52,code=sm_52', 13 | '-gencode', 'arch=compute_60,code=sm_60', 14 | '-gencode', 'arch=compute_61,code=sm_61', 15 | '-gencode', 'arch=compute_70,code=sm_70', 16 | '-gencode', 'arch=compute_70,code=compute_70' 17 | ] 18 | 19 | setup( 20 | name='correlation_cuda', 21 | ext_modules=[ 22 | CUDAExtension('correlation_cuda', [ 23 | 'correlation_cuda.cc', 24 | 'correlation_cuda_kernel.cu' 25 | ], extra_compile_args={'cxx': cxx_args, 'nvcc': nvcc_args}) 26 | ], 27 | cmdclass={ 28 | 'build_ext': BuildExtension 29 | }) 30 | -------------------------------------------------------------------------------- /models/correlation_package/correlation_cuda_kernel.cuh: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include 5 | #include 6 | 7 | int correlation_forward_cuda_kernel(at::Tensor& output, 8 | int ob, 9 | int oc, 10 | int oh, 11 | int ow, 12 | int osb, 13 | int osc, 14 | int osh, 15 | int osw, 16 | 17 | at::Tensor& input1, 18 | int ic, 19 | int ih, 20 | int iw, 21 | int isb, 22 | int isc, 23 | int ish, 24 | int isw, 25 | 26 | at::Tensor& input2, 27 | int gc, 28 | int gsb, 29 | int gsc, 30 | int gsh, 31 | int gsw, 32 | 33 | at::Tensor& rInput1, 34 | at::Tensor& rInput2, 35 | int pad_size, 36 | int kernel_size, 37 | int max_displacement, 38 | int stride1, 39 | int stride2, 40 | int corr_type_multiply, 41 | cudaStream_t stream); 42 | 43 | 44 | int correlation_backward_cuda_kernel( 45 | at::Tensor& gradOutput, 46 | int gob, 47 | int goc, 48 | int goh, 49 | int gow, 50 | int gosb, 51 | int gosc, 52 | int gosh, 53 | int gosw, 54 | 55 | at::Tensor& input1, 56 | int ic, 57 | int ih, 58 | int iw, 59 | int isb, 60 | int isc, 61 | int ish, 62 | int isw, 63 | 64 | at::Tensor& input2, 65 | int gsb, 66 | int gsc, 67 | int gsh, 68 | int gsw, 69 | 70 | at::Tensor& gradInput1, 71 | int gisb, 72 | int gisc, 73 | int gish, 74 | int gisw, 75 | 76 | at::Tensor& gradInput2, 77 | int ggc, 78 | int ggsb, 79 | int ggsc, 80 | int ggsh, 81 | int ggsw, 82 | 83 | at::Tensor& rInput1, 84 | at::Tensor& rInput2, 85 | int pad_size, 86 | int kernel_size, 87 | int max_displacement, 88 | int stride1, 89 | int stride2, 90 | int corr_type_multiply, 91 | cudaStream_t stream); 92 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | PWC-Net adapted for pytorch > 1.0 & Python 3 from the the official pubilshed code. 2 | 3 | * The PWC model is from https://github.com/NVlabs/PWC-Net/tree/master/PyTorch. 4 | * The correlation_package is from https://github.com/NVIDIA/flownet2-pytorch/tree/master/networks/correlation_package. 5 | 6 | 7 | #### Installation 8 | 9 | ``` 10 | cd models correlation_package 11 | python setup.py install 12 | ``` 13 | 14 | > Gcc version may affect the compilation. I compiled the correlation_package successfully with gcc 5.4.0. 15 | 16 | #### Download pretrained models 17 | 18 | Download from https://github.com/NVlabs/PWC-Net/tree/master/PyTorch 19 | 20 | - pwc_net_chairs.pth.tar is the pretrained weight using flyingthings3D dataset 21 | - pwc_net.pth.tar is the fine-tuned weight on MPI Sintel 22 | 23 | 24 | #### Paper & Citation 25 | Deqing Sun, Xiaodong Yang, Ming-Yu Liu, Jan Kautz. PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. CVPR 2018 Oral. 26 | Sun, Deqing, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume." arXiv preprint arXiv:1709.02371(https://arxiv.org/abs/1709.02371), 2017. 27 | Project webpage: http://research.nvidia.com/publication/2018-02_PWC-Net:-CNNs-for 28 | 29 | If you use PWC-Net, please cite the following paper: 30 | ``` 31 | @InProceedings{Sun2018PWC-Net, 32 | author = {Deqing Sun and Xiaodong Yang and Ming-Yu Liu and Jan Kautz}, 33 | title = {{PWC-Net}: {CNNs} for Optical Flow Using Pyramid, Warping, and Cost Volume}, 34 | booktitle = CVPR, 35 | year = {2018}, 36 | } 37 | ``` 38 | or the arXiv paper 39 | ``` 40 | @article{sun2017pwc, 41 | author={Sun, Deqing and Yang, Xiaodong and Liu, Ming-Yu and Kautz, Jan}, 42 | title={{PWC-Net}: {CNNs} for Optical Flow Using Pyramid, Warping, and Cost Volume}, 43 | journal={arXiv preprint arXiv:1709.02371}, 44 | year={2017} 45 | } 46 | ``` 47 | -------------------------------------------------------------------------------- /models/correlation_package/correlation.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.nn.modules.module import Module 3 | from torch.autograd import Function 4 | import correlation_cuda 5 | 6 | class CorrelationFunction(Function): 7 | 8 | def __init__(self, pad_size=3, kernel_size=3, max_displacement=20, stride1=1, stride2=2, corr_multiply=1): 9 | super(CorrelationFunction, self).__init__() 10 | self.pad_size = pad_size 11 | self.kernel_size = kernel_size 12 | self.max_displacement = max_displacement 13 | self.stride1 = stride1 14 | self.stride2 = stride2 15 | self.corr_multiply = corr_multiply 16 | # self.out_channel = ((max_displacement/stride2)*2 + 1) * ((max_displacement/stride2)*2 + 1) 17 | 18 | def forward(self, input1, input2): 19 | self.save_for_backward(input1, input2) 20 | 21 | with torch.cuda.device_of(input1): 22 | rbot1 = input1.new() 23 | rbot2 = input2.new() 24 | output = input1.new() 25 | 26 | correlation_cuda.forward(input1, input2, rbot1, rbot2, output, 27 | self.pad_size, self.kernel_size, self.max_displacement,self.stride1, self.stride2, self.corr_multiply) 28 | 29 | return output 30 | 31 | def backward(self, grad_output): 32 | input1, input2 = self.saved_tensors 33 | 34 | with torch.cuda.device_of(input1): 35 | rbot1 = input1.new() 36 | rbot2 = input2.new() 37 | 38 | grad_input1 = input1.new() 39 | grad_input2 = input2.new() 40 | 41 | correlation_cuda.backward(input1, input2, rbot1, rbot2, grad_output, grad_input1, grad_input2, 42 | self.pad_size, self.kernel_size, self.max_displacement,self.stride1, self.stride2, self.corr_multiply) 43 | 44 | return grad_input1, grad_input2 45 | 46 | 47 | class Correlation(Module): 48 | def __init__(self, pad_size=0, kernel_size=0, max_displacement=0, stride1=1, stride2=2, corr_multiply=1): 49 | super(Correlation, self).__init__() 50 | self.pad_size = pad_size 51 | self.kernel_size = kernel_size 52 | self.max_displacement = max_displacement 53 | self.stride1 = stride1 54 | self.stride2 = stride2 55 | self.corr_multiply = corr_multiply 56 | 57 | def forward(self, input1, input2): 58 | 59 | result = CorrelationFunction(self.pad_size, self.kernel_size, self.max_displacement,self.stride1, self.stride2, self.corr_multiply)(input1, input2) 60 | 61 | return result 62 | 63 | -------------------------------------------------------------------------------- /script_pwc.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import cv2 3 | import torch 4 | import numpy as np 5 | from math import ceil 6 | from torch.autograd import Variable 7 | from scipy.ndimage import imread 8 | import models 9 | """ 10 | Contact: Deqing Sun (deqings@nvidia.com); Zhile Ren (jrenzhile@gmail.com) 11 | """ 12 | def writeFlowFile(filename,uv): 13 | """ 14 | According to the matlab code of Deqing Sun and c++ source code of Daniel Scharstein 15 | Contact: dqsun@cs.brown.edu 16 | Contact: schar@middlebury.edu 17 | """ 18 | TAG_STRING = np.array(202021.25, dtype=np.float32) 19 | if uv.shape[2] != 2: 20 | sys.exit("writeFlowFile: flow must have two bands!"); 21 | H = np.array(uv.shape[0], dtype=np.int32) 22 | W = np.array(uv.shape[1], dtype=np.int32) 23 | with open(filename, 'wb') as f: 24 | f.write(TAG_STRING.tobytes()) 25 | f.write(W.tobytes()) 26 | f.write(H.tobytes()) 27 | f.write(uv.tobytes()) 28 | 29 | 30 | im1_fn = 'data/frame_0010.png'; 31 | im2_fn = 'data/frame_0011.png'; 32 | flow_fn = './tmp/frame_0010.flo'; 33 | 34 | if len(sys.argv) > 1: 35 | im1_fn = sys.argv[1] 36 | if len(sys.argv) > 2: 37 | im2_fn = sys.argv[2] 38 | if len(sys.argv) > 3: 39 | flow_fn = sys.argv[3] 40 | 41 | pwc_model_fn = './pwc_net_chairs.pth.tar'; 42 | 43 | im_all = [imread(img) for img in [im1_fn, im2_fn]] 44 | im_all = [im[:, :, :3] for im in im_all] 45 | 46 | # rescale the image size to be multiples of 64 47 | divisor = 64. 48 | H = im_all[0].shape[0] 49 | W = im_all[0].shape[1] 50 | 51 | H_ = int(ceil(H/divisor) * divisor) 52 | W_ = int(ceil(W/divisor) * divisor) 53 | for i in range(len(im_all)): 54 | im_all[i] = cv2.resize(im_all[i], (W_, H_)) 55 | 56 | for _i, _inputs in enumerate(im_all): 57 | im_all[_i] = im_all[_i][:, :, ::-1] 58 | im_all[_i] = 1.0 * im_all[_i]/255.0 59 | 60 | im_all[_i] = np.transpose(im_all[_i], (2, 0, 1)) 61 | im_all[_i] = torch.from_numpy(im_all[_i]) 62 | im_all[_i] = im_all[_i].expand(1, im_all[_i].size()[0], im_all[_i].size()[1], im_all[_i].size()[2]) 63 | im_all[_i] = im_all[_i].float() 64 | 65 | im_all = torch.autograd.Variable(torch.cat(im_all,1).cuda(), volatile=True) 66 | 67 | net = models.pwc_dc_net(pwc_model_fn) 68 | net = net.cuda() 69 | net.eval() 70 | 71 | flo = net(im_all) 72 | flo = flo[0] * 20.0 73 | flo = flo.cpu().data.numpy() 74 | 75 | # scale the flow back to the input size 76 | flo = np.swapaxes(np.swapaxes(flo, 0, 1), 1, 2) # 77 | u_ = cv2.resize(flo[:,:,0],(W,H)) 78 | v_ = cv2.resize(flo[:,:,1],(W,H)) 79 | u_ *= W/ float(W_) 80 | v_ *= H/ float(H_) 81 | flo = np.dstack((u_,v_)) 82 | 83 | import ipdb; ipdb.set_trace() 84 | writeFlowFile(flow_fn, flo) 85 | -------------------------------------------------------------------------------- /models/correlation_package/correlation_cuda.cc: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | 8 | #include "correlation_cuda_kernel.cuh" 9 | 10 | int correlation_forward_cuda(at::Tensor& input1, at::Tensor& input2, at::Tensor& rInput1, at::Tensor& rInput2, at::Tensor& output, 11 | int pad_size, 12 | int kernel_size, 13 | int max_displacement, 14 | int stride1, 15 | int stride2, 16 | int corr_type_multiply) 17 | { 18 | 19 | int batchSize = input1.size(0); 20 | 21 | int nInputChannels = input1.size(1); 22 | int inputHeight = input1.size(2); 23 | int inputWidth = input1.size(3); 24 | 25 | int kernel_radius = (kernel_size - 1) / 2; 26 | int border_radius = kernel_radius + max_displacement; 27 | 28 | int paddedInputHeight = inputHeight + 2 * pad_size; 29 | int paddedInputWidth = inputWidth + 2 * pad_size; 30 | 31 | int nOutputChannels = ((max_displacement/stride2)*2 + 1) * ((max_displacement/stride2)*2 + 1); 32 | 33 | int outputHeight = ceil(static_cast(paddedInputHeight - 2 * border_radius) / static_cast(stride1)); 34 | int outputwidth = ceil(static_cast(paddedInputWidth - 2 * border_radius) / static_cast(stride1)); 35 | 36 | rInput1.resize_({batchSize, paddedInputHeight, paddedInputWidth, nInputChannels}); 37 | rInput2.resize_({batchSize, paddedInputHeight, paddedInputWidth, nInputChannels}); 38 | output.resize_({batchSize, nOutputChannels, outputHeight, outputwidth}); 39 | 40 | rInput1.fill_(0); 41 | rInput2.fill_(0); 42 | output.fill_(0); 43 | 44 | int success = correlation_forward_cuda_kernel( 45 | output, 46 | output.size(0), 47 | output.size(1), 48 | output.size(2), 49 | output.size(3), 50 | output.stride(0), 51 | output.stride(1), 52 | output.stride(2), 53 | output.stride(3), 54 | input1, 55 | input1.size(1), 56 | input1.size(2), 57 | input1.size(3), 58 | input1.stride(0), 59 | input1.stride(1), 60 | input1.stride(2), 61 | input1.stride(3), 62 | input2, 63 | input2.size(1), 64 | input2.stride(0), 65 | input2.stride(1), 66 | input2.stride(2), 67 | input2.stride(3), 68 | rInput1, 69 | rInput2, 70 | pad_size, 71 | kernel_size, 72 | max_displacement, 73 | stride1, 74 | stride2, 75 | corr_type_multiply, 76 | at::cuda::getCurrentCUDAStream() 77 | //at::globalContext().getCurrentCUDAStream() 78 | ); 79 | 80 | //check for errors 81 | if (!success) { 82 | AT_ERROR("CUDA call failed"); 83 | } 84 | 85 | return 1; 86 | 87 | } 88 | 89 | int correlation_backward_cuda(at::Tensor& input1, at::Tensor& input2, at::Tensor& rInput1, at::Tensor& rInput2, at::Tensor& gradOutput, 90 | at::Tensor& gradInput1, at::Tensor& gradInput2, 91 | int pad_size, 92 | int kernel_size, 93 | int max_displacement, 94 | int stride1, 95 | int stride2, 96 | int corr_type_multiply) 97 | { 98 | 99 | int batchSize = input1.size(0); 100 | int nInputChannels = input1.size(1); 101 | int paddedInputHeight = input1.size(2)+ 2 * pad_size; 102 | int paddedInputWidth = input1.size(3)+ 2 * pad_size; 103 | 104 | int height = input1.size(2); 105 | int width = input1.size(3); 106 | 107 | rInput1.resize_({batchSize, paddedInputHeight, paddedInputWidth, nInputChannels}); 108 | rInput2.resize_({batchSize, paddedInputHeight, paddedInputWidth, nInputChannels}); 109 | gradInput1.resize_({batchSize, nInputChannels, height, width}); 110 | gradInput2.resize_({batchSize, nInputChannels, height, width}); 111 | 112 | rInput1.fill_(0); 113 | rInput2.fill_(0); 114 | gradInput1.fill_(0); 115 | gradInput2.fill_(0); 116 | 117 | int success = correlation_backward_cuda_kernel(gradOutput, 118 | gradOutput.size(0), 119 | gradOutput.size(1), 120 | gradOutput.size(2), 121 | gradOutput.size(3), 122 | gradOutput.stride(0), 123 | gradOutput.stride(1), 124 | gradOutput.stride(2), 125 | gradOutput.stride(3), 126 | input1, 127 | input1.size(1), 128 | input1.size(2), 129 | input1.size(3), 130 | input1.stride(0), 131 | input1.stride(1), 132 | input1.stride(2), 133 | input1.stride(3), 134 | input2, 135 | input2.stride(0), 136 | input2.stride(1), 137 | input2.stride(2), 138 | input2.stride(3), 139 | gradInput1, 140 | gradInput1.stride(0), 141 | gradInput1.stride(1), 142 | gradInput1.stride(2), 143 | gradInput1.stride(3), 144 | gradInput2, 145 | gradInput2.size(1), 146 | gradInput2.stride(0), 147 | gradInput2.stride(1), 148 | gradInput2.stride(2), 149 | gradInput2.stride(3), 150 | rInput1, 151 | rInput2, 152 | pad_size, 153 | kernel_size, 154 | max_displacement, 155 | stride1, 156 | stride2, 157 | corr_type_multiply, 158 | at::cuda::getCurrentCUDAStream() 159 | //at::globalContext().getCurrentCUDAStream() 160 | ); 161 | 162 | if (!success) { 163 | AT_ERROR("CUDA call failed"); 164 | } 165 | 166 | return 1; 167 | } 168 | 169 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 170 | m.def("forward", &correlation_forward_cuda, "Correlation forward (CUDA)"); 171 | m.def("backward", &correlation_backward_cuda, "Correlation backward (CUDA)"); 172 | } 173 | 174 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | ## creative commons 2 | 3 | # Attribution-NonCommercial-ShareAlike 4.0 International 4 | 5 | Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. 