Refactor PSNR and SSIM

ignite/metrics/psnr.py

@@ -118,7 +118,7 @@ def update(self, output: Sequence[torch.Tensor]) -> None:
         self._num_examples += y.shape[0]
 
     @sync_all_reduce("_sum_of_batchwise_psnr", "_num_examples")
-    def compute(self) -> torch.Tensor:
+    def compute(self) -> float:
         if self._num_examples == 0:
             raise NotComputableError("PSNR must have at least one example before it can be computed.")
-        return self._sum_of_batchwise_psnr / self._num_examples
+        return (self._sum_of_batchwise_psnr / self._num_examples).item()

ignite/metrics/ssim.py

@@ -180,7 +180,7 @@ def update(self, output: Sequence[torch.Tensor]) -> None:
         self._num_examples += y.shape[0]
 
     @sync_all_reduce("_sum_of_ssim", "_num_examples")
-    def compute(self) -> torch.Tensor:
+    def compute(self) -> float:
         if self._num_examples == 0:
             raise NotComputableError("SSIM must have at least one example before it can be computed.")
-        return self._sum_of_ssim / self._num_examples
+        return (self._sum_of_ssim / self._num_examples).item()

tests/ignite/conftest.py

@@ -13,6 +13,16 @@
 import ignite.distributed as idist
 
 
+@pytest.fixture(
+    params=[
+        "cpu",
+        pytest.param("cuda", marks=pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no CUDA support")),
+    ]
+)
+def available_device(request):
+    return request.param
+
+
 @pytest.fixture()
 def dirname():
     path = Path(tempfile.mkdtemp())

tests/ignite/metrics/test_psnr.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 import pytest
 import torch
@@ -11,8 +9,6 @@
 from ignite.metrics import PSNR
 from ignite.utils import manual_seed
 
-from tests.ignite import cpu_and_maybe_cuda
-
 
 def test_zero_div():
     psnr = PSNR(1.0)
@@ -32,9 +28,32 @@ def test_invalid_psnr():
         psnr.update((y_pred, y.squeeze(dim=0)))
 
 
-def _test_psnr(y_pred, y, data_range, device):
-    psnr = PSNR(data_range=data_range, device=device)
-    psnr.update((y_pred, y))
+@pytest.fixture(params=["float", "YCbCr", "uint8", "NHW shape"])
+def test_data(request, available_device):
+    manual_seed(42)
+    if request.param == "float":
+        y_pred = torch.rand(8, 3, 28, 28, device=available_device)
+        y = y_pred * 0.8
+    elif request.param == "YCbCr":
+        y_pred = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=available_device)
+        y = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=available_device)
+    elif request.param == "uint8":
+        y_pred = torch.randint(0, 256, (4, 3, 16, 16), dtype=torch.uint8, device=available_device)
+        y = (y_pred * 0.8).to(torch.uint8)
+    elif request.param == "NHW shape":
+        y_pred = torch.rand(8, 28, 28, device=available_device)
+        y = y_pred * 0.8
+    else:
+        raise ValueError(f"Wrong fixture parameter, given {request.param}")
+    return (y_pred, y)
+
+
+def test_psnr(test_data, available_device):
+    y_pred, y = test_data
+    data_range = (y.max() - y.min()).cpu().item()
+
+    psnr = PSNR(data_range=data_range, device=available_device)
+    psnr.update(test_data)
     psnr_compute = psnr.compute()
 
     np_y_pred = y_pred.cpu().numpy()
@@ -43,43 +62,9 @@ def _test_psnr(y_pred, y, data_range, device):
     for np_y_pred_, np_y_ in zip(np_y_pred, np_y):
         np_psnr += ski_psnr(np_y_, np_y_pred_, data_range=data_range)
 
-    assert torch.gt(psnr_compute, 0.0)
-    assert isinstance(psnr_compute, torch.Tensor)
-    assert psnr_compute.dtype == torch.float64
-    assert psnr_compute.device.type == torch.device(device).type
-    assert np.allclose(psnr_compute.cpu().numpy(), np_psnr / np_y.shape[0])
-
-
-@pytest.mark.parametrize("device", cpu_and_maybe_cuda())
-def test_psnr(device):
-
-    # test for float
-    manual_seed(42)
-    y_pred = torch.rand(8, 3, 28, 28, device=device)
-    y = y_pred * 0.8
-    data_range = (y.max() - y.min()).cpu().item()
-    _test_psnr(y_pred, y, data_range, device)
-
-    # test for YCbCr
-    manual_seed(42)
-    y_pred = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=device)
-    y = torch.randint(16, 236, (4, 1, 12, 12), dtype=torch.uint8, device=device)
-    data_range = (y.max() - y.min()).cpu().item()
-    _test_psnr(y_pred, y, data_range, device)
-
-    # test for uint8
-    manual_seed(42)
-    y_pred = torch.randint(0, 256, (4, 3, 16, 16), dtype=torch.uint8, device=device)
-    y = (y_pred * 0.8).to(torch.uint8)
-    data_range = (y.max() - y.min()).cpu().item()
-    _test_psnr(y_pred, y, data_range, device)
-
-    # test with NHW shape
-    manual_seed(42)
-    y_pred = torch.rand(8, 28, 28, device=device)
-    y = y_pred * 0.8
-    data_range = (y.max() - y.min()).cpu().item()
-    _test_psnr(y_pred, y, data_range, device)
+    assert psnr_compute > 0.0
+    assert isinstance(psnr_compute, float)
+    assert np.allclose(psnr_compute, np_psnr / np_y.shape[0])
 
