mirror of
https://github.com/zebrajr/pytorch.git
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53 lines
2.4 KiB
Python
53 lines
2.4 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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from caffe2.python import workspace, cnn, memonger
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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from hypothesis import given
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class MemongerTest(hu.HypothesisTestCase):
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@given(input_dim=st.integers(min_value=1, max_value=10),
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output_dim=st.integers(min_value=1, max_value=10),
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batch_size=st.integers(min_value=1, max_value=10),
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do=st.sampled_from(hu.device_options))
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def test_simple_memonger(self, input_dim, output_dim, batch_size, do):
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m = cnn.CNNModelHelper()
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fc1 = m.FC("data", "fc1", dim_in=input_dim, dim_out=output_dim)
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fc2 = m.FC(fc1, "fc2", dim_in=output_dim, dim_out=output_dim)
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fc3 = m.FC(fc2, "fc3", dim_in=output_dim, dim_out=output_dim)
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fc3.Relu([], fc3)\
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.Softmax([], "pred") \
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.LabelCrossEntropy(["label"], ["xent"]) \
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.AveragedLoss([], "loss")
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input_to_grad = m.AddGradientOperators(["loss"])
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m.net.Proto().device_option.CopyFrom(do)
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m.param_init_net.Proto().device_option.CopyFrom(do)
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static_blobs = \
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[o for op in m.param_init_net.Proto().op for o in op.output] + \
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["data", "label", "loss", input_to_grad["fc1_w"]]
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optimization = memonger.optimize_interference(m.Proto(), static_blobs)
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data = np.random.randn(batch_size, input_dim).astype(np.float32)
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label = np.random.randint(
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low=0, high=output_dim, size=(batch_size,)).astype(np.int32)
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workspace.RunNetOnce(m.param_init_net)
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workspace.FeedBlob("data", data, device_option=do)
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workspace.FeedBlob("label", label, device_option=do)
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workspace.RunNetOnce(m.net)
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loss = workspace.FetchBlob("loss")
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grad = workspace.FetchBlob(str(input_to_grad["fc1_w"]))
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workspace.RunNetOnce(optimization.net)
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optimized_loss = workspace.FetchBlob("loss")
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optimized_grad = workspace.FetchBlob(str(input_to_grad["fc1_w"]))
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np.testing.assert_almost_equal(loss, optimized_loss)
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np.testing.assert_almost_equal(grad, optimized_grad)
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stats = memonger.compute_statistics(optimization.assignments)
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self.assertLess(stats.optimized_nbytes, stats.baseline_nbytes)
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