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Json #48

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Oct 1, 2023
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Json #48

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3fe3a77
changed load data from YOLO to JSON
naseemap47 Sep 30, 2023
0944965
fixed issue with DetectionCollateFN, used - CrowdDetectionCollateFN
naseemap47 Sep 30, 2023
e598d8b
updated based on JSON formating
naseemap47 Sep 30, 2023
9325fe9
README -updated based on JSON formating
naseemap47 Sep 30, 2023
46a5460
fixed typo error in testdata loader
naseemap47 Sep 30, 2023
d6e68a6
updated Install dependencies - Recommended, changed torch1.12 to torc…
naseemap47 Sep 30, 2023
2047f5b
replaced super-gradients git install with pip
naseemap47 Sep 30, 2023
318b334
updated version of super-gradients==3.1.3
naseemap47 Oct 1, 2023
8c050ee
removed class number input option to training, it will take from anno…
naseemap47 Oct 1, 2023
f39268c
updated data.yaml format, removed class names
naseemap47 Oct 1, 2023
c0b4957
added link to JSON converter
naseemap47 Oct 1, 2023
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63 changes: 26 additions & 37 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -40,64 +40,53 @@ cd YOLO-NAS
```
conda create -n yolo-nas python=3.9 -y
conda activate yolo-nas
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch -y
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip install pytorch-quantization==2.1.2 --extra-index-url https://pypi.ngc.nvidia.com
pip install super-gradients==3.1.3
pip install urllib3==1.25.9
```
#### OR
```
pip3 install -r requirements.txt
```
### 🎒 Prepare Dataset
Your custom dataset should be in **COCO** data format.<br>
Your custom dataset should be in **COCO JSON** data format.<br>
To convert **YOLO (.txt) / PASCAL VOC (.XML)** format to **COCO JSON**.<br>
Using JSON Converter https://github.com/naseemap47/autoAnnoter#10-yolo_to_jsonpy <br>
**COCO Data Format**:
```
├── Dataset
| ├── annotations
│ │ ├── train.json
│ │ ├── valid.json
│ │ ├── test.json
│ ├── train
│ │ ├── images
│ │ │ ├── 1.jpg
│ │ │ ├── abc.png
| | | ├── ....
│ │ ├── labels
│ │ │ ├── 1.txt
│ │ │ ├── abc.txt
| | | ├── ....
│ │ ├── 1.jpg
│ │ ├── abc.png
| | ├── ....
│ ├── val
│ │ ├── images
│ │ │ ├── 2.jpg
│ │ │ ├── fram.png
| | | ├── ....
│ │ ├── labels
│ │ │ ├── 2.txt
│ │ │ ├── fram.txt
| | | ├── ....
│ │ ├── 2.jpg
│ │ ├── fram.png
| | ├── ....
│ ├── test
│ │ ├── images
│ │ │ ├── img23.jpeg
│ │ │ ├── 50.jpg
| | | ├── ....
│ │ ├── labels
│ │ │ ├── img23.txt
│ │ │ ├── 50.txt
| | | ├── ....
│ │ ├── img23.jpeg
│ │ ├── 50.jpg
| | ├── ....
```

To training custom model using your custom data.
You need to create [data.yaml](https://github.com/naseemap47/YOLO-NAS/blob/master/data.yaml)
Example:
```
names:
- class1
- class2
- class3
Dir: '/home/user/my_data' # path to your custom data
Dir: 'Data'
images:
test: test/images
train: train/images
val: valid/images
test: test
train: train
val: valid
labels:
test: test/labels
train: train/labels
val: valid/labels
test: annotations/test.json
train: annotations/train.json
val: annotations/valid.json
```

## 🤖 Train
Expand Down
16 changes: 6 additions & 10 deletions data.yaml
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@@ -1,13 +1,9 @@
names:
- Paper
- Rock
- Scissors
Dir: 'Data'
images:
test: test/images
train: train/images
val: valid/images
test: test
train: train
val: valid
labels:
test: test/labels
train: train/labels
val: valid/labels
test: annotations/test.json
train: annotations/train.json
val: annotations/valid.json
157 changes: 98 additions & 59 deletions train.py
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@@ -1,16 +1,23 @@
from super_gradients.training.dataloaders.dataloaders import coco_detection_yolo_format_train, coco_detection_yolo_format_val
from super_gradients.training.models.detection_models.pp_yolo_e import PPYoloEPostPredictionCallback
from super_gradients.training.datasets.detection_datasets.coco_format_detection import COCOFormatDetectionDataset
from super_gradients.training.transforms.transforms import DetectionMosaic, DetectionRandomAffine, DetectionHSV, \
DetectionHorizontalFlip, DetectionPaddedRescale, DetectionStandardize, DetectionTargetsFormatTransform
from super_gradients.training.datasets.datasets_utils import worker_init_reset_seed
from super_gradients.training.utils.detection_utils import CrowdDetectionCollateFN
from super_gradients.training.metrics import DetectionMetrics_050
from super_gradients.training.losses import PPYoloELoss
from super_gradients.training import dataloaders
from super_gradients.training import Trainer
from super_gradients.training import models
import argparse
import torch
import time
import yaml
import json
import os



