en tapant :
biasRestaurant = to_np(m.ib(V(topRestIdx))) #converting the torch embedding to numpy matrix
j’aurai cette erreur :
AttributeError Traceback (most recent call last)
in ()
----> 1 biasRestaurant = to_np(m.ib(V(topRestIdx))) #converting the torch embedding to numpy matrix
2 frames
/usr/local/lib/python3.6/dist-packages/fastai/core.py in to_gpu(x, *args, **kwargs)
28 def to_gpu(x, *args, **kwargs):
29 if torch.cuda.is_available():
—> 30 return x.cuda(*args, **kwargs)
31 else:
32 return x
voici le code core.py
AttributeError: ‘numpy.ndarray’ object has no attribute ‘cuda’
from .imports import *
from .torch_imports import *
def sum_geom(a,r,n):
return a*n if r==1 else math.ceil(a*(1-r**n)/(1-r))
conv_dict = {np.dtype(‘int8’): torch.LongTensor, np.dtype(‘int16’): torch.LongTensor,
np.dtype('int32'): torch.LongTensor, np.dtype('int64'): torch.LongTensor,
np.dtype('float32'): torch.FloatTensor, np.dtype('float64'): torch.FloatTensor}
def T(a):
a = np.array(a)
if a.dtype in (np.int8, np.int16, np.int32, np.int64):
return torch.LongTensor(a.astype(np.int64))
if a.dtype in (np.float32, np.float64):
return torch.FloatTensor(a.astype(np.float32))
raise NotImplementedError
def V_(x): return to_gpu(x, async=True) if isinstance(x, Variable) else Variable(to_gpu(x, async=True))
def V(x): return [V_(o) for o in x] if isinstance(x,list) else V_(x)
def VV_(x): return to_gpu(x, async=True) if isinstance(x, Variable) else Variable(to_gpu(x, async=True), volatile=True)
def VV(x): return [VV_(o) for o in x] if isinstance(x,list) else VV_(x)
def to_np(v):
if isinstance(v, Variable): v=v.data
return v.cpu().Numpy ( )
def to_gpu(x, *args, **kwargs):
if torch.cuda.is_available():
return x.cuda(*args, **kwargs)
else:
return x
def noop(*args, **kwargs): return
def split_by_idxs(seq, idxs):
last, sl = 0, len(seq)
for idx in idxs:
yield seq[last:idx]
last = idx
yield seq[last:]
def trainable_params_(m):
return [p for p in m.parameters() if p.requires_grad]
def chain_params§:
if isinstance(p, (list,tuple)):
return list(chain(*[trainable_params_(o) for o in p]))
return trainable_params_(p)
def set_trainable_attr(m,b):
m.trainable=b
for p in m.parameters(): p.requires_grad=b
def apply_leaf(m, f):
c = children(m)
if isinstance(m, nn.Module): f(m)
if len(c)>0:
for l in c: apply_leaf(l,f)
def set_trainable(l, b):
apply_leaf(l, lambda m: set_trainable_attr(m,b))
def SGD_Momentum(momentum):
return lambda *args, **kwargs: optim.SGD(*args, momentum=momentum, **kwargs)
def one_hot(a,c): return np.eye©[a]
ok i’ll try
thank you verry much sir 