From 5b2cd4da61f0d2ab19f8a18393edabe1eb19e593 Mon Sep 17 00:00:00 2001
From: Colin <colin>
Date: Mon, 28 Oct 2024 16:31:42 +0800
Subject: [PATCH] Add model.conv1.weight normal after update grad.

---
 unsuper/.gitignore |  4 +++-
 unsuper/minist.py  | 22 ++++++++++++++--------
 2 files changed, 17 insertions(+), 9 deletions(-)

diff --git a/unsuper/.gitignore b/unsuper/.gitignore
index 4809819..e25ce62 100644
--- a/unsuper/.gitignore
+++ b/unsuper/.gitignore
@@ -1,2 +1,4 @@
 dump1
-dump2
\ No newline at end of file
+dump2
+*.png
+*.log
\ No newline at end of file
diff --git a/unsuper/minist.py b/unsuper/minist.py
index 8be7062..024fa69 100644
--- a/unsuper/minist.py
+++ b/unsuper/minist.py
@@ -5,15 +5,23 @@ import torch.nn as nn
 import torch.nn.functional as F  # Add this line
 import torchvision
 import torchvision.transforms as transforms
+import numpy as np
+import random
 
 sys.path.append("..")
 from tools import show
 
-seed = 4321
+seed = 42
 torch.manual_seed(seed)
-torch.cuda.manual_seed_all(seed)
+if torch.cuda.is_available():
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+np.random.seed(seed)
+random.seed(seed)
+
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# device = torch.device("cpu")
 # device = torch.device("mps")
 
 num_epochs = 1
@@ -60,15 +68,12 @@ class ConvNet(nn.Module):
 
     def printFector(self, x, label, dir=""):
         show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), dir + "/input_image.png", Contrast=[0, 1.0])
-        # show.DumpTensorToLog(x, "input_image.log")
 
         w = self.normal_conv1_weight()
         x = torch.conv2d(x, w)
         show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), dir + "/conv1_weight.png")
-        # show.DumpTensorToLog(w, "conv1_weight.log")
 
         show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), dir + "/conv1_output.png")
-        # show.DumpTensorToLog(x, "conv1_output.png")
 
         x = self.pool(x)
         x = self.conv2(x)
@@ -132,16 +137,17 @@ for epoch in range(epochs):
         model.conv1.weight.grad = None
         loss.backward()
 
-        model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 10000
+        model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 1000
+        model.conv1.weight.data = model.normal_conv1_weight()
 
         if (i + 1) % 100 == 0:
             print(f"Epoch [{epoch+1}/{epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.8f}")
 
 show.DumpTensorToImage(images.view(-1, images.shape[2], images.shape[3]), "input_image.png", Contrast=[0, 1.0])
 g = model.conv1.weight.grad
-show.DumpTensorToImage(g.view(-1, g.shape[2], g.shape[3]).cpu(), "conv1_weight_grad.png")
+show.DumpTensorToImage(g.view(-1, g.shape[2], g.shape[3]).cpu(), "conv1_weight_grad.png", Value2Log=True)
 w = model.conv1.weight.data
-show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), "conv1_weight_update.png")
+show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), "conv1_weight_update.png", Value2Log=True)
 
 # model.conv1.weight.data = torch.rand(model.conv1.weight.data.shape, device=device)