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ecbsr.py
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ecbsr.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from PIL import Image
'''
Add some future on ECBSR
support 1x1_3x3, 3x3_1x1, residual, 1x1
'''
class SeqConv3x3(nn.Module):
def __init__(self, seq_type, inp_planes, out_planes, depth_multiplier):
super(SeqConv3x3, self).__init__()
self.type = seq_type
self.inp_planes = inp_planes
self.out_planes = out_planes
if self.type == 'conv1x1-conv3x3':
# all have bias
self.mid_planes = int(out_planes * depth_multiplier)
conv0 = torch.nn.Conv2d(self.inp_planes, self.mid_planes, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
# without padding is important, NEED to add bias1 for padding
# plain version 3*3 has padding=1
conv1 = torch.nn.Conv2d(self.mid_planes, self.out_planes, kernel_size=3)
self.k1 = conv1.weight
self.b1 = conv1.bias
elif self.type == 'conv1x1-sobelx':
conv0 = torch.nn.Conv2d(self.inp_planes, self.out_planes, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
# init scale & bias
scale = torch.randn(size=(self.out_planes, 1, 1, 1)) * 1e-3
self.scale = nn.Parameter(scale)
# bias = 0.0
# bias = [bias for c in range(self.out_planes)]
# bias = torch.FloatTensor(bias)
bias = torch.randn(self.out_planes) * 1e-3
bias = torch.reshape(bias, (self.out_planes,))
self.bias = nn.Parameter(bias)
# init mask
self.mask = torch.zeros((self.out_planes, 1, 3, 3), dtype=torch.float32)
for i in range(self.out_planes):
self.mask[i, 0, 0, 0] = 1.0
self.mask[i, 0, 1, 0] = 2.0
self.mask[i, 0, 2, 0] = 1.0
self.mask[i, 0, 0, 2] = -1.0
self.mask[i, 0, 1, 2] = -2.0
self.mask[i, 0, 2, 2] = -1.0
self.mask = nn.Parameter(data=self.mask, requires_grad=False)
elif self.type == 'conv1x1-sobely':
conv0 = torch.nn.Conv2d(self.inp_planes, self.out_planes, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
# init scale & bias
scale = torch.randn(size=(self.out_planes, 1, 1, 1)) * 1e-3
self.scale = nn.Parameter(torch.FloatTensor(scale))
# bias = 0.0
# bias = [bias for c in range(self.out_planes)]
# bias = torch.FloatTensor(bias)
bias = torch.randn(self.out_planes) * 1e-3
bias = torch.reshape(bias, (self.out_planes,))
self.bias = nn.Parameter(torch.FloatTensor(bias))
# init mask
self.mask = torch.zeros((self.out_planes, 1, 3, 3), dtype=torch.float32)
for i in range(self.out_planes):
self.mask[i, 0, 0, 0] = 1.0
self.mask[i, 0, 0, 1] = 2.0
self.mask[i, 0, 0, 2] = 1.0
self.mask[i, 0, 2, 0] = -1.0
self.mask[i, 0, 2, 1] = -2.0
self.mask[i, 0, 2, 2] = -1.0
self.mask = nn.Parameter(data=self.mask, requires_grad=False)
elif self.type == 'conv1x1-laplacian':
conv0 = torch.nn.Conv2d(self.inp_planes, self.out_planes, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
# init scale & bias
scale = torch.randn(size=(self.out_planes, 1, 1, 1)) * 1e-3
self.scale = nn.Parameter(torch.FloatTensor(scale))
# bias = 0.0
# bias = [bias for c in range(self.out_planes)]
# bias = torch.FloatTensor(bias)
bias = torch.randn(self.out_planes) * 1e-3
bias = torch.reshape(bias, (self.out_planes,))
self.bias = nn.Parameter(torch.FloatTensor(bias))
# init mask
self.mask = torch.zeros((self.out_planes, 1, 3, 3), dtype=torch.float32)
for i in range(self.out_planes):
self.mask[i, 0, 0, 1] = 1.0
self.mask[i, 0, 1, 0] = 1.0
self.mask[i, 0, 1, 2] = 1.0
self.mask[i, 0, 2, 1] = 1.0
self.mask[i, 0, 1, 1] = -4.0
self.mask = nn.Parameter(data=self.mask, requires_grad=False)
elif self.type == 'conv3x3-conv1x1':
# all have bias
self.mid_planes = int(out_planes * depth_multiplier)
conv0 = torch.nn.Conv2d(self.inp_planes, self.mid_planes, kernel_size=3, padding=1)
self.k0 = conv0.weight
self.b0 = conv0.bias
# without padding is important, NEED to add bias1 for padding
# plain version 3*3 has padding=1
conv1 = torch.nn.Conv2d(self.mid_planes, self.out_planes, kernel_size=1, padding=0)
self.k1 = conv1.weight
self.b1 = conv1.bias
elif self.type == 'conv1x1':
conv0 = torch.nn.Conv2d(self.inp_planes, self.out_planes, kernel_size=1, padding=0)
self.k0 = conv0.weight
self.b0 = conv0.bias
else:
raise ValueError('the type of seqconv is not supported!')
