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Fix some bugs in oneflow backend implement #82

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Fix some bugs in oneflow backend implement #82

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24 changes: 12 additions & 12 deletions tensorlayerx/backend/ops/oneflow_backend.py
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Original file line number Diff line number Diff line change
Expand Up @@ -179,7 +179,7 @@ def random_uniform(shape, minval=0, maxval=1, dtype=None, seed=None):
if seed is not None:
flow.manual_seed(seed)
else:
flow.manual_seed(flow.random.gen_seed())
flow.manual_seed(flow.initial_seed())

w = flow.randn(shape, dtype=_dtypeDict[dtype])
out = w.uniform_(minval, maxval)
Expand Down Expand Up @@ -211,7 +211,7 @@ def random_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if seed is not None:
flow.manual_seed(seed)
else:
flow.manual_seed(flow.random.gen_seed())
flow.manual_seed(flow.initial_seed())

return flow.normal(shape, mean=mean, std=stddev, dtype=_dtypeDict[dtype])

Expand Down Expand Up @@ -241,7 +241,7 @@ def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
if seed is not None:
flow.manual_seed(seed)
else:
flow.manual_seed(flow.random.gen_seed())
flow.manual_seed(flow.initial_seed())

w = flow.empty(shape, dtype=_dtypeDict[dtype])
out = nn.init.truncated_normal_(w, mean=mean, std=stddev)
Expand Down Expand Up @@ -271,7 +271,7 @@ def he_normal(shape, dtype=None, seed=None):
if seed is not None:
flow.manual_seed(seed)
else:
flow.manual_seed(flow.random.gen_seed())
flow.manual_seed(flow.initial_seed())

w = flow.empty(shape, dtype=_dtypeDict[dtype])
out = nn.init.kaiming_normal_(w)
Expand Down Expand Up @@ -301,7 +301,7 @@ def he_uniform(shape, dtype=None, seed=None):
if seed is not None:
flow.manual_seed(seed)
else:
flow.manual_seed(flow.random.gen_seed())
flow.manual_seed(flow.initial_seed())

w = flow.empty(shape, dtype=_dtypeDict[dtype])
out = nn.init.kaiming_uniform_(w)
Expand Down Expand Up @@ -331,7 +331,7 @@ def xavier_normal(shape, dtype=None, seed=None):
if seed is not None:
flow.manual_seed(seed)
else:
flow.manual_seed(flow.random.gen_seed())
flow.manual_seed(flow.initial_seed())

w = flow.empty(shape, dtype=_dtypeDict[dtype])
out = nn.init.xavier_normal_(w)
Expand Down Expand Up @@ -363,7 +363,7 @@ def xavier_uniform(shape, gain=1.0, dtype=None, seed=None):
if seed is not None:
flow.manual_seed(seed)
else:
flow.manual_seed(flow.random.gen_seed())
flow.manual_seed(flow.initial_seed())

w = flow.empty(shape, dtype=_dtypeDict[dtype])
out = nn.init.xavier_uniform_(w, gain=gain)
Expand Down Expand Up @@ -674,7 +674,7 @@ def reduce_mean(input_tensor, axis=None, keepdims=False):
if axis is not None:
return flow.mean(input_tensor, dim=axis, keepdim=keepdims)
else:
return flow.mean(input_tensor, keepdim=keepdims)
return flow.mean(input_tensor)


class ReduceMax(object):
Expand Down Expand Up @@ -718,7 +718,7 @@ def reduce_max(input_tensor, axis=None, keepdims=False):
if axis is not None:
return flow.max(input_tensor, dim=axis, keepdim=keepdims)
else:
return flow.max(input_tensor, keepdim=keepdims)
return flow.max(input_tensor)


def reduce_min(input_tensor, axis=None, keepdims=False):
Expand Down Expand Up @@ -1582,11 +1582,11 @@ def count_nonzero(x, axis=None, keepdims=None, dtype="int64"):
return convert_to_tensor(non_zero)


def cumprod(x, axis=None, dtype=None, out=None):
def cumprod(x, axis=0, dtype=None, out=None):
return flow.cumprod(x, dim=axis)


def cumsum(x, axis=None, dtype=None, out=None):
def cumsum(x, axis=0, dtype=None, out=None):
return flow.cumsum(x, dim=axis)

def equal(x, y):
Expand Down Expand Up @@ -1892,7 +1892,7 @@ def mask_select(x, mask, axis = 0):
elif axis == 3:
return x[:,:,:, mask]

def eye(n, m=None, dtype=None):
def eye(n, m=None, dtype=flow.float32):
if m is None:
m = n
return flow.eye(n, m, dtype=dtype)
Expand Down