diff --git a/script/build.py b/script/build.py index 61cdb2d..b75a675 100644 --- a/script/build.py +++ b/script/build.py @@ -4,21 +4,21 @@ #this_file = os.path.dirname(__file__) -sources = ['src/my_lib.c'] -headers = ['src/my_lib.h'] +sources = ['src/my_lib.c', 'src/my_lib_invert.c'] +headers = ['src/my_lib.h', 'src/my_lib_invert.h'] defines = [] with_cuda = False if torch.cuda.is_available(): print('Including CUDA code.') - sources += ['src/my_lib_cuda.c'] - headers += ['src/my_lib_cuda.h'] + sources += ['src/my_lib_cuda.c', 'src/my_lib_invert_cuda.c'] + headers += ['src/my_lib_cuda.h', 'src/my_lib_invert_cuda.h'] defines += [('WITH_CUDA', None)] with_cuda = True this_file = os.path.dirname(os.path.realpath(__file__)) print(this_file) -extra_objects = ['src/my_lib_cuda_kernel.cu.o'] +extra_objects = ['src/my_lib_cuda_kernel.cu.o', 'src/my_lib_invert_cuda_kernel.cu.o'] extra_objects = [os.path.join(this_file, fname) for fname in extra_objects] ffi = create_extension( diff --git a/script/functions/stn.py b/script/functions/stn.py index ea2c693..286c5a8 100644 --- a/script/functions/stn.py +++ b/script/functions/stn.py @@ -76,4 +76,4 @@ def backward(self, grad_output): grad_input1 = grad_input1.transpose(2,3).transpose(1,2) grad_input2 = grad_input2.transpose(2,3).transpose(1,2) - return grad_input1, grad_input2 \ No newline at end of file + return grad_input1, grad_input2 diff --git a/script/functions/stn_invert.py b/script/functions/stn_invert.py new file mode 100644 index 0000000..e68ca23 --- /dev/null +++ b/script/functions/stn_invert.py @@ -0,0 +1,58 @@ +# functions/add.py +import torch +from torch.autograd import Function +from _ext import my_lib +from cffi import FFI +ffi = FFI() +import time + +class STNInvertFunction(Function): + def forward(self, input1, input2, depth_map): + #self.input1 = input1 + #self.input2 = input2 + if input1.is_cuda: + invgrid = torch.zeros(input2.size()).cuda() + output = torch.zeros(input1.size()).cuda() + else: + invgrid = torch.zeros(input2.size()) + output = torch.zeros(input1.size()) + + + self.device = torch.cuda.current_device() + self.device_c = ffi.new("int *") + self.device_c[0] = self.device + print(self.device_c[0]) + + if not input1.is_cuda: + my_lib.InvSamplerBHWD_updateOutput(input1, input2, invgrid, output, depth_map) + else: + self.target_depth_map = torch.zeros(depth_map.size()).cuda(self.device) + start_time = time.time() + my_lib.InvSamplerBHWD_updateOutput_cuda(input1, input2, invgrid, output, depth_map, self.target_depth_map, self.device_c) + print("--- %s seconds ---" % (time.time() - start_time)) + + self.save_for_backward(input1, input2, depth_map, invgrid) + return output, invgrid + + def backward(self, grad_output, grad_invgrid): + + input1, input2, depth_map, invgrid = self.saved_tensors + if not grad_output.is_cuda: + grad_input1 = torch.zeros(input1.size()) + grad_input2 = torch.zeros(input2.size()) + else: + grad_input1 = torch.zeros(input1.size()).cuda() + grad_input2 = torch.zeros(input2.size()).cuda() + + self.device_c = ffi.new("int *") + self.device_c[0] = self.device + print(self.device_c[0]) + + if not grad_output.is_cuda: + my_lib.InvSamplerBHWD_updateGradInput(input1, input2, invgrid, grad_input1, grad_input2, grad_output) + else: + start_time = time.time() + my_lib.InvSamplerBHWD_updateGradInput_cuda(input1, input2, invgrid, grad_input1, grad_input2, grad_output, self.device_c) + print("--- %s seconds ---" % (time.time() - start_time)) + + return grad_input1, grad_input2, depth_map \ No newline at end of file diff --git a/script/invert_test.py b/script/invert_test.py new file mode 100644 index 0000000..323c471 --- /dev/null +++ b/script/invert_test.py @@ -0,0 +1,57 @@ + +# coding: utf-8 + +# In[1]: + +import torch +import numpy as np +import torch.nn as nn +from torch.autograd import Variable +from modules.stn_invert import STNInvert +from modules.gridgen import CylinderGridGen, AffineGridGen +from PIL import Image +import matplotlib.pyplot as plt + + + +img = Image.open('cat.jpg').convert('RGB') +img = np.array(img)/255.0 + + +# In[3]: + +img_batch = np.expand_dims(img, 0) +inputImages = torch.from_numpy(img_batch.astype(np.float32)) +inputImages.size() +s = STNInvert() +g = AffineGridGen(328, 582) +input = Variable(torch.from_numpy(np.array([[[1, 0.2, 0], [0.5, 1, 0]]], dtype=np.float32)), requires_grad = True) +#print input +out = g(input) +input1 = Variable(inputImages) + + +# In[9]: + +res = s(input1, out) + + +# In[10]: + +res.size() + + +# In[6]: + + + +# In[11]: + +res.backward(torch.rand(res.data.size())) + +print(input.grad) + +# In[ ]: + + + diff --git a/script/make.sh b/script/make.sh index ae3cbb0..d7979af 100755 --- a/script/make.sh +++ b/script/make.sh @@ -5,6 +5,7 @@ CUDA_PATH=/usr/local/cuda/ cd src echo "Compiling my_lib kernels by nvcc..." nvcc -c -o my_lib_cuda_kernel.cu.o my_lib_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52 +nvcc -c -o my_lib_invert_cuda_kernel.cu.o my_lib_invert_cuda_kernel.cu -x cu -Xcompiler -fPIC -arch=sm_52 cd ../ python build.py diff --git a/script/modules/gridgen.py b/script/modules/gridgen.py index fe990f3..e3c7956 100644 --- a/script/modules/gridgen.py +++ b/script/modules/gridgen.py @@ -153,7 +153,7 @@ def __init__(self, height, width, lr = 1, aux_loss = False): self.theta = self.grid[:,:,0] * np.pi/2 + np.pi/2 self.phi = self.grid[:,:,1] * np.pi - self.x = torch.sin(self.theta) * torch.cos(self.phi) + self.x = torch.sin(self.theta) * torch.cos(self.phi) self.y = torch.sin(self.theta) * torch.sin(self.phi) self.z = torch.cos(self.theta) @@ -305,32 +305,46 @@ def forward(self, depth, trans0, trans1, rotate): self.batchgrid = Variable(self.batchgrid) - x = self.batchgrid3d[:,:,:,0:1] * depth + trans0.view(-1,1,1,1).repeat(1, self.height, self.width, 1) + if depth.is_cuda: + self.batchgrid3d = self.batchgrid3d.cuda() + self.batchgrid = self.batchgrid.cuda() + + + x0 = self.batchgrid3d[:,:,:,0:1] * depth + trans0.view(-1,1,1,1).repeat(1, self.height, self.width, 1) + y0 = self.batchgrid3d[:,:,:,1:2] * depth + trans1.view(-1,1,1,1).repeat(1, self.height, self.width, 1) + rotate0 = rotate.view(-1,1,1,1).repeat(1,self.height, self.width,1) * np.pi + + x = x0 * torch.cos(rotate0) - y0 * torch.sin(rotate0) + y = x0 * torch.sin(rotate0) + y0 * torch.cos(rotate0) + + + #x += trans0.view(-1,1,1,1).repeat(1, self.height, self.width, 1) + #y += trans1.view(-1,1,1,1).repeat(1, self.height, self.width, 1) - y = self.batchgrid3d[:,:,:,1:2] * depth + trans1.view(-1,1,1,1).repeat(1, self.height, self.width, 1) z = self.batchgrid3d[:,:,:,2:3] * depth #print(x.size(), y.size(), z.size()) - r = torch.sqrt(x**2 + y**2 + z**2) + 1e-5 + r = torch.sqrt(x**2 + y**2 + z**2) + 1e-3 + + #if depth.is_cuda: + # r_large_enough = r.gt(0.2).type(torch.cuda.FloatTensor) + #else: + # r_large_enough = r.gt(0.2).type(torch.FloatTensor) - #print(r) theta = torch.acos(z/r)/(np.pi/2) - 1 #phi = torch.atan(y/x) - phi = torch.atan(y/(x + 1e-5)) + np.pi * x.lt(0).type(torch.FloatTensor) * (y.ge(0).type(torch.FloatTensor) - y.lt(0).type(torch.FloatTensor)) - phi = phi/np.pi - #print(theta.size(), phi.size()) + if depth.is_cuda: + phi = torch.atan(y/(x + 1e-5)) + np.pi * x.lt(0).type(torch.cuda.FloatTensor) * (y.ge(0).type(torch.cuda.FloatTensor) - y.lt(0).type(torch.cuda.FloatTensor)) + else: + phi = torch.atan(y/(x + 1e-5)) + np.pi * x.lt(0).type(torch.FloatTensor) * (y.ge(0).type(torch.FloatTensor) - y.lt(0).type(torch.FloatTensor)) - input_u = rotate.view(-1,1,1,1).repeat(1,self.height, self.width,1) + phi = phi/np.pi output = torch.cat([theta,phi], 3) - #print(output.size()) - - output1 = torch.atan(torch.tan(np.pi/2.0*(output[:,:,:,1:2] + self.batchgrid[:,:,:,2:] * input_u[:,:,:,:]))) /(np.pi/2) - output2 = torch.cat([output[:,:,:,0:1], output1], 3) - - return output2 + output2 = torch.cat([output[:,:,:,0:1] , output[:,:,:,1:2] ], 3) + return output2, r @@ -410,5 +424,82 @@ def forward(self, depth, trans0, trans1, rotate): phi = phi/np.pi - output = torch.cat([theta,phi], 3) - return output + if self.ray_tracing: + theta_np = theta[:,:,:,0].cpu().detach().data.numpy() + phi_np = phi[:,:,:,0].cpu().detach().data.numpy() + r_np = r[:,:,:,0].cpu().detach().data.numpy() + depth_np = depth[:,:,:,0].cpu().detach().data.numpy() + + #import pickle as pkl + #pkl.dump([theta_np, phi_np, r_np, depth_np], open('save.pkl', 'w')) + + dia = np.arctan(np.tan(np.pi/float(self.height)) / (depth_np+1e-5) * r_np) / np.tan(np.pi/float(self.height)) + dia = np.ceil(dia) + occupancy, occupancy_input = ray_tracing_v2.trace(theta_np, phi_np, r_np, dia) + + occupancy = torch.from_numpy(occupancy) + occupancy_input = torch.from_numpy(occupancy_input) + + if depth.is_cuda: + occupancy, occupancy_input = occupancy.cuda(), occupancy_input.cuda() + + output = torch.cat([theta,phi], 3) + return output, occupancy, occupancy_input + + else: + output = torch.cat([theta,phi], 3) + return output + + + + +class RotateGridGen(Module): + def __init__(self, height, width, lr = 1, aux_loss = False): + super(RotateGridGen, self).__init__() + self.height, self.width = height, width + self.aux_loss = aux_loss + self.lr = lr + + self.grid = np.zeros( [self.height, self.width, 3], dtype=np.float32) + self.grid[:,:,0] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/self.height), 0), repeats = self.width, axis = 0).T, 0) + self.grid[:,:,1] = np.expand_dims(np.repeat(np.expand_dims(np.arange(-1, 1, 2.0/self.width), 0), repeats = self.height, axis = 0), 0) + self.grid[:,:,2] = np.ones([self.height, width]) + self.grid = torch.from_numpy(self.grid.astype(np.float32)) + + self.theta = self.grid[:,:,0] * np.pi/2 + np.pi/2 + self.phi = self.grid[:,:,1] * np.pi + + self.x = torch.sin(self.theta) * torch.cos(self.phi) + self.y = torch.sin(self.theta) * torch.sin(self.phi) + self.z = torch.cos(self.theta) + + self.grid3d = torch.from_numpy(np.zeros( [self.height, self.width, 4], dtype=np.float32)) + + self.grid3d[:,:,0] = self.x + self.grid3d[:,:,1] = self.y + self.grid3d[:,:,2] = self.z + self.grid3d[:,:,3] = self.grid[:,:,2] + + + def forward(self, grid, rotate): + self.batchgrid = torch.zeros(torch.Size([grid.size(0)]) + self.grid.size()) + + for i in range(grid.size(0)): + self.batchgrid[i] = self.grid + + self.batchgrid = Variable(self.batchgrid) + + if grid.is_cuda: + self.batchgrid = self.batchgrid.cuda() + + input_u = rotate.view(-1,1,1,1).repeat(1,self.height, self.width,1) + + output = grid + self.batchgrid[:,:,:,:2] + + output1 = torch.atan(torch.tan(np.pi/2.0*(output[:,:,:,1:2] + self.batchgrid[:,:,:,2:] * input_u[:,:,:,:]))) /(np.pi/2) + output2 = torch.cat([output[:,:,:,0:1], output1], 3) + + + return output2 + + diff --git a/script/modules/stn_invert.py b/script/modules/stn_invert.py new file mode 100644 index 0000000..2e81d41 --- /dev/null +++ b/script/modules/stn_invert.py @@ -0,0 +1,9 @@ +from torch.nn.modules.module import Module +from functions.stn_invert import STNInvertFunction + +class STNInvert(Module): + def __init__(self): + super(STNInvert, self).__init__() + self.f = STNInvertFunction() + def forward(self, input1, input2, input3): + return self.f(input1, input2, input3) \ No newline at end of file diff --git a/script/src/my_lib_invert.c b/script/src/my_lib_invert.c new file mode 100644 index 0000000..11bfa19 --- /dev/null +++ b/script/src/my_lib_invert.c @@ -0,0 +1,676 @@ +#include +#include +#include + +#define real float + +void dot43(real A[4][3], real B[3][3]) { + int i,j,k; + for (i = 0; i<3; i++) + { + for (j = 0; j<3; j++) { + B[i][j] = 0; + for (k = 0; k < 4; k++) + B[i][j] += A[k][i] * A[k][j]; + //printf("%f ", B[i][j]); + } + //printf("\n"); + } + //printf("\n"); +} + + +void inv3(real B[3][3], real invB[3][3]) { + float determinant = +B[0][0]*(B[1][1]*B[2][2]-B[2][1]*B[1][2]) + -B[0][1]*(B[1][0]*B[2][2]-B[1][2]*B[2][0]) + +B[0][2]*(B[1][0]*B[2][1]-B[1][1]*B[2][0]); + float invdet = 1/determinant; + + //printf("det %f\n", determinant); + invB[0][0] = (B[1][1]*B[2][2]-B[2][1]*B[1][2])*invdet; + invB[1][0] = -(B[0][1]*B[2][2]-B[0][2]*B[2][1])*invdet; + invB[2][0] = (B[0][1]*B[1][2]-B[0][2]*B[1][1])*invdet; + invB[0][1] = -(B[1][0]*B[2][2]-B[1][2]*B[2][0])*invdet; + invB[1][1] = (B[0][0]*B[2][2]-B[0][2]*B[2][0])*invdet; + invB[2][1] = -(B[0][0]*B[1][2]-B[1][0]*B[0][2])*invdet; + invB[0][2] = (B[1][0]*B[2][1]-B[2][0]*B[1][1])*invdet; + invB[1][2] = -(B[0][0]*B[2][1]-B[2][0]*B[0][1])*invdet; + invB[2][2] = (B[0][0]*B[1][1]-B[1][0]*B[0][1])*invdet; + +} + + +void dot34(real invB[3][3], real A[4][3], real m[3][4]) { + int i, j, k; + for (i = 0; i < 3; i++) + for (j = 0; j < 4; j++){ + m[i][j] = 0; + for (k = 0; k < 3; k++) { + m[i][j] += invB[i][k] * A[j][k]; + } + } +} + + +void dot41(real m[3][4], real x[4], real alpha[3]) { + int i,j; + for (i = 0; i < 3; i++) { + alpha[i] = 0; + for (j = 0; j < 4; j++) + alpha[i] += m[i][j] * x[j]; + //printf("%.2f ", alpha[i]); + } + //printf("\n"); +} + +real min(real * array, int len) { + real m = array[0]; + int i; + for (i = 0; i < len; i++) + if (array[i] < m) m = array[i]; + return m; +} + +real max(real * array, int len) { + real m = array[0]; + int i; + for (i = 0; i < len; i++) + if (array[i] > m) m = array[i]; + return m; +} + + +void dot21(real im2[2][2], real d[2], real r[2]) { + int i,j; + for (i = 0; i < 2; i++) { + r[i] = 0; + for (j = 0; j < 2; j++) + r[i] += im2[i][j] * d[j]; + } +} + + + +void dot22(real m1[2][2], real m2[2][2], real result[2][2]) { + int i,j,k; + for (i = 0; i < 2; i++ ) + for (j = 0; j < 2; j++) + { + result[i][j] = 0; + for (k = 0; k < 2; k++) + result[i][j] += m1[i][k] * m2[k][j]; + } +} + + +void dot32(real gradalphar[3][2], real gradr[2], real gradalpha[3]) { + int i,j; + for (i = 0; i < 3; i++) { + gradalpha[i] = 0; + for (j = 0; j < 2; j++) + gradalpha[i] += gradalphar[i][j] * gradr[j]; + } +} + + +void inv2(real m2[2][2], real im2[2][2]) { + real determinant = m2[0][0] * m2[1][1] - m2[0][1] * m2[1][0]; + //printf("det %.5f\n", determinant); + im2[0][0] = m2[1][1] / determinant; + im2[1][1] = m2[0][0] / determinant; + im2[0][1] = -m2[0][1] / determinant; + im2[1][0] = -m2[1][0] / determinant; +} + +void dot34t(real m[3][4], real alpha[3], real gradx[4]) { + int i,j; + for (i = 0; i < 4; i++) { + gradx[i] = 0; + for (j = 0; j < 3; j++) + gradx[i] += m[j][i] * alpha[j]; + } +} + +real abs_real(real num) { + return (num > 0)?num:-num; +} + +int InvSamplerBHWD_updateOutput(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *invgrids, THFloatTensor *output, THFloatTensor *depth_map) +{ + + int batchsize = inputImages->size[0]; + int inputImages_height = inputImages->size[1]; + int inputImages_width = inputImages->size[2]; + int output_height = output->size[1]; + int output_width = output->size[2]; + int inputImages_channels = inputImages->size[3]; + + int output_strideBatch = output->stride[0]; + int output_strideHeight = output->stride[1]; + int output_strideWidth = output->stride[2]; + + int depth_strideBatch = depth_map->stride[0]; + int depth_strideHeight = depth_map->stride[1]; + int depth_strideWidth = depth_map->stride[2]; + + + int inputImages_strideBatch = inputImages->stride[0]; + int inputImages_strideHeight = inputImages->stride[1]; + int inputImages_strideWidth = inputImages->stride[2]; + + int grids_strideBatch = grids->stride[0]; + int grids_strideHeight = grids->stride[1]; + int grids_strideWidth = grids->stride[2]; + + + real *inputImages_data, *output_data, *grids_data, *invgrids_data, *depth_data; + inputImages_data = THFloatTensor_data(inputImages); + output_data = THFloatTensor_data(output); + grids_data = THFloatTensor_data(grids); + invgrids_data = THFloatTensor_data(invgrids); + + depth_data = THFloatTensor_data(depth_map); + + real * target_depth_data = (real *)malloc(sizeof(real) * output_height * output_width * batchsize); + + + int tradeb, yOut, xOut, k; + + real x[4], y[4], alpha[3], beta[3]; + + real m2[2][2], im2[2][2]; + + real minx, miny, minbasex, minbasey; + real maxx, maxy, maxbasex, maxbasey; + + int b; + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < output_height - 1; yOut++) + { + for(xOut=0; xOut < output_width - 1; xOut++) { + const int outdepthAddress = depth_strideBatch * b + depth_strideHeight * yOut + depth_strideWidth * xOut; + target_depth_data[outdepthAddress] = 1e5; + } + } + } + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < output_height - 1; yOut++) + { + for(xOut=0; xOut < output_width - 1; xOut++) + { + //read the grid + + + const int inTopLeftAddress = grids_strideBatch * b + grids_strideHeight * yOut + grids_strideWidth * xOut; + const int inTopRightAddress = inTopLeftAddress + grids_strideWidth; + const int inBottomLeftAddress = inTopLeftAddress + grids_strideHeight; + const int inBottomRightAddress = inBottomLeftAddress + grids_strideWidth; + + + x[0] = grids_data[inTopLeftAddress + 1]; + x[1] = grids_data[inBottomLeftAddress + 1]; + x[2] = grids_data[inTopRightAddress + 1]; + x[3] = grids_data[inBottomRightAddress + 1]; + + y[0] = grids_data[inTopLeftAddress]; + y[1] = grids_data[inBottomLeftAddress]; + y[2] = grids_data[inTopRightAddress]; + y[3] = grids_data[inBottomRightAddress]; + + + //if (abs_real(x[2] - x[0]) > 1) { + // if (x[0] < 0) x[0] += 2; + // if (x[2] < 0) x[2] += 2; + //} + // + //if (abs_real(x[3] - x[1]) > 1) { + // if (x[1] < 0) x[1] += 2; + // if (x[3] < 0) x[3] += 2; + //} + + + float dx1 = x[2] - x[0]; + float dy1 = y[2] - y[0]; + + float dx2 = x[1] - x[0]; + float dy2 = y[1] - y[0]; + float normal = (dx1 * dy2) - (dx2 * dy1); + + + + real m[3][4] = {{ 0.7500, 0.2500, 0.2500, -0.2500},{-0.5000, -0.5000, 0.5000, 0.5000},{-0.5000, 0.5000, -0.5000, 0.5000}}; + + dot41(m, x, alpha); + dot41(m, y, beta); + + //printf("recon %.4f = %.4f\n", A[0][0] * alpha[0] + A[0][1] * alpha[1] + A[0][2] * alpha[2], x[0]); + //printf("%.2f %.2f %.2f %.2f %.2f %.2f\n", alpha[0], alpha[1], alpha[2], beta[0], beta[1], beta[2]); + + minx = min(x, 4); + miny = min(y, 4); + maxx = max(x, 4); + maxy = max(y, 4); + + + int minxcoord = floor((minx + 1) * (inputImages_width - 1) / 2); + int maxxcoord = ceil((maxx + 1) * (inputImages_width - 1) / 2); + + int minycoord = floor((miny + 1) * (inputImages_height - 1) / 2); + int maxycoord = ceil((maxy + 1) * (inputImages_height - 1) / 2); + + //printf("%d %d %d %d\n", minxcoord, maxxcoord, minycoord, maxycoord); + + m2[0][0] = alpha[1]; + m2[0][1] = alpha[2]; + m2[1][0] = beta[1]; + m2[1][1] = beta[2]; + + inv2(m2, im2); + + //printf("%.2f, %.2f \n%.2f, %.2f \n\n", im2[0][0], im2[0][1], im2[1][0], im2[1][1]); + + int xcoord, ycoord; + + int scaling = (maxxcoord - minxcoord) * (maxycoord - minycoord); + + if (normal > 0) + if ((maxxcoord - minxcoord) < inputImages_width / 2) + for (xcoord = minxcoord; xcoord < maxxcoord; xcoord ++) + for (ycoord = minycoord; ycoord < maxycoord; ycoord ++) { + + real d2[2]; + real yf = (float)ycoord / (float)(output_height-1) * 2 - 1; + real xf = (float)xcoord / (float)(output_width-1) * 2 - 1; + + d2[0] = xf - alpha[0]; + d2[1] = yf - beta[0]; + + real r[2]; + dot21(im2, d2, r); // r[0] x, r[1] y; + + real slack = 0; + //printf("%f %f\n", r[0], r[1]); + //printf("%.4f = %.4f\n", alpha[0] + alpha[1] * r[0] + alpha[2] * r[1], xf); + if ((-slack < r[0]) && (r[0] < 1+slack) &&(-slack < r[1]) && (r[1] < 1 + slack)) { + //printf("%.4f, %.4f | %.4f %.4f \n", r[0], r[1], basex[0], basey[0]); + int yInTopLeft, xInTopLeft; + real yWeightTopLeft, xWeightTopLeft; + + real xcoord_source = r[0] + xOut; + xInTopLeft = floor(xcoord_source); + xWeightTopLeft = 1 - (xcoord_source - xInTopLeft); + + real ycoord_source = r[1] + yOut; + yInTopLeft = floor(ycoord_source); + yWeightTopLeft = 1 - (ycoord_source - yInTopLeft); + + const int outAddress = output_strideBatch * b + output_strideHeight * ycoord + output_strideWidth * xcoord; + const int outGridAddress = grids_strideBatch * b + grids_strideHeight * ycoord + grids_strideWidth * xcoord; + const int inTopLeftAddress = inputImages_strideBatch * b + inputImages_strideHeight * yInTopLeft + inputImages_strideWidth * xInTopLeft; + const int inTopRightAddress = inTopLeftAddress + inputImages_strideWidth; + const int inBottomLeftAddress = inTopLeftAddress + inputImages_strideHeight; + const int inBottomRightAddress = inBottomLeftAddress + inputImages_strideWidth; + + const int indepthAddress = depth_strideBatch * b + depth_strideHeight * yInTopLeft + depth_strideWidth * xInTopLeft; + const int outdepthAddress = depth_strideBatch * b + depth_strideHeight * ycoord + depth_strideWidth * xcoord; + + real v=0; + real inTopLeft=0; + real inTopRight=0; + real inBottomLeft=0; + real inBottomRight=0; + + bool topLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool topRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool bottomLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + bool bottomRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + + bool outIsIn = xcoord >= 0 && xcoord <= inputImages_width-1 && ycoord >= 0 && ycoord <= inputImages_height-1; + + int t; + + + + //if (scaling < 36){ + if (outIsIn) + { + + if ((scaling < 36) && ((depth_data[indepthAddress] < target_depth_data[outdepthAddress]) && (depth_data[indepthAddress] > 0))) + { + for(t=0; tsize[0]; + int inputImages_height = inputImages->size[1]; + int inputImages_width = inputImages->size[2]; + int gradOutput_height = gradOutput->size[1]; + int gradOutput_width = gradOutput->size[2]; + int inputImages_channels = inputImages->size[3]; + + int gradOutput_strideBatch = gradOutput->stride[0]; + int gradOutput_strideHeight = gradOutput->stride[1]; + int gradOutput_strideWidth = gradOutput->stride[2]; + + int inputImages_strideBatch = inputImages->stride[0]; + int inputImages_strideHeight = inputImages->stride[1]; + int inputImages_strideWidth = inputImages->stride[2]; + + int gradInputImages_strideBatch = gradInputImages->stride[0]; + int gradInputImages_strideHeight = gradInputImages->stride[1]; + int gradInputImages_strideWidth = gradInputImages->stride[2]; + + int grids_strideBatch = grids->stride[0]; + int grids_strideHeight = grids->stride[1]; + int grids_strideWidth = grids->stride[2]; + + int gradGrids_strideBatch = gradGrids->stride[0]; + int gradGrids_strideHeight = gradGrids->stride[1]; + int gradGrids_strideWidth = gradGrids->stride[2]; + + + + real *inputImages_data, *gradOutput_data, *grids_data, *gradGrids_data, *gradInputImages_data, *invgrids_data; + + inputImages_data = THFloatTensor_data(inputImages); + gradOutput_data = THFloatTensor_data(gradOutput); + grids_data = THFloatTensor_data(grids); + invgrids_data = THFloatTensor_data(invgrids); + + gradGrids_data = THFloatTensor_data(gradGrids); + gradInputImages_data = THFloatTensor_data(gradInputImages); + + int b, yOut, xOut; + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < gradOutput_height; yOut++) + { + for(xOut=0; xOut < gradOutput_width; xOut++) + { + const int Address = gradGrids_strideBatch * b + gradGrids_strideHeight * yOut + gradGrids_strideWidth * xOut; + gradGrids_data[Address] = 0; + gradGrids_data[Address + 1] = 0; + + } + } + } + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < gradOutput_height; yOut++) + { + for(xOut=0; xOut < gradOutput_width; xOut++) + { + const int gradOutputAddress = gradOutput_strideBatch * b + gradOutput_strideHeight * yOut + gradOutput_strideWidth * xOut; + const int invgridAddress = grids_strideBatch * b + grids_strideHeight * yOut + grids_strideWidth * xOut; + + real r[2], gradr[2]; + + int xSource, ySource; + + xSource = (int)invgrids_data[invgridAddress + 1]; + ySource = (int)invgrids_data[invgridAddress]; + + //printf("%d %d\n", xSource ,ySource); + + const int gridinTopLeftAddress = grids_strideBatch * b + grids_strideHeight * ySource + grids_strideWidth * xSource; + const int gridinTopRightAddress = gridinTopLeftAddress + grids_strideWidth; + const int gridinBottomLeftAddress = gridinTopLeftAddress + grids_strideHeight; + const int gridinBottomRightAddress = gridinBottomLeftAddress + grids_strideWidth; + + int i,j; + + real m[3][4] = {{ 0.7500, 0.2500, 0.2500, -0.2500},{-0.5000, -0.5000, 0.5000, 0.5000},{-0.5000, 0.5000, -0.5000, 0.5000}}; + + real gradalpha[3], gradbeta[3], alpha[3], beta[3]; + + real x[4], y[4]; + x[0] = grids_data[gridinTopLeftAddress + 1]; + x[1] = grids_data[gridinBottomLeftAddress + 1]; + x[2] = grids_data[gridinTopRightAddress + 1]; + x[3] = grids_data[gridinBottomRightAddress + 1]; + + y[0] = grids_data[gridinTopLeftAddress]; + y[1] = grids_data[gridinBottomLeftAddress]; + y[2] = grids_data[gridinTopRightAddress]; + y[3] = grids_data[gridinBottomRightAddress]; + + dot41(m, x, alpha); + dot41(m, y, beta); + real target_yf, target_xf; + target_yf = (float)yOut / (float)(inputImages_height - 1) * 2 - 1; + target_xf = (float)xOut / (float)(inputImages_width - 1) * 2 - 1; + + real m2[2][2], im2[2][2]; + m2[0][0] = alpha[1]; + m2[0][1] = alpha[2]; + m2[1][0] = beta[1]; + m2[1][1] = beta[2]; + inv2(m2, im2); + + real d2[2]; + d2[0] = target_xf - alpha[0]; + d2[1] = target_yf - beta[0]; + + real r2[2]; + + dot21(im2, d2, r2); + + + if ((xSource != 0) || (ySource != 0)) { + //printf("%d %d %.8f %.8f\n", xSource ,ySource, r2[0], r2[1]); + + // get the weights for interpolation + int yInTopLeft, xInTopLeft; + real yWeightTopLeft, xWeightTopLeft; + real xgrad,ygrad; + + xInTopLeft = xSource; + xWeightTopLeft = r[0]; + + yInTopLeft = ySource; + yWeightTopLeft = r[1]; + + const int inTopLeftAddress = inputImages_strideBatch * b + inputImages_strideHeight * yInTopLeft + inputImages_strideWidth * xInTopLeft; + const int inTopRightAddress = inTopLeftAddress + inputImages_strideWidth; + const int inBottomLeftAddress = inTopLeftAddress + inputImages_strideHeight; + const int inBottomRightAddress = inBottomLeftAddress + inputImages_strideWidth; + + const int gradInputImagesTopLeftAddress = gradInputImages_strideBatch * b + gradInputImages_strideHeight * yInTopLeft + gradInputImages_strideWidth * xInTopLeft; + const int gradInputImagesTopRightAddress = gradInputImagesTopLeftAddress + gradInputImages_strideWidth; + const int gradInputImagesBottomLeftAddress = gradInputImagesTopLeftAddress + gradInputImages_strideHeight; + const int gradInputImagesBottomRightAddress = gradInputImagesBottomLeftAddress + gradInputImages_strideWidth; + + const int gradOutputAddress = gradOutput_strideBatch * b + gradOutput_strideHeight * yOut + gradOutput_strideWidth * xOut; + + real topLeftDotProduct = 0; + real topRightDotProduct = 0; + real bottomLeftDotProduct = 0; + real bottomRightDotProduct = 0; + + real v=0; + real inTopLeft=0; + real inTopRight=0; + real inBottomLeft=0; + real inBottomRight=0; + + // we are careful with the boundaries + bool topLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool topRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool bottomLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + bool bottomRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + + int t; + + for(t=0; t +#include +#include +#include "my_lib_cuda_kernel.h" + +#define real float + +// this symbol will be resolved automatically from PyTorch libs +extern THCState *state; + +// Bilinear sampling is done in BHWD (coalescing is not obvious in BDHW) +// we assume BHWD format in inputImages +// we assume BHW(YX) format on grids + +int InvSamplerBHWD_updateOutput_cuda(THCudaTensor *inputImages, + THCudaTensor *grids, + THCudaTensor *invgrids, + THCudaTensor *output, + THCudaTensor *depth_map, + THCudaTensor *target_depth_map, + int * device) +{ +// THCState *state = getCutorchState(L); +// THCudaTensor *inputImages = (THCudaTensor *)luaT_checkudata(L, 2, "torch.CudaTensor"); +// THCudaTensor *grids = (THCudaTensor *)luaT_checkudata(L, 3, "torch.CudaTensor"); +// THCudaTensor *output = (THCudaTensor *)luaT_checkudata(L, 4, "torch.CudaTensor"); + + cudaSetDevice(device[0]); + int success = 0; + + success = InvSamplerBHWD_updateOutput_cuda_kernel( + THCudaTensor_size(state, inputImages, 0), //int batchsize ,//= inputImages->size[0]; + THCudaTensor_size(state, inputImages, 1), //int inputImages_height ,//= inputImages->size[1]; + THCudaTensor_size(state, inputImages, 2), //int inputImages_width ,//= inputImages->size[2]; + THCudaTensor_size(state, output, 1), //int output_height ,//= output->size[1]; + THCudaTensor_size(state, output, 2), //int output_width ,//= output->size[2]; + THCudaTensor_size(state, inputImages, 3), //int inputImages_channels ,//= inputImages->size[3]; + THCudaTensor_stride(state, output, 0),//int output_strideBatch ,//= output->stride[0]; + THCudaTensor_stride(state, output, 1),//int output_strideHeight ,//= output->stride[1]; + THCudaTensor_stride(state, output, 2),//int output_strideWidth ,//= output->stride[2]; + THCudaTensor_stride(state, depth_map, 0),//int depth_strideBatch ,//= depth_map->stride[0]; + THCudaTensor_stride(state, depth_map, 1),//int depth_strideHeight ,//= depth_map->stride[1]; + THCudaTensor_stride(state, depth_map, 2),//int depth_strideWidth ,//= depth_map->stride[2]; + THCudaTensor_stride(state, inputImages, 0),//int inputImages_strideBatch ,//= inputImages->stride[0]; + THCudaTensor_stride(state, inputImages, 1),//int inputImages_strideHeight ,//= inputImages->stride[1]; + THCudaTensor_stride(state, inputImages, 2),//int inputImages_strideWidth ,//= inputImages->stride[2]; + THCudaTensor_stride(state, grids, 0),//int grids_strideBatch ,//= grids->stride[0]; + THCudaTensor_stride(state, grids, 1),//int grids_strideHeight ,//= grids->stride[1]; + THCudaTensor_stride(state, grids, 2),//int grids_strideWidth ,//= grids->stride[2]; + THCudaTensor_data(state, inputImages),//float *inputImages_data, + THCudaTensor_data(state, output),//float *output_data, + THCudaTensor_data(state, grids),//float *grids_data, + THCudaTensor_data(state, invgrids),//float *invgrids_data, + THCudaTensor_data(state, depth_map),//float *depth_data, + THCudaTensor_data(state, target_depth_map),//float *target_depth_data, + THCState_getCurrentStream(state)); //cudaStream_t stream + + + +// success = BilinearSamplerBHWD_updateOutput_cuda_kernel(output->size[2], +// output->size[1], +// output->size[0], +// THCudaTensor_size(state, inputImages, 3), +// THCudaTensor_size(state, inputImages, 1), +// THCudaTensor_size(state, inputImages, 2), +// THCudaTensor_size(state, output, 2), +// THCudaTensor_data(state, inputImages), +// THCudaTensor_stride(state, inputImages, 0), +// THCudaTensor_stride(state, inputImages, 3), +// THCudaTensor_stride(state, inputImages, 1), +// THCudaTensor_stride(state, inputImages, 2), +// THCudaTensor_data(state, grids), +// THCudaTensor_stride(state, grids, 0), +// THCudaTensor_stride(state, grids, 3), +// THCudaTensor_stride(state, grids, 1), +// THCudaTensor_stride(state, grids, 2), +// THCudaTensor_data(state, output), +// THCudaTensor_stride(state, output, 0), +// THCudaTensor_stride(state, output, 3), +// THCudaTensor_stride(state, output, 1), +// THCudaTensor_stride(state, output, 2), +// THCState_getCurrentStream(state)); + + //check for errors + if (!success) { + THError("aborting"); + } + return 1; +} + +int InvSamplerBHWD_updateGradInput_cuda(THCudaTensor *inputImages, + THCudaTensor *grids, + THCudaTensor *invgrids, + THCudaTensor *gradInputImages, + THCudaTensor *gradGrids, + THCudaTensor *gradOutput, + int * device) +{ +// THCState *state = getCutorchState(L); +// THCudaTensor *inputImages = (THCudaTensor *)luaT_checkudata(L, 2, "torch.CudaTensor"); +// THCudaTensor *grids = (THCudaTensor *)luaT_checkudata(L, 3, "torch.CudaTensor"); +// THCudaTensor *gradInputImages = (THCudaTensor *)luaT_checkudata(L, 4, "torch.CudaTensor"); +// THCudaTensor *gradGrids = (THCudaTensor *)luaT_checkudata(L, 5, "torch.CudaTensor"); +// THCudaTensor *gradOutput = (THCudaTensor *)luaT_checkudata(L, 6, "torch.CudaTensor"); + + cudaSetDevice(device[0]); + int success = 0; + + success = InvSamplerBHWD_updateGradInput_cuda_kernel( + THCudaTensor_size(state, inputImages, 0),//int batchsize ,//= inputImages->size[0]; + THCudaTensor_size(state, inputImages, 1),//int inputImages_height ,//= inputImages->size[1]; + THCudaTensor_size(state, inputImages, 2),//int inputImages_width ,//= inputImages->size[2]; + THCudaTensor_size(state, gradOutput, 1),//int gradOutput_height ,//= gradOutput->size[1]; + THCudaTensor_size(state, gradOutput, 2),//int gradOutput_width ,//= gradOutput->size[2]; + THCudaTensor_size(state, inputImages, 3), //int inputImages_channels ,//= inputImages->size[3]; + THCudaTensor_stride(state, gradOutput, 0),//int gradOutput_strideBatch ,//= gradOutput->stride[0]; + THCudaTensor_stride(state, gradOutput, 1),//int gradOutput_strideHeight ,//= gradOutput->stride[1]; + THCudaTensor_stride(state, gradOutput, 2),//int gradOutput_strideWidth ,//= gradOutput->stride[2]; + THCudaTensor_stride(state, inputImages, 0),//int inputImages_strideBatch ,//= inputImages->stride[0]; + THCudaTensor_stride(state, inputImages, 1),//int inputImages_strideHeight ,//= inputImages->stride[1]; + THCudaTensor_stride(state, inputImages, 2),//int inputImages_strideWidth ,//= inputImages->stride[2]; + THCudaTensor_stride(state, gradInputImages, 0),//int gradInputImages_strideBatch ,//= gradInputImages->stride[0]; + THCudaTensor_stride(state, gradInputImages, 1),//int gradInputImages_strideHeight ,//= gradInputImages->stride[1]; + THCudaTensor_stride(state, gradInputImages, 2),//int gradInputImages_strideWidth ,//= gradInputImages->stride[2]; + THCudaTensor_stride(state, grids, 0),//int grids_strideBatch ,//= grids->stride[0]; + THCudaTensor_stride(state, grids, 1),//int grids_strideHeight ,//= grids->stride[1]; + THCudaTensor_stride(state, grids, 2),//int grids_strideWidth ,//= grids->stride[2]; + THCudaTensor_stride(state, gradGrids, 0),//int gradGrids_strideBatch ,//= gradGrids->stride[0]; + THCudaTensor_stride(state, gradGrids, 1),//int gradGrids_strideHeight ,//= gradGrids->stride[1]; + THCudaTensor_stride(state, gradGrids, 2),//int gradGrids_strideWidth ,//= gradGrids->stride[2]; + THCudaTensor_data(state, inputImages),//float *inputImages_data, + THCudaTensor_data(state, gradOutput),//float *gradOutput_data, + THCudaTensor_data(state, grids),//float *grids_data, + THCudaTensor_data(state, gradGrids),//float *gradGrids_data, + THCudaTensor_data(state, gradInputImages),//float *gradInputImages_data, + THCudaTensor_data(state, invgrids),//float *invgrids_data, + THCState_getCurrentStream(state));//cudaStream_t stream + +// success = BilinearSamplerBHWD_updateGradInput_cuda_kernel(gradOutput->size[2], +// gradOutput->size[1], +// gradOutput->size[0], +// THCudaTensor_size(state, inputImages, 3), +// THCudaTensor_size(state, inputImages, 1), +// THCudaTensor_size(state, inputImages, 2), +// THCudaTensor_size(state, gradOutput, 2), +// THCudaTensor_data(state, inputImages), +// THCudaTensor_stride(state, inputImages, 0), +// THCudaTensor_stride(state, inputImages, 3), +// THCudaTensor_stride(state, inputImages, 1), +// THCudaTensor_stride(state, inputImages, 2), +// THCudaTensor_data(state, grids), +// THCudaTensor_stride(state, grids, 0), +// THCudaTensor_stride(state, grids, 3), +// THCudaTensor_stride(state, grids, 1), +// THCudaTensor_stride(state, grids, 2), +// THCudaTensor_data(state, gradInputImages), +// THCudaTensor_stride(state, gradInputImages, 0), +// THCudaTensor_stride(state, gradInputImages, 3), +// THCudaTensor_stride(state, gradInputImages, 1), +// THCudaTensor_stride(state, gradInputImages, 2), +// THCudaTensor_data(state, gradGrids), +// THCudaTensor_stride(state, gradGrids, 0), +// THCudaTensor_stride(state, gradGrids, 3), +// THCudaTensor_stride(state, gradGrids, 1), +// THCudaTensor_stride(state, gradGrids, 2), +// THCudaTensor_data(state, gradOutput), +// THCudaTensor_stride(state, gradOutput, 0), +// THCudaTensor_stride(state, gradOutput, 3), +// THCudaTensor_stride(state, gradOutput, 1), +// THCudaTensor_stride(state, gradOutput, 2), +// THCState_getCurrentStream(state)); +// + //check for errors + if (!success) { + THError("aborting"); + } + return 1; +} + + + diff --git a/script/src/my_lib_invert_cuda.h b/script/src/my_lib_invert_cuda.h new file mode 100644 index 0000000..5313f16 --- /dev/null +++ b/script/src/my_lib_invert_cuda.h @@ -0,0 +1,18 @@ +int InvSamplerBHWD_updateOutput_cuda(THCudaTensor *inputImages, + THCudaTensor *grids, + THCudaTensor *invgrids, + THCudaTensor *output, + THCudaTensor *depth_map, + THCudaTensor *target_depth_map, + int * device); + +int InvSamplerBHWD_updateGradInput_cuda(THCudaTensor *inputImages, + THCudaTensor *grids, + THCudaTensor *invgrids, + THCudaTensor *gradInputImages, + THCudaTensor *gradGrids, + THCudaTensor *gradOutput, + int * device); + +//int InvSamplerBHWD_updateGradInput_num(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *invgrids, THFloatTensor *gradInputImages, THFloatTensor *gradGrids, THFloatTensor *gradOutput, THFloatTensor *msave); + diff --git a/script/src/my_lib_invert_cuda_kernel.cu b/script/src/my_lib_invert_cuda_kernel.cu new file mode 100644 index 0000000..a72ab06 --- /dev/null +++ b/script/src/my_lib_invert_cuda_kernel.cu @@ -0,0 +1,804 @@ +//#include +#include +#include +#include "my_lib_invert_cuda_kernel.h" +#define real float + +__device__ void dot43(real A[4][3], real B[3][3]) { + int i,j,k; + for (i = 0; i<3; i++) + { + for (j = 0; j<3; j++) { + B[i][j] = 0; + for (k = 0; k < 4; k++) + B[i][j] += A[k][i] * A[k][j]; + //printf("%f ", B[i][j]); + } + //printf("\n"); + } + //printf("\n"); +} + + +__device__ void inv3(real B[3][3], real invB[3][3]) { + float determinant = +B[0][0]*(B[1][1]*B[2][2]-B[2][1]*B[1][2]) + -B[0][1]*(B[1][0]*B[2][2]-B[1][2]*B[2][0]) + +B[0][2]*(B[1][0]*B[2][1]-B[1][1]*B[2][0]); + float invdet = 1/determinant; + + //printf("det %f\n", determinant); + invB[0][0] = (B[1][1]*B[2][2]-B[2][1]*B[1][2])*invdet; + invB[1][0] = -(B[0][1]*B[2][2]-B[0][2]*B[2][1])*invdet; + invB[2][0] = (B[0][1]*B[1][2]-B[0][2]*B[1][1])*invdet; + invB[0][1] = -(B[1][0]*B[2][2]-B[1][2]*B[2][0])*invdet; + invB[1][1] = (B[0][0]*B[2][2]-B[0][2]*B[2][0])*invdet; + invB[2][1] = -(B[0][0]*B[1][2]-B[1][0]*B[0][2])*invdet; + invB[0][2] = (B[1][0]*B[2][1]-B[2][0]*B[1][1])*invdet; + invB[1][2] = -(B[0][0]*B[2][1]-B[2][0]*B[0][1])*invdet; + invB[2][2] = (B[0][0]*B[1][1]-B[1][0]*B[0][1])*invdet; + +} + + +__device__ void dot34(real invB[3][3], real A[4][3], real m[3][4]) { + int i, j, k; + for (i = 0; i < 3; i++) + for (j = 0; j < 4; j++){ + m[i][j] = 0; + for (k = 0; k < 3; k++) { + m[i][j] += invB[i][k] * A[j][k]; + } + } +} + + +__device__ void dot41(real m[3][4], real x[4], real alpha[3]) { + int i,j; + for (i = 0; i < 3; i++) { + alpha[i] = 0; + for (j = 0; j < 4; j++) + alpha[i] += m[i][j] * x[j]; + //printf("%.2f ", alpha[i]); + } + //printf("\n"); +} + +__device__ real min(real * array, int len) { + real m = array[0]; + int i; + for (int i = 0; i < len; i++) + if (array[i] < m) m = array[i]; + return m; +} + +__device__ real max(real * array, int len) { + real m = array[0]; + int i; + for (int i = 0; i < len; i++) + if (array[i] > m) m = array[i]; + return m; +} + + +__device__ void dot21(real im2[2][2], real d[2], real r[2]) { + int i,j; + for (i = 0; i < 2; i++) { + r[i] = 0; + for (j = 0; j < 2; j++) + r[i] += im2[i][j] * d[j]; + } +} + + + +__device__ void dot22(real m1[2][2], real m2[2][2], real result[2][2]) { + int i,j,k; + for (i = 0; i < 2; i++ ) + for (j = 0; j < 2; j++) + { + result[i][j] = 0; + for (k = 0; k < 2; k++) + result[i][j] += m1[i][k] * m2[k][j]; + } +} + + +__device__ void dot32(real gradalphar[3][2], real gradr[2], real