mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
131 lines
4.3 KiB
Python
131 lines
4.3 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 hypothesis import given
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import hypothesis.strategies as st
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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class TestActivations(hu.HypothesisTestCase):
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@given(X=hu.tensor(),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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inplace=st.booleans(),
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**hu.gcs)
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def test_elu(self, X, alpha, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def elu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = alpha * (np.exp(Y[neg_indices]) - 1)
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return (Y,)
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op = core.CreateOperator(
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"Elu",
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["X"], ["Y" if not inplace else "X"],
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alpha=alpha)
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self.assertReferenceChecks(gc, op, [X], elu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@given(X=hu.tensor(min_dim=4, max_dim=4),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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inplace=st.booleans(),
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shared=st.booleans(),
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order=st.sampled_from(["NCHW", "NHWC"]),
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seed=st.sampled_from([20, 100]),
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**hu.gcs)
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def test_prelu(self, X, alpha, inplace, shared, order, seed, gc, dc):
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np.random.seed(seed)
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W = np.random.randn(
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X.shape[1] if order == "NCHW" else X.shape[3]).astype(np.float32)
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if shared:
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W = np.random.randn(1).astype(np.float32)
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def prelu_ref(X, W):
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Y = X.copy()
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W = W.reshape(1, -1, 1, 1) if order == "NCHW" \
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else W.reshape(1, 1, 1, -1)
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assert len(X.shape) == 4
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neg_indices = X <= 0
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assert len(neg_indices.shape) == 4
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assert X.shape == neg_indices.shape
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Y[neg_indices] = (Y * W)[neg_indices]
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return (Y,)
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op = core.CreateOperator(
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"PRelu", ["X", "W"], ["Y" if not inplace else "X"],
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alpha=alpha, order=order)
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self.assertReferenceChecks(gc, op, [X, W], prelu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X, W], [0])
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if not inplace:
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X, W], 0, [0], stepsize=1e-2)
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# Gradient check wrt W
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self.assertGradientChecks(gc, op, [X, W], 1, [0], stepsize=1e-2)
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@given(X=hu.tensor(),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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inplace=st.booleans(),
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**hu.gcs)
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def test_leaky_relu(self, X, alpha, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def leaky_relu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = Y[neg_indices] * alpha
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return (Y,)
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op = core.CreateOperator(
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"LeakyRelu",
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["X"], ["Y" if not inplace else "X"],
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alpha=alpha)
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self.assertReferenceChecks(gc, op, [X], leaky_relu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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@given(X=hu.tensor(),
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inplace=st.booleans(),
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**hu.gcs)
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def test_leaky_relu_default(self, X, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def leaky_relu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = Y[neg_indices] * 0.01
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return (Y,)
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op = core.CreateOperator(
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"LeakyRelu",
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["X"], ["Y" if not inplace else "X"])
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self.assertReferenceChecks(gc, op, [X], leaky_relu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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if __name__ == "__main__":
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import unittest
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unittest.main()
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