reduced numbers of pylint warnings (#304)

glasses/models/AutoModel.py

@@ -2,8 +2,23 @@
 from glasses.utils.storage import HuggingFaceStorage
 from typing import List, Optional, OrderedDict
 from torch import nn
-from .classification import *
-from .segmentation import *
+
+from .classification import (
+    ResNetXt,
+    WideResNet,
+    DenseNet,
+    SEResNet,
+    RegNet,
+    ResNeSt,
+    VGG,
+    MobileNet,
+    EfficientNet,
+    EfficientNetLite,
+    FishNet,
+    ViT,
+    DeiT,
+)
+from .segmentation import UNet
 from rich.table import Table
 from .classification.resnet import (
     ResNet,
@@ -273,9 +288,7 @@ def models_table(storage: Optional[Storage] = HuggingFaceStorage()) -> Table:
         table.add_column("Name", justify="left", no_wrap=True)
         table.add_column("Pretrained", justify="left", no_wrap=True)
 
-        [
+        for k in AutoModel.zoo.keys():
             table.add_row(k, "true" if k in storage.models else "false")
-            for k in AutoModel.zoo.keys()
-        ]
 
         return table

glasses/models/AutoTransform.py

@@ -1,10 +1,11 @@
 import torch
 import torchvision.transforms as T
-from glasses.models import *
 from typing import Tuple, List, Callable
 from torchvision.transforms import InterpolationMode
 from functools import partial
 
+# from glasses.models import *
+
 IMAGENET_DEFAULT_MEAN = torch.Tensor([0.485, 0.456, 0.406])
 IMAGENET_DEFAULT_STD = torch.Tensor([0.229, 0.224, 0.225])
 
@@ -22,8 +23,10 @@ def __init__(
         std: Tuple[float],
         mean: Tuple[float],
         interpolation: str = "bilinear",
-        transforms: List[Callable] = list(),
+        transforms: List[Callable] = None,
     ):
+        if transforms is None:
+            transforms = []
         self.input_size: int = input_size
         base_transforms = [
             T.Resize(resize, Transform.interpolations[interpolation]),
@@ -56,9 +59,6 @@ class AutoTransform:
         "eca_resnet26t": ImageNetTransform(
             resize=320, input_size=320, interpolation="bicubic"
         ),
-        "eca_resnet26t": ImageNetTransform(
-            resize=320, input_size=320, interpolation="bicubic"
-        ),
         "eca_resnet50t": ImageNetTransform(
             resize=320, input_size=320, interpolation="bicubic"
         ),

glasses/models/base/protocols.py

@@ -37,7 +37,7 @@ def unfreeze(self, who: Optional[nn.Module] = None):
 
 
 class Interpretable:
-    """Protocol that allows the clas that subclass it to interpret an input using and instance of `Interpretability`"""
+    """Protocol that allows the class that subclass it to interpret an input using and instance of `Interpretability`"""
 
     def interpret(self, x: torch.Tensor, using: Interpretability, *args, **kwargs):
         return using(x, self, *args, **kwargs)

glasses/nn/att/CBAM.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 from torch import Tensor
-from einops.layers.torch import Rearrange, Reduce
+from einops.layers.torch import Reduce
 
 
 class CBAMChannelAtt(nn.Module):

glasses/nn/blocks/__init__.py

@@ -1,25 +1,24 @@
-import torch.nn as nn
 import math
-from functools import partial
 from typing import Callable, Tuple, Optional
-from torch import Tensor
+from functools import partial
+from torch import nn, Tensor
 from torch.nn import functional as F
 from torchvision.ops import StochasticDepth
 
-from glasses.nn import regularization
-
 
 class Lambda(nn.Module):
-    def __init__(self, lambd: Callable[[Tensor], Tensor]):
-        """An utility Module, it allows custom function to be passed
+    """An utility Module, it allows custom function to be passed
 
-        Args:
-            lambd (Callable[Tensor]): A function that does something on a tensor
+    Args:
+        lambd (Callable[Tensor]): A function that does something on a tensor
+
+    Examples:
+        >>> add_two = Lambda(lambd x: x + 2)
+        >>> add_two(Tensor([0])) // 2
+    """
+
+    def __init__(self, lambd: Callable[[Tensor], Tensor]):
 
-        Examples:
-            >>> add_two = Lambda(lambd x: x + 2)
-            >>> add_two(Tensor([0])) // 2
-        """
         super().__init__()
         self.lambd = lambd
 

glasses/nn/pool/SpatialPyramidPool.py

@@ -24,10 +24,13 @@ class SpatialPyramidPool(nn.Module):
     """
 
     def __init__(
-        self, num_pools: List[int] = [1, 4, 16], pool: nn.Module = nn.AdaptiveMaxPool2d
+        self, num_pools: List[int] = None, pool: nn.Module = nn.AdaptiveMaxPool2d
     ):
         super().__init__()
 
+        if num_pools is None:
+            num_pools = [1, 4, 16]
+
         self.pools = nn.ModuleList([])
         for p in num_pools:
             # the output of 2d pool is B X C X P X P

glasses/utils/ModuleTransfer.py

@@ -2,7 +2,6 @@
 from torch import Tensor
 from dataclasses import dataclass, field
 from .Tracker import Tracker
-from pprint import pprint
 from typing import List
 
 

glasses/utils/storage/Storage.py

@@ -1,4 +1,3 @@
-from re import L
 from typing import Any, List
 from abc import ABC, abstractmethod
 

glasses/utils/storage/hugging_face/HFModelHub.py

@@ -1,9 +1,9 @@
 import json
 import logging
 import os
+from typing import Dict, Optional
 import torch
 import requests
-from typing import Dict, Optional
 from torch import nn
 from torch import Tensor
 

glasses/utils/storage/hugging_face/ModelCardProducer.py

@@ -1,10 +1,10 @@
 import os
-import time
-from glasses.models import AutoModel
-from huggingface_hub import Repository, HfFolder
-from .HuggingFaceStorage import HuggingFaceStorage
 from pathlib import Path
 from torch import nn
+from huggingface_hub import Repository
+from glasses.models import AutoModel
+from .HuggingFaceStorage import HuggingFaceStorage
+
 
 # ---
 # tags:

setup.py

@@ -28,6 +28,7 @@
         "matplotlib",
         "einops",
         "rich",
+        "huggingface-hub",
     ],
     python_requires=">=3.8",
     extras_require={