我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用tensorflow.linspace()。
def _meshgrid(self, height, width): with tf.variable_scope('_meshgrid'): # This should be equivalent to: # x_t, y_t = np.meshgrid(np.linspace(-1, 1, width), # np.linspace(-1, 1, height)) # ones = np.ones(np.prod(x_t.shape)) # grid = np.vstack([x_t.flatten(), y_t.flatten(), ones]) x_t = tf.matmul(tf.ones(shape=tf.pack([height, 1])), tf.transpose(tf.expand_dims(tf.linspace(-1.0, 1.0, width), 1), [1, 0])) y_t = tf.matmul(tf.expand_dims(tf.linspace(-1.0, 1.0, height), 1), tf.ones(shape=tf.pack([1, width]))) x_t_flat = tf.reshape(x_t, (1, -1)) y_t_flat = tf.reshape(y_t, (1, -1)) ones = tf.ones_like(x_t_flat) grid = tf.concat(0, [x_t_flat, y_t_flat, ones]) return grid
def __init__(self,input_shape,control_points_ratio): self.num_batch = input_shape[0] self.depth = input_shape[1] self.height = input_shape[2] self.width = input_shape[3] self.num_channels = input_shape[4] self.out_height = self.height self.out_width = self.width self.out_depth = self.depth self.X_controlP_number = int(input_shape[3] / \ (control_points_ratio)) self.Y_controlP_number = int(input_shape[2] / \ (control_points_ratio)) self.Z_controlP_number = int(input_shape[1] / \ (control_points_ratio)) init_x = np.linspace(-5,5,self.X_controlP_number) init_y = np.linspace(-5,5,self.Y_controlP_number) init_z = np.linspace(-5,5,self.Z_controlP_number) x_s = np.tile(init_x, [self.Y_controlP_number*self.Z_controlP_number]) y_s = np.tile(np.repeat(init_y,self.X_controlP_number),[self.Z_controlP_number]) z_s = np.repeat(init_z,self.X_controlP_number*self.Y_controlP_number) self.initial = np.array([x_s,y_s,z_s])
def _local_Networks(self,input_dim,x): with tf.variable_scope('_local_Networks'): x = tf.reshape(x,[-1,self.height*self.width*self.depth*self.num_channels]) W_fc_loc1 = weight_variable([self.height*self.width*self.depth*self.num_channels, 20]) b_fc_loc1 = bias_variable([20]) W_fc_loc2 = weight_variable([20, self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number*3]) initial = self.initial.astype('float32') initial = initial.flatten() b_fc_loc2 = tf.Variable(initial_value=initial, name='b_fc_loc2') h_fc_loc1 = tf.nn.tanh(tf.matmul(x, W_fc_loc1) + b_fc_loc1) h_fc_loc2 = tf.nn.tanh(tf.matmul(h_fc_loc1, W_fc_loc2) + b_fc_loc2) #temp use if Debug == True: x = np.linspace(-1.0,1.0,self.X_controlP_number) y = np.linspace(-1.0,1.0,self.Y_controlP_number) z = np.linspace(-1.0,1.0,self.Z_controlP_number) x_s = tf.tile(x,[self.Y_controlP_number*self.Z_controlP_number],'float64') y_s = tf.tile(self._repeat(y,self.X_controlP_number,'float64'),[self.Z_controlP_number]) z_s = self._repeat(z,self.X_controlP_number*self.Y_controlP_number,'float64') h_fc_loc2 = tf.concat([x_s,y_s,z_s],0) h_fc_loc2 = tf.tile(h_fc_loc2,[self.num_batch]) h_fc_loc2 = tf.reshape(h_fc_loc2,[self.num_batch,-1]) #2*(4*4*4)*3->(2,192) return h_fc_loc2
def _local_Networks(self,input_dim,x): with tf.variable_scope('_local_Networks'): x = tf.reshape(x,[-1,self.height*self.width*self.depth*self.num_channels]) W_fc_loc1 = weight_variable([self.height*self.width*self.depth*self.num_channels, 20]) b_fc_loc1 = bias_variable([20]) W_fc_loc2 = weight_variable([20, self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number*3]) initial = self.initial.astype('float32') initial = initial.flatten() b_fc_loc2 = tf.Variable(initial_value=initial, name='b_fc_loc2') h_fc_loc1 = tf.nn.tanh(tf.matmul(x, W_fc_loc1) + b_fc_loc1) h_fc_loc2 = tf.nn.tanh(tf.matmul(h_fc_loc1, W_fc_loc2) + b_fc_loc2) #temp use if Debug == True: x = np.linspace(-1.0,1.0,self.X_controlP_number) y = np.linspace(-1.0,1.0,self.Y_controlP_number) z = np.linspace(-1.0,1.0,self.Z_controlP_number) x_s = tf.tile(x,[self.Y_controlP_number*self.Z_controlP_number],'float64') y_s = tf.tile(self._repeat(y,self.X_controlP_number,'float64'),[self.Z_controlP_number]) z_s = self._repeat(z,self.X_controlP_number*self.Y_controlP_number,'float64') h_fc_loc2 = tf.concat([x_s,y_s,z_s],0) h_fc_loc2 = tf.tile(h_fc_loc2,[self.num_batch]) h_fc_loc2 = tf.reshape(h_fc_loc2,[self.num_batch,-1]) return h_fc_loc2
def gauss(mean, stddev, ksize): """Use Tensorflow to compute a Gaussian Kernel. Parameters ---------- mean : float Mean of the Gaussian (e.g. 0.0). stddev : float Standard Deviation of the Gaussian (e.g. 1.0). ksize : int Size of kernel (e.g. 16). Returns ------- kernel : np.ndarray Computed Gaussian Kernel using Tensorflow. """ g = tf.Graph() with tf.Session(graph=g): x = tf.linspace(-3.0, 3.0, ksize) z = (tf.exp(tf.neg(tf.pow(x - mean, 2.0) / (2.0 * tf.pow(stddev, 2.0)))) * (1.0 / (stddev * tf.sqrt(2.0 * 3.1415)))) return z.eval()
def gabor(ksize=32): """Use Tensorflow to compute a 2D Gabor Kernel. Parameters ---------- ksize : int, optional Size of kernel. Returns ------- gabor : np.ndarray Gabor kernel with ksize x ksize dimensions. """ g = tf.Graph() with tf.Session(graph=g): z_2d = gauss2d(0.0, 1.0, ksize) ones = tf.ones((1, ksize)) ys = tf.sin(tf.linspace(-3.0, 3.0, ksize)) ys = tf.reshape(ys, [ksize, 1]) wave = tf.matmul(ys, ones) gabor = tf.mul(wave, z_2d) return gabor.eval()
def mu_law_bins(num_bins): """ this functions returns the mu-law bin (right) edges and bin centers, with num_bins number of bins """ #all edges bins_edge = np.linspace(-1, 1, num_bins + 1) #center of all edges bins_center = np.linspace(-1 + 1.0 / num_bins, 1 - 1.0 / num_bins, num_bins) #get the right edges bins_trunc = bins_edge[1:] #if sample >= right edges, it might be assigned to the next bin, add 0.1 to avoid this bins_trunc[-1] += 0.1 #convert edges and centers to mu-law scale bins_edge_mu = np.multiply(np.sign(bins_trunc), (num_bins ** np.absolute(bins_trunc) - 1) / (num_bins - 1)) bins_center_mu = np.multiply(np.sign(bins_center), (num_bins ** np.absolute(bins_center) - 1) / (num_bins - 1)) return (bins_edge_mu, bins_center_mu)
def mu_law_bins_tf(num_bins): """ this functions returns the mu-law bin (right) edges and bin centers, with num_bins number of bins """ #all edges bins_edge = tf.linspace(-1.0, 1.0, num_bins + 1) #center of all edges bins_center = tf.linspace(-1.0 + 1.0 / num_bins, 1.0 - 1.0 / num_bins, num_bins) #get the right edges bins_trunc = tf.concat([bins_edge[1:-1], [1.1]], 0) #if sample >= right edges, it might be assigned to the next bin, add 0.1 to avoid this #convert edges and centers to mu-law scale bins_edge_mu = tf.multiply(tf.sign(bins_trunc), (num_bins ** tf.abs(bins_trunc) - 1) / (num_bins - 1)) bins_center_mu = tf.multiply(tf.sign(bins_center), (num_bins ** tf.abs(bins_center) - 1) / (num_bins - 1)) return (bins_edge_mu, bins_center_mu)
