Add in_chans arg to resnet (#673)

* Add in_chans arg to resnet

* Update CHANGELOG.md

CHANGELOG.md

@@ -50,6 +50,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added support for flash zero with the `InstanceSegmentation` and `KeypointDetector` tasks ([#672](https://github.com/PyTorchLightning/lightning-flash/pull/672))
 
+- Added support for `in_chans` argument to the flash ResNet to control the expected number of input channels ([#673](https://github.com/PyTorchLightning/lightning-flash/pull/673))
+
 ### Changed
 
 - Changed how pretrained flag works for loading weights for ImageClassifier task ([#560](https://github.com/PyTorchLightning/lightning-flash/pull/560))

flash/image/classification/backbones/resnet.py

@@ -169,6 +169,7 @@ def __init__(
         norm_layer: Optional[Callable[..., nn.Module]] = None,
         first_conv3x3: bool = False,
         remove_first_maxpool: bool = False,
+        in_chans: int = 3,
     ) -> None:
 
         super().__init__()
@@ -194,9 +195,9 @@ def __init__(
         num_out_filters = width_per_group * widen
 
         if first_conv3x3:
-            self.conv1 = nn.Conv2d(3, num_out_filters, kernel_size=3, stride=1, padding=1, bias=False)
+            self.conv1 = nn.Conv2d(in_chans, num_out_filters, kernel_size=3, stride=1, padding=1, bias=False)
         else:
-            self.conv1 = nn.Conv2d(3, num_out_filters, kernel_size=7, stride=2, padding=3, bias=False)
+            self.conv1 = nn.Conv2d(in_chans, num_out_filters, kernel_size=7, stride=2, padding=3, bias=False)
 
         self.bn1 = norm_layer(num_out_filters)
         self.relu = nn.ReLU(inplace=True)