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For the avoidance of doubt, this paragraph does not form part of the public licenses. 175 | 176 | Creative Commons may be contacted at [creativecommons.org](http://creativecommons.org/). 177 | ``` 178 | -------------------------------------------------------------------------------- /models/correlation_package/correlation_cuda_kernel.cu: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | #include "correlation_cuda_kernel.cuh" 4 | 5 | #define CUDA_NUM_THREADS 1024 6 | #define THREADS_PER_BLOCK 32 7 | #define FULL_MASK 0xffffffff 8 | 9 | #include 10 | #include 11 | #include 12 | #include 13 | 14 | using at::Half; 15 | 16 | template 17 | __forceinline__ __device__ scalar_t warpReduceSum(scalar_t val) { 18 | for (int offset = 16; offset > 0; offset /= 2) 19 | val += __shfl_down_sync(FULL_MASK, val, offset); 20 | return val; 21 | } 22 | 23 | template 24 | __forceinline__ __device__ scalar_t blockReduceSum(scalar_t val) { 25 | 26 | static __shared__ scalar_t shared[32]; 27 | int lane = threadIdx.x % warpSize; 28 | int wid = threadIdx.x / warpSize; 29 | 30 | val = warpReduceSum(val); 31 | 32 | if (lane == 0) 33 | shared[wid] = val; 34 | 35 | __syncthreads(); 36 | 37 | val = (threadIdx.x < blockDim.x / warpSize) ? shared[lane] : 0; 38 | 39 | if (wid == 0) 40 | val = warpReduceSum(val); 41 | 42 | return val; 43 | } 44 | 45 | 46 | template 47 | __global__ void channels_first(const scalar_t* __restrict__ input, scalar_t* rinput, int channels, int height, int width, int pad_size) 48 | { 49 | 50 | // n (batch size), c (num of channels), y (height), x (width) 51 | int n = blockIdx.x; 52 | int y = blockIdx.y; 53 | int x = blockIdx.z; 54 | 55 | int ch_off = threadIdx.x; 56 | scalar_t value; 57 | 58 | int dimcyx = channels * height * width; 59 | int dimyx = height * width; 60 | 61 | int p_dimx = (width + 2 * pad_size); 62 | int p_dimy = (height + 2 * pad_size); 63 | int p_dimyxc = channels * p_dimy * p_dimx; 64 | int p_dimxc = p_dimx * channels; 65 | 66 | for (int c = ch_off; c < channels; c += THREADS_PER_BLOCK) { 67 | value = input[n * dimcyx + c * dimyx + y * width + x]; 68 | rinput[n * p_dimyxc + (y + pad_size) * p_dimxc + (x + pad_size) * channels + c] = value; 69 | } 70 | } 71 | 72 | 73 | template 74 | __global__ void correlation_forward(scalar_t* __restrict__ output, const int nOutputChannels, 75 | const int outputHeight, const int outputWidth, const scalar_t* __restrict__ rInput1, 76 | const int nInputChannels, const int inputHeight, const int inputWidth, 77 | const scalar_t* __restrict__ rInput2, const int pad_size, const int kernel_size, 78 | const int max_displacement, const int stride1, const int stride2) { 79 | 80 | int32_t pInputWidth = inputWidth + 2 * pad_size; 81 | int32_t pInputHeight = inputHeight + 2 * pad_size; 82 | 83 | int32_t kernel_rad = (kernel_size - 1) / 2; 84 | 85 | int32_t displacement_rad = max_displacement / stride2; 86 | 87 | int32_t displacement_size = 2 * displacement_rad + 1; 88 | 89 | int32_t n = blockIdx.x; 90 | int32_t y1 = blockIdx.y * stride1 + max_displacement; 91 | int32_t x1 = blockIdx.z * stride1 + max_displacement; 92 | int32_t c = threadIdx.x; 93 | 94 | int32_t pdimyxc = pInputHeight * pInputWidth * nInputChannels; 95 | 96 | int32_t pdimxc = pInputWidth * nInputChannels; 97 | 98 | int32_t pdimc = nInputChannels; 99 | 100 | int32_t tdimcyx = nOutputChannels * outputHeight * outputWidth; 101 | int32_t tdimyx = outputHeight * outputWidth; 102 | int32_t tdimx = outputWidth; 103 | 104 | int32_t nelems = kernel_size * kernel_size * pdimc; 105 | 106 | // element-wise product along channel axis 107 | for (int tj = -displacement_rad; tj <= displacement_rad; ++tj) { 108 | for (int ti = -displacement_rad; ti <= displacement_rad; ++ti) { 109 | int x2 = x1 + ti * stride2; 110 | int y2 = y1 + tj * stride2; 111 | 112 | float acc0 = 0.0f; 113 | 114 | for (int j = -kernel_rad; j <= kernel_rad; ++j) { 115 | for (int i = -kernel_rad; i <= kernel_rad; ++i) { 116 | // THREADS_PER_BLOCK 117 | #pragma unroll 118 | for (int ch = c; ch < pdimc; ch += blockDim.x) { 119 | 120 | int indx1 = n * pdimyxc + (y1 + j) * pdimxc 121 | + (x1 + i) * pdimc + ch; 122 | int indx2 = n * pdimyxc + (y2 + j) * pdimxc 123 | + (x2 + i) * pdimc + ch; 124 | acc0 += static_cast(rInput1[indx1] * rInput2[indx2]); 125 | } 126 | } 127 | } 128 | 129 | if (blockDim.x == warpSize) { 130 | __syncwarp(); 131 | acc0 = warpReduceSum(acc0); 132 | } else { 133 | __syncthreads(); 134 | acc0 = blockReduceSum(acc0); 135 | } 136 | 137 | if (threadIdx.x == 0) { 138 | 139 | int tc = (tj + displacement_rad) * displacement_size 140 | + (ti + displacement_rad); 141 | const int tindx = n * tdimcyx + tc * tdimyx + blockIdx.y * tdimx 142 | + blockIdx.z; 143 | output[tindx] = static_cast(acc0 / nelems); 144 | } 145 | } 146 | } 147 | } 148 | 149 | 150 | template 151 | __global__ void correlation_backward_input1(int item, scalar_t* gradInput1, int nInputChannels, int inputHeight, int inputWidth, 152 | const scalar_t* __restrict__ gradOutput, int nOutputChannels, int outputHeight, int outputWidth, 153 | const scalar_t* __restrict__ rInput2, 154 | int pad_size, 155 | int kernel_size, 156 | int max_displacement, 157 | int stride1, 158 | int stride2) 159 | { 160 | // n (batch size), c (num of channels), y (height), x (width) 161 | 162 | int n = item; 163 | int y = blockIdx.x * stride1 + pad_size; 164 | int x = blockIdx.y * stride1 + pad_size; 165 | int c = blockIdx.z; 166 | int tch_off = threadIdx.x; 167 | 168 | int kernel_rad = (kernel_size - 1) / 2; 169 | int displacement_rad = max_displacement / stride2; 170 | int displacement_size = 2 * displacement_rad + 1; 171 | 172 | int xmin = (x - kernel_rad - max_displacement) / stride1; 173 | int ymin = (y - kernel_rad - max_displacement) / stride1; 174 | 175 | int xmax = (x + kernel_rad - max_displacement) / stride1; 176 | int ymax = (y + kernel_rad - max_displacement) / stride1; 177 | 178 | if (xmax < 0 || ymax < 0 || xmin >= outputWidth || ymin >= outputHeight) { 179 | // assumes gradInput1 is pre-allocated and zero filled 180 | return; 181 | } 182 | 183 | if (xmin > xmax || ymin > ymax) { 184 | // assumes gradInput1 is pre-allocated and zero filled 185 | return; 186 | } 187 | 188 | xmin = max(0,xmin); 189 | xmax = min(outputWidth-1,xmax); 190 | 191 | ymin = max(0,ymin); 192 | ymax = min(outputHeight-1,ymax); 193 | 194 | int pInputWidth = inputWidth + 2 * pad_size; 195 | int pInputHeight = inputHeight + 2 * pad_size; 196 | 197 | int pdimyxc = pInputHeight * pInputWidth * nInputChannels; 198 | int pdimxc = pInputWidth * nInputChannels; 199 | int pdimc = nInputChannels; 200 | 201 | int tdimcyx = nOutputChannels * outputHeight * outputWidth; 202 | int tdimyx = outputHeight * outputWidth; 203 | int tdimx = outputWidth; 204 | 205 | int odimcyx = nInputChannels * inputHeight* inputWidth; 206 | int odimyx = inputHeight * inputWidth; 207 | int odimx = inputWidth; 208 | 209 | scalar_t nelems = kernel_size * kernel_size * nInputChannels; 210 | 211 | __shared__ scalar_t prod_sum[THREADS_PER_BLOCK]; 212 | prod_sum[tch_off] = 0; 213 | 214 | for (int tc = tch_off; tc < nOutputChannels; tc += THREADS_PER_BLOCK) { 215 | 216 | int i2 = (tc % displacement_size - displacement_rad) * stride2; 217 | int j2 = (tc / displacement_size - displacement_rad) * stride2; 218 | 219 | int indx2 = n * pdimyxc + (y + j2)* pdimxc + (x + i2) * pdimc + c; 220 | 221 | scalar_t val2 = rInput2[indx2]; 222 | 223 | for (int j = ymin; j <= ymax; ++j) { 224 | for (int i = xmin; i <= xmax; ++i) { 225 | int tindx = n * tdimcyx + tc * tdimyx + j * tdimx + i; 226 | prod_sum[tch_off] += gradOutput[tindx] * val2; 227 | } 228 | } 229 | } 230 | __syncthreads(); 231 | 232 | if(tch_off == 0) { 233 | scalar_t reduce_sum = 0; 234 | for(int idx = 0; idx < THREADS_PER_BLOCK; idx++) { 235 | reduce_sum += prod_sum[idx]; 236 | } 237 | const int indx1 = n * odimcyx + c * odimyx + (y - pad_size) * odimx + (x - pad_size); 238 | gradInput1[indx1] = reduce_sum / nelems; 239 | } 240 | 241 | } 242 | 243 | template 244 | __global__ void correlation_backward_input2(int item, scalar_t* gradInput2, int nInputChannels, int inputHeight, int inputWidth, 245 | const scalar_t* __restrict__ gradOutput, int nOutputChannels, int outputHeight, int outputWidth, 246 | const scalar_t* __restrict__ rInput1, 247 | int pad_size, 248 | int kernel_size, 249 | int max_displacement, 250 | int stride1, 251 | int stride2) 252 | { 253 | // n (batch size), c (num of channels), y (height), x (width) 254 | 255 | int n = item; 256 | int y = blockIdx.x * stride1 + pad_size; 257 | int x = blockIdx.y * stride1 + pad_size; 258 | int c = blockIdx.z; 259 | 260 | int tch_off = threadIdx.x; 261 | 262 | int kernel_rad = (kernel_size - 1) / 2; 263 | int displacement_rad = max_displacement / stride2; 264 | int displacement_size = 2 * displacement_rad + 1; 265 | 266 | int pInputWidth = inputWidth + 2 * pad_size; 267 | int pInputHeight = inputHeight + 2 * pad_size; 268 | 269 | int pdimyxc = pInputHeight * pInputWidth * nInputChannels; 270 | int pdimxc = pInputWidth * nInputChannels; 271 | int pdimc = nInputChannels; 272 | 273 | int tdimcyx = nOutputChannels * outputHeight * outputWidth; 274 | int tdimyx = outputHeight * outputWidth; 275 | int tdimx = outputWidth; 276 | 277 | int odimcyx = nInputChannels * inputHeight* inputWidth; 278 | int odimyx = inputHeight * inputWidth; 279 | int odimx = inputWidth; 280 | 281 | scalar_t nelems = kernel_size * kernel_size * nInputChannels; 282 | 283 | __shared__ scalar_t prod_sum[THREADS_PER_BLOCK]; 284 | prod_sum[tch_off] = 0; 285 | 286 | for (int tc = tch_off; tc < nOutputChannels; tc += THREADS_PER_BLOCK) { 287 | int i2 = (tc % displacement_size - displacement_rad) * stride2; 288 | int j2 = (tc / displacement_size - displacement_rad) * stride2; 289 | 290 | int xmin = (x - kernel_rad - max_displacement - i2) / stride1; 291 | int ymin = (y - kernel_rad - max_displacement - j2) / stride1; 292 | 293 | int xmax = (x + kernel_rad - max_displacement - i2) / stride1; 294 | int ymax = (y + kernel_rad - max_displacement - j2) / stride1; 295 | 296 | if (xmax < 0 || ymax < 0 || xmin >= outputWidth || ymin >= outputHeight) { 297 | // assumes gradInput2 is pre-allocated and zero filled 298 | continue; 299 | } 300 | 301 | if (xmin > xmax || ymin > ymax) { 302 | // assumes gradInput2 is pre-allocated and zero filled 303 | continue; 304 | } 305 | 306 | xmin = max(0,xmin); 307 | xmax = min(outputWidth-1,xmax); 308 | 309 | ymin = max(0,ymin); 310 | ymax = min(outputHeight-1,ymax); 311 | 312 | int indx1 = n * pdimyxc + (y - j2)* pdimxc + (x - i2) * pdimc + c; 313 | scalar_t val1 = rInput1[indx1]; 314 | 315 | for (int j = ymin; j <= ymax; ++j) { 316 | for (int i = xmin; i <= xmax; ++i) { 317 | int tindx = n * tdimcyx + tc * tdimyx + j * tdimx + i; 318 | prod_sum[tch_off] += gradOutput[tindx] * val1; 319 | } 320 | } 321 | } 322 | 323 | __syncthreads(); 324 | 325 | if(tch_off == 0) { 326 | scalar_t reduce_sum = 0; 327 | for(int idx = 0; idx < THREADS_PER_BLOCK; idx++) { 328 | reduce_sum += prod_sum[idx]; 329 | } 330 | const int indx2 = n * odimcyx + c * odimyx + (y - pad_size) * odimx + (x - pad_size); 331 | gradInput2[indx2] = reduce_sum / nelems; 332 | } 333 | 334 | } 335 | 336 | int correlation_forward_cuda_kernel(at::Tensor& output, 337 | int ob, 338 | int oc, 339 | int oh, 340 | int ow, 341 | int osb, 342 | int osc, 343 | int osh, 344 | int osw, 345 | 346 | at::Tensor& input1, 347 | int ic, 348 | int ih, 349 | int iw, 350 | int isb, 351 | int isc, 352 | int ish, 353 | int isw, 354 | 355 | at::Tensor& input2, 356 | int gc, 357 | int gsb, 358 | int gsc, 359 | int gsh, 360 | int gsw, 361 | 362 | at::Tensor& rInput1, 363 | at::Tensor& rInput2, 