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test.py
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r""" Visual Prompt Encoder training (validation) code """
import os
import argparse
import torch.nn as nn
import torch
import torch.distributed as dist
from model.VRP_encoder import VRP_encoder
from common.logger import Logger, AverageMeter
from common.vis import Visualizer
from common.evaluation import Evaluator
from common import utils
from data.dataset import FSSDataset
from SAM2pred import SAM_pred
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
def test(args, epoch, model, sam_model, dataloader, training):
r""" Train VRP_encoder model """
training = False
utils.fix_randseed(args.seed + epoch) if training else utils.fix_randseed(args.seed)
model.module.eval()
average_meter = AverageMeter(dataloader.dataset)
for idx, batch in enumerate(dataloader):
batch = utils.to_cuda(batch)
pred_mask, attn_lst, _ = model.module.predict_mask_nshot( args, batch, sam_model, nshot=args.nshot)
area_inter, area_union = Evaluator.classify_prediction(pred_mask.squeeze(1), batch)
average_meter.update(area_inter, area_union, batch['class_id'], loss=None)
average_meter.write_process(idx, len(dataloader), epoch, write_batch_idx=1)
# # Visualize predictions
# if Visualizer.visualize:
# Visualizer.visualize_prediction_batch(batch['support_imgs'], batch['support_masks'],
# batch['query_img'], batch['query_mask'],
# pred_mask, batch['class_id'], idx,
# area_inter[1].float() / area_union[1].float())
"""visualization"""
img_size = batch['query_img'].shape
spt_img = batch['support_imgs'].squeeze(1).squeeze(0).cpu().numpy()
spt_img = np.transpose(spt_img, (1, 2, 0))
qry_img = batch['query_img'].cpu().squeeze(0).numpy()
qry_img = np.transpose(qry_img, (1, 2, 0))
# """show attn map"""
# target_img = spt_img
# target_attn_map = attn_lst[0]
# dir_path = './vis_s_attn'
# if not os.path.exists(dir_path):
# os.makedirs(dir_path)
# for j in range(target_attn_map.shape[1]):
# attn = target_attn_map[:,j,:]
# attn = (attn - attn.min())/(attn.max() - attn.min())
# attn = attn.reshape(64,64)
# attn = F.interpolate(attn.unsqueeze(0).unsqueeze(0), size=img_size[2:], mode='bilinear', align_corners=True).cpu().squeeze(0).squeeze(0).numpy()
# cmap = plt.get_cmap('jet')
# attn_colormap = cmap(attn)
# attn_colormap = attn_colormap[..., :3]
# alpha = 0.8
# combined_img = (1 - alpha) * target_img + alpha * attn_colormap
# plt.figure(figsize=(10, 10))
# plt.imshow(combined_img)
# plt.axis('off') # 축 제거
# plt.savefig(dir_path + '/{}_{}.png'.format(idx,j), bbox_inches='tight', pad_inches=0)
# plt.close()
# """show attn map"""
# target_img = qry_img
# target_attn_map = attn_lst[1]
# dir_path = './vis_q_attn'
# if not os.path.exists(dir_path):
# os.makedirs(dir_path)
# for j in range(target_attn_map.shape[1]):
# attn = target_attn_map[:,j,:]
# attn = (attn - attn.min())/(attn.max() - attn.min())
# attn = attn.reshape(64,64)
# attn = F.interpolate(attn.unsqueeze(0).unsqueeze(0), size=img_size[2:], mode='bilinear', align_corners=True).cpu().squeeze(0).squeeze(0).numpy()
# cmap = plt.get_cmap('jet')
# attn_colormap = cmap(attn)
# attn_colormap = attn_colormap[..., :3]
# alpha = 0.8
# combined_img = (1 - alpha) * target_img + alpha * attn_colormap
# plt.figure(figsize=(10, 10))
# plt.imshow(combined_img)
# plt.axis('off') # 축 제거
# plt.savefig(dir_path + '/{}_{}.png'.format(idx,j), bbox_inches='tight', pad_inches=0)
# plt.close()
# """show attn