def spatial_expected_softmax(x, temp=1): assert len(x.get_shape()) == 4 vals = [] for dim in [0, 1]: dim_val = x.get_shape()[dim + 1].value lin = tf.linspace(-1.0, 1.0, dim_val) lin = tf.expand_dims(lin, 1 - dim) lin = tf.expand_dims(lin, 0) lin = tf.expand_dims(lin, 3) m = tf.reduce_max(x, [1, 2], keep_dims=True) e = tf.exp((x - m) / temp) + 1e-5 val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2])) vals.append(tf.expand_dims(val, 2)) return tf.reshape(tf.concat(2, vals), [-1, x.get_shape()[-1].value * 2])
def get_output_for(self, input, **kwargs): return spatial_expected_softmax(input)#, self.temp) # max_ = tf.reduce_max(input, reduction_indices=[1, 2], keep_dims=True) # exp = tf.exp(input - max_) + 1e-5 # vals = [] # # for dim in [0, 1]: # dim_val = input.get_shape()[dim + 1].value # lin = tf.linspace(-1.0, 1.0, dim_val) # lin = tf.expand_dims(lin, 1 - dim) # lin = tf.expand_dims(lin, 0) # lin = tf.expand_dims(lin, 3) # m = tf.reduce_max(input, [1, 2], keep_dims=True) # e = tf.exp(input - m) + 1e-5 # val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2])) # vals.append(tf.expand_dims(val, 2)) # # return tf.reshape(tf.concat(2, vals), [-1, input.get_shape()[-1].value * 2]) # import ipdb; ipdb.set_trace() # input.get_shape() # exp / tf.reduce_sum(exp, reduction_indices=[1, 2], keep_dims=True) # import ipdb; # ipdb.set_trace() # spatial softmax? # for dim in range(2): # val = obs.get_shape()[dim + 1].value # lin = tf.linspace(-1.0, 1.0, val) # lin = tf.expand_dims(lin, 1 - dim) # lin = tf.expand_dims(lin, 0) # lin = tf.expand_dims(lin, 3) # m = tf.reduce_max(e, [1, 2], keep_dims=True) # e = tf.exp(e - m) + 1e-3 # val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2]))
def site_rdf(distances, cutoff, step, width, eps=1e-5, use_mean=False, lower_cutoff=None): with tf.variable_scope('srdf'): if lower_cutoff is None: vrange = cutoff else: vrange = cutoff - lower_cutoff distances = tf.expand_dims(distances, -1) n_centers = np.ceil(vrange / step) gap = vrange - n_centers * step n_centers = int(n_centers) if lower_cutoff is None: centers = tf.linspace(0., cutoff - gap, n_centers) else: centers = tf.linspace(lower_cutoff + 0.5 * gap, cutoff - 0.5 * gap, n_centers) centers = tf.reshape(centers, (1, 1, 1, -1)) gamma = -0.5 / width / step ** 2 rdf = tf.exp(gamma * (distances - centers) ** 2) mask = tf.cast(distances >= eps, tf.float32) rdf *= mask rdf = tf.reduce_sum(rdf, 2) if use_mean: N = tf.reduce_sum(mask, 2) N = tf.maximum(N, 1) rdf /= N new_shape = [None, None, n_centers] rdf.set_shape(new_shape) return rdf
def main(): sess = tf.Session() # inputs U=tf.linspace(1.0,10.0,2*8*8*8*2) U =tf.reshape(U,[2,8,8,8,2]) #network initial dtn_input_shape = [2,8,8,8,2] control_points_ratio = 2 # initial DTN class transform = DSN_Transformer_3D(dtn_input_shape,control_points_ratio) # encoder conv1= transform.Encoder(U,U) #decoder conv2 = transform.Decoder(conv1,conv1)
def _meshgrid(self): with tf.variable_scope('_meshgrid'): x_use = tf.linspace(-1.0, 1.0, self.out_height) y_use = tf.linspace(-1.0, 1.0, self.out_width) z_use = tf.linspace(-1.0, 1.0, self.out_depth) x_t = tf.tile(x_use,[self.out_width*self.out_depth]) y_t = tf.tile(self._repeat(y_use,self.out_height,'float32'),[self.out_depth]) z_t = self._repeat(z_use,self.out_height*self.out_width,'float32') x_t_flat = tf.reshape(x_t, (1, -1)) y_t_flat = tf.reshape(y_t, (1, -1)) z_t_flat = tf.reshape(z_t, (1, -1)) px,py,pz = tf.stack([x_t_flat],axis=2),tf.stack([y_t_flat],axis=2),tf.stack([z_t_flat],axis=2) #source control points x,y,z = tf.linspace(-1.,1.,self.X_controlP_number),tf.linspace(-1.,1.,self.Y_controlP_number),tf.linspace(-1.,1.,self.Z_controlP_number) x = tf.tile(x,[self.Y_controlP_number*self.Z_controlP_number]) y = tf.tile(self._repeat(y,self.X_controlP_number,'float32'),[self.Z_controlP_number]) z = self._repeat(z,self.X_controlP_number*self.Y_controlP_number,'float32') xs,ys,zs = tf.transpose(tf.reshape(x,(-1,1))),tf.transpose(tf.reshape(y,(-1,1))),tf.transpose(tf.reshape(z,(-1,1))) cpx,cpy,cpz = tf.transpose(tf.stack([xs],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([ys],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([zs],axis=2),perm=[1,0,2]) px, cpx = tf.meshgrid(px,cpx);py, cpy = tf.meshgrid(py,cpy); pz, cpz = tf.meshgrid(pz,cpz) #Compute distance R Rx,Ry,Rz = tf.square(tf.subtract(px,cpx)),tf.square(tf.subtract(py,cpy)),tf.square(tf.subtract(pz,cpz)) R = tf.add(tf.add(Rx,Ry),Rz) R = tf.multiply(R,tf.log(tf.clip_by_value(R,1e-10,1e+10))) #Source coordinates ones = tf.ones_like(x_t_flat) grid = tf.concat([ones, x_t_flat, y_t_flat,z_t_flat,R],0) return grid
def __init__(self,input_shape,control_points_ratio): self.num_batch = input_shape[0] self.height = input_shape[1] self.width = input_shape[2] self.num_channels = input_shape[3] self.out_height = self.height self.out_width = self.width self.Column_controlP_number = int(input_shape[1] / \ (control_points_ratio)) self.Row_controlP_number = int(input_shape[2] / \ (control_points_ratio)) init_x = np.linspace(-5,5,self.Column_controlP_number) init_y = np.linspace(-5,5,self.Row_controlP_number) x_s,y_s = np.meshgrid(init_x, init_y) self.initial = np.array([x_s,y_s])
def _makeT(self,cp): with tf.variable_scope('_makeT'): cp = tf.reshape(cp,(-1,2,self.Column_controlP_number*self.Row_controlP_number)) cp = tf.cast(cp,'float32') N_f = tf.shape(cp)[0] #c_s x,y = tf.linspace(-1.,1.,self.Column_controlP_number),tf.linspace(-1.,1.,self.Row_controlP_number) x,y = tf.meshgrid(x,y) xs,ys = tf.transpose(tf.reshape(x,(-1,1))),tf.transpose(tf.reshape(y,(-1,1))) cp_s = tf.concat([xs,ys],0) cp_s_trans = tf.transpose(cp_s) ##===Compute distance R xs_trans,ys_trans = tf.transpose(tf.stack([xs],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([ys],axis=2),perm=[1,0,2]) xs, xs_trans = tf.meshgrid(xs,xs_trans);ys, ys_trans = tf.meshgrid(ys,ys_trans) Rx,Ry = tf.square(tf.subtract(xs,xs_trans)),tf.square(tf.subtract(ys,ys_trans)) R = tf.add(Rx,Ry) R = tf.multiply(R,tf.log(tf.clip_by_value(R,1e-10,1e+10))) ones = tf.ones([tf.multiply(self.Row_controlP_number,self.Column_controlP_number),1],tf.float32) ones_trans = tf.transpose(ones) zeros = tf.zeros([3,3],tf.float32) Deltas1 = tf.concat([ones, cp_s_trans, R],1) Deltas2 = tf.concat([ones_trans,cp_s],0) Deltas2 = tf.concat([zeros,Deltas2],1) Deltas = tf.concat([Deltas1,Deltas2],0) ##get deltas_inv Deltas_inv = tf.matrix_inverse(Deltas) Deltas_inv = tf.expand_dims(Deltas_inv,0) Deltas_inv = tf.reshape(Deltas_inv,[-1]) Deltas_inv_f = tf.tile(Deltas_inv,tf.stack([N_f])) Deltas_inv_f = tf.reshape(Deltas_inv_f,tf.stack([N_f,self.Column_controlP_number*self.Row_controlP_number+3, -1])) cp_trans =tf.transpose(cp,perm=[0,2,1]) zeros_f_In = tf.zeros([N_f,3,2],tf.float32) cp = tf.concat([cp_trans,zeros_f_In],1) T = tf.transpose(tf.matmul(Deltas_inv_f,cp),[0,2,1]) return T
def spatial_expected_softmax(x, temp=1): assert len(x.get_shape()) == 4 vals = [] for dim in [0, 1]: dim_val = x.get_shape()[dim + 1].value lin = tf.linspace(-1.0, 1.0, dim_val) lin = tf.expand_dims(lin, 1 - dim) lin = tf.expand_dims(lin, 0) lin = tf.expand_dims(lin, 3) m = tf.reduce_max(x, [1, 2], keep_dims=True) e = tf.exp((x - m) / temp) + 1e-5 val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2])) vals.append(tf.expand_dims(val, 2)) return tf.reshape(tf.concat(axis=2, values=vals), [-1, x.get_shape()[-1].value * 2])
