How can i test my controlnet_model after i finishing the training.

Nobody has claimed this yet.

Please help me!!! i have trained my controlnet model use my own data,but there is only the tensorboard for the training loss.so i want to use the training dataset agian to test the model and get the loss of every test image,how can i do?
Below is my training code:

from share import *
import pytorch_lightning as pl
from torch.utils.data import DataLoader
from tutorial_dataset import MyDataset
from cldm.logger import ImageLogger
from cldm.model import create_model, load_state_dict

# Configs
resume_path = './models/control_sd15.ckpt'
batch_size = 4
logger_freq = 300
learning_rate = 1e-5
sd_locked = True
only_mid_control = False

# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs.
model = create_model('./models/cldm_v15.yaml').cpu()
model.load_state_dict(load_state_dict(resume_path, location='cpu'))
model.learning_rate = learning_rate
model.sd_locked = sd_locked
model.only_mid_control = only_mid_control

# Misc
dataset = MyDataset()
dataloader = DataLoader(dataset, num_workers=0, batch_size=batch_size, shuffle=True)
logger = ImageLogger(batch_frequency=logger_freq)
trainer = pl.Trainer(gpus=1, precision=32, callbacks=[logger])

# Train!
trainer.fit(model, dataloader)

Start with the posted training script, MyDataset, ImageLogger, and the trainer.fit entry point to understand what evaluation support is currently available. Determine how a trained ControlNet model could be evaluated on the dataset and how per-image loss should be reported; done would be a documented, reproducible testing workflow.

Written by the indexing model from the issue text.