File size: 3,334 Bytes
2692210 c1333ba 2692210 c1333ba c975e16 c1333ba c975e16 c1333ba 77185e0 c1333ba 77185e0 c1333ba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
license: mit
datasets:
- imagenet-1k
language:
- en
metrics:
- accuracy
pipeline_tag: image-classification
---
[Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations](https://hmichaeli.github.io/alias_free_convnets/)
Official PyTorch trained model.
This is a ConvNeXt-Tiny variant.
`convnext_tiny_baseline` is ConvNeXt-Tiny with circular-padded convolutions.
`convnext_tiny_afc` is the full ConvNeXt-Tiny-AFC which is shift invariant to circular shifts.
For more details see the [paper](https://arxiv.org/abs/2303.08085) or [implementation](https://github.com/hmichaeli/alias_free_convnets).
```bash
git clone https://github.com/hmichaeli/alias_free_convnets.git
```
```python
from huggingface_hub import hf_hub_download
import torch
from torchvision import datasets, transforms
from alias_free_convnets.models.convnext_afc import convnext_afc_tiny
# baseline
path = hf_hub_download(repo_id="hmichaeli/convnext-afc", filename="convnext_tiny_basline.pth")
ckpt = torch.load(path)
base_model = convnext_afc_tiny(pretrained=False, num_classes=1000)
base_model.load_state_dict(ckpt, strict=True)
# AFC
path = hf_hub_download(repo_id="hmichaeli/convnext-afc", filename="convnext_tiny_afc.pth")
ckpt = torch.load(path)
afc_model = convnext_afc_tiny(
pretrained=False,
num_classes=1000,
activation='up_poly_per_channel',
activation_kwargs={'in_scale': 7, 'out_scale': 7, 'train_scale': True},
blurpool_kwargs={"filter_type": "ideal", "scale_l2": False},
normalization_type='CHW2',
stem_activation_kwargs={"in_scale": 7, "out_scale": 7, "train_scale": True, "cutoff": 0.75},
normalization_kwargs={},
stem_mode='activation_residual', stem_activation='lpf_poly_per_channel'
)
afc_model.load_state_dict(ckpt, strict=False)
# evaluate model
interpolation = transforms.InterpolationMode.BICUBIC
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
transform = transforms.Compose([
transforms.Resize(256, interpolation=interpolation),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD),
])
data_path = "/path/to/imagenet/val"
dataset_val = datasets.ImageFolder(data_path, transform=transform)
nb_classes = 1000
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val,
batch_size=8,
num_workers=8,
drop_last=False
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@torch.no_grad()
def evaluate(data_loader, model, device):
model.eval()
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(data_loader):
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
acc = 100. * correct / total
print("Acc@1 {:.3f}".format(acc))
print("evaluate baseline")
base_model.to(device)
test_stats = evaluate(data_loader_val, base_model, device)
print("evaluate AFC")
afc_model.to(device)
test_stats = evaluate(data_loader_val, afc_model, device)
```
|