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Applying a trained model

Open wolfam0108 opened this issue 5 years ago • 11 comments

My model based on "ESRGAN_PSNR_SRx4_DF2K_official-150ff491.pth" has finished training. The files net_g_XXX and net_d_XXX appeared in the folder experiments \ model name \ model.

I don't understand python at all. How can I apply the trained model, for example, in your ESRGAN project?

If I put in "test.py" ESRGAN the path to net_g_XXX.pth, this should work for image processing?

wolfam0108 avatar Jul 24 '20 17:07 wolfam0108

When I apply the model, I get this:

(base) vova@wolfram-pc:/tmp/ESRGAN$ python test.py
Traceback (most recent call last):
  File "test.py", line 15, in <module>
    model.load_state_dict(torch.load(model_path), strict=True)
  File "/home/vova/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 847, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for RRDBNet:
        Missing key(s) in state_dict: "conv_first.weight", "conv_first.bias", "RRDB_trunk.0.RDB1.conv1.weight", "RRDB_trunk.0.RDB1.conv1.bias", "RRDB_trunk.0.RDB1.conv2.weight", "RRDB_trunk.0.RDB1.conv2.bias", "RRDB_trunk.0.RDB1.conv3.weight", "RRDB_trunk.0.RDB1.conv3.bias", "RRDB_trunk.0.RDB1.conv4.weight", "RRDB_trunk.0.RDB1.conv4.bias", "RRDB_trunk.0.RDB1.conv5.weight", "RRDB_trunk.0.RDB1.conv5.bias", "RRDB_trunk.0.RDB2.conv1.weight", "RRDB_trunk.0.RDB2.conv1.bias", "RRDB_trunk.0.RDB2.conv2.weight", "RRDB_trunk.0.RDB2.conv2.bias", "RRDB_trunk.0.RDB2.conv3.weight", "RRDB_trunk.0.RDB2.conv3.bias", "RRDB_trunk.0.RDB2.conv4.weight", "RRDB_trunk.0.RDB2.conv4.bias", "RRDB_trunk.0.RDB2.conv5.weight", "RRDB_trunk.0.RDB2.conv5.bias", "RRDB_trunk.0.RDB3.conv1.weight", "RRDB_trunk.0.RDB3.conv1.bias", "RRDB_trunk.0.RDB3.conv2.weight", "RRDB_trunk.0.RDB3.conv2.bias", "RRDB_trunk.0.RDB3.conv3.weight", "RRDB_trunk.0.RDB3.conv3.bias", "RRDB_trunk.0.RDB3.conv4.weight", "RRDB_trunk.0.RDB3.conv4.bias", "RRDB_trunk.0.RDB3.conv5.weight", "RRDB_trunk.0.RDB3.conv5.bias", "RRDB_trunk.1.RDB1.conv1.weight", "RRDB_trunk.1.RDB1.conv1.bias", "RRDB_trunk.1.RDB1.conv2.weight", "RRDB_trunk.1.RDB1.conv2.bias", "RRDB_trunk.1.RDB1.conv3.weight", "RRDB_trunk.1.RDB1.conv3.bias", "RRDB_trunk.1.RDB1.conv4.weight", "RRDB_trunk.1.RDB1.conv4.bias", "RRDB_trunk.1.RDB1.conv5.weight", "RRDB_trunk.1.RDB1.conv5.bias", "RRDB_trunk.1.RDB2.conv1.weight", "RRDB_trunk.1.RDB2.conv1.bias", "RRDB_trunk.1.RDB2.conv2.weight", "RRDB_trunk.1.RDB2.conv2.bias", "RRDB_trunk.1.RDB2.conv3.weight", "RRDB_trunk.1.RDB2.conv3.bias", "RRDB_trunk.1.RDB2.conv4.weight", "RRDB_trunk.1.RDB2.conv4.bias", "RRDB_trunk.1.RDB2.conv5.weight", "RRDB_trunk.1.RDB2.conv5.bias", "RRDB_trunk.1.RDB3.conv1.weight", "RRDB_trunk.1.RDB3.conv1.bias", "RRDB_trunk.1.RDB3.conv2.weight", "RRDB_trunk.1.RDB3.conv2.bias", "RRDB_trunk.1.RDB3.conv3.weight", "RRDB_trunk.1.RDB3.conv3.bias", "RRDB_trunk.1.RDB3.conv4.weight", 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wolfam0108 avatar Jul 24 '20 17:07 wolfam0108

