mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
5eb836880d
Summary:
Needed by oss.
This is done by running the following line:
find . -name "*_test.py" -exec sed -i '$ a \\nif __name__ == "__main__":\n import unittest\n unittest.main()' {} \;
Reviewed By: ajtulloch
Differential Revision: D4223848
fbshipit-source-id: ef4696e9701d45962134841165c53e76a2e19233
316 lines
11 KiB
Python
316 lines
11 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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from caffe2.python import core, workspace
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from hypothesis import given
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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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import numpy as np
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import unittest
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class TestSoftmaxOps(hu.HypothesisTestCase):
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@given(n=st.integers(2, 10), D=st.integers(4, 16), **hu.gcs)
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def test_softmax(self, n, D, gc, dc):
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# n = number of examples, D = |labels|
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# Initialize X and add 1e-2 for numerical stability
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X = np.random.rand(n, D).astype(np.float32)
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X = X + 1e-2
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# Reference implementation of cross entropy with soft labels
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def label_softmax(X):
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probs = np.zeros((n, D))
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rowmax = np.zeros(n)
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for i in range(n):
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rowmax[i] = max(X[i, ])
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# We need to subtract the max to avoid numerical issues
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probs[i] = X[i] - rowmax[i]
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exps = np.exp(probs[i, ])
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norm = sum(exps)
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probs[i, ] = exps / norm
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return [probs]
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op = core.CreateOperator(
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"Softmax",
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["X"],
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["probs"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=label_softmax,
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)
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self.assertGradientChecks(
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gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2)
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@given(axis=st.integers(min_value=1, max_value=4), **hu.gcs)
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def test_softmax_axis(self, axis, gc, dc):
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np.random.seed(1)
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X = np.random.randn(1, 2, 3, 2, 1).astype(np.float32)
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X = X + 1e-2
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def prod(xs):
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p = 1
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for x in xs:
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p *= x
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return p
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N = prod(list(X.shape)[:axis])
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D = prod(list(X.shape)[axis:])
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# Reference implementation of cross entropy with soft labels
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def label_softmax(X):
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X_ = X.reshape(N, D)
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probs = np.zeros((N, D))
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rowmax = np.zeros(N)
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for i in range(N):
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rowmax[i] = max(X_[i, ])
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# We need to subtract the max to avoid numerical issues
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probs[i] = X_[i] - rowmax[i]
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exps = np.exp(probs[i, ])
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norm = sum(exps)
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probs[i, ] = exps / norm
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return [probs.reshape(*X.shape)]
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op = core.CreateOperator(
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"Softmax",
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["X"],
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["probs"],
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axis=axis,
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=label_softmax,
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)
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self.assertGradientChecks(
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gc, op, [X], 0, [0], stepsize=1e-4, threshold=1e-2)
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@given(n=st.integers(2, 10), D=st.integers(4, 16), **hu.gcs)
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def test_softmax_with_loss(self, n, D, gc, dc):
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# n = number of examples, D = |labels|
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# Initialize X and add 1e-2 for numerical stability
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X = np.random.rand(n, D).astype(np.float32)
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X = X + 1e-2
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# Initialize label
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label = (np.random.rand(n) * D).astype(np.int32)
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# Reference implementation of cross entropy with soft labels
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def label_softmax_crossent(X, label):
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probs = np.zeros((n, D))
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rowmax = np.zeros(n)
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for i in range(n):
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rowmax[i] = max(X[i, ])
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# We need to subtract the max to avoid numerical issues
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probs[i] = X[i] - rowmax[i]
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exps = np.exp(probs[i, ])
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norm = sum(exps)
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probs[i, ] = exps / norm
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label_xent = [-np.log(max(probs[i][label[i]], 1e-20))
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for i in range(n)]
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avgloss = np.sum(label_xent) / float(n)
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return (probs, avgloss)
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op = core.CreateOperator(