def generate_anchors(boxes, height, width, conv_height, conv_width): '''Generate anchors for given geometry boxes: K x 2 tensor for anchor geometries, K different sizes height: source image height width: source image width conv_height: convolution layer height conv_width: convolution layer width returns: conv_height x conv_width x K x 4 tensor with boxes for all positions. Last dimension 4 numbers are (y, x, h, w) ''' k, _ = boxes.get_shape().as_list() height, width = tf.cast(height, tf.float32), tf.cast(width, tf.float32) grid = tf.transpose(tf.stack(tf.meshgrid( tf.linspace(-0.5, height - 0.5, conv_height), tf.linspace(-0.5, width - 0.5, conv_width)), axis=2), [1, 0, 2]) # convert boxes from K x 2 to 1 x 1 x K x 2 boxes = tf.expand_dims(tf.expand_dims(boxes, 0), 0) # convert grid from H' x W' x 2 to H' x W' x 1 x 2 grid = tf.expand_dims(grid, 2) # combine them into single H' x W' x K x 4 tensor return tf.concat( 3, [tf.tile(grid, [1, 1, k, 1]), tf.tile(boxes, [conv_height, conv_width, 1, 1])] )
def _makeT(self,cp): with tf.variable_scope('_makeT'): cp = tf.reshape(cp,(-1,3,self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number)) cp = tf.cast(cp,'float32') N_f = tf.shape(cp)[0] #c_s x,y,z = tf.linspace(-1.,1.,self.X_controlP_number),tf.linspace(-1.,1.,self.Y_controlP_number),tf.linspace(-1.,1.,self.Z_controlP_number) x = tf.tile(x,[self.Y_controlP_number*self.Z_controlP_number]) y = tf.tile(self._repeat(y,self.X_controlP_number,'float32'),[self.Z_controlP_number]) z = self._repeat(z,self.X_controlP_number*self.Y_controlP_number,'float32') xs,ys,zs = tf.transpose(tf.reshape(x,(-1,1))),tf.transpose(tf.reshape(y,(-1,1))),tf.transpose(tf.reshape(z,(-1,1))) cp_s = tf.concat([xs,ys,zs],0) cp_s_trans = tf.transpose(cp_s) # (4*4*4)*3 -> 64 * 3 ##===Compute distance R xs_trans,ys_trans,zs_trans = tf.transpose(tf.stack([xs],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([ys],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([zs],axis=2),perm=[1,0,2]) xs, xs_trans = tf.meshgrid(xs,xs_trans);ys, ys_trans = tf.meshgrid(ys,ys_trans);zs, zs_trans = tf.meshgrid(zs,zs_trans) Rx,Ry, Rz = tf.square(tf.subtract(xs,xs_trans)),tf.square(tf.subtract(ys,ys_trans)),tf.square(tf.subtract(zs,zs_trans)) R = tf.add_n([Rx,Ry,Rz]) R = tf.multiply(R,tf.log(tf.clip_by_value(R,1e-10,1e+10))) ones = tf.ones([self.Y_controlP_number*self.X_controlP_number*self.Z_controlP_number,1],tf.float32) ones_trans = tf.transpose(ones) zeros = tf.zeros([4,4],tf.float32) Deltas1 = tf.concat([ones, cp_s_trans, R],1) Deltas2 = tf.concat([ones_trans,cp_s],0) Deltas2 = tf.concat([zeros,Deltas2],1) Deltas = tf.concat([Deltas1,Deltas2],0) ##get deltas_inv Deltas_inv = tf.matrix_inverse(Deltas) Deltas_inv = tf.expand_dims(Deltas_inv,0) Deltas_inv = tf.reshape(Deltas_inv,[-1]) Deltas_inv_f = tf.tile(Deltas_inv,tf.stack([N_f])) Deltas_inv_f = tf.reshape(Deltas_inv_f,tf.stack([N_f,self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number+4, -1])) cp_trans =tf.transpose(cp,perm=[0,2,1]) zeros_f_In = tf.zeros([N_f,4,3],tf.float32) cp = tf.concat([cp_trans,zeros_f_In],1) T = tf.transpose(tf.matmul(Deltas_inv_f,cp),[0,2,1]) return T