map"""
# target_img = spt_img
# target_attn_map = attn_lst[0]
# dir_path = './vis_s_c_attn'
# if not os.path.exists(dir_path):
# os.makedirs(dir_path)
# for j in range(target_attn_map.shape[1]):
# attn = target_attn_map[:,j,:]
# attn = (attn - attn.min())/(attn.max() - attn.min())
# attn = attn.reshape(64,64)
# attn = F.interpolate(attn.unsqueeze(0).unsqueeze(0), size=img_size[2:], mode='bilinear', align_corners=True).cpu().squeeze(0).squeeze(0).numpy()
# cmap = plt.get_cmap('jet')
# attn_colormap = cmap(attn)
# attn_colormap = attn_colormap[..., :3]
# alpha = 0.8
# combined_img = (1 - alpha) * target_img + alpha * attn_colormap
# plt.figure(figsize=(10, 10))
# plt.imshow(combined_img)
# plt.axis('off') # 축 제거
# plt.savefig(dir_path + '/{}_{}.png'.format(idx,j), bbox_inches='tight', pad_inches=0)
# plt.close()
# """show attn map"""
# target_img = qry_img
# target_attn_map = attn_lst[1]
# dir_path = './vis_q_c_attn'
# if not os.path.exists(dir_path):
# os.makedirs(dir_path)
# for j in range(target_attn_map.shape[1]):
# attn = target_attn_map[:,j,:]
# attn = (attn - attn.min())/(attn.max() - attn.min())
# attn = attn.reshape(64,64)
# attn = F.interpolate(attn.unsqueeze(0).unsqueeze(0), size=img_size[2:], mode='bilinear', align_corners=True).cpu().squeeze(0).squeeze(0).numpy()
# cmap = plt.get_cmap('jet')
# attn_colormap = cmap(attn)
# attn_colormap = attn_colormap[..., :3]
# alpha = 0.8
# combined_img = (1 - alpha) * target_img + alpha * attn_colormap
# plt.figure(figsize=(10, 10))
# plt.imshow(combined_img)
# plt.axis('off') # 축 제거
# plt.savefig(dir_path + '/{}_{}.png'.format(idx,j), bbox_inches='tight', pad_inches=0)
# plt.close()
# """show sub map"""
# target_img = qry_img
# target_attn_map = attn_lst[3]
# dir_path = './vis_sub_map'
# if not os.path.exists(dir_path):
# os.makedirs(dir_path)
# for j in range(target_attn_map.shape[1]):
# attn = target_attn_map[:,j,:]
# attn = (attn - attn.min())/(attn.max() - attn.min())
# attn = attn.reshape(64,64)
# attn = F.interpolate(attn.unsqueeze(0).unsqueeze(0), size=img_size[2:], mode='bilinear', align_corners=True).cpu().squeeze(0).squeeze(0).numpy()
# cmap = plt.get_cmap('jet')
# attn_colormap = cmap(attn)
# attn_colormap = attn_colormap[..., :3]
# alpha = 0.8
# combined_img = (1 - alpha) * target_img + alpha * attn_colormap
# plt.figure(figsize=(10, 10))
# plt.imshow(combined_img)
# plt.axis('off') # 축 제거
# plt.savefig(dir_path + '/{}_{}.png'.format(idx,j), bbox_inches='tight', pad_inches=0)
# plt.close()
"""show pseudo mask"""
pseudo_mask_vis = attn_lst[2]
for k in range(len(pseudo_mask_vis)):
target_img = qry_img
target_attn_map = pseudo_mask_vis[k]
dir_path = './vis_pseudo_mask'
if not os.path.exists(dir_path):
os.makedirs(dir_path)
attn = target_attn_map[:,:]
attn = (attn - attn.min())/(attn.max() - attn.min())
attn = attn.reshape(64,64)
attn = F.interpolate(attn.unsqueeze(0).unsqueeze(0), size=img_size[2:], mode='bilinear', align_corners=True).cpu().squeeze(0).squeeze(0).numpy()
cmap = plt.get_cmap('jet')
attn_colormap = cmap(attn)
attn_colormap = attn_colormap[..., :3]
alpha = 0.8
combined_img = (1 - alpha) * target_img + alpha * attn_colormap
plt.figure(figsize=(10, 10))
plt.imshow(combined_img)
plt.axis('off') # 축 제거
plt.savefig(dir_path + '/{}_{}.png'.format(idx, k), bbox_inches='tight', pad_inches=0)
plt.close()
average_meter.write_result('Validation', epoch)
avg_loss = utils.mean(average_meter.loss_buf)
miou, fb_iou = average_meter.compute_iou()
