From dc08bfa17edb3688b7b7c654ceda05dd60bc0a1b Mon Sep 17 00:00:00 2001
From: chun <zhengchh6@mail2.sysu.edu.cn>
Date: Tue, 19 Dec 2023 21:33:50 +0800
Subject: [PATCH] Encoder test

---
 environment.yml | Bin 10368 -> 0 bytes
 model.py        |  59 +++++++++++++++++++++++++++++++++++++++++++-----
 train.py        |  21 +++++++++++++++++
 3 files changed, 74 insertions(+), 6 deletions(-)
 delete mode 100644 environment.yml

diff --git a/environment.yml b/environment.yml
deleted file mode 100644
index 6507ad17c9511188b37d47a64885684430dfe566..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/model.py b/model.py
index 5ac275b..2a444c8 100644
--- a/model.py
+++ b/model.py
@@ -1,5 +1,13 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  11:00:00 2023
+
+@author: chun
+"""
+
 import torch
 import torch.nn as nn
+import numpy as np
 
 
 class _ConvWithPReLU(nn.Module):
@@ -26,11 +34,50 @@ class _TransConvWithPReLU(nn.Module):
         return x
 
 
-class Nomalization(nn.Module):
-    def __init__(self, in_channels):
-        super(Nomalization, self).__init__()
-        self.norm = nn.BatchNorm2d(in_channels)
+def _image_normalization(tensor, norm_type):
+    if norm_type == 'nomalization':
+        return tensor / 255.0
+    elif norm_type == 'denormalization':
+        return tensor * 255.0
+    else:
+        raise Exception('Unknown type of normalization')
+
+
+def _NormlizationLayer(norm_type='nomalization'):
+    pass
+
+
+def ratio2filter_size(x, ratio):
+    before_size = np.prod(x.size())
+    after_size = before_size*ratio
+    encoder_temp = Encoder(c=after_size)
+
+
+class Encoder(nn.Module):
+    def __init__(self, c, is_temp=False):
+        super(Encoder, self).__init__()
+        self.is_temp = is_temp
+        self.imgae_normalization = _image_normalization(norm_type='nomalization')
+        self.conv1 = _ConvWithPReLU(in_channels=3, out_channels=16, kernel_size=5, stride=2)
+        self.conv2 = _ConvWithPReLU(in_channels=16, out_channels=32, kernel_size=5, stride=2)
+        self.conv3 = _ConvWithPReLU(in_channels=32, out_channels=32, kernel_size=5, stride=1)
+        self.conv4 = _ConvWithPReLU(in_channels=32, out_channels=32, kernel_size=5, stride=1)
+        self.conv5 = _ConvWithPReLU(in_channels=32, out_channels=c, kernel_size=5, stride=1)
+        self.norm = _NormlizationLayer(norm_type='nomalization')
 
     def forward(self, x):
-        x = self.norm(x)
-        return x
+        x = self.imgae_normalization(x)
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = self.conv3(x)
+        x = self.conv4(x)
+        if not self.is_temp:
+            x = self.conv5(x)
+        z = self.norm(x)
+        del x
+        return z
+
+
+class Decoder(nn.Module):
+    def __init__(self):
+        super(Decoder, self).__init__()
diff --git a/train.py b/train.py
index e69de29..fa323c5 100644
--- a/train.py
+++ b/train.py
@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  11:00:00 2023
+
+@author: chun
+"""
+
+import torch
+import torch.nn as nn
+
+
+
+def main():
+    pass
+
+
+
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file