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"SoftmaxWithLoss",
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["X", "label"],
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["probs", "avgloss"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X, label],
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reference=label_softmax_crossent,
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)
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self.assertGradientChecks(
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gc, op, [X, label], 0, [1], stepsize=1e-4, threshold=1e-2)
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@given(n=st.integers(2, 10), D=st.integers(4, 16), **hu.gcs)
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def test_softmax_with_loss_weighted(self, n, D, gc, dc):
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# n = number of examples, D = |labels|
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# Initialize X and add 1e-2 for numerical stability
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X = np.random.rand(n, D).astype(np.float32)
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X = X + 1e-2
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# Initialize label
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label = (np.random.rand(n) * D).astype(np.int32)
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# Init weights (weight by sample)
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weights = np.random.rand(n).astype(np.float32)
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# Reference implementation of cross entropy with soft labels
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def label_softmax_crossent_weighted(X, label, weights):
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probs = np.zeros((n, D))
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rowmax = np.zeros(n)
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for i in range(n):
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rowmax[i] = max(X[i, ])
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# We need to subtract the max to avoid numerical issues
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probs[i] = X[i] - rowmax[i]
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exps = np.exp(probs[i, ])
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norm = sum(exps)
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probs[i, ] = exps / norm
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label_xent = [-weights[i] * np.log(max(probs[i][label[i]], 1e-20))
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for i in range(n)]
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avgloss = np.sum(label_xent) / sum(weights)
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return (probs, avgloss)
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op = core.CreateOperator(
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"SoftmaxWithLoss",
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["X", "label", "weights"],
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["probs", "avgloss"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X, label, weights],
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reference=label_softmax_crossent_weighted,
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)
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self.assertGradientChecks(
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gc, op, [X, label, weights], 0, [1], stepsize=1e-4, threshold=1e-2)
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@given(n=st.integers(2, 5), D=st.integers(2, 4),
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weighted=st.booleans(), **hu.gcs)
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def test_spatial_softmax_with_loss(self, n, D, weighted, gc, dc):
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# n = number of examples, D = |labels|
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# Initialize X and add 1e-2 for numerical stability
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W = 18
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H = 12
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X = np.random.rand(n, D, H, W).astype(np.float32)
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X = X + 1e-2
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weighted = True
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weights = None
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if weighted:
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weights = np.random.rand(n, H, W).astype(np.float32)
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# Initialize label. Some of the labels are (-1), i.e "DONT CARE"
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label = (np.random.rand(n, H, W) * (D + 1)).astype(np.int32) - 1
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def label_softmax_crossent_spatial(X, label, weights=None):
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probs = np.zeros((n, D, H, W))
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rowmax = np.zeros((n, H, W))
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label_xent = np.zeros((n, H, W))
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for i in range(n):
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for x in range(W):
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for y in range(H):
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rowmax[i, y, x] = max(X[i, :, y, x])
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# We need to subtract the max to avoid numerical issues
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probs[i, :, y, x] = X[i, :, y, x] - rowmax[i, y, x]
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exps = np.exp(probs[i, :, y, x])
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probs[i, :, y, x] = exps / sum(exps)
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label_xent[:, y, x] = \
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[-np.log(max(probs[j, label[i, y, x], y, x], 1e-20))
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for j in range(n)]
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total_xent = 0.0
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total_weight = 0.0
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for y in range(H):
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for x in range(W):
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for i in range(n):
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l = label[i, y, x]
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if (l != (-1)):
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w = 1.0 if weights is None else weights[i, y, x]
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total_xent += \
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-np.log(max(probs[i, l, y, x], 1e-20)) * w
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total_weight += w
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print("Total weight {}".format(total_weight))
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return (probs, total_xent / total_weight)
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op = core.CreateOperator(
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"SoftmaxWithLoss",
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["X", "label"] + ([] if weights is None else ["weights"]),
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["probs", "avgloss"],
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spatial=1
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)
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inputs = [X, label