def __getitem__(self,idx):
idx%=self.size
inputs=cv2.imread(PJ(self.imgdir,self.namelist[idx]),0)
targets=cv2.imread(PJ(self.labdir,self.namelist[idx]),0)
crop=self.transforms(np.concatenate((inputs[...,np.newaxis],targets[...,np.newaxis]),axis=-1))
inputs=crop[0:1]
targets=(crop[1:2]<=self.th).float()
return inputs,(inputs,targets)
def __getitem__(self,idx):
idx=idx%self.size
inputs=cv2.imread(PJ(self.imgdir,self.namelist[idx]),0)
targets=cv2.imread(PJ(self.labdir,self.namelist[idx]),0)
if self.train:
crop=self.transforms(np.concatenate((inputs[...,np.newaxis],targets[...,np.newaxis]),axis=-1))
inputs=crop[0:1].unsqueeze(0)
targets=(crop[1:2]<=self.th).float().unsqueeze(0)
else:
h,w=inputs.shape
H,W=h//self.cell,w//self.cell
inputs=self.transforms(inputs)
targets=(self.transforms(targets)<=self.th).float()
inputs=inputs.reshape(H,self.cell,W,self.cell).permute(0,2,1,3).reshape(H*W,self.cell,self.cell).unsqueeze(1)
targets=targets.reshape(H,self.cell,W,self.cell).permute(0,2,1,3).reshape(H*W,self.cell,self.cell).unsqueeze(1)
return inputs,targets
def __init__(self, data_root, data_file, class2idx, transform=None):
self.data_root = PJ(os.getcwd(), *data_root)
self.class2idx = class2idx
self.transform = transform
# Load data path
images = []
dir = os.path.expanduser(PJ(self.data_root, data_file))
for target in sorted(self.class2idx.keys()):
d = os.path.join(dir, target)
if not os.path.isdir(d):
continue
for root, _, fnames in sorted(os.walk(d)):
for fname in sorted(fnames):
path = os.path.join(root, fname)
item = [path, target]
images.append(item)
self.data = images
transforms = T.Compose([
T.ToPILImage(),
T.ToTensor(),
])
dataset = Dataset(cfg, mode, transforms)
loader = torch.utils.data.DataLoader(dataset,
batch_size=cfg['batch_size'],
shuffle=cfg['shuffle'],
num_workers=cfg['num_workers'])
return loader
if __name__ == "__main__":
mode = 'test'
direction = {
'test': PJ("D:\\Data", "ISBI-2012-EM", "new_test_set"),
"train": PJ("D:\\Data", "ISBI-2012-EM", "new train set")
}
cfg = {
"file_name": "ISBI",
"num_workers": 1,
"th": 0.6,
'repeat_time': 10,
"batch_size": 1,
"shuffle": True,
'direction': direction[mode]
}
d = iter(dataloader(cfg, mode))
for kkk in range(10):
i, t = next(d)
print(t.sum() / 512 / 512)
import os
from os.path import join as PJ
import scipy.io as sio
from pandas import DataFrame as df
import yaml
# Setting
DATASET = "apy"
ROOT = PJ("..", "dataset")
XLSA17 = PJ(ROOT, "xlsa17", "data", DATASET)
CONCEPT = PJ(ROOT, DATASET, "list", "concepts", "concepts.txt")
CONCEPT_LIST = PJ(ROOT, DATASET, "list", "concepts")
SPLIT_LIST = PJ(ROOT, DATASET, "list", "train_test")
ATT_SPLITS = sio.loadmat(PJ(XLSA17, "att_splits.mat"))
RES101 = sio.loadmat(PJ(XLSA17, "res101.mat"))
# check path exist
if not os.path.isdir(PJ(ROOT, DATASET, "list")):
os.makedirs(PJ(ROOT, DATASET, "list"))
if not os.path.isdir(CONCEPT_LIST):
os.makedirs(CONCEPT_LIST)
if not os.path.isdir(SPLIT_LIST):
os.makedirs(SPLIT_LIST)
# reorganize data
img_files = [filter(None, i[0][0].split('/')) for i in RES101['image_files']]
img_files = [PJ(*list(i)[5:]) for i in img_files]
########################################
# Environment and Experiment setting
########################################
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument('--exp', '-e', type=str, default='exp1_ResNet18')
parser.add_argument('--model_weights', '-w', type=str, default='')
args = parser.parse_args()
# Load experiment config
config_path = f"./configs/{args.exp}.yaml"
config = config(config_path)
exp_name = config['exp_name']
print(f"EXP: {exp_name}")
save_root = PJ(f"./results", exp_name)
# Show some experiment info
model_weights = args.model_weights
print(f"Model: {config['model']}, Weights: {model_weights}")
print(f"Pretrained: {config['pretrained']}, Freeze: {config['freeze']}")
########################################
# Data loader
########################################
class2idx = config['class2idx']
idx2class = {class2idx[k]: k for k in class2idx.keys()}
# Dataset
transform = data_transform(config, train=False)
testset = CIFAR10Dataset(config['data_root'], config['test_file'],
class2idx, transform)
import re
import pandas as pd
import numpy as np
from os.path import join as PJ
import scipy.io as sio
from gensim.models import KeyedVectors
DATASET = "cub"
concept_vec_filename = "concepts_new.txt"
# concept_vec_filename = "concepts_old.txt"
weight_path = "self-enwiki-gensim-normed-win10.bin"
# weight_path = "enwiki-gensim-normed.bin"
ROOT = PJ("..", "dataset")
concept_filename = PJ(ROOT, DATASET, "list", "concepts", "concepts.txt")
concept_vec_filename = PJ(ROOT, DATASET, "list", "concepts",
