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predict_one.py
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predict_one.py
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import torch
import torch.nn as nn
import network
import utils
from PIL import Image
import torchvision.transforms as transforms
import numpy as np
import cv2 as cv
from torch.nn import functional as F
from datasets import Cityscapes
from utils import ext_transforms as et
model_map = {
'deeplabv3_resnet50': network.deeplabv3_resnet50,
'deeplabv3plus_resnet50': network.deeplabv3plus_resnet50,
'deeplabv3_resnet101': network.deeplabv3_resnet101,
'deeplabv3plus_resnet101': network.deeplabv3plus_resnet101,
'deeplabv3_mobilenet': network.deeplabv3_mobilenet,
'deeplabv3plus_mobilenet': network.deeplabv3plus_mobilenet,
'deeplabv3plus_mobilenetSA': network.deeplabv3plus_mobilenetSA,
'deeplabv3plus_mobilenetSAc': network.deeplabv3plus_mobilenetSAc,
'deeplabv3plus_mobilenet_M': network.deeplabv3plus_mobilenetM,
'deeplabv3plus_mobilenetSA_M': network.deeplabv3plus_mobilenetSAM,
'deeplabv3plus_mobilenetSAc_M': network.deeplabv3plus_mobilenetSAcM
}
# 把label映射到不同的颜色 (voc数据集)
def voc_cmap(N=256, normalized=False):
def bitget(byteval, idx):
return ((byteval & (1 << idx)) != 0)
dtype = 'float32' if normalized else 'uint8'
cmap = np.zeros((N, 3), dtype=dtype)
for i in range(N):
r = g = b = 0
c = i
for j in range(8):
r = r | (bitget(c, 0) << 7 - j)
g = g | (bitget(c, 1) << 7 - j)
b = b | (bitget(c, 2) << 7 - j)
c = c >> 3
cmap[i] = np.array([r, g, b])
cmap = cmap / 255 if normalized else cmap
return cmap
if __name__ == "__main__":
# img_path = r"samples/114_image.png" # 要测试的图片
# result_path = r"samples/114_image_results.png" # 要保存的路径
img_path = r'samples/frankfurt_000000_000294_leftImg8bit.png'
result_path = r"samples/image_results.png" # 要保存的路径
# model_name ='deeplabv3plus_mobilenet' #用什么模型什么方式
# model_name ='deeplabv3plus_mobilenetSA'
# model_name ='deeplabv3plus_mobilenetSAc'
model_name = 'deeplabv3plus_mobilenet_M'
# model_name ='deeplabv3plus_mobilenetSA_M'
# model_name ='deeplabv3plus_mobilenetSAc_M'
dataset = 'cityscapes'
# dataset='voc'
# 单模型读取的路径 （sin_model）
ckpt_path = r'checkpoints/best_deeplabv3plus_mobilenet_cityscapes_os16.pth'
# ckpt_path = r'checkpoints/best_deeplabv3plus_mobilenet_voc_os16.pth' # 模型参数的位置
# ckpt_path = r'checkpoints/best_deeplabv3plus_mobilenetSA_voc_os16.pth'
# ckpt_path = r'checkpoints/best_deeplabv3plus_mobilenetSAc_voc_os16.pth'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if dataset == 'voc':
N_class = 21
sin_model_class = 21
val_transform = transforms.Compose([
transforms.Resize(513),
transforms.CenterCrop(513),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
else:
N_class = 20
sin_model_class = 19
val_transform = transforms.Compose([
# et.ExtResize( 512 ),
# et.ExtToTensor(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# et.ExtNormalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225]),
])
# 图像读取和预处理
# img = Image.open(img_path).convert('RGB')
# input = val_transform(img).unsqueeze(0)
img = Image.open(img_path).convert('RGB')
img = val_transform(img).unsqueeze(0)
# 如果是多模型融合
if 'M' in model_name:
preds = []
for class_num in range(1, N_class):
modelname = 'checkpoints/multiple_model/latest_%s_%s_class%d_os%d.pth' % (
model_name, dataset, class_num, 16,)
checkpoint = torch.load(modelname, map_location=torch.device('cpu'))
model = model_map[model_name](num_classes=N_class, output_stride=16)
model.load_state_dict(checkpoint["model_state"])
model = nn.DataParallel(model)
model.to(device)
model.eval()
with torch.no_grad():
outputs = nn.Sigmoid()(model(img))
pred = outputs.detach().cpu().numpy()
preds.append(pred)
# 读取单个模型
checkpoint = torch.load(ckpt_path, map_location=torch.device('cpu'))
model = model_map[model_name.split('_')[0] + '_' + model_name.split('_')[1]](num_classes=sin_model_class,
output_stride=16)
model.load_state_dict(checkpoint["model_state"])
if device=='cuda':
model = nn.DataParallel(model)
model.to(device)
model.eval()
# sin_outputs = model(images).cpu().numpy()
sin_outputs = F.softmax(model(img.to(device)), dim=1).detach().cpu().numpy()
# 融合
alpha = 0.5
preds = np.concatenate(preds, 1)
if dataset.lower() != 'cityscapes':
background_p = np.expand_dims(sin_outputs[:, 0, :, :], axis=1) # 抽取单类模型预测的背景概率
preds = np.concatenate((background_p, preds), 1) # 单类模型与多雷模型分数融合
final_preds = alpha * preds + sin_outputs
preds = np.argmax(final_preds, axis=1)
if dataset == 'voc':
pred = voc_cmap()[preds.squeeze(axis=0)].astype(np.uint8)
else:
pred = Cityscapes.decode_target(preds.squeeze(axis=0)).astype(np.uint8)
Image.fromarray(pred).save(result_path)
print("Prediction is saved in %s" % result_path)
else:
# 模型加载
model = model_map[model_name](num_classes=sin_model_class, output_stride=16)
weights = torch.load(ckpt_path)["model_state"]
model.load_state_dict(weights)
model.to(device)
model.eval()
with torch.no_grad():
print(img.shape)
img = img.to(device)
# preds = outputs.detach().max(dim=1)[1].cpu().numpy()
pred = model(img).max(dim=1)[1].cpu().numpy()[0, :, :]
if dataset == 'voc':
pred = voc_cmap()[pred].astype(np.uint8)
else:
pred= Cityscapes.decode_target(pred).astype(np.uint8)
Image.fromarray(pred).save(result_path)
print("Prediction is saved in %s" % result_path)