[NOMERGE] drop in transforms v2 into the v1 tests

test/test_functional_tensor.py

@@ -8,8 +8,8 @@
 import numpy as np
 import pytest
 import torch
-import torchvision.transforms as T
-import torchvision.transforms.functional as F
+import torchvision.prototype.transforms as T
+import torchvision.prototype.transforms.functional as F
 import torchvision.transforms.functional_pil as F_pil
 import torchvision.transforms.functional_tensor as F_t
 from common_utils import (
@@ -87,13 +87,20 @@ class TestRotate:
         "fill",
         [
             None,
-            [0, 0, 0],
-            (1, 2, 3),
-            [255, 255, 255],
+            # TODO: torscript fails with errors like
+            # RuntimeError: rotate() Expected a value of type 'Union[List[float], float, int, NoneType]' for argument 'fill' but instead found type 'tuple'.
+            # Position: 5
+            # Value: (1.0, 2.0, 3.0)
+            # We don't support ints (only float) nor tuples
+            [0.0, 0.0, 0.0],
+            [1.0, 2.0, 3.0],
+            [255.0, 255.0, 255.0],
             [
-                1,
+                1.0,
+            ],
+            [
+                2.0,
             ],
-            (2.0,),
         ],
     )
     @pytest.mark.parametrize("fn", [F.rotate, scripted_rotate])
@@ -312,6 +319,10 @@ def test_translations(self, device, height, width, dt, t, fn):
     @pytest.mark.parametrize("fn", [F.affine, scripted_affine])
     def test_all_ops(self, device, height, width, dt, a, t, s, sh, f, fn):
         # 4) Test rotation + translation + scale + shear
+        # TODO: similar breakage as for rotate(): we don't support ints nor
+        # tuples(of anything) anymore
+        if isinstance(f, (tuple, list)):
+            f = [float(x) for x in f]
         tensor, pil_img = _create_data(height, width, device=device)
 
         if dt == torch.float16 and device == "cpu":
@@ -392,6 +403,10 @@ def _get_data_dims_and_points_for_perspective():
 @pytest.mark.parametrize("fn", [F.perspective, torch.jit.script(F.perspective)])
 def test_perspective_pil_vs_tensor(device, dims_and_points, dt, fill, fn):
 
+    # TODO: see test_rotate()
+    if fill is not None:
+        fill = [float(x) for x in fill]
+
     if dt == torch.float16 and device == "cpu":
         # skip float16 on CPU case
         return
@@ -522,8 +537,9 @@ def test_resize_asserts(device):
 
     for img in (tensor, pil_img):
         exp_msg = "max_size should only be passed if size specifies the length of the smaller edge"
-        with pytest.raises(ValueError, match=exp_msg):
-            F.resize(img, size=(32, 34), max_size=35)
+        # TODO: is this a BC break or something we handle smoothly now?
+        # with pytest.raises(ValueError, match=exp_msg):
+        #     F.resize(img, size=(32, 34), max_size=35)
         with pytest.raises(ValueError, match="max_size = 32 must be strictly greater"):
             F.resize(img, size=32, max_size=32)
 
@@ -885,6 +901,9 @@ def test_pad(device, dt, pad, config):
     script_fn = torch.jit.script(F.pad)
     tensor, pil_img = _create_data(7, 8, device=device)
     batch_tensors = _create_data_batch(16, 18, num_samples=4, device=device)
+    # TODO: we don't support tuples in jit anymore. Not sure if floats are an issue (or relevant)
+    if isinstance(pad, tuple):
+        pad = [x for x in pad]
 
     if dt == torch.float16 and device == "cpu":
         # skip float16 on CPU case
@@ -1269,15 +1288,19 @@ def test_ten_crop(device):
 
 
 def test_elastic_transform_asserts():
-    with pytest.raises(TypeError, match="Argument displacement should be a Tensor"):
-        _ = F.elastic_transform("abc", displacement=None)
+    img_tensor = torch.rand(1, 3, 32, 24)
+    # TODO: passing None currently fails with:
+    # grid = _create_identity_grid((image_height, image_width), device=device).add_(displacement.to(device))
+    # AttributeError: 'NoneType' object has no attribute 'to'
+    # with pytest.raises(TypeError, match="Argument displacement should be a Tensor"):
+    #     _ = F.elastic_transform(img_tensor, displacement=None)
 
-    with pytest.raises(TypeError, match="img should be PIL Image or Tensor"):
+    with pytest.raises(TypeError, match="Input can either be"):
         _ = F.elastic_transform("abc", displacement=torch.rand(1))
 
-    img_tensor = torch.rand(1, 3, 32, 24)
-    with pytest.raises(ValueError, match="Argument displacement shape should"):
-        _ = F.elastic_transform(img_tensor, displacement=torch.rand(1, 2))
+    # TODO: this doesnt raise
+    # with pytest.raises(ValueError, match="Argument displacement shape should"):
+    #     _ = F.elastic_transform(img_tensor, displacement=torch.rand(1, 2))
 
 
 @pytest.mark.parametrize("device", cpu_and_gpu())
@@ -1287,7 +1310,19 @@ def test_elastic_transform_asserts():
     "fill",
     [None, [255, 255, 255], (2.0,)],
 )
+# TODO: This one is completly broken and mostly fails with:
+#   File "/home/nicolashug/dev/vision/torchvision/prototype/transforms/functional/_geometry.py", line 420, in _apply_grid_transform
+#     float_img = grid_sample(float_img, grid, mode=mode, padding_mode="zeros", align_corners=False)
+#   File "/home/nicolashug/.miniconda3/envs/pt/lib/python3.9/site-packages/torch/nn/functional.py", line 4243, in grid_sample
+#     return torch.grid_sampler(input, grid, mode_enum, padding_mode_enum, align_corners)
+# RuntimeError: expected scalar type Double but found Float
+
+# I couldn't make it work even when just setting dt to float64 and fixing the
+# fill param as for the other tests.
+# One thing is clear is that float16 is clearly not supported anymore. But there
+# are other underlying issues that I don't understand yet.
 def test_elastic_transform_consistency(device, interpolation, dt, fill):
+    return  # FIXME!!!!!!
     script_elastic_transform = torch.jit.script(F.elastic_transform)
     img_tensor, _ = _create_data(32, 34, device=device)
     # As there is no PIL implementation for elastic_transform,