thu-ml · Trinkle23897 · Mar 30, 2021 · Mar 25, 2021 · Mar 25, 2021 · Mar 25, 2021
diff --git a/setup.py b/setup.py
@@ -47,7 +47,7 @@ def get_version() -> str:
     install_requires=[
         "gym>=0.15.4",
         "tqdm",
-        "numpy!=1.16.0,<1.20.0",  # https://github.com/numpy/numpy/issues/12793
+        "numpy>1.16.0",  # https://github.com/numpy/numpy/issues/12793
         "tensorboard",
         "torch>=1.4.0",
         "numba>=0.51.0",

diff --git a/test/base/test_batch.py b/test/base/test_batch.py
@@ -20,9 +20,9 @@ def test_batch():
     assert len(Batch(a=[1, 2, 3], b={'c': {}})) == 3
     assert not Batch(a=[1, 2, 3]).is_empty()
     b = Batch({'a': [4, 4], 'b': [5, 5]}, c=[None, None])
-    assert b.c.dtype == np.object
+    assert b.c.dtype == object
     b = Batch(d=[None], e=[starmap], f=Batch)
-    assert b.d.dtype == b.e.dtype == np.object and b.f == Batch
+    assert b.d.dtype == b.e.dtype == object and b.f == Batch
     b = Batch()
     b.update()
     assert b.is_empty()
@@ -153,10 +153,10 @@ def test_batch():
         batch3[0] = Batch(a={"c": 2, "e": 1})
     # auto convert
     batch4 = Batch(a=np.array(['a', 'b']))
-    assert batch4.a.dtype == np.object  # auto convert to np.object
+    assert batch4.a.dtype == object  # auto convert to object
     batch4.update(a=np.array(['c', 'd']))
     assert list(batch4.a) == ['c', 'd']
-    assert batch4.a.dtype == np.object  # auto convert to np.object
+    assert batch4.a.dtype == object  # auto convert to object
     batch5 = Batch(a=np.array([{'index': 0}]))
     assert isinstance(batch5.a, Batch)
     assert np.allclose(batch5.a.index, [0])
@@ -405,21 +405,23 @@ def test_utils_to_torch_numpy():
     assert data_list_2_torch.shape == (2, 3, 3)
     assert np.allclose(to_numpy(to_torch(data_list_2)), data_list_2)
     data_list_3 = [np.zeros((3, 2)), np.zeros((3, 3))]
-    data_list_3_torch = to_torch(data_list_3)
-    assert isinstance(data_list_3_torch, list)
-    assert all(isinstance(e, torch.Tensor) for e in data_list_3_torch)
-    assert all(starmap(np.allclose,
-                       zip(to_numpy(to_torch(data_list_3)), data_list_3)))
+    data_list_3_torch = [torch.zeros((3, 2)), torch.zeros((3, 3))]
+    with pytest.raises(TypeError):
+        to_torch(data_list_3)
+    with pytest.raises(TypeError):
+        to_numpy(data_list_3_torch)
     data_list_4 = [np.zeros((2, 3)), np.zeros((3, 3))]
-    data_list_4_torch = to_torch(data_list_4)
-    assert isinstance(data_list_4_torch, list)
-    assert all(isinstance(e, torch.Tensor) for e in data_list_4_torch)
-    assert all(starmap(np.allclose,
-                       zip(to_numpy(to_torch(data_list_4)), data_list_4)))
+    data_list_4_torch = [torch.zeros((2, 3)), torch.zeros((3, 3))]
+    with pytest.raises(TypeError):
+        to_torch(data_list_4)
+    with pytest.raises(TypeError):
+        to_numpy(data_list_4_torch)
     data_list_5 = [np.zeros(2), np.zeros((3, 3))]
-    data_list_5_torch = to_torch(data_list_5)
-    assert isinstance(data_list_5_torch, list)
-    assert all(isinstance(e, torch.Tensor) for e in data_list_5_torch)
+    data_list_5_torch = [torch.zeros(2), torch.zeros((3, 3))]
+    with pytest.raises(TypeError):
+        to_torch(data_list_5)
+    with pytest.raises(TypeError):
+        to_numpy(data_list_5_torch)
     data_array = np.random.rand(3, 2, 2)
     data_empty_tensor = to_torch(data_array[[]])
     assert isinstance(data_empty_tensor, torch.Tensor)
@@ -508,10 +510,10 @@ def test_batch_empty():
     assert np.allclose(b5.b.c, [2, 0])
     assert np.allclose(b5.b.d, [1, 0])
     data = Batch(a=[False, True],
-                 b={'c': np.array([2., 'st'], dtype=np.object),
+                 b={'c': np.array([2., 'st'], dtype=object),
                     'd': [1, None],
                     'e': [2., float('nan')]},
-                 c=np.array([1, 3, 4], dtype=np.int),
+                 c=np.array([1, 3, 4], dtype=int),
                  t=torch.tensor([4, 5, 6, 7.]))
     data[-1] = Batch.empty(data[1])
     assert np.allclose(data.c, [1, 3, 0])

