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predict.py
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predict.py
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import os
import argparse
import torch
from torchvision import transforms
from model import FastSCNN
from PIL import Image
from utils.transforms import NewPad
from utils.transforms import pred_2_img
from metrics import pixel_accuracy
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
parser = argparse.ArgumentParser(
description='Predict segmentation result from a given image')
parser.add_argument('--checkpoint', type=str, default='checkpoints/FastSCNN11_29',
help='which checkpoint to use')
parser.add_argument('--input_image', type=str,
help='path to the input picture')
parser.add_argument('--outdir', default='data/test_result', type=str,
help='path to save the predict result')
parser.add_argument('--num_classes', type=int, default=6,
help='num of classes in model')
parser.add_argument('--eval', default=None, type=str,
help='image label for evaluation score')
args = parser.parse_args()
def predict():
#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device = "cpu"
# output folder
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# image transform
image_transformer = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.3396, 0.3628, 0.3362], [0.1315, 0.1287, 0.1333])
])
#img = np.load(args.input_image)
img = np.load('img-0a00f663-3528-4ac2-86f3-36ffbdf5e69b.npy')
img = img.transpose(1,2,0)
fig = plt.figure(1)
canvas = FigureCanvas(fig)
plt.imshow(img)
canvas.print_figure('test-pic.png')
image = Image.fromarray(img).convert('RGB')
image = image_transformer(image).unsqueeze(0).to(device)
model = FastSCNN(args.num_classes).to(device)
model.load_state_dict(torch.load(args.checkpoint, map_location=device))
# model = torch.load(args.checkpoint, map_location=device)
print('Finished loading model!')
model.eval()
with torch.no_grad():
outputs = model(image)
pred = torch.argmax(outputs[0], 1).squeeze(0).to(device)
#outname = os.path.splitext(os.path.split(args.input_image)[-1])[0] + '.png'
outname = 'test-pred.png'
#pred_2_img(pred, os.path.join(args.outdir, outname))
pred_2_img(pred, os.path.join(outname))
mask = np.load('img-0a00f663-3528-4ac2-86f3-36ffbdf5e69b-mask.npy')
mask = np.argmax(mask, 0)
ClassesColors = {
(255,0,0):0,
(255,255,255):1,
(255,255,0):2,
(0,0,255):3,
(0,255,255):4,
(0,255,0):5
}
Class1H2RGB = dict([[str(val),key] for key,val in ClassesColors.items()])
cmap = ListedColormap(np.array([Class1H2RGB[k] for k in sorted(Class1H2RGB.keys())])/255.0)
plt.figure(2)
plt.imshow(mask,cmap=cmap,rasterized=True)
plt.figure(3)
plt.imshow(pred, cmap=cmap, rasterized=True)
plt.show(block=True)
if not args.eval is None:
# only one label for now, so add batch=1 dim
label = torch.load(args.eval).unsqueeze(0)
output = outputs[0]
print("image score: ", pixel_accuracy(output, label))
if __name__ == '__main__':
predict()