from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import torch # from torch.autograd import Variable from lpips.trainer import * from lpips.lpips import * # class PerceptualLoss(torch.nn.Module): # def __init__(self, model='lpips', net='alex', spatial=False, use_gpu=False, gpu_ids=[0], version='0.1'): # VGG using our perceptually-learned weights (LPIPS metric) # # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss # super(PerceptualLoss, self).__init__() # print('Setting up Perceptual loss...') # self.use_gpu = use_gpu # self.spatial = spatial # self.gpu_ids = gpu_ids # self.model = dist_model.DistModel() # self.model.initialize(model=model, net=net, use_gpu=use_gpu, spatial=self.spatial, gpu_ids=gpu_ids, version=version) # print('...[%s] initialized'%self.model.name()) # print('...Done') # def forward(self, pred, target, normalize=False): # """ # Pred and target are Variables. # If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1] # If normalize is False, assumes the images are already between [-1,+1] # Inputs pred and target are Nx3xHxW # Output pytorch Variable N long # """ # if normalize: # target = 2 * target - 1 # pred = 2 * pred - 1 # return self.model.forward(target, pred) def normalize_tensor(in_feat,eps=1e-10): norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True)) return in_feat/(norm_factor+eps) def l2(p0, p1, range=255.): return .5*np.mean((p0 / range - p1 / range)**2) def psnr(p0, p1, peak=255.): return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2)) def dssim(p0, p1, range=255.): from skimage.measure import compare_ssim return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2. def rgb2lab(in_img,mean_cent=False): from skimage import color img_lab = color.rgb2lab(in_img) if(mean_cent): img_lab[:,:,0] = img_lab[:,:,0]-50 return img_lab def tensor2np(tensor_obj): # change dimension of a tensor object into a numpy array return tensor_obj[0].cpu().float().numpy().transpose((1,2,0)) def np2tensor(np_obj): # change dimenion of np array into tensor array return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1))) def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False): # image tensor to lab tensor from skimage import color img = tensor2im(image_tensor) img_lab = color.rgb2lab(img) if(mc_only): img_lab[:,:,0] = img_lab[:,:,0]-50 if(to_norm and not mc_only): img_lab[:,:,0] = img_lab[:,:,0]-50 img_lab = img_lab/100. return np2tensor(img_lab) def tensorlab2tensor(lab_tensor,return_inbnd=False): from skimage import color import warnings warnings.filterwarnings("ignore") lab = tensor2np(lab_tensor)*100. lab[:,:,0] = lab[:,:,0]+50 rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1) if(return_inbnd): # convert back to lab, see if we match lab_back = color.rgb2lab(rgb_back.astype('uint8')) mask = 1.*np.isclose(lab_back,lab,atol=2.) mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis]) return (im2tensor(rgb_back),mask) else: return im2tensor(rgb_back) def load_image(path): if(path[-3:] == 'dng'): import rawpy with rawpy.imread(path) as raw: img = raw.postprocess() elif(path[-3:]=='bmp' or path[-3:]=='jpg' or path[-3:]=='png'): import cv2 return cv2.imread(path)[:,:,::-1] else: img = (255*plt.imread(path)[:,:,:3]).astype('uint8') return img def rgb2lab(input): from skimage import color return color.rgb2lab(input / 255.) def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): image_numpy = image_tensor[0].cpu().float().numpy() image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor return image_numpy.astype(imtype) def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): return torch.Tensor((image / factor - cent) [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) def tensor2vec(vector_tensor): return vector_tensor.data.cpu().numpy()[:, :, 0, 0] def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): # def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.): image_numpy = image_tensor[0].cpu().float().numpy() image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor return image_numpy.astype(imtype) def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): # def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): return torch.Tensor((image / factor - cent) [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) def voc_ap(rec, prec, use_07_metric=False): """ ap = voc_ap(rec, prec, [use_07_metric]) Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11 point method (default:False). """ if use_07_metric: # 11 point metric ap = 0. for t in np.arange(0., 1.1, 0.1): if np.sum(rec >= t) == 0: p = 0 else: p = np.max(prec[rec >= t]) ap = ap + p / 11. else: # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], rec, [1.])) mpre = np.concatenate(([0.], prec, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap