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reader.py
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reader.py
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import json
import os
from configparser import Interpolation
import numpy as np
import scipy.io as sio
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as TF
from PIL import Image
from torchvision import transforms
"""
1 plant
2 sky
3 cloud
4 snow
5 building
6 desert
7 mountain
8 water
9 sun
10 virtual
"""
arc_categories = [
"asymmetric",
"color",
"crystallographic",
"flowing",
"isolation",
"progressive",
"regular",
"shape",
"symmetric",
]
class ArchitectureReader(torch.utils.data.Dataset):
def __init__(self, main_path, mode="train", domain="ALL"):
self.paths = []
self.labels = []
self.dataset_path = main_path
with open(os.path.join(self.dataset_path, "splitted_labels.json"), "r") as f:
data_json = json.load(f)
if domain == "ALL":
path_label_pairs = []
for _, labels in data_json.items():
for label in labels[mode]:
path_label_pairs.append(label)
else:
path_label_pairs = []
for label in data_json[domain][mode]:
path_label_pairs.append(label)
self.transform = transforms.Compose(
[
transforms.RandAugment(
num_ops=3,
magnitude=10,
num_magnitude_bins=40,
interpolation=TF.InterpolationMode.BILINEAR,
),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
self.transform_test = transforms.Compose(
[
transforms.Resize((224, 224), interpolation=Image.BILINEAR),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
self.pairs = path_label_pairs
self.mode = mode
def load_image(self, path):
return Image.open(path)
def __len__(self):
return len(self.pairs)
def __getitem__(self, idx):
path, label = self.pairs[idx]
img_path = os.path.join(self.dataset_path, path)
ranked_one_hot_label = torch.zeros(len(arc_categories), dtype=torch.int32)
ranked_one_hot_label[label[0] - 1] = 3
if label[1] != -1:
ranked_one_hot_label[label[1] - 1] = 2
if label[2] != -1:
ranked_one_hot_label[label[2] - 1] = 1
if self.mode == "train":
img = self.load_image(img_path)
img = self.transform(img)
else:
img = self.load_image(img_path)
img = self.transform_test(img)
if self.mode != "test":
return img, ranked_one_hot_label
else:
return img, ranked_one_hot_label, img_path
class LandscapeReader(torch.utils.data.Dataset):
def __init__(self, main_path, mode="train"):
self.main_path = main_path
self.config_path = os.path.join(main_path, "config.json")
self.mode = mode
self.paths = sorted(
os.listdir(os.path.join(main_path, "images")),
key=lambda x: int(x.split(".")[0]),
)
self.paths = [os.path.join(main_path, "images", path) for path in self.paths]
# Load data config
with open(self.config_path, "r") as f:
self.config = json.load(f)[mode]
self.raw_labels = sio.loadmat(os.path.join(main_path, "pic_scene.mat"))
self.raw_labels = [
[
[self.raw_labels["pic"][0][i][0][j][k] for k in range(10)]
for j in range(10)
]
for i in range(2000)
]
self.raw_labels = np.array(self.raw_labels)
self.labels = np.ones_like(self.raw_labels) * 0
for i in range(2000):
for j in range(10):
row = self.raw_labels[i, j, :]
vl_idx = np.where(row == 10)[0][0]
self.labels[i, j, row - 1] = np.arange(10)
self.labels[i, j, self.labels[i, j, :] > vl_idx] = vl_idx + 1
self.labels = np.mean(self.labels, axis=1)
virtual_label_threshold = np.expand_dims(self.labels[:, -1], axis=1).repeat(
10, axis=1
)
self.labels[self.labels > virtual_label_threshold] = float("inf")
self.labels = self.labels[:, :-1]
for i in range(self.labels.shape[0]):
uniques = np.unique(self.labels[i, :])
ranks = np.arange(0, len(uniques))[::-1]
for j in range(len(uniques)):
self.labels[i, self.labels[i, :] == uniques[j]] = ranks[j]
# for i in range(self.labels.shape[0]):
# if np.sum(self.labels[i, :]) == 0:
# print(self.labels[i, :], i)
# INDEX 253 IS ILL LABELED
self.config = [x for x in self.config if x != 253]
self.transform = transforms.Compose(
[
transforms.RandAugment(
num_ops=3,
magnitude=10,
num_magnitude_bins=40,
interpolation=TF.InterpolationMode.BILINEAR,
),
# transforms.RandomHorizontalFlip(p=0.5),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
self.transform_test = transforms.Compose(
[
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
),
]
)
self.mode = mode
def load_image(self, path):
return Image.open(path)
def __len__(self):
return len(self.config)
def __getitem__(self, idx):
iidx = self.config[idx] - 1
img = self.load_image(self.paths[iidx])
label = torch.tensor(self.labels[iidx]).long()
# Label: [0, 0, 2, 0, 0, 1, 0, 0, 3] -> falan filan gibi
if self.mode == "train":
img = self.transform(img)
else:
img = self.transform_test(img)
if self.mode != "test":
return img, label
else:
return img, label, self.paths[iidx]
class RankedMNISTReader(torch.utils.data.Dataset):
def __init__(self, main_path, config_path, mode="train", subset=False):
self.main_path = main_path
self.paths = []
self.labels = []
self.mode = mode
# Load data config
with open(config_path, "r") as f:
self.data_json = json.load(f)
self.transform = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[1.0, 1.0, 1.0]),
]
)
self.transform_test = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[1.0, 1.0, 1.0]),
]
)
self.mode = mode
if subset:
self.data_json[self.mode] = self.data_json[self.mode][:2000]
def load_image(self, path):
return Image.open(path)
def __len__(self):
return len(self.data_json[self.mode])
def __getitem__(self, idx):
path, label = self.data_json[self.mode][idx]
# Label: [0, 0, 2, 0, 0, 1, 0, 0, 0, 3] -> falan filan gibi
# Join path with main path
path = os.path.join(self.main_path, path)
if self.mode == "train":
img = self.load_image(path)
img = self.transform(img)
else:
img = self.load_image(path)
img = self.transform_test(img)
if self.mode != "test":
return img, torch.tensor(label)
else:
return img, torch.tensor(label), path
# reader = LandscapeReader("landscape_dataset", "test")