Вопрос о различных параметрах согласованного измерения в фазе отбора проб и образных сетей в диффузионной модели ⇐ Python

[Anonymous]

Конкретная ошибка:
down = torch.size ([4, 128, 12, 64, 64]) up = torch.size ([4, 128, 12, 64, 64])
down = torch.size ([4, 64, 25, 64, 64]) up = torch.size ([4, 64, 24, 64, 64])
traceback (самый последний вызов последним):
file "/media/xd/hdd/wxy/spectraldiff_diffusion-master/codes/feature_extract_unet.py", строка 237, в
extract ()
file "/media/xd/hdd/ wxy/spectraldiff_diffusion-master/codes/feature_extract_unet.py ", line 231, в извлечении
ancure_by_t (dataloader, diffusion, model, x, ti)
file"/media/xd/hdd/wxy/spectraldiff_diffusion -master/codes/feature_extract_unet.py", line 159, in inference_by_t
feature_t_list = inference_mini_batch(model, xt, t)
File "/media/xd/hdd/wxy/spectraldiff_diffusion-master/codes/ feature_extract_unet.py ", Line 133, в inference_mini_batch
ushy_pred = model (temp_xt, t, feature = true)
file" /home/xd/.local/lib/python3.8/site-packages/ TORCH/NN/MODULES/MODULE.PY ", LINE 1553, в _WRAND_CALL_IMPL
return self._call_impl (*args, ** kwargs)
file" /home/xd/.local/lib/python3. 8/site-packages/torch/nn/modules/module.py ", line 1562, в _call_impl
return forward_call (*args, ** kwargs)
file"/media/xd/hdd/wxy /spectraldiff_diffusion-master/codes/unet3d.py ", строка 113, в прямом стороже Размер 1. Ожидаемый размер 24, но получил размер 25 для тензора номер 1 в списке.
import torch
import torchvision
import matplotlib.pyplot as plt
from torch import nn
import math
import numpy as np

class Block3d(nn.Module):
def __init__(self, in_ch, out_ch, time_emb_dim, kernal=(4,3,3), stride=(2,1,1), padding=(1,1,1), up=False):
super().__init__()
self.time_mlp = nn.Linear(time_emb_dim, out_ch)
if up:
self.conv1 = nn.Conv3d(2*in_ch, out_ch, 3, padding=1)
self.transform = nn.ConvTranspose3d(out_ch, out_ch, kernal, stride, padding)
else:
self.conv1 = nn.Conv3d(in_ch, out_ch, 3, padding=1)
self.transform = nn.Conv3d(out_ch, out_ch, kernal, stride, padding)
self.conv2 = nn.Conv3d(out_ch, out_ch, 3, padding=1)
self.bnorm1 = nn.BatchNorm3d(out_ch)
self.bnorm2 = nn.BatchNorm3d(out_ch)
self.relu = nn.ReLU()

def forward(self, x, t, ):
# First Conv
h = self.bnorm1(self.relu(self.conv1(x)))
# Time embedding
time_emb = self.relu(self.time_mlp(t))
# Extend last 2 dimensions
time_emb = time_emb[(..., ) + (None, ) * 3]
# Add time channel
h = h + time_emb
# Second Conv
h = self.bnorm2(self.relu(self.conv2(h)))
# Down or Upsample
return self.transform(h)

class SinusoidalPositionEmbeddings(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim

def forward(self, time):
device = time.device
half_dim = self.dim // 2
embeddings = math.log(10000) / (half_dim - 1)
embeddings = torch.exp(torch.arange(half_dim, device=device) * -embeddings)
embeddings = time[:, None] * embeddings[None, :]
embeddings = torch.cat((embeddings.sin(), embeddings.cos()), dim=-1)
# TODO: Double check the ordering here
return embeddings

class SimpleUnet(nn.Module):
"""
A simplified variant of the Unet architecture.
"""
def __init__(self, _image_channels):
super().__init__()
image_channels = _image_channels
down_channels = (16,32,64,128)
down_params = [
[(4,5,5),(2,1,1),(1,2,2)],
[(4,5,5),(2,1,1),(1,2,2)],
[(4,5,5),(2,1,1),(1,2,2)],
]
up_channels = (128,64,32,16)
up_params = [
[(4,5,5),(2,1,1),(1,2,2)],
[(4,5,5),(2,1,1),(1,2,2)],
[(4,5,5),(2,1,1),(1,2,2)],
]
out_dim = 1
time_emb_dim = 32
self.features = []

# Time embedding
self.time_mlp = nn.Sequential(
SinusoidalPositionEmbeddings(time_emb_dim),
nn.Linear(time_emb_dim, time_emb_dim),
nn.ReLU()
)

# Initial projection
self.conv0 = nn.Conv3d(image_channels, down_channels[0], 3, padding=1)

# Downsample
self.downs = nn.ModuleList([Block3d(down_channels, down_channels[i+1], time_emb_dim, \
down_params[0], down_params[1], down_params[2]) \
for i in range(len(down_channels)-1)])
# Upsample
self.ups = nn.ModuleList([Block3d(up_channels, up_channels[i+1], time_emb_dim, \
up_params[0], up_params[1], up_params[2], up=True) \
for i in range(len(up_channels)-1)])

self.output = nn.Conv3d(up_channels[-1], image_channels, out_dim)

def forward(self, x, timestep, feature=False):
# Embedd time
t = self.time_mlp(timestep)
# Initial conv
x = self.conv0(x)
# Unet
residual_inputs = []
for down in self.downs:
x = down(x, t)
residual_inputs.append(x)
for up in self.ups:
residual_x = residual_inputs.pop()
# print("down=",residual_x.shape, "up=", x.shape)
# Add residual x as additional channels
x = torch.cat((x, residual_x), dim=1)
if feature:
self.features.append(x.detach().cpu().numpy())
x = up(x, t)
return self.output(x)

def return_features(self):
temp_features = []
temp_features = self.features[:]
self.features = []
return temp_features

if __name__ == "__main__":
model = SimpleUnet(_image_channels=1)
t = torch.full((1,), 100, dtype=torch.long)
a = torch.randn((100,1,104,16,16))
print(model(a, t))
< /code>
Основная задача - dim = 1, то есть измерение спектрального канала неверно. Большинство людей думают, что предыдущая настройка параметров неверна, но они не знают, как его модифицировать. Искренне спасибо за любую полезную помощь и предложения!

Подробнее здесь: https://stackoverflow.com/questions/794 ... p-and-down