def __init__(self, dataset, num_epochs=-1, output_numpy=False, do_copy=True):
self._col_names = None
# create a copy of tree and work on it.
self.ori_dataset = dataset
self.ir_tree, self.dataset = dataset.create_ir_tree()
self._runtime_context = cde.PythonRuntimeContext()
self._runtime_context.Init()
consumer = cde.PythonIteratorConsumer(num_epochs)
consumer.Init(self.ir_tree)
self._runtime_context.AssignConsumer(consumer)
self._iterator = self._runtime_context.GetConsumer()
self._transform_tensor = lambda t: t.as_array()
if not output_numpy:
if do_copy:
self._transform_tensor = lambda t: Tensor(t.as_array())
else:
self._transform_tensor = lambda t: Tensor.from_numpy(t.as_array())
self._index = 0
# todo remove next when ContextManager is done
ITERATORS_LIST.append(weakref.ref(self))
_unset_iterator_cleanup()
# x = Tensor.from_numpy(np.random.random([Batch,Seq,Heads*Dim_head]).astype(np.float32))
# mask = Tensor.from_numpy(np.ones(x.shape).astype(np.float32))
# model = Performer(depth =2, dim=Heads*Dim_head, heads=Heads, causal=True)
# out = model(x,mask)
# print(out)
# print(out.shape)
# print(model)
# test for PerformerLM
np.random.seed(777)
Batch, Seq, Dim, Heads = 2, 10, 8, 2
#[2,10]
input_ids = Tensor.from_numpy(
np.random.random([
Batch,
Seq,
]).astype(np.int))
mask_np = np.ones(input_ids.shape).astype(np.float32)
mask_np[0, 2:] = 0.
#[2,10,8]
x = Tensor.from_numpy(
np.random.random([Batch, Seq, Dim]).astype(np.float32))
mask = Tensor.from_numpy(mask_np)
# model = Performer(depth =2, dim=Dim, heads=Heads, causal=True)
# print(model)
# out = model(x,mask)
# model = SelfAttention(dim=Dim, heads=Heads, dim_head=Dim//Heads, causal = False, nb_features = None, qr_uniform_q = False, dropout = 0.9)
# out = model(x,mask)
model = PerformerLM(num_tokens=100,
max_seq_len=Seq,
