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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 dump1
dump2 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 torch.nn.functional as F # Add this line
import torchvision import torchvision
import torchvision.transforms as transforms import torchvision.transforms as transforms
import numpy as np
import random
sys.path.append("..") sys.path.append("..")
from tools import show from tools import show
seed = 4321 seed = 42
torch.manual_seed(seed) torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(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("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("cpu")
# device = torch.device("mps") # device = torch.device("mps")
num_epochs = 1 num_epochs = 1
@ -60,15 +68,12 @@ class ConvNet(nn.Module):
def printFector(self, x, label, dir=""): 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.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() w = self.normal_conv1_weight()
x = torch.conv2d(x, w) x = torch.conv2d(x, w)
show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), dir + "/conv1_weight.png") 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.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.pool(x)
x = self.conv2(x) x = self.conv2(x)
@ -132,16 +137,17 @@ for epoch in range(epochs):
model.conv1.weight.grad = None model.conv1.weight.grad = None
loss.backward() 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: if (i + 1) % 100 == 0:
print(f"Epoch [{epoch+1}/{epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.8f}") 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]) show.DumpTensorToImage(images.view(-1, images.shape[2], images.shape[3]), "input_image.png", Contrast=[0, 1.0])
g = model.conv1.weight.grad 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 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) # model.conv1.weight.data = torch.rand(model.conv1.weight.data.shape, device=device)