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generate_pseudo.py
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generate_pseudo.py
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import numpy as np
import os
import time
import matplotlib.pyplot as plt
import torch
import torchvision
import torchvision.transforms as T
from sklearn.linear_model import LogisticRegression
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR100
from tqdm import tqdm
import argparse
from pkg_resources import packaging
print("Torch version:", torch.__version__)
os.sys.path.append('../')
import clip
import utils
def parse_args(argv):
"""
Parsing input arguments.
"""
# Arguments
parser = argparse.ArgumentParser(description='arguments.')
# data args
parser.add_argument('--dataset', type=str, default='ucm', required=False,
help='Dataset (default=%(default)s)')
parser.add_argument('--season', type=str, default=None, required=False,
help='only used for sen12ms dataset')
parser.add_argument('--topk', default=20, type=int, required=False,
help='Number of classes to train with')
parser.add_argument('--pseudo-version', default='2', type=str, required=False,
help='Number ofs classes to train with')
# model args
parser.add_argument('--vis-model', type=str, required=False, default='ViT-B/32',
help='Vision Model')
parser.add_argument('--gpu', type=str, required=False, default='0',
help='GPU assigned')
# training args
parser.add_argument('--batch-size', default=64, type=int, required=False,
help='Batch size to use (default=%(default)s)')
parser.add_argument('--load-from', type=str, default='', required=False,
help='Resume training with the last saved model (default=%(default)s)')
args = parser.parse_args(argv)
return args
prompt = "This is an aerial image of a {}."
def evaluate(model, test_loader, device):
"""
Evaluating the resulting classifier using a given test set loader.
"""
model.eval()
model.float()
text_descriptions = [prompt.format(label) for label in test_loader.dataset.classes]
text_tokens = clip.tokenize(text_descriptions).cuda()
text_features = model.encode_text(text_tokens).float()
text_features /= text_features.norm(dim=-1, keepdim=True)
total_top1_hits, total_top5_hits, N = 0, 0, 0
top1_avg, top5_avg = 0, 0
pre_probs = []
gts = []
with torch.no_grad():
cnt = 0
for data in test_loader:
images, targets, _ = data
images = images.to(device)
targets = targets.to(device).squeeze()
image_features = model.encode_image(images)
similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
pre_probs.append(similarity.cpu().numpy())
gts.append(targets.cpu().numpy())
top5_hits, top1_hits = utils.calculate_metrics(similarity, targets)
total_top1_hits += top1_hits
total_top5_hits += top5_hits
N += targets.shape[0]
top1_avg = 100 * (float(total_top1_hits) / N)
top5_avg = 100 * (float(total_top5_hits) / N)
pre_probs = np.concatenate(pre_probs, 0)
return top1_avg, top5_avg, pre_probs, gts
def run_gen(args):
os.environ['CUDA_DEVICE_ORDER']='PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES']=str(args.gpu)
model, preprocess = clip.load(args.vis_model)
model.cuda().eval()
input_resolution = model.visual.input_resolution
context_length = model.context_length
vocab_size = model.vocab_size
print("Model parameters:", f"{np.sum([int(np.prod(p.shape)) for p in model.parameters()]):,}")
print("Input resolution:", input_resolution)
print("Context length:", context_length)
print("Vocab size:", vocab_size)
test_transforms = T.Compose([
T.Resize(256),
T.CenterCrop(224),
T.ToTensor(),
T.Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
])
