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# --------------------------------------------------------
# Two Stream Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Hangyan Jiang
# --------------------------------------------------------
# Testing part
import torch
import torch.nn as nn
import torch.nn.functional as F
import cv2
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import argparse
class SRMConv2d(nn.Module):
def __init__(self, learnable=False):
super(SRMConv2d, self).__init__()
self.weight = nn.Parameter(torch.Tensor(30, 3, 5, 5),
requires_grad=learnable)
self.bias = nn.Parameter(torch.Tensor(30), \
requires_grad=learnable)
self.reset_parameters()
def reset_parameters(self):
SRM_npy = np.load('lib/component/SRM_Kernels.npy')
# print(SRM_npy.shape)
SRM_npy = np.repeat(SRM_npy, 3, axis=1)
# print(SRM_npy.shape)
self.weight.data.numpy()[:] = SRM_npy
self.bias.data.zero_()
def forward(self, input):
return F.conv2d(input, self.weight, stride=1, padding=2)
class SRMConv2d_simple(nn.Module):
def __init__(self, inc=3, learnable=False):
super(SRMConv2d_simple, self).__init__()
self.truc = nn.Hardtanh(-3, 3)
kernel = self._build_kernel(inc) # (3,3,5,5)
self.kernel = nn.Parameter(data=kernel, requires_grad=learnable)
# self.hor_kernel = self._build_kernel().transpose(0,1,3,2)
def forward(self, x):
'''
x: imgs (Batch, H, W, 3)
'''
out = F.conv2d(x, self.kernel, stride=1, padding=2)
out = self.truc(out)
return out
def _build_kernel(self, inc):
# filter1: KB
filter1 = [[0, 0, 0, 0, 0],
[0, -1, 2, -1, 0],
[0, 2, -4, 2, 0],
[0, -1, 2, -1, 0],
[0, 0, 0, 0, 0]]
# filter2：KV
filter2 = [[-1, 2, -2, 2, -1],
[2, -6, 8, -6, 2],
[-2, 8, -12, 8, -2],
[2, -6, 8, -6, 2],
[-1, 2, -2, 2, -1]]
# # filter3：hor 2rd
filter3 = [[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, -2, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
# filter3：hor 2rd
# filter3 = [[0, 0, 0, 0, 0],
# [0, 0, 1, 0, 0],
# [0, 1, -4, 1, 0],
# [0, 0, 1, 0, 0],
# [0, 0, 0, 0, 0]]
filter1 = np.asarray(filter1, dtype=float) / 4.
filter2 = np.asarray(filter2, dtype=float) / 12.
filter3 = np.asarray(filter3, dtype=float) / 2.
# statck the filters
filters = [[filter1],#, filter1, filter1],
[filter2],#, filter2, filter2],
[filter3]]#, filter3, filter3]] # (3,3,5,5)
filters = np.array(filters)
filters = np.repeat(filters, inc, axis=1)
filters = torch.FloatTensor(filters) # (3,3,5,5)
return filters
class SRMConv2d_Separate(nn.Module):
def __init__(self, inc, outc, learnable=False):
super(SRMConv2d_Separate, self).__init__()
self.inc = inc
self.truc = nn.Hardtanh(-3, 3)
kernel = self._build_kernel(inc) # (3,3,5,5)
self.kernel = nn.Parameter(data=kernel, requires_grad=learnable)
# self.hor_kernel = self._build_kernel().transpose(0,1,3,2)
self.out_conv = nn.Sequential(
nn.Conv2d(3*inc, outc, 1, 1, 0, 1, 1, bias=False),
nn.BatchNorm2d(outc),
nn.ReLU(inplace=True)
)
for ly in self.out_conv.children():
if isinstance(ly, nn.Conv2d):
nn.init.kaiming_normal_(ly.weight, a=1)
def forward(self, x):
'''
x: imgs (Batch,inc, H, W)
kernel: (outc,inc,kH,kW)
'''
out = F.conv2d(x, self.kernel, stride=1, padding=2, groups=self.inc)
out = self.truc(out)
out = self.out_conv(out)
return out
def _build_kernel(self, inc):
# filter1: KB
filter1 = [[0, 0, 0, 0, 0],
[0, -1, 2, -1, 0],
[0, 2, -4, 2, 0],
[0, -1, 2, -1, 0],
[0, 0, 0, 0, 0]]
# filter2：KV
filter2 = [[-1, 2, -2, 2, -1],
[2, -6, 8, -6, 2],
[-2, 8, -12, 8, -2],
[2, -6, 8, -6, 2],
[-1, 2, -2, 2, -1]]
# # filter3：hor 2rd
filter3 = [[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 1, -2, 1, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
# filter3：hor 2rd
# filter3 = [[0, 0, 0, 0, 0],
# [0, 0, 1, 0, 0],
# [0, 1, -4, 1, 0],
# [0, 0, 1, 0, 0],
# [0, 0, 0, 0, 0]]
filter1 = np.asarray(filter1, dtype=float) / 4.
filter2 = np.asarray(filter2, dtype=float) / 12.
filter3 = np.asarray(filter3, dtype=float) / 2.
# statck the filters
filters = [[filter1],#, filter1, filter1],
[filter2],#, filter2, filter2],
[filter3]]#, filter3, filter3]] # (3,3,5,5) => (3,1,5,5)
filters = np.array(filters)
# filters = np.repeat(filters, inc, axis=1)
filters = np.repeat(filters, inc, axis=0)
filters = torch.FloatTensor(filters) # (3*inc,1,5,5)
# print(filters.size())
return filters
if __name__ == "__main__":
im = cv2.imread('E:\SRM\component\FF-F2F_0.png')
im_ten = im/255*2-1
im_ten = torch.from_numpy(im_ten).unsqueeze(0).permute(0, 3, 1, 2).float()
# im_ten = torch.cat((im_ten, im_ten), dim=1)
srm_conv = SRMConv2d_simple(inc=3)
srm_conv1 = SRMConv2d_Separate(inc=3, outc=3)
srm = srm_conv(im_ten)
print(srm.size())
def t2im(t):
# t = (t+1)/2*255
t = t*255
im = t.squeeze().detach().cpu().numpy().transpose(1, 2, 0).astype(np.uint8)
return im
cv2.imshow('ori', im)
cv2.imshow('srm', t2im(srm))
cv2.imshow('srm1', t2im(srm_conv1(im_ten)))
# cv2.imshow('srm2', t2im(srm_conv(srm)))
cv2.waitKey()
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