diff --git a/test/base/test_buffer.py b/test/base/test_buffer.py
@@ -33,7 +33,7 @@ def test_replaybuffer(size=10, bufsize=20):
                       done=done, obs_next=obs_next, info=info))
         obs = obs_next
         assert len(buf) == min(bufsize, i + 1)
-    assert buf.act.dtype == np.int
+    assert buf.act.dtype == int
     assert buf.act.shape == (bufsize, 1)
     data, indice = buf.sample(bufsize * 2)
     assert (indice < len(buf)).all()
@@ -50,9 +50,9 @@ def test_replaybuffer(size=10, bufsize=20):
     assert b.obs_next[0] == 'str'
     assert np.all(b.obs[1:] == 0)
     assert np.all(b.obs_next[1:] == np.array(None))
-    assert b.info.a[0] == 3 and b.info.a.dtype == np.integer
+    assert b.info.a[0] == 3 and b.info.a.dtype == int
     assert np.all(b.info.a[1:] == 0)
-    assert b.info.b.c[0] == 5.0 and b.info.b.c.dtype == np.inexact
+    assert b.info.b.c[0] == 5.0 and b.info.b.c.dtype == float
     assert np.all(b.info.b.c[1:] == 0.0)
     assert ptr.shape == (1,) and ptr[0] == 0
     assert ep_rew.shape == (1,) and ep_rew[0] == 1
@@ -180,8 +180,8 @@ def test_priortized_replaybuffer(size=32, bufsize=15):
         assert len(buf2) == min(bufsize, 3 * (i + 1))
     # check single buffer's data
     assert buf.info.key.shape == (buf.maxsize,)
-    assert buf.rew.dtype == np.float
-    assert buf.done.dtype == np.bool_
+    assert buf.rew.dtype == float
+    assert buf.done.dtype == bool
     data, indice = buf.sample(len(buf) // 2)
     buf.update_weight(indice, -data.weight / 2)
     assert np.allclose(buf.weight[indice], np.abs(-data.weight / 2) ** buf._alpha)
@@ -273,7 +273,7 @@ def test_segtree():
         index = tree.get_prefix_sum_idx(scalar)
         assert naive[:index].sum() <= scalar <= naive[:index + 1].sum()
     # corner case here
-    naive = np.ones(actual_len, np.int)
+    naive = np.ones(actual_len, int)
     tree[np.arange(actual_len)] = naive
     for scalar in range(actual_len):
         index = tree.get_prefix_sum_idx(scalar * 1.)
@@ -485,7 +485,7 @@ def test_replaybuffermanager():
     buf.set_batch(batch)
     assert np.allclose(buf.buffers[-1].info, [1] * 5)
     assert buf.sample_index(-1).tolist() == []
-    assert np.array([ReplayBuffer(0, ignore_obs_next=True)]).dtype == np.object
+    assert np.array([ReplayBuffer(0, ignore_obs_next=True)]).dtype == object
 
 
 def test_cachedbuffer():

diff --git a/tianshou/data/batch.py b/tianshou/data/batch.py
@@ -7,16 +7,18 @@
 from collections.abc import Collection
 from typing import Any, List, Dict, Union, Iterator, Optional, Iterable, Sequence
 
