mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from caffe2.python import workspace, scope
|
|
from caffe2.python.model_helper import ModelHelper
|
|
|
|
import numpy as np
|
|
|
|
|
|
def sigmoid(x):
|
|
return 1.0 / (1.0 + np.exp(-x))
|
|
|
|
|
|
def tanh(x):
|
|
return 2.0 * sigmoid(2.0 * x) - 1
|
|
|
|
|
|
def _prepare_rnn(
|
|
t, n, dim_in, create_rnn, outputs_with_grads,
|
|
forget_bias, memory_optim=False,
|
|
forward_only=False, drop_states=False, T=None,
|
|
two_d_initial_states=None, dim_out=None,
|
|
num_states=2,
|
|
**kwargs
|
|
):
|
|
if dim_out is None:
|
|
dim_out = [dim_in]
|
|
print("Dims: ", t, n, dim_in, dim_out)
|
|
|
|
model = ModelHelper(name='external')
|
|
|
|
if two_d_initial_states is None:
|
|
two_d_initial_states = np.random.randint(2)
|
|
|
|
def generate_input_state(n, d):
|
|
if two_d_initial_states:
|
|
return np.random.randn(n, d).astype(np.float32)
|
|
else:
|
|
return np.random.randn(1, n, d).astype(np.float32)
|
|
|
|
states = []
|
|
for layer_id, d in enumerate(dim_out):
|
|
for i in range(num_states):
|
|
state_name = "state_{}/layer_{}".format(i, layer_id)
|
|
states.append(model.net.AddExternalInput(state_name))
|
|
workspace.FeedBlob(
|
|
states[-1], generate_input_state(n, d).astype(np.float32))
|
|
|
|
# Due to convoluted RNN scoping logic we make sure that things
|
|
# work from a namescope
|
|
with scope.NameScope("test_name_scope"):
|
|
input_blob, seq_lengths = model.net.AddScopedExternalInputs(
|
|
'input_blob', 'seq_lengths')
|
|
|
|
outputs = create_rnn(
|
|
model, input_blob, seq_lengths, states,
|
|
dim_in=dim_in, dim_out=dim_out, scope="external/recurrent",
|
|
outputs_with_grads=outputs_with_grads,
|
|
memory_optimization=memory_optim,
|
|
forget_bias=forget_bias,
|
|
forward_only=forward_only,
|
|
drop_states=drop_states,
|
|
static_rnn_unroll_size=T,
|
|
**kwargs
|
|
)
|
|
|
|
workspace.RunNetOnce(model.param_init_net)
|
|
|
|
workspace.FeedBlob(
|
|
seq_lengths,
|
|
np.random.randint(1, t + 1, size=(n,)).astype(np.int32)
|
|
)
|
|
return outputs, model.net, states + [input_blob]
|