fix the scatter when input is cpu

mmcv/parallel/_functions.py

@@ -22,10 +22,7 @@ def scatter(input, devices, streams=None):
         if devices != [-1]:
             with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
                 output = output.cuda(devices[0], non_blocking=True)
-        else:
-            # unsqueeze the first dimension thus the tensor's shape is the
-            # same as those scattered with GPU.
-            output = output.unsqueeze(0)
+
         return output
     else:
         raise Exception(f'Unknown type {type(input)}.')
@@ -76,4 +73,4 @@ def forward(target_gpus, input):
         if streams is not None:
             synchronize_stream(outputs, target_gpus, streams)
 
-        return tuple(outputs)
+        return tuple(outputs) if isinstance(outputs, list) else (outputs, )

tests/test_parallel.py

@@ -1,11 +1,13 @@
 from unittest.mock import MagicMock, patch
 
+import pytest
 import torch
 import torch.nn as nn
 from torch.nn.parallel import DataParallel, DistributedDataParallel
 
 from mmcv.parallel import (MODULE_WRAPPERS, MMDataParallel,
                            MMDistributedDataParallel, is_module_wrapper)
+from mmcv.parallel._functions import Scatter, get_input_device, scatter
 from mmcv.parallel.distributed_deprecated import \
     MMDistributedDataParallel as DeprecatedMMDDP
 
@@ -64,3 +66,83 @@ def forward(self, *args, **kwargs):
 
     module_wraper = ModuleWrapper(model)
     assert is_module_wrapper(module_wraper)
+
+
+def test_get_input_device():
+    # if the device is CPU, return -1
+    input = torch.zeros([1, 3, 3, 3])
+    assert get_input_device(input) == -1
+    inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
+    assert get_input_device(inputs) == -1
+
+    # if the device is GPU, return the index of device
+    if torch.cuda.is_available():
+        input = torch.zeros([1, 3, 3, 3]).cuda()
+        assert get_input_device(input) == 0
+        inputs = [
+            torch.zeros([1, 3, 3, 3]).cuda(),
+            torch.zeros([1, 4, 4, 4]).cuda()
+        ]
+        assert get_input_device(inputs) == 0
+
+    # input should be a tensor or list of tensor
+    with pytest.raises(Exception):
+        get_input_device(5)
+
+
+def test_scatter():
+    # if the device is CPU, just return the input
+    input = torch.zeros([1, 3, 3, 3])
+    output = scatter(input=input, devices=[-1])
+    assert torch.allclose(input, output)
+
+    inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
+    outputs = scatter(input=inputs, devices=[-1])
+    for input, output in zip(inputs, outputs):
+        assert torch.allclose(input, output)
+
+    # if the device is GPU, copy the input from CPU to GPU
+    if torch.cuda.is_available():
+        input = torch.zeros([1, 3, 3, 3])
+        output = scatter(input=input, devices=[0])
+        assert torch.allclose(input.cuda(), output)
+
+        inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
+        outputs = scatter(input=inputs, devices=[0])
+        for input, output in zip(inputs, outputs):
+            assert torch.allclose(input.cuda(), output)
+
+    # input should be a tensor or list of tensor
+    with pytest.raises(Exception):
+        scatter(5, [-1])
+
+
+def test_Scatter():
+    # if the device is CPU, just return the input
+    target_gpus = [-1]
+    input = torch.zeros([1, 3, 3, 3])
+    outputs = Scatter.forward(target_gpus, input)
+    assert isinstance(outputs, tuple)
+    assert torch.allclose(input, outputs[0])
+
+    target_gpus = [-1]
+    inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
+    outputs = Scatter.forward(target_gpus, inputs)
+    assert isinstance(outputs, tuple)
+    for input, output in zip(inputs, outputs):
+        assert torch.allclose(input, output)
+
+    # if the device is GPU, copy the input from CPU to GPU
+    if torch.cuda.is_available():
+        target_gpus = [0]
+        input = torch.zeros([1, 3, 3, 3])
+        outputs = Scatter.forward(target_gpus, input)
+        assert isinstance(outputs, tuple)
+        assert torch.allclose(input.cuda(), outputs[0])
+
+        target_gpus = [0]
+        inputs = [torch.zeros([1, 3, 3, 3]), torch.zeros([1, 4, 4, 4])]
+        outputs = Scatter.forward(target_gpus, inputs)
+        assert isinstance(outputs, tuple)
+        for input, output in zip(inputs, outputs):
+            assert torch.allclose(input.cuda(), output[0])