gradalpha[3]) { + int i,j; + for (i = 0; i < 3; i++) { + gradalpha[i] = 0; + for (j = 0; j < 2; j++) + gradalpha[i] += gradalphar[i][j] * gradr[j]; + } +} + + +__device__ void inv2(real m2[2][2], real im2[2][2]) { + real determinant = m2[0][0] * m2[1][1] - m2[0][1] * m2[1][0]; + //printf("det %.5f\n", determinant); + im2[0][0] = m2[1][1] / determinant; + im2[1][1] = m2[0][0] / determinant; + im2[0][1] = -m2[0][1] / determinant; + im2[1][0] = -m2[1][0] / determinant; +} + +__device__ void dot34t(real m[3][4], real alpha[3], real gradx[4]) { + int i,j; + for (i = 0; i < 4; i++) { + gradx[i] = 0; + for (j = 0; j < 3; j++) + gradx[i] += m[j][i] * alpha[j]; + } +} + +__device__ real abs_real(real num) { + return (num > 0)?num:-num; +} + +__global__ void test(float * a, float * b, int c, int d) { + +} +__global__ void InvSamplerBHWD_updateOutput(//(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *invgrids, THFloatTensor *output, THFloatTensor *depth_map) + int batchsize ,//= inputImages->size[0]; + int inputImages_height ,//= inputImages->size[1]; + int inputImages_width ,//= inputImages->size[2]; + int output_height ,//= output->size[1]; + int output_width ,//= output->size[2]; + int inputImages_channels ,//= inputImages->size[3]; + int output_strideBatch ,//= output->stride[0]; + int output_strideHeight ,//= output->stride[1]; + int output_strideWidth ,//= output->stride[2]; + int depth_strideBatch ,//= depth_map->stride[0]; + int depth_strideHeight ,//= depth_map->stride[1]; + int depth_strideWidth ,//= depth_map->stride[2]; + int inputImages_strideBatch ,//= inputImages->stride[0]; + int inputImages_strideHeight ,//= inputImages->stride[1]; + int inputImages_strideWidth ,//= inputImages->stride[2]; + int grids_strideBatch ,//= grids->stride[0]; + int grids_strideHeight ,//= grids->stride[1]; + int grids_strideWidth ,//= grids->stride[2]; + float *inputImages_data, float *output_data, float *grids_data, float *invgrids_data, float *depth_data, + float *target_depth_data) //= (real *)malloc(sizeof(real) * output_height * output_width * batchsize); + //inputImages_data = THFloatTensor_data(inputImages); + //output_data = THFloatTensor_data(output); + //grids_data = THFloatTensor_data(grids); + //invgrids_data = THFloatTensor_data(invgrids); + //depth_data = THFloatTensor_data(depth_map); + { + + int tradeb, yOut, xOut, k; + + real x[4], y[4], alpha[3], beta[3]; + + real m2[2][2], im2[2][2]; + + real minx, miny, minbasex, minbasey; + real maxx, maxy, maxbasex, maxbasey; + + int b; + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < output_height - 1; yOut++) + { + for(xOut=0; xOut < output_width - 1; xOut++) { + const int outdepthAddress = depth_strideBatch * b + depth_strideHeight * yOut + depth_strideWidth * xOut; + target_depth_data[outdepthAddress] = 1e5; + } + } + } + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < output_height - 1; yOut++) + { + for(xOut=0; xOut < output_width - 1; xOut++) + { + //read the grid + + + const int inTopLeftAddress = grids_strideBatch * b + grids_strideHeight * yOut + grids_strideWidth * xOut; + const int inTopRightAddress = inTopLeftAddress + grids_strideWidth; + const int inBottomLeftAddress = inTopLeftAddress + grids_strideHeight; + const int inBottomRightAddress = inBottomLeftAddress + grids_strideWidth; + + + x[0] = grids_data[inTopLeftAddress + 1]; + x[1] = grids_data[inBottomLeftAddress + 1]; + x[2] = grids_data[inTopRightAddress + 1]; + x[3] = grids_data[inBottomRightAddress + 1]; + + y[0] = grids_data[inTopLeftAddress]; + y[1] = grids_data[inBottomLeftAddress]; + y[2] = grids_data[inTopRightAddress]; + y[3] = grids_data[inBottomRightAddress]; + + + //if (abs_real(x[2] - x[0]) > 1) { + // if (x[0] < 0) x[0] += 2; + // if (x[2] < 0) x[2] += 2; + //} + // + //if (abs_real(x[3] - x[1]) > 1) { + // if (x[1] < 0) x[1] += 2; + // if (x[3] < 0) x[3] += 2; + //} + + + float dx1 = x[2] - x[0]; + float dy1 = y[2] - y[0]; + + float dx2 = x[1] - x[0]; + float dy2 = y[1] - y[0]; + float normal = (dx1 * dy2) - (dx2 * dy1); + + + real m[3][4] = {{ 0.7500, 0.2500, 0.2500, -0.2500},{-0.5000, -0.5000, 0.5000, 0.5000},{-0.5000, 0.5000, -0.5000, 0.5000}}; + + dot41(m, x, alpha); + dot41(m, y, beta); + + //printf("recon %.4f = %.4f\n", A[0][0] * alpha[0] + A[0][1] * alpha[1] + A[0][2] * alpha[2], x[0]); + //printf("%.2f %.2f %.2f %.2f %.2f %.2f\n", alpha[0], alpha[1], alpha[2], beta[0], beta[1], beta[2]); + + minx = min(x, 4); + miny = min(y, 4); + maxx = max(x, 4); + maxy = max(y, 4); + + + int minxcoord = floor((minx + 1) * (inputImages_width - 1) / 2); + int maxxcoord = ceil((maxx + 1) * (inputImages_width - 1) / 2); + + int minycoord = floor((miny + 1) * (inputImages_height - 1) / 2); + int maxycoord = ceil((maxy + 1) * (inputImages_height - 1) / 2); + + //printf("%d %d %d %d\n", minxcoord, maxxcoord, minycoord, maxycoord); + + m2[0][0] = alpha[1]; + m2[0][1] = alpha[2]; + m2[1][0] = beta[1]; + m2[1][1] = beta[2]; + + inv2(m2, im2); + + //printf("%.2f, %.2f \n%.2f, %.2f \n\n", im2[0][0], im2[0][1], im2[1][0], im2[1][1]); + + int xcoord, ycoord; + + int scaling = (maxxcoord - minxcoord) * (maxycoord - minycoord); + + if (normal > 0) + if ((maxxcoord - minxcoord) < inputImages_width / 2) + for (xcoord = minxcoord; xcoord < maxxcoord; xcoord ++) + for (ycoord = minycoord; ycoord < maxycoord; ycoord ++) { + + real d2[2]; + real yf = (float)ycoord / (float)(output_height-1) * 2 - 1; + real xf = (float)xcoord / (float)(output_width-1) * 2 - 1; + + d2[0] = xf - alpha[0]; + d2[1] = yf - beta[0]; + + real r[2]; + dot21(im2, d2, r); // r[0] x, r[1] y; + + real slack = 0; + //printf("%f %f\n", r[0], r[1]); + //printf("%.4f = %.4f\n", alpha[0] + alpha[1] * r[0] + alpha[2] * r[1], xf); + if ((-slack < r[0]) && (r[0] < 1+slack) &&(-slack < r[1]) && (r[1] < 1 + slack)) { + //printf("%.4f, %.4f | %.4f %.4f \n", r[0], r[1], basex[0], basey[0]); + int yInTopLeft, xInTopLeft; + real yWeightTopLeft, xWeightTopLeft; + + real xcoord_source = r[0] + xOut; + xInTopLeft = floor(xcoord_source); + xWeightTopLeft = 1 - (xcoord_source - xInTopLeft); + + real ycoord_source = r[1] + yOut; + yInTopLeft = floor(ycoord_source); + yWeightTopLeft = 1 - (ycoord_source - yInTopLeft); + + const int outAddress = output_strideBatch * b + output_strideHeight * ycoord + output_strideWidth * xcoord; + const int outGridAddress = grids_strideBatch * b + grids_strideHeight * ycoord + grids_strideWidth * xcoord; + const int inTopLeftAddress = inputImages_strideBatch * b + inputImages_strideHeight * yInTopLeft + inputImages_strideWidth * xInTopLeft; + const int inTopRightAddress = inTopLeftAddress + inputImages_strideWidth; + const int inBottomLeftAddress = inTopLeftAddress + inputImages_strideHeight; + const int inBottomRightAddress = inBottomLeftAddress + inputImages_strideWidth; + + const int indepthAddress = depth_strideBatch * b + depth_strideHeight * yInTopLeft + depth_strideWidth * xInTopLeft; + const int outdepthAddress = depth_strideBatch * b + depth_strideHeight * ycoord + depth_strideWidth * xcoord; + + real v=0; + real inTopLeft=0; + real inTopRight=0; + real inBottomLeft=0; + real inBottomRight=0; + + bool topLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool topRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool bottomLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + bool bottomRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + + bool outIsIn = xcoord >= 0 && xcoord <= inputImages_width-1 && ycoord >= 0 && ycoord <= inputImages_height-1; + + int t; + + for(t=0; t 0)) + output_data[outAddress + t] = v; + } + else { + if (outIsIn) { + output_data[outAddress + t] = 0; + target_depth_data[outdepthAddress] = 0; + } + } + + } + + if (outIsIn) + if ((depth_data[indepthAddress] < target_depth_data[outdepthAddress]) && (depth_data[indepthAddress] > 0)) { + invgrids_data[outGridAddress] = (float)yOut; + invgrids_data[outGridAddress+1] = (float)xOut; // x - [+1], y - [0] + } + + if (outIsIn) + if ((depth_data[indepthAddress] < target_depth_data[outdepthAddress]) && (depth_data[indepthAddress] > 0)) { + target_depth_data[outdepthAddress] = depth_data[indepthAddress]; + } + } + } + + } + } + } + + //free(target_depth_data); + return; +} + +__global__ void InvSamplerBHWD_updateGradInput//(THFloatTensor *inputImages, THFloatTensor *grids, THFloatTensor *invgrids, THFloatTensor *gradInputImages, THFloatTensor *gradGrids, THFloatTensor *gradOutput) +( + int batchsize ,//= inputImages->size[0]; + int inputImages_height ,//= inputImages->size[1]; + int inputImages_width ,//= inputImages->size[2]; + int gradOutput_height ,//= gradOutput->size[1]; + int gradOutput_width ,//= gradOutput->size[2]; + int inputImages_channels ,//= inputImages->size[3]; + int gradOutput_strideBatch ,//= gradOutput->stride[0]; + int gradOutput_strideHeight ,//= gradOutput->stride[1]; + int gradOutput_strideWidth ,//= gradOutput->stride[2]; + int inputImages_strideBatch ,//= inputImages->stride[0]; + int inputImages_strideHeight ,//= inputImages->stride[1]; + int inputImages_strideWidth ,//= inputImages->stride[2]; + int gradInputImages_strideBatch ,//= gradInputImages->stride[0]; + int gradInputImages_strideHeight ,//= gradInputImages->stride[1]; + int gradInputImages_strideWidth ,//= gradInputImages->stride[2]; + int grids_strideBatch ,//= grids->stride[0]; + int grids_strideHeight ,//= grids->stride[1]; + int grids_strideWidth ,//= grids->stride[2]; + int gradGrids_strideBatch ,//= gradGrids->stride[0]; + int gradGrids_strideHeight ,//= gradGrids->stride[1]; + int gradGrids_strideWidth ,//= gradGrids->stride[2]; + float *inputImages_data, float *gradOutput_data, float *grids_data, float *gradGrids_data, float *gradInputImages_data, float *invgrids_data) +{ + //inputImages_data = THFloatTensor_data(inputImages); + //gradOutput_data = THFloatTensor_data(gradOutput); + //grids_data = THFloatTensor_data(grids); + //invgrids_data = THFloatTensor_data(invgrids); + //gradGrids_data = THFloatTensor_data(gradGrids); + //gradInputImages_data = THFloatTensor_data(gradInputImages); + bool onlyGrid=false; + int b, yOut, xOut; + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < gradOutput_height; yOut++) + { + for(xOut=0; xOut < gradOutput_width; xOut++) + { + const int Address = gradGrids_strideBatch * b + gradGrids_strideHeight * yOut + gradGrids_strideWidth * xOut; + gradGrids_data[Address] = 0; + gradGrids_data[Address + 1] = 0; + + } + } + } + + for(b=0; b < batchsize; b++) + { + for(yOut=0; yOut < gradOutput_height; yOut++) + { + for(xOut=0; xOut < gradOutput_width; xOut++) + { + const int gradOutputAddress = gradOutput_strideBatch * b + gradOutput_strideHeight * yOut + gradOutput_strideWidth * xOut; + const int invgridAddress = grids_strideBatch * b + grids_strideHeight * yOut + grids_strideWidth * xOut; + + real r[2], gradr[2]; + + int xSource, ySource; + + xSource = (int)invgrids_data[invgridAddress + 1]; + ySource = (int)invgrids_data[invgridAddress]; + + //printf("%d %d\n", xSource ,ySource); + + const int gridinTopLeftAddress = grids_strideBatch * b + grids_strideHeight * ySource + grids_strideWidth * xSource; + const int gridinTopRightAddress = gridinTopLeftAddress + grids_strideWidth; + const int gridinBottomLeftAddress = gridinTopLeftAddress + grids_strideHeight; + const int gridinBottomRightAddress = gridinBottomLeftAddress + grids_strideWidth; + + int i,j; + + real m[3][4] = {{ 0.7500, 0.2500, 0.2500, -0.2500},{-0.5000, -0.5000, 0.5000, 0.5000},{-0.5000, 0.5000, -0.5000, 0.5000}}; + + real gradalpha[3], gradbeta[3], alpha[3], beta[3]; + + real x[4], y[4]; + x[0] = grids_data[gridinTopLeftAddress + 1]; + x[1] = grids_data[gridinBottomLeftAddress + 1]; + x[2] = grids_data[gridinTopRightAddress + 1]; + x[3] = grids_data[gridinBottomRightAddress + 1]; + + y[0] = grids_data[gridinTopLeftAddress]; + y[1] = grids_data[gridinBottomLeftAddress]; + y[2] = grids_data[gridinTopRightAddress]; + y[3] = grids_data[gridinBottomRightAddress]; + + dot41(m, x, alpha); + dot41(m, y, beta); + real target_yf, target_xf; + target_yf = (float)yOut / (float)(inputImages_height - 1) * 2 - 1; + target_xf = (float)xOut / (float)(inputImages_width - 1) * 2 - 1; + + real m2[2][2], im2[2][2]; + m2[0][0] = alpha[1]; + m2[0][1] = alpha[2]; + m2[1][0] = beta[1]; + m2[1][1] = beta[2]; + inv2(m2, im2); + + real d2[2]; + d2[0] = target_xf - alpha[0]; + d2[1] = target_yf - beta[0]; + + real r2[2]; + + dot21(im2, d2, r2); + + + if ((xSource != 0) || (ySource != 0)) { + //printf("%d %d %.8f %.8f\n", xSource ,ySource, r2[0], r2[1]); + + // get the weights for interpolation + int yInTopLeft, xInTopLeft; + real yWeightTopLeft, xWeightTopLeft; + real xgrad,ygrad; + + xInTopLeft = xSource; + xWeightTopLeft = r[0]; + + yInTopLeft = ySource; + yWeightTopLeft = r[1]; + + const int inTopLeftAddress = inputImages_strideBatch * b + inputImages_strideHeight * yInTopLeft + inputImages_strideWidth * xInTopLeft; + const int inTopRightAddress = inTopLeftAddress + inputImages_strideWidth; + const int inBottomLeftAddress = inTopLeftAddress + inputImages_strideHeight; + const int inBottomRightAddress = inBottomLeftAddress + inputImages_strideWidth; + + const int gradInputImagesTopLeftAddress = gradInputImages_strideBatch * b + gradInputImages_strideHeight * yInTopLeft + gradInputImages_strideWidth * xInTopLeft; + const int gradInputImagesTopRightAddress = gradInputImagesTopLeftAddress + gradInputImages_strideWidth; + const int gradInputImagesBottomLeftAddress = gradInputImagesTopLeftAddress + gradInputImages_strideHeight; + const int gradInputImagesBottomRightAddress = gradInputImagesBottomLeftAddress + gradInputImages_strideWidth; + + const int gradOutputAddress = gradOutput_strideBatch * b + gradOutput_strideHeight * yOut + gradOutput_strideWidth * xOut; + + real topLeftDotProduct = 0; + real topRightDotProduct = 0; + real bottomLeftDotProduct = 0; + real bottomRightDotProduct = 0; + + real v=0; + real inTopLeft=0; + real inTopRight=0; + real inBottomLeft=0; + real inBottomRight=0; + + // we are careful with the boundaries + bool topLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool topRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft >= 0 && yInTopLeft <= inputImages_height-1; + bool bottomLeftIsIn = xInTopLeft >= 0 && xInTopLeft <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + bool bottomRightIsIn = xInTopLeft+1 >= 0 && xInTopLeft+1 <= inputImages_width-1 && yInTopLeft+1 >= 0 && yInTopLeft+1 <= inputImages_height-1; + + int t; + + for(t=0; tsize[0]; + int inputImages_height ,//= inputImages->size[1]; + int inputImages_width ,//= inputImages->size[2]; + int output_height ,//= output->size[1]; + int output_width ,//= output->size[2]; + int inputImages_channels ,//= inputImages->size[3]; + int output_strideBatch ,//= output->stride[0]; + int output_strideHeight ,//= output->stride[1]; + int output_strideWidth ,//= output->stride[2]; + int depth_strideBatch ,//= depth_map->stride[0]; + int depth_strideHeight ,//= depth_map->stride[1]; + int depth_strideWidth ,//= depth_map->stride[2]; + int inputImages_strideBatch ,//= inputImages->stride[0]; + int inputImages_strideHeight ,//= inputImages->stride[1]; + int inputImages_strideWidth ,//= inputImages->stride[2]; + int grids_strideBatch ,//= grids->stride[0]; + int grids_strideHeight ,//= grids->stride[1]; + int grids_strideWidth ,//= grids->stride[2]; + float *inputImages_data, float *output_data, float *grids_data, float *invgrids_data, float *depth_data, + float *target_depth_data, + cudaStream_t stream +) +{ + InvSamplerBHWD_updateOutput<<<1, 1, 0, stream>>> ( + batchsize ,//= inputImages->size[0]; + inputImages_height ,//= inputImages->size[1]; + inputImages_width ,//= inputImages->size[2]; + output_height ,//= output->size[1]; + output_width ,//= output->size[2]; + inputImages_channels ,//= inputImages->size[3]; + output_strideBatch ,//= output->stride[0]; + output_strideHeight ,//= output->stride[1]; + output_strideWidth ,//= output->stride[2]; + depth_strideBatch ,//= depth_map->stride[0]; + depth_strideHeight ,//= depth_map->stride[1]; + depth_strideWidth ,//= depth_map->stride[2]; + inputImages_strideBatch ,//= inputImages->stride[0]; + inputImages_strideHeight ,//= inputImages->stride[1]; + inputImages_strideWidth ,//= inputImages->stride[2]; + grids_strideBatch ,//= grids->stride[0]; + grids_strideHeight ,//= grids->stride[1]; + grids_strideWidth ,//= grids->stride[2]; + inputImages_data, + output_data, + grids_data, + invgrids_data, + depth_data, + target_depth_data + ); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in BilinearSampler.updateGradInput: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; +} + +int InvSamplerBHWD_updateGradInput_cuda_kernel( + int batchsize ,//= inputImages->size[0]; + int inputImages_height ,//= inputImages->size[1]; + int inputImages_width ,//= inputImages->size[2]; + int gradOutput_height ,//= gradOutput->size[1]; + int gradOutput_width ,//= gradOutput->size[2]; + int inputImages_channels ,//= inputImages->size[3]; + int gradOutput_strideBatch ,//= gradOutput->stride[0]; + int gradOutput_strideHeight ,//= gradOutput->stride[1]; + int gradOutput_strideWidth ,//= gradOutput->stride[2]; + int inputImages_strideBatch ,//= inputImages->stride[0]; + int inputImages_strideHeight ,//= inputImages->stride[1]; + int inputImages_strideWidth ,//= inputImages->stride[2]; + int gradInputImages_strideBatch ,//= gradInputImages->stride[0]; + int gradInputImages_strideHeight ,//= gradInputImages->stride[1]; + int gradInputImages_strideWidth ,//= gradInputImages->stride[2]; + int grids_strideBatch ,//= grids->stride[0]; + int grids_strideHeight ,//= grids->stride[1]; + int grids_strideWidth ,//= grids->stride[2]; + int gradGrids_strideBatch ,//= gradGrids->stride[0]; + int gradGrids_strideHeight ,//= gradGrids->stride[1]; + int gradGrids_strideWidth ,//= gradGrids->stride[2]; + float *inputImages_data, + float *gradOutput_data, + float *grids_data, + float *gradGrids_data, + float *gradInputImages_data, + float *invgrids_data, + cudaStream_t stream +){ + + printf("%d %d %d %d\n", batchsize, inputImages_height, inputImages_width, inputImages_channels); + + InvSamplerBHWD_updateGradInput<<<1, 1, 0, stream>>> ( + batchsize ,//= inputImages->size[0]; + inputImages_height ,//= inputImages->size[1]; + inputImages_width ,//= inputImages->size[2]; + gradOutput_height ,//= gradOutput->size[1]; + gradOutput_width ,//= gradOutput->size[2]; + inputImages_channels ,//= inputImages->size[3]; + gradOutput_strideBatch ,//= gradOutput->stride[0]; + gradOutput_strideHeight ,//= gradOutput->stride[1]; + gradOutput_strideWidth ,//= gradOutput->stride[2]; + inputImages_strideBatch ,//= inputImages->stride[0]; + inputImages_strideHeight ,//= inputImages->stride[1]; + inputImages_strideWidth ,//= inputImages->stride[2]; + gradInputImages_strideBatch ,//= gradInputImages->stride[0]; + gradInputImages_strideHeight ,//= gradInputImages->stride[1]; + gradInputImages_strideWidth ,//= gradInputImages->stride[2]; + grids_strideBatch ,//= grids->stride[0]; + grids_strideHeight ,//= grids->stride[1]; + grids_strideWidth ,//= grids->stride[2]; + gradGrids_strideBatch ,//= gradGrids->stride[0]; + gradGrids_strideHeight ,//= gradGrids->stride[1]; + gradGrids_strideWidth ,//= gradGrids->stride[2]; + inputImages_data, + gradOutput_data, + grids_data, + gradGrids_data, + gradInputImages_data, + invgrids_data + ); + + cudaError_t err = cudaGetLastError(); + if (err != cudaSuccess) { + printf("error in BilinearSampler.updateGradInput: %s\n", cudaGetErrorString(err)); + //THError("aborting"); + return 0; + } + return 1; + +} + + + + +#ifdef __cplusplus +} +#endif diff --git a/script/src/my_lib_invert_cuda_kernel.h b/script/src/my_lib_invert_cuda_kernel.h new file mode 100644 index 0000000..215c675 --- /dev/null +++ b/script/src/my_lib_invert_cuda_kernel.h @@ -0,0 +1,66 @@ +#ifdef __cplusplus +extern "C" { +#endif + +int InvSamplerBHWD_updateOutput_cuda_kernel( + int batchsize ,//= inputImages->size[0]; + int inputImages_height ,//= inputImages->size[1]; + int inputImages_width ,//= inputImages->size[2]; + int output_height ,//= output->size[1]; + int output_width ,//= output->size[2]; + int inputImages_channels ,//= inputImages->size[3]; + int output_strideBatch ,//= output->stride[0]; + int output_strideHeight ,//= output->stride[1]; + int output_strideWidth ,//= output->stride[2]; + int depth_strideBatch ,//= depth_map->stride[0]; + int depth_strideHeight ,//= depth_map->stride[1]; + int depth_strideWidth ,//= depth_map->stride[2]; + int inputImages_strideBatch ,//= inputImages->stride[0]; + int inputImages_strideHeight ,//= inputImages->stride[1]; + int inputImages_strideWidth ,//= inputImages->stride[2]; + int grids_strideBatch ,//= grids->stride[0]; + int grids_strideHeight ,//= grids->stride[1]; + int grids_strideWidth ,//= grids->stride[2]; + float *inputImages_data, + float *output_data, + float *grids_data, + float *invgrids_data, + float *depth_data, + float *target_depth_data, + cudaStream_t stream); + + +int InvSamplerBHWD_updateGradInput_cuda_kernel( + int batchsize ,//= inputImages->size[0]; + int inputImages_height ,//= inputImages->size[1]; + int inputImages_width ,//= inputImages->size[2]; + int gradOutput_height ,//= gradOutput->size[1]; + int gradOutput_width ,//= gradOutput->size[2]; + int inputImages_channels ,//= inputImages->size[3]; + int gradOutput_strideBatch ,//= gradOutput->stride[0]; + int gradOutput_strideHeight ,//= gradOutput->stride[1]; + int gradOutput_strideWidth ,//= gradOutput->stride[2]; + int inputImages_strideBatch ,//= inputImages->stride[0]; + int inputImages_strideHeight ,//= inputImages->stride[1]; + int inputImages_strideWidth ,//= inputImages->stride[2]; + int gradInputImages_strideBatch ,//= gradInputImages->stride[0]; + int gradInputImages_strideHeight ,//= gradInputImages->stride[1]; + int gradInputImages_strideWidth ,//= gradInputImages->stride[2]; + int grids_strideBatch ,//= grids->stride[0]; + int grids_strideHeight ,//= grids->stride[1]; + int grids_strideWidth ,//= grids->stride[2]; + int gradGrids_strideBatch ,//= gradGrids->stride[0]; + int gradGrids_strideHeight ,//= gradGrids->stride[1]; + int gradGrids_strideWidth ,//= gradGrids->stride[2]; + float *inputImages_data, + float *gradOutput_data, + float *grids_data, + float *gradGrids_data, + float *gradInputImages_data, + float *invgrids_data, + cudaStream_t stream +); + +#ifdef __cplusplus +} +#endif diff --git a/script/test.py b/script/test.py index 6544b18..f153e41 100644 --- a/script/test.py +++ b/script/test.py @@ -41,7 +41,7 @@ out.backward(input1.data) print(input1.grad.size(), 'time:', time.time() - start) -with torch.cuda.device(3): +with torch.cuda.device(0): input1 = input1.cuda() input2 = input2.cuda() start = time.time() @@ -62,7 +62,7 @@ out.backward(input1.data) print(input1.grad.size(), 'time:', time.time() - start) -with torch.cuda.device(1): +with torch.cuda.device(0): input1 = input1.cuda() input2 = input2.cuda() start = time.time() diff --git a/script/test2.py b/script/test2.py new file mode 100644 index 0000000..48fe94b --- /dev/null +++ b/script/test2.py @@ -0,0 +1,53 @@ +import torch +import numpy as np +import torch.nn as nn +from torch.autograd import Variable + +from modules.stn_invert import STNInvert +from modules.gridgen import AffineGridGen, CylinderGridGen, CylinderGridGenV2, DenseAffine3DGridGen, DenseAffine3DGridGen_rotate + +nframes = 12 +height = 64 +width = 128 +channels = 3 + +inputImages = torch.randn(nframes, height, width, channels) +grids = torch.zeros(nframes, height, width, 2) +depth = torch.zeros(nframes, height, width) + + +input1, input2 = Variable(inputImages, requires_grad=True), Variable(grids, requires_grad=True) +input1.data.uniform_() +input2.data.uniform_(-1,1) + +depth = Variable(depth) + +input = Variable(torch.from_numpy(np.array([[[1, 0, 0.1], [0, 1, 0]]], dtype=np.float32)), requires_grad = True) + +g = AffineGridGen(64, 128, aux_loss = True) +out, aux = g(input) +print out.size() +out.backward(out.data) + +print input2.size() +s = STNInvert() +out, _ = s(input1, input2, depth) +print(out.size()) +torch.sum(out).backward() +print(input1.grad.size()) + + +input1, input2 = Variable(inputImages.cuda(0), requires_grad=True), Variable(grids.cuda(0), requires_grad=True) +input1.data.uniform_() +input2.data.uniform_(-1,1) +depth = Variable(torch.zeros(nframes, height, width).cuda()) + +with torch.cuda.device(0): + s = STNInvert() + #input1 = input1.cuda() + #input2 = input2.cuda() + #depth = depth.cuda() + out, _ = s(input1, input2, depth) + print('cuda',out.size()) + #torch.sum(out).backward() +print('done') diff --git a/script/test_stn.ipynb b/script/test_stn.ipynb deleted file mode 100644 index 84fd534..0000000 --- a/script/test_stn.ipynb +++ /dev/null @@ -1,477 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "from __future__ import print_function \n", - "import torch\n", - "import numpy as np\n", - "import torch.nn as nn\n", - "from torch.autograd import Variable\n", - "from modules.stn import STN\n", - "from modules.gridgen import CylinderGridGen, AffineGridGen\n", - "from PIL import Image\n", - "from matplotlib import