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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/49980 From ``` ./python/libcst/libcst codemod remove_unused_imports.RemoveUnusedImportsWithGlean --no-format caffe2/ ``` Test Plan: Standard sandcastle tests Reviewed By: xush6528 Differential Revision: D25727359 fbshipit-source-id: c4f60005b10546423dc093d31d46deb418352286
134 lines
5.1 KiB
Python
134 lines
5.1 KiB
Python
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from hypothesis import given, settings
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import hypothesis.strategies as st
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import numpy as np
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import unittest
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class TestSpatialBN(hu.HypothesisTestCase):
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@given(size=st.integers(7, 10),
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input_channels=st.integers(7, 10),
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batch_size=st.integers(1, 3),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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inplace=st.sampled_from([True, False]),
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**mu.gcs)
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@settings(deadline=1000)
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def test_spatialbn_test_mode(
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self, size, input_channels, batch_size, seed, order, epsilon,
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inplace, gc, dc):
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op = core.CreateOperator(
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"SpatialBN",
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["X", "scale", "bias", "mean", "var"],
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["X" if inplace else "Y"],
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order=order,
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is_test=True,
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epsilon=epsilon
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)
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def reference_spatialbn_test(X, scale, bias, mean, var):
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if order == "NCHW":
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scale = scale[np.newaxis, :, np.newaxis, np.newaxis]
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bias = bias[np.newaxis, :, np.newaxis, np.newaxis]
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mean = mean[np.newaxis, :, np.newaxis, np.newaxis]
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var = var[np.newaxis, :, np.newaxis, np.newaxis]
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return ((X - mean) / np.sqrt(var + epsilon) * scale + bias,)
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np.random.seed(1701)
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scale = np.random.rand(input_channels).astype(np.float32) + 0.5
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bias = np.random.rand(input_channels).astype(np.float32) - 0.5
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mean = np.random.randn(input_channels).astype(np.float32)
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var = np.random.rand(input_channels).astype(np.float32) + 0.5
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X = np.random.rand(
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batch_size, input_channels, size, size).astype(np.float32) - 0.5
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if order == "NHWC":
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X = X.swapaxes(1, 2).swapaxes(2, 3)
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self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0])
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@given(size=st.integers(7, 10),
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input_channels=st.integers(7, 10),
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batch_size=st.integers(1, 3),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW"]),
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epsilon=st.floats(1e-5, 1e-2),
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inplace=st.sampled_from([True, False]),
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**mu.gcs)
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def test_spatialbn_train_mode(
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self, size, input_channels, batch_size, seed, order, epsilon,
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inplace, gc, dc):
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print("dc0: {}, dc1: {}".format(dc[0], dc[1]))
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op = core.CreateOperator(
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"SpatialBN",
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["X", "scale", "bias", "running_mean", "running_var"],
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["X" if inplace else "Y",
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"running_mean", "running_var", "saved_mean", "saved_var"],
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order=order,
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is_test=False,
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epsilon=epsilon,
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)
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np.random.seed(1701)
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scale = np.random.rand(input_channels).astype(np.float32) + 0.5
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bias = np.random.rand(input_channels).astype(np.float32) - 0.5
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running_mean = np.random.randn(input_channels).astype(np.float32)
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running_var = np.random.rand(input_channels).astype(np.float32) + 0.5
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X = np.random.rand(
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batch_size, input_channels, size, size).astype(np.float32) - 0.5
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if order == "NHWC":
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X = X.swapaxes(1, 2).swapaxes(2, 3)
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# TODO: It looks like IDEEP spatial_bn op outputs save_var (output[4])
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# as the reciprocal of CPU op's output. Need to check back and add
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# output[4] for comparison
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self.assertDeviceChecks(dc, op, [X, scale, bias, running_mean, running_var],
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[0, 1, 2, 3])
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@given(size=st.integers(7, 10),
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input_channels=st.integers(1, 10),
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batch_size=st.integers(1, 3),
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seed=st.integers(0, 65535),
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order=st.sampled_from(["NCHW"]),
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epsilon=st.floats(min_value=1e-5, max_value=1e-2),
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**mu.gcs)
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@settings(deadline=None, max_examples=50)
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def test_spatialbn_train_mode_gradient_check(
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self, size, input_channels, batch_size, seed, order, epsilon,
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gc, dc):
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op = core.CreateOperator(
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"SpatialBN",
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["X", "scale", "bias", "mean", "var"],
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["Y", "mean", "var", "saved_mean", "saved_var"],
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order=order,
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is_test=False,
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epsilon=epsilon,
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)
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np.random.seed(seed)
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scale = np.random.rand(input_channels).astype(np.float32) + 0.5
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bias = np.random.rand(input_channels).astype(np.float32) - 0.5
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mean = np.random.randn(input_channels).astype(np.float32)
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var = np.random.rand(input_channels).astype(np.float32) + 0.5
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X = np.random.rand(
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batch_size, input_channels, size, size).astype(np.float32) - 0.5
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if order == "NHWC":
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X = X.swapaxes(1, 2).swapaxes(2, 3)
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for input_to_check in [0, 1, 2]: # dX, dScale, dBias
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self.assertGradientChecks(gc, op, [X, scale, bias, mean, var],
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input_to_check, [0])
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if __name__ == "__main__":
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unittest.main()
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