适配rocm

setup.py

@@ -36,7 +36,59 @@ def choose_requirement(primary, secondary):
     return str(primary)
 
 
+def get_sha(pytorch_root: Union[str, Path]) -> str:
+    try:
+        return subprocess.check_output(['git', 'rev-parse', 'HEAD'], cwd=pytorch_root).decode('ascii').strip()
+    except Exception:
+        return 'Unknown'
+
+
+def get_abi():
+    try:
+        command = "echo '#include <string>' | gcc -x c++ -E -dM - | fgrep _GLIBCXX_USE_CXX11_ABI" 
+        result = subprocess.run(command, shell=True, capture_output=True, text=True) 
+        output = result.stdout.strip() 
+        abi = "abi" + output.split(" ")[-1]
+        return abi
+    except Exception:
+        return 'abiUnknown'
+
+
+def get_version_add(sha: Optional[str] = None) -> str:
+    version=''
+    mmcv_root = os.path.dirname(os.path.abspath(__file__))
+    add_version_path = os.path.join(os.path.join(mmcv_root, "mmcv"), "version.py")
+    if sha != 'Unknown':
+        if sha is None:
+            sha = get_sha(mmcv_root)
+        version = 'git' + sha[:7]
+
+    # abi
+    version += "." + get_abi()
+
+    # dtk version
+    if os.getenv("ROCM_PATH"):
+        rocm_path = os.getenv('ROCM_PATH', "")
+        rocm_version_path = os.path.join(rocm_path, '.info', "rocm_version")
+        with open(rocm_version_path, 'r',encoding='utf-8') as file:
+            lines = file.readlines()
+        rocm_version=lines[0][:-2].replace(".", "")
+        version += ".dtk" + rocm_version
+    
+    # torch version
+    version += ".torch" + torch.__version__[:4]
+
+    lines=[]
+    with open(add_version_path, 'r',encoding='utf-8') as file:
+        lines = file.readlines()
+    lines[2] = "__dcu_version__ = '2.0.0+{}'\n".format(version)
+    with open(add_version_path, encoding="utf-8",mode="w") as file:
+        file.writelines(lines)
+    file.close()
+
+
 def get_version():
+    get_version_add()
     version_file = 'mmcv/version.py'
     with open(version_file, encoding='utf-8') as f:
         exec(compile(f.read(), version_file, 'exec'))

tests/test_ops/test_carafe.py

@@ -48,15 +48,15 @@ class TestCarafe:
             pytest.skip('test requires compilation')
 
         np_feat = np.fromfile(
-            'tests/data/for_carafe/carafe_feat.bin', dtype=np.float32)
+            './data/for_carafe/carafe_feat.bin', dtype=np.float32)
         np_mask = np.fromfile(
-            'tests/data/for_carafe/carafe_mask.bin', dtype=np.float32)
+            './data/for_carafe/carafe_mask.bin', dtype=np.float32)
         np_output = np.fromfile(
-            'tests/data/for_carafe/carafe_output.bin', dtype=np.float32)
+            './data/for_carafe/carafe_output.bin', dtype=np.float32)
         np_feat_grad = np.fromfile(
-            'tests/data/for_carafe/carafe_feat_grad.bin', dtype=np.float32)
+            './data/for_carafe/carafe_feat_grad.bin', dtype=np.float32)
         np_mask_grad = np.fromfile(
-            'tests/data/for_carafe/carafe_mask_grad.bin', dtype=np.float32)
+            './data/for_carafe/carafe_mask_grad.bin', dtype=np.float32)
 
         np_feat = np_feat.reshape((2, 64, 3, 3))
         np_mask = np_mask.reshape((2, 100, 6, 6))

tests/test_ops/test_cc_attention.py

@@ -24,10 +24,10 @@ class TestCrissCrossAttention:
         loss_func = Loss()
 
         input = np.fromfile(
-            'tests/data/for_ccattention/ccattention_input.bin',
+            './data/for_ccattention/ccattention_input.bin',
             dtype=np.float32)
         output = np.fromfile(
-            'tests/data/for_ccattention/ccattention_output.bin',
+            './data/for_ccattention/ccattention_output.bin',
             dtype=np.float32)
         input = input.reshape((1, 32, 45, 45))
         output = output.reshape((1, 32, 45, 45))

tests/test_ops/test_furthest_point_sample.py

@@ -41,9 +41,9 @@ def test_fps_with_dist():
     assert torch.all(idx == expected_idx)
 
     import numpy as np
-    fps_idx = np.load('tests/data/for_3d_ops/fps_idx.npy')
+    fps_idx = np.load('./data/for_3d_ops/fps_idx.npy')
     features_for_fps_distance = np.load(
-        'tests/data/for_3d_ops/features_for_fps_distance.npy')
+        './data/for_3d_ops/features_for_fps_distance.npy')
     expected_idx = torch.from_numpy(fps_idx).cuda()
     features_for_fps_distance = torch.from_numpy(
         features_for_fps_distance).cuda()

tests/test_ops/test_masked_conv2d.py

@@ -21,15 +21,15 @@ class TestMaskedConv2d:
     def test_masked_conv2d_all_close(self, device):
         from mmcv.ops import MaskedConv2d
         np_input = np.load(
-            'tests/data/for_masked_conv2d/masked_conv2d_for_input.npy')
+            './data/for_masked_conv2d/masked_conv2d_for_input.npy')
         np_mask = np.load(
-            'tests/data/for_masked_conv2d/masked_conv2d_for_mask.npy')
+            './data/for_masked_conv2d/masked_conv2d_for_mask.npy')
         np_weight = np.load(
-            'tests/data/for_masked_conv2d/masked_conv2d_for_weight.npy')
+            './data/for_masked_conv2d/masked_conv2d_for_weight.npy')
         np_bias = np.load(
-            'tests/data/for_masked_conv2d/masked_conv2d_for_bias.npy')
+            './data/for_masked_conv2d/masked_conv2d_for_bias.npy')
         np_output = np.load(
-            'tests/data/for_masked_conv2d/masked_conv2d_for_output.npy')
+            './data/for_masked_conv2d/masked_conv2d_for_output.npy')
         input = torch.tensor(np_input, dtype=torch.float, device=device)
         mask = torch.tensor(np_mask, dtype=torch.float, device=device)
         weight = torch.tensor(np_weight, dtype=torch.float, device=device)

tests/test_ops/test_psa_mask.py

@@ -39,9 +39,9 @@ class TestPSAMask:
         test_loss = Loss()
 
         input = np.fromfile(
-            'tests/data/for_psa_mask/psa_input.bin', dtype=np.float32)
+            './data/for_psa_mask/psa_input.bin', dtype=np.float32)
         output_collect = np.fromfile(
-            'tests/data/for_psa_mask/psa_output_collect.bin', dtype=np.float32)
+            './data/for_psa_mask/psa_output_collect.bin', dtype=np.float32)
 
         input = input.reshape((4, 16, 8, 8))
         output_collect = output_collect.reshape((4, 64, 8, 8))
@@ -91,9 +91,9 @@ class TestPSAMask:
         test_loss = Loss()
 
         input = np.fromfile(
-            'tests/data/for_psa_mask/psa_input.bin', dtype=np.float32)
+            './data/for_psa_mask/psa_input.bin', dtype=np.float32)
         output_distribute = np.fromfile(
-            'tests/data/for_psa_mask/psa_output_distribute.bin',
+            './data/for_psa_mask/psa_output_distribute.bin',
             dtype=np.float32)
 
         input = input.reshape((4, 16, 8, 8))