if __name__ == '__main__':

ap = argparse.ArgumentParser()
Expand Down Expand Up @@ -52,15 +59,15 @@
help="LR Mode")
ap.add_argument("--cosine_final_lr_ratio", type=float, default=0.1,
help="Cosine Final LR Ratio")
ap.add_argument("--optimizer", type=str, default='Adam',
ap.add_argument("--optimizer", type=str, default='AdamW',
help="Optimizer")
ap.add_argument("--weight_decay", type=float, default=0.0001,
help="Weight Decay")
args = vars(ap.parse_args())

# Start Time
s_time = time.time()


if args['name'] is None:
name = 'train'
else:
Expand All @@ -77,8 +84,8 @@
break
else:
n += 1

print(f"[INFO] Checkpoints saved in \033[1m{os.path.join('runs', name)}\033[0m")

# Training on GPU or CPU
if args['cpu']:
print('[INFO] Training on \033[1mCPU\033[0m')
Expand All @@ -90,67 +97,100 @@
print(f'[INFO] Training on GPU: \033[1m{torch.cuda.get_device_name()}\033[0m')
trainer = Trainer(experiment_name=name, ckpt_root_dir='runs')

# Load Path Params
yaml_params = yaml.safe_load(open(args['data'], 'r'))
with open(os.path.join(yaml_params['Dir'], yaml_params['labels']['train'])) as f:
no_class = len(json.load(f)['categories'])
f.close()
print(f"\033[1m[INFO] Number of Classes: {no_class}\033[0m")

# Reain Dataset
trainset = COCOFormatDetectionDataset(data_dir=yaml_params['Dir'],
images_dir=yaml_params['images']['train'],
json_annotation_file=yaml_params['labels']['train'],
input_dim=(args['size'], args['size']),
ignore_empty_annotations=False,
transforms=[
DetectionMosaic(prob=1., input_dim=(args['size'], args['size'])),
DetectionRandomAffine(degrees=0., scales=(0.5, 1.5), shear=0.,
target_size=(args['size'], args['size']),
filter_box_candidates=False, border_value=128),
DetectionHSV(prob=1., hgain=5, vgain=30, sgain=30),
DetectionHorizontalFlip(prob=0.5),
DetectionPaddedRescale(input_dim=(args['size'], args['size']), max_targets=300),
DetectionStandardize(max_value=255),
DetectionTargetsFormatTransform(max_targets=300, input_dim=(args['size'], args['size']),
output_format="LABEL_CXCYWH")
])
train_loader = dataloaders.get(dataset=trainset, dataloader_params={
"shuffle": True,
"batch_size": args['batch'],
"drop_last": False,
"pin_memory": True,
"collate_fn": CrowdDetectionCollateFN(),
"worker_init_fn": worker_init_reset_seed,
"min_samples": 512
})
# Valid Data
valset = COCOFormatDetectionDataset(data_dir=yaml_params['Dir'],
images_dir=yaml_params['images']['val'],
json_annotation_file=yaml_params['labels']['val'],
input_dim=(args['size'], args['size']),
ignore_empty_annotations=False,
transforms=[
DetectionPaddedRescale(input_dim=(args['size'], args['size']), max_targets=300),
DetectionStandardize(max_value=255),
DetectionTargetsFormatTransform(max_targets=300, input_dim=(args['size'], args['size']),
output_format="LABEL_CXCYWH")
])
valid_loader = dataloaders.get(dataset=valset, dataloader_params={
"shuffle": False,
"batch_size": int(args['batch']*2),
"num_workers": args['worker'],
"drop_last": False,
"pin_memory": True,
"collate_fn": CrowdDetectionCollateFN(),
"worker_init_fn": worker_init_reset_seed
})

train_data = coco_detection_yolo_format_train(
dataset_params={
'data_dir': yaml_params['Dir'],
'images_dir': yaml_params['images']['train'],
'labels_dir': yaml_params['labels']['train'],
'classes': yaml_params['names'],
'input_dim': (args['size'], args['size'])
},
dataloader_params={
'batch_size': args['batch'],
'num_workers': args['worker']
}
)

val_data = coco_detection_yolo_format_val(
dataset_params={
'data_dir': yaml_params['Dir'],
'images_dir': yaml_params['images']['val'],
'labels_dir': yaml_params['labels']['val'],
'classes': yaml_params['names'],
'input_dim': (args['size'], args['size'])
},
dataloader_params={
'batch_size':args['batch'],
'num_workers': args['worker']
}
)