mlab\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "%reload_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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BvfdnP43O839/P4eNYyhjHmLZHl5TxjOmBJIunP+L6YJ1YqP/nnWy7uvsskRM/VeeRUb/\nXS/1eRgOnbxZQzDTTM84eTa2lDebFlCQQEotdz6+zWvf+P/44N03ePHoBi/cuMWiCogYIRgasqQg\nnsFQFagEQkCiYQkaM5p1w+psxWp1Tr08BglgtpUC1szH0ZkzdyQlOoSR2aDtDUv1NeYwuAxNtdbb\n54u9f3DiE2FQF6vmp+ei90uAsaZhfOXoJU/2Ve4tDGZaSzRFu95Tf3zMtGT092B1FL+GCzRIn4Qv\nw27q35ntOF6iW7ZGOHi0SVPBlcZxONyYAcFMMz3jJCIEqcBcCZgMRIXTRye88/qrvP/Wt5G44uat\n7+F4eUSlhmqLqnhuAlWoAlIpQUGCIhIAw0QIGJEKSTkRkRma+xI8wiANbNzDDIX7gMBFiYGKh3Vx\nlhqrrg/JRLgLkBysHi+MfgKZdJJccerKT77xDKPbhu1fJo/C8J6hw2AeIp6wZ2czwxaZ3vx3MYQi\ny3of43uGCYSmkyBdlQH3mTD9/j4h0cZI848N8LDhBFlo24v+SeSEuBztZsKWT2+ZB0a47dB1fdn+\nd9EMCGaa6XkgwyVzKwxizb07b/P2a19jde8eL914gXpRE6oqJzDKzN+VCVRVhQYlSADx1MSiAijB\nhEVds1zUqDoTUPGCx8X+WHIgBNNunylZDOF6N11nuhcDg33nLwMm+k53DCaf3MVm9+VjuAyVzX9X\nsqFtn4HNew97B1MPKj7+0eHd2e2uw8p9WBjc4bRbY/F0tQXT9Lg1EZ4U2JkBwUwzPQ8kCTPFTAka\nOTv5iLe/81XufvdNjjTwuZsvsVwsCcFzFCiCGqgG1xQgVKqdU2An8Zu55KsCQfz+DCjUMhCgbDzq\nYVApbUuzZYMqw2U3oy60czOz/v7hffvaugooGLezpeAdMvk9KupdNvRxmCNQdAyDK3cxsX2b/b57\nps/vbm+g8dm676Lfh9Gud3690Ry2G9Q9jzSFb4aYzfp/dut+Uoln4dCJmQHBTDM9ByRZojcxUnvG\nd998hTe+9lU4fcjnljdZ6oI6LAjB6xkE8eItYh5qGEIgqDrfVj8uAikHwZklRHrmlvKGVJiEDnLG\nppQ6kLBrgy/b0CGSTJfZbaAmPSTK4KB5u8CEMTz+pCTJ7Tko/V0k4W1v5uVxdmsKLg8khuPcvPd6\nAMFMV6Ty6vdZgPa+Ei86ZWw7/e6iGRDMNNNzQCniyYRoeXD3Pd569Q+5f/sdbiIc10s3EYSSQ8BA\nxasgqqEqBHVpX0UhuMlAk2FJiGZQBSoJ3ldKSEpES51PgZjbe4uK3FP/7qGRGv0y0vq+lq+LaR8q\n2Q9OdCLblJ/AsN0p6q4rxuPR8c0kQKWfod+Abd23WxswcGrbI5FfPI9jyXLs7Dfd/0z7aQgLD9KR\njC1bo9+7SbpXNuchmGmmTx0lVud3+e6b3+CjN1/jKCk3Fkd4HeOAqGQe7BX4NND5ElRB0KBuSlDB\nNEA0UKNGkaqmripPXGTmBZMsEZOBRcwiapsMc8wehlvOpHJ6n9rfBtKtZNX6Dl6zS+twab+Bx7zu\nsiR7RL5N5j+4o9MITN3j1+wf65S6eN8c9UWQ+j6nxnzR297V/rNnz39adCGLtu1vLH8ew0v2tO8+\nQpexzMyAYKaZnmHqmG8IkM64++GbvPGtr3Ly8fu8WB2zqI9oFbQCk4QldSdCFeqgVJKdC8XNBjqI\nD4/QqxPzpiGqiCpIKZs6kkhE/Hwe2669pjghjmksXe9l4FnCuQwDuchEcZGkZIPrShud+WM8ngk+\neUj/m6aD0sCU9N3ddUWHQtkBGvaZSMbXDaHewTLtXnoyDnHDcs/PNhXz2JZT4Y6pvfqMD9/drCGY\n6ROjXRvZYzQ1aq7fimxwkW7dsMdSvXVEuiTh01c+1tPsacSwvBm4T0C5vpeWR5uyASTW5w94541X\nuP32a3C+5ujmC4RwhC4k5+UHFSOIUItQk00FoqgGT3OcIwzcEmCkZERLxBRJlgiihOAah5ACyYL7\ngye3Qoj2krul1IXsdWp+g85jfTTlNvHXeF4mbruST8F1SKKd+WLQjI3+veh+2GR8/trLe06bsQuy\nHdb4OLS7nR587DM9PC4A2DR3eL9PTjvw5LQOlx/z7vUtbAKBDcNL0QgM19u+vjfm93qefwYEM/V0\njXz9+qlk3cp/C7Cl0txSsPV/FjVo/k+tO8FTf/Au5MvHbOax/5AwS15pMJ3ywduv8e43X6G9f8qt\nGwviMqECS7uBJiGEBg2RShNKQ7CKSoWqVkL2G0gSSBIwKZUIDTFBpKaNQshTKxqoqYgasWSICgG3\nTmDud2AFKQBdSeTCQGVTVb0loRYsVpijQEoZEmg+1oep7wUF033sl0SnchhMaTw6LQnWLbeutTBC\nrBeBky3gqeMDe8d90fPsOjcxIoaD2aU0GVfI2xUOWdra1mJcBj7tp4vzO4z06we0td3G9WkvOuOQ\n5EqVZWhlGdj29d7vZUwCGVxIgdJTlEb/7qcZEMx0ZeqXoFw/K5Xxj6kexox8zODzsc7BrQcI27JP\nf+RanmVvI9LFIYsJiJBwhijg0r5AMncKBDi9f5t3X3+F+x98xEIrjo6WhHqJEkpDIIbk3AGqIMEd\nCYOETtUvQQiqGAENirUAgbhcEkoeghyNYJqBifbq2ByTsFnhiG0oduDePNMV6FCmNTb3XIauV5J/\nJiWMa6FdosTUb73GKe3B60WN2uC/i2kGBDM9W7S1d2QpbsD0JXvDl7J/HfIWvy51RcZTPpkZmliu\na9637IxLeAKQZifJxh9j1S2YiPPbbO9vmnNuv/UK73zn68SzB7ywXFCFhdcp0CrXOXBTgYozcRnk\nE3BnwzxXWawokgtVIBJgUbFY1lTBExuJbM5JkUByV5hAytoAl6InbO4TUQYXM7Hrty9fReK+fB+b\nzHf4vCIykq59hvpjY3g6/N2v0t2MdQBmh+/Bpq/Z3d+YvW1fNz3m8d8Mnm9by+Bj+/TDRdnxdxFI\nJrUERX45oP1Dr7sMzYBgpp4uubqutBgP0c531wyRrY7+3m6ks9GV3zbYqAQw9Yp/HcjY7HLTA+Hi\np7vc1btaMCAhRYrPIKGYDcQid+68zxuv/D5333+LYxGO6ptgwZ8ngIghyZ9PxdAgaAhZI6CgimUm\nb3iOgY5hiziw8njFHjhA9jLP9+C5CkLWauTuOutNqeE+Zi3dk26AgavpDz4JD/VD+phkagXMjcwQ\n0zTQrXVAYsoMMDw3nNkxAx7PaxmD5Xe465rN9MP9m9x3zzbj3xz71DWfXrrKd781K4MpvO4ZM+sj\nVQ6hGRDM9Hh0CIN/LNreXHyTHG9a/mdxS3Nellwlb55IR7I0iwkmif12teGGOTj0OM85OVcGZkiW\n8BMl+52hYpyd3Ofd11/h/Te+TWjOOT66RVAhiVehs5hQcqZBBVH1tMOaTQKiRAmo5DwFVcA0YEnd\nLo6RRDCtPFRBhpCqWD79X8mAq9PZZHOH4epQk77+gbAvs+C0xfNQZnxRBMFVQcPjZM6zHfdPtzmW\nujP4EmCUj2C32n9qMdrgv15jUADm7numzm1BuskxlNTWDjzShqZr+57eD2gOPxzQE52GXETswD5m\nQDBTpl0FTg6ji5SecAleKlPjKZvJGAj0DGvrt1neYDX/7Cu1SRFtLzOuHZLtBBsYjXH7imHeHsmb\nteUDLqwnUnzEB+9+m7df/QPO7z/gRrVgITVqECp3DLQ2ucNfHVzQ11K4CBDFJGDZkVBUUAl01RIt\nYWpEUUQqTAKC1zEwEYIIbQYXWEKSZg8CGSlvMvPBNnTnY8e9jTTHO53DBrN4RVX/42Y6nGq/853Y\n0Z7KZhrjix3/hmaAvvrd6A56pjsGwrvmYaqh/WGLPW2202OZwvBLv6Wf4bPtZzqundKNLj4r5oPt\nt7E9VzI8d419F43joVh3BgSfeXrc2N1OXtw4us9+NrxTtq4oujMl68G3byKHu0mpmGYgJZNezF9b\n1hTQAhWCYuU+Qh7xyORgguyrODI+N2Rwg6H31/X21sJQyiY4/EBNXGuRMqMQBbGWh3fe5e1v/QEf\nvfsalVQs62OEAObFimIGPMEClUFQNzt4jWMhZQfDAjSI4sWNQtYGAGLRcxxowCy4eaF7tlzoSAOd\n8SD3aXk2gc7BUGQ7U/90Vj/joh3qEGn9Kl73182ENpn1oWMoeRy6I4OzQ0A+1NYMmfrmNSJh1MfY\npFbeinbnezAz7GN8T398WI1w1z1lHJvjjP2anwBVzztNmohGW9aWjtP2CCH7zl2aLg8vZkDwmacJ\nG6AN4uMpzN7yZQOx1qQLxZLhXpGbmVIZWP5RlMtW1NFFepfxplM2luH15b84+pdsCihMa1i4RRhq\nGDxMLozmYqiuLXNR1MG7oM8ElWe1fgQ2PNGBHh9TAQpieYOxSDy/zwdvvMr7r79KPLnPcXUMVmVf\nAyW1DaJCFQK1KIGQQZJhkkgEBC9jTN6cDSGaolJThyVVJXSal/qYqlpmTYr1/5a3IUJS82yFyBaO\nHDsQwuFM96LMg9eVgXDXPReBB3BoeZm99aoq8asClqnn2WxHD9QSXC89jhnmeaDx842h0pTl5bq1\nALvJsiZya1Q7aQYEn3kaLxTrmagkeo6f/KhlCTI7wmXOl2/tN5/SVteH+KZqRYW1ETZjSCdvFqap\n3bnC9DuPZWmBBlKW6CWCQbJc3ctasIhYQLq2FCxLUwiS1eD+BZeNNDNnG0p9OvjoMwPPH1n/ofmY\ni/9Ccd7bhA5FcsrRDnkuffqka16lJbYn3Hv/bd77zrc4/eh9jiyy0BqV0DkLphRRqQghe3OLuWpf\n+7lO+SF8lJFkFZVI52wYwsJ9DaolHL1IWN5Cg+bogf7di0iGStY9h+RMhPsiB3aaDA6kMSC5Cii4\nTlv1vvj1i+479Pp91z3O/A3Hcp108Zg+OfZ3HXSVtbLPkXSsHXickse72t5FQzPPoTQDgpkoqmEb\nin3iTmRFwCzsrvPEF8PT3bnUXZjFUC3mjEko6k6XR13KNIu4k1pLUfELeFlegaFa1Bd+kd4TYiuI\nDRYz09WEpQSpgJYWSw1m2vePq8XRHhBYAR1dV0oSQ/MzxWSIVDnLn5ce9i+6T+1rxb1+pAfotCHm\ndQH8ocvUePlgj0sOpIIHJCLpjId33+GNb32V99/6DnZ6znGgTwoUwDIIKPPZSqIKSoEhliBKluI1\nokmwqARRByRlbnQJy5tURy+gy1todROqCs917EOuJFDy6ZkIlhKSfDVo5e1ZitlxaZct/HA6NJnQ\ndbQ5Pv4kHd2m2t4+dvW+hyGO+/q8aEyXG+9u2ghPvGBN7AIpz4pvweXWRd6/xnM3OHQFPj05Jm9s\nuqGrzt21AwIR+RXgV0aHv2lmfyqfXwK/CvwVYAn8NvCLZvbBdY9lpkNo6IU6/jC1Y9at9WxPMEjZ\n0ajYIcXc9l3U9QKluK4UZyJJeCLcFlLbMW6ISLlHBmaIwUhMxIvxEEnxHNomKwbUtQQx5cx72bAQ\nGxDt8ugJwav8iTPzaFnyzwDEUsKCfw4xc3PRCqQmaUCsRqTyOdHK7eZZA1FgQEkiZAw2aDJMSoJJ\n9OczLydcPAtCh5ZWnNy/zQdvfZMP3voW5w/vohi1LvoNYABewEMIVYSIR1SElHz8Kt27ILUo4s6F\nIaKpRVJFsuiYQbzSoYqQ1J0JVYVkCbViO3ZfBMlJk0QchDkeGoGhwbu7jAr8caMMroM+ae/3zf4e\nj1Psi3J4kkBnV/sl4uAqNSmeBxrmmJC8h5bInU5Hml9pp7QvfHzUlgz+MDtE+reuXdi1bi8/309K\nQ/BHwM/Tj7cdnPt14C8C/z7wAPj7wP8G/LknNJaZ9lJesd2elJlkCQ/Khw3DNCeoLQsxS6GuWReQ\n5AyoSy3ceFpcXFVu1kCKpNRCTKTUYGmNWERTIol5MiHc0z0N9GsJQSwhMULbQoybqrpkXYKeaENd\nh4OBwszcbGGIBvdb6ACIA5dug5OASYXoGtGAtgtE61z4p0JCDRJzoZ/sm2Ap99H3rCIQnPnHjqm6\nWcLzJhmKS+2rBx9x9+3v8OHr3+LRB++T1msWocKqOhcl0s4MIVJMNrkv8w0h4gzdELDo4YXq21Ei\nEjrmnovp2FZ7AAAgAElEQVQdxhaJDcQWqoRqSW4EQiBZRDurkL8TL4i3aQ7JKGhzZxuvtDK3dA1u\nnX8c4HDV6IOr0LgtmXikQ5nxhrMll5PaDzl3nXR508ynK7ywT/q0+W+BckUI2LqPkWZghP1s+9B1\njJbLRo09KUDQmtmH44Mi8iLwHwNfNrN/nI/9deAbIvJnzOwrT2g8zxSVl3+1O2FSRT1q00ZHd/dX\n7PLSXzVwFtSseUZzedYu133uWbNGwIrkH8FSx+SI+ZwlLK5IbYOlhtQaMbWYrR1ERHD+5lK0S7S9\nCtoBgbkmobVu2B4K74BAzJ1oIpGUEgwYqFFs/hkQiHja38Kk1YGDqP82CVn7ENBQE6VBQ+UmhOyl\nr6FCgjv6USoASnJgUYwsmZG7PV8Rg25UEpG2xdoVzdl97n33DT5+/Vs8ePdN4qP7BEuI1iQClfZg\nxRUK+e8slSAecqhSNB/9M0s2zqiE7r/M1TFLpLgmWIPREsQ8dDGvIckaJPFqSJDSBuNzyWhz5W36\nThRoNlBld+st37GHye06f1XmeKgWYtzvRWGHh1Df7jBMtifzk3lqps0J3Rq2/ljf/i4N8tDzfwg+\nNl7DJSTMfc9czumg3z2XP2u0b3MueHfqeTotwBXY+kgmG3YxtbdneDG6anxHoYTZ4ev0SQGCnxSR\nd4Fz4J8C/5mZvQ386dznPyoXmtkrIvIW8GeBzwQguCy5ampc2W/4t0wcO5yGXqi+4Es7zv3FXPgX\n1Jly9i9wHZmrpEktZpnZR9cEaEyINVhqSTGSWj9nqSXGmG3POWwws9HyPFLMEEKG1nlRi6f19ZwC\nPmBRy3HgZAbXIjFuSOtDiFT0ezF73qM5vE6qDu0k1eykV2OpdXNDDCA1qarc0S4sXWsQgl8bFLHs\naCmucbDoYAbJoXtmXmfAErQr4uqM1cP7PLjzHh+8+S0+fuc7nN35EFudIVKRJBFTcdwrUkkGQqMV\nICIO5sjzkAKacwQoAWWgYTDrtTUWCWJUljKgC25awfJ1mQGZQRJSSogyGJOVheR/iwyWYg9iLtqT\nPilJ8pM2CxxKQilS08mbG2e3j+2iIRsZ5vOwHefGK2l4fHMMPZiZau/Zm9PrpqK03Io1kjJzuZLp\neEphE2wces2Oy82yv9QGQtnFHw5/L08CEPwu8NeAV4AfBP5L4J+IyM8CPwCszezB6J7387nPBG0t\nmCna+A57RllOba4l29o7+pxxww+40CDHf9n4pS89K5KlQXoP8lqKP0FCLPsAJMPa1pl+syI2Z8TY\nYLHtGI21EfcVSNBGUmwxa4kpbTA5RUglaqH4BZBhQpdvABJKK3QfYPk/0d4XQhKEaJg2bCQzSgnJ\nz54sb73qef9BScQuhS+oS//BQFpI7n9gskat8nmLLYQKi35OtYJQu2pfSvy/RzaIemIWSSDi/hPp\n/Izm5D4PPr7Nx++9w73bb3Jy9yPWp+ek1iAYWgtaDUwZA5Vkp2pORhIvZWwSCRFiAoIQ1RMKqeLO\ngAYpJVKMqBiWXJuSUvI5t+jvVsPADyJrOkzdVKG+8fnK8ggRB0B5ZdpQGt61qIfLe0IiHznHjc+P\nr9l3/6He9heFHXbapql+bFPaHl637byoG78Hd2x82L3fmJUPc+vc9njGYxjeEwbHjXHYba9FKMen\nNqpxnoTyDevmeAfnn7QfQzeuUd2EK9FwXidOl6Jkw2vGuytkmWPUpDG4d9dUHDRFuZ7LDk1T32tZ\nb9vrYhddOyAws98e/PwjEfkK8CbwH+Iag5kOpE3MbRvHy1+FqXaIdePF71ld3XVx46aczLazd/mW\nYK5ctxZLayw2QAvNmtSuSO2aZnVCXJ8S20iKyZkr5tnw2kiKETEIpphFUsoOdtn0kEQ7UOKq/g6K\nIwTM0+y4+iurvwV3qkMlq+4hpRZrU3ZlKCwqZru9ZZV3GmxS4kBBgqv7U/YJkEiylhAbRNzz3nBN\nQAqt96kNEhWTnAEwLED7XAFITkakimiNWcCSodaS1iecntzj9MGH3H//uzz64DYP79zh/GRFSopJ\njWiNhkAI/XZjZiQzwsY7c4ATYySEQEpGqAqwKkloBMvSPSkRoyMnU0OSawtiikj0ZyseJJ6S2LMb\najCSNCTz9+r1DcTHex2b8ROioUZgX4jYs0qS1SvPpvzdaxefPl2vlqJ7qm4fZMt0tPn0lrOKjO4v\nvw9V8BxMpbDyEHJMfYeHv6MnHnZoZvdF5FXgJ4DfARYi8uJIS/D9wO2L2vqlX/olXnrppY1jX/7y\nl/mFX/iF6xzy06e8aHpv+/FCHy852fO+hyi+LAylCxHDOtjqXvqR4ilLZj7uJrBCotu7U2ywuMKa\nc9r1Gc36jLg+z+AgufRtdCGLKSViG1Ggysy9G3MHAlLOMJw6uzeagU4O94t5uCFmhJxV/aggKbrQ\nHh1sdKrxnMTIzFxLkc0cyVJvihFF1D3upaT2JSIhYVR4yKJCUJIs0KpGgiKhzaWKPSsg2kABA1r3\nZo/gqYFjBGtbrDlndfaIh/ff59Hd93n08Yc8vPMhJ/fvcr46hwihqtGqBgn0znoDMdTcNtjblD1v\ngOIZC0MIVMFNBu4JnUA8N4OlXKlQWggNkhpSbEltA7pwh0uKoqTc78eimjsjZgBiyQFlSpLNP49H\nT0qaLKBgHxB4uhEGF128//RIQejH9rR9kWPmRffvo+uMnOhpLFpP6NUfq5/99w5LF2+o7tl0Irww\nQmBq2Fekzlogm0d/5x//Lr/zT35v49qzs8Nk8ScOCETkFvAngP8R+H084uDngf89n/9p4EdwX4O9\n9Gu/9mt88YtfBJ5PpL+TbMD8u3VvRPM0tgHXAXQqorI6LSurugQyWQpKZfPLDGRj5WQ1XpffX5CQ\n241rNK5IKaGpBTy0zDDIPgBxvaZpV6R4TmwamtWKZn1GatfuMGj0qXKzodt9BRJtMtpszhC8CI8/\nS54ASTnToN+vQTofgq5qlxQ1qMfbJ6EHEJIjE3P72XpPB4jMXJduCSxm0COdWrtLLZy1FSLmwf8S\nctXAgIYjoi0Rq5AoBCQ78lUkaXN0hGBaYfQliM0isW2IqxWrk/ucPrzPg3t3OH14l0d3PuLk3ses\nzh8Q28a1H+KRBUFK3YDo2gcKJijFhgzFx1GpEjQXMRJBQp8B0XGfO2VKarO5wNwUYg0ptW42MHFz\nhbXuJyB4lIXgSYykIkZ1cKV9kqaeegnlsgzlceLm99F1Jvt5nHEc1PbuTikLdavn4ruRT3Zf+icM\ncMZ9ddrLa9iiO4Nm96jl9whw7HzcfcBhqIe1DZ5dHHaHj+Bd2nDKByc3QcEh83/YOxoDrIm5yNf8\nm//Gv8rP/+t/prtNBN589z3+o7/xn1/Yy5PIQ/D3gP8TNxP8EPBf4SDgfzazByLy3wO/KiJ3gYfA\nbwD/z2clwgDYCyHLgvQifZ7SN4lkD/rMpKxI8CUXQAYLQg6L81A2K0l0kmwYksxKpkDtN5p4jq3P\niOsTrGmwuHbnPwBzlb+1LU3T0jRrYjwntpG29d/WNNkOnaVW6zUOiJFSyX/vzn6BbGMvjoPRuucv\nC9tUCVUgWXHUUQhCEmjzfBQFnaXCzB0cVIRe8yH5mZO5FJsMM4+IcA2G9VEK4smYggiVBqK6gyEh\noIsarSJqCcw1ADlIEhWl1toTAmkgydq1ClJjSYh2TmxOOH/4kAd3PuDe3bs0Zw3r81POHz1gff7I\nNS/EnI0wjx2vP1Ckb4+GoJvnbBggKFQhdABAUiIl1wz4nqFYDnVUSa54yLuX40YPp9Sca8GSkWic\nuVgFWNY21IQyvhgdW9F4aKIUoJbggD2uMKyreervlm4Pbe9xJOQrRyvsbV8m7PA7+hztIVLMhyP+\nuK+/QwDYkKY0LONnm2rvIs3MYbQtoh8OePooi5IbYbLdQdswmq+N4/mnDOs39ucuGMrGKzp87W8C\nmn46jd0fW+qvO7CfJ6Eh+GHgHwIvAx8C/zfwr5nZx/n8L+GG6/8VT0z0fwF/8wmM49mlsZpngEBN\nsvXcjCDQp5DNCWzMNQDgjnQEsmRXmGmb8wm525dv5lVWvTtDNNocHgeCkdZnxNUJqTl3QLBqsHZN\nskibVevECBkAmEVSbEgx0caG1EQstlhqM5MGSeZ+AoOqggPM7etTwLKvgaQ+kU+eEjQE2uBOgaae\nJEdFSdmdLaU+FwFd1IHlsLra/Qsy4DAiElP2IXB1t6TWGV/xRiyVEQWCCi3qUnBQpAqEdkmoE6E2\npFpm34AS1gem66xRqNDgrDrFBmsSbbvi/PQej+7f4+HHdzl7dEbbRNr1mvV6RduuN5waC8P3p0uu\nxi9gS8U1D4Yzac19pdRV3TPzqTfBsxsiIFX+z/MlhLBAqiOCLqnDkrqukZycSTS5GcQ2E1ErwqKq\nSBgxCC1GI4nUZKdGRwgXSqh9COV+hjV1vJw7NFzxIqfE61GNH3b9kJ6UWeQy5z8JLcKzoMUd+5Ds\nok6ZOj72WJ1vNjRsfv/85494orFh/oPt8z2D8b/2gYZNehJOhXsN+ma2Av6T/N919PdMLLjHpQlZ\nwRlZtnublup1ZTGU8DK3nZN8247ZXt7fnzdeRxIYbbaXr7HUILGlPT8hnp/RrM5p16fYak1qGhLJ\nAQF4zqHYkmLyPi1i0f9LbQNNCSdsHQjEDAgyQ9Ycp4/1jN8skcxBBMXG72zIZyAoWuViPmoEK4mR\ncvx89o4vzwsu6UoIrFQR1ZwFMGf2bxOW8yKk1Hax9R3ogS40z53xEmhAQkWoA6ldYYuItYm6brAQ\nMNXs+yBEDRC85kBKijUgEdI6cnb+iEcPH/Dw3n1WJytnoOvE+mzF+dk567XPm2idSxgXUABINgFI\nCY/0EM3hsW5jKZqTDuS441/RZiiKUQEBT8tcoWFBqBaEECCETvrpHQVzdIL5uxFVgnoOKY9RibQp\nkbIph6K3PpB2Map9569CT4L5db4J/uNCzcUQpFwuX4J/8920dBaE8f3bO0k53jdnW+f637bz+MBq\nMepnfI9snPsErRYH07R5g177wOZT7KJdaX/KLNigzWH7h30fV524EfC8xJ1zLYNnibJtSjBMPN5e\n1EFBa9mOS1YbS8aP2dlLu5VZYs1zvH5mKoIh1iLWotYg6Zzm/BHt+px2dY6tG5qzM+Lq1NX/sSVS\nSg5lJ8EYsZTQ7JRHbLOT3BprPdwwtg1t05Ciq+ZLSuMyvkRmNIkuDt5i9LIIFjFJhOwsJ0Hcy14r\nUvDKfQ4IvO0YowOQDgBJV7iHEDCBSnoQlaIDFcxD7VwaMOLAQ95l7eBSenBQoUFJrZLaGmsTLFo0\nrj1bYcgpkXOdBE2RlEP2iJ5wab1a8fDhfe7dvcvq0RlEI1AhMdKcn7NarWjbxoGT2MbnrJKTJuWk\nQyriGQVE+kREvQ51S/oggxvXmmQzjQZPrKS1L7DgfhJQnAn77bBoKfqQu0TMKY3dtyIRqqrzEzHD\nzUs7ucB0NMJVmPUhoYWHaAX2Odj1kth2YqKtcQyEk0OfZXz9kJ1ujmsXo978LUXE7dLilX+Lmrmk\norbReQZtCX24MhvX9KBg+1zfJoM2tp541N8nQftZ+6SZ44IWx7Bnq7shJh5f+EQffwpxHApvZkDw\ndGjXuxmsoGRGmxJBFQ2eejfiEkYlOc3uoKnytyL5qxVMKpKAh+y1JDsjrVaeRGj1iPb8lKY5p00t\nae3hg+3qzBm9lMJA1knhzuRjp3632JKaSGocFMS4pm0bmvUqq+RTr+IuDo9ZSkopefri2HRahaIh\ngBw3L0JVVWiosco8ht88D4KZkVqjzcmNRAQle2BqlTUSiTb7FXTjN8uhf2W+rfNRMHG+qKaIVp6q\nOVTOLyvQdukaktiSYkMIS6SqsUVAtcJCRQiRYJX7I6yNZnXOo5OHPLh3jwcf3aNdr1GtqMOCdt2w\nWp/RrNeYedpgUm/jFBEPbshx65r9I8YasaGWgKwJQHK9BFF3MVFFCZSsip6N0fMniAltMjQFailg\nU1ArgDCDA/P/cw0LvjZVc7rjwoyKqWNi13uCiryrAoon5hx4gLZg6xyX4BVW+oDO/lZUOwz/5TKt\nTtBVXtpedsnTAQU9Ded+8v1fJMVfNPwxL74WKs7W4wYvpXK4kGZA8IxQ/1qFZM5A68q9uF2WjQSM\nIJ7Pvzk5o12tu7tch+gSM8lz7OuiItRLQhBPcrNekVbuDGjtGlu3WVJPWITYRLwcQJYUNfhvSWAN\nHr4X3Z8g/yuxhdiQ2si6WdM0K5qmccCQGXUvQbnEkFLOTWBt74eQtQ+ejd8lMlVB60CoarqE+smz\nH2KJFPEER2THuGx/16C4tFvyKXjJYc/Chzs7mpJKHKP4bxVBKskOjwpVTdBEDIkQAxpLZmbDYiJV\niWppmNSk2hmliTsbSjRs3XJ+dsKjhw85efiI9fmK1fkaZE2l56S2pYlr98mwhJh2jnmYerGo7Dug\nphvfvBYb/I7VlPJzOo/IiY3UQZ55A55VreRLEM15BXrmIqo5rloGCZtkIFjG7p2WbJLFr2Mc5teb\ndYKvowuk7euy9++y2w99GMZmxyknwGFiokPGcREYmPZzsK3yuP24ClAc6LShA2E2Ygz94/icD9Ml\n92BFB9eMtQXZZNb1Xc6VBEQpj7974uGouzZ22cA/7dS9Ddtk2eX9Xt8sDLUym9Q7PO6+ZkzPPSB4\nLv0H9gBo6S7QnGHXX2YtIDGxOrnPve++w3e//RoPPvyY9foUsxyqpkqMa5r1GjGhPlrw0uc+x0vf\n83mOb93k+OiI4+ObHN96AakWWBuJMaJdyJyStM5z6sl6kuHV8rRFkqvXLWcjtHVLaqP32ayJTaRp\nEk3TZMa9GXJkKQOKZJ68KEVibIgxZd+ElpQatEyQCCFWhKolVOphhh7Mn8PksvlhIFG7D4G4U1+M\nnQQlUnwUsvo7GZbLHHup5yztRiVmFb2ZkoJb3Pt9OfjzWQYdUhGy1K1VlspjIq4amrNzTk9OOT09\n5Wx1ThM9tC+ZEcXnsbU1SYbP4P+qSufP4NEY/nGrjXLFWZHce+nBN6Gs3bHsN5BNJ6JlnuiTOIpn\nKez9U/JK1AELVLLDq3SMMQ02mRJemVKvHdhQucvFyYs2r5fJLewyzoT7rrnouselqzlL7osy2PQf\nGt6D5LfdnZvSEoyPjzMODpk4W3/7+7OJc0VyHfc9fKaiHeQp0QUagUu0cFluM34DV6fL3z2Ee4fe\n/XwBgiLMHXrxxmvcNSVjlUu574IqUR1atyw0lY+ik8E6qcn/9A9HzJntOJWwbXx8ye3vKJUZWAun\nD/no3Xd4+1vf5K1Xv8HH777L+tR9AFbrE8wSi+WSEAKxbWnWkWgNt26+wAsvvsjiaMny5gu8/AM/\nxA994Qt8/gd+gProBovlMaSWYEIbFbE1ljyzX0Ty997b/GOMxLZF2jVxnc0DbQEGLbFdd6YFEbyi\noImnyC0ZCi2nz229xkEbi3NgyoluMuNWJTZGjUFaeNRDbLIUWhwcMwMpG48qIQW0zZnhYwJKvYSe\nNDvbFWlYJRCs8nuKg4ZFPNmPkrIUptn50UxI0UjBnR1rXeQKiIHUROJ5w9mjE85OHrE6P6NZNVgC\n1QoxBwZtWpGSawgU3IGRgaNgKZiUzRndajFPFywd/i+