This is the code from our ICDAR 2019 paper, "Deep Visual Template-free Form Parsing." (http://arxiv.org/abs/1909.02576)
This code is licensed under GNU GPL v3. If you would like it distributed to you under a different license, please contact me (briandavis@byu.net).
- Python 3.x (at least 3.7 for doing global pairing optimization)
- PyTorch 1.2+
I used conda, but it shouldn't be hard to install the packages another way.
First check gcc version (must be atleast 4.9) and cuda version (8 requires gcc to be 5.3 or lower). If upgrading cuda, remove the old version first either with apt-get or uninstall script in /usr/cuda/bin. (Be sure CUDA_HOME and PATH are right after installation)
conda install $
ipython(maybe not needed anymore)pytorch torchvision -c pytorchor what ever the command is on pytorch.org given cuda version, etcopencvscikit-image
Run this in the Visual-Template-Free-Form-Parsing directoty: python setup.py build develop (I don't think this is needed since I replaced RoIAlign with the torchvision implementation).
To run the global pairing optimization, you need cvxpy, which requires python 3.7 or later:
Clone github: https://github.com/cvxgrp/cvxpy
python setup.py install in the cvxpy repo`
see https://github.com/herobd/NAF_dataset
python train.py -c cf_detector.json
python train.py -c cf_pairing.json
If you want to run on GPU, add -g #, where # is the GPU number.
Remove the -T flag to run on the validation set.
Detection, full set: python eval.py -c saved/detector/checkpoint-iteration150000.pth -n 0 -T
Detection, pairing set: python eval.py -c saved/detector/checkpoint-iteration150000.pth -n 0 -T -a data_loader=special_dataset=simple
Pairing, no pairing optimization: python eval.py -c saved/pairing/checkpoint-iteration125000.pth -n 0 -T
`
Pairing, with pairing optimization: python eval.py -c saved/pairing/checkpoint-iteration125000.pth -n 0 -T -a optimize=true
Pairing, GT detections: python eval.py -c saved/pairing/checkpoint-iteration125000.pth -n 0 -T -a useDetect=gt
Pairing, optimized with GT num neighnors: python eval.py -c saved/pairing/checkpoint-iteration125000.pth -n 0 -T -a optimize=gt
python train.py -c cf_baseline_detector.json
Note: This will take a while before it begins training on your first run as it caches smaller sizes of the dataset.
Make training data for no visual feature pairing:
mkdir outpython eval.py -c saved/detector/checkpoint-iteration150000.pth -g 0 -n 10000 -a save_json=out/detection_data,data_loader=batch_size=1,data_loader=num_workers=0,data_loader=rescale_range=0.52,data_loader=crop_params=,validation=rescale_range=0.52,validation=crop_params=,data_loader=cache_resized_images=0
Train no visual feature pairing: python train.py -c cf_no_vis_pairing.json
Detection with regular convs, full set: python eval.py -c saved/baseline_detector/checkpoint-iteration150000.pth -n 0 -T
Detection with regular convs, pairing set: python eval.py -c saved/baseline_detector/checkpoint-iteration150000.pth -n 0 -T -a data_loader=special_dataset=simple
Distance based pairing: python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a rule=closest
Scoring functions pairing: python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a rule=icdar
No visual features pairing: python eval.py -f cf_test_no_vis_pairing.json -n 0 -T
No visual features pairing, with global optimization: python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a optimize=true
For GT detections:
python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a rule=closest,useDetect=gt
python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a rule=icdar,useDetect=gt
python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a useDetect=gt
For global optimization with GT num neighbors:
python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a rule=closest,optimize=gt
python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a rule=icdar,optimize=gt
python eval.py -f cf_test_no_vis_pairing.json -n 0 -T -a optimize=gt
This is the script that executes training based on a configuration file. The training code is found in trainer/. The config file specifies which trainer is used.
The usage is: python train.py -c CONFIG.json (see below for example config file)
A training session can be resumed with: python train.py -r CHECKPOINT.pth
If you want to override the config file on a resume, just use the -c flag and be sure it has "override": true
This script runs a trained model (from a snapshot) through the dataset and prints its scores. It is also used to save images with the predictions on them.
Usage: python eval.py -c CHECKPOINT.pth -f OVERRIDE_CONFIG.pth -g (gpu number) -n (number of images to save) -d (directory to save images) -T
The only flags required is -c or -f.
If -T is ommited it will run on the validation set instead of the test set.
