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Add model.conv1.weight normal after update grad.

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Colin 2024-10-28 16:31:42 +08:00
parent 6a0b47c674
commit 5b2cd4da61
2 changed files with 17 additions and 9 deletions
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2
unsuper/.gitignore vendored
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@ -1,2 +1,4 @@
dump1
dump2
*.png
*.log

20
unsuper/minist.py
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@ -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)
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)