FnRKJgCTP6hjFQWWynEjIvOZiVv90JglV\nQdUIWhzAUi5e5ZbzYkIQfE7cT8AdC8Uiaq59yMkKyMUm+vdIydKfNqTs4pLYvRsrEmWOVtAM3DLE\n0M0Arx54bDC6/pv3L86/TP/C+r5LuOhm/+zUXJTzhUQkV9e8PO1jSj4a23gS75At7cGGYDESAg9m\nYMaor+HeNKyDME0FpPRJsuKmJqP7dwgGh6MrAx9eV/bN1F89lLS7RGq5QFenWRG89LpsFP0as+Xu\nHW/4Zwyum5jHMQ0rF5Z0xmUIvSA3QUXBtrns+vcgvv43uVL5PXzJl1l7h0OC5wsQyOaLuPDig2nH\nwjikhyzJqJStx8Cih9GNk17nxShISRvg6F6DM1HoitEkawmmpPMz7r5/m3e/9Yd8+2t/wO23vs3q\n5AG14GGJNIBL2esVLJYLVJUgkfN1w4N7d1idPXBpXyreeettvvvuW/zQF77Ay9//g7zwuc/zwosv\ncvP4Bqle8ujRA1ars06K9Dh2I8a1J9aJLTE1pPU5qcn2/xyOGNMaSa0nUhLJ2QBbME9FXCTEEupn\nWWTVnBuf6NJOUU9aSljjX5osgjPR1hMLeRGj1G335LkMgjPrjKlSil5m2VJ+nsFGlc0EIavSk9f8\nLb6QLp+rYlIRtSKol0RWrbKPQY1VNSyOCItb6LImxcj67JTT0xMePXzA2dkp6/MWLBA0eLcGsV3T\nxBWkdXYkLMmQirQdCHn9pBTRSr0Co2WFe4JKoaE4W7qjnxAxCzn0NC/HZLQxElhjWmdgXSFiGQzo\nxoZj4OCly1Xg2h1EsqbFupRPw1wPIq4lKNWwuq292+x67QIwoTEY/u6993t24X8VxzY3K7lZrcwL\nSA5NJSd26p7I/UZkAPdz/g4jg5jybU7a9/uNp9vAR3vRrm3pYF+FIReQzcMl6+dkpzK4Xfrx0c0T\nFD+aqZ1tc2TTe59lgLdZ2XJwl0AHAEu/IpvveEpfvnEsb4ojj/8t1YIVFud7Q+qODfdxcQDaRVkN\ndvduTUw8xxD0sbkid1F5BeV6k0HuuEFb43uGz9ndb0NA4EfLuaEzaDnam4LGvzdB3KHw4fkCBIWu\nFRRMIa6p+4Zo1jmGvx/P9N+Hrfju5K+nMCxnPmifea9sjSVm3Dc2t+GqGAHj9O4HvP71r/GNr36V\n9779Vc7u3aGShuOjClGhTRGp4KhegNXUiyVHx8csF0ushfP1mrPTu7TrU1TcR6A9ecDH76yJqxPO\n7t3jhc9/nsXxTW699Dn+2Msvc3xziQY4PTshto0LbdZ4vvs2InGNxRVtPPcIg+j58S2uvOgRlgW7\nrPNwNUFWAaunu5Vsy1c/L0kJJh7ClyQXQDKQmN+MkcxT5PqGrVjK6nTzxEIJI6i4lG0eVqfi0oKZ\n5953rFXs5F6zQDRAcMdFkeDRmwkHCzgTlqpGqyVa14Rq6f/Vx+hySTi6gSxvQrUEqUhp5cma1mva\ndk0s4XgS0Eog4sAqNbTtihTX3fdcJFYR88Q/A7VzKuWESillgLxWPMOiuqQuYJqBVpe50t+domhI\nVCKE4L4ZoV6gGqglOPMUZ9yEzPy7ZZ0zPIqnnu6SMolHgwCo5BoMYqARNcWyv4CZ18PAcv2JKcob\nY8lwWTQLkutNeNbEfKmUd4+/c1V3ti3/5SZVpEs2VbQWGcK4cymwbjwHRNfflBnSpnLU5/kemjn2\nmDOm8iJMmRY6/4bNqSGVtN4dUx5znQKbbOPmIrlSpgD6Ij1be+nUyxlGHGz7BEgurLX5vJlx5W/O\n98VByvKNgbPNiTOzHx7qQGH32Hm/HIyrAFCzNGCJ/VjMioZt2GZ/WQf2/A53XconyrWDXX3zfXff\nMd28brxD61efDccw0FSMdTFdDpCO0ffXbgJqG50/BMps0/MJCAZ0/XkIellk+MuKdNJdV9RZ/dJy\njYB/tCblnLpqq8sw3H8Qjghd3VapOoDIGenOHt7l1T/4Z/zR7/1T3vjWt7GTj7hZB24cVdRVRUvE\nYkJNqIJSh4p6uWBRL1hUNZbD1zQesbbWw8Jqdam5aXh05y5E4/TRA7SuQSs+/t7v5Yd/7Ed4+fv+\nGHV9i0cnp5yt11h0R0ax5KGHTYO1DbF1p0TLJoKyIC1nyOpzA4gzfnFJtpWSykf7DdWUpBFLSot4\nyeTuQy7qyECoNIcrOoBSM1IKWR0vVFqhYYmwxJMeNSR1B0PEs/RhXlIYCYSwoKorQrXICX4Ac898\nqbwtrZZUy2Pq5TGLxTGLxRH1YolUS6rFEqk9fC+2kfWqoVm1tDHl0Mfg70Ks26hSjMR1Q9usfQ4l\nS7o9p89rycU9hYGpyRlCAZRF+ktAFC+ypFkT4ZqHNvseeJRBUKWqaqrFgnp5TFgsoK4JITtTBg9P\nLCKO4YDLwYEfUwkYuO9E8UfAPBpCIGlRp5fsmvmpshRYHq13Ni1fWQYPDKVZIQRPDuVJpbIEJoJm\nIICETstReGX+mrzsdkrdJl4YuzHYiB/TuP24CY4uRQPh+Uoy0QhLwHWMf3+Gxa0OnyINlvbESbYw\nVhHuzKZ1CgVAdDK6+T1DZ8Lu2smXVgTMvr/J85ujys9y/VEyzz0guF7aBSwKZMyMH6CUEs1Mvi9p\nLFlroF3WwSJNgS/GhFerS2Y5m16x83oT60cPePOVr/P1f/b/8v4br6LNGTdvLKmKOSGBhgWLoASg\nQlmIIDFiqxXrdYO1xjq2qBnLakFqWi+Hm2DdtqxPT3mYjNQ2vPC5FxFR3nv9Ho8efMyP/+Sf5Ed+\n/Au89MKLxHv3OD1toWlIbUNq1ljbom2/2brMmojFvo7l/P6aE+goFrL8JdG99YsHc3aYIwmmQkyu\nYbAETXTJTlU9aY5VWcrUDhD46zEsebx+qAJBl8AxLStolCBeSwDN7wmPshCpkLBEQk1YLAmVMxb3\nrfdiRtViyeLoBvXRMYvlLRZHN1gcHXlWP619+LkSaWwbmmZN00ZPryQ1GhJBY3b+i0RaUg7PTK3X\nKTZ1Bls2DFeFevInzeWpNRXpuJgGeulCUwKtvB1xl0w1z/4IDZIzEyIVUlVIXTuoWS6ReoEFBxuS\npb2g2kkynsWxVIgsq9+6vl0BlDppKqnnnSjJkIo/jHVbZqnB0Mnww68sux941bhsh3BQRY6cMbqE\nVXXOo5DyJlyyZxbtVASIbQczAA+YGYjLEZdmDxEpLuO0eMhGfaVMhxOMZuu6IuleSU4qPgSHSpgF\n9PfC2VbSHx5rQBP9HTpnl+/PTU2DAyPgNDa7dKC8ALUdXe59zwcP8zLPfjX69AGC4Xxdaj0ML/aP\nQl+Oe0oAACAASURBVIbnMlAr21nKkluvIurtOn5/7KQRcvY9cX91l1JJOZNfDgmLfm1qV3z3zdf5\nxld+l9vfeZV4+pDjqqKqrJdoct7+IBWVJBYhICgSjRTP8xiCS4gGoAStwJQ6GEGMxsRDytbnNCdw\nvDyGZNz74DavrFra9Yof/KEf5EZdEauas/NEs1rRrtZYTuoTsJyyOObdvHUbo3ifnmNf+9A5BRND\nNdJK20m3RiSJYRIIBKqqom0DpEDCqETRukJlQUBo4xlxaJoEMJeyQx0I4QiRIxqU2LSEWHlp3+KJ\nbwlZB/cZqBau+l8sqBcLqnpBpZol5ZpqccTR8S2WR7eolzeoFkuq5ZHnhrAWi+veoa/xioExNnn/\n8+evQ6ClhZhobEWM65zMaaC+trKZZEfJFDEr7QSwnKqYiuIuJ4CmwjzJFQcNkjvQtdoSKq/p4GAH\nqALV4ojFYkFVLSBURBU3SdgaZelziZG0RB+kbiN0VXBvvqHUh8jvwNeCa66csUtnU00DYG3FUQMG\nYpZ10SkAhJA1aN5+qGrquqaqKmrNPiUxIsl9WNq2dZNUZkzS9VdmOX/G1nlAePdDdeA17bf7mf2U\ng92u38NTI2YkZezbbUw/iu3426mHAVdTN09Tp4/Z2e/10OO8PJn4a5OJ79RCX6Cxud6nnQHB5ek6\nQOgGGKBzYIL+dSjiG1f2aPEgAkE0QWohNV3VP7OcA05bF/OyJsHj9HMxGQFNLQ/ev823/8Xv89a3\nv8765AEL8cyAbRupq4oQKrI13hmg9t7iKUXEXMJDQasKYrazBkGib8R1qEmVp0K2FDm9d5dVeMDN\nWy9ydLzk9P4dvv7V3+fOhz/Iv/THf5RbL7zIgs/xsF2zPluxXrVgbVbhJmLJ+teBHRCiA4HgJg0N\nHn9O8tS8zlDL/CSiRS/Go7Wr9GID1jjwcVd4al04AIuBkL3dbfBWgni7oT4mhFtUtB2jKDkRvD8j\n2QteVTlUhKrmaLF0QLBY5mRQC7Q64ujoBY6PX2CxuOFmguBzGy23m/B0yG3jqY3bFZLWHpqZGU8V\nFJWKdWqzFuGcpm0894B6RUMvS5GzVFp0MJRirpVgWKqzLdwjHyRHflhR08fk4EQ73QGE6PkKKlft\na1VRLReExSJrRGpPv0x2AJXYm2rUayMUj70i1Ws2BSQRjLZ7f0bKIakpF0saZFCXvAYt+88LrnWg\nbHGbG28Bim1O1VlVgcWiYrFYEDQQU2TVNMTG5zMl10x1fiY68NEZb6ADpjq22Xf4obuWyf3k8VW1\nfS+b/gQXWC/Kect6Rykgckoqn6a9o5bBPvcYz1e0Bde0GR9I18Qod5gGCm28+8FC6Z0ot9t7EsN8\nUvTcA4InkYdg31J29WL+eFP+u7g5e6A8ltbQrjtVWVHIehW7PtWtaDYvJLeHticPuf2dV3jrG3/E\n6b07VGJUQTvnO82pdHGHbz+X7c8enZA5ZBBCyOmCRV3dbC4pWvJCOVYL0dwZSAjEmDh59IBFPCZU\nFadna946fcjpg3v88R/9Ai9/3w9wfOt7aJqWNq27pEiimtX4Pg9p8MxeilcJlVJVHtcfU1Zn5w2j\nKHOr7LCG1iSDKjZEWrcN5xS9lXpBocbWRGKXXdBwz/cgPsfV4gb14iZmnt7YAUE2YwCWr5PgZY1D\nqKirmlDVaFW5LTwDi+OjF7hx9AJ1teyyHzbpjPb8zM0OybMtWnsG7SnanhNii5r7XAAErQiaSC0Q\nW2K7wojZw5+ckti1PK6MSrSp9UKRmXlqqLI5RoAWMVejO6hLDhBUEHNTjVnObGi470D2lajq4NoC\nqTEqkonXIRAgCgk3pWhSByCdujebAsSldqcIOXVRV2gqDZLZlLebvaOLdagAipE81jsEZsYnotTV\nguPlknpRQYLVes15TvWcUuvJsbLBVoozZFe5smRrzN4XlqMIum+5jx1wRzzb3L/3bC270iJPJjXa\ncjocP3uvat6Xq6E4C+rmwYHsXcTV3kAzbqHvsChPbHC2V5WIjJ3WLqbh8+1yonx8ejocdRsEDE0F\nmxqGQx5TLkR/10mH9/PcA4JpmlJP2eD39ge5t7UeioNFDI8dN8vqzRjRHGMvFqFtu+JAznABFapQ\n5zS8glUVpksMd4wTEo/ufcw7r7/KvdtvsUwtoVr02WiyU5eZed37HGNeqt2JhE49rPm8a8czQ7WE\n1NaF9iUlf/RCXS+IMbFarzh/9IjFYsFyobSrE95/41Xu3/mIH/7Jn+Hl7/t+Xvz8y9TBuH8v0TYr\nNwxYQqJ5lsTi9WU+Za4yr6iqQGuCmTu8SZY6TbxiYBUUtMak8pDFFDFp0KpCRUgEal0QQk0DXf2B\nzg8w4fMQlMXiiKP6BklCzoSYekDg6f/QuiKEZTYxqKvP1esfuOTtUvLx4phFfezMOiVSXGWzwApi\n4xkL2zXt+pzUrFxKl0hQIyheujoEz3jYKKrFTg9ah65AEZbHb+Zqd3Gw6Kap2iVgcOdCE1IJ98sZ\n5ZS2S3PtEROayxV7GmYNC0RCdngVWhM0egrp1lr3YA8RZZFNO9lHo3MAta62RgGekj34MdeQdQ6E\nRnbCGn5zdN+QYF4nIjtNpOIDIsFNEmKoViyWx0jwNNCrVcOqbWjWnufCmbm7DvYCvnRWCF8T1vWr\nhd0V504Gcvqhu/jEvnCorwCU+bkGR7ACZGyoHxv0451N37pxXkbjkdGVjzvMAr4+CcY31O08WdV6\nmZmy1vo5hc5iuo0fNm6+KAT0esXcw1v7dAECG/2xNQ9TE9Mnwth8GaPNLBmuf2+I7QlIk6vnNRAb\nJLnqkgQavRxwm7PSgX8csa5ZVkukDtDWULUgC0QX0AoPPvyAj26/R1ydcSS5SxGqOrh2IHp526rK\nRW0s54MJpTqeIuoOiwq0kh2EsjTW2fHNN10J7qHdtm6jrasKkpKSERsjVEpqjQ/fu839k8hP/cy/\nzJ/40R/iez7/vVTVgocPPma9XnnNAiKoeknhXD3Q6/1orkcQcqGhivIJqWhW2QcWVU2oj7BqiTuK\nufe9OwkKLVBJTVUdkcKCmA3TQXIoYnYq1CpQVwuqsCQqXmshurQT1E0umGVNTXCfgbrOxX8CSbxG\nQrI1MZ65PwGJ2KzdoTKdk9pTQrvyHAy5joOs19CcI3Htqn9JBMVLJ0tFFcxV3qH2qAZZelGnLlzK\n3NyEdGF2Ka1Rq30lJiGRzUUWPLSQrLEy59TBSm4HQ6tcubBeQL2AeknSisYgxIS2LSEFIkZrLSYR\nsRoL1qnVJeuoLYedeV/F6cyZcXmXIlDl3APl0zGKyWEz66CVrHuKI7lIXps5J4IoWtVU/z95bxdq\n25btd/1a732Mudbe55xbVfdW3XvzRRKjcF8UiUQFJQ8hiOBDniVEEF8CkbwYBPXhanwSBEF9EBH0\nRSGooCgkGJEoItGHiE+KiUbMx71VdT723mvNOcfovbfmQ2t9zDHXmmt/nKqKHhzFqT3XnOOjj6/e\nWvu3f/u3MlH7yvl8ZlmrO0cpuZ6DWTzvnoq6tLC+HMdLDrlE0UKkLJ5LBH/YiPxijQzciKCfoBVy\n+Xg9rCfL0H14to+Lf/QLOJOBJjwNun5WPsIFcbr8+/S7/XH3n2/jJD/LcrW3uF+3aJibw3bj8B+U\nvR7HeeYdfNtr+cSWvWf5TjkEL2hiwJh4uHjJ4Dr3bgjTRijzgOZSIjgg2tjYow4Fh0OdsORtft3w\nW1vQ9ujkvdrovYJW6EYnoQqFDn1lbZ3ePWJ3uHaiFv+XXLDpjl7u6OmeSYXHr7/izZdfg0xo8vK4\njHl3O4RcyiaW46JwKSB1LzHz6BOSKVlHc6I4t3S5NoJQfKPoppfIWbE80/E0gqcTBPLE/b2ideVv\n/O//G3p8y9/19/zd/Mqv/hqpTLx790BrldRWrK90W0Ea4Pn1VAoyH5BU3PiWFHXjOQR+vPXuNM9M\nd5+RpntGBYdkIadMwmjWERPKdID5FV0c1izZ2x1bz4gm500U70JouqC10deFFNyK0AKk06OZkWDi\nZYg5RSmbCKIrYo16emQ5e+MndCWp32+xjq6dvjaSdno9UY9vqW1FN1ljr/cXUXLJlFyY5jte3X8B\n2lmWM60tmPXt/gEbDwMVRDtJhaQroFgyf647SC5bSkVKRkvBciZNE1YKqRxgOkCZ0OjGaCawrmg7\nkvKKkdwpyVBSQdXHnMqMiKDm55doHmVrRySHMYdEZ2hzer9ECwTKK1yGu5MJZIrRf2IHpWdBmFwb\nIRem7Kmh4+ktva3QYRLXkhAN8u2YVKO85+bUIJdJ+Wltw5VanTli5QjD7YnzVmb5Wd7+E/++9duV\ndJNcvjdzF2xoasD1XHcF/e+jz51R2dAA8+N8coXDjUVNuUgTXwzglm6wi2swfLYnZx7rDtTjYvTG\n3/LUsdjJ1Y4eEJfjX67jc/4Im7O7fWXXzsVzR/H6el7Ega5X7vuUwvZsPtd4HKiVvXA9r7DrIeRm\ngGhcj3619nOB7+vnYDOaH6mq+Z1yCG7drPiFS9ZsJ2Sakk+AET0/Fc/wh7FzeSgBkt80W6Gv3tmu\nrQ4Ht4W2LrT1EWsLViu9VaCjltwhQMjWMK3U1umGG4I+oX1CS0H7TMoF6Qb3iZQP1HPl3VdfsR7P\nIAlLkEXJ4ikFL9/LO95ADkJhRIk7XQSL6CwFjuVAw5iyL5NHshTXVb2rXilILkBBxVi70TrMGLWd\nOL57y//xV0+sS+V3/d7fzavPvuB7P/yRR3APD5E2OaMsGA2zRMl3TPNnlHJPvrtD5oKk7OI+eYY0\nk/JEKQfS3R1lvvN8eaAjSVx9K+mCKOTpQJoOoc1vLvaTMliGJn7/cnYiXYjWaBbXSVD1yNUUbYuX\nADbPg6eUaH2ErYCutPM7Tg9vWU8nejsjWrG+Qmver6C7aE7Syvn4NaeHr2itYlIQmbA80aWQywFw\n1bxpKpgecAngTkrmSEBA5dbHhOMdBXvzu+a3TOjsUgHxHKQQV7Jc3DEITomJl3JaGroYuAaCVFqC\npFOUKwoJJ51IdtQmleLpFQ3sLIzmeBG9g+ZIDY2p9xKZeamtn4vKMEA+qbmYUpAhI9Iv4QxIcBR6\nXdHWoqLFS0FTGMgroe9BTNAwDsOIxnSwh9Yvy1aUuHMQBoog24R+w3ZxtdHPe7kdUP78dv+LyFm/\nEAWP5QMC8E939v6fR9i8HVNePP5ObeLlo9mH13nfOF8a7TWK8PxL2VzodGPF23veK2W+b73n212c\nk49ZvlMOwctLQJg7VGmbu+LBMdiimo0SEN6caY8C6O7teFWhr1BXtK/0daW1E30909aVen5E9YxV\nrykXM7qVyEG73j2EDGWoxokqKSD/ljRqtxuSO3fFePf2LT/5yU9Z1xUxh5tL8hp+5wTIpd2sXKoL\n2LxzIo9u6FYDbiAXBfhnz4T5lcnJFflKzqTiLXJNhFI6vbukb88zPCbeff2Gv/7X/hrv3n3D7/n9\nf4Df8Xt/F1/80hecyh3WqkflFrXvycvk5vyaw/3n5MMrmGZyKUzlzvkUAd0nCmkqyKEEMWB/M5Xc\nLeR0S6TX/b7papCapyJMvP1AU1j9BAUlmXdn7Kq05vurbWFZj/R+dg7B5JK+1jomRhZYH48spwfq\nckbXCjWeg+bll60qOQlmC+vxDefjG+8kKYmUJhdImu6xg5FEUSoldSg+GdnsCEjvE6MMtTE6CPpd\n691o4SCkuComhpSLM+BFJV6tMJ5tMyHRyWbeeVJ7VMP6c9eSua5FSVigDEjxds9l8uuBITWhSTYJ\n2M353oymhejPPvom3kcB8Wd+MPskHlav906k7NyBkkffhk5Xr+CA0LAI9vZwEq+e5ZEWeBot3ILS\nt0+X6PXpfGlcjvEir+Ajbcgnk+l2c/7TQ2w/yYt4iK83tr9lP26M7+fBZ3h2IPMBpJsDeOqC7c/0\n4qjdXHaBud343j/LjS93h3729ce5LC9dp1vffujx8PfnqUvwkrGX7Z35dG9xCOW/NNLny3fbIdje\nEL/TPl+E8RfblN9c3z2NVDregCMYytqhVqRX1Cpaj9Cay8o2zxv3daGuJ1qr9OVMW8/0HjBy35Hj\n8gRpouccjPSEkF09ziagoAi4TUDXirKQ88o3X37FVz/+bawtZFGKeK+CnMQrFbMT9IpkJMRfRHYq\njYEGoKFsNxjbLzxFmzMhQsnuACiNXhWR6tnh5KmKkmfK559xONwzWeb4zRve/Phr/nb5G8yv7/nh\n7/ydfPH971Nrp64uy1smJysmJhIzd6/uSYd7KPekkslz8eh0GBNNkEo09NFdG92R7z+4Z23iZZ1m\ntOrkPjXv2JjkgBmouWNivW2wvYkb195d8rjXlXY+U+tCzsC8IOqdG1WUCeC0IMsZ1hPUBVsaWlfW\n9UhbjqznIJdKo51PrGdPn/QOQqaUmen+e2CZhNL7A2pnUvJujSW7HgVJQ+jR4tkcrXiiksCMngoW\n5EHBCXkSEZNZR5FIFxWSKolONyHTUavkDlbPvv8c1RzFSZjYjMkEMjFJoaQJkeJlmWoUU0wUFXO+\niF1PMimsz8baj/fMH8nArgSXOFbQ0B9OyfklORdcXbnRW/cywkAeRsQu0i87lZjq9jr14rRBM7uS\nAd/LGOutSV24wKnvZX4/tyYvFDbeftd2ywWNtN06XN7hq2O8FI0+We/Kttr1D/Fc+Y8pHIG++/l9\nZ/B+WORFv0NGp82n+3/JhL702/MjflI/m93w970x/Du/Nt/OJXp5EPLiH2Ozob89vNwhS/xkg/g9\nwsv3IOTvG+Pwkf7/4BCMiYFrb9z/Zpsw/fu6aQYoXjcuraL1TF/d8Dsa8IBG1766nml1odeFHjKz\nbV3pdcG04U2M/LLnafYJO3q7WHdSnZPeHKrX0CtyNMIJWCqN9XTi6y9/wps3X7vyXFQVSEpRliZk\nyVFRMM7V3/5tstk/R8MZSEMq6TpSkVC/g4BsLbr3Yc6VMKFzgYFTr/QspDTz+f0rZoXzeub47h0/\n/pt/i2me+ZUf/Q7u7g5M08y6LpCTS/0eXgWbf0ZKxspELiVqIuP+GFg2TKuT57qyLm5sy1SYpuLX\nq2mI7nQQo9WFZT3TWkUskfMByZmkDasLy3qiq3olQfHmQmbZnTSDuRxIKs59OHmap7cF1Uq3TmqN\nvpzdKWw1HMNKX1fW88rp8ZGuC9C9qdHirafX1RXzpvnAK5lI0x0pdep6otezOxHdZaAlTD/J/PmQ\niz670dDRK4POkE816+EotIiWJHgMYCX6BpghyftCaE7ULuT1jGhDc6bnxkSkicocDkYOtUMJmB8i\nl4TQSJqiD4GG3bzEQiOKsXhux2S8VYEEbGfJKCSU0IxIGcF5K9Y61uomSaw6HAKcSDkcoPH0jmc/\nXRTkhs7/XtL8JZM2IrXR5vmDMugbcLCHFW673B9WMzRs6+542fk2n105GzKu4NVxL/fgyQnatso4\n4CUgujHay9wpPKfIfTgsfRaT75wT/TaB7c29PznQzYv+dDDybL1nY3zp76ejuEJ7PtojeWFfl7v6\nzIG7gZBc3bZPPewnXvzvrkOwveXD4PuXWxkU3vnNF5dlNXX5XdMekf+Zfj7S1kf6skBzhKC1hraF\ndXWJ2YtDUJ2Rr074c7VA8whUzfXio8yr5OIkwDKRcvFJLgdhzaCuDUuJNAnL6cg3X37FcjyBdpJ4\nOaPnTRMlR5mhXM7X9eUvD+r27j+5RGlMPno9SV2cCqNHbnewkz3X67wKU285vPaKUCgqFJTDlFhb\n5e2XP6EUQWvj+7/8Q15//gX3r16zKmi+xw6fk+5eQ8o0FCfzO1teLYxcEJMMQJPD5N1zyK565xFO\nrQ3rRhFv1SvqZDxMsdZopkhPjq6oYjVEgBZjmhJTlH0KhVJmSi6U3GmdKGcLQaG60uoZo/p9r43W\n+1ZpoLXRa6fXaFCEc0XUhNqN87nSauXQjGlesfuOJqUuZ+p69OdRDYJwSjhwpIRm7wxJsPvNhqOw\nuvaAGpbjnplF+WaUCHYnnFlxXYJEobcWz4Dfy9SrixFNjVQSYgeHLXNxTkcunkIIs+TGFo9qTKCP\niKa7ATF/cIaGwtWrGV55QraKmfG0luQCVT1SOaIwiVeluCG0bfuhZ6ADMYgSTE8XRAojximb03vt\nBI/vnhnqj5jbLe7F9q7d2M+nQO/D5F9bsAsPKlz9J4P8kAV8+j1cIs/Ec2h8B1ZfjV12v78Hvv/Y\n5Weznb+Y5X2XjEu67lN29emn+B5UxPW1A0lIt9f5BS3fXYeA4TmNWxIP/zAy6oDrVganLsdrdXGe\nQHUuwLo8UpdH2nKGumLr4lFV82ivNldD63V15CDgzJySd4crKcrtPCeapVCmO+bDHTkX8pRDutfL\nqRx2BV2qE9Ik8e7dW7766Zf0tVLMu8Jl8dLB0cQlZdmC6gEhmYU6oaTtmXEnIAxKEPIw9YqB7aIx\nPKjYwgmIo3bcoy0Lxrgbbj13uiZ6966Blo2mSn+38lgSqa+cT2/40a/9Ln7pl3+NaX6NTge03GHl\nHi0e5SfBhZlwxpxZx5JiobwoEuVsVuhuIzfNgtEcqKsr4oGSBebi2vtqIRetQg/FPNTRHlsVze4U\nIJleQhNCG2090k6nC+rTm4sN0bxfQ2/Y6DtgFvls2YxmV0ERNB2QIpC8x0NfhX426uLE1bUutLY4\np6E75J1K2bQWktcpouLpDYmqPkERW8DKdj+creyGPqccSoZE2sDv+/hPtWNVEWnkXLDi74pOB1cj\nJCFpJuU7Upk8RSOjXDUjWTHLaJMN5hqKiRsLXpwNLiG0NRpzbE7BFnD6NctZ4j1VTJ2gODQ1fMye\nKhpBsIMFxiACJcAkeT+QwOBGauKl5abR3kWwt4SFbtfof3j5mPz8TWW7q5rA9x/zw8f4NrH5zxbP\n39zji1yK/5c8hRf8qCc4zC/guJcg7GVn7smIRlvKrT3lL3b5DjoEl4uyiUDgHvWIan3idkjWc/3N\njfn5hK4nzwMvC+vywLI8siyPngaoKywrfa1Yr4EUeASjWr1rmgEp0ZOr75U0kTTuNRmkOGydCtM0\nR60/yOgM58C8M6rTjKnw9ps3fPnVT1FtTnATl7L1yUpC8W/DK9GuDGbNKOzyAOwicjMcy6GNoFve\nkgj8RnMg5yOoQwiOsAihzx/RO0JRqLXStWMZNxpqUJVJPa3wzW//LdbjiVqVH/6eP8Crz16T5tfY\nfIeUCSx60meD3sES2So28pumaKvOnSgJJQfB09cR7Ygqra6uUoeSknr9uqg3tlGjqztoktyx6n2l\n6upPTvKSuaZ4kxzMEYHzCtbI4tp7wiX15JfMAlKVIOCJVzdErh28C+VU7uk10xYvOW210WqHZLTV\nHQzMnUyxaSMDusMXvR9wMmQzkCCQGg3Gv5ZA/UlqVrA8kQ1yyRvITAgWGUqPTpRDbluifNG6N1jK\nKFkin1+cWOjP8tB4MFJResWLDE19HwOWNW/XtbVcugpuLzGUp7EGSTCe462kK8Yd6p0pJs0LW8GR\nBpHh6LpP6yTeMb06gjAMz6Yw8iSd