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https://github.com/zebrajr/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
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from caffe2.python import workspace, scope
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from caffe2.python.model_helper import ModelHelper
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import numpy as np
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def sigmoid(x):
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return 1.0 / (1.0 + np.exp(-x))
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def tanh(x):
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return 2.0 * sigmoid(2.0 * x) - 1
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def _prepare_rnn(
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t, n, dim_in, create_rnn, outputs_with_grads,
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forget_bias, memory_optim=False,
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forward_only=False, drop_states=False, T=None,
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two_d_initial_states=None, dim_out=None,
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num_states=2,
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**kwargs
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):
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if dim_out is None:
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dim_out = [dim_in]
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print("Dims: ", t, n, dim_in, dim_out)
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model = ModelHelper(name='external')
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if two_d_initial_states is None:
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two_d_initial_states = np.random.randint(2)
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def generate_input_state(n, d):
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if two_d_initial_states:
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return np.random.randn(n, d).astype(np.float32)
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else:
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return np.random.randn(1, n, d).astype(np.float32)
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states = []
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for layer_id, d in enumerate(dim_out):
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for i in range(num_states):
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state_name = "state_{}/layer_{}".format(i, layer_id)
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states.append(model.net.AddExternalInput(state_name))
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workspace.FeedBlob(
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states[-1], generate_input_state(n, d).astype(np.float32))
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# Due to convoluted RNN scoping logic we make sure that things
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# work from a namescope
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with scope.NameScope("test_name_scope"):
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input_blob, seq_lengths = model.net.AddScopedExternalInputs(
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'input_blob', 'seq_lengths')
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outputs = create_rnn(
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model, input_blob, seq_lengths, states,
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dim_in=dim_in, dim_out=dim_out, scope="external/recurrent",
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outputs_with_grads=outputs_with_grads,
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memory_optimization=memory_optim,
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forget_bias=forget_bias,
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forward_only=forward_only,
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drop_states=drop_states,
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static_rnn_unroll_size=T,
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**kwargs
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)
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workspace.RunNetOnce(model.param_init_net)
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workspace.FeedBlob(
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seq_lengths,
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np.random.randint(1, t + 1, size=(n,)).astype(np.int32)
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)
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return outputs, model.net, states + [input_blob]
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