# Just need to do this one time
caffemodel_dir_path = 'PATH_TO_PSPNET_DIR/evaluation/model'
psp.load_pretrained_model(model_path=os.path.join(
caffemodel_dir_path, 'pspnet101_cityscapes.caffemodel'))
#psp.load_pretrained_model(model_path=os.path.join(caffemodel_dir_path, 'pspnet50_ADE20K.caffemodel'))
#psp.load_pretrained_model(model_path=os.path.join(caffemodel_dir_path, 'pspnet101_VOC2012.caffemodel'))
# psp.load_state_dict(torch.load('psp.pth'))
psp.float()
psp.cuda(cd)
psp.eval()
dataset_root_dir = 'PATH_TO_CITYSCAPES_DIR'
dst = cl(root=dataset_root_dir)
img = m.imread(
os.path.join(
dataset_root_dir,
'leftImg8bit/demoVideo/stuttgart_00/stuttgart_00_000000_000010_leftImg8bit.png'
))
m.imsave('cropped.png', img)
orig_size = img.shape[:-1]
img = img.transpose(2, 0, 1)
img = img.astype(np.float64)
img -= np.array([123.68, 116.779, 103.939])[:, None, None]
img = np.copy(img[::-1, :, :])
img = torch.from_numpy(img).float() # convert to torch tensor
img = img.unsqueeze(0)
out = psp.tile_predict(img)
from torch.autograd import Variable
import scipy.misc as m
from ptsemseg.loader.cityscapes_loader import cityscapesLoader as cl
psp = pspnet(n_classes=19)
# Just need to do this one time
#psp.load_pretrained_model(model_path='/home/meet/models/pspnet101_cityscapes.caffemodel')
torch.save(psp.state_dict(), "psp.pth")
psp.load_state_dict(torch.load('psp.pth'))
psp.float()
psp.cuda(cd)
psp.eval()
dst = cl(root='/home/meet/datasets/cityscapes/')
img = m.imread(
'/home/meet/seg/leftImg8bit/demoVideo/stuttgart_00/stuttgart_00_000000_000010_leftImg8bit.png'
)
m.imsave('cropped.png', img)
orig_size = img.shape[:-1]
img = img.transpose(2, 0, 1)
img = img.astype(np.float64)
img -= np.array([123.68, 116.779, 103.939])[:, None, None]
img = np.copy(img[::-1, :, :])
flp = np.copy(img[:, :, ::-1])
# Warmup model
#warmup = Variable(torch.unsqueeze(torch.from_numpy(flp).float(), 0).cuda(cd))
#for i in range(5):
# _ = psp(warmup[:,:,300:300+713,300:300+713])