 
 def _test(
@@ -109,9 +94,9 @@ def update(engine, i):
     y = idist.all_gather(y)
     y_pred = idist.all_gather(y_pred)
 
+    assert "psnr" in engine.state.metrics
     result = engine.state.metrics["psnr"]
     assert result > 0.0
-    assert "psnr" in engine.state.metrics
 
     if compute_y_channel:
         np_y_pred = y_pred[:, 0, ...].cpu().numpy()
@@ -127,7 +112,9 @@ def update(engine, i):
     assert np.allclose(result, np_psnr / np_y.shape[0], atol=atol)
 
 
-def _test_distrib_input_float(device, atol=1e-8):
+def test_distrib_input_float(distributed):
+    device = idist.device()
+
     def get_test_cases():
 
         y_pred = torch.rand(n_iters * batch_size, 2, 2, device=device)
@@ -143,12 +130,14 @@ def get_test_cases():
         # check multiple random inputs as random exact occurencies are rare
         torch.manual_seed(42 + rank + i)
         y_pred, y = get_test_cases()
-        _test(y_pred, y, 1, "cpu", n_iters, batch_size, atol=atol)
+        _test(y_pred, y, 1, "cpu", n_iters, batch_size, atol=1e-8)
         if device.type != "xla":
-            _test(y_pred, y, 1, idist.device(), n_iters, batch_size, atol=atol)
+            _test(y_pred, y, 1, idist.device(), n_iters, batch_size, atol=1e-8)
+
 
+def test_distrib_multilabel_input_YCbCr(distributed):
+    device = idist.device()
 
-def _test_distrib_multilabel_input_YCbCr(device, atol=1e-8):
     def get_test_cases():
 
         y_pred = torch.randint(16, 236, (n_iters * batch_size, 1, 12, 12), dtype=torch.uint8, device=device)
@@ -171,13 +160,15 @@ def out_fn(x):
         # check multiple random inputs as random exact occurencies are rare
         torch.manual_seed(42 + rank + i)
         y_pred, y = get_test_cases()
-        _test(y_pred, y, 220, "cpu", n_iters, batch_size, atol, output_transform=out_fn, compute_y_channel=True)
+        _test(y_pred, y, 220, "cpu", n_iters, batch_size, atol=1e-8, output_transform=out_fn, compute_y_channel=True)
         if device.type != "xla":
             dev = idist.device()
-            _test(y_pred, y, 220, dev, n_iters, batch_size, atol, output_transform=out_fn, compute_y_channel=True)
+            _test(y_pred, y, 220, dev, n_iters, batch_size, atol=1e-8, output_transform=out_fn, compute_y_channel=True)
 
 
-def _test_distrib_multilabel_input_uint8(device, atol=1e-8):
+def test_distrib_multilabel_input_uint8(distributed):
+    device = idist.device()
+
     def get_test_cases():
 
         y_pred = torch.randint(0, 256, (n_iters * batch_size, 3, 16, 16), device=device, dtype=torch.uint8)
@@ -193,12 +184,14 @@ def get_test_cases():
         # check multiple random inputs as random exact occurencies are rare
         torch.manual_seed(42 + rank + i)
         y_pred, y = get_test_cases()
-        _test(y_pred, y, 100, "cpu", n_iters, batch_size, atol)
+        _test(y_pred, y, 100, "cpu", n_iters, batch_size, atol=1e-8)
         if device.type != "xla":
-            _test(y_pred, y, 100, idist.device(), n_iters, batch_size, atol)
+            _test(y_pred, y, 100, idist.device(), n_iters, batch_size, atol=1e-8)
 
 
-def _test_distrib_multilabel_input_NHW(device, atol=1e-8):
+def test_distrib_multilabel_input_NHW(distributed):
+    device = idist.device()
+
     def get_test_cases():
 
         y_pred = torch.rand(n_iters * batch_size, 28, 28, device=device)
@@ -214,13 +207,13 @@ def get_test_cases():
         # check multiple random inputs as random exact occurencies are rare
         torch.manual_seed(42 + rank + i)
         y_pred, y = get_test_cases()
-        _test(y_pred, y, 10, "cpu", n_iters, batch_size, atol)
+        _test(y_pred, y, 10, "cpu", n_iters, batch_size, atol=1e-8)
         if device.type != "xla":
-            _test(y_pred, y, 10, idist.device(), n_iters, batch_size, atol)
+            _test(y_pred, y, 10, idist.device(), n_iters, batch_size, atol=1e-8)
 