# Test Data
if 'test' in (yaml_params['images'].keys() or yaml_params['labels'].keys()):
test_data = coco_detection_yolo_format_val(
dataset_params={
'data_dir': yaml_params['Dir'],
'images_dir': yaml_params['images']['test'],
'labels_dir': yaml_params['labels']['test'],
'classes': yaml_params['names'],
'input_dim': (args['size'], args['size'])
},
dataloader_params={
'batch_size':args['batch'],
'num_workers': args['worker']
}
)
testset = COCOFormatDetectionDataset(data_dir=yaml_params['Dir'],
images_dir=yaml_params['images']['test'],
json_annotation_file=yaml_params['labels']['test'],
input_dim=(args['size'], args['size']),
ignore_empty_annotations=False,
transforms=[
DetectionPaddedRescale(input_dim=(args['size'], args['size']), max_targets=300),
DetectionStandardize(max_value=255),
DetectionTargetsFormatTransform(max_targets=300, input_dim=(args['size'], args['size']),
output_format="LABEL_CXCYWH")
])
test_loader = dataloaders.get(dataset=testset, dataloader_params={
"shuffle": False,
"batch_size": int(args['batch']*2),
"num_workers": args['worker'],
"drop_last": False,
"pin_memory": True,
"collate_fn": CrowdDetectionCollateFN(),
"worker_init_fn": worker_init_reset_seed
})

# To Resume Training
if args['resume']:
model = models.get(
args['model'],
num_classes=len(yaml_params['names']),
num_classes=no_class,
checkpoint_path=args["weight"]
)
else:
model = models.get(
args['model'],
num_classes=len(yaml_params['names']),
num_classes=no_class,
pretrained_weights=args["weight"]
)

train_params = {
# ENABLING SILENT MODE
'silent_mode': False,
"average_best_models":True,
"warmup_mode": args['warmup_mode'],
Expand All @@ -168,15 +208,14 @@
"mixed_precision": True,
"loss": PPYoloELoss(
use_static_assigner=False,
num_classes=len(yaml_params['names']),
num_classes=no_class,
reg_max=16
),
"valid_metrics_list": [
DetectionMetrics_050(
score_thres=0.1,
top_k_predictions=300,
# NOTE: num_classes needs to be defined here
num_cls=len(yaml_params['names']),
num_cls=no_class,
normalize_targets=True,
post_prediction_callback=PPYoloEPostPredictionCallback(
score_threshold=0.01,
Expand All @@ -196,24 +235,25 @@
# Print Training Params
print('[INFO] Training Params:\n', train_params)

# Model Training...
trainer.train(
model=model,
training_params=train_params,
train_loader=train_data,
valid_loader=val_data
train_loader=train_loader,
valid_loader=valid_loader
)

# Load best model
best_model = models.get(args['model'],
num_classes=len(yaml_params['names']),
num_classes=no_class,
checkpoint_path=os.path.join('runs', name, 'ckpt_best.pth'))

# Evaluating on Val Dataset
eval_model = trainer.test(model=best_model,
test_loader=val_data,
test_loader=valid_loader,
test_metrics_list=DetectionMetrics_050(score_thres=0.1,
top_k_predictions=300,
num_cls=len(yaml_params['names']),
num_cls=no_class,
normalize_targets=True,
post_prediction_callback=PPYoloEPostPredictionCallback(score_threshold=0.01,
nms_top_k=1000,
Expand All @@ -227,10 +267,10 @@
# Evaluating on Test Dataset
if 'test' in (yaml_params['images'].keys() or yaml_params['labels'].keys()):
test_result = trainer.test(model=best_model,
test_loader=test_data,
test_loader=test_loader,
test_metrics_list=DetectionMetrics_050(score_thres=0.1,
top_k_predictions=300,
num_cls=len(yaml_params['names']),
num_cls=no_class,
normalize_targets=True,
post_prediction_callback=PPYoloEPostPredictionCallback(score_threshold=0.01,
nms_top_k=1000,
Expand All @@ -241,4 +281,3 @@
for i in test_result:
print(f"{i}: {float(test_result[i])}")
print(f'[INFO] Training Completed in \033[1m{(time.time()-s_time)/3600} Hours\033[0m')