return miou, fb_iou
if __name__ == '__main__':
# Arguments parsing
parser = argparse.ArgumentParser(description='Visual Prompt Encoder Pytorch Implementation')
parser.add_argument('--datapath', type=str, default='/root/paddlejob/workspace/env_run/datsets/')
parser.add_argument('--benchmark', type=str, default='coco', choices=['pascal', 'coco', 'fss'])
parser.add_argument('--logpath', type=str, default='')
parser.add_argument('--bsz', type=int, default=2) # batch size = num_gpu * bsz default num_gpu = 4
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--supp_ratio', type=float, default=0.0)
parser.add_argument('--mask_loss_lower_bound', type=float, default=0.5)
parser.add_argument('--pseudo_mask_upper_th', type=float, default=0.5)
parser.add_argument('--weight_decay', type=float, default=1e-6)
parser.add_argument('--epochs', type=int, default=50)
parser.add_argument('--concat_th', type=bool, default=False)
parser.add_argument('--nworker', type=int, default=8)
parser.add_argument('--fold', type=int, default=0, choices=[0, 1, 2, 3])
parser.add_argument('--condition', type=str, default='scribble', choices=['point', 'scribble', 'box', 'mask'])
parser.add_argument('--use_ignore', type=bool, default=True, help='Boundaries are not considered during pascal training')
parser.add_argument('--num_query', type=int, default=50)
parser.add_argument('--seed', type=int, default=321)
parser.add_argument('--num_layers', type=int, default=3)
parser.add_argument('--attn_drop_out', type=float, default=0.3)
parser.add_argument('--local_rank', type=int, default=-1, help='number of cpu threads to use during batch generation')
parser.add_argument('--backbone', type=str, default='resnet50', choices=['vgg16', 'resnet50', 'resnet101'])
parser.add_argument('--load', type=str, default="./best_model.pt")
parser.add_argument('--nshot', type=int, default=1)
parser.add_argument('--visualize', type=bool, default=True, help='Boundaries are not considered during pascal training')
parser.add_argument('--vispath', type=str, default='./vis')
args = parser.parse_args()
Logger.initialize(args, training=False)
# Distributed setting
local_rank = args.local_rank
dist.init_process_group(backend='nccl')
print('local_rank: ', local_rank)
torch.cuda.set_device(local_rank)
device = torch.device('cuda', local_rank)
# Model initialization
model = VRP_encoder(args, args.backbone, False)
model.eval()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Logger.info('# available GPUs: %d' % torch.cuda.device_count())
# model = nn.DataParallel(model)
model.to(device)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True)
sam_model = SAM_pred()
sam_model.to(device)
model.to(device)
# Load trained model
if args.load == '': raise Exception('Pretrained model not specified.')
model.load_state_dict(torch.load(args.load))
# Helper classes (for testing) initialization
Evaluator.initialize(args)
Visualizer.initialize(args.visualize, "./vis/")
# Dataset initialization
FSSDataset.initialize(img_size=512, datapath=args.datapath, use_original_imgsize=False)
dataloader_test = FSSDataset.build_dataloader(args.benchmark, args.bsz, args.nworker, args.fold, 'val', shot=args.nshot)
# Test
with torch.no_grad():
test_miou, test_fb_iou = test(args, 0, model, sam_model, dataloader_test, False)
Logger.info('Fold %d mIoU: %5.2f \t FB-IoU: %5.2f' % (args.fold, test_miou.item(), test_fb_iou.item()))
Logger.info('==================== Finished Testing ====================')