364 | int pad_size, 365 | int kernel_size, 366 | int max_displacement, 367 | int stride1, 368 | int stride2, 369 | int corr_type_multiply, 370 | cudaStream_t stream) 371 | { 372 | 373 | int batchSize = ob; 374 | 375 | int nInputChannels = ic; 376 | int inputWidth = iw; 377 | int inputHeight = ih; 378 | 379 | int nOutputChannels = oc; 380 | int outputWidth = ow; 381 | int outputHeight = oh; 382 | 383 | dim3 blocks_grid(batchSize, inputHeight, inputWidth); 384 | dim3 threads_block(THREADS_PER_BLOCK); 385 | 386 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(input1.type(), "channels_first_fwd_1", ([&] { 387 | 388 | channels_first<<>>( 389 | input1.data(), rInput1.data(), nInputChannels, inputHeight, inputWidth, pad_size); 390 | 391 | })); 392 | 393 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(input2.type(), "channels_first_fwd_2", ([&] { 394 | 395 | channels_first<<>> ( 396 | input2.data(), rInput2.data(), nInputChannels, inputHeight, inputWidth, pad_size); 397 | 398 | })); 399 | 400 | dim3 threadsPerBlock(THREADS_PER_BLOCK); 401 | dim3 totalBlocksCorr(batchSize, outputHeight, outputWidth); 402 | 403 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(input1.type(), "correlation_forward", ([&] { 404 | 405 | correlation_forward<<>> 406 | (output.data(), nOutputChannels, outputHeight, outputWidth, 407 | rInput1.data(), nInputChannels, inputHeight, inputWidth, 408 | rInput2.data(), 409 | pad_size, 410 | kernel_size, 411 | max_displacement, 412 | stride1, 413 | stride2); 414 | 415 | })); 416 | 417 | cudaError_t err = cudaGetLastError(); 418 | 419 | 420 | // check for errors 421 | if (err != cudaSuccess) { 422 | printf("error in correlation_forward_cuda_kernel: %s\n", cudaGetErrorString(err)); 423 | return 0; 424 | } 425 | 426 | return 1; 427 | } 428 | 429 | 430 | int correlation_backward_cuda_kernel( 431 | at::Tensor& gradOutput, 432 | int gob, 433 | int goc, 434 | int goh, 435 | int gow, 436 | int gosb, 437 | int gosc, 438 | int gosh, 439 | int gosw, 440 | 441 | at::Tensor& input1, 442 | int ic, 443 | int ih, 444 | int iw, 445 | int isb, 446 | int isc, 447 | int ish, 448 | int isw, 449 | 450 | at::Tensor& input2, 451 | int gsb, 452 | int gsc, 453 | int gsh, 454 | int gsw, 455 | 456 | at::Tensor& gradInput1, 457 | int gisb, 458 | int gisc, 459 | int gish, 460 | int gisw, 461 | 462 | at::Tensor& gradInput2, 463 | int ggc, 464 | int ggsb, 465 | int ggsc, 466 | int ggsh, 467 | int ggsw, 468 | 469 | at::Tensor& rInput1, 470 | at::Tensor& rInput2, 471 | int pad_size, 472 | int kernel_size, 473 | int max_displacement, 474 | int stride1, 475 | int stride2, 476 | int corr_type_multiply, 477 | cudaStream_t stream) 478 | { 479 | 480 | int batchSize = gob; 481 | int num = batchSize; 482 | 483 | int nInputChannels = ic; 484 | int inputWidth = iw; 485 | int inputHeight = ih; 486 | 487 | int nOutputChannels = goc; 488 | int outputWidth = gow; 489 | int outputHeight = goh; 490 | 491 | dim3 blocks_grid(batchSize, inputHeight, inputWidth); 492 | dim3 threads_block(THREADS_PER_BLOCK); 493 | 494 | 495 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(input1.type(), "lltm_forward_cuda", ([&] { 496 | 497 | channels_first<<>>( 498 | input1.data(), 499 | rInput1.data(), 500 | nInputChannels, 501 | inputHeight, 502 | inputWidth, 503 | pad_size 504 | ); 505 | })); 506 | 507 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(input2.type(), "lltm_forward_cuda", ([&] { 508 | 509 | channels_first<<>>( 510 | input2.data(), 511 | rInput2.data(), 512 | nInputChannels, 513 | inputHeight, 514 | inputWidth, 515 | pad_size 516 | ); 517 | })); 518 | 519 | dim3 threadsPerBlock(THREADS_PER_BLOCK); 520 | dim3 totalBlocksCorr(inputHeight, inputWidth, nInputChannels); 521 | 522 | for (int n = 0; n < num; ++n) { 523 | 524 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(input2.type(), "lltm_forward_cuda", ([&] { 525 | 526 | 527 | correlation_backward_input1<<>> ( 528 | n, gradInput1.data(), nInputChannels, inputHeight, inputWidth, 529 | gradOutput.data(), nOutputChannels, outputHeight, outputWidth, 530 | rInput2.data(), 531 | pad_size, 532 | kernel_size, 533 | max_displacement, 534 | stride1, 535 | stride2); 536 | })); 537 | } 538 | 539 | for(int n = 0; n < batchSize; n++) { 540 | 541 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(rInput1.type(), "lltm_forward_cuda", ([&] { 542 | 543 | correlation_backward_input2<<>>( 544 | n, gradInput2.data(), nInputChannels, inputHeight, inputWidth, 545 | gradOutput.data(), nOutputChannels, outputHeight, outputWidth, 546 | rInput1.data(), 547 | pad_size, 548 | kernel_size, 549 | max_displacement, 550 | stride1, 551 | stride2); 552 | 553 | })); 554 | } 555 | 556 | // check for errors 557 | cudaError_t err = cudaGetLastError(); 558 | if (err != cudaSuccess) { 559 | printf("error in correlation_backward_cuda_kernel: %s\n", cudaGetErrorString(err)); 560 | return 0; 561 | } 562 | 563 | return 1; 564 | } 565 | -------------------------------------------------------------------------------- /models/PWCNet.py: -------------------------------------------------------------------------------- 1 | """ 2 | implementation of the PWC-DC network for optical flow estimation by Sun et al., 2018 3 | 4 | Jinwei Gu and Zhile Ren 5 | 6 | """ 7 | 8 | import torch 9 | import torch.nn as nn 10 | from torch.autograd import Variable 11 | import os 12 | os.environ['PYTHON_EGG_CACHE'] = 'tmp/' # a writable directory 13 | from .correlation_package.correlation import Correlation 14 | import numpy as np 15 | 16 | 17 | 18 | 19 | 20 | __all__ = [ 21 | 'pwc_dc_net', 'pwc_dc_net_old' 22 | ] 23 | 24 | def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1): 25 | return nn.Sequential( 26 | nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, 27 | padding=padding, dilation=dilation, bias=True), 28 | nn.LeakyReLU(0.1)) 29 | 30 | def predict_flow(in_planes): 31 | return nn.Conv2d(in_planes,2,kernel_size=3,stride=1,padding=1,bias=True) 32 | 33 | def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1): 34 | return nn.ConvTranspose2d(in_planes, out_planes, kernel_size, stride, padding, bias=True) 35 | 36 | 37 | 38 | class PWCDCNet(nn.Module): 39 | """ 40 | PWC-DC net. add dilation convolution and densenet connections 41 | 42 | """ 43 | def __init__(self, md=4): 44 | """ 45 | input: md --- maximum displacement (for correlation. default: 4), after warpping 46 | 47 | """ 48 | super(PWCDCNet,self).__init__() 49 | 50 | self.conv1a = conv(3, 16, kernel_size=3, stride=2) 51 | self.conv1aa = conv(16, 16, kernel_size=3, stride=1) 52 | self.conv1b = conv(16, 16, kernel_size=3, stride=1) 53 | self.conv2a = conv(16, 32, kernel_size=3, stride=2) 54 | self.conv2aa = conv(32, 32, kernel_size=3, stride=1) 55 | self.conv2b = conv(32, 32, kernel_size=3, stride=1) 56 | self.conv3a = conv(32, 64, kernel_size=3, stride=2) 57 | self.conv3aa = conv(64, 64, kernel_size=3, stride=1) 58 | self.conv3b = conv(64, 64, kernel_size=3, stride=1) 59 | self.conv4a = conv(64, 96, kernel_size=3, stride=2) 60 | self.conv4aa = conv(96, 96, kernel_size=3, stride=1) 61 | self.conv4b = conv(96, 96, kernel_size=3, stride=1) 62 | self.conv5a = conv(96, 128, kernel_size=3, stride=2) 63 | self.conv5aa = conv(128,128, kernel_size=3, stride=1) 64 | self.conv5b = conv(128,128, kernel_size=3, stride=1) 65 | self.conv6aa = conv(128,196, kernel_size=3, stride=2) 66 | self.conv6a = conv(196,196, kernel_size=3, stride=1) 67 | self.conv6b = conv(196,196, kernel_size=3, stride=1) 68 | 69 | self.corr = Correlation(pad_size=md, kernel_size=1, max_displacement=md, stride1=1, stride2=1, corr_multiply=1) 70 | self.leakyRELU = nn.LeakyReLU(0.1) 71 | 72 | nd = (2*md+1)**2 73 | dd = np.cumsum([128,128,96,64,32]) 74 | 75 | od = nd 76 | self.conv6_0 = conv(od, 128, kernel_size=3, stride=1) 77 | self.conv6_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 78 | self.conv6_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 79 | self.conv6_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 80 | self.conv6_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 81 | self.predict_flow6 = predict_flow(od+dd[4]) 82 | self.deconv6 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 83 | self.upfeat6 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 84 | 85 | od = nd+128+4 86 | self.conv5_0 = conv(od, 128, kernel_size=3, stride=1) 87 | self.conv5_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 88 | self.conv5_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 89 | self.conv5_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 90 | self.conv5_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 91 | self.predict_flow5 = predict_flow(od+dd[4]) 92 | self.deconv5 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 93 | self.upfeat5 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 94 | 95 | od = nd+96+4 96 | self.conv4_0 = conv(od, 128, kernel_size=3, stride=1) 97 | self.conv4_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 98 | self.conv4_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 99 | self.conv4_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 100 | self.conv4_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 101 | self.predict_flow4 = predict_flow(od+dd[4]) 102 | self.deconv4 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 103 | self.upfeat4 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 104 | 105 | od = nd+64+4 106 | self.conv3_0 = conv(od, 128, kernel_size=3, stride=1) 107 | self.conv3_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 108 | self.conv3_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 109 | self.conv3_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 110 | self.conv3_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 111 | self.predict_flow3 = predict_flow(od+dd[4]) 112 | self.deconv3 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 113 | self.upfeat3 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 114 | 115 | od = nd+32+4 116 | self.conv2_0 = conv(od, 128, kernel_size=3, stride=1) 117 | self.conv2_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 118 | self.conv2_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 119 | self.conv2_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 120 | self.conv2_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 121 | self.predict_flow2 = predict_flow(od+dd[4]) 122 | self.deconv2 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 123 | 124 | self.dc_conv1 = conv(od+dd[4], 128, kernel_size=3, stride=1, padding=1, dilation=1) 125 | self.dc_conv2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2) 126 | self.dc_conv3 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4) 127 | self.dc_conv4 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8) 128 | self.dc_conv5 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16) 129 | self.dc_conv6 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1) 130 | self.dc_conv7 = predict_flow(32) 131 | 132 | for m in self.modules(): 133 | if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): 134 | nn.init.kaiming_normal(m.weight.data, mode='fan_in') 135 | if m.bias is not None: 136 | m.bias.data.zero_() 137 | 138 | 139 | def warp(self, x, flo): 140 | """ 141 | warp an image/tensor (im2) back to im1, according to the optical flow 142 | 143 | x: [B, C, H, W] (im2) 144 | flo: [B, 2, H, W] flow 145 | 146 | """ 147 | B, C, H, W = x.size() 148 | # mesh grid 149 | xx = torch.arange(0, W).view(1,-1).repeat(H,1) 150 | yy = torch.arange(0, H).view(-1,1).repeat(1,W) 151 | xx = xx.view(1,1,H,W).repeat(B,1,1,1) 152 | yy = yy.view(1,1,H,W).repeat(B,1,1,1) 153 | grid = torch.cat((xx,yy),1).float() 154 | 155 | if x.is_cuda: 156 | grid = grid.cuda() 157 | vgrid = Variable(grid) + flo 158 | 159 | # scale grid to [-1,1] 160 | vgrid[:,0,:,:] = 2.0*vgrid[:,0,:,:].clone() / max(W-1,1)-1.0 161 | vgrid[:,1,:,:] = 2.0*vgrid[:,1,:,:].clone() / max(H-1,1)-1.0 162 | 163 | vgrid = vgrid.permute(0,2,3,1) 164 | output = nn.functional.grid_sample(x, vgrid) 165 | mask = torch.autograd.Variable(torch.ones(x.size())).cuda() 166 | mask = nn.functional.grid_sample(mask, vgrid) 167 | 168 | # if W==128: 169 | # np.save('mask.npy', mask.cpu().data.numpy()) 170 | # np.save('warp.npy', output.cpu().data.numpy()) 171 | 172 | mask[mask<0.9999] = 0 173 | mask[mask>0] = 1 174 | 175 | return output*mask 176 | 177 | 178 | def forward(self,x): 179 | im1 = x[:,:3,:,:] 180 | im2 = x[:,3:,:,:] 181 | 182 | c11 = self.conv1b(self.conv1aa(self.conv1a(im1))) 183 | c21 = self.conv1b(self.conv1aa(self.conv1a(im2))) 184 | c12 = self.conv2b(self.conv2aa(self.conv2a(c11))) 185 | c22 = self.conv2b(self.conv2aa(self.conv2a(c21))) 186 | c13 = self.conv3b(self.conv3aa(self.conv3a(c12))) 187 | c23 = self.conv3b(self.conv3aa(self.conv3a(c22))) 188 | c14 = self.conv4b(self.conv4aa(self.conv4a(c13))) 189 | c24 = self.conv4b(self.conv4aa(self.conv4a(c23))) 190 | c15 = self.conv5b(self.conv5aa(self.conv5a(c14))) 191 | c25 = self.conv5b(self.conv5aa(self.conv5a(c24))) 192 | c16 = self.conv6b(self.conv6a(self.conv6aa(c15))) 