if 'sen12ms' in args.dataset.lower():
bands_mean = {'s1_mean': [-11.76858, -18.294598],
's2_mean': [1226.4215, 1137.3799, 1139.6792, 1350.9973, 1932.9058,
2211.1584, 2154.9846, 2409.1128, 2001.8622, 1356.0801]}
bands_std = {'s1_std': [4.525339, 4.3586307],
's2_std': [741.6254, 740.883, 960.1045, 946.76056, 985.52747,
1082.4341, 1057.7628, 1136.1942, 1132.7898, 991.48016]}
# data path
data_root = './data/SEN12MS/'
list_dir = "./data/SEN12MS/SEN12MS/splits/" # split lists/ label dirs
# define image transform
from data.sen12ms_dataset import SEN12MS, ToTensor, Normalize, CenterCrop
imgTransform = T.Compose([
ToTensor(),
Normalize(bands_mean, bands_std),
CenterCrop(224),
])
# test multi_label part with normalization
if args.season is None:
season = 'all_seasons'
else:
season = args.season
assert season in ['sprint', 'summer', 'fall', 'winter']
print("\n\nSEN12MS test")
test_dataset = SEN12MS(data_root, list_dir, imgTransform, season=season,
label_type="single_label", threshold=0.1, subset="test",
use_s1=False, use_s2=False, use_RGB=True, IGBP_s=True)
else:
if 'ucm' in args.dataset.lower():
data_root='./data/UCMerced_LandUse/'
elif 'whurs' in args.dataset.lower():
data_root='./data/WHU-RS19/'
elif 'nwpu' in args.dataset.lower():
data_root='./data/NWPU-RESISC45/'
elif 'aid' in args.dataset.lower():
data_root='./data/AID/'
elif 'eurosat' in args.dataset.lower():
data_root = './data/EuroSAT/'
else:
print('dataset not defined')
return
from data.ucm_dataset import RSDataset
test_dataset = RSDataset(data_root=data_root, txt_path='all.txt', transform=test_transforms)
print('test size', len(test_dataset))
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=128, shuffle=False,
num_workers=4, pin_memory=True)
device = torch.device("cuda:0")
if os.path.isfile(args.load_from):
checkpoint = torch.load(open(args.load_from, 'rb'), map_location="cpu")
model.load_state_dict(checkpoint['state_dict'])
else:
print('file not exist')
top1_test_acc, top5_test_acc, pre_probs, gts = evaluate(model, test_loader, device)
print('top1_test_acc: {:.3f}, top5_test_acc: {:.3f}'.format(top1_test_acc, top5_test_acc))
topk=args.topk
fp_train = open(data_root + 'pseudo_v{}_train_{}shot.txt'.format(args.pseudo_version, topk), 'w')
fp_valid = open(data_root + 'pseudo_v{}_valid_{}shot.txt'.format(args.pseudo_version, topk), 'w')
correct_all = 0.0
train_cnt, test_cnt = 0, 0
classes = test_dataset.classes
for c, name in enumerate(classes):
pre_probs_per_class = pre_probs[:, c]
indices = np.argsort(-pre_probs_per_class)[:topk]
# print(c, name, pre_probs_per_class[indices])
n_ims = len(indices)
correct = 0.0
for ind in indices:
im, label, im_path = test_dataset[ind]
correct += (label==c)
correct_all += (label==c)
im = np.array(torch.squeeze(im).permute(1, 2, 0))
fp_train.writelines(im_path + ', ' + str(c) + ', ' + str(label) + '\n')
train_cnt += 1
print('pseudo accuracy: {:.4f}'.format(correct_all/(len(classes)*topk)))
fp_train.close()
if 'sen12ms' in args.dataset.lower():
all_list = np.loadtxt(data_root + 'test_{}.txt'.format(season), delimiter=' ', dtype='str')
else:
all_list = np.loadtxt(data_root + 'all.txt', delimiter=' ', dtype='str')
all_train = np.loadtxt(data_root + 'pseudo_v{}_train_{}shot.txt'.format(args.pseudo_version, topk), delimiter=' ', dtype='str')
for item in all_list:
im_path, label = item
if im_path not in all_train[:, 0]:
fp_valid.writelines(im_path + ', ' + str(label) + '\n')
test_cnt += 1
fp_valid.close()
print(train_cnt, test_cnt, train_cnt+test_cnt)
def main(argv=None):
args = parse_args(argv)
run_gen(args)
if __name__ == "__main__":
main()