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with st.echo():
plost.bar_chart(
data=datasets['pageviews'],
bar='pagenum',
value='pageviews',
width=500,
pan_zoom='minimap')
"---"
with st.echo():
plost.bar_chart(
data=datasets['pageviews'],
bar='pagenum',
value='pageviews',
direction='horizontal',
width=500,
height=500,
pan_zoom='minimap')
""
""
""
""
"🍅"
# <FILESEP>
from __future__ import print_function
import argparse
import os.path as osp
import sys
import time
import warnings
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torchvision.transforms as transforms
from torch.autograd import Variable
from model import embed_net
from utils.data_utils import *
from utils.eval_utils import *
from utils.misc import Logger, set_seed
warnings.filterwarnings("ignore", category=UserWarning)
parser = argparse.ArgumentParser(description='PyTorch Cross-Modality Testing')
### dataloader config
parser.add_argument('--dataset', default='sysu', help='dataset name: [regdb or sysu]')
parser.add_argument('--workers', default=4, type=int, help='number of data loading workers (default: 4)')
parser.add_argument('--img_h', default=288, type=int, help='img height')
parser.add_argument('--img_w', default=144, type=int, help='img width')
parser.add_argument('--num_pos', default=4, type=int, help='num of pos per identity in each modality')
parser.add_argument('--batch_size', default=8, type=int, help='training batch size')
parser.add_argument('--test-batch', default=64, type=int, help='testing batch size')
### directory config
parser.add_argument('--save_path', default='log/', type=str, help='parent save directory')
parser.add_argument('--exp_name', default='exp', type=str, help='child save directory')
parser.add_argument('--model_name', default='ep_80', type=str,help='model save path')
### model/training config
parser.add_argument('--method', default='full', type=str, help='method type: [baseline or full]')
### evaluation protocols
parser.add_argument('--trial', default=1, type=int, help='trial (only for RegDB dataset)')
parser.add_argument('--tvsearch', action='store_true', help='whether thermal to visible search on RegDB')
parser.add_argument('--mode', default='all', type=str, help='all or indoor for sysu')
### misc
parser.add_argument('--seed', default=0, type=int, help='random seed')
parser.add_argument('--nvidia_device', default=0, type=int, help='gpu device to use')
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.nvidia_device)
set_seed(args.seed)
dataset = args.dataset
if dataset == 'sysu':
data_path = '/workspace/dataset/SYSU-MM01/'
n_class = 395
test_mode = [1, 2]
elif dataset =='regdb':
data_path = '/workspace/dataset/RegDB/'
n_class = 206
test_mode = [2, 1]
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
pool_dim = 2048
print('==> Building model..')
net = embed_net(args, n_class)
net.to(device)
cudnn.benchmark = True
sys.stdout = Logger(osp.join(args.save_path, '{}/os_test.txt'.format(args.exp_name)))
print('==> Loading data..')
# Data loading code