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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
119 lines
4.1 KiB
Python
119 lines
4.1 KiB
Python
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import click.testing
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import numpy as np
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import os
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import tempfile
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import unittest
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from caffe2.python import brew, core, model_helper
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import caffe2.contrib.tensorboard.tensorboard as tb
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import caffe2.contrib.tensorboard.tensorboard_exporter as tb_exporter
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try:
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# tensorboard>=1.14.0
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from tensorboard.compat.proto.graph_pb2 import GraphDef
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except ImportError:
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from tensorflow import GraphDef
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def load_events(filename):
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try:
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# tensorboard>=1.14.0
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from tensorboard.backend.event_processing import event_file_loader
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loader = event_file_loader.EventFileLoader(filename)
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return list(loader.Load())
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except ImportError:
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import tensorflow as tf
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return list(tf.train.summary_iterator(filename))
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class TensorboardTest(unittest.TestCase):
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def test_events(self):
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runner = click.testing.CliRunner()
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c2_dir = tempfile.mkdtemp()
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np.random.seed(1701)
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n_iters = 2
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blobs = ["w", "b"]
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data = np.random.randn(len(blobs), n_iters, 10)
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for i, blob in enumerate(blobs):
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with open(os.path.join(c2_dir, blob), "w") as f:
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for row in data[i]:
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stats = [row.min(), row.max(), row.mean(), row.std()]
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f.write(" ".join(str(s) for s in stats) + "\n")
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# Test error handling path
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with open(os.path.join(c2_dir, "not-a-summary"), "w") as f:
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f.write("not-a-summary")
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tf_dir = tempfile.mkdtemp()
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result = runner.invoke(
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tb.cli,
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["tensorboard-events", "--c2-dir", c2_dir, "--tf-dir", tf_dir])
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self.assertEqual(result.exit_code, 0)
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entries = list(os.walk(tf_dir))
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self.assertEqual(len(entries), 1)
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((d, _, (fname,)),) = entries
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self.assertEqual(tf_dir, d)
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events = load_events(os.path.join(tf_dir, fname))
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self.assertEqual(len(events), n_iters + 1)
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events = events[1:]
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self.maxDiff = None
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self.assertEqual(len(events), 2)
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def test_tensorboard_graphs(self):
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model = model_helper.ModelHelper(name="overfeat")
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data, label = brew.image_input(
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model, ["db"], ["data", "label"], is_test=0
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)
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with core.NameScope("conv1"):
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conv1 = brew.conv(model, data, "conv1", 3, 96, 11, stride=4)
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relu1 = brew.relu(model, conv1, conv1)
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pool1 = brew.max_pool(model, relu1, "pool1", kernel=2, stride=2)
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with core.NameScope("classifier"):
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fc = brew.fc(model, pool1, "fc", 4096, 1000)
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pred = brew.softmax(model, fc, "pred")
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xent = model.LabelCrossEntropy([pred, label], "xent")
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loss = model.AveragedLoss(xent, "loss")
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model.AddGradientOperators([loss], skip=1)
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c2_dir = tempfile.mkdtemp()
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tf_dir = tempfile.mkdtemp()
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with open(os.path.join(c2_dir, "init"), "w") as f:
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f.write(str(model.param_init_net.Proto()))
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with open(os.path.join(c2_dir, "net"), "w") as f:
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f.write(str(model.net.Proto()))
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runner = click.testing.CliRunner()
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result = runner.invoke(
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tb.cli,
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["tensorboard-graphs",
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"--c2-netdef", os.path.join(c2_dir, "init"),
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"--c2-netdef", os.path.join(c2_dir, "net"),
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"--tf-dir", tf_dir])
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self.assertEqual(result.exit_code, 0)
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entries = list(os.walk(tf_dir))
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self.assertEqual(len(entries), 1)
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((d, _, (fname,)),) = entries
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self.assertEqual(tf_dir, d)
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events = load_events(os.path.join(tf_dir, fname))
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self.assertEqual(len(events), 3)
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events = events[1:]
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nets = [model.param_init_net, model.net]
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for i, (event, net) in enumerate(zip(events, nets), start=1):
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self.assertEqual(event.step, i)
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self.assertEqual(event.wall_time, i)
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g = GraphDef()
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g.ParseFromString(event.graph_def)
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self.assertMultiLineEqual(
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str(g),
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str(tb_exporter.nets_to_graph_def([net])))
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if __name__ == "__main__":
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unittest.main()
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