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from PIL import Image |
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from torch.utils.data import Dataset, DataLoader |
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from augmentations import ha_augment_sample, resize_sample, spatial_augment_sample |
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from lanet_utils import to_tensor_sample |
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def image_transforms(shape, jittering): |
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def train_transforms(sample): |
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sample = resize_sample(sample, image_shape=shape) |
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sample = spatial_augment_sample(sample) |
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sample = to_tensor_sample(sample) |
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sample = ha_augment_sample(sample, jitter_paramters=jittering) |
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return sample |
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return {"train": train_transforms} |
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class GetData(Dataset): |
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def __init__(self, config, transforms=None): |
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""" |
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Get the list containing all images and labels. |
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""" |
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datafile = open(config.train_txt, "r") |
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lines = datafile.readlines() |
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dataset = [] |
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for line in lines: |
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line = line.rstrip() |
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data = line.split() |
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dataset.append(data[0]) |
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self.config = config |
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self.dataset = dataset |
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self.root = config.train_root |
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self.transforms = transforms |
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def __getitem__(self, index): |
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""" |
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Return image'data and its label. |
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""" |
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img_path = self.dataset[index] |
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img_file = self.root + img_path |
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img = Image.open(img_file) |
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if img.mode == "L": |
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img_new = Image.new("RGB", img.size) |
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img_new.paste(img) |
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sample = {"image": img_new, "idx": index} |
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else: |
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sample = {"image": img, "idx": index} |
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if self.transforms: |
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sample = self.transforms(sample) |
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return sample |
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def __len__(self): |
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""" |
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Return the number of all data. |
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""" |
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return len(self.dataset) |
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def get_data_loader( |
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config, |
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transforms=None, |
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sampler=None, |
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drop_last=True, |
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): |
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""" |
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Return batch data for training. |
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""" |
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transforms = image_transforms(shape=config.image_shape, jittering=config.jittering) |
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dataset = GetData(config, transforms=transforms["train"]) |
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train_loader = DataLoader( |
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dataset, |
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batch_size=config.batch_size, |
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shuffle=config.shuffle, |
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sampler=sampler, |
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num_workers=config.num_workers, |
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pin_memory=config.pin_memory, |
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drop_last=drop_last, |
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) |
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return train_loader |
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