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import os.path |
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from data.base_dataset import BaseDataset, get_transform |
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from data.image_folder import make_dataset |
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from PIL import Image |
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import numpy as np |
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import csv |
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import torch |
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import torchvision.transforms as transforms |
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def getfeats(featpath): |
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trans_points = np.empty([5,2],dtype=np.int64) |
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with open(featpath, 'r') as csvfile: |
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reader = csv.reader(csvfile, delimiter=' ') |
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for ind,row in enumerate(reader): |
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trans_points[ind,:] = row |
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return trans_points |
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def getSoft(size,xb,yb,boundwidth=5.0): |
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xarray = np.tile(np.arange(0,size[1]),(size[0],1)) |
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yarray = np.tile(np.arange(0,size[0]),(size[1],1)).transpose() |
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cxdists = [] |
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cydists = [] |
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for i in range(len(xb)): |
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xba = np.tile(xb[i],(size[1],1)).transpose() |
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yba = np.tile(yb[i],(size[0],1)) |
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cxdists.append(np.abs(xarray-xba)) |
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cydists.append(np.abs(yarray-yba)) |
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xdist = np.minimum.reduce(cxdists) |
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ydist = np.minimum.reduce(cydists) |
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manhdist = np.minimum.reduce([xdist,ydist]) |
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im = (manhdist+1) / (boundwidth+1) * 1.0 |
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im[im>=1.0] = 1.0 |
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return im |
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class SingleDataset(BaseDataset): |
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@staticmethod |
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def modify_commandline_options(parser, is_train): |
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return parser |
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def initialize(self, opt): |
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self.opt = opt |
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self.root = opt.dataroot |
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self.dir_A = os.path.join(opt.dataroot) |
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imglist = 'datasets/apdrawing_list/%s/%s.txt' % (opt.phase, opt.dataroot) |
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if os.path.exists(imglist): |
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lines = open(imglist, 'r').read().splitlines() |
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self.A_paths = sorted(lines) |
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else: |
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self.A_paths = make_dataset(self.dir_A) |
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self.A_paths = sorted(self.A_paths) |
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self.transform = get_transform(opt) |
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def __getitem__(self, index): |
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A_path = self.A_paths[index] |
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A_img = Image.open(A_path).convert('RGB') |
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A = self.transform(A_img) |
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if self.opt.which_direction == 'BtoA': |
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input_nc = self.opt.output_nc |
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output_nc = self.opt.input_nc |
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else: |
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input_nc = self.opt.input_nc |
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output_nc = self.opt.output_nc |
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if input_nc == 1: |
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tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114 |
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A = tmp.unsqueeze(0) |
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item = {'A': A, 'A_paths': A_path} |
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if self.opt.use_local: |
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regions = ['eyel','eyer','nose','mouth'] |
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basen = os.path.basename(A_path)[:-4]+'.txt' |
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featdir = self.opt.lm_dir |
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featpath = os.path.join(featdir,basen) |
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feats = getfeats(featpath) |
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mouth_x = int((feats[3,0]+feats[4,0])/2.0) |
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mouth_y = int((feats[3,1]+feats[4,1])/2.0) |
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ratio = self.opt.fineSize / 256 |
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EYE_H = self.opt.EYE_H * ratio |
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EYE_W = self.opt.EYE_W * ratio |
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NOSE_H = self.opt.NOSE_H * ratio |
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NOSE_W = self.opt.NOSE_W * ratio |
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MOUTH_H = self.opt.MOUTH_H * ratio |
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MOUTH_W = self.opt.MOUTH_W * ratio |
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center = torch.LongTensor([[feats[0,0],feats[0,1]-4*ratio],[feats[1,0],feats[1,1]-4*ratio],[feats[2,0],feats[2,1]-NOSE_H/2+16*ratio],[mouth_x,mouth_y]]) |
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item['center'] = center |
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rhs = [int(EYE_H),int(EYE_H),int(NOSE_H),int(MOUTH_H)] |
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rws = [int(EYE_W),int(EYE_W),int(NOSE_W),int(MOUTH_W)] |
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if self.opt.soft_border: |
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soft_border_mask4 = [] |
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for i in range(4): |
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xb = [np.zeros(rhs[i]),np.ones(rhs[i])*(rws[i]-1)] |
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yb = [np.zeros(rws[i]),np.ones(rws[i])*(rhs[i]-1)] |
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soft_border_mask = getSoft([rhs[i],rws[i]],xb,yb) |
