clean up publishing

featup/adaptive_conv_cuda/adaptive_conv.py

@@ -1,8 +1,5 @@
-import math
-from torch import nn
 from torch.autograd import Function
 import torch
-import time
 
 from featup.adaptive_conv_cuda import cuda_impl
 

featup/train_jbu_upsampler.py

@@ -14,14 +14,14 @@
 from torch.utils.data import DataLoader
 from torchvision.transforms import InterpolationMode
 
-from datasets.JitteredImage import apply_jitter, sample_transform
-from datasets.util import get_dataset, SingleImageDataset
-from downsamplers import SimpleDownsampler, AttentionDownsampler
-from featurizers.util import get_featurizer
-from layers import ChannelNorm
-from losses import TVLoss, SampledCRFLoss, entropy
-from upsamplers import get_upsampler
-from util import pca, RollingAvg, unnorm, norm, prep_image
+from featup.datasets.JitteredImage import apply_jitter, sample_transform
+from featup.datasets.util import get_dataset, SingleImageDataset
+from featup.downsamplers import SimpleDownsampler, AttentionDownsampler
+from featup.featurizers.util import get_featurizer
+from featup.layers import ChannelNorm
+from featup.losses import TVLoss, SampledCRFLoss, entropy
+from featup.upsamplers import get_upsampler
+from featup.util import pca, RollingAvg, unnorm, norm, prep_image
 
 torch.multiprocessing.set_sharing_strategy('file_system')
 

featup/train_probes.py

@@ -14,9 +14,9 @@
 from torch.utils.data import DataLoader
 from torchmetrics.classification import Accuracy, JaccardIndex
 
-from datasets.COCO import Coco
-from datasets.EmbeddingFile import EmbeddingFile
-from losses import ScaleAndShiftInvariantLoss
+from featup.datasets.COCO import Coco
+from featup.datasets.EmbeddingFile import EmbeddingFile
+from featup.losses import ScaleAndShiftInvariantLoss
 from featup.util import pca
 from featup.util import remove_axes
 

featup/upsamplers.py

@@ -3,7 +3,6 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from mmcv.ops import CARAFEPack
 from timm.models.layers import trunc_normal_
 
 from featup.adaptive_conv_cuda.adaptive_conv import AdaptiveConv
@@ -143,6 +142,7 @@ class CarafeUpsampler(torch.nn.Module):
 
     def __init__(self, dim, kernel_size, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        from mmcv.ops import CARAFEPack
         self.up1 = CARAFEPack(dim, up_kernel=3, up_group=1, scale_factor=2)
         self.up2 = CARAFEPack(dim, up_kernel=3, up_group=1, scale_factor=2)
         self.up3 = CARAFEPack(dim, up_kernel=3, up_group=1, scale_factor=2)

setup.py

@@ -7,6 +7,17 @@
     packages=find_packages(),
     install_requires=[
         'torch',
+        'kornia',
+        'omegaconf',
+        'pytorch-lightning',
+        'torchvision',
+        'tqdm',
+        'torchemetrics',
+        'sklearn',
+        'numpy',
+        'matplotlib',
+        'glob',
+
     ],
     author='Mark Hamilton, Stephanie Fu',
     author_email='markth@mit.edu, fus@berkeley.edu',