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HuangJunJie2017 authored Nov 9, 2018
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44 changes: 44 additions & 0 deletions run_a_pair.py
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import torch
import numpy as np
import argparse

from Networks.FlowNet2 import FlowNet2 # the path is depended on where you create this module
from frame_utils import read_gen # the path is depended on where you create this module

if __name__ == '__main__':
# obtain the necessary args for construct the flownet framework
parser = argparse.ArgumentParser()
parser.add_argument('--fp16', action='store_true', help='Run model in pseudo-fp16 mode (fp16 storage fp32 math).')
parser.add_argument("--rgb_max", type=float, default=255.)
args = parser.parse_args()

# initial a Net
net = FlowNet2(args).cuda()
# load the state_dict
dict = torch.load("/home/hjj/PycharmProjects/flownet2_pytorch/FlowNet2_checkpoint.pth.tar")
net.load_state_dict(dict["state_dict"])

# load the image pair, you can find this operation in dataset.py
pim1 = read_gen("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img0.ppm")
pim2 = read_gen("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img1.ppm")
images = [pim1, pim2]
images = np.array(images).transpose(3, 0, 1, 2)
im = torch.from_numpy(images.astype(np.float32)).unsqueeze(0).cuda()

# process the image pair to obtian the flow
result = net(im).squeeze()


# save flow, I reference the code in scripts/run-flownet.py in flownet2-caffe project
def writeFlow(name, flow):
f = open(name, 'wb')
f.write('PIEH'.encode('utf-8'))
np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
flow = flow.astype(np.float32)
flow.tofile(f)
f.flush()
f.close()


data = result.data.cpu().numpy().transpose(1, 2, 0)
writeFlow("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img.flo", data)

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