jamdodot
commented
1 year ago Like you, I also made an error in the test after the training, but the channel of my args is 3. The problem I encountered during the test is that args["img_size"] is of float type, resulting in the following error.
Since image dtype is floating point, you must specify ' 'the data_range parameter. Please read the documentation ' 'carefully (including the note). It is recommended that ' 'you always specify the data_range anyway.
The outermost layer of the faulting method stack is the following line of code https://github.com/Julian-Wyatt/AnoDDPM/blob/3052f0441a472af55d6e8b1028f5d3156f3d6ed3/detection.py#L243 https://github.com/Julian-Wyatt/AnoDDPM/blob/3052f0441a472af55d6e8b1028f5d3156f3d6ed3/evaluation.py#L48
My training args are as follows
{
"img_size": [
256,
256
],
"Batch_Size": 1,
"EPOCHS": 3000,
"T": 1000,
"base_channels": 128,
"beta_schedule": "linear",
"channel_mults": "",
"loss-type": "l2",
"loss_weight": "none",
"train_start": true,
"lr": 1e-4,
"random_slice": true,
"sample_distance": 800,
"weight_decay": 0.0,
"save_imgs": true,
"save_vids": false,
"dropout": 0,
"attention_resolutions": "16,8",
"num_heads": 2,
"num_head_channels": -1,
"noise_fn": "simplex",
"channels": 3,
"dataset": "leather"
}
Julian-Wyatt
ZZZGGGG
Hello author, I am honored to read your paper. After completing leather data set training, I tried to detect, but encountered some problems: Here is my args file:
args19.json { "img_size": [256,256], "Batch_Size": 1, "EPOCHS": 1000, "T": 1000, "base_channels": 128, "beta_schedule": "linear", "channel_mults": "", "loss-type": "l2", "loss_weight": "none", "train_start": true, "lr": 1e-4, "random_slice": true, "sample_distance": 600, "weight_decay": 0.0, "save_imgs":true, "save_vids":true, "dropout":0, "attention_resolutions":"16,8", "num_heads":2, "num_head_channels":-1, "noise_fn":"simplex", "dataset":"leather", "channels": 1 }
After the training:
File "diffusion_training.py", line 153, in train evaluation.testing(testing_dataset_loader, diffusion, ema=ema, args=args, model=model) File "/home/dell/local/HK/AnoDDPM-master/evaluation.py", line 124, in testing x = x.to(device) NameError: name 'device' is not defined
python3 detection.py args19.jsonHere, args19.json is the configuration file I trained with But the following error is reported:This is the shape of the layer for the row where the error occurred Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1）
Traceback (most recent call last): File "detection.py", line 959, in
anomalous_metric_calculation()
File "detection.py", line 226, in anomalous_metric_calculation
t_distance=200, denoise_fn=args["noise_fn"]
File "/home/dell/local/HK/AnoDDPM-master/GaussianDiffusion.py", line 354, in forward_backward
out = self.sample_p(model, x, t_batch, denoise_fn)
File "/home/dell/local/HK/AnoDDPM-master/GaussianDiffusion.py", line 299, in sample_p
out = self.p_mean_variance(model, x_t, t)
File "/home/dell/local/HK/AnoDDPM-master/GaussianDiffusion.py", line 279, in p_mean_variance
estimate_noise = model(x_t, t)
File "/home/dell/anaconda3/envs/pytorch181/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, kwargs)
File "/home/dell/local/HK/AnoDDPM-master/UNet.py", line 400, in forward
h = module(h, time_embed)
File "/home/dell/anaconda3/envs/pytorch181/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(*input, *kwargs)
File "/home/dell/local/HK/AnoDDPM-master/UNet.py", line 35, in forward
x = layer(x)
File "/home/dell/anaconda3/envs/pytorch181/lib/python3.6/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
result = self.forward(input, kwargs)
File "/home/dell/anaconda3/envs/pytorch181/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 399, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/home/dell/anaconda3/envs/pytorch181/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 396, in _conv_forward
self.padding, self.dilation, self.groups)
RuntimeError: Given groups=1, weight of size [128, 1, 3, 3], expected input[1, 3, 256, 256] to have 1 channels, but got 3 channels instead
I wonder if there is something wrong with my detection call (I did not change your code) :
Maybe my question is relatively simple, but it has really caused me a lot of trouble. I would like to express my deep thanks again for troubling you.