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aff_infer.py
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import torch
import torchvision
from tool import imutils
import argparse
import importlib
import numpy as np
import voc12.data
from torch.utils.data import DataLoader
import torch.nn.functional as F
import os.path
import imageio
from tqdm import tqdm
def get_indices_in_radius(height, width, radius):
search_dist = []
for x in range(1, radius):
search_dist.append((0, x))
for y in range(1, radius):
for x in range(-radius+1, radius):
if x*x + y*y < radius*radius:
search_dist.append((y, x))
full_indices = np.reshape(np.arange(0, height * width, dtype=np.int64),
(height, width))
radius_floor = radius-1
cropped_height = height - radius_floor
cropped_width = width - 2 * radius_floor
indices_from = np.reshape(full_indices[:-radius_floor, radius_floor:-radius_floor], [-1])
indices_from_to_list = []
for dy, dx in search_dist:
indices_to = full_indices[dy:dy + cropped_height, radius_floor + dx:radius_floor + dx + cropped_width]
indices_to = np.reshape(indices_to, [-1])
indices_from_to = np.stack((indices_from, indices_to), axis=1)
indices_from_to_list.append(indices_from_to)
concat_indices_from_to = np.concatenate(indices_from_to_list, axis=0)
return concat_indices_from_to
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--weights", required=True, type=str)
parser.add_argument("--network", default="network.resnet38_aff", type=str)
parser.add_argument("--infer_list", default="voc12/val.txt", type=str)
parser.add_argument("--num_workers", default=8, type=int)
parser.add_argument("--cam_dir", required=True, type=str)
parser.add_argument("--voc12_root", default='VOC2012', type=str)
parser.add_argument("--alpha", default=6, type=float)
parser.add_argument("--out_rw", default='out_rw', type=str)
parser.add_argument("--beta", default=8, type=int)
parser.add_argument("--logt", default=6, type=int)
parser.add_argument("--crf", default=False, type=bool)
args = parser.parse_args()
if not os.path.exists(args.out_rw):
os.makedirs(args.out_rw)
model = getattr(importlib.import_module(args.network), 'Net')()
model.load_state_dict(torch.load(args.weights), strict=False)
model.eval()
model.cuda()
infer_dataset = voc12.data.VOC12ImageDataset(args.infer_list, voc12_root=args.voc12_root,
transform=torchvision.transforms.Compose([np.asarray,
model.normalize,
imutils.HWC_to_CHW]))
infer_data_loader = DataLoader(infer_dataset, shuffle=False, num_workers=args.num_workers, pin_memory=True)
for iter, (name, img) in tqdm(enumerate(infer_data_loader), total=len(infer_data_loader)):
name = name[0]
# print(iter)
orig_shape = img.shape
padded_size = (int(np.ceil(img.shape[2]/8)*8), int(np.ceil(img.shape[3]/8)*8))
p2d = (0, padded_size[1] - img.shape[3], 0, padded_size[0] - img.shape[2])
img = F.pad(img, p2d)
dheight = int(np.ceil(img.shape[2]/8))
dwidth = int(np.ceil(img.shape[3]/8))
cam = np.load(os.path.join(args.cam_dir, name + '.npy'), allow_pickle=True).item()
cam_full_arr = np.zeros((21, orig_shape[2], orig_shape[3]), np.float32)
for k, v in cam.items():
cam_full_arr[k+1] = v
cam_full_arr[0] = 0.27
cam_full_arr = np.pad(cam_full_arr, ((0, 0), (0, p2d[3]), (0, p2d[1])), mode='constant')
with torch.no_grad():
aff_mat = torch.pow(model.forward(img.cuda(), True), args.beta)
trans_mat = aff_mat / torch.sum(aff_mat, dim=0, keepdim=True)
for _ in range(args.logt):
trans_mat = torch.matmul(trans_mat, trans_mat)
cam_full_arr = torch.from_numpy(cam_full_arr)
cam_full_arr = F.avg_pool2d(cam_full_arr, 8, 8)
cam_vec = cam_full_arr.view(21, -1)
cam_rw = torch.matmul(cam_vec.cuda(), trans_mat)
cam_rw = cam_rw.view(1, 21, dheight, dwidth)
cam_rw = torch.nn.Upsample((img.shape[2], img.shape[3]), mode='bilinear')(cam_rw)
# if args.crf:
# img_8 = img[0].numpy().transpose((1,2,0)) #F.interpolate(img, (dheight,dwidth), mode='bilinear')[0].numpy().transpose((1,2,0))
# img_8 = np.ascontiguousarray(img_8)
# mean = (0.485, 0.456, 0.406)
# std = (0.229, 0.224, 0.225)
# img_8[:,:,0] = (img_8[:,:,0]*std[0] + mean[0])*255
# img_8[:,:,1] = (img_8[:,:,1]*std[1] + mean[1])*255
# img_8[:,:,2] = (img_8[:,:,2]*std[2] + mean[2])*255
# img_8[img_8 > 255] = 255
# img_8[img_8 < 0] = 0
# img_8 = img_8.astype(np.uint8)
# cam_rw = cam_rw[0].cpu().numpy()
# cam_rw = imutils.crf_inference(img_8, cam_rw, t=1)
# cam_rw = torch.from_numpy(cam_rw).view(1, 21, img.shape[2], img.shape[3]).cuda()
_, cam_rw_pred = torch.max(cam_rw, 1)
res = np.uint8(cam_rw_pred.cpu().data[0])[:orig_shape[2], :orig_shape[3]]
# scipy.misc.imsave(os.path.join(args.out_rw, name + '.png'), res)
imageio.imwrite(os.path.join(args.out_rw, name + '.png'), res)