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import socket
import timeit
import numpy as np
from PIL import Image
from datetime import datetime
import os
import sys
from collections import OrderedDict
sys.path.append('./')
# PyTorch includes
import torch
from torch.autograd import Variable
from torchvision import transforms
import cv2
# Custom includes
from networks import deeplab_xception_transfer, graph
from dataloaders import custom_transforms as tr
#
import argparse
import torch.nn.functional as F
import warnings
warnings.filterwarnings("ignore")
label_colours = [(0,0,0)
, (128,0,0), (255,0,0), (0,85,0), (170,0,51), (255,85,0), (0,0,85), (0,119,221), (85,85,0), (0,85,85), (85,51,0), (52,86,128), (0,128,0)
, (0,0,255), (51,170,221), (0,255,255), (85,255,170), (170,255,85), (255,255,0), (255,170,0)]
def flip(x, dim):
indices = [slice(None)] * x.dim()
indices[dim] = torch.arange(x.size(dim) - 1, -1, -1,
dtype=torch.long, device=x.device)
return x[tuple(indices)]
def flip_cihp(tail_list):
'''
:param tail_list: tail_list size is 1 x n_class x h x w
:return:
'''
# tail_list = tail_list[0]
tail_list_rev = [None] * 20
for xx in range(14):
tail_list_rev[xx] = tail_list[xx].unsqueeze(0)
tail_list_rev[14] = tail_list[15].unsqueeze(0)
tail_list_rev[15] = tail_list[14].unsqueeze(0)
tail_list_rev[16] = tail_list[17].unsqueeze(0)
tail_list_rev[17] = tail_list[16].unsqueeze(0)
tail_list_rev[18] = tail_list[19].unsqueeze(0)
tail_list_rev[19] = tail_list[18].unsqueeze(0)
return torch.cat(tail_list_rev,dim=0)
def decode_labels(mask, num_images=1, num_classes=20):
"""Decode batch of segmentation masks.
Args:
mask: result of inference after taking argmax.
num_images: number of images to decode from the batch.
num_classes: number of classes to predict (including background).
Returns:
A batch with num_images RGB images of the same size as the input.
"""
n, h, w = mask.shape
assert (n >= num_images), 'Batch size %d should be greater or equal than number of images to save %d.' % (
n, num_images)
outputs = np.zeros((num_images, h, w, 3), dtype=np.uint8)
for i in range(num_images):
img = Image.new('RGB', (len(mask[i, 0]), len(mask[i])))
pixels = img.load()
for j_, j in enumerate(mask[i, :, :]):
for k_, k in enumerate(j):
if k < num_classes:
pixels[k_, j_] = label_colours[k]
outputs[i] = np.array(img)
return outputs
def read_img(img_path):
_img = Image.open(img_path).convert('RGB') # return is RGB pic
return _img
def img_transform(img, transform=None):
sample = {'image': img, 'label': 0}
sample = transform(sample)
return sample
def inference(net, img_path='', output_path='./', output_name='f', use_gpu=True):
'''
:param net:
:param img_path:
:param output_path:
:return:
'''
# adj
adj2_ = torch.from_numpy(graph.cihp2pascal_nlp_adj).float()
adj2_test = adj2_.unsqueeze(0).unsqueeze(0).expand(1, 1, 7, 20).cuda().transpose(2, 3)
adj1_ = Variable(torch.from_numpy(graph.preprocess_adj(graph.pascal_graph)).float())
adj3_test = adj1_.unsqueeze(0).unsqueeze(0).expand(1, 1, 7, 7).cuda()
cihp_adj = graph.preprocess_adj(graph.cihp_graph)
adj3_ = Variable(torch.from_numpy(cihp_adj).float())
adj1_test = adj3_.unsqueeze(0).unsqueeze(0).expand(1, 1, 20, 20).cuda()
# multi-scale
scale_list = [1, 0.5, 0.75, 1.25, 1.5, 1.75]
img = read_img(img_path)
testloader_list = []
testloader_flip_list = []
for pv in scale_list:
composed_transforms_ts = transforms.Compose([
tr.Scale_only_img(pv),
tr.Normalize_xception_tf_only_img(),
tr.ToTensor_only_img()])
composed_transforms_ts_flip = transforms.Compose([
tr.Scale_only_img(pv),
tr.HorizontalFlip_only_img(),
tr.Normalize_xception_tf_only_img(),
tr.ToTensor_only_img()])
testloader_list.append(img_transform(img, composed_transforms_ts))
# print(img_transform(img, composed_transforms_ts))
testloader_flip_list.append(img_transform(img, composed_transforms_ts_flip))
# print(testloader_list)
start_time = timeit.default_timer()
# One testing epoch
net.eval()
# 1 0.5 0.75 1.25 1.5 1.75 ; flip:
for iii, sample_batched in enumerate(zip(testloader_list, testloader_flip_list)):
inputs, labels = sample_batched[0]['image'], sample_batched[0]['label']
inputs_f, _ = sample_batched[1]['image'], sample_batched[1]['label']
inputs = inputs.unsqueeze(0)
inputs_f = inputs_f.unsqueeze(0)
inputs = torch.cat((inputs, inputs_f), dim=0)
if iii == 0:
_, _, h, w = inputs.size()
# assert inputs.size() == inputs_f.size()
# Forward pass of the mini-batch
inputs = Variable(inputs, requires_grad=False)
with torch.no_grad():
if use_gpu >= 0:
inputs = inputs.cuda()
# outputs = net.forward(inputs)
outputs = net.forward(inputs, adj1_test.cuda(), adj3_test.cuda(), adj2_test.cuda())
outputs = (outputs[0] + flip(flip_cihp(outputs[1]), dim=-1)) / 2
outputs = outputs.unsqueeze(0)
if iii > 0:
outputs = F.upsample(outputs, size=(h, w), mode='bilinear', align_corners=True)
outputs_final = outputs_final + outputs
else:
outputs_final = outputs.clone()
################ plot pic
predictions = torch.max(outputs_final, 1)[1]
results = predictions.cpu().numpy()
vis_res = decode_labels(results)
parsing_im = Image.fromarray(vis_res[0])
parsing_im.save(output_path+'/{}.png'.format(output_name))
cv2.imwrite(output_path+'/{}_gray.png'.format(output_name), results[0, :, :])
end_time = timeit.default_timer()
print('time used for the multi-scale image inference' + ' is :' + str(end_time - start_time))
if __name__ == '__main__':
'''argparse begin'''
parser = argparse.ArgumentParser()
# parser.add_argument('--loadmodel',default=None,type=str)
parser.add_argument('--loadmodel', default='', type=str)
parser.add_argument('--img_path', default='', type=str)
parser.add_argument('--output_path', default='', type=str)
parser.add_argument('--output_name', default='', type=str)
parser.add_argument('--use_gpu', default=1, type=int)
opts = parser.parse_args()
net = deeplab_xception_transfer.deeplab_xception_transfer_projection_savemem(n_classes=20,
hidden_layers=128,
source_classes=7, )
if not opts.loadmodel == '':
x = torch.load(opts.loadmodel)
net.load_source_model(x)
print('load model:', opts.loadmodel)
else:
print('no model load !!!!!!!!')
raise RuntimeError('No model!!!!')
if opts.use_gpu >0 :
net.cuda()
use_gpu = True
else:
use_gpu = False
raise RuntimeError('must use the gpu!!!!')
inference(net=net, img_path=opts.img_path,output_path=opts.output_path , output_name=opts.output_name, use_gpu=use_gpu)
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