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https://github.com/saymrwulf/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
132 lines
4.7 KiB
Python
132 lines
4.7 KiB
Python
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from caffe2.python.test_util import TestCase
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from caffe2.python import workspace, brew
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from caffe2.python.model_helper import ModelHelper
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from caffe2.python.predictor import mobile_exporter
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import numpy as np
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class TestMobileExporter(TestCase):
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def test_mobile_exporter(self):
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model = ModelHelper(name="mobile_exporter_test_model")
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# Test LeNet
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brew.conv(model, 'data', 'conv1', dim_in=1, dim_out=20, kernel=5)
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brew.max_pool(model, 'conv1', 'pool1', kernel=2, stride=2)
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brew.conv(model, 'pool1', 'conv2', dim_in=20, dim_out=50, kernel=5)
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brew.max_pool(model, 'conv2', 'pool2', kernel=2, stride=2)
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brew.fc(model, 'pool2', 'fc3', dim_in=50 * 4 * 4, dim_out=500)
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brew.relu(model, 'fc3', 'fc3')
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brew.fc(model, 'fc3', 'pred', 500, 10)
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brew.softmax(model, 'pred', 'out')
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# Create our mobile exportable networks
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workspace.RunNetOnce(model.param_init_net)
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init_net, predict_net = mobile_exporter.Export(
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workspace, model.net, model.params
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)
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# Populate the workspace with data
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np_data = np.random.rand(1, 1, 28, 28).astype(np.float32)
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workspace.FeedBlob("data", np_data)
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workspace.CreateNet(model.net)
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workspace.RunNet(model.net)
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ref_out = workspace.FetchBlob("out")
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# Clear the workspace
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workspace.ResetWorkspace()
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# Populate the workspace with data
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workspace.RunNetOnce(init_net)
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# Fake "data" is populated by init_net, we have to replace it
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workspace.FeedBlob("data", np_data)
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# Overwrite the old net
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workspace.CreateNet(predict_net, True)
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workspace.RunNet(predict_net.name)
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manual_run_out = workspace.FetchBlob("out")
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np.testing.assert_allclose(
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ref_out, manual_run_out, atol=1e-10, rtol=1e-10
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)
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# Clear the workspace
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workspace.ResetWorkspace()
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# Predictor interface test (simulates writing to disk)
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predictor = workspace.Predictor(
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init_net.SerializeToString(), predict_net.SerializeToString()
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)
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# Output is a vector of outputs but we only care about the first and only result
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predictor_out = predictor.run([np_data])
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assert len(predictor_out) == 1
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predictor_out = predictor_out[0]
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np.testing.assert_allclose(
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ref_out, predictor_out, atol=1e-10, rtol=1e-10
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)
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def test_mobile_exporter_datatypes(self):
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model = ModelHelper(name="mobile_exporter_test_model")
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model.Copy("data_int", "out")
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model.params.append("data_int")
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model.Copy("data_obj", "out_obj")
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model.params.append("data_obj")
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# Create our mobile exportable networks
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workspace.RunNetOnce(model.param_init_net)
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np_data_int = np.random.randint(100, size=(1, 1, 28, 28), dtype=np.int32)
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workspace.FeedBlob("data_int", np_data_int)
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np_data_obj = np.array(['aa', 'bb']).astype(np.dtype('O'))
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workspace.FeedBlob("data_obj", np_data_obj)
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init_net, predict_net = mobile_exporter.Export(
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workspace, model.net, model.params
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)
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workspace.CreateNet(model.net)
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workspace.RunNet(model.net)
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ref_out = workspace.FetchBlob("out")
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ref_out_obj = workspace.FetchBlob("out_obj")
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# Clear the workspace
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workspace.ResetWorkspace()
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# Populate the workspace with data
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workspace.RunNetOnce(init_net)
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# Overwrite the old net
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workspace.CreateNet(predict_net, True)
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workspace.RunNet(predict_net.name)
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manual_run_out = workspace.FetchBlob("out")
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manual_run_out_obj = workspace.FetchBlob("out_obj")
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np.testing.assert_allclose(
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ref_out, manual_run_out, atol=1e-10, rtol=1e-10
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)
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np.testing.assert_equal(ref_out_obj, manual_run_out_obj)
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# Clear the workspace
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workspace.ResetWorkspace()
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# Predictor interface test (simulates writing to disk)
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predictor = workspace.Predictor(
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init_net.SerializeToString(), predict_net.SerializeToString()
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)
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# Output is a vector of outputs.
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predictor_out = predictor.run([])
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assert len(predictor_out) == 2
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predictor_out_int = predictor_out[1]
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predictor_out_obj = predictor_out[0]
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# The order in predictor_out is non-deterministic. Use type of the entry
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# to figure out what to compare it to.
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if isinstance(predictor_out[1][0], bytes):
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predictor_out_int = predictor_out[0]
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predictor_out_obj = predictor_out[1]
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np.testing.assert_allclose(
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ref_out, predictor_out_int, atol=1e-10, rtol=1e-10
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)
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np.testing.assert_equal(ref_out_obj, predictor_out_obj)
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