There is an additional -a flag which allows overwriting of specific values of the config file using this format key1=nestedkey=value,key2=value. It also allows setting these special options (which are part of config):
Evaluating detector:
-a pretty=true: Makes printed pictured cleaner (less details)-a save_json=path/to/dir: Save the detection results as jsons matching the dataset format.-a THRESH=[float]: Modify the threshold for displaying and precision/recall calculations. Default is 0.92
Evaluatring pairing:
-a useDetect=[gt,path]: Whether to use GT detections (gt) or can be directory with jsons with saved detections.-a rule=[closest,icdar]: Use a rule (nearest or scoring functions) to do pairing (instead of model).-a optimize=[true,gt]: Use global pairing optimization. Ifgtspecified it will use the GT number of neighbors.-a penalty=[float]: The variable c in Equation 1. Default is 0.25-a THRESH=[float]: Modify the thresh for calculating prec/recall for relationships. Also is T in Equation 1. Default is 0.7-a sweep_threshold=true: Run metrics using a range of thresholds-a draw_thresh=[float]: Seperate threshold for which relationships get saved in images.-a confThresh=[float]: Threshold used for detections.-a pretty=[true,light,clean]: Different ways of displaying the results.
This script will load a single image and create a copy of it with the predicted annotations drawn on it, either detection and pairing, or just detection.
Usage: python run.py image.png image_result.png -c checkpoint/path.pth
Use the -d flag if running a detection model.
It automatically scales images to 0.52 as the NAF model is trainined. If this isn't desired use the --scale-image flag to override this.
This is based on victoresque's pytorch template: https://github.com/victoresque/pytorch-template
│
├── train.py - Training script
├── eval.py - Evaluation and display script
│
├── base/ - abstract base classes
│ ├── base_data_loader.py - abstract base class for data loaders
│ ├── base_model.py - abstract base class for models
│ └── base_trainer.py - abstract base class for trainers
│
├── data_loader/ -
│ └── data_loaders.py - This provides access to all the dataset objects
│
├── datasets/ - default datasets folder
│ ├── box_detect.py - base class for detection
│ ├── forms_box_detect.py - detection for NAF dataset
│ ├── forms_feature_pair.py - dataset for training non-visual classifier
│ ├── graph_pair.py - base class for pairing
│ ├── forms_graph_pair.py - pairing for NAD dataset
│ └── test*.py - scripts to test the datasets and display the images for visual inspection
│
├── logger/ - for training process logging
│ └── logger.py
│
├── model/ - models, losses, and metrics
│ ├── binary_pair_real.py - Provides classifying network for pairing and final prediction network for detection. Also can have secondary using non-visual features only classifier
│ ├── coordconv.py - Implements a few variations of CoordConv. I didn't get better results using it.
│ ├── csrc/ - Contains Facebook's implementation for ROIAlign from https://github.com/facebookresearch/maskrcnn-benchmark
│ ├── roi_align.py - End point for ROIAlign code
│ ├── loss.py - Imports all loss functions
│ ├── net_builder.py - Defines basic layers and interpets config syntax into networks.
│ ├── optimize.py - pairing descision optimization code
│ ├── pairing_graph.py - pairing network class
│ ├── simpleNN.py - defines non-convolutional network
│ ├── yolo_box_detector.py - detector network class
│ └── yolo_loss.py - loss used by detector
│
├── saved/ - default checkpoints folder
│
├── trainer/ - trainers
│ ├── box_detect_trainer.py - detector training code
│ ├── feature_pair_trainer.py - non-visual pairing training code
│ ├── graph_pair_trainer.py - pairing training code
│ └── trainer.py
│
└── utils/
├── util.py
└── ...
Config files are in .json format. Example:
{
"name": "pairing", # Checkpoints will be saved in saved/name/checkpoint-...pth
"cuda": true, # Whether to use GPU
"gpu": 0, # GPU number. Only single GPU supported.
