TorchUtils

TorchUtils is a pytorch lib with several useful tools and training tricks. (Work In Progress)

Install

bash git clone https://github.com/seefun/TorchUtils.git cd TorchUtils bash pip install -r requirements.txt pip install .

Import

import torch_utils as tu

Seed All

SEED = 42 tu.tools.seed_everything(SEED)

Data Augmentation

``` import albumentations from albumentations import pytorch as AT train_transform = albumentations.Compose([ albumentations.Resize(IMAGE_SIZE, IMAGE_SIZE), albumentations.HorizontalFlip(p=0.5), tu.dataset.randAugment(image_size=IMAGE_SIZE, N=2, M=12, p=0.9, mode='all', cut_out=False), albumentations.Normalize(), albumentations.CoarseDropout(max_holes=8, max_height=IMAGE_SIZE // 8, max_width=IMAGE_SIZE // 8, fill_value=0, p=0.25), AT.ToTensorV2(), ])

mixup_dataset = tu.dataset.MixupDataset(dataset, alpha=0.2, prob=0.2, mixup_to_cutmix=0.25)

0.15 mixup and 0.05 cutmix

```

Model

fast build models with torch_utils: model = tu.ImageModel(name='resnest50d', pretrained=True, pooling='concat', fc='multi-dropout', num_feature=2048, classes=1) model.cuda()

using other libs along with torch_utils: ``` import timm

model = timm.create_model('tresnet_m', pretrained=True) model.global_pool = tu.layers.FastGlobalConcatPool2d(flatten=True) model.head = tu.layers.get_attention_fc(2048*2, 1) model.cuda() ```

``` from pytorchcv.model_provider import get_model as ptcv_get_model

model = ptcv_get_model('seresnext50_32x4d', pretrained=True) model.features.final_pool = tu.layers.GeM() model.output = tu.layers.get_simple_fc(2048, 1)
model.cuda() ```

segmentation models: hrnet = tu.get_hrnet(name='hrnet_w18', out_channel=1, pretrained=True).cuda() unet = tu.get_unet(name='resnest50d', out_channel=1, aspp=False, pretrained=True).cuda()

recommanded pretrained models:

• ResNeSt
• SEResNext-50
• GPU-Efficient
• swsl_ResNeXt
• BiT/ResNetV2
• TResNet
• EfficientNet_ns
• ResNext_WSL
• MixNet
• SKNet
• SGENet
• HRNet
• Res2Net

recommanded github repos：

• pytorch-image-models(timm)
• imgclsmob(pytorchcv)
• gen-efficientnet-pytorch
• efficientnet-pytorch
• pytorch-encoding
• pretrained-models-pytorch

model utils: ```

model summary

tu.summary(model, input_size=(batch_size, 3, 224, 224))

macs and flops

tu.profile(model, input_shape=(batch_size, 3, 224, 224))

3 channels pretrained weights to 1 channel

weight_rgb = model.conv1.weight.data weight_grey = weight_rgb.sum(dim=1, keepdim=True) model.conv1 = nn.Conv2d(1, 64, kernel_size=xxx, stride=xxx, padding=xxx, bias=False) model.conv1.weight.data = weight_grey

3 channels pretrained weights to 4 channel

weight_rgb = model.conv1.weight.data weight_y = weight_rgb.mean(dim=1, keepdim=True) weight_rgby = torch.cat([weight_rgb,weight_y], axis=1) * 3 / 4 model.conv1 = nn.Conv2d(4, 64, kernel_size=xxx, stride=xxx, padding=xxx, bias=False) model.conv1.weight.data = weight_rgby

2D models to 3d models using ACSConv (advanced)

using code in this repo: https://github.com/M3DV/ACSConv

```

Optimizer

``` optimizer_ranger = tu.Ranger(model_conv.parameters(), lr=LR)

optimizer = torch.optim.AdamW(model_conv.parameters(), lr=LR, weight_decay=2e-4)

```

Criterion

```

for example:

criterion = tu.SmoothBCEwLogits(smoothing=0.02)

criterion = tu.LabelSmoothingCrossEntropy()

```

Find LR

lr_finder = tu.LRFinder(model, optimizer, criterion, device="cuda") lr_finder.range_test(train_loader, end_lr=10, num_iter=500, accumulation_steps=1) lr_finder.plot() # to inspect the loss-learning rate graph lr_finder.reset() # to reset the model and optimizer to their initial state

LR Scheduler

``` scheduler = tu.get_flat_anneal_scheduler(optimizer, max_iter, warmup_iter=0, decay_start=0.5, anneal='cos', gamma=0.05)

scheduler = tu.CosineAnnealingWarmUpRestarts(optimizer, T_0=T, T_mult=1, eta_max=LR, T_up=0, gamma=0.05)

torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0, T_mult=1, eta_min=0, last_epoch=-1)

torch.optim.lr_scheduler.OneCycleLR or tu.OneCycleScheduler

```

AMP

Ref: https://pytorch.org/docs/master/notes/amp_examples.html

TODO

• [ ] add unit test for models
• [x] add Hybrid Vision Transformer
• [ ] channels_last
• [ ] inplace_abn
• [ ] grad-CAM
• [ ] convert paddle ssld model to pytorch