From 29412a2ec160a7656558d024afb1a0e81327cef2 Mon Sep 17 00:00:00 2001
From: fat rabbit <531041610@qq.com>
Date: Fri, 7 Sep 2018 00:31:35 +0800
Subject: [PATCH] remove

---
 plugin/facesr4/net.py | 76 -------------------------------------------
 1 file changed, 76 deletions(-)
 delete mode 100644 plugin/facesr4/net.py

diff --git a/plugin/facesr4/net.py b/plugin/facesr4/net.py
deleted file mode 100644
index 6a0d000..0000000
--- a/plugin/facesr4/net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from torch.autograd import Variable
-import torch
-import torch.nn  as nn
-from torchvision.models import alexnet, vgg
-import torch.nn.functional as F
-import math
-from torch.autograd import Variable
-import gc
-from  PIL import Image
-
-class GNet(nn.Module):
-    def __init__(self):
-        super(GNet, self).__init__()
-
-        self.pad1 = nn.ReflectionPad2d(3)
-        self.conv1 = nn.Conv2d(3, 64, 7, 2)
-        self.in1 = nn.InstanceNorm2d(64)
-        # relu
-
-        self.pad2 = nn.ReflectionPad2d(1)
-        self.conv2 = nn.Conv2d(64,64, 3, 2)
-        self.in2 = nn.InstanceNorm2d(64)
-        # relu
-
-        self.pad3_1 = nn.ReflectionPad2d(1)
-        self.con3_1 = nn.Conv2d(64, 64, 3, 1)
-
-
-        self.pad3_2 = nn.ReflectionPad2d(1)
-        self.con3_2 = nn.Conv2d(64, 64, 3, 1)
-        self.in3 = nn.InstanceNorm2d(64)
-
-        self.deconv = nn.Sequential(
-            nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(128, 128, 3, 2, 1, 1),
-            nn.InstanceNorm2d(128),
-            nn.ReLU(inplace=True),
-            nn.ConvTranspose2d(128, 3, 3, 2, 1, 1),
-            nn.ReflectionPad2d(3),
-            nn.Conv2d(3, 3, 7, 1),
-            nn.Tanh()
-        )
-        self._initialize_weights()
-
-    def _initialize_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.data.normal_(0, math.sqrt(2. / n))
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-            elif isinstance(m, nn.Linear):
-                m.weight.data.normal_(0, 0.01)
-                m.bias.data.zero_()
-
-    def forward(self, x):
-        y1 = F.relu(self.in1(self.conv1(self.pad1(x))))
-        y2 = F.relu(self.in2(self.conv2(self.pad2(y1))))
-        y3 = F.relu(self.in3(self.con3_2(self.pad3_2(self.con3_1(self.pad3_1(y2))))))
-        y3_cat_y2 = torch.cat((y2, y3), dim=1)
-        output = self.deconv(y3_cat_y2)
-        output = torch.clamp(2*output + x, -1, 1)
-        return output
-
-class Preprocess:
-    def __init__(self):
-        pass
-
-    def __call__(self, pil_img):
-        a,b = pil_img.size
-        a,b = int(a*4),int(b*4)
-        pil_img = pil_img.resize((a,b),Image.BICUBIC)
-        return pil_img