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77 lines
3.2 KiB
Python
Executable File
77 lines
3.2 KiB
Python
Executable File
### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
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### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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import os.path
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import torch
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from data.base_dataset import BaseDataset, get_img_params, get_transform, concat_frame
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from data.image_folder import make_grouped_dataset, check_path_valid
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from PIL import Image
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import numpy as np
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class TestDataset(BaseDataset):
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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, opt.phase + '_A')
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self.dir_B = os.path.join(opt.dataroot, opt.phase + '_B')
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self.use_real = opt.use_real_img
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self.A_is_label = self.opt.label_nc != 0
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self.A_paths = sorted(make_grouped_dataset(self.dir_A))
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if self.use_real:
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self.B_paths = sorted(make_grouped_dataset(self.dir_B))
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check_path_valid(self.A_paths, self.B_paths)
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if self.opt.use_instance:
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self.dir_inst = os.path.join(opt.dataroot, opt.phase + '_inst')
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self.I_paths = sorted(make_grouped_dataset(self.dir_inst))
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check_path_valid(self.A_paths, self.I_paths)
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self.init_frame_idx(self.A_paths)
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def __getitem__(self, index):
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self.A, self.B, self.I, seq_idx = self.update_frame_idx(self.A_paths, index)
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tG = self.opt.n_frames_G
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A_img = Image.open(self.A_paths[seq_idx][0]).convert('RGB')
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params = get_img_params(self.opt, A_img.size)
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transform_scaleB = get_transform(self.opt, params)
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transform_scaleA = get_transform(self.opt, params, method=Image.NEAREST, normalize=False) if self.A_is_label else transform_scaleB
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frame_range = list(range(tG)) if self.A is None else [tG-1]
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for i in frame_range:
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A_path = self.A_paths[seq_idx][self.frame_idx + i]
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Ai = self.get_image(A_path, transform_scaleA, is_label=self.A_is_label)
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self.A = concat_frame(self.A, Ai, tG)
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if self.use_real:
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B_path = self.B_paths[seq_idx][self.frame_idx + i]
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Bi = self.get_image(B_path, transform_scaleB)
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self.B = concat_frame(self.B, Bi, tG)
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else:
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self.B = 0
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if self.opt.use_instance:
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I_path = self.I_paths[seq_idx][self.frame_idx + i]
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Ii = self.get_image(I_path, transform_scaleA) * 255.0
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self.I = concat_frame(self.I, Ii, tG)
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else:
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self.I = 0
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self.frame_idx += 1
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return_list = {'A': self.A, 'B': self.B, 'inst': self.I, 'A_path': A_path, 'change_seq': self.change_seq}
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return return_list
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def get_image(self, A_path, transform_scaleA, is_label=False):
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A_img = Image.open(A_path)
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A_scaled = transform_scaleA(A_img)
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if is_label:
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A_scaled *= 255.0
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return A_scaled
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def __len__(self):
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return sum(self.frames_count)
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def n_of_seqs(self):
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return len(self.A_paths)
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def name(self):
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return 'TestDataset' |