concept_vec_filename)
weight_path = PJ(ROOT, "enwiki", weight_path)
ATT_SPLITS = sio.loadmat(PJ(ROOT, "xlsa17", "data", DATASET, "att_splits.mat"))
RES101 = sio.loadmat(PJ(ROOT, "xlsa17", "data", DATASET, "res101.mat"))
setting = {
'apy': {
'replace_word': {
'diningtable': 'tables',
'pottedplant': 'houseplant',
# 'tvmonitor': 'tv'
'tvmonitor': 'flat_panel_display'
}
for tn in STATE['split_list']:
df = datasets[tn].data.iloc[:, 1:].sum(axis=0)
print(tn + ": " + str(df[df > 0].skew()))
train_loader = DataLoader(datasets['trainval'],
batch_size=CONFIG['train_batch_size'], shuffle=True)
test_loaders = {tn: DataLoader(datasets[tn],
batch_size=CONFIG['test_batch_size'], shuffle=False)
for tn in STATE['split_list'][1:]}
##########################################################################################
# experiment for n times
for exp_times in range(CONFIG['exp_times']):
SAVE_PATH = PJ('.', 'runs_test', DATASET, EXP_NAME, str(exp_times))
writer = SummaryWriter(PJ(SAVE_PATH))
# set experiment type: classifier / transformer
if CONFIG['type'] == "classifier":
model = classifier(backbone=CONFIG['model'],
k=CONFIG['k'], d=CONFIG['d'][CONFIG['concepts']][DATASET],
pretrained=CONFIG['pretrained'], freeze=CONFIG['freeze'])
elif CONFIG['type'] == "transformer":
model = transformer(backbone=CONFIG['model'], linear=CONFIG['linear'],
k=CONFIG['k'], d=CONFIG['d'][CONFIG['concepts']][DATASET],
pretrained=CONFIG['pretrained'], freeze=CONFIG['freeze'])
else:
assert False, "Must Assign the model type: classifier or transformer"
import pymarc
#Please load the supplied version of pymarc (install using pip install ./)
import numpy as np
import ujson
import json
import bgzf
import struct
import os
from tqdm.auto import tqdm
from os.path import join as PJ
#Set to the location where INSPIRE dump is.
inspireDumpPath = PJ("..", "Data", "INSPIRE", "20210108")
processedDataPath = PJ(inspireDumpPath, "Processed")
os.makedirs(processedDataPath, exist_ok=True)
def getTagSubtag(data, subtag, onErrorParent=True):
if (isinstance(data, dict)):
value = None
# try:
if (subtag not in data):
value = None
else:
value = data[subtag]
# except:
import os
from os.path import join as PJ
import scipy.io as sio
from pandas import DataFrame as df
# Setting
DATASET = "awa2"
ROOT = PJ("..", "dataset")
XLSA17 = PJ(ROOT, "xlsa17", "data", DATASET)
CONCEPT = PJ(ROOT, DATASET, "list", "concepts", "concepts.txt")
CONCEPT_LIST = PJ(ROOT, DATASET, "list", "concepts")
SPLIT_LIST = PJ(ROOT, DATASET, "list", "train_val")
ATT_SPLITS = sio.loadmat(PJ(XLSA17, "att_splits.mat"))
RES101 = sio.loadmat(PJ(XLSA17, "res101.mat"))
# check path exist
if not os.path.isdir(PJ(ROOT, DATASET, "list")):
os.makedirs(PJ(ROOT, DATASET, "list"))
if not os.path.isdir(CONCEPT_LIST):
os.makedirs(CONCEPT_LIST)
if not os.path.isdir(SPLIT_LIST):
os.makedirs(SPLIT_LIST)
# reorganize data
img_files = [filter(None, i[0][0].split('/')) for i in RES101['image_files']]
img_files = [PJ(*list(i)[5:]) for i in img_files]
import os
from os.path import join as PJ
import scipy.io as sio
from pandas import DataFrame as df
import yaml
from sklearn.preprocessing import LabelBinarizer
# Setting
DATASET = "awa2"
ROOT = PJ("..", "dataset")
XLSA17 = PJ(ROOT, "xlsa17", "data", DATASET)
CONCEPT = PJ(ROOT, DATASET, "list", "concepts", "concepts.txt")
CONCEPT_LIST = PJ(ROOT, DATASET, "list", "concepts")
SPLIT_LIST = {
"train_test": PJ(ROOT, DATASET, "list", "train_test"),
"train_val": PJ(ROOT, DATASET, "list", "train_test")
}
ATT_SPLITS = sio.loadmat(PJ(XLSA17, "att_splits.mat"))
RES101 = sio.loadmat(PJ(XLSA17, "res101.mat"))
####################
# check path exist #
####################
if not os.path.isdir(PJ(ROOT, DATASET, "list")):
os.makedirs(PJ(ROOT, DATASET, "list"))
if not os.path.isdir(CONCEPT_LIST):
os.makedirs(CONCEPT_LIST)
if __name__ == '__main__':
from models.network import *
import argparse
import os
from os.path import join as PJ
from RandAugment import RandAugment
from custom_data_io import myset
import torch.optim as optim
from DML import DML
parser = argparse.ArgumentParser()
parser.add_argument('-e', '--exp', type=str, default='exp_1')
args = parser.parse_args()
config_path = PJ(os.getcwd(), f"{args.exp}.yaml")
config = config(config_path)
exp_name = config['exp_name']
print(f"EXP: {exp_name}")
train_transforms = transforms.Compose([transforms.Resize((385, 505)),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
test_transforms = transforms.Compose([transforms.Resize((385, 505)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225])])
train_transforms.transforms.insert(0, RandAugment(2, 14))