193 | c26 = self.conv6b(self.conv6a(self.conv6aa(c25))) 194 | 195 | 196 | corr6 = self.corr(c16, c26) 197 | corr6 = self.leakyRELU(corr6) 198 | 199 | 200 | x = torch.cat((self.conv6_0(corr6), corr6),1) 201 | x = torch.cat((self.conv6_1(x), x),1) 202 | x = torch.cat((self.conv6_2(x), x),1) 203 | x = torch.cat((self.conv6_3(x), x),1) 204 | x = torch.cat((self.conv6_4(x), x),1) 205 | flow6 = self.predict_flow6(x) 206 | up_flow6 = self.deconv6(flow6) 207 | up_feat6 = self.upfeat6(x) 208 | 209 | 210 | warp5 = self.warp(c25, up_flow6*0.625) 211 | corr5 = self.corr(c15, warp5) 212 | corr5 = self.leakyRELU(corr5) 213 | x = torch.cat((corr5, c15, up_flow6, up_feat6), 1) 214 | x = torch.cat((self.conv5_0(x), x),1) 215 | x = torch.cat((self.conv5_1(x), x),1) 216 | x = torch.cat((self.conv5_2(x), x),1) 217 | x = torch.cat((self.conv5_3(x), x),1) 218 | x = torch.cat((self.conv5_4(x), x),1) 219 | flow5 = self.predict_flow5(x) 220 | up_flow5 = self.deconv5(flow5) 221 | up_feat5 = self.upfeat5(x) 222 | 223 | 224 | warp4 = self.warp(c24, up_flow5*1.25) 225 | corr4 = self.corr(c14, warp4) 226 | corr4 = self.leakyRELU(corr4) 227 | x = torch.cat((corr4, c14, up_flow5, up_feat5), 1) 228 | x = torch.cat((self.conv4_0(x), x),1) 229 | x = torch.cat((self.conv4_1(x), x),1) 230 | x = torch.cat((self.conv4_2(x), x),1) 231 | x = torch.cat((self.conv4_3(x), x),1) 232 | x = torch.cat((self.conv4_4(x), x),1) 233 | flow4 = self.predict_flow4(x) 234 | up_flow4 = self.deconv4(flow4) 235 | up_feat4 = self.upfeat4(x) 236 | 237 | 238 | warp3 = self.warp(c23, up_flow4*2.5) 239 | corr3 = self.corr(c13, warp3) 240 | corr3 = self.leakyRELU(corr3) 241 | 242 | 243 | x = torch.cat((corr3, c13, up_flow4, up_feat4), 1) 244 | x = torch.cat((self.conv3_0(x), x),1) 245 | x = torch.cat((self.conv3_1(x), x),1) 246 | x = torch.cat((self.conv3_2(x), x),1) 247 | x = torch.cat((self.conv3_3(x), x),1) 248 | x = torch.cat((self.conv3_4(x), x),1) 249 | flow3 = self.predict_flow3(x) 250 | up_flow3 = self.deconv3(flow3) 251 | up_feat3 = self.upfeat3(x) 252 | 253 | 254 | warp2 = self.warp(c22, up_flow3*5.0) 255 | corr2 = self.corr(c12, warp2) 256 | corr2 = self.leakyRELU(corr2) 257 | x = torch.cat((corr2, c12, up_flow3, up_feat3), 1) 258 | x = torch.cat((self.conv2_0(x), x),1) 259 | x = torch.cat((self.conv2_1(x), x),1) 260 | x = torch.cat((self.conv2_2(x), x),1) 261 | x = torch.cat((self.conv2_3(x), x),1) 262 | x = torch.cat((self.conv2_4(x), x),1) 263 | flow2 = self.predict_flow2(x) 264 | 265 | x = self.dc_conv4(self.dc_conv3(self.dc_conv2(self.dc_conv1(x)))) 266 | flow2 = flow2 + self.dc_conv7(self.dc_conv6(self.dc_conv5(x))) 267 | 268 | if self.training: 269 | return flow2,flow3,flow4,flow5,flow6 270 | else: 271 | return flow2 272 | 273 | 274 | 275 | class PWCDCNet_old(nn.Module): 276 | """ 277 | PWC-DC net. add dilation convolution and densenet connections 278 | 279 | """ 280 | def __init__(self, md=4): 281 | """ 282 | input: md --- maximum displacement (for correlation. default: 4), after warpping 283 | 284 | """ 285 | super(PWCDCNet_old,self).__init__() 286 | 287 | self.conv1a = conv(3, 16, kernel_size=3, stride=2) 288 | self.conv1b = conv(16, 16, kernel_size=3, stride=1) 289 | self.conv2a = conv(16, 32, kernel_size=3, stride=2) 290 | self.conv2b = conv(32, 32, kernel_size=3, stride=1) 291 | self.conv3a = conv(32, 64, kernel_size=3, stride=2) 292 | self.conv3b = conv(64, 64, kernel_size=3, stride=1) 293 | self.conv4a = conv(64, 96, kernel_size=3, stride=2) 294 | self.conv4b = conv(96, 96, kernel_size=3, stride=1) 295 | self.conv5a = conv(96, 128, kernel_size=3, stride=2) 296 | self.conv5b = conv(128,128, kernel_size=3, stride=1) 297 | self.conv6a = conv(128,196, kernel_size=3, stride=2) 298 | self.conv6b = conv(196,196, kernel_size=3, stride=1) 299 | 300 | self.corr = Correlation(pad_size=md, kernel_size=1, max_displacement=md, stride1=1, stride2=1, corr_multiply=1) 301 | self.leakyRELU = nn.LeakyReLU(0.1) 302 | 303 | nd = (2*md+1)**2 304 | dd = np.cumsum([128,128,96,64,32]) 305 | 306 | od = nd 307 | self.conv6_0 = conv(od, 128, kernel_size=3, stride=1) 308 | self.conv6_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 309 | self.conv6_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 310 | self.conv6_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 311 | self.conv6_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 312 | self.predict_flow6 = predict_flow(od+dd[4]) 313 | self.deconv6 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 314 | self.upfeat6 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 315 | 316 | od = nd+128+4 317 | self.conv5_0 = conv(od, 128, kernel_size=3, stride=1) 318 | self.conv5_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 319 | self.conv5_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 320 | self.conv5_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 321 | self.conv5_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 322 | self.predict_flow5 = predict_flow(od+dd[4]) 323 | self.deconv5 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 324 | self.upfeat5 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 325 | 326 | od = nd+96+4 327 | self.conv4_0 = conv(od, 128, kernel_size=3, stride=1) 328 | self.conv4_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 329 | self.conv4_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 330 | self.conv4_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 331 | self.conv4_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 332 | self.predict_flow4 = predict_flow(od+dd[4]) 333 | self.deconv4 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 334 | self.upfeat4 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 335 | 336 | od = nd+64+4 337 | self.conv3_0 = conv(od, 128, kernel_size=3, stride=1) 338 | self.conv3_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 339 | self.conv3_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 340 | self.conv3_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 341 | self.conv3_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 342 | self.predict_flow3 = predict_flow(od+dd[4]) 343 | self.deconv3 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 344 | self.upfeat3 = deconv(od+dd[4], 2, kernel_size=4, stride=2, padding=1) 345 | 346 | od = nd+32+4 347 | self.conv2_0 = conv(od, 128, kernel_size=3, stride=1) 348 | self.conv2_1 = conv(od+dd[0],128, kernel_size=3, stride=1) 349 | self.conv2_2 = conv(od+dd[1],96, kernel_size=3, stride=1) 350 | self.conv2_3 = conv(od+dd[2],64, kernel_size=3, stride=1) 351 | self.conv2_4 = conv(od+dd[3],32, kernel_size=3, stride=1) 352 | self.predict_flow2 = predict_flow(od+dd[4]) 353 | self.deconv2 = deconv(2, 2, kernel_size=4, stride=2, padding=1) 354 | 355 | self.dc_conv1 = conv(od+dd[4], 128, kernel_size=3, stride=1, padding=1, dilation=1) 356 | self.dc_conv2 = conv(128, 128, kernel_size=3, stride=1, padding=2, dilation=2) 357 | self.dc_conv3 = conv(128, 128, kernel_size=3, stride=1, padding=4, dilation=4) 358 | self.dc_conv4 = conv(128, 96, kernel_size=3, stride=1, padding=8, dilation=8) 359 | self.dc_conv5 = conv(96, 64, kernel_size=3, stride=1, padding=16, dilation=16) 360 | self.dc_conv6 = conv(64, 32, kernel_size=3, stride=1, padding=1, dilation=1) 361 | self.dc_conv7 = predict_flow(32) 362 | 363 | for m in self.modules(): 364 | if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d): 365 | nn.init.kaiming_normal(m.weight.data, mode='fan_in') 366 | if m.bias is not None: 367 | m.bias.data.zero_() 368 | 369 | 370 | def warp(self, x, flo): 371 | """ 372 | warp an image/tensor (im2) back to im1, according to the optical flow 373 | 374 | x: [B, C, H, W] (im2) 375 | flo: [B, 2, H, W] flow 376 | 377 | """ 378 | B, C, H, W = x.size() 379 | # mesh grid 380 | xx = torch.arange(0, W).view(1,-1).repeat(H,1) 381 | yy = torch.arange(0, H).view(-1,1).repeat(1,W) 382 | xx = xx.view(1,1,H,W).repeat(B,1,1,1) 383 | yy = yy.view(1,1,H,W).repeat(B,1,1,1) 384 | grid = torch.cat((xx,yy),1).float() 385 | 386 | if x.is_cuda: 387 | grid = grid.cuda() 388 | vgrid = Variable(grid) + flo 389 | 390 | # scale grid to [-1,1] 391 | vgrid[:,0,:,:] = 2.0*vgrid[:,0,:,:].clone() / max(W-1,1)-1.0 392 | vgrid[:,1,:,:] = 2.0*vgrid[:,1,:,:].clone() / max(H-1,1)-1.0 393 | 394 | vgrid = vgrid.permute(0,2,3,1) 395 | output = nn.functional.grid_sample(x, vgrid) 396 | mask = torch.autograd.Variable(torch.ones(x.size())).cuda() 397 | mask = nn.functional.grid_sample(mask, vgrid) 398 | 399 | mask[mask<0.999] = 0 400 | mask[mask>0] = 1 401 | 402 | return output*mask 403 | 404 | 405 | def forward(self,x): 406 | im1 = x[:,:3,:,:] 407 | im2 = x[:,3:,:,:] 408 | 409 | c11 = self.conv1b(self.conv1a(im1)) 410 | c21 = self.conv1b(self.conv1a(im2)) 411 | c12 = self.conv2b(self.conv2a(c11)) 412 | c22 = self.conv2b(self.conv2a(c21)) 413 | c13 = self.conv3b(self.conv3a(c12)) 414 | c23 = self.conv3b(self.conv3a(c22)) 415 | c14 = self.conv4b(self.conv4a(c13)) 416 | c24 = self.conv4b(self.conv4a(c23)) 417 | c15 = self.conv5b(self.conv5a(c14)) 418 | c25 = self.conv5b(self.conv5a(c24)) 419 | c16 = self.conv6b(self.conv6a(c15)) 420 | c26 = self.conv6b(self.conv6a(c25)) 421 | 422 | corr6 = self.corr(c16, c26) 423 | corr6 = self.leakyRELU(corr6) 424 | x = torch.cat((corr6, self.conv6_0(corr6)),1) 425 | x = torch.cat((self.conv6_1(x), x),1) 426 | x = torch.cat((x, self.conv6_2(x)),1) 427 | x = torch.cat((x, self.conv6_3(x)),1) 428 | x = torch.cat((x, self.conv6_4(x)),1) 429 | flow6 = self.predict_flow6(x) 430 | up_flow6 = self.deconv6(flow6) 431 | up_feat6 = self.upfeat6(x) 432 | 433 | warp5 = self.warp(c25, up_flow6*0.625) 434 | corr5 = self.corr(c15, warp5) 435 | corr5 = self.leakyRELU(corr5) 436 | x = torch.cat((corr5, c15, up_flow6, up_feat6), 1) 437 | x = torch.cat((x, self.conv5_0(x)),1) 438 | x = torch.cat((self.conv5_1(x), x),1) 439 | x = torch.cat((x, self.conv5_2(x)),1) 440 | x = torch.cat((x, self.conv5_3(x)),1) 441 | x = torch.cat((x, self.conv5_4(x)),1) 442 | flow5 = self.predict_flow5(x) 443 | up_flow5 = self.deconv5(flow5) 444 | up_feat5 = self.upfeat5(x) 445 | 446 | warp4 = self.warp(c24, up_flow5*1.25) 447 | corr4 = self.corr(c14, warp4) 448 | corr4 = self.leakyRELU(corr4) 449 | x = torch.cat((corr4, c14, up_flow5, up_feat5), 1) 450 | x = torch.cat((x, self.conv4_0(x)),1) 451 | x = torch.cat((self.conv4_1(x), x),1) 452 | x = torch.cat((x, self.conv4_2(x)),1) 453 | x = torch.cat((x, self.conv4_3(x)),1) 454 | x = torch.cat((x, self.conv4_4(x)),1) 455 | flow4 = self.predict_flow4(x) 456 | up_flow4 = self.deconv4(flow4) 457 | up_feat4 = self.upfeat4(x) 458 | 459 | warp3 = self.warp(c23, up_flow4*2.5) 460 | corr3 = self.corr(c13, warp3) 461 | corr3 = self.leakyRELU(corr3) 462 | x = torch.cat((corr3, c13, up_flow4, up_feat4), 1) 463 | x = torch.cat((x, self.conv3_0(x)),1) 464 | x = torch.cat((self.conv3_1(x), x),1) 465 | x = torch.cat((x, self.conv3_2(x)),1) 466 | x = torch.cat((x, self.conv3_3(x)),1) 467 | x = torch.cat((x, self.conv3_4(x)),1) 468 | flow3 = self.predict_flow3(x) 469 | up_flow3 = self.deconv3(flow3) 470 | up_feat3 = self.upfeat3(x) 471 | 472 | warp2 = self.warp(c22, up_flow3*5.0) 473 | corr2 = self.corr(c12, warp2) 474 | corr2 = self.leakyRELU(corr2) 475 | x = torch.cat((corr2, c12, up_flow3, up_feat3), 1) 476 | x = torch.cat((x, self.conv2_0(x)),1) 477 | x = torch.cat((self.conv2_1(x), x),1) 478 | x = torch.cat((x, self.conv2_2(x)),1) 479 | x = torch.cat((x, self.conv2_3(x)),1) 480 | x = torch.cat((x, self.conv2_4(x)),1) 481 | flow2 = self.predict_flow2(x) 482 | 483 | x = self.dc_conv4(self.dc_conv3(self.dc_conv2(self.dc_conv1(x)))) 484 | flow2 = flow2 + self.dc_conv7(self.dc_conv6(self.dc_conv5(x))) 485 | 486 | if self.training: 487 | return flow2,flow3,flow4,flow5,flow6 488 | else: 489 | return flow2 490 | 491 | 492 | 493 | 494 | 495 | def pwc_dc_net(path=None): 496 | 497 | model = PWCDCNet() 498 | if path is not None: 499 | data = torch.load(path) 500 | if 'state_dict' in data.keys(): 501 | model.load_state_dict(data['state_dict']) 502 | else: 503 | model.load_state_dict(data) 504 | return model 505 | 506 | 507 | 508 | 509 | def pwc_dc_net_old(path=None): 510 | 511 | model = PWCDCNet_old() 512 | if path is not None: 513 | data = torch.load(path) 514 | if 'state_dict' in data.keys(): 515 | model.load_state_dict(data['state_dict']) 516 | else: 517 | model.load_state_dict(data) 518 | return model 519 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/demo-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "/home/yongzhang/anaconda2/lib/python2.7/site-packages/pkg_resources/__init__.py:1243: UserWarning: tmp/ is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable).\n", 13 | " warnings.warn(msg, UserWarning)\n" 14 | ] 15 | } 16 | ], 17 | "source": [ 18 | "import sys\n", 19 | "import cv2\n", 20 | "import torch\n", 21 | "import numpy as np\n", 22 | "from math import ceil\n", 23 | "from torch.autograd import Variable\n", 24 | "# from scipy.ndimage import imread\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "import models\n", 27 | "import PIL.Image as Image" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 15, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "data": { 37 | "text/plain": [ 38 | "PWCDCNet(\n", 39 | " (conv1a): Sequential(\n", 40 | " (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 41 | " (1): LeakyReLU(negative_slope=0.1)\n", 42 | " )\n", 43 | " (conv1aa): Sequential(\n", 44 | " (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 45 | " (1): LeakyReLU(negative_slope=0.1)\n", 46 | " )\n", 47 | " (conv1b): Sequential(\n", 48 | " (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 49 | " (1): LeakyReLU(negative_slope=0.1)\n", 50 | " )\n", 51 | " (conv2a): Sequential(\n", 52 | " (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 53 | " (1): LeakyReLU(negative_slope=0.1)\n", 54 | " )\n", 55 | " (conv2aa): Sequential(\n", 56 | " (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 57 | " (1): LeakyReLU(negative_slope=0.1)\n", 58 | " )\n", 59 | " (conv2b): Sequential(\n", 60 | " (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 61 | " (1): LeakyReLU(negative_slope=0.1)\n", 62 | " )\n", 63 | " (conv3a): Sequential(\n", 64 | " (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 65 | " (1): LeakyReLU(negative_slope=0.1)\n", 66 | " )\n", 67 | " (conv3aa): Sequential(\n", 68 | " (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 69 | " (1): LeakyReLU(negative_slope=0.1)\n", 70 | " )\n", 71 | " (conv3b): Sequential(\n", 72 | " (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 73 | " (1): LeakyReLU(negative_slope=0.1)\n", 74 | " )\n", 75 | " (conv4a): Sequential(\n", 76 | " (0): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 77 | " (1): LeakyReLU(negative_slope=0.1)\n", 78 | " )\n", 79 | " (conv4aa): Sequential(\n", 80 | " (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 81 | " (1): LeakyReLU(negative_slope=0.1)\n", 82 | " )\n", 83 | " (conv4b): Sequential(\n", 84 | " (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 85 | " (1): LeakyReLU(negative_slope=0.1)\n", 86 | " )\n", 87 | " (conv5a): Sequential(\n", 88 | " (0): Conv2d(96, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 89 | " (1): LeakyReLU(negative_slope=0.1)\n", 90 | " )\n", 91 | " (conv5aa): Sequential(\n", 92 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 93 | " (1): LeakyReLU(negative_slope=0.1)\n", 94 | " )\n", 95 | " (conv5b): Sequential(\n", 96 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 97 | " (1): LeakyReLU(negative_slope=0.1)\n", 98 | " )\n", 99 | " (conv6aa): Sequential(\n", 100 | " (0): Conv2d(128, 196, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 101 | " (1): LeakyReLU(negative_slope=0.1)\n", 102 | " )\n", 103 | " (conv6a): Sequential(\n", 104 | " (0): Conv2d(196, 196, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 105 | " (1): LeakyReLU(negative_slope=0.1)\n", 106 | " )\n", 107 | " (conv6b): Sequential(\n", 108 | " (0): Conv2d(196, 196, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 109 | " (1): LeakyReLU(negative_slope=0.1)\n", 110 | " )\n", 111 | " (corr): Correlation()\n", 112 | " (leakyRELU): LeakyReLU(negative_slope=0.1)\n", 113 | " (conv6_0): Sequential(\n", 114 | " (0): Conv2d(81, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 115 | " (1): LeakyReLU(negative_slope=0.1)\n", 116 | " )\n", 117 | " (conv6_1): Sequential(\n", 118 | " (0): Conv2d(209, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 119 | " (1): LeakyReLU(negative_slope=0.1)\n", 120 | " )\n", 121 | " (conv6_2): Sequential(\n", 122 | " (0): Conv2d(337, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 123 | " (1): LeakyReLU(negative_slope=0.1)\n", 124 | " )\n", 125 | " (conv6_3): Sequential(\n", 126 | " (0): Conv2d(433, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 127 | " (1): LeakyReLU(negative_slope=0.1)\n", 128 | " )\n", 129 | " (conv6_4): Sequential(\n", 130 | " (0): Conv2d(497, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 131 | " (1): LeakyReLU(negative_slope=0.1)\n", 132 | " )\n", 133 | " (predict_flow6): Conv2d(529, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 134 | " (deconv6): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 135 | " (upfeat6): ConvTranspose2d(529, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 136 | " (conv5_0): Sequential(\n", 137 | " (0): Conv2d(213, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 138 | " (1): LeakyReLU(negative_slope=0.1)\n", 139 | " )\n", 140 | " (conv5_1): Sequential(\n", 141 | " (0): Conv2d(341, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 142 | " (1): LeakyReLU(negative_slope=0.1)\n", 143 | " )\n", 144 | " (conv5_2): Sequential(\n", 145 | " (0): Conv2d(469, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 146 | " (1): LeakyReLU(negative_slope=0.1)\n", 147 | " )\n", 148 | " (conv5_3): Sequential(\n", 149 | " (0): Conv2d(565, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 150 | " (1): LeakyReLU(negative_slope=0.1)\n", 151 | " )\n", 152 | " (conv5_4): Sequential(\n", 153 | " (0): Conv2d(629, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 154 | " (1): LeakyReLU(negative_slope=0.1)\n", 155 | " )\n", 156 | " (predict_flow5): Conv2d(661, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 157 | " (deconv5): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 158 | " (upfeat5): ConvTranspose2d(661, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 159 | " (conv4_0): Sequential(\n", 160 | " (0): Conv2d(181, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 161 | " (1): LeakyReLU(negative_slope=0.1)\n", 162 | " )\n", 163 | " (conv4_1): Sequential(\n", 164 | " (0): Conv2d(309, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 165 | " (1): LeakyReLU(negative_slope=0.1)\n", 166 | " )\n", 167 | " (conv4_2): Sequential(\n", 168 | " (0): Conv2d(437, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 169 | " (1): LeakyReLU(negative_slope=0.1)\n", 170 | " )\n", 171 | " (conv4_3): Sequential(\n", 172 | " (0): Conv2d(533, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 173 | " (1): LeakyReLU(negative_slope=0.1)\n", 174 | " )\n", 175 | " (conv4_4): Sequential(\n", 176 | " (0): Conv2d(597, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 177 | " (1): LeakyReLU(negative_slope=0.1)\n", 178 | " )\n", 179 | " (predict_flow4): Conv2d(629, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 180 | " (deconv4): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 181 | " (upfeat4): ConvTranspose2d(629, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 182 | " (conv3_0): Sequential(\n", 183 | " (0): Conv2d(149, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 184 | " (1): LeakyReLU(negative_slope=0.1)\n", 185 | " )\n", 186 | " (conv3_1): Sequential(\n", 187 | " (0): Conv2d(277, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 188 | " (1): LeakyReLU(negative_slope=0.1)\n", 189 | " )\n", 190 | " (conv3_2): Sequential(\n", 191 | " (0): Conv2d(405, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 192 | " (1): LeakyReLU(negative_slope=0.1)\n", 193 | " )\n", 194 | " (conv3_3): Sequential(\n", 195 | " (0): Conv2d(501, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 196 | " (1): LeakyReLU(negative_slope=0.1)\n", 197 | " )\n", 198 | " (conv3_4): Sequential(\n", 199 | " (0): Conv2d(565, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 200 | " (1): LeakyReLU(negative_slope=0.1)\n", 201 | " )\n", 202 | " (predict_flow3): Conv2d(597, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 203 | " (deconv3): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 204 | " (upfeat3): ConvTranspose2d(597, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 205 | " (conv2_0): Sequential(\n", 206 | " (0): Conv2d(117, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 207 | " (1): LeakyReLU(negative_slope=0.1)\n", 208 | " )\n", 209 | " (conv2_1): Sequential(\n", 210 | " (0): Conv2d(245, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 211 | " (1): LeakyReLU(negative_slope=0.1)\n", 212 | " )\n", 213 | " (conv2_2): Sequential(\n", 214 | " (0): Conv2d(373, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 215 | " (1): LeakyReLU(negative_slope=0.1)\n", 216 | " )\n", 217 | " (conv2_3): Sequential(\n", 218 | " (0): Conv2d(469, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 219 | " (1): LeakyReLU(negative_slope=0.1)\n", 220 | " )\n", 221 | " (conv2_4): Sequential(\n", 222 | " (0): Conv2d(533, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 223 | " (1): LeakyReLU(negative_slope=0.1)\n", 224 | " )\n", 225 | " (predict_flow2): Conv2d(565, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 226 | " (deconv2): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 227 | " (dc_conv1): Sequential(\n", 228 | " (0): Conv2d(565, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 229 | " (1): LeakyReLU(negative_slope=0.1)\n", 230 | " )\n", 231 | " (dc_conv2): Sequential(\n", 232 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2))\n", 233 | " (1): LeakyReLU(negative_slope=0.1)\n", 234 | " )\n", 235 | " (dc_conv3): Sequential(\n", 236 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4))\n", 237 | " (1): LeakyReLU(negative_slope=0.1)\n", 238 | " )\n", 239 | " (dc_conv4): Sequential(\n", 240 | " (0): Conv2d(128, 96, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8))\n", 241 | " (1): LeakyReLU(negative_slope=0.1)\n", 242 | " )\n", 243 | " (dc_conv5): Sequential(\n", 244 | " (0): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16))\n", 245 | " (1): LeakyReLU(negative_slope=0.1)\n", 246 | " )\n", 247 | " (dc_conv6): Sequential(\n", 248 | " (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 249 | " (1): LeakyReLU(negative_slope=0.1)\n", 250 | " )\n", 251 | " (dc_conv7): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 252 | ")" 253 | ] 254 | }, 255 | "execution_count": 15, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "def writeFlowFile(filename,uv):\n", 262 | " TAG_STRING = np.array(202021.25, dtype=np.float32)\n", 263 | " if uv.shape[2] != 2:\n", 264 | " sys.exit(\"writeFlowFile: flow must have two bands!\");\n", 265 | " H = np.array(uv.shape[0], dtype=np.int32)\n", 266 | " W = np.array(uv.shape[1], dtype=np.int32)\n", 267 | " with open(filename, 'wb') as f:\n", 268 | " f.write(TAG_STRING.tobytes())\n", 269 | " f.write(W.tobytes())\n", 270 | " f.write(H.tobytes())\n", 271 | " f.write(uv.tobytes())\n", 272 | " \n", 273 | "def draw_flow(img, flow, step=16):\n", 274 | " h, w = img.shape[:2]\n", 275 | " y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)\n", 276 | " fx, fy = flow[y,x].T\n", 277 | " lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)\n", 278 | " lines = np.int32(lines + 0.5)\n", 279 | " vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)\n", 280 | " cv.polylines(vis, lines, 0, (0, 255, 0))\n", 281 | " for (x1, y1), (_x2, _y2) in lines:\n", 282 | " cv.circle(vis, (x1, y1), 1, (0, 255, 0), -1)\n", 283 | " return vis\n", 284 | "\n", 285 | "def flow2rgb(flow_map_np, max_value=None):\n", 286 | " _, h, w = flow_map_np.shape\n", 287 | " hsv = np.ones((h,w,3), dtype=np.uint8)\n", 288 | " hsv[..., 1] = 255\n", 289 | " mag, ang = cv2.cartToPolar(flow_map_np[0].squeeze(), flow_map_np[1].squeeze())\n", 290 | " hsv[..., 0] = ang / (2 * np.pi) * 180\n", 291 | " hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n", 292 | " rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)\n", 293 | " return rgb\n", 294 | "\n", 295 | "\n", 296 | "# pwc_model_fn = './pwc_net_chairs.pth.tar'\n", 297 | "pwc_model_fn = 'pwc_net.pth.tar'\n", 298 | "\n", 299 | "net = models.pwc_dc_net(pwc_model_fn)\n", 300 | "net = net.cuda()\n", 301 | "net.eval()" 302 | ] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": 16, 307 | "metadata": {}, 308 | "outputs": [ 309 | { 310 | "data": { 311 | "text/plain": [ 312 | "" 313 | ] 314 | }, 315 | "execution_count": 16, 316 | "metadata": {}, 317 | "output_type": "execute_result" 318 | }, 319 | { 320 | "data": { 321 | "image/png": 