"save_mode": "state_dict", # Whether to save/load just state_dict, or whole object in checkpoint
"override": true, # Override a checkpoints config
"super_computer":false, # Whether to mute training info printed
"data_loader": {
"data_set_name": "FormsGraphPair", # Class of dataset
"special_dataset": "simple", # Use partial dataset. "simple" is the set used for pairing in the paper
"data_dir": "../data/NAF_dataset", # Directory of dataset
"batch_size": 1,
"shuffle": true,
"num_workers": 1,
"crop_to_page":false,
"color":false,
"rescale_range": [0.4,0.65], # Form images are randomly resized in this range
"crop_params": {
"crop_size":[652,1608], # Crop size for training instance
"pad":0
},
"no_blanks": true, # Removed fields that are blank
"swap_circle":true, # Treat text that should be circled/crossed-out as pre-printed text
"no_graphics":true, # Images not considered elements
"cache_resized_images": true, # Cache images at maximum size of rescale_range to make reading them faster
"rotation": false, # Bounding boxes are converted to axis-aligned rectangles
"only_opposite_pairs": true # Only label-value pairs
},
"validation": { # Enherits all values from data_loader, specified values are changed
"shuffle": false,
"rescale_range": [0.52,0.52],
"crop_params": null,
"batch_size": 1
},
"lr_scheduler_type": "none",
"optimizer_type": "Adam",
"optimizer": { # Any parameters of the optimizer object go here
"lr": 0.001,
"weight_decay": 0
},
"loss": { # Name of functions (in loss.py) for various components
"box": "YoloLoss", # Detection loss
"edge": "sigmoid_BCE_loss", # Pairing loss
"nn": "MSE", # Num neighbor loss
"class": "sigmoid_BCE_loss" # Class of detections loss
},
"loss_weights": { # Respective weighting of losses (multiplier)
"box": 1.0,
"edge": 0.5,
"nn": 0.25,
"class": 0.25
},
"loss_params":
{
"box": {"ignore_thresh": 0.5,
"bad_conf_weight": 20.0,
"multiclass":true}
},
"metrics": [],
"trainer": {
"class": "GraphPairTrainer", # Training class name
"iterations": 125000, # Stop iteration
"save_dir": "saved/", # save directory
"val_step": 5000, # Run validation set every X iterations
"save_step": 25000, # Save distinct checkpoint every X iterations
"save_step_minor": 250, # Save 'latest' checkpoint (overwrites) every X iterations
"log_step": 250, # Print training metrics every X iterations
"verbosity": 1,
"monitor": "loss",
"monitor_mode": "none",
"warmup_steps": 1000, # Defines length of ramp up from 0 learning rate
"conf_thresh_init": 0.5,
"conf_thresh_change_iters": 0, # Allows slowly lowering of detection conf thresh from higher value
"retry_count":1,
"unfreeze_detector": 2000, # Iteration to unfreeze detector network
"partial_from_gt": 0, # Iteration to start using detection predictions
"stop_from_gt": 20000, # When to maximize predicted detection use
"max_use_pred": 0.5, # Maximum predicted detection use
"use_all_bb_pred_for_rel_loss": true,
"use_learning_schedule": true,
"adapt_lr": false
},
"arch": "PairingGraph", # Class name of model
"model": {
"detector_checkpoint": "saved/detector/checkpoint-iteration150000.pth",
"conf_thresh": 0.5,
"start_frozen": true,
"use_rel_shape_feats": "corner",
"use_detect_layer_feats": 16, # Assumes this is from final level of detection network
"use_2nd_detect_layer_feats": 0, # Specify conv after pool
"use_2nd_detect_scale_feats": 2, # Scale (from pools)
"use_2nd_detect_feats_size": 64,
"use_fixed_masks": true,
"no_grad_feats": true,
"expand_rel_context": 150, # How much to pad around relationship candidates before passing to conv layers
"featurizer_start_h": 32, # Size ROIPooling resizes relationship crops to
"featurizer_start_w": 32,
"featurizer_conv": ["sep128","M","sep128","sep128","M","sep256","sep256","M",238], # Network for featurizing relationship, see below for syntax
"featurizer_fc": null,
"pred_nn": true, # Predict a new num neighbors for detections
"pred_class": false, # Predict a new class for detections
"expand_bb_context": 150, # How much to pad around detections
"featurizer_bb_start_h": 32, # Size ROIPooling resizes detection crops to
"featurizer_bb_start_w": 32,
"bb_featurizer_conv": ["sep64","M","sep64","sep64","M","sep128","sep128","M",250], # Network for featurizing detections
"graph_config": {
"arch": "BinaryPairReal",
"in_channels": 256,
"layers": ["FC256","FC256"], # Relationship classifier
"rel_out": 1, # one output, probability of true relationship
"layers_bb": ["FC256"] # Detection predictor
"bb_out": 1, # one output, num neighbors
}
}
}
Config network layer syntax:
[int]: Regular 3x3 convolution with specified output channels, normalization (if any), and ReLU"ReLU""drop[float]"/"dropout[float]": Dropout, if no float amount is 0.5"M"": Maxpool (2x2)"R[int]": Residual block with specified output channels, two 3x3 convs with correct ReLU+norm ordering (expects non-acticated input)"k[int]-[int]": conv, norm, relu. First int specifies kernel size, second specifier output channels."d[int]-[int]": dilated conv, norm, relu. First int specifies dilation, second specifier output channels."[h/v]d[int]-[int]": horizontal or vertical dilated conv, norm, relu (horizontal is 1x3 and vertical is 3x1 kernel). First int specifies dilation, second specifier output channels."sep[int]": Two conv,norm,relu blocks, the first is depthwise seperated, the second is (1x1). The int is the out channels"cc[str]-k[int],d[int],[hd/vd]-[int]": CoordConv, str is type, k int is kernel size (default 3), d is dilation size (default 1), hd makes it horizontal (kernel is height 1), vd makes it vertical, final int is out channels"FC[int]": Fully-connected layer with given output channels
The checkpoints will be saved in save_dir/name.
The config file is saved in the same folder. (as a reference only, the config is loaded from the checkpoint)
Note: checkpoints contain:
{
'arch': arch,
'epoch': epoch,
'logger': self.train_logger,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'monitor_best': self.monitor_best,
'config': self.config
}