 
-def _test_distrib_accumulator_device(device):
-
+def test_distrib_accumulator_device(distributed):
+    device = idist.device()
     metric_devices = [torch.device("cpu")]
     if torch.device(device).type != "xla":
         metric_devices.append(idist.device())
@@ -235,99 +228,3 @@ def _test_distrib_accumulator_device(device):
         psnr.update((y_pred, y))
         dev = psnr._sum_of_batchwise_psnr.device
         assert dev == metric_device, f"{dev} vs {metric_device}"
-
-
-@pytest.mark.distributed
-@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
-def test_distrib_gloo_cpu_or_gpu(distributed_context_single_node_gloo):
-
-    device = idist.device()
-    _test_distrib_input_float(device)
-    _test_distrib_multilabel_input_YCbCr(device)
-    _test_distrib_multilabel_input_uint8(device)
-    _test_distrib_multilabel_input_NHW(device)
-    _test_distrib_accumulator_device(device)
-
-
-@pytest.mark.distributed
-@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
-@pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU")
-def test_distrib_nccl_gpu(distributed_context_single_node_nccl):
-
-    device = idist.device()
-    _test_distrib_input_float(device)
-    _test_distrib_multilabel_input_YCbCr(device)
-    _test_distrib_multilabel_input_uint8(device)
-    _test_distrib_multilabel_input_NHW(device)
-    _test_distrib_accumulator_device(device)
-
-
-@pytest.mark.multinode_distributed
-@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
-@pytest.mark.skipif("MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
-def test_multinode_distrib_gloo_cpu_or_gpu(distributed_context_multi_node_gloo):
-
-    device = idist.device()
-    _test_distrib_input_float(device)
-    _test_distrib_multilabel_input_YCbCr(device)
-    _test_distrib_multilabel_input_uint8(device)
-    _test_distrib_multilabel_input_NHW(device)
-    _test_distrib_accumulator_device(device)
-
-
-@pytest.mark.multinode_distributed
-@pytest.mark.skipif(not idist.has_native_dist_support, reason="Skip if no native dist support")
-@pytest.mark.skipif("GPU_MULTINODE_DISTRIB" not in os.environ, reason="Skip if not multi-node distributed")
-def test_multinode_distrib_nccl_gpu(distributed_context_multi_node_nccl):
-
-    device = idist.device()
-    _test_distrib_input_float(device)
-    _test_distrib_multilabel_input_YCbCr(device)
-    _test_distrib_multilabel_input_uint8(device)
-    _test_distrib_multilabel_input_NHW(device)
-    _test_distrib_accumulator_device(device)
-
-
-@pytest.mark.tpu
-@pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars")
-@pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package")
-def test_distrib_single_device_xla():
-
-    device = idist.device()
-    _test_distrib_input_float(device)
-    _test_distrib_multilabel_input_YCbCr(device)
-    _test_distrib_multilabel_input_uint8(device)
-    _test_distrib_multilabel_input_NHW(device)
-    _test_distrib_accumulator_device(device)
-
-
-def _test_distrib_xla_nprocs(index):
-    device = idist.device()
-    _test_distrib_input_float(device)
-    _test_distrib_multilabel_input_YCbCr(device)
-    _test_distrib_multilabel_input_uint8(device)
-    _test_distrib_multilabel_input_NHW(device)
-    _test_distrib_accumulator_device(device)
-
-
-@pytest.mark.tpu
-@pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars")
-@pytest.mark.skipif(not idist.has_xla_support, reason="Skip if no PyTorch XLA package")
-def test_distrib_xla_nprocs(xmp_executor):
-    n = int(os.environ["NUM_TPU_WORKERS"])
-    xmp_executor(_test_distrib_xla_nprocs, args=(), nprocs=n)
-
-
-@pytest.mark.distributed
-@pytest.mark.skipif(not idist.has_hvd_support, reason="Skip if no Horovod dist support")
-@pytest.mark.skipif("WORLD_SIZE" in os.environ, reason="Skip if launched as multiproc")
-def test_distrib_hvd(gloo_hvd_executor):
-
-    device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
-    nproc = 4 if not torch.cuda.is_available() else torch.cuda.device_count()
-
-    gloo_hvd_executor(_test_distrib_input_float, (device,), np=nproc, do_init=True)
-    gloo_hvd_executor(_test_distrib_multilabel_input_YCbCr, (device,), np=nproc, do_init=True)
-    gloo_hvd_executor(_test_distrib_multilabel_input_uint8, (device,), np=nproc, do_init=True)
-    gloo_hvd_executor(_test_distrib_multilabel_input_NHW, (device,), np=nproc, do_init=True)
-    gloo_hvd_executor(_test_distrib_accumulator_device, (device,), np=nproc, do_init=True)