+D4b+pIC4YeM16cQ9K4vzxNj+Z5d3EppvJ+4KFfrvTS+6/LF\ncdU+tHzken8nbf6zlMFuGc7pyMkPNJSnZ/ExhvpnGWAM5tYAr5ZdSvVnGY992tbfMYfAJ4mBpmyw\n2oAKo++71TNmgQJUlzm13qjHR+x8otaFdT1Rzw/U5UhdFup69lK08+JiNtq36FBV6ZsimpDyBJEK\nIM0w3ZPme2QqSHGjrKrUWilWgujoD0APuFjyRJknzr3x9Zc/4eHtN6QUkU+2rbpgSBMPQ689JuJw\nFuijdDJhyYllfWuX4J6lavQrNzeegtC38FO8e6FFE5tBRFR3CNQs2izHxJ0zqbiu/BwNZsQyYpm+\nVr76yU9c1IjMr/6+ify9O4SJ1r2SQwykeTMkayu1nr3vQ2tuZMJ4iQmtuwMiQNeGNfMGQbFdzuJo\niN8WzIYp9XK5JJ1cjFyEfmos9UwXcRKbmnMPhvPTPX/d6O4USEZFvFmOVnpXlOYp5xBb6pP3F9Aq\naErM84Ekhd6F1lbW8yOrVda2OOLevMGRmjqKYYr0huXMlHyyd5EpJ9y5stEoOQ0HJCVSylg3d4qS\nBqdCcJGr7B/BUQ4z1DKoUFJGk6MqmHlVSPdUWaJRUohVIUHACwNkcmkcxEhDEffJjfLgMiBBhhys\naLukQzYtEJI39jLzfYWktIYctasvOuoTdMnwbwdXYDgdQxyJbeK7CIG5Ouc1GL6zFvH+vEQpGyqN\nLxEN9wb2uTTxe6avy2C2/W4G6gPG8zk58VOt7bVzdk1ulDjGGJZeoOvBKYj7uQ3WLlLQe+b/Vc+q\nqw+7094FvuNL4QOEwXGPr45zKTj3BlpsPqljR5GC2iE9ly4b+4g9NrRw/jePQWK/fv7JBmld4xyu\naymuAf4nHsp478Y4nvgHMsIQzbsLNh6MGxfFBqA0Lvju4u29nk9YvoMOAVcvk4yHtHlKoC9HtB6x\nekbbidY6rVVaPbOejuj5TF1OLOdH+nqi14W2eBVBXRd6c3hYVbf/ujYvRcveNdBV+JypLdOBPN2R\npkNEVwnMoUxRpbXQ45fLC6xAUzf+p8cj3/z4J7TTA3cF0B5dEMMRSN54xmX3LLq5Rc5/TGji10ZU\nd1k/n8z8vXVoViKiM/B8bDhVpjhZTOPlToL28QBmkB4IiESeeWY6vGa+u2ddzyy1Y4/VORLaefOT\n3/ZWzNb4pV8/8vpXfh0tM703uhlibnRVG8v5uFVyeOthCx0IaG2ha4sIWWh1jUi9Y3UlHyZMjbWu\nHimmQppmN+ba0dTIwJwLTYRlWWlm3M2zi7ycF++WlxMJl2LuvW5cCgm2upoLF/XmDZUkuipOOpGT\nlxS2aNucUubu/pVrC2Sjtc65Pjhc0RtiQu9elWLRtMnj+HjxJaqUDTLRYAghyxwk02CJZ/GxoKiV\nHwAAIABJREFUW/PIXBSz6khBEkQa2v0+emWnkwO3nDvmglpt9XelnmjtRF5fYdFwS6aMGLTWnOTZ\nO0VCYhicGLkVHaYw5APmH+qR8dbG80M4bYP0alfzZQhSmb/YiR6biTt7TwxwN7zSwD2BQBBiit9S\nKrspOX4bhvtpxL05E+ym3xEEXMSPn41jv3xIw3/b9/hqP2ffMPjfxgl4CTW47CocqxFPbQOS3Xr7\ndMbeorsjYDZi2IHi7EtPhw4FpJ2g0IUAvjOK8R2B+lwQueGU7cfoQxlGd4/2XEZvV8O9Nvgv/T2O\ncflt0CplG+l4jkMZlA8hQi8dZ//vDWsdL4ubNeWDSMzeqXjRk/r45TvmEEgw5IExWRhIr6zLkeX0\nSD09QH3E1jHJNWpdWdYz9fxAXxbW85nleERrOATVteh7dY6A9o6hm+StWnOhl1xIkyBlpiSPlCW7\n6qAEOxs1VLxzH2pYUo9KR6WJeDSppljtfPXVl7z96U/dwM2uPJeG27y9mEEW6xcnZXv4/Q27pB4l\nDL7BpdGMN59JY/aVIRpz6eqYLG/kPi8V20VjJCLohpTIZUIOB6b7V1gS9HxG1WHvLInaTxzf/pSf\n/q1CbStC4/Nf/hFVHTZ3Iy+IKFrP1HWlt5WSIjKNFrbrcqK2ylQyORXaegaMnF3kh9qjOK97vwJx\nWV+fcCpYiNqkxFwmWpnprXkFwFQoKbEuZ9q5BQze6eaIkFmoIG5cDGfB99EciuhHkARK9skx+T3L\nOTPNB3o/oOczvVdqddXBkjKWij9fEOTR29HUiOScf5Ivz3tA2INy5CREvfynrh5Iyo40of7eiN8/\nkhtkb37UsX5mPb9DHgqlF+b5nny4R8yRLh2OUJ78nE1RbahVH4fsJuM49kDwdCcvulUcDLW1PRS/\ns5QmQWoMo2UWmhqwM2RuTFJEtDaee19p2+H7eQF7Z4Xr7/jwd/t9Pv37vVUGY5/fev5+2eHYW4jR\nq8SXp2S5953l3gG4/nubZ4bhkssW6cnWafC6ZPx9Gavf1325nW/5QTmJLZp+vo6nkOM5GY7GzqCP\n75+f7+XvtLuXKTa8VH6wBWCh9LKZ9ecm+8a13MZ77SBe/5Z2n2VzlF5erqtPxrpXAeMnLN85h2B4\nT47BBlnwfKae3rEc37Ie39DXR2w5o6EdsC6ry8SeH9B1YTkvnM8ndxr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l7Rvqu3e0xzes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Yrw8C7z8HBGEWrdaB3qVml1eKe5rBAGUwWW04pZp+01UhxuFHsf3jdA1evcux0CT+4cAYxDTGYa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\nIbD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4iyYPaVeP5FhXZ9JHBW7dUQIVNwRSdde6qRR6D6JiMrroyOywHU0JUpi4YQ2BJiQ3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\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75d7P4/RrHKW+aVsyGy40fbYcIrlBmblWWBMJE07kBCdVv3kK8iK8iOnsajAQ3x93uhMv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\nwHAIBgYGBgYGBhgOwcDAwMDAwADw/wBpYQDT3oik/wAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "img = Image.open('cat.jpg').convert('RGB')\n", - "img = np.array(img)/255.0\n", - "plt.imshow(img)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "img_batch = np.expand_dims(img, 0)\n", - "inputImages = torch.from_numpy(img_batch.astype(np.float32))\n", - "inputImages.size()\n", - "s = STN()\n", - "g = AffineGridGen(328, 582)\n", - "input = Variable(torch.from_numpy(np.array([[[1, 0.5, 0], [0.5, 1, 0]]], dtype=np.float32)).cuda(), requires_grad = True)\n", - "out = g(input).repeat(20,1,1,1)\n", - "\n", - "input1 = Variable(inputImages.cuda()).repeat(20,1,1,1)\n", - "res = s(input1, out)\n", - "#print res.size()\n", - "#res = res.cpu().data.numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "target = Variable(res.data.cuda())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "crt = nn.L1Loss()" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "loss = crt(res, target)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "loss.backward()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Variable containing:\n", - "(0 ,.,.) = \n", - " -0.3326 -0.0469 -0.2243\n", - " -0.0819 -0.5957 0.1721\n", - "[torch.cuda.FloatTensor of size 1x2x3 (GPU 0)]\n", - "\n" - ] - } - ], - "source": [ - "print input.grad" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "input = Variable(torch.from_numpy(np.array([[[1, 0.5, 0], [0.5, 1, 0]]], dtype=np.float32)).cuda(), requires_grad = True)\n", - "out = g(input).repeat(20,1,1,1)\n", - "out.backward(out.data)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Variable containing:\n", - "(0 ,.,.) = \n", - "1.00000e+06 *\n", - " 1.2727 0.6363 -0.0149\n", - " 0.6363 1.2727 -0.0124\n", - "[torch.cuda.FloatTensor of size 1x2x3 (GPU 0)]\n", - "\n" - ] - } - ], - "source": [ - "print(input.grad)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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WscDhfa4s0c+Dge5Ce2k7aURSqTim4a/0zLfg3sm+3/d7+T0p29LsX5CAXLpKSLkKUhKD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G5H6m6sLbUUs0LGsvCQOpAdtYLmgRCwElJn71PzjKqVaALmR2ALvQGlilI6C9Q1QR0L8/BAnq1l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\nTt0CL9asuqBWzUW4O4z4Q8VLYegj5+uB8/XA1VNNBva7mdvbHYf9SPQR53t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NiNPss5u1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\nL5elej8NLy6Jzdn762+3x+S7aACnN84pmaGtW8uetbW07iACZyeA8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K2rNA8BcHT2aTNa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\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K+Rll/Zy6fgrbMyjnXgAVnFaz5tZKtzek0GiyIsVCq0xGaOFSTQu1nVAgqJH/VBLVOcRp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\nAritFtdq5R8AAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(res.cpu().data.numpy()[1])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "target = Variable(res.data.cuda())[0:1,:,:,:]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([20, 328, 582, 3]) torch.Size([1, 328, 582, 3])\n" - ] - } - ], - "source": [ - "print(input1.size(), target.size())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "crt = nn.L1Loss().cuda()\n", - "crt2 = nn.L1Loss().cuda()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Variable containing:\n", - " 0.2754\n", - "[torch.cuda.FloatTensor of size 1 (GPU 0)]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "crt.forward(input1[0:1], target)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "class Transformer(nn.Module):\n", - " def __init__(self):\n", - " super(Transformer, self).__init__()\n", - " self.s = STN()\n", - " self.g = AffineGridGen(328, 582, lr = 0.01)\n", - " def forward(self,input1, input2):\n", - " out = self.g(input2)\n", - " out2 = self.s(input1, out)\n", - " return out2" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "t = Transformer()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [], - "source": [ - "x = np.arange(0.1, 2, 0.01)\n", - "y = []\n", - "g_ = []\n", - "input1 = Variable(inputImages.cuda())\n", - "\n", - "for v in x:\n", - " input2 = Variable(torch.from_numpy(np.array([[[1, 0.5, 0], [0.48, v, 0]]], dtype=np.float32)).cuda() , requires_grad = True)\n", - " out = t(input1, input2)\n", - " err = crt(out, target)\n", - " y.append(err.data[0])\n", - " err.backward()\n", - " \n", - " #print input2.grad.size()\n", - " g_.append(input2.grad.data[0,1,1])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 1)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print input1.size()\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" - ] - } - ], - "source": [ - "print input1.size()\n", - "print input2.size()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false, - "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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GTkxx/h+A50MII0MIM0MIfwFKgbPS0NaskiygVdmBB8I333h4uOCC1NdutBF8\n9lndPueDD6B374rXQ4f6zqoPPljvJousQuFCJD/FGi7MrBnQD3gxeSyEEICJwIAUlw1IvF/ZhBrO\nz1nz51dM5kzaZx8ftrj9dh/KSGW77XxiZm1WrICPPoKttqo41qmTT+gcPdrneIg0lMKFSH6Ku+ei\nA1AAVN12ay7QOcU1net5fs5KDotUts468MknPjxSk4ED4euvax8a+fRT+PXXVXsuwOd0LF4MO+4I\nX3xR/7aLgMKFSL7SapEsVt2wSF0NSPTzvPVWzed98IE/Vw0XPXrAm2/6fw8cCLfcUrF6RaSuyst9\nrlAT/UsjklfirnPxA7AC6FTleCdgzuqnQ+J4fc4HoLi4mLZt265yrKioiKKiojo3NttUNyxSV+uv\nD926eUA4/PDU533wga8uqa4eRrdu8PrrcNZZvu37BRf4KpIhQxrWJsk/S5eq10IkW5SUlFBSUrLK\nsbKyslg+K9ZwEUJYZmZTgUHAMwBmZonXt6S47K1q3t8rcTylUaNG0bdv3zVuczZZk54L8B6HZO9D\nKlUnc1bVqZOvHPn+ezj+eLj6ai9FXrXA1ltvQfv23uMhklRe7pvuiUjmVfcLd2lpKf369Yv8s9LR\nWTkSOMXMhphZD2AM0BIYC2Bm48zs6krnjwb2MbPzzGwLM/srPin0tjS0NWusWOF1LBracwE+X6K0\n1OdOpFJbuEjq2NF3Z50504t4VfbNN14fo1cvOOkk7cgqFcrL1XMhko9iDxchhMeA84HLgXeBrYDB\nIYR5iVMKqTRZM4TwFnA0cCrwHvB74OAQwrS425pNysp8y/U1CRcDB8Ly5fDOO9W//8svvly1LuEC\nYI89fAnsAw+sevyvf/WJptdfD8884ytPausxkfygcCGSn9IyzSqEcEcIoWsIoUUIYUAI4Z1K7+0R\nQjixyvn/DCH0SJy/VQhhQjramU2qlv5uiC239B1TU/2gnzbNA0xdw0VBgQ+JPPKIrzBJ3mPsWPjz\nn31exscf++fuuSc891zD2y65QeFCJD9pDneWqrwjakMVFMAOO/ikzAULYG6VBb7//a/PnejVq+73\nPP54v1dyd9aLL4aNN4Zhw/x1+/YwYQLsvbfXyvj664a3Xxo/hQuR/KRwkaWq7ojaUDvu6JuRrbsu\ndO7swxZJJSW+G2rLlnW/X48efk1xsQ+RPP2018SoPGmvRQu4804fknn33TVrvzRuChci+Ulbrmep\nKIZFAM6hMWi6AAAbzUlEQVQ5B7p39wAxapTPjzjwQPjwQ3jxRQ8Y9XXJJXDbbbDNNrDzzrBvNdvK\nde7sQzIff7xm7ZfGTeFCJD8pXGSp+fO9B6CmEt910b59RV2K9dbzSZnPPQdPPOE9D4ceWv97HnCA\nP2pi5tvBK1zkN4ULkfykcJGlqiv9vaZ2282HSS6+2JeU/vWv0KxZtJ9RmcKFKFyI5CfNuchSa1pA\nqzpmvqrjv//1csynnBLt/atSuBCFC5H8pJ6LLLUmpb9rsvfe3oPRt288969s881hzhxYtMjrYEj+\nKS/Xdy+SjxQuslQcwyLgvRcvvxz9fauz+eb+/MknEEN1WWkE1HMhkp80LJKl4hgWSbfNNvNnDY3k\nL4ULkfykcJGl4hoWSad27XxPEoWL/KVwIZKfFC6yVC70XIAPjcycmelWSKYoXIjkJ4WLDFm5Es46\nC2bMWP29Zcvgp58af88FwBZbqOcinylciOQnhYsMmTIFbr8dHn109fcWLPDnXOm5+Phj3yBN8o/C\nhUh+UrjIkCef9Of//Gf195KbluVKuPjpp9U3TZP8oHAhkp8ULjIgBA8XTZt6uKj6W32y56J9+/S3\nLWrJ5aj1HRr55Rffu+SwwzRnozFTuBDJTwoXGTB9utd+OPFE+P57+PbbVd/PpZ6LTTbx2hr1DRdX\nXeX/O02Z4lvCX3ttPO2TeClciOQnhYsMePJJ3zH0T3/y11WHRnKp52LttWHTTeH99+t+zccfw403\n+v8+H38MJ58Ml18OZWXxtVOit2KFPxQuRPKPwkUGPPkk7LcfdOsGXbrAO++s+v78+dC8+ZrviJot\nBg6EN96o27kh+DbxG2wAf/yj/+/w5z/7b8D/+Ee87ZRolZf7s8KFSP5RuEizr7+GqVPhd7/z1/37\nV99zkQu9Fkk77eQ9Fz/9VPu5L78M//d/MGpURbjaYAPYc0944IF42ynRUrgQyV8KF2n2/PNQUAD7\n7uuvt93Wey4qT+qcPz835lsk7bST1/WYPLn2c++9F3r0gIMPXvX48cfD66/Dp5/G00aJnsKFSP5S\nuEizSZO8t6JtW3/dvz8sXAiffVZxTq71XGyxhRcEe/31ms9buNCHjE44wSeBVnbIIb675rhx8bVT\noqVwIZK/FC7SKAQPF7vsUnGsf39/rjw0kms9F2aw4461h4tHHvHqpMcdt/p7LVvCEUf40MjKlfG0\nU6KlcCGSvxQu0uizz2D2bNh114pjHTpA166rTurMtZ4L8KGRyZM9PKRy//2wzz6w/vrVv3/SST5n\n5emn42mjREvhQiR/Nc10A/LJq69Ckyb+g7ayPn3gww8rXudazwX4n3nxYp/YmeytqWzaNK9p8fjj\nqe+xww6w225eA+OQQ7xHZMoUKCnxWiHz5vl5zZrB6afD738fyx9F6kjhQiR/qecijSZN8iCRnG+R\nVFjoPRpJudhz0bevLytNNTQybpzPyzjwwJrvc8klvtpmwgQvsrXXXh5IFizwZb2FhbB0KRx6KJx9\ndsUPOEk/hQuR/KVwEaNly+Dww+HZZ/111fkWSRtsALNm+X+HkJs9F2uvDdttlzpcPP88HHAArLVW\nzffZYw/Yfnv4y1/goINgww3ho49g4kR4+GF48EF45RXfFO7uu6FTJx+GuuoqzdVIN4ULkfylcBGj\nkhL/rbqoCP79b/jqq1XnWyR16eK/eS9Z4kMHy5blXs8FwIAB8Pbbqx+fOxf++1/vhaiNmfde/Oc/\nHsKeecZXkVQ954wzoLQULrgA2rXza+payEuioXAhkr8ULmKyciVcf73/pl1YWFG3YeedVz93gw38\nefbs3NpXpKo+fXxuxI8/rnp84kR/3nPPut1n//3hwgth/Hjo3j31eb16wYgRvry1a1cV4Uo3hQuR\n/KVwEZPnn/fu+ksvhSee8B1Qe/f2eQVVdeniz7NnV+wrkqvhAlbfZ+SFF2CrrXwIoy7M4LrrvKx4\nXTRp4stbH3vMe4YkPRQuRPKXwkVMrrvOVzfsvLNXnHz5Za8+WZ1kuJg1q6LnIheHRTbbzEt6Vw4X\nIXi4qMuQyJoYMsTLj6/pMtaZM314S2qncCGSvxQuYvDSS/Daa77xVrLSZP/+PqGxOuusA61a5X7P\nRUGB9968917FsRkz/M8dd7jYdFMv5NXQoZEQfJLoVlv5n+GZZ6JtXy5KhovaJumKSO5RuIjY9997\nF/wuu/hqhrow896LynMu2rWLr42Z1KfPqj0XL7zgP3yqm4sStSFD/PO+/bZ+1y1bBkcfDWedBaec\n4nNDDj4YLrts1T1hFi+u2+Zs+aK83L/bqqXcRST3KVxEaMUKOOYYWL7cV4o0qcf/usnlqAsWeB2M\ngoL42plJW2/tBbOWLvXXL7zgBbZatoz/s484wnuJ+veHMWNqrhZaWXGxr/p59FG47Tb/7yuvhL/+\nFU4+2b/viRNh44294uoBB8BTT8X6R2kUkuFCRPJPrOHCzNqb2UNmVmZmC8zsXjNrVcv5t5jZDDNb\nbGZfmdloM1sn1TXZZORIePFFr7eQnEdRV5V7LnJxvkVSnz7+Q33aNA9SL74Igwen57PbtfMhmb33\n9qWqvXp5UKjc+1DV3/7mwyG33urhBDw0jhgBf/+7F//abjv/M/Tt63Nt5s+H3/3O59nks/JyzbcQ\nyVdxl/9+GOgEDALWAsYCdwHHpji/C7A+cB4wHdg4cf76wBExt3WNrFgBt9ziv8kOGlT/6zfYwGtA\nLFiQm/Mtknr39m7y99/3XosVK3w79XTZeGMPBMOHw0UXeZGzHj1go408fBQW+rLVZct88ubYsXDq\nqXDaaavf69hjvafiuOM8bFx6qfc4/eEP/v+BE07w+h1V63DkC4ULkfwVW7gwsx7AYKBfCOHdxLGz\ngWfN7PwQwpyq14QQPgIOr3ToCzMbAfzdzJqEELK2xuIrr/hY/oknNuz6fOm5aNMGNtnEN2p7+mkv\nMFbXJahR2npreO457114+GEPdT/8AO++66tBmjb11S0nnQQ335z6Pvvs4/NsKs8raNLEN2Hr3RvO\nOy/1KqFcp3Ahkr/i7LkYACxIBouEiUAAtgfquiiwHbAom4MF+G/Dm23mpakboksXr9D5+efQrVu0\nbcs2ffrAfff5BMhzzslsW3bf3R+VrVzpYaGuExGrO69rVxg1yieAdujgkz/z7QetwoVI/opzzkVn\n4PvKB0IIK4D5ifdqZWYdgEvwoZGs9csv8M9/+mqEhs6MT1bpnDYtt3suwHsNFi/2FTXbbJPp1qyu\nSZNoVjicdBJce63Pxenf37/bfKJwIZK/6h0uzOwaM1tZw2OFmW2+pg0zszbAs8CHwGVrer84Pfmk\nB4xjU80kqYPkBNDFi3N7zgVUBIpzz81sO+Jm5rVO3nnH53AMHVrz5NFco3Ahkr8aMixyI3B/Led8\nDswBOlY+aGYFwLqJ91Iys9bABGAh8PtEj0eNiouLaVtlL/OioiKKiopqu3SNjRvnv4V37drwe1Re\nXZLrPReDB3tJ9OR+K7luq61g9GifnzFxYvwFw7KFwoVIdikpKaGkpGSVY2VlZbF8loWYfpVKTOj8\nCOhfaULn3sBzQGF1EzoT57T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