Update minist unsuper.

This commit is contained in:
Colin 2024-10-22 19:13:19 +08:00
parent 385c438c1c
commit a0da2565fe
2 changed files with 14 additions and 23 deletions

View File

@ -2,4 +2,8 @@
1. 重复的权重,虽然权重看起来都一样,但是有稍微的不同,不是完全一样
2. 3x3太小了导致了样本的信噪比太低大部分的样本切出来都是0
2. 5x5的时候会有网格状重复
3. 7x7的时候边框区域问题
3. 7x7的时候边框区域问题
1. 输入的信噪比
2. loss函数的设计
3. grad信息的应用

View File

@ -30,7 +30,7 @@ test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, s
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 8, 3, 1, 0)
self.conv1 = nn.Conv2d(1, 8, 5, 1, 0)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(8, 1, 5, 1, 0)
self.fc1 = nn.Linear(1 * 4 * 4, 10)
@ -105,27 +105,11 @@ model = ConvNet().to(device)
model.train()
# Train the model unsuper
epochs = 1
epochs = 2
n_total_steps = len(train_loader)
for epoch in range(epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.to(device)
# # images = images[:,:,12:15,12:15]
# kernel_size = 3
# mean_filter = torch.ones((1, 1, kernel_size, kernel_size), device=device) / (kernel_size * kernel_size)
# images = F.conv2d(images, mean_filter, padding=1)
# images = F.conv2d(images, mean_filter, padding=1)
# images = F.conv2d(images, mean_filter, padding=1)
# # images = F.conv2d(images, mean_filter, padding=1)
# # images = F.conv2d(images, mean_filter, padding=1)
# images = torch.rand(3, 3).to(device=device)
# # images[1, 1] = images[1, 1] * 2
# # images[0, 0] = images[1, 1] * 2
# # images[2, 2] = images[1, 1] * 2
# images = images.view(1, 1, 3, 3)
outputs = model.forward_unsuper(images)
outputs = outputs.permute(0, 2, 3, 1) # 64 8 24 24 -> 64 24 24 8
@ -137,9 +121,10 @@ for epoch in range(epochs):
max = torch.expand_copy(max.reshape(-1, 1), sample.shape)
all = range(0, sample.shape[0])
ratio_max = abs / mean
ratio_nor = (max - abs) / max
ratio_nor[all, max_index] = ratio_max[all, max_index].clone()
# ratio_max = abs / mean
# ratio_nor = (max - abs) / max
ratio_nor = torch.pow(abs / mean, 4)
# ratio_nor[all, max_index] = ratio_max[all, max_index].clone()
ratio_nor = torch.where(torch.isnan(ratio_nor), 1.0, ratio_nor)
label = sample * ratio_nor
@ -147,7 +132,7 @@ for epoch in range(epochs):
model.conv1.weight.grad = None
loss.backward()
model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 100
model.conv1.weight.data = model.conv1.weight.data - model.conv1.weight.grad * 10000
if (i + 1) % 100 == 0:
print(f"Epoch [{epoch+1}/{epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.8f}")
@ -158,6 +143,8 @@ show.DumpTensorToImage(g.view(-1, g.shape[2], g.shape[3]).cpu(), "conv1_weight_g
w = model.conv1.weight.data
show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), "conv1_weight_update.png")
# model.conv1.weight.data = torch.rand(model.conv1.weight.data.shape, device=device)
# loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
# images, labels = next(iter(loader))
# images = images.to(device)