def meshgrid(height, width): x = tf.tile(tf.linspace(-1.,1.,width), [height]) y = repeat(tf.linspace(-1.,1.,height), width) return x, y
def gaussian_blur(input, filter_size, filter_sampling_range=3.5, strides=[1, 1, 1, 1], padding='SAME'): """ Blur input with a 2D Gaussian filter of size filter_size x filter_size. The filter's values are sampled from an evenly spaced grid on the 2D standard normal distribution in the range [-filter_sampling_range, filter_sampling_range] in both dimensions. :param input: A rank-4 tensor with shape=(samples, x, y, n_channels). The same Gaussian filter will be applied to all n_channels feature maps of input. :param filter_size: The size of one edge of the square-shaped Gaussian filter. :param filter_sampling_range: The range in which to sample from the standard normal distribution in both dimensions, i.e. a sampling range of 1 corresponds to sampling in a square grid that bounds the standard deviation circle. :param strides: Param strides as passed to tf.nn.depthwise_conv2d. :param padding: Param padding as passed to tf.nn.depthwise_conv2d. :return: The result of the Gaussian blur as a rank-4 tensor with the same shape as input. """ # make 2D distribution mu = np.repeat(np.float32(0.), 2) sig = np.repeat(np.float32(1.), 2) dist = tf.contrib.distributions.MultivariateNormalDiag(mu, sig) # sample from distribution on a grid sampling_range = tf.cast(filter_sampling_range, tf.float32) x_1D = tf.linspace(-sampling_range, sampling_range, filter_size) x = tf.stack(tf.meshgrid(x_1D, x_1D), 2) kern = dist.pdf(x) kern /= tf.reduce_sum(kern) kern = tf.reshape(kern, kern.shape.as_list() + [1, 1]) kern = tf.tile(kern, [1, 1, input.shape.as_list()[-1], 1]) return tf.nn.depthwise_conv2d(input, kern, strides, padding)
def _meshgrid(height, width): with tf.variable_scope('_meshgrid'): x_t = tf.matmul(tf.ones(shape=tf.stack([height, 1])), tf.transpose( tf.expand_dims(tf.linspace(-1.0, 1.0, width), 1), [1, 0])) y_t = tf.matmul(tf.expand_dims( tf.linspace(-1.0, 1.0, height), 1), tf.ones(shape=tf.stack([1, width]))) x_t_flat = tf.reshape(x_t, (1, -1)) y_t_flat = tf.reshape(y_t, (1, -1)) ones = tf.ones_like(x_t_flat) grid = tf.concat([x_t_flat, y_t_flat, ones], 0) return grid
def uv_grid(shape): u, v = tf.meshgrid(tf.linspace(0.0, 1.0, shape[1]), tf.linspace(0.0, 1.0, shape[0])) return u, v
def tf_percentile(images): min = tf.reduce_min(tf.log(1.0 + images)) max = tf.reduce_max(tf.log(1.0 + images)) histogram = tf.histogram_fixed_width(tf.reshape(images, [-1]), [min, max]) values = tf.linspace(min, max, 100) csum = tf.cumsum(histogram) csum_float = tf.cast(csum, tf.float32) / tf.cast(tf.size(csum), tf.float32) argmin_index = tf.cast(tf.argmin((csum_float - 0.95) ** 2.0, axis = 0), tf.int32) return tf.exp(values[argmin_index]) - 1.0
def lat_long_grid(shape, epsilon = 1.0e-12): return tf.meshgrid(tf.linspace(-np.pi, np.pi, shape[1]), tf.linspace(-np.pi / 2.0 + epsilon, np.pi / 2.0 - epsilon, shape[0]))
def uv_grid(shape): return tf.meshgrid(tf.linspace(-0.5, 0.5, shape[1]), tf.linspace(-0.5, 0.5, shape[0])) # Restricted rotations of (a, b, c) to (x, y, z), implemented using # permutations and negations.
def xyz_grid(shape, face = "front"): a, b = tf.meshgrid(tf.linspace(-1.0, 1.0, shape[1]), tf.linspace(-1.0, 1.0, shape[0])) c = tf.constant(1.0, dtype = tf.float32, shape = shape) return switch_face(a, b, c, face) # Convert Cartesian coordinates (x, y, z) to latitude (T) and longitude (S).