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wsBDe8suqHOzhZ4NoOiJrFPvfbjfW5zSLuabcMFi5563Os9t1SXAfvQAYVrWaBGY0z1TWdoBLNkdKoC5EK0wj78V9Klftpa9+knSQHPhJ0kFaFvUBjOxKjzkcs23PlGHRn2O5WVGfpyKsJMRiEotQfRtYIVADPFF/Yr8/ObabVUf6y4+BUqTnPCvqc9r2kfM26XhgyhViepmFEZXdpvtj49QpPQt7nUPKY7HfiqhsQbC/Q4UaE1hRhn0/rxDvqdzItLUd7FLLaXseq3VeIBXAGFgOq2ZkII4kSY5ADvwk6SA58JOkg7RvzrPbUu/DQQzsPMDYyVNTLojpUKTZ5GXjvPcCcx7r7qzP2Zj1hf5l49lXrmgT59jWYfN4BRp7qvVNOQ5s2dBbaU6l2LY5MPtZU9/rbI6kvNPBhoHBQsnSk8SuVqRr2whWVEqgx48dr0zbXt+cx96dVavn0JxT4VckWmk4Dsy4+xxXII4kSX7yyIGfJB1kDZ57FaL+eOLnTZ1joBTRGsExuD72kLPmvCCPK2Hx3i74YOzlFhaqIIBHFH+fv69t2yzSs5fchqmDn8NWsN0YAnGeF40MTZ4Xj9967hXx/awXonMPNoPAIbWeezbOYC9QA2qDY/Cxs1PcSqwSm39F8hc/STpIDvwk6SA58JOkg7TvsmsDZOwTrUqq1fFZX7QqUM8xhfTsarGBn1e44nJsfmtuC9xLozw3uEf5/dwrJizKvCmcaxuZtjgYkt3iekrmPN4brm/KcfqSeRYc7ITnBvrOHA+wZHtq3sMv+I3aYpdaU8emo+OPAz3eBiYpnplpe+K8V6u45e44ZteBnb/h+Senrmi+ichf/CTpIDnwk6SDtCvqA0EAgWAFlxfz3HqS8WmNmPVOEAO78o09+RoBMPh2cX1lscKrz4qNLNWZ0/hbuF+4qpUN9KnkwOg0LOqzRW26U9Yx4u2prSluSmK6sNhvRH1OWzXgdRb1uQ7rJVhpltqiu7Vrf684bp/J47IcxzDaktsSxdVnc2exZ8AKW7P1nPptW+wBGW0HXkH+4idJB8mBnyQdZA275XqLC4JYdIVYo3452xbDcc6KmGdBWzY2WtE2z/TaXW8X5/WNKN6jtBXTB8Xxov5+Y1a/T+XKRzilsltUx8iInlNKXx6V/Ri9zupOMCPP6WjG/zU6bix8CvBmxjfNs93lOk0ei9G8XZfd26XwzjN5xWx9sKNvYS0KVhztRdtrcR/t/aZj797krH6SJB458JOkg+TAT5IO8uYx5xVmjGhVkqPvr5IX6fhRHkN6vZjb2K/U44fme3dY6O59+nxgyi286foo9yNk/Z/VWOsvt1fMIZT8mHTQCa/A2zD3g9M2oEnfMfW9toKOzysKd/xi2KJyVn/edPT6bVNH5GnH75/dP4DNxoWOb+oobLXWxOusumu0xccnrOOLyJaIfF1Evi0iz4jIb88/f7eIPCkiz4vIl0Ukd8RMkrcINaL+LoA7VPX9AG4BcJeI3ArgMwA+q6o3A/ghgHtPrptJkhwnNXvnKYDX58nh/E8B3AHg1+afPwjgtwB8/uAmPVEk2v/KC0AQBHVobPcXLOCphr8nF22L6YcUeWryltcGlN56G5S7YYTxDRL1N42or/RIi3U4pq0JtdUz/Z9S+keXKW/LbG02JNnZBrYY0tVtBOI9mwSteZYXqWzT8WVTjs/bsuZZjokXeOAVAUICrzvLyPHIa+zJ0KvL47iADQ8/NwjhguPcQktE+vOdci8CeAzA3wB4VfVKtMsLAG6oajFJkrVTNfBVdaKqtwC4EcAHAbx3WbFl54rIWRE5LyLnCx/wJEnWxkrmPFV9FcATAG4FcLXIlcXrNwJ42TnnnKqeUdUzzRh2SZKsgwNHooi8E8BIVV8VkW0Av4TZxN7XAHwUwEMA7gHwcFWLnrpUKL/W3VaXl2vIGLXKe7ifdnAef08udFM1GjTryBNTX4/KTozuPinO869FCv28rKNPj3QSzHmwy+4UpSS2i4Uuv0F5e5eMTY2DRlhzHve/2Arb6tYU6KMRzNMJurJldOQx72loXmnWmXnlnt1LINKto5V1bCKs1c8bLruUt+XE6QfKuQFv3sELdGOo+Qm+HsCDItLH7M3/iqo+KiLfAfCQiPx7AN8C8IWqFpMkWTs1s/p/AeADSz5/ETN9P0mStxjtK93e1j9MYIk7PJ54b01DUbssii7O04Y5bFFOGmpAj47L8+pF/UUd1qtvQKY+VgkmZjqnzCsv9BSL99T/8V4ZLL4I7mHj5RdLDcUvx8E8GoE+nJWBU/Pa8nbgNg4ji9+e6c2mozz7Unh1hiqBrZ/6yJ6NVqXh7brEmZ6rFPXTVz9JOkgO/CTpIO2L+jWz+g1RX5bnhVpDlOmHpy7yGsE8eNbWDxzCor8V56MZ/36R5nIw5RY3YRh49bFKIOY6WeWwasAezerv0PHYzP5femO56jPv5AL24rOLbYq4fTZEt3OPN41KwDP5tg7ePZcdDyNvPJtXiPNBWO7iHJPmd8nmFZYHunGXzPDcpnSK+kmSrEoO/CTpIDnwk6SDtKvjC+q+ahpeT56OdVg7X+15gVdfGLCDTH3q6/hNc57SsQ+b4sRcCwf3YK++qZkLsHMDzCV6LbboeM/0iuP2j2zQiF3Sw98IVvGxTm63jBo4q+l2zWu7wfMEJu8Nx5vOvmPkQNjYhis6j+8/6+4mgGmh4+/ZuSPeApyey4YZLLuBV98+k+jNodOrSiVJ8hNFDvwk6SBvzuVykThViF0rxG/zG1uhqGNLtLHWp76ZSysX30RGmTHljk3JEaX7JM73zHUK5dlAImwSHJJ4b18Wjic4soteeCEKqwGv1YmiM1g85vh+xpy3OfDzNpyFLdYcthMssGEapjiO98cmuyDmoxX1maGfVcQg9BjVLX3PX/wk6SA58JOkg+TAT5IO8ibS8R23XMDXzRpbLjvnRG014u8HpxVVeAFAUej81pzHDVh939Pr7Uo9Derw5hCs2a9H3/n9xr4AizCdG9Qruw/AZhFUpGSPt6cuqg8CdkSwzmzndniPORvYszCB0fHQut4G71Wkk7NKXej45mly/+07wWXZhGlXMhZ7rDt9Gqc5L0kShxz4SdJB3kSiPhN5TjkxyIHSwy+qo4jJYcV0Tlg1wFNHfM89mzel9iKT3TQw7U0KUd83F/KxFfWl8PAr8/pkU+JtvW18/1Fh6jNeiOSsZ0OR+Iz9JMfVt+bTEbWwZ2PWsyccHe+acmwSbNQfrKwbOSa8hqg/XV4OAJSDjASrFZXyvC3ix7k6L0kShxz4SdJB2hX1Fb6kF2x060fpsDP3UYhkzuO2gsUUtiOFuhAsFqoOAW5n9ZfnRd5+kZ9WGYijFG179Oit/+MmtbdVxAi0QvuidRtbkAX8XZrtnthHNqFrs0EkJnR1HN/PhuEuZujNb9nQEfU3zKvPorhVIfkmW1G/yAs897j+ceDVx16ffdPYlNPO7L23G7Uhf/GTpIPkwE+SDpIDP0k6SPvmvIaSt4SpKePp/6HTV+UKv4YeX1snr/oyWvLU1/+tn12Z8mL1+xfaM9/dSv1ij7yBWfY1KLzzyjp4ay+l8/pGr+yTgjsMlN8B6/smYMfuLl3bprnOS5zHATuC7dHtG806/oCOt21QTkrb5zmuDLYZBeKI5gkY3nfCmuZ6foDXVan+xZ9vlf0tEXl0nn63iDwpIs+LyJdFZOOgOpIkeXOwiqj/SQDPUvozAD6rqjcD+CGAe4+zY0mSnBxVor6I3AjgHwL4DwD+pYgIgDsA/Nq8yIMAfgvA5w+szJNaWbxvOMI5XndWLtdAJIMjfq8Uts8R9TVoy5oVOYxcw5zH8fh4N95Ip/EDbJQmO1/Ut4t0Thdlfe88FvV7xR5RgFB6SOW2jQFyh4OK7KrJIzPgG8EClUFlHl9muRtYmTeM9lOwz8J5r6zJji97HKihhWUyMPu5on7doqfaX/zPAfhNLIbkOwC8qnrFh/ACgBsq60qSZM0cOPBF5FcAXFTVb/LHS4ou/aoRkbMicl5Ezhd+yEmSrI0aUf82AB8RkQ8D2ALwNswkgKtFZDD/1b8RwMvLTlbVcwDOAYAMTteudk+S5AQ5cOCr6gMAHgAAEbkdwL9S1V8Xkd8H8FEADwG4B8DDB7amaJrqruSxjm8DbDiusla3liAPjltnbbyOxgd95xhlIIdJvTnP33q7LFfG1S+vs+eY86yOP8Smm8dzDaep7aHpx4AU102jNHPAzg3S90fGJLjN23CbOYTL1F6RZ1x7x5S2dYx5noAfk5lPwBalrY7P8wQNHZ/z4GNj9Rfw3AB9bHV8Nuf1HJfdyp/Wozjw3IfZRN8LmOn8XzhCXUmStMhKDjyq+gSAJ+bHLwL44PF3KUmSk2YNnnuOkMGiuRX1p2w64+PItGLbYTkv2iLZzyozA7VC/X6IZ/4x6WiL69JkV6oZnnjP5rtZ3oabxy8FO5lZUZ/F9NeMutCnM7foeM+Y8zg9MmL6Kap/VByXdWihLpQiMJsLd0jt2r0UrMCLRH27zZcte6U++2zVz2MTJB9bkyCL+o14fEvaCUhf/STpIDnwk6SDtByIQypFfbvohbo5ZbHU1sWz/1HcvuWnzKoMRCVxTmx450XWheWedbOc5WJ6ryGmD5ce27QG4jzP6ts8hu+GfVl2SYy+2tSxS7LzLi/SaQTzGC09ByjVgGmlurBnVsCU6sjiahrXsrt4ho0I1ZGob8XxK0Qhue37wvH4eOb+EIt0WpjVT5LkLUoO/CTpIDnwk6SDtB9sc+xtbc3L1kq9VSjuuBTebSYIRaTfFNtrUaKxAorL2TqcOYSGOc/36uuFOj6b31jHL+9Hr9Dx68x0A9LpbTrS8UvTnpg8PyoKt7ZbBA4tFegJ6eQ7ZoUf500ob9fo8Xye1fG5LAcEHRrTobCXoNHBd3hKobFXuLP6z1JMS9h5AsdMZ012rNd7cwup4ydJ4pEDP0k6SMuee2JWSjALGcWaqFi8528qbXhw1W0tNS3imBtxirdPClWHSNRnc54v6ksg6rM4P2yY7DaWnmPTLMJPQ3NeqQZ4DE2aFwuNzG/IdnGdizz7XEa0uGdiRP03KM15l82CoDGld0zea3RerxD7y3eH1YDLwYMfN3b5chbYNKjUQ9U5tmnPcy/UdxfkL36SdJAc+EnSQXLgJ0kHaV3HH7hNLnSlfiPwxCJdbulc1sUBKqdGlyzTFNRSjWLGgULsdsneirxAx48DZZT6v2B5EA1rzotcdgeO/r/K6rzaCKQc03/TPIvSbMlx+stnO6H5hbHR8U+TTs56/CVcLspdorY3G/eUnwW55cLHBvNgHXynWbiqzmqifSP6ka/5QZ+X5C9+knSQHPhJ0kFaFfUFgqHTpJKI0jMyTt8JUGG9wDgGvDQ8syZLj61oVIj+drsvb5tsI+oLpfuBqD8wYunQybP3jNOROW+LRPhTxmR3GHOemHslxTOrE/UtXoxAoFTdRnRdVn3iZ3ip4UHIKwN5yy+/jtgBT026dmOG2uCOwd4Qg2jV52rkL36SdJAc+EnSQVoV9XuQIuwyo4WnVykW9YqZWZ4hLudReTfXSSPgA8dlW5ynpi0ljzxbf+mtx/2NFuKUeQOwGmDj5fWo3CJvI/Dw2zIz8lvuIp2y3GkS74eVon4TtsT4or7NY6ZF4BBrieFntij3I1PHaWfrMQCY0DN8nawEdndffiv9fX+b1A8gX9Qv1QX6Le4HIeIHjvpkg8I45C9+knSQHPhJ0kFy4CdJB2ndnOfr+LwCz5rAuJusn5fdZ31OAl2vDODh60STKO5m4Vnn6/jDhh6/OM+a8zwTnjWVbYaee8u99U4f2pzn3wQ2q0X3oNTxIx3Ub4tX8dm5gB/Rsz5lNPLL5A3I3oUbjXvvm/MGzjEQ6f/BVtiN+tmjMPDOY3Oe3Q784GZMmxWIyEsAXsPMUD5W1TMicg2ALwO4CcBLAP6Rqv6wrtkkSdbJKqL+L6rqLap6Zp6+H8DjqnozgMfn6SRJ3gIcRdS/G8Dt8+MHMdtT777oBIEUoigTi/rLzWPWdCOFSdAGWvC2tYpEMl9u6sFfRMNqQNOzjkV9u4OtF4jDmsoiNWORtx2oC6eCQB9849E1AAALpklEQVTMzjGI+qeKfhxuWmlUHJemSb7OXVP/prMVmTUZD0NRfPnxLB0FaWT8cq73n3VM5Vf6EOH8mdqnoAD+TES+KSJn559dp6qvAMD8/7WVdSVJsmZqf/FvU9WXReRaAI+JyF/VNjD/ojgLAH2cOkQXkyQ5bqp+8VX15fn/iwC+itn22D8QkesBYP7/onPuOVU9o6pneof2EEuS5Dg58BdfRE4D6Knqa/PjXwbw7wA8AuAeAJ+e/3+4rkFvpRbri3Zb6MnScvZ7a+joc7P08lj3VteLdHxWn3qBHl/q+H6Qy+aeeJt07AfULAN22utcHgCzeU99/ZxhGW23kStUrs5lt3m/64jO43mIXWPq4736bBAQ28I+Vt+3K/KYQbWy7QcB4TrKfftMHf0oSkf8saVG1L8OwFdl5gM8APBfVfVPROQbAL4iIvcC+C6Aj9U1mSTJujlw4KvqiwDev+Tz/wfgzpPoVJIkJ0vrnns2lvw+HGev14iJ31tarrkSi4NtWPMS5/lmnWmgSjClOD9w86w4z2m7Kq5W1C+vzVdVpDiOYv/5rwGLx9uNlvi5HF3Ut6I4p5vBN8qS+9h3Qost15xY9AdQmvOsLL1c/B4H/W16//GeEovjiTXn7XJc/dxCK0mSFcmBnyQdJAd+knSQN02wTdZtrK7HOn9ZrtTjx0Vc/VJB6lWaBDlt94MrFSh2vfV1fBv5Zljo7r6Ov0nlokCWkc5czmXUuUGvwnHo+Bo891odn8s1zXmL9Ch0P65z1Y5X5ymVq1S2DdFZk8jUd+Xj3DsvSRKHHPhJ0kHWsIXWcrGy75hFAOt15m+FzZ571pzCakHp4eeLdU3jD4uebM6z2zb5wTbKFYRlHweOaB5550V50efshdd0pK6roxfcR99kassdnd1QXZguzWtuo758i7X9XC9v4Ly3zQAdkSrhtW33lwhMfSuSv/hJ0kFy4CdJB2l5Vr8UZ5l+IIYx5fZadjEFx6W3IuVyr6rmfrj+zGw5y+xvhVXOdpf94JLWQ3FKAmJ5bL3RuB++iF2e48esf8PUv10ppkdwe1PHm3BWrm4Wetc53q9lWbsAsFeI+hP6fGLKLb/3ts6RqZ931vVm+AG7U69VQz1Rv2xrEm6luxr5i58kHSQHfpJ0kBz4SdJBWt47D9hyv2t0ydF+mvU0ccuxXj9pmJ4WlFtt+2ad5lzDIi1GW2f6prWyfl8f9XT8VVaVeea8aaDjW532Ml3bdmF+bLa26KO/as17fpam7l4Ht70X9IP1favj8z6JI3M/xvBNbMuMvsvKjYrrtlttc18isyKlvdV5lWa+/MVPkg6SAz9JOsgaFul4C0I4mIKXA0zZe6lRP8eR8wNsRKJ+ZBrSQtT3Pev6RT9sXtmrMlUr6vsmTU/U3zXi5VawoKmss25BUBMvOIb1IPRNfZzeoc9trP+9ImXzlqtuaq55VGzJbfPYZGfNectF7knjczbn+eoC19+sI815SZIcgRz4SdJBcuAnSQdpfXWeF/Sh3DuvhLUqdr21nff0eKB0nWXTyrBh1mHdt2yBdb9yJZ3vstsMgOEHASkDbPiBMni75+1GzP3l++qtssKPz9ujvNPBdcbU6abWJHi5MBf6JrXI7ZfzRpU6vjbW1vmr87wVeZMlzuCLcr6ZblL0y8y99D2z3/L+ROQvfpJ0kBz4SdJBWhb1I/Ew2pK6rlw/MA1NqBYWjqeN2qNAGdyn5WK5PS8KxNHc/ppX/G1dOd42Ee05b0jHNj2gEBtXmXAbHNPfxgXkOP6n6bgZV49XOVrT50LkvFwppl8OzFeTcLVinWejFmK57ceizpGpPzKxeavzrOfopHiXzDvc5/6PnGMAk7KF5RyjqC8iV4vIH4jIX4nIsyLy8yJyjYg8JiLPz/+/varFJEnWTq2o/x8B/Imq/gxm22k9C+B+AI+r6s0AHp+nkyR5C1CzW+7bAPwCgH8CAKq6B2BPRO4GcPu82IMAngBw38FN1sVzKzsZedotsOIVwzugsvfftOFL13PzJoUqEYn6PCNvxWNfdGYRe0ji/SAQ55ui/ubSvIER9Vm8j0T9YdFfu5UX39NS9OTgHvzr0lQJ/EVR3kKieJusSNSNgr34i2PYmmNF/d1CDSD6QVy9KI8VhrER9QvR/+RF/Z8G8LcA/ouIfEtE/vN8u+zrVPUVAJj/v7aqxSRJ1k7NwB8A+FkAn1fVDwC4hBXEehE5KyLnReT8GJcP2c0kSY6TmoF/AcAFVX1ynv4DzL4IfiAi1wPA/P/FZSer6jlVPaOqZwaN/VaTJFkHB+r4qvp9EfmeiLxHVZ8DcCeA78z/7gHw6fn/hw+qa7ZN9sHfNUOjq5fBK/zzo5pZH+VgDU2PNt9jrvQM9MsNCu85q8fX6db8JTk0X5gbRV6k/28uPZ7V7+eV/Rgs/Rwo7+nrxvRU7oSwuN87DT3e3+Ka9fo3Ah2/fqVatPLSX52HIk9NDm9djeXHQKnXDwIdf0w6vhoPwuniHvccHX9aeS9q7fj/AsCXRGQDwIsA/ilmz/YrInIvgO8C+FhlXUmSrJmqga+qTwE4syTrzuPtTpIkbbAGz73VN00qF5QcLQABUG6htRcGsvC3e2ITow22IY5KYNNWReg5prOmx5yvSnB621mwY9NRXj+og++V3SOAReko2EZJ+WynrqmvfpGOX8564PkLcSZBHlxR31wnP6bGOjWqc1QZc2+FOIzLSF/9JOkgOfCTpIPkwE+SDpIDP0k6SA78JOkgOfCTpIOI6tHNY9WNifwtgP8D4KcA/N/WGl7Om6EPQPbDkv0oWbUff1dV33lQoVYH/pVGRc6r6jKHoE71IfuR/VhXP1LUT5IOkgM/STrIugb+uTW1y7wZ+gBkPyzZj5IT6cdadPwkSdZLivpJ0kFaHfgicpeIPCciL4hIa1F5ReSLInJRRJ6mz1oPDy4i7xKRr81DlD8jIp9cR19EZEtEvi4i357347fnn79bRJ6c9+PL8/gLJ46I9OfxHB9dVz9E5CUR+UsReUpEzs8/W8c70koo+9YGvoj0AfwnAP8AwPsAfFxE3tdS878L4C7z2TrCg48B/IaqvhfArQA+Mb8HbfdlF8Adqvp+ALcAuEtEbgXwGQCfnffjhwDuPeF+7PNJzEK277Oufvyiqt5C5rN1vCPthLJX1Vb+APw8gD+l9AMAHmix/ZsAPE3p5wBcPz++HsBzbfWF+vAwgA+tsy8ATgH4XwB+DjNHkcGy53WC7d84f5nvAPAoZmEP1tGPlwD8lPms1ecC4G0A/jfmc28n2Y82Rf0bAHyP0hfmn62LtYYHF5GbAHwAwJPr6MtcvH4KsyCpjwH4GwCvql4J9NbW8/kcgN/EIrLEO9bUDwXwZyLyTRE5O/+s7efSWij7Ngf+stArnTQpiMhVAP4QwKdU9cfr6IOqTlT1Fsx+cT8I4L3Lip1kH0TkVwBcVNVv8sdt92PObar6s5ipop8QkV9ooU3LkULZr0KbA/8CgHdR+kYAL7fYvqUqPPhxIyJDzAb9l1T1j9bZFwBQ1Vcx2wXpVgBXi8h+kKg2ns9tAD4iIi8BeAgzcf9za+gHVPXl+f+LAL6K2Zdh28/lSKHsV6HNgf8NADfPZ2w3APwqgEdabN/yCGZhwYHK8OBHRUQEwBcAPKuqv7OuvojIO0Xk6vnxNoBfwmwS6WsAPtpWP1T1AVW9UVVvwux9+O+q+utt90NETovI39k/BvDLAJ5Gy89FVb8P4Hsi8p75R/uh7I+/Hyc9aWImKT4M4K8x0yf/TYvt/h6AVzDbfOwCZrPE78BsUun5+f9rWujH38dMbP0LAE/N/z7cdl8A/D0A35r342kA/3b++U8D+DqAFwD8PoDNFp/R7QAeXUc/5u19e/73zP67uaZ35BYA5+fP5r8BePtJ9CM995Kkg6TnXpJ0kBz4SdJBcuAnSQfJgZ8kHSQHfpJ0kBz4SdJBcuAnSQfJgZ8kHeT/A2ASOeWK1mq/AAAAAElFTkSuQmCC\n", 322 | "text/plain": [ 323 | "
" 324 | ] 325 | }, 326 | "metadata": { 327 | "needs_background": "light" 328 | }, 329 | "output_type": "display_data" 330 | } 331 | ], 332 | "source": [ 333 | "im1_fn = './data/input6_1.jpg'\n", 334 | "im2_fn = './data/input6_2.jpg'\n", 335 | "flow_fn = './tmp/frame_0010.flo'\n", 336 | "\n", 337 | "im_all = [plt.imread(img) for img in [im1_fn, im2_fn]]\n", 338 | "im_all = [im[:, :, :3] for im in im_all]\n", 339 | "\n", 340 | "# rescale the image size to be multiples of 64\n", 341 | "divisor = 64.\n", 342 | "H = im_all[0].shape[0]\n", 343 | "W = im_all[0].shape[1]\n", 344 | "\n", 345 | "H_ = int(ceil(H/divisor) * divisor)\n", 346 | "W_ = int(ceil(W/divisor) * divisor)\n", 347 | "for i in range(len(im_all)):\n", 348 | " im_all[i] = cv2.resize(im_all[i], (W_, H_))\n", 349 | "\n", 350 | "for _i, _inputs in enumerate(im_all):\n", 351 | " im_all[_i] = im_all[_i][:, :, ::-1]\n", 352 | " im_all[_i] = 1.0 * im_all[_i]/255.0\n", 353 | "\n", 354 | " im_all[_i] = np.transpose(im_all[_i], (2, 0, 1))\n", 355 | " im_all[_i] = torch.from_numpy(im_all[_i])\n", 356 | " im_all[_i] = im_all[_i].expand(1, im_all[_i].size()[0], im_all[_i].size()[1], im_all[_i].size()[2])\t\n", 357 | " im_all[_i] = im_all[_i].float()\n", 358 | " \n", 359 | "im_all = torch.cat(im_all,1).cuda()\n", 360 | "\n", 361 | "flo = net(im_all)\n", 362 | "import torch.nn.functional as F\n", 363 | "flo = F.interpolate(flo, size=im_all[0].size()[-2:], mode='bilinear', align_corners=False)\n", 364 | "flo = flo[0]\n", 365 | "flo = flo.cpu().data.numpy()\n", 366 | "\n", 367 | "\n", 368 | "# import ipdb; ipdb.set_trace()\n", 369 | "rgb_flow = flow2rgb(flo)\n", 370 | "# Image.fromarray(rgb_flow, 'RGB').show()\n", 371 | "# imwrite(filename + '.png', to_save)\n", 372 | "plt.imshow(rgb_flow)" 373 | ] 374 | }, 375 | { 376 | "cell_type": "code", 377 | "execution_count": 22, 378 | "metadata": {}, 379 | "outputs": [ 380 | { 381 | "data": { 382 | "text/plain": [ 383 | "array([[[ 78, 78, 78, ..., 38, 35, 35],\n", 384 | " [ 78, 78, 78, ..., 38, 35, 35],\n", 385 | " [ 77, 77, 78, ..., 36, 32, 32],\n", 386 | " ...,\n", 387 | " [214, 214, 215, ..., 159, 162, 162],\n", 388 | " [214, 214, 215, ..., 160, 163, 163],\n", 389 | " [214, 214, 215, ..., 160, 163, 163]],\n", 390 | "\n", 391 | " [[161, 161, 162, ..., 202, 202, 202],\n", 392 | " [161, 161, 162, ..., 202, 202, 202],\n", 393 | " [164, 164, 165, ..., 203, 203, 203],\n", 394 | " ...,\n", 395 | " [ 87, 87, 87, ..., 122, 122, 122],\n", 396 | " [ 86, 86, 87, ..., 123, 123, 123],\n", 397 | " [ 86, 86, 87, ..., 123, 123, 123]]], dtype=uint8)" 398 | ] 399 | }, 400 | "execution_count": 22, 401 | "metadata": {}, 402 | "output_type": "execute_result" 403 | } 404 | ], 405 | "source": [ 406 | "flo_norm = np.array((flo - flo.min()) / (flo.max() - flo.min()) * 255, 'uint8')" 407 | ] 408 | }, 409 | { 410 | "cell_type": "code", 411 | "execution_count": 5, 412 | "metadata": {}, 413 | "outputs": [ 414 | { 415 | "data": { 416 | "text/plain": [ 417 | "-0.44682392" 418 | ] 419 | }, 420 | "execution_count": 5, 421 | "metadata": {}, 422 | "output_type": "execute_result" 423 | } 424 | ], 425 | "source": [ 426 | "flo[0,:,:,None].min()" 427 | ] 428 | }, 429 | { 430 | "cell_type": "code", 431 | "execution_count": null, 432 | "metadata": {}, 433 | "outputs": [], 434 | "source": [] 435 | } 436 | ], 437 | "metadata": { 438 | "kernelspec": { 439 | "display_name": "Python 2", 440 | "language": "python", 441 | "name": "python2" 442 | }, 443 | "language_info": { 444 | "codemirror_mode": { 445 | "name": "ipython", 446 | "version": 2 447 | }, 448 | "file_extension": ".py", 449 | "mimetype": "text/x-python", 450 | "name": "python", 451 | "nbconvert_exporter": "python", 452 | "pygments_lexer": "ipython2", 453 | "version": "2.7.16" 454 | } 455 | }, 456 | "nbformat": 4, 457 | "nbformat_minor": 2 458 | } 459 | -------------------------------------------------------------------------------- /demo.