Added gray images support for adjust_contrast

test/common_utils.py

@@ -128,7 +128,12 @@ def needs_cuda(test_func):
 def _create_data(height=3, width=3, channels=3, device="cpu"):
     # TODO: When all relevant tests are ported to pytest, turn this into a module-level fixture
     tensor = torch.randint(0, 256, (channels, height, width), dtype=torch.uint8, device=device)
-    pil_img = Image.fromarray(tensor.permute(1, 2, 0).contiguous().cpu().numpy())
+    data = tensor.permute(1, 2, 0).contiguous().cpu().numpy()
+    mode = "RGB"
+    if channels == 1:
+        mode = "L"
+        data = data[..., 0]
+    pil_img = Image.fromarray(data, mode=mode)
     return tensor, pil_img
 
 

test/test_functional_tensor.py

@@ -641,12 +641,14 @@ def backward(ctx, grad_output):
     assert torch.autograd.gradcheck(F.apply, x, eps=1e-8, atol=1e-6, rtol=1e-6, fast_mode=False)
 
 
-def check_functional_vs_PIL_vs_scripted(fn, fn_pil, fn_t, config, device, dtype, tol=2.0 + 1e-10, agg_method="max"):
+def check_functional_vs_PIL_vs_scripted(
+    fn, fn_pil, fn_t, config, device, dtype, channels=3, tol=2.0 + 1e-10, agg_method="max"
+):
 
     script_fn = torch.jit.script(fn)
     torch.manual_seed(15)
-    tensor, pil_img = _create_data(26, 34, device=device)
-    batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
+    tensor, pil_img = _create_data(26, 34, channels=channels, device=device)
+    batch_tensors = _create_data_batch(16, 18, num_samples=4, channels=channels, device=device)
 
     if dtype is not None:
         tensor = F.convert_image_dtype(tensor, dtype)
@@ -798,14 +800,16 @@ def test_equalize(device):
 @pytest.mark.parametrize('device', cpu_and_gpu())
 @pytest.mark.parametrize('dtype', (None, torch.float32, torch.float64))
 @pytest.mark.parametrize('config', [{"contrast_factor": f} for f in [0.2, 0.5, 1.0, 1.5, 2.0]])
-def test_adjust_contrast(device, dtype, config):
+@pytest.mark.parametrize('channels', [1, 3])
+def test_adjust_contrast(device, dtype, config, channels):
     check_functional_vs_PIL_vs_scripted(
         F.adjust_contrast,
         F_pil.adjust_contrast,
         F_t.adjust_contrast,
         config,
         device,
-        dtype
+        dtype,
+        channels=channels
     )
 
 

torchvision/transforms/functional_tensor.py

@@ -169,10 +169,13 @@ def adjust_contrast(img: Tensor, contrast_factor: float) -> Tensor:
 
     _assert_image_tensor(img)
 
-    _assert_channels(img, [3])
-
+    _assert_channels(img, [3, 1])
+    c = get_image_num_channels(img)
     dtype = img.dtype if torch.is_floating_point(img) else torch.float32
-    mean = torch.mean(rgb_to_grayscale(img).to(dtype), dim=(-3, -2, -1), keepdim=True)
+    if c == 3:
+        mean = torch.mean(rgb_to_grayscale(img).to(dtype), dim=(-3, -2, -1), keepdim=True)
+    else:
+        mean = torch.mean(img.to(dtype), dim=(-3, -2, -1), keepdim=True)
 
     return _blend(img, mean, contrast_factor)