def backproject_cubic(depth, shape, face): a, b = tf.meshgrid(tf.linspace(-1.0, 1.0, shape[2]), tf.linspace(-1.0, 1.0, shape[1])) A = depth * tf.expand_dims(tf.tile(tf.expand_dims(a, 0), [shape[0], 1, 1]), 3) B = depth * tf.expand_dims(tf.tile(tf.expand_dims(b, 0), [shape[0], 1, 1]), 3) C = depth x, y, z = switch_face(A, B, C, face) return tf.sqrt(x ** 2.0 + z ** 2.0)
def backproject_rectilinear(depth, K, shape, face): u, v = tf.meshgrid(tf.linspace(-1.0, 1.0, shape[2]), tf.linspace(-1.0, 1.0, shape[1])) u = tf.expand_dims(tf.tile(tf.expand_dims(u, 0), [shape[0], 1, 1]), 3) v = tf.expand_dims(tf.tile(tf.expand_dims(v, 0), [shape[0], 1, 1]), 3) A = (u - K[2]) * depth / K[0] B = (v - K[3]) * depth / K[1] C = depth x, y, z = switch_face(A, B, C, face) return tf.sqrt(x ** 2.0 + z ** 2.0)
def rectilinear_xyz(K, shape, face = "front"): u, v = tf.meshgrid(tf.linspace(-1.0, 1.0, shape[1]), tf.linspace(-1.0, 1.0, shape[0])) # X = (u - c_x) * z / f_x # Y = (v - c_y) * z / f_y a = (u - K[2]) / K[0] b = (v - K[3]) / K[1] c = tf.ones([shape[1], shape[0]], dtype = tf.float32) return switch_face(a, b, c, face)
def get_output_for(self, input, **kwargs): return spatial_expected_softmax(input) # , self.temp) # max_ = tf.reduce_max(input, reduction_indices=[1, 2], keep_dims=True) # exp = tf.exp(input - max_) + 1e-5 # vals = [] # # for dim in [0, 1]: # dim_val = input.get_shape()[dim + 1].value # lin = tf.linspace(-1.0, 1.0, dim_val) # lin = tf.expand_dims(lin, 1 - dim) # lin = tf.expand_dims(lin, 0) # lin = tf.expand_dims(lin, 3) # m = tf.reduce_max(input, [1, 2], keep_dims=True) # e = tf.exp(input - m) + 1e-5 # val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2])) # vals.append(tf.expand_dims(val, 2)) # # return tf.reshape(tf.concat(2, vals), [-1, # input.get_shape()[-1].value * 2]) # import ipdb; ipdb.set_trace() # input.get_shape() # exp / tf.reduce_sum(exp, reduction_indices=[1, 2], keep_dims=True) # import ipdb; # ipdb.set_trace() # spatial softmax? # for dim in range(2): # val = obs.get_shape()[dim + 1].value # lin = tf.linspace(-1.0, 1.0, val) # lin = tf.expand_dims(lin, 1 - dim) # lin = tf.expand_dims(lin, 0) # lin = tf.expand_dims(lin, 3) # m = tf.reduce_max(e, [1, 2], keep_dims=True) # e = tf.exp(e - m) + 1e-3 # val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2]))
def _meshgrid(height, width): x_t_flat, y_t_flat = tf.meshgrid(tf.linspace(-1., 1., width), tf.linspace(-1., 1., height)) ones = tf.ones_like(x_t_flat) grid = tf.concat(values=[x_t_flat, y_t_flat, ones], axis=0) return grid
def warping_meshgrid(height, width): x_t_flat, y_t_flat = tf.meshgrid(tf.linspace(-1., 1., width), tf.linspace(-1., 1., height)) grid = tf.concat(values=[x_t_flat, y_t_flat], axis=0) return grid
def test_LinSpace(self): t = tf.linspace(0., 10., 15) self.check(t)