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "/home/yongzhang/anaconda2/lib/python2.7/site-packages/pkg_resources/__init__.py:1243: UserWarning: tmp/ is writable by group/others and vulnerable to attack when used with get_resource_filename. Consider a more secure location (set with .set_extraction_path or the PYTHON_EGG_CACHE environment variable).\n", 13 | " warnings.warn(msg, UserWarning)\n" 14 | ] 15 | } 16 | ], 17 | "source": [ 18 | "import sys\n", 19 | "import cv2\n", 20 | "import torch\n", 21 | "import numpy as np\n", 22 | "from math import ceil\n", 23 | "from torch.autograd import Variable\n", 24 | "# from scipy.ndimage import imread\n", 25 | "import matplotlib.pyplot as plt\n", 26 | "import models\n", 27 | "import PIL.Image as Image" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 15, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "data": { 37 | "text/plain": [ 38 | "PWCDCNet(\n", 39 | " (conv1a): Sequential(\n", 40 | " (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 41 | " (1): LeakyReLU(negative_slope=0.1)\n", 42 | " )\n", 43 | " (conv1aa): Sequential(\n", 44 | " (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 45 | " (1): LeakyReLU(negative_slope=0.1)\n", 46 | " )\n", 47 | " (conv1b): Sequential(\n", 48 | " (0): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 49 | " (1): LeakyReLU(negative_slope=0.1)\n", 50 | " )\n", 51 | " (conv2a): Sequential(\n", 52 | " (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 53 | " (1): LeakyReLU(negative_slope=0.1)\n", 54 | " )\n", 55 | " (conv2aa): Sequential(\n", 56 | " (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 57 | " (1): LeakyReLU(negative_slope=0.1)\n", 58 | " )\n", 59 | " (conv2b): Sequential(\n", 60 | " (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 61 | " (1): LeakyReLU(negative_slope=0.1)\n", 62 | " )\n", 63 | " (conv3a): Sequential(\n", 64 | " (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 65 | " (1): LeakyReLU(negative_slope=0.1)\n", 66 | " )\n", 67 | " (conv3aa): Sequential(\n", 68 | " (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 69 | " (1): LeakyReLU(negative_slope=0.1)\n", 70 | " )\n", 71 | " (conv3b): Sequential(\n", 72 | " (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 73 | " (1): LeakyReLU(negative_slope=0.1)\n", 74 | " )\n", 75 | " (conv4a): Sequential(\n", 76 | " (0): Conv2d(64, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 77 | " (1): LeakyReLU(negative_slope=0.1)\n", 78 | " )\n", 79 | " (conv4aa): Sequential(\n", 80 | " (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 81 | " (1): LeakyReLU(negative_slope=0.1)\n", 82 | " )\n", 83 | " (conv4b): Sequential(\n", 84 | " (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 85 | " (1): LeakyReLU(negative_slope=0.1)\n", 86 | " )\n", 87 | " (conv5a): Sequential(\n", 88 | " (0): Conv2d(96, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 89 | " (1): LeakyReLU(negative_slope=0.1)\n", 90 | " )\n", 91 | " (conv5aa): Sequential(\n", 92 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 93 | " (1): LeakyReLU(negative_slope=0.1)\n", 94 | " )\n", 95 | " (conv5b): Sequential(\n", 96 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 97 | " (1): LeakyReLU(negative_slope=0.1)\n", 98 | " )\n", 99 | " (conv6aa): Sequential(\n", 100 | " (0): Conv2d(128, 196, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", 101 | " (1): LeakyReLU(negative_slope=0.1)\n", 102 | " )\n", 103 | " (conv6a): Sequential(\n", 104 | " (0): Conv2d(196, 196, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 105 | " (1): LeakyReLU(negative_slope=0.1)\n", 106 | " )\n", 107 | " (conv6b): Sequential(\n", 108 | " (0): Conv2d(196, 196, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 109 | " (1): LeakyReLU(negative_slope=0.1)\n", 110 | " )\n", 111 | " (corr): Correlation()\n", 112 | " (leakyRELU): LeakyReLU(negative_slope=0.1)\n", 113 | " (conv6_0): Sequential(\n", 114 | " (0): Conv2d(81, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 115 | " (1): LeakyReLU(negative_slope=0.1)\n", 116 | " )\n", 117 | " (conv6_1): Sequential(\n", 118 | " (0): Conv2d(209, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 119 | " (1): LeakyReLU(negative_slope=0.1)\n", 120 | " )\n", 121 | " (conv6_2): Sequential(\n", 122 | " (0): Conv2d(337, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 123 | " (1): LeakyReLU(negative_slope=0.1)\n", 124 | " )\n", 125 | " (conv6_3): Sequential(\n", 126 | " (0): Conv2d(433, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 127 | " (1): LeakyReLU(negative_slope=0.1)\n", 128 | " )\n", 129 | " (conv6_4): Sequential(\n", 130 | " (0): Conv2d(497, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 131 | " (1): LeakyReLU(negative_slope=0.1)\n", 132 | " )\n", 133 | " (predict_flow6): Conv2d(529, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 134 | " (deconv6): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 135 | " (upfeat6): ConvTranspose2d(529, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 136 | " (conv5_0): Sequential(\n", 137 | " (0): Conv2d(213, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 138 | " (1): LeakyReLU(negative_slope=0.1)\n", 139 | " )\n", 140 | " (conv5_1): Sequential(\n", 141 | " (0): Conv2d(341, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 142 | " (1): LeakyReLU(negative_slope=0.1)\n", 143 | " )\n", 144 | " (conv5_2): Sequential(\n", 145 | " (0): Conv2d(469, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 146 | " (1): LeakyReLU(negative_slope=0.1)\n", 147 | " )\n", 148 | " (conv5_3): Sequential(\n", 149 | " (0): Conv2d(565, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 150 | " (1): LeakyReLU(negative_slope=0.1)\n", 151 | " )\n", 152 | " (conv5_4): Sequential(\n", 153 | " (0): Conv2d(629, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 154 | " (1): LeakyReLU(negative_slope=0.1)\n", 155 | " )\n", 156 | " (predict_flow5): Conv2d(661, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 157 | " (deconv5): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 158 | " (upfeat5): ConvTranspose2d(661, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 159 | " (conv4_0): Sequential(\n", 160 | " (0): Conv2d(181, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 161 | " (1): LeakyReLU(negative_slope=0.1)\n", 162 | " )\n", 163 | " (conv4_1): Sequential(\n", 164 | " (0): Conv2d(309, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 165 | " (1): LeakyReLU(negative_slope=0.1)\n", 166 | " )\n", 167 | " (conv4_2): Sequential(\n", 168 | " (0): Conv2d(437, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 169 | " (1): LeakyReLU(negative_slope=0.1)\n", 170 | " )\n", 171 | " (conv4_3): Sequential(\n", 172 | " (0): Conv2d(533, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 173 | " (1): LeakyReLU(negative_slope=0.1)\n", 174 | " )\n", 175 | " (conv4_4): Sequential(\n", 176 | " (0): Conv2d(597, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 177 | " (1): LeakyReLU(negative_slope=0.1)\n", 178 | " )\n", 179 | " (predict_flow4): Conv2d(629, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 180 | " (deconv4): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 181 | " (upfeat4): ConvTranspose2d(629, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 182 | " (conv3_0): Sequential(\n", 183 | " (0): Conv2d(149, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 184 | " (1): LeakyReLU(negative_slope=0.1)\n", 185 | " )\n", 186 | " (conv3_1): Sequential(\n", 187 | " (0): Conv2d(277, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 188 | " (1): LeakyReLU(negative_slope=0.1)\n", 189 | " )\n", 190 | " (conv3_2): Sequential(\n", 191 | " (0): Conv2d(405, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 192 | " (1): LeakyReLU(negative_slope=0.1)\n", 193 | " )\n", 194 | " (conv3_3): Sequential(\n", 195 | " (0): Conv2d(501, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 196 | " (1): LeakyReLU(negative_slope=0.1)\n", 197 | " )\n", 198 | " (conv3_4): Sequential(\n", 199 | " (0): Conv2d(565, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 200 | " (1): LeakyReLU(negative_slope=0.1)\n", 201 | " )\n", 202 | " (predict_flow3): Conv2d(597, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 203 | " (deconv3): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 204 | " (upfeat3): ConvTranspose2d(597, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 205 | " (conv2_0): Sequential(\n", 206 | " (0): Conv2d(117, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 207 | " (1): LeakyReLU(negative_slope=0.1)\n", 208 | " )\n", 209 | " (conv2_1): Sequential(\n", 210 | " (0): Conv2d(245, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 211 | " (1): LeakyReLU(negative_slope=0.1)\n", 212 | " )\n", 213 | " (conv2_2): Sequential(\n", 214 | " (0): Conv2d(373, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 215 | " (1): LeakyReLU(negative_slope=0.1)\n", 216 | " )\n", 217 | " (conv2_3): Sequential(\n", 218 | " (0): Conv2d(469, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 219 | " (1): LeakyReLU(negative_slope=0.1)\n", 220 | " )\n", 221 | " (conv2_4): Sequential(\n", 222 | " (0): Conv2d(533, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 223 | " (1): LeakyReLU(negative_slope=0.1)\n", 224 | " )\n", 225 | " (predict_flow2): Conv2d(565, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 226 | " (deconv2): ConvTranspose2d(2, 2, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n", 227 | " (dc_conv1): Sequential(\n", 228 | " (0): Conv2d(565, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 229 | " (1): LeakyReLU(negative_slope=0.1)\n", 230 | " )\n", 231 | " (dc_conv2): Sequential(\n", 232 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2))\n", 233 | " (1): LeakyReLU(negative_slope=0.1)\n", 234 | " )\n", 235 | " (dc_conv3): Sequential(\n", 236 | " (0): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(4, 4), dilation=(4, 4))\n", 237 | " (1): LeakyReLU(negative_slope=0.1)\n", 238 | " )\n", 239 | " (dc_conv4): Sequential(\n", 240 | " (0): Conv2d(128, 96, kernel_size=(3, 3), stride=(1, 1), padding=(8, 8), dilation=(8, 8))\n", 241 | " (1): LeakyReLU(negative_slope=0.1)\n", 242 | " )\n", 243 | " (dc_conv5): Sequential(\n", 244 | " (0): Conv2d(96, 64, kernel_size=(3, 3), stride=(1, 1), padding=(16, 16), dilation=(16, 16))\n", 245 | " (1): LeakyReLU(negative_slope=0.1)\n", 246 | " )\n", 247 | " (dc_conv6): Sequential(\n", 248 | " (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 249 | " (1): LeakyReLU(negative_slope=0.1)\n", 250 | " )\n", 251 | " (dc_conv7): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", 252 | ")" 253 | ] 254 | }, 255 | "execution_count": 15, 256 | "metadata": {}, 257 | "output_type": "execute_result" 258 | } 259 | ], 260 | "source": [ 261 | "def writeFlowFile(filename,uv):\n", 262 | " TAG_STRING = np.array(202021.25, dtype=np.float32)\n", 263 | " if uv.shape[2] != 2:\n", 264 | " sys.exit(\"writeFlowFile: flow must have two bands!\");\n", 265 | " H = np.array(uv.shape[0], dtype=np.int32)\n", 266 | " W = np.array(uv.shape[1], dtype=np.int32)\n", 267 | " with open(filename, 'wb') as f:\n", 268 | " f.write(TAG_STRING.tobytes())\n", 269 | " f.write(W.tobytes())\n", 270 | " f.write(H.tobytes())\n", 271 | " f.write(uv.tobytes())\n", 272 | " \n", 273 | "def draw_flow(img, flow, step=16):\n", 274 | " h, w = img.shape[:2]\n", 275 | " y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int)\n", 276 | " fx, fy = flow[y,x].T\n", 277 | " lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2)\n", 278 | " lines = np.int32(lines + 0.5)\n", 279 | " vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)\n", 280 | " cv.polylines(vis, lines, 0, (0, 255, 0))\n", 281 | " for (x1, y1), (_x2, _y2) in lines:\n", 282 | " cv.circle(vis, (x1, y1), 1, (0, 255, 0), -1)\n", 283 | " return vis\n", 284 | "\n", 285 | "def flow2rgb(flow_map_np, max_value=None):\n", 286 | " _, h, w = flow_map_np.shape\n", 287 | " hsv = np.ones((h,w,3), dtype=np.uint8)\n", 288 | " hsv[..., 1] = 255\n", 289 | " mag, ang = cv2.cartToPolar(flow_map_np[0].squeeze(), flow_map_np[1].squeeze())\n", 290 | " hsv[..., 0] = ang / (2 * np.pi) * 180\n", 291 | " hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n", 292 | " rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)\n", 293 | " return rgb\n", 294 | "\n", 295 | "\n", 296 | "# pwc_model_fn = './pwc_net_chairs.pth.tar'\n", 297 | "pwc_model_fn = 'pwc_net.pth.tar'\n", 298 | "\n", 299 | "net = models.pwc_dc_net(pwc_model_fn)\n", 300 | "net = net.cuda()\n", 301 | "net.eval()" 302 | ] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": 16, 307 | "metadata": {}, 308 | "outputs": [ 309 | { 310 | "data": { 311 | "text/plain": [ 312 | "" 313 | ] 314 | }, 315 | "execution_count": 16, 316 | "metadata": {}, 317 | "output_type": "execute_result" 318 | }, 319 | { 320 | "data": { 321 | "image/png": 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\n", 322 | "text/plain": [ 323 | "
