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utils.py
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import torch
import glob
import os
import shutil
import numpy as np
import matplotlib.pyplot as plt
from torch.optim.lr_scheduler import _LRScheduler
from PIL import Image
def depth2label_sid(depth, K=80.0, alpha=1.0, beta=90.4414):
alpha = torch.tensor(alpha)
beta = torch.tensor(beta)
K = torch.tensor(K)
if torch.cuda.is_available():
alpha = alpha.cuda()
beta = beta.cuda()
K = K.cuda()
label = K * torch.log(depth / alpha) / torch.log(beta / alpha)
label = torch.max(label, torch.zeros(label.shape).cuda()) # prevent negative label.
if torch.cuda.is_available():
label = label.cuda()
return label.int()
def label2depth_sid(label, K=80.0, alpha=1.0, beta=89.4648, gamma=-0.9766):
if torch.cuda.is_available():
alpha = torch.tensor(alpha).cuda()
beta = torch.tensor(beta).cuda()
K = torch.tensor(K).cuda()
else:
alpha = torch.tensor(alpha)
beta = torch.tensor(beta)
K = torch.tensor(K)
label = label.float()
ti_0 = torch.exp(torch.log(alpha) + torch.log(beta/alpha)*label/K) # t(i)
ti_1 = torch.exp(torch.log(alpha) + torch.log(beta/alpha)*(label+1)/K) # t(i+1)
depth = (ti_0 + ti_1) / 2 - gamma # avg of t(i) & t(i+1)
return depth.float()
def colored_depthmap(depth, d_min=None, d_max=None):
if d_min is None:
d_min = np.min(depth)
if d_max is None:
d_max = np.max(depth)
depth_relative = (depth - d_min) / (d_max - d_min)
return 255 * plt.cm.jet(depth_relative)[:, :, :3] # H, W, C
def batch_merge_into_row(input, depth_target, depth_pred):
# _input, depth_target, and depth_pred should in shape N,C,H,W
N,C,H,W = input.shape
if N > 6:
N = 6 # plot maximum 6 figures
for i in range(N):
_input = input[i,:,:,:]
_depth_target = depth_target[i,:,:]
_depth_pred = depth_pred[i,:,:]
_input = 256 * np.transpose(_input.cpu().numpy(), (1, 2, 0)) # H, W, C
_depth_target = 256* np.squeeze(_depth_target.cpu().numpy())
_depth_pred = 256* np.squeeze(_depth_pred.data.cpu().numpy())
d_min = min(np.min(_depth_target), np.min(_depth_pred))
d_max = max(np.max(_depth_target), np.max(_depth_pred))
_depth_target = colored_depthmap(_depth_target, d_min, d_max)
_depth_pred = colored_depthmap(_depth_pred, d_min, d_max)
if i==0:
img_merge = np.hstack([_input, _depth_target, _depth_pred])
else:
row = np.hstack([_input, _depth_target, _depth_pred])
img_merge = add_row(img_merge, row)
return img_merge
def merge_into_row(_input, depth_target, depth_pred):
if _input.dim() == 4:
_input = _input[0,:,:,:]
if depth_target.dim() == 4:
depth_target = depth_target[0,:,:]
if depth_pred.dim() == 4:
depth_pred = depth_pred[0,:,:]
_input = 255 * np.transpose(_input.cpu().numpy(), (1, 2, 0)) # H, W, C
depth_target_cpu = np.squeeze(depth_target.cpu().numpy())
depth_pred_cpu = np.squeeze(depth_pred.data.cpu().numpy())
d_min = min(np.min(depth_target_cpu), np.min(depth_pred_cpu))
d_max = max(np.max(depth_target_cpu), np.max(depth_pred_cpu))
depth_target_col = colored_depthmap(depth_target_cpu, d_min, d_max)
depth_pred_col = colored_depthmap(depth_pred_cpu, d_min, d_max)
img_merge = np.hstack([_input, depth_target_col, depth_pred_col])
return img_merge
def merge_into_row_with_gt(_input, depth_input, depth_target, depth_pred):
rgb = 255 * np.transpose(np.squeeze(_input.cpu().numpy()), (1, 2, 0)) # H, W, C
depth_input_cpu = np.squeeze(depth_input.cpu().numpy())
depth_target_cpu = np.squeeze(depth_target.cpu().numpy())
depth_pred_cpu = np.squeeze(depth_pred.data.cpu().numpy())
d_min = min(np.min(depth_input_cpu), np.min(depth_target_cpu), np.min(depth_pred_cpu))
d_max = max(np.max(depth_input_cpu), np.max(depth_target_cpu), np.max(depth_pred_cpu))
depth_input_col = colored_depthmap(depth_input_cpu, d_min, d_max)
depth_target_col = colored_depthmap(depth_target_cpu, d_min, d_max)
depth_pred_col = colored_depthmap(depth_pred_cpu, d_min, d_max)
img_merge = np.hstack([rgb, depth_input_col, depth_target_col, depth_pred_col])
return img_merge
def add_row(img_merge, row):
return np.vstack([img_merge, row])
def save_image(img_merge, filename):
img_merge = Image.fromarray(img_merge.astype('uint8'))
img_merge.save(filename)
class PolynomialLRDecay(_LRScheduler):
"""Polynomial learning rate decay until step reach to max_decay_step
Args:
optimizer (Optimizer): Wrapped optimizer.
max_decay_steps: after this step, we stop decreasing learning rate
end_learning_rate: scheduler stoping learning rate decay, value of learning rate must be this value
power: The power of the polynomial.
"""
def __init__(self, optimizer, max_decay_steps, end_learning_rate=0.0001, power=1.0):
if max_decay_steps <= 1.:
raise ValueError('max_decay_steps should be greater than 1.')
self.max_decay_steps = max_decay_steps
self.end_learning_rate = end_learning_rate
self.power = power
self.last_step = 0
super().__init__(optimizer)
def get_lr(self):
if self.last_step > self.max_decay_steps:
return [self.end_learning_rate for _ in self.base_lrs]
return [(base_lr - self.end_learning_rate) *
((1 - self.last_step / self.max_decay_steps) ** (self.power)) +
self.end_learning_rate for base_lr in self.base_lrs]
def step(self, step=None):
if step is None:
step = self.last_step + 1
self.last_step = step if step != 0 else 1
if self.last_step <= self.max_decay_steps:
decay_lrs = [(base_lr - self.end_learning_rate) *
((1 - self.last_step / self.max_decay_steps) ** (self.power)) +
self.end_learning_rate for base_lr in self.base_lrs]
for param_group, lr in zip(self.optimizer.param_groups, decay_lrs):
param_group['lr'] = lr