def get_output_for(self, input, **kwargs): return spatial_expected_softmax(input)#, self.temp) # max_ = tf.reduce_max(input, reduction_indices=[1, 2], keep_dims=True) # exp = tf.exp(input - max_) + 1e-5 # vals = [] # # for dim in [0, 1]: # dim_val = input.get_shape()[dim + 1].value # lin = tf.linspace(-1.0, 1.0, dim_val) # lin = tf.expand_dims(lin, 1 - dim) # lin = tf.expand_dims(lin, 0) # lin = tf.expand_dims(lin, 3) # m = tf.reduce_max(input, [1, 2], keep_dims=True) # e = tf.exp(input - m) + 1e-5 # val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2])) # vals.append(tf.expand_dims(val, 2)) # # return tf.reshape(tf.concat(axis=2, values=vals), [-1, input.get_shape()[-1].value * 2]) # import ipdb; ipdb.set_trace() # input.get_shape() # exp / tf.reduce_sum(exp, reduction_indices=[1, 2], keep_dims=True) # import ipdb; # ipdb.set_trace() # spatial softmax? # for dim in range(2): # val = obs.get_shape()[dim + 1].value # lin = tf.linspace(-1.0, 1.0, val) # lin = tf.expand_dims(lin, 1 - dim) # lin = tf.expand_dims(lin, 0) # lin = tf.expand_dims(lin, 3) # m = tf.reduce_max(e, [1, 2], keep_dims=True) # e = tf.exp(e - m) + 1e-3 # val = tf.reduce_sum(e * lin, [1, 2]) / (tf.reduce_sum(e, [1, 2]))
def _makeT(self,cp): with tf.variable_scope('_makeT'): cp = tf.reshape(cp,(-1,3,self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number)) cp = tf.cast(cp,'float32') N_f = tf.shape(cp)[0] #c_s x,y,z = tf.linspace(-1.,1.,self.X_controlP_number),tf.linspace(-1.,1.,self.Y_controlP_number),tf.linspace(-1.,1.,self.Z_controlP_number) x = tf.tile(x,[self.Y_controlP_number*self.Z_controlP_number]) y = tf.tile(self._repeat(y,self.X_controlP_number,'float32'),[self.Z_controlP_number]) z = self._repeat(z,self.X_controlP_number*self.Y_controlP_number,'float32') xs,ys,zs = tf.transpose(tf.reshape(x,(-1,1))),tf.transpose(tf.reshape(y,(-1,1))),tf.transpose(tf.reshape(z,(-1,1))) cp_s = tf.concat([xs,ys,zs],0) cp_s_trans = tf.transpose(cp_s) # (4*4*4)*3 -> 64 * 3 ##===Compute distance R xs_trans,ys_trans,zs_trans = tf.transpose(tf.stack([xs],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([ys],axis=2),perm=[1,0,2]),tf.transpose(tf.stack([zs],axis=2),perm=[1,0,2]) xs, xs_trans = tf.meshgrid(xs,xs_trans);ys, ys_trans = tf.meshgrid(ys,ys_trans);zs, zs_trans = tf.meshgrid(zs,zs_trans) Rx,Ry, Rz = tf.square(tf.subtract(xs,xs_trans)),tf.square(tf.subtract(ys,ys_trans)),tf.square(tf.subtract(zs,zs_trans)) R = tf.add_n([Rx,Ry,Rz]) #print("R",sess.run(R)) R = tf.multiply(R,tf.log(tf.clip_by_value(R,1e-10,1e+10))) #print("R",sess.run(R)) ones = tf.ones([self.Y_controlP_number*self.X_controlP_number*self.Z_controlP_number,1],tf.float32) ones_trans = tf.transpose(ones) zeros = tf.zeros([4,4],tf.float32) Deltas1 = tf.concat([ones, cp_s_trans, R],1) Deltas2 = tf.concat([ones_trans,cp_s],0) Deltas2 = tf.concat([zeros,Deltas2],1) Deltas = tf.concat([Deltas1,Deltas2],0) #print("Deltas",sess.run(Deltas)) ##get deltas_inv Deltas_inv = tf.matrix_inverse(Deltas) Deltas_inv = tf.expand_dims(Deltas_inv,0) Deltas_inv = tf.reshape(Deltas_inv,[-1]) Deltas_inv_f = tf.tile(Deltas_inv,tf.stack([N_f])) Deltas_inv_f = tf.reshape(Deltas_inv_f,tf.stack([N_f,self.X_controlP_number*self.Y_controlP_number*self.Z_controlP_number+4, -1])) cp_trans =tf.transpose(cp,perm=[0,2,1]) zeros_f_In = tf.zeros([N_f,4,3],tf.float32) cp = tf.concat([cp_trans,zeros_f_In],1) #print("cp",sess.run(cp)) #print("Deltas_inv_f",sess.run(Deltas_inv_f)) T = tf.transpose(tf.matmul(Deltas_inv_f,cp),[0,2,1]) #print("T",sess.run(T)) return T
