latest_net_G.pth not exists yet!

[sureshbona]

Hi

I am getting 'latest_net_G.pth not exists yet' error when I run below command.

Could you please rectify this issue. Thank you in advance.

output:

$ python batch.py --dataroot data/ --model pix2pixHD --checkpoint_dir models/pix2pix-model/

Generating images
21:42:13.108 [main] INFO  o.a.p.FigureExtractorBatchCli$ - Finished processing 0 files
21:42:13.111 [main] INFO  o.a.p.FigureExtractorBatchCli$ - Took 0.0 seconds
21:42:13.112 [main] INFO  o.a.p.FigureExtractorBatchCli$ - No errors
Padding images
0it [00:00, ?it/s]
Predicting outlines
------------ Options -------------
aspect_ratio: 1.0
batchSize: 1
checkpoints_dir: models/pix2pix-model/
cluster_path: features_clustered_010.npy
data_type: 32
dataroot: data/
display_winsize: 512
engine: None
export_onnx: None
feat_num: 3
fineSize: 1024
fp16: False
gpu_ids: [0]
how_many: inf
input_nc: 3
instance_feat: False
isPredict: True
isTrain: False
label_feat: False
label_nc: 0
loadSize: 1024
load_features: False
local_rank: 0
max_dataset_size: inf
mode: val
model: pix2pixHD
nThreads: 2
n_blocks_global: 9
n_blocks_local: 3
n_clusters: 10
n_downsample_E: 4
n_downsample_global: 4
n_local_enhancers: 1
name: gen-tables
nef: 16
netG: global
ngf: 64
niter_fix_global: 0
no_flip: False
no_instance: True
no_move: False
norm: instance
ntest: inf
onnx: None
output_nc: 3
phase: test
resize_or_crop: scale_width
results_dir: data/outlines_pix2pixHD
serial_batches: False
tf_log: False
use_dropout: False
use_encoded_image: False
verbose: False
which_epoch: latest
-------------- End ----------------
CustomDatasetDataLoader
Creating dataset for prediction
dataset [AlignedDataset] was created
Found 0 images
GlobalGenerator(
  (model): Sequential(
    (0): ReflectionPad2d((3, 3, 3, 3))
    (1): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
    (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (3): ReLU(inplace=True)
    (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (6): ReLU(inplace=True)
    (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (9): ReLU(inplace=True)
    (10): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (11): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (12): ReLU(inplace=True)
    (13): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (14): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (15): ReLU(inplace=True)
    (16): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (17): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (18): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (19): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (20): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (21): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (22): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (23): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (24): ResnetBlock(
      (conv_block): Sequential(
        (0): ReflectionPad2d((1, 1, 1, 1))
        (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
        (3): ReLU(inplace=True)
        (4): ReflectionPad2d((1, 1, 1, 1))
        (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1))
        (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
      )
    )
    (25): ConvTranspose2d(1024, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (26): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (27): ReLU(inplace=True)
    (28): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (29): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (30): ReLU(inplace=True)
    (31): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (32): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (33): ReLU(inplace=True)
    (34): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))
    (35): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)
    (36): ReLU(inplace=True)
    (37): ReflectionPad2d((3, 3, 3, 3))
    (38): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
    (39): Tanh()
  )
)
models/pix2pix-model/gen-tables/latest_net_G.pth not exists yet!
Traceback (most recent call last):
  File "./pix2pixHD/predict.py", line 25, in <module>
    model = create_model(opt)
  File "/content/PDF-Table-Structure-Recognition-using-deep-learning/pipeline/pix2pixHD/models/models.py", line 15, in create_model
    model.initialize(opt)
  File "/content/PDF-Table-Structure-Recognition-using-deep-learning/pipeline/pix2pixHD/models/pix2pixHD_model.py", line 60, in initialize
    self.load_network(self.netG, 'G', opt.which_epoch, pretrained_path)            
  File "/content/PDF-Table-Structure-Recognition-using-deep-learning/pipeline/pix2pixHD/models/base_model.py", line 60, in load_network
    raise('Generator must exist!')
TypeError: exceptions must derive from BaseException
Finding cells
Extracting text
Creating csv
Finished

[rinkstiekema]

Seems like you have not downloaded the generator model yet. This file should be located in 'models/pix2pix-model/'.

[sureshbona]

Thanks for the response. Could you please provide the link to download generator model.

[rinkstiekema]

I would strongly advise you to read the readme of this repository. The link is mentioned there: https://drive.google.com/drive/u/0/folders/1dgKISbhBNfR8XXnIxUD_sIhwYNurKHbb

[sureshbona]

I tried with this. Still, I got the error.

Here is the folder structure of pipeline.

├── batch-own.py
├── batch.py
├── batch-tabula-papers.py
├── batch-tabula.py
├── data
│   ├── auer-katalog_de.pdf
│   ├── csv_pred_encoder-decoder-skip
│   ├── csv_pred_pix2pixHD
│   ├── json
│   ├── outlines_encoder-decoder-skip
│   ├── outlines_pix2pixHD
│   ├── pdf
│   └── png
├── json2csv.py
├── json2png.py
├── models
│   └── pix2pix-model
│       ├── 50_net_D.pth
│       ├── 50_net_G.pth
│       └── gen-tables
│           └── loss_log.txt

[rinkstiekema]

Ah. As you have not defined an epoch to use, you should rename the models to latest_net_G.pth and latest_net_D.pth. The 50 indicates the epoch.

[sureshbona]

Thanks for the help. The code is working without any errors now.