File "test.py" from ESRGAN project:

import os.path as osp
import glob
import cv2
import numpy as np
import torch
import RRDBNet_arch as arch

model_path = 'models/net_g_18000.pth'  # models/RRDB_ESRGAN_x4.pth OR models/RRDB_PSNR_x4.pth
device = torch.device('cuda')  # if you want to run on CPU, change 'cuda' -> cpu
# device = torch.device('cpu')

test_img_folder = '/mnt/e/test/006/IN/*'

model = arch.RRDBNet(3, 3, 64, 23, gc=32)
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
model = model.to(device)

print('Model path {:s}. \nTesting...'.format(model_path))

idx = 0
for path in glob.glob(test_img_folder):
    idx += 1
    base = osp.splitext(osp.basename(path))[0]
    print(idx, base)
    # read images
    img = cv2.imread(path, cv2.IMREAD_COLOR)
    img = img * 1.0 / 255
    img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
    img_LR = img.unsqueeze(0)
    img_LR = img_LR.to(device)

    with torch.no_grad():
        output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
    output = (output * 255.0).round()
    cv2.imwrite('/mnt/e/test/006/OUT/{:s}_rlt.png'.format(base), output)

wolfam0108 avatar Jul 24 '20 17:07 wolfam0108

I tried running transer_RRDB_models.py. But the result:

(base) vova@wolfram-pc:/tmp/ESRGAN$ python3 ./transer_RRDB_models.py
###################################

Traceback (most recent call last):
  File "./transer_RRDB_models.py", line 31, in <module>
    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
KeyError: 'model.0.weight'

wolfam0108 avatar Jul 25 '20 17:07 wolfam0108

Hi @wolfam0108 ， the ESRGAN repo and current BasicSR repo are not compatible now and we will update later~

You can use the BasicSR repo to do the test. the cmd is:

CUDA_VISIBLE_DEVICES=0 python basicsr/test.py -opt options/test/ESRGAN/test_ESRGAN_SRx4_woGT.yml

or

CUDA_VISIBLE_DEVICES=0 python basicsr/test.py -opt options/test/ESRGAN/test_ESRGAN_SRx4.yml

You may change some options accordingly~

xinntao avatar Jul 27 '20 03:07 xinntao

Is it possible to do without dataroot_gt in testing? If I have already trained the model. And I need to apply this to images.

wolfam0108 avatar Aug 09 '20 12:08 wolfam0108

@wolfam0108 sorry for the late reply.

You can inference with only LR image. Reference: #260

xinntao avatar Aug 20 '20 10:08 xinntao

Thank you, it works! Will this work with EDVR?

wolfam0108 avatar Aug 23 '20 04:08 wolfam0108

If I change the type: VideoTestDataset to the type: SingleImageDataset in the file test_EDVR_L_x4_SR_Vid4?

wolfam0108 avatar Aug 24 '20 03:08 wolfam0108

@wolfam0108 Now, BasicSR does not support inference w/o GT for video model. You can write a new video test dataset w/o reading GT just like SingleImageDataset (SingleImageDataset does not support video) I may add this feature later in this weekend~

xinntao avatar Aug 24 '20 08:08 xinntao

Fine! I will be very grateful for this functionality! Thanks for your work!

vova0108 avatar Aug 24 '20 09:08 vova0108

When I inference with inference/inference_basicvsr.py, the input and output results look the same, only the resolution is different. Do you have this problem?

thd-ux avatar Dec 16 '24 08:12 thd-ux