Fix support for torch 11 (#1234)

CHANGELOG.md

@@ -33,6 +33,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed a bug where buffers in loss functions were not correctly registered in the `Task` ([#1203](https://github.com/PyTorchLightning/lightning-flash/pull/1203))
 - Fixed support for passing a sampler instance to `from_*` methods / the `DataModule` ([#1204](https://github.com/PyTorchLightning/lightning-flash/pull/1204))
 
+- Fixed support for `torch==1.11.0` ([#1234](https://github.com/PyTorchLightning/lightning-flash/pull/1234))
+
 ## [0.7.0] - 2022-02-15
 
 ### Added

flash/core/serve/types/bbox.py

@@ -20,12 +20,11 @@ class BBox(BaseType):
 
     def __post_init__(self):
         self._valid_size = torch.Size([4])
-        self._invalid_types = {torch.bool, torch.complex32, torch.complex64, torch.complex128}
 
     def _validate(self, elem):
         if elem.shape != self._valid_size:
             raise ValueError("Each box must consist of (only) four elements each " "corresponding to x1, x2, y1 and y2")
-        if elem.dtype in self._invalid_types:
+        if elem.dtype == torch.bool or torch.is_complex(elem):
             raise TypeError(f"Found unsupported datatype for " f"bounding boxes: {elem.dtype}")
 
     def deserialize(self, box: Tuple[float, ...]) -> torch.Tensor:

flash/text/question_answering/input.py

@@ -77,7 +77,7 @@ def load_data(
 
         if flash._IS_TESTING:
             # NOTE: must subset in this way to return a Dataset
-            hf_dataset = [sample for sample in hf_dataset.select(range(40), keep_in_memory=True)]
+            hf_dataset = [sample for sample in hf_dataset][:40]
 
         return hf_dataset
 

flash/text/seq2seq/core/input.py

@@ -44,7 +44,7 @@ def load_data(
 
         if flash._IS_TESTING:
             # NOTE: must subset in this way to return a Dataset
-            hf_dataset = [sample for sample in hf_dataset.select(range(40), keep_in_memory=True)]
+            hf_dataset = [sample for sample in hf_dataset][:40]
 
         return hf_dataset