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run.py
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run.py
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#!/usr/bin/env python
import getopt
import numpy
import PIL
import PIL.Image
import sys
import torch
##########################################################
torch.set_grad_enabled(False) # make sure to not compute gradients for computational performance
torch.backends.cudnn.enabled = True # make sure to use cudnn for computational performance
##########################################################
arguments_strModel = 'bsds500' # only 'bsds500' for now
arguments_strIn = './images/sample.png'
arguments_strOut = './out.png'
for strOption, strArgument in getopt.getopt(sys.argv[1:], '', [ strParameter[2:] + '=' for strParameter in sys.argv[1::2] ])[0]:
if strOption == '--model' and strArgument != '': arguments_strModel = strArgument # which model to use
if strOption == '--in' and strArgument != '': arguments_strIn = strArgument # path to the input image
if strOption == '--out' and strArgument != '': arguments_strOut = strArgument # path to where the output should be stored
# end
##########################################################
class Network(torch.nn.Module):
def __init__(self):
super().__init__()
self.netVggOne = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggTwo = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggThr = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggFou = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netVggFiv = torch.nn.Sequential(
torch.nn.MaxPool2d(kernel_size=2, stride=2),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False),
torch.nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
torch.nn.ReLU(inplace=False)
)
self.netScoreOne = torch.nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreTwo = torch.nn.Conv2d(in_channels=128, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreThr = torch.nn.Conv2d(in_channels=256, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreFou = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netScoreFiv = torch.nn.Conv2d(in_channels=512, out_channels=1, kernel_size=1, stride=1, padding=0)
self.netCombine = torch.nn.Sequential(
torch.nn.Conv2d(in_channels=5, out_channels=1, kernel_size=1, stride=1, padding=0),
torch.nn.Sigmoid()
)
self.load_state_dict({ strKey.replace('module', 'net'): tenWeight for strKey, tenWeight in torch.hub.load_state_dict_from_url(url='http://content.sniklaus.com/github/pytorch-hed/network-' + arguments_strModel + '.pytorch', file_name='hed-' + arguments_strModel).items() })
# end
def forward(self, tenInput):
tenInput = tenInput * 255.0
tenInput = tenInput - torch.tensor(data=[104.00698793, 116.66876762, 122.67891434], dtype=tenInput.dtype, device=tenInput.device).view(1, 3, 1, 1)
tenVggOne = self.netVggOne(tenInput)
tenVggTwo = self.netVggTwo(tenVggOne)
tenVggThr = self.netVggThr(tenVggTwo)
tenVggFou = self.netVggFou(tenVggThr)
tenVggFiv = self.netVggFiv(tenVggFou)
tenScoreOne = self.netScoreOne(tenVggOne)
tenScoreTwo = self.netScoreTwo(tenVggTwo)
tenScoreThr = self.netScoreThr(tenVggThr)
tenScoreFou = self.netScoreFou(tenVggFou)
tenScoreFiv = self.netScoreFiv(tenVggFiv)
tenScoreOne = torch.nn.functional.interpolate(input=tenScoreOne, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreTwo = torch.nn.functional.interpolate(input=tenScoreTwo, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreThr = torch.nn.functional.interpolate(input=tenScoreThr, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreFou = torch.nn.functional.interpolate(input=tenScoreFou, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
tenScoreFiv = torch.nn.functional.interpolate(input=tenScoreFiv, size=(tenInput.shape[2], tenInput.shape[3]), mode='bilinear', align_corners=False)
return self.netCombine(torch.cat([ tenScoreOne, tenScoreTwo, tenScoreThr, tenScoreFou, tenScoreFiv ], 1))
# end
# end
netNetwork = None
##########################################################
def estimate(tenInput):
global netNetwork
if netNetwork is None:
netNetwork = Network().cuda().eval()
# end
intWidth = tenInput.shape[2]
intHeight = tenInput.shape[1]
assert(intWidth == 480) # remember that there is no guarantee for correctness, comment this line out if you acknowledge this and want to continue
assert(intHeight == 320) # remember that there is no guarantee for correctness, comment this line out if you acknowledge this and want to continue
return netNetwork(tenInput.cuda().view(1, 3, intHeight, intWidth))[0, :, :, :].cpu()
# end
##########################################################
if __name__ == '__main__':
tenInput = torch.FloatTensor(numpy.ascontiguousarray(numpy.array(PIL.Image.open(arguments_strIn))[:, :, ::-1].transpose(2, 0, 1).astype(numpy.float32) * (1.0 / 255.0)))
tenOutput = estimate(tenInput)
PIL.Image.fromarray((tenOutput.clip(0.0, 1.0).numpy().transpose(1, 2, 0)[:, :, 0] * 255.0).astype(numpy.uint8)).save(arguments_strOut)
# end