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soft_border_mask4.append(torch.Tensor(soft_border_mask).unsqueeze(0)) |
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item['soft_'+regions[i]+'_mask'] = soft_border_mask4[i] |
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for i in range(4): |
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item[regions[i]+'_A'] = A[:,(center[i,1]-rhs[i]/2).to(torch.long): |
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(center[i,1]+rhs[i]/2).to(torch.long), |
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(center[i,0]-rws[i]/2).to(torch.long): |
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(center[i,0]+rws[i]/2).to(torch.long)] |
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if self.opt.soft_border: |
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item[regions[i]+'_A'] = item[regions[i]+'_A'] * soft_border_mask4[i].repeat(int(input_nc/output_nc),1,1) |
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if self.opt.compactmask: |
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cmasks0 = [] |
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cmasks = [] |
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for i in range(4): |
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cmaskpath = os.path.join(self.opt.cmask_dir,regions[i],basen[:-4]+'.png') |
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im_cmask = Image.open(cmaskpath) |
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cmask0 = transforms.ToTensor()(im_cmask) |
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if output_nc == 1 and cmask0.shape[0] == 3: |
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tmp = cmask0[0, ...] * 0.299 + cmask0[1, ...] * 0.587 + cmask0[2, ...] * 0.114 |
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cmask0 = tmp.unsqueeze(0) |
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cmask0 = (cmask0 >= 0.5).float() |
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cmasks0.append(cmask0) |
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cmask = cmask0.clone() |
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cmask = cmask[:,(center[i,1]-rhs[i]/2).to(torch.long):(center[i,1]+rhs[i]/2).to(torch.long),(center[i,0]-rws[i]/2).to(torch.long):(center[i,0]+rws[i]/2).to(torch.long)] |
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cmasks.append(cmask) |
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item['cmaskel'] = cmasks[0] |
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item['cmasker'] = cmasks[1] |
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item['cmask'] = cmasks[2] |
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item['cmaskmo'] = cmasks[3] |
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if self.opt.hair_local: |
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output_nc = self.opt.output_nc |
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mask = torch.ones([output_nc,A.shape[1],A.shape[2]]) |
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for i in range(4): |
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mask[:,(center[i,1]-rhs[i]/2).to(torch.long):(center[i,1]+rhs[i]/2).to(torch.long),(center[i,0]-rws[i]/2).to(torch.long):(center[i,0]+rws[i]/2).to(torch.long)] = 0 |
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if self.opt.soft_border: |
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imgsize = self.opt.fineSize |
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maskn = mask[0].numpy() |
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masks = [np.ones([imgsize,imgsize]),np.ones([imgsize,imgsize]),np.ones([imgsize,imgsize]),np.ones([imgsize,imgsize])] |
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masks[0][1:] = maskn[:-1] |
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masks[1][:-1] = maskn[1:] |
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masks[2][:,1:] = maskn[:,:-1] |
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masks[3][:,:-1] = maskn[:,1:] |
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masks2 = [maskn-e for e in masks] |
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bound = np.minimum.reduce(masks2) |
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bound = -bound |
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xb = [] |
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yb = [] |
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for i in range(4): |
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xbi = [(center[i,0]-rws[i]/2).to(torch.long), (center[i,0]+rws[i]/2-1).to(torch.long)] |
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ybi = [(center[i,1]-rhs[i]/2).to(torch.long), (center[i,1]+rhs[i]/2-1).to(torch.long)] |
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for j in range(2): |
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maskx = bound[:,xbi[j]] |
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masky = bound[ybi[j],:] |
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tmp_a = torch.from_numpy(maskx)*xbi[j].double() |
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tmp_b = torch.from_numpy(1-maskx) |
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xb += [tmp_b*10000 + tmp_a] |
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tmp_a = torch.from_numpy(masky)*ybi[j].double() |
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tmp_b = torch.from_numpy(1-masky) |
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yb += [tmp_b*10000 + tmp_a] |
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soft = 1-getSoft([imgsize,imgsize],xb,yb) |
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soft = torch.Tensor(soft).unsqueeze(0) |
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mask = (torch.ones(mask.shape)-mask)*soft + mask |
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hair_A = (A/2+0.5) * mask.repeat(int(input_nc/output_nc),1,1) * 2 - 1 |
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item['hair_A'] = hair_A |
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item['mask'] = mask |
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if self.opt.bg_local: |
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bgdir = self.opt.bg_dir |
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bgpath = os.path.join(bgdir,basen[:-4]+'.png') |
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im_bg = Image.open(bgpath) |
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mask2 = transforms.ToTensor()(im_bg) |
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mask2 = (mask2 >= 0.5).float() |
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hair_A = (A/2+0.5) * mask.repeat(int(input_nc/output_nc),1,1) * mask2.repeat(int(input_nc/output_nc),1,1) * 2 - 1 |
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bg_A = (A/2+0.5) * (torch.ones(mask2.shape)-mask2).repeat(int(input_nc/output_nc),1,1) * 2 - 1 |
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item['hair_A'] = hair_A |
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item['bg_A'] = bg_A |
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item['mask'] = mask |
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item['mask2'] = mask2 |
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return item |
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def __len__(self): |
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return len(self.A_paths) |
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def name(self): |
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return 'SingleImageDataset' |
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