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|
| | from caffe2.python import workspace, scope |
| | from caffe2.python.model_helper import ModelHelper |
| |
|
| | import numpy as np |
| |
|
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|
| | def sigmoid(x): |
| | return 1.0 / (1.0 + np.exp(-x)) |
| |
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| |
|
| | def tanh(x): |
| | return 2.0 * sigmoid(2.0 * x) - 1 |
| |
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| |
|
| | 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)) |
| |
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| | |
| | |
| | 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] |
| |
|
