Fixed issues in elastic transform (#7257)

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

torchvision/prototype/transforms/functional/_geometry.py

@@ -1538,10 +1538,21 @@ def elastic_image_tensor(
 
     device = image.device
     dtype = image.dtype if torch.is_floating_point(image) else torch.float32
+
+    # Patch: elastic transform should support (cpu,f16) input
+    is_cpu_half = device.type == "cpu" and dtype == torch.float16
+    if is_cpu_half:
+        image = image.to(torch.float32)
+        dtype = torch.float32
+
     # We are aware that if input image dtype is uint8 and displacement is float64 then
     # displacement will be casted to float32 and all computations will be done with float32
     # We can fix this later if needed
 
+    expected_shape = (1,) + shape[-2:] + (2,)
+    if expected_shape != displacement.shape:
+        raise ValueError(f"Argument displacement shape should be {expected_shape}, but given {displacement.shape}")
+
     if ndim > 4:
         image = image.reshape((-1,) + shape[-3:])
         needs_unsquash = True
@@ -1561,6 +1572,9 @@ def elastic_image_tensor(
     if needs_unsquash:
         output = output.reshape(shape)
 
+    if is_cpu_half:
+        output = output.to(torch.float16)
+
     return output
 
 
@@ -1676,6 +1690,9 @@ def elastic(
     if not torch.jit.is_scripting():
         _log_api_usage_once(elastic)
 
+    if not isinstance(displacement, torch.Tensor):
+        raise TypeError("Argument displacement should be a Tensor")
+
     if torch.jit.is_scripting() or is_simple_tensor(inpt):
         return elastic_image_tensor(inpt, displacement, interpolation=interpolation, fill=fill)
     elif isinstance(inpt, datapoints._datapoint.Datapoint):