" 324 | ] 325 | }, 326 | "metadata": { 327 | "needs_background": "light" 328 | }, 329 | "output_type": "display_data" 330 | } 331 | ], 332 | "source": [ 333 | "im1_fn = './data/input6_1.jpg'\n", 334 | "im2_fn = './data/input6_2.jpg'\n", 335 | "flow_fn = './tmp/frame_0010.flo'\n", 336 | "\n", 337 | "im_all = [plt.imread(img) for img in [im1_fn, im2_fn]]\n", 338 | "im_all = [im[:, :, :3] for im in im_all]\n", 339 | "\n", 340 | "# rescale the image size to be multiples of 64\n", 341 | "divisor = 64.\n", 342 | "H = im_all[0].shape[0]\n", 343 | "W = im_all[0].shape[1]\n", 344 | "\n", 345 | "H_ = int(ceil(H/divisor) * divisor)\n", 346 | "W_ = int(ceil(W/divisor) * divisor)\n", 347 | "for i in range(len(im_all)):\n", 348 | " im_all[i] = cv2.resize(im_all[i], (W_, H_))\n", 349 | "\n", 350 | "for _i, _inputs in enumerate(im_all):\n", 351 | " im_all[_i] = im_all[_i][:, :, ::-1]\n", 352 | " im_all[_i] = 1.0 * im_all[_i]/255.0\n", 353 | "\n", 354 | " im_all[_i] = np.transpose(im_all[_i], (2, 0, 1))\n", 355 | " im_all[_i] = torch.from_numpy(im_all[_i])\n", 356 | " im_all[_i] = im_all[_i].expand(1, im_all[_i].size()[0], im_all[_i].size()[1], im_all[_i].size()[2])\t\n", 357 | " im_all[_i] = im_all[_i].float()\n", 358 | " \n", 359 | "im_all = torch.cat(im_all,1).cuda()\n", 360 | "\n", 361 | "flo = net(im_all)\n", 362 | "import torch.nn.functional as F\n", 363 | "flo = F.interpolate(flo, size=im_all[0].size()[-2:], mode='bilinear', align_corners=False)\n", 364 | "flo = flo[0]\n", 365 | "flo = flo.cpu().data.numpy()\n", 366 | "\n", 367 | "\n", 368 | "# import ipdb; ipdb.set_trace()\n", 369 | "rgb_flow = flow2rgb(flo)\n", 370 | "# Image.fromarray(rgb_flow, 'RGB').show()\n", 371 | "# imwrite(filename + '.png', to_save)\n", 372 | "plt.imshow(rgb_flow)" 373 | ] 374 | }, 375 | { 376 | "cell_type": "code", 377 | "execution_count": 25, 378 | "metadata": {}, 379 | "outputs": [ 380 | { 381 | "data": { 382 | "text/plain": [ 383 | "" 384 | ] 385 | }, 386 | "execution_count": 25, 387 | "metadata": {}, 388 | "output_type": "execute_result" 389 | }, 390 | { 391 | "data": { 392 | "image/png": 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v44txz7PDmzpvM87sWfYkw9gPwhdDmUJU2aGUTGw+Os+c+BBFOpySbu10/IY8vLyTmXWF4zc5nZPKTjdTZ+qjZ8jltVtehzSv1i3lz9YE99R4/7nvhLmPdOLLdbN+7lX1QMcP27pSSEba0uNNws5MtpkkyZrkwk+SAdKzqK/FIB2w59fYyloVtY1J1K+cSF3Tubi2hryeGk764UV99k7zYikPyX8yW4kbyiZH2zFo6ugE5tWAhtQYvtd1y9zG5anKn9NYLZ15k02E9Y4T9el6lSmn7a7FKl6Q5IJF+NYtjW6QMefR0N70xvnsWqa+jqJ+pI5smVbO/asN3d6fv/hJMkBy4SfJAMmFnyQDpHdzHkaH6/jVxOpfbMIbU6TXyPl/sj4d6f8Nu+U6PZ5NT61IMr4W7ye02iiasBWIVdb//Zy74Meo6+Xf8unB8vHWM5fUYX+pXNctex4ng2BTnO3V0H5I7cxj1YR0/B0ewyeQXA5aud8hIR9eUzLbRa2ZBB5edy/p9eu47HYlqtsXYeodcjKPcl79chhiRuclSVIgF36SDJB+RX2g/KeGrUtO5B2ReD8ekRef68dirzo1oGZzHomvjRf1SRTtmkuhctfi+VdOJGP1hNUWAJjQZ+NZ+TH43Iv603opHu7Plo/38tQ+6unest/BZVvsoBY6Jw8x8SZB9rpzpj42H7Jpz/cz3pZO3jZqBl2rlVPeeO75L9hqdaFlzouKKAQec1wDQiKzn3lTkO8vyr8/5pDHTMSRJMma5MJPkgFyAqL+ahFFOK+eE51Z1J+w2O/6qUlXZv+mNbRDzAkk1AW58HkT5MerjAXBifr0NlZNAGCXzndHNnLm1HgZOTOuyJLhxyexlPsBwIySGv54tnv1+OJ0x/S7TGrAj8anTNsljmWpl2J/NbX3lEX4xqkBXNWYxX5vGeB966a1q0/XYjVgjVx8VvQPAnHCQYK02Saopmw1kK5efcGuvnDOvVLSj/TcS5KkRC78JBkgufCTZID077lX0vHp9ZHz7hsJ67RL3WZ3bHVk1sm9Oa8p6O5+NqbNJ7mU1Wa6UWBu83r8mcn+1ePTY1vv6Mx42bZLmTMmzgbG92CvsmNMWcevlzr+y7M90+/ibKnzT9y9+iF5/10mD7/msv2dqGnbQFyiD87Bz21VoJ97p072omQzWuUUdC2ewGwUFCPaDiN634jtunTsvAuV5+Hz5Zvxgtz5vE9QFX6zt2XOE5E9EfmKiHxTRL4tIr+5eP2NIvKkiDwtIp8XkZ3DxkqS5Nqgi6i/D+CdqvpWAHcCuEdE7gLwMQAfV9U7ALwA4L7jm2aSJNukS+08BfDjxelk8U8BvBPAryxefxjAbwD45GHj+QQZXQiTWRDGw80nxyCx3eZ+cGaosNzq6mv5YKGobYfNeS4R3qnRUmw/XR0sj0cHpt8uifd7TsauyZ5zA4n6N4z2Tb8fj3dR4tJ0acIzgT4HztxGKkGr3JgpRUbH7v6OwttdMMU5zz3znWrVFOMhuJ/PVchlvqI5lYmCgDg/oXZN5rFJfr9teu6JyGhRKfcCgMcA/CWAF1X1yrfuPIDXd7pikiQnTqeFr6q1qt4J4DYAbwfw5lXdVr1XRO4XkXMicq5++eLmM02SZGusZc5T1RcBPAHgLgA3ilwtlHQbgOcK73lIVc+q6tnRq84cZa5JkmyJQ3V8EXkdgKmqvigipwD8POYbe18G8D4AnwNwL4BHulxQ69V/a4TsPD5ibkbut3xc1dal0Uex9UXLPZjNfk4Q4vlPnUnmgAoIsgmvdhFhIxpz15nzRqQLc7+J20/g/YWZ2vv4wt7pq8evnF7q+z/et/1mdVmftAk8yvX3OteA4zLno7L+7CM7TR5/kzi0rD+v9TUq6dpRZGdgpjOuvVHu/9J1S2Y+Rxc7/q0AHhaREeYSwhdU9Usi8h0AnxORfwfg6wA+1emKSZKcOF129f8UwNtWvP4M5vp+kiTXGf167imKIpBGefBIPGaxug6886LkFdsgjNyj45mWxfmxi7CaNkvxflq5fGsEqwFnqn3XthTh2avvYmPNd2wubFxI14t7y2g9Nu0dHNivywHn3HPqgjHhGXHbm/P4uUSFBnhsn8OfTGVeDTAlxQNxPoie27YG2S7lTcck3svMJSYpqQQ8dse8f+mrnyQDJBd+kgyQ/hNxFFJWs3jvxWhOGT1jbzEp7+p7j7ltE4n6UdsBWSJ23A7sPgXY7DZlUb8i+XVPXKAPif4HJH57lYAtBY1TR17YXe7qczIPn7fvJX4u7nbX9DmrIBEHy/B+t7s2QVfL133lX9Yy2h6Eq1WJ9jx4wKDN0dqhvzKEE7nNLW4l01sestoiLiW6aSupJinqJ0lSIhd+kgyQXPhJMkD61/FLaNnko8aZiXO0O4+2IPKvKSTROG5a+xXKerHzhKPzKevggZlr5JRVTlKxR20+kSUP+arRK6bp1ePLy+PJ8vjFHZuUc39vub9Qu0ScDZ3XU44+s5+Fv4B1a2+E6yTQHpD71nJyD6//W1PicZjsVj+bZlTW8dVv3xgdn4+dSZpNfYWEJtrxpzx/8ZNkgOTCT5IB0r+oXxKpwvwDXPKKREg/BL2xdiKlzZdXnt5GST+CMfylTO4/1zorqAFTpxJMSVY8cHLjni8v1QGf04/PJyRHT1yNADaZ+grHNZ3XXC3XPbRZoMZwAA+rcX4MrtpbuW+09dxjc17g4efnEd1SkxZwtfkRAMwjdG2s4ZgEJv47bD7L6umYPIUB+YufJAMkF36SDJBc+EkyQK4dcx7jdXxT6275ujeV8XlXXd1rRFEJalOGW8rX4r+m6tp4jgcukciYlFc25021rONP1UXMURub+kbwc6R6hC5hJ79vTMcTlxR/Z7w83x/btnpnOQ8l19MopbzX980nq8r7BHzetJJXrO4X6s+ecpVsa6YLTHZK5r2WN7b5whSO0c3k2DIVFshf/CQZILnwk2SAXJuivqNkzotqHauTybSgBrTFeRTbStF/rbRxdOmWk1ZQootz31kvPifqk+uabzMeejT9UcnVC201gM15nJtvz5UD26ESZpOJ/SrNdilnIIn6TVP+rWkHNZKZjj6mL8NlRHgfnVevPq68ShCU9gpFeDbhUZv3LmSNzIv6RXPhBhW/gqBOQ/7iJ8kAyYWfJAOkf1G/JL509Kazx7Zf05TFaFt9iMX+sodfJMKDRFaf9KOr5cHn4xvRmOzV1xqD2g7crj4HAVXBVrXZ8Xf9OGX3KSrfdcPEJvN4ZbbMx9dKMU7PYp/mX7vkKTVVh9WRU88KyTf8xypV5gWsWmCq9s7sPTXBPd6qFIr6q9t8PxbBdWLbtGJrETX47185jm1JBukkSVIiF36SDJBc+EkyQPrV8QXAaLX7kYzIPOb6VNTGJZIqp1uzvu7LcJXMaJGHX6uENnuB0RzbpbajtvWTdPpkmNvAlNdyijHn3P9b42WSjv0d+3WJPCV53+Nlen77Y6vgzg6Wym+z40yTe5RYdbq6JBfgctEH5jyuNlYdlHX8lmmvi26NshcfYM156hLGlBJxtq4V6f9r0vkbtSiV/XUR+dLi/I0i8qSIPC0inxeRnaNNJUmSvljnp+RDAJ6i848B+Liq3gHgBQD3bXNiSZIcH51EfRG5DcA/BPDvAfwLmcvU7wTwK4suDwP4DQCfPHSs0WoTE4vOrfJX1WqPOW9GMyYlL2oZc15ZFLfvCcRycy33mThZiBuC5xyJ/RG+em4XqsDLceJSmry6IvF+RKK5k+n4OY2dOx17+U3oM190wTz7u5RUZNd5/1Ee/4bMb42vuMw3uSXqk4pA4r0rM2DVgFkkz9tTNi2ax9JKxKHFtjDnfunShX5dUwd2/QZ9AsCvY+kj+1oAL6rqlad7HsDrO46VJMkJc+jCF5FfBHBBVb/GL6/ouvKPjYjcLyLnRORc/fLFDaeZJMk26SLqvwPAL4nIewDsAXg15hLAjSIyXvzq3wbguVVvVtWHADwEALtvvK2/vNZJkhQ5dOGr6oMAHgQAEbkbwL9U1V8Vkd8B8D4AnwNwL4BHDr2aaMtUd7WJzXRuH2BE52NK+Bjl0Yd3IbVXo2NnWjFmv9YsqS0w2fGxG6MJovNK/SJaeftpjhPfmWB3Xm+O8Xn2r47nzH6lKD4AODVaKs174+XxS5M904/dfi87HX+fdPwpJS3xplqurdhqm9H7XlmOpzvu+7FP+r8tR+gSYLq2jvntI/2/i+5+aNs6fdpTWIuPYL7R9z3Mdf5PHWGsJEl6ZC0HHlV9AsATi+NnALx9+1NKkuS46T06TwriOXvhjZyoP6a2yahszmPz0tQlWjD5+IuJPQAWvHzk3raJvAb7LPPlo/gmupRfT5Pdy/erTK0CN0ZBDThD0X4A8EpNon5tv46XZkslhGsLTGufmIRz+tnnyaW9L5PX4PQVqwjpiHIEOq8+4TLfzlxorJhsMo4i6/xXrvSsj/Hrl776STJAcuEnyQDpVdQXaYvxVzDeea7PhLy9dmhX34vDJobBtdW0vV7Rzq/L2+B23aMyWcUmgy/XZcXjck6/CmUx+rgZmR3/cjK6G0dlvwwW9fdom/wyVeIFgH1KTrfvEtXx+QH3cyoBqwiXayvCX5ou1YWXSDW85FTOA/JQ1MqqEsqivvPqM238mNxzN6J/8HMbGnO6qH897OonSXKdkgs/SQZILvwkGSA9J+LQoo7Pr/tyzHzOx2On+46qsllnRvpRHdTJnpGpqJUwoWR1CXT1Vt+gH5sto70A1sG7mv0a9ze+CZJymjz75K3nPfd2yOy341zYLjdLnflitdTrL7tMk7YcWLf6AZca62vIewGvOB3/R9NTV49H1Q1XjyNT6tQ99+aATX3ut5KTbwQ6/iYeeb78Wie6lnlff+QkSa53cuEnyQDp15yHsjmv5J0HWBPeLiV42HHJHw7Iu6sWl7Oe1AD2/OIqpoAr1xW4X5n8+ygTqQGVCxDiarmRCc+/bxM4mMf/9edrT6IEHmyyUxvZckD5828cXVp5XcAmFfHqSF2oGPxSc8r0Y9H/Ur1r2v5mdObq8bijivSKE/VZ9K997v99rpu1oatdV5H+GgnSSZLkOiUXfpIMkFz4STJAenbZ1Zap7upESK/fcX1Yl9+jBA/jVnTe8nzm3C6lXl0/uFWXjnR+bxLUgl7vowQlMsUFyULNPgfr2c5UZureoWyKM2Y/lKPnIrfcylyrrIvWzof5dKHfOjUCeD9gSonpX6U2UcjL9VLnf6my+j+z35QjAfdnlNjTP3d24/Zz5MSfQaltQ6TTb5iAdV3yFz9JBkgu/CQZIL1H5+1OfDzcHM69vju2ffjcROd5wcuUsXYX4DLFXLbZl7jG6n6AzZ/Hzn9R+aiJG7+ktvg2Nlv6fHalyLd5G3nakex5xiWS3zMeeUeP/ovy+1lPQNvmzXslpkr3IHi23gzK4j2X/Oa8/4DzCA1yPtYz5wFJbbqNUmdaOAa6mQs7WgbzFz9JBkgu/CQZIL2K+pUo9sarRX3e0T41tuKr2ck33le+dBVfDMW2GXnx+fJRvMs/dtaFUpXdUeCd560YJbUFsCL9KapY68X5XTpvBc4U1IA9XxGXznfcfSyJ33Ww4+zvQckCsM4vDc/qgN9ZXS53dBfgnIGnSdQ/5dQsVq38cx+Rhcinfhfy5FOfS70rpfvqh4vUgDXJX/wkGSC58JNkgOTCT5IB0q+OD23p71dgU9aOM7UYMxfpwWESCm/yoT9xnEDCe+6VPAs9fO0oAm/SKh+9/Pxez+Rz/pzec8943bXalu/bE9bxbb8zbHKEZUoKJJcn8Hq8va49518U9pkcbVir4ICzXDRun4h0/krtg79I5cBO076JN5Hyc9kd2UQfB/SdmLnovBlF7pXKwx2GmkfDCfhdfn8255UssB09/zotfBF5FsDLmDslzlT1rIjcBODzAG4H8CyAf6SqL3S6apIkJ8o6ov7Pqeqdqnp2cf4AgMdV9Q4Ajy/OkyS5DjiKqP9eAHcvjh/GvKbeR6I3VKI4PT4otl3BJ9hgsWx3tNoceCgsKbL33DpDsKmP8+M52wqL/t5DjM2RPmfgmObFKkIrSCdIjmGCdOjYe+exeD9x4jeL5jWN4ZUglH8VAAALuklEQVS0kjgPWJF+wkk/NhT1Ocho6j7LlO+Hk7btPSDvPPcd2zFVmN29IhPegUvSwaXfMCp/tqBEgy29xeO7x6zGW7RwrS0n4lAAfywiXxOR+xev3aKqzwPA4v+bO46VJMkJ0/UX/x2q+pyI3AzgMRH5864XWPyhuB8ATt1ywyG9kyTpg06/+Kr63OL/CwC+iHl57B+KyK0AsPj/QuG9D6nqWVU9u3tjOVY6SZL+OPQXX0TOAKhU9eXF8S8A+LcAHgVwL4CPLv5/5PCxtJU84+pESG/zphbjTmkSUm6/lPQOKUmt6DzSqyp0M+etU1o60t270jXazV7Xv4ej6YT6lfG/IKOCXt++Fo9R/h1qaE6NU+SnhaScgM3j79tKtOs10L6M+/7O2ISn5ecXJfOwULSfvx9s6Steqtua6CLq3wLgi4vNhDGA/6KqfygiXwXwBRG5D8D3Aby/0xWTJDlxDl34qvoMgLeueP1vALzrOCaVJMnx0nNefcVOtdocx15h3tTC4r1XAwwbOCA3TvRs2I3K3Z0DyttXmfk6cd6UuPZqAOWwC1SVrrnz6yD5QyT2Rx50LI53E45jM50R+4OH5OfEfadBQjv+nL5E18VmmWefRX3vsRmpbua76aLzxuPV8/LmOyPqu4Qa/AjN6E7lUM7+UvLQ67gG0lc/SQZILvwkGSC58JNkgPReO6+U2HFciMDz56GOH2Xg2QSvvnFSR5TNcozX/1lf9Hp8V/NkHXy4SOcv0VU/b71vg5sc6fGVu9aI6h+Ogqgz1t0vt0poL3X+6N4Yl3EXoblfkznPtY0prz7XYfQ6Pufq9+WvG9L5+f40vpx7MP5Vskx2kiQlcuEnyQDpPdmmTz6xbCt77nFUlS0L5USmKPKr8CeuVUKLxveiVlWQryIRvRWBFyQLrUwCz3K/TTz8fKLMmly/vCdcSfT3ov2mSTWYJtLPaI4cJXjgRHYur+XNeawG8PHMefG1S6LTrILna8qlkVrny3BZE6FLsMFWOlYh3bXMLI546/MXP0kGSC78JBkg/efcGx2eiCOqDuvbGJPfLpCGWSWonTjPnnxtNWB1kI4nyrlXGRF+syAjUy3XqwEFC4P3UGSFy+fAnxSk0q0YSoKMFNNWqo8ll5rlfdx34jyL95ebsqi/3yy/7tPGivoHdM6VcwFbNdkHbjHGOy8qiNvy6uOTcpDYNkPS8hc/SQZILvwkGSC58JNkgPRszmtww2j/0H5eT+0aqRZFxXGNPGP2W+NPX6l2XKt2XjQP1s/RzUzX0sE5GUlrjNX3ykfqTbnkt38PdWV9v/H7B1soC81Rd41Tfjm//yVquqz2axsl2yib81x+/CZqW577fZ9I5y/1a72n0NZ6kh2v1YX8xU+SAZILP0kGSO+ee6c7iPpdaZxI5k1Wpq3kZeYrbVNgCJwHIYuDUe48xgclcTBSZM6LgoBYJfAltEpz8SLqgQmO8e/hay9f9aW2NqFx15qSd97UifqX6fQiifcX1QbisAlv6tQAI96zya62/dicVzuvuzoQ05uSmN5SCVYft95nGjbM09+B/MVPkgGSCz9JBkgu/CQZIL0n24xcbkt4XX45YLkeXCtyr+B76pNa1GxeckkRUXGU4NH183YizvUVt7Y5b/3IvaNn81815upRpy4hPJ95h90pPTObO9+51HJSTmfO4+Qbxh3b7QexXj9zOj7r4HVT1vH5++K/O01dNgluYqY7amBk/uInyQDJhZ8kA6T3nHslz7KIUUE98DnaOKGEVynYc61qlv1qcSZBznnWkqfInNfVm3AbEXhObK6i6LyOZbJHJhrSXdscl3PicyKOqfr7TeW1TdIPy2XlfraNzWiblAaLaEVeomBSQ6wG1CTCz2YUxef6seivdfmzCJXJdl9Nk8zD5/5fvl4c2tDpF19EbhSR3xWRPxeRp0TkZ0TkJhF5TESeXvz/mm6XTJLkpOkq6v8HAH+oqn8H83JaTwF4AMDjqnoHgMcX50mSXAd0qZb7agA/C+CfAICqHgA4EJH3Arh70e1hAE8A+MhxTLLkjdb6qyUc8FG5vqt39Sc+9xqLr2Lbyjn3uu+Lm7x6fke+9Dl9SaeCOO/nMgnyE46C3G4TTjjClW7X2EoueeS1du5pWq1ceuCd8OVxO+CoW6GvUpDVfPxyApba7NaXRX1ua2bOMsDnXtTn5ztZ3jcW++fn9Dyro7nudfnF/0kAfw3gP4vI10XkPy3KZd+iqs8DwOL/m480kyRJeqPLwh8D+CkAn1TVtwG4iDXEehG5X0TOici5H//fcmqlJEn6o8vCPw/gvKo+uTj/Xcz/EPxQRG4FgMX/F1a9WVUfUtWzqnr2hpu2EeaRJMlROVTHV9W/EpEfiMibVPW7AN4F4DuLf/cC+Oji/0eOMpFIT4683Up4zzH+k8P67VRcNBfp9V4HD/P2b5nwfgQ593fAZkDO0++TedB4fnxTJrtszuN7XLc8JZdwlN3Ue8yRPj314/dozjNz8no8nbNODziPPDbnTd1vKuv1LR2fTH38NleCm/X60Wj196Nk5vN0teP/cwCfFZEdAM8A+KeYSwtfEJH7AHwfwPs7jpUkyQnTaeGr6jcAnF3R9K7tTidJkj7o1XOvK21PtbIJrDiGk6Y4kIMDWyYu2cZIr71b0rofRpwv348qMvvx+IEKE1XEjdo4f35jXi97zHVlk4rAhxGJ/ibtfZREg0V4H+BF5+LaTPXcQFLnx5RBOkmSrE0u/CQZILnwk2SA5MJPkgGSCz9JBkgu/CQZIKJHTdC9zsVE/hrA/wbwEwD+T28XXs21MAcg5+HJeVjWncffVtXXHdap14V/9aIi51R1lUPQoOaQ88h5nNQ8UtRPkgGSCz9JBshJLfyHTui6zLUwByDn4cl5WI5lHiei4ydJcrKkqJ8kA6TXhS8i94jId0XkeyLSW1ZeEfm0iFwQkW/Ra72nBxeRN4jIlxcpyr8tIh86ibmIyJ6IfEVEvrmYx28uXn+jiDy5mMfnF/kXjh0RGS3yOX7ppOYhIs+KyJ+JyDdE5NzitZP4jvSSyr63hS8iIwD/EcA/APAWAB8Qkbf0dPnPALjHvXYS6cFnAH5NVd8M4C4AH1zcg77nsg/gnar6VgB3ArhHRO4C8DEAH1/M4wUA9x3zPK7wIcxTtl/hpObxc6p6J5nPTuI70k8qe1Xt5R+AnwHwR3T+IIAHe7z+7QC+ReffBXDr4vhWAN/tay40h0cAvPsk5wLgNID/CeCnMXcUGa96Xsd4/dsWX+Z3AvgS5gWXTmIezwL4Cfdar88FwKsB/C8s9t6Ocx59ivqvB/ADOj+/eO2kONH04CJyO4C3AXjyJOayEK+/gXmS1McA/CWAF1X1SmaSvp7PJwD8Opb5Ol57QvNQAH8sIl8TkfsXr/X9XHpLZd/nwl+VM2SQJgURuQHA7wH4sKq+dBJzUNVaVe/E/Bf37QDevKrbcc5BRH4RwAVV/Rq/3Pc8FrxDVX8Kc1X0gyLysz1c03OkVPbr0OfCPw/gDXR+G4Dnery+p1N68G0jIhPMF/1nVfX3T3IuAKCqL2JeBekuADeKXE073MfzeQeAXxKRZwF8DnNx/xMnMA+o6nOL/y8A+CLmfwz7fi5HSmW/Dn0u/K8CuGOxY7sD4JcBPNrj9T2PYp4WHNhCevAuiIgA+BSAp1T1t05qLiLyOhG5cXF8CsDPY76J9GUA7+trHqr6oKrepqq3Y/59+G+q+qt9z0NEzojIq64cA/gFAN9Cz89FVf8KwA9E5E2Ll66kst/+PI5708RtUrwHwF9grk/+6x6v+9sAnse8dNt5zHeJX4v5ptLTi/9v6mEefx9zsfVPAXxj8e89fc8FwN8D8PXFPL4F4N8sXv9JAF8B8D0AvwNgt8dndDeAL53EPBbX++bi37evfDdP6DtyJ4Bzi2fzXwG85jjmkZ57STJA0nMvSQZILvwkGSC58JNkgOTCT5IBkgs/SQZILvwkGSC58JNkgOTCT5IB8v8AQ1w8T7BxH3UAAAAASUVORK5CYII=\n", 393 | "text/plain": [ 394 | "
" 395 | ] 396 | }, 397 | "metadata": { 398 | "needs_background": "light" 399 | }, 400 | "output_type": "display_data" 401 | } 402 | ], 403 | "source": [ 404 | "flo_norm = np.array((flo - flo.min()) / (flo.max() - flo.min()) * 255, 'uint8')" 405 | ] 406 | }, 407 | { 408 | "cell_type": "code", 409 | "execution_count": 31, 410 | "metadata": {}, 411 | "outputs": [ 412 | { 413 | "data": { 414 | "text/plain": [ 415 | "" 416 | ] 417 | }, 418 | "execution_count": 31, 419 | "metadata": {}, 420 | "output_type": "execute_result" 421 | }, 422 | { 423 | "data": { 424 | "image/png": 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425 | "text/plain": [ 426 | "
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