mirror of
https://github.com/NVIDIA/vid2vid.git
synced 2026-02-01 17:26:51 +00:00
77 lines
3.4 KiB
Python
Executable File
77 lines
3.4 KiB
Python
Executable File
### Copyright (C) 2017 NVIDIA Corporation. All rights reserved.
|
|
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
|
|
import os.path
|
|
import random
|
|
import torch
|
|
from data.base_dataset import BaseDataset, get_img_params, get_transform, get_video_params
|
|
from data.image_folder import make_grouped_dataset, check_path_valid
|
|
from PIL import Image
|
|
import numpy as np
|
|
|
|
class TemporalDataset(BaseDataset):
|
|
def initialize(self, opt):
|
|
self.opt = opt
|
|
self.root = opt.dataroot
|
|
self.dir_A = os.path.join(opt.dataroot, opt.phase + '_A')
|
|
self.dir_B = os.path.join(opt.dataroot, opt.phase + '_B')
|
|
self.A_is_label = self.opt.label_nc != 0
|
|
|
|
self.A_paths = sorted(make_grouped_dataset(self.dir_A))
|
|
self.B_paths = sorted(make_grouped_dataset(self.dir_B))
|
|
check_path_valid(self.A_paths, self.B_paths)
|
|
if opt.use_instance:
|
|
self.dir_inst = os.path.join(opt.dataroot, opt.phase + '_inst')
|
|
self.I_paths = sorted(make_grouped_dataset(self.dir_inst))
|
|
check_path_valid(self.A_paths, self.I_paths)
|
|
|
|
self.n_of_seqs = len(self.A_paths) # number of sequences to train
|
|
self.seq_len_max = max([len(A) for A in self.A_paths])
|
|
self.n_frames_total = self.opt.n_frames_total # current number of frames to train in a single iteration
|
|
|
|
def __getitem__(self, index):
|
|
tG = self.opt.n_frames_G
|
|
A_paths = self.A_paths[index % self.n_of_seqs]
|
|
B_paths = self.B_paths[index % self.n_of_seqs]
|
|
if self.opt.use_instance:
|
|
I_paths = self.I_paths[index % self.n_of_seqs]
|
|
|
|
# setting parameters
|
|
n_frames_total, start_idx, t_step = get_video_params(self.opt, self.n_frames_total, len(A_paths), index)
|
|
|
|
# setting transformers
|
|
B_img = Image.open(B_paths[0]).convert('RGB')
|
|
params = get_img_params(self.opt, B_img.size)
|
|
transform_scaleB = get_transform(self.opt, params)
|
|
transform_scaleA = get_transform(self.opt, params, method=Image.NEAREST, normalize=False) if self.A_is_label else transform_scaleB
|
|
|
|
# read in images
|
|
A = B = inst = 0
|
|
for i in range(n_frames_total):
|
|
A_path = A_paths[start_idx + i * t_step]
|
|
B_path = B_paths[start_idx + i * t_step]
|
|
Ai = self.get_image(A_path, transform_scaleA, is_label=self.A_is_label)
|
|
Bi = self.get_image(B_path, transform_scaleB)
|
|
|
|
A = Ai if i == 0 else torch.cat([A, Ai], dim=0)
|
|
B = Bi if i == 0 else torch.cat([B, Bi], dim=0)
|
|
|
|
if self.opt.use_instance:
|
|
I_path = I_paths[start_idx + i * t_step]
|
|
Ii = self.get_image(I_path, transform_scaleA) * 255.0
|
|
inst = Ii if i == 0 else torch.cat([inst, Ii], dim=0)
|
|
|
|
return_list = {'A': A, 'B': B, 'inst': inst, 'A_path': A_path, 'B_paths': B_path}
|
|
return return_list
|
|
|
|
def get_image(self, A_path, transform_scaleA, is_label=False):
|
|
A_img = Image.open(A_path)
|
|
A_scaled = transform_scaleA(A_img)
|
|
if is_label:
|
|
A_scaled *= 255.0
|
|
return A_scaled
|
|
|
|
def __len__(self):
|
|
return len(self.A_paths)
|
|
|
|
def name(self):
|
|
return 'TemporalDataset' |