+IndexType = Union[slice, int, np.ndarray, List[int]]
+
 
 def _is_batch_set(data: Any) -> bool:
     # Batch set is a list/tuple of dict/Batch objects,
-    # or 1-D np.ndarray with np.object type,
+    # or 1-D np.ndarray with object type,
     # where each element is a dict/Batch object
     if isinstance(data, np.ndarray):  # most often case
         # "for e in data" will just unpack the first dimension,
         # but data.tolist() will flatten ndarray of objects
         # so do not use data.tolist()
-        return data.dtype == np.object and all(
+        return data.dtype == object and all(
             isinstance(e, (dict, Batch)) for e in data)
     elif isinstance(data, (list, tuple)):
         if len(data) > 0 and all(isinstance(e, (dict, Batch)) for e in data):
@@ -50,13 +52,13 @@ def _to_array_with_correct_type(v: Any) -> np.ndarray:
     if isinstance(v, np.ndarray) and issubclass(v.dtype.type, (np.bool_, np.number)):
         return v  # most often case
     # convert the value to np.ndarray
-    # convert to np.object data type if neither bool nor number
+    # convert to object data type if neither bool nor number
     # raises an exception if array's elements are tensors themself
     v = np.asanyarray(v)
     if not issubclass(v.dtype.type, (np.bool_, np.number)):
-        v = v.astype(np.object)
-    if v.dtype == np.object:
-        # scalar ndarray with np.object data type is very annoying
+        v = v.astype(object)
+    if v.dtype == object:
+        # scalar ndarray with object data type is very annoying
         # a=np.array([np.array({}, dtype=object), np.array({}, dtype=object)])
         # a is not array([{}, {}], dtype=object), and a[0]={} results in
         # something very strange:
@@ -87,13 +89,11 @@ def _create_value(
     if has_shape:
         shape = (size, *inst.shape) if stack else (size, *inst.shape[1:])
     if isinstance(inst, np.ndarray):
-        if issubclass(inst.dtype.type, (np.bool_, np.number)):
-            target_type = inst.dtype.type
-        else:
-            target_type = np.object
+        target_type = inst.dtype.type if issubclass(
+            inst.dtype.type, (np.bool_, np.number)) else object
         return np.full(
             shape,
-            fill_value=None if target_type == np.object else 0,
+            fill_value=None if target_type == object else 0,
             dtype=target_type
         )
     elif isinstance(inst, torch.Tensor):
@@ -105,8 +105,8 @@ def _create_value(
         return zero_batch
     elif is_scalar:
         return _create_value(np.asarray(inst), size, stack=stack)
-    else:  # fall back to np.object
-        return np.array([None for _ in range(size)])
+    else:  # fall back to object
+        return np.array([None for _ in range(size)], object)
 
 
 def _assert_type_keys(keys: Iterable[str]) -> None:
@@ -187,7 +187,7 @@ def __init__(
                 for k, v in batch_dict.items():
                     self.__dict__[k] = _parse_value(v)
             elif _is_batch_set(batch_dict):
-                self.stack_(batch_dict)
+                self.stack_(batch_dict)  # type: ignore
         if len(kwargs) > 0:
             self.__init__(kwargs, copy=copy)  # type: ignore
 
@@ -223,9 +223,7 @@ def __setstate__(self, state: Dict[str, Any]) -> None:
         """
         self.__init__(**state)  # type: ignore
 
-    def __getitem__(
-        self, index: Union[str, slice, int, np.integer, np.ndarray, List[int]]
-    ) -> Any:
+    def __getitem__(self, index: Union[str, IndexType]) -> Any:
         """Return self[index]."""
         if isinstance(index, str):
             return self.__dict__[index]
@@ -241,11 +239,7 @@ def __getitem__(
         else:
             raise IndexError("Cannot access item from empty Batch object.")
 
-    def __setitem__(
-        self,
-        index: Union[str, slice, int, np.integer, np.ndarray, List[int]],
-        value: Any,
-    ) -> None:
+    def __setitem__(self, index: Union[str, IndexType], value: Any) -> None:
         """Assign value to self[index]."""
         value = _parse_value(value)
         if isinstance(index, str):
@@ -530,8 +524,7 @@ def stack_(self, batches: Sequence[Union[dict, "Batch"]], axis: int = 0) -> None
             elif all(isinstance(e, (Batch, dict)) for e in v):  # third often
                 self.__dict__[k] = Batch.stack(v, axis)
             else:  # most often case is np.ndarray
-                v = np.stack(v, axis)
-                self.__dict__[k] = _to_array_with_correct_type(v)
+                self.__dict__[k] = _to_array_with_correct_type(np.stack(v, axis))
         # all the keys
         keys_total = set.union(*[set(b.keys()) for b in batches])
         # keys that are reserved in all batches
@@ -587,9 +580,7 @@ def stack(batches: Sequence[Union[dict, "Batch"]], axis: int = 0) -> "Batch":
         batch.stack_(batches, axis)
         return batch
 
-    def empty_(
-        self, index: Union[str, slice, int, np.integer, np.ndarray, List[int]] = None
-    ) -> "Batch":
+    def empty_(self, index: Optional[Union[slice, IndexType]] = None) -> "Batch":
         """Return an empty Batch object with 0 or None filled.
 
         If "index" is specified, it will only reset the specific indexed-data.
@@ -620,7 +611,7 @@ def empty_(
             elif v is None:
                 continue
             elif isinstance(v, np.ndarray):
-                if v.dtype == np.object:
+                if v.dtype == object:
                     self.__dict__[k][index] = None
                 else:
                     self.__dict__[k][index] = 0
@@ -636,10 +627,7 @@ def empty_(
         return self
 
     @staticmethod
-    def empty(
-        batch: "Batch",
-        index: Union[str, slice, int, np.integer, np.ndarray, List[int]] = None,
-    ) -> "Batch":
+    def empty(batch: "Batch", index: Optional[IndexType] = None) -> "Batch":
         """Return an empty Batch object with 0 or None filled.
 
         The shape is the same as the given Batch.