def forward(self, x):
if self.type == 'conv1x1-conv3x3':
# conv-1x1
y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
# explicitly padding with bias
y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
b0_pad = self.b0.view(1, -1, 1, 1) # 1*B*1*1
y0[:, :, 0:1, :] = b0_pad
y0[:, :, -1:, :] = b0_pad
y0[:, :, :, 0:1] = b0_pad
y0[:, :, :, -1:] = b0_pad
# conv-3x3, without padding
y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1)
elif self.type == 'conv3x3-conv1x1':
y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1, padding=1)
y1 = F.conv2d(input=y0, weight=self.k1, bias=self.b1, stride=1)
elif self.type == 'conv1x1':
y1 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1, padding=0)
else:
y0 = F.conv2d(input=x, weight=self.k0, bias=self.b0, stride=1)
# explicitly padding with bias
y0 = F.pad(y0, (1, 1, 1, 1), 'constant', 0)
b0_pad = self.b0.view(1, -1, 1, 1)
y0[:, :, 0:1, :] = b0_pad
y0[:, :, -1:, :] = b0_pad
y0[:, :, :, 0:1] = b0_pad
y0[:, :, :, -1:] = b0_pad
# conv-3x3
y1 = F.conv2d(input=y0, weight=self.scale * self.mask, bias=self.bias, stride=1, groups=self.out_planes)
return y1
def rep_params(self):
device = self.k0.get_device()
if device < 0:
device = None
if self.type == 'conv1x1-conv3x3':
# re-param conv kernel, tranpose 1*1 weight, A * B * 1 * 1
# new kernel is C * A * 1 * 1
RK = F.conv2d(input=self.k1, weight=self.k0.permute(1, 0, 2, 3))
# re-param conv bias
RB = torch.ones(1, self.mid_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
RB = F.conv2d(input=RB, weight=self.k1).view(-1,) + self.b1
elif self.type == 'conv3x3-conv1x1':
RK = torch.zeros(self.k1.data.shape[0], self.k0.data.shape[1], 3, 3)
RB = torch.zeros(self.k1.data.shape[0])
for i in range(self.k1.data.shape[0]):
RK[i, ...] = torch.sum(self.k0.data * \
self.k1.data[i, ...].unsqueeze(1), dim=0)
RB[i] = self.b1.data[i] + torch.sum(self.b0.data * self.k1.data[i, ...].squeeze(1).squeeze(1))
elif self.type == 'conv1x1':
RK = F.pad(self.k0, (1, 1, 1, 1), 'constant', 0)
RB = self.b0.data
else:
tmp = self.scale * self.mask
k1 = torch.zeros((self.out_planes, self.out_planes, 3, 3), device=device)
for i in range(self.out_planes):
k1[i, i, :, :] = tmp[i, 0, :, :]
b1 = self.bias
# re-param conv kernel
RK = F.conv2d(input=k1, weight=self.k0.permute(1, 0, 2, 3))
# re-param conv bias
RB = torch.ones(1, self.out_planes, 3, 3, device=device) * self.b0.view(1, -1, 1, 1)
RB = F.conv2d(input=RB, weight=k1).view(-1,) + b1
return RK, RB
def unittest():
#test seq-conv
x = torch.randn(1, 3, 100, 100) * 100
#conv = SeqConv3x3('conv3x3-conv1x1', 3, 3, 2)
conv = SeqConv3x3('conv1x1', 3, 3, 2)
y0 = conv(x)
print(y0.shape)
RK, RB = conv.rep_params()
y1 = F.conv2d(input=x, weight=RK, bias=RB, stride=1, padding=1) # default valid 3x3 conv， padding is zero， for inference
print(torch.sum(y0-y1), torch.mean(y0-y1))
#tensor(0.0001, grad_fn=<SumBackward0>) tensor(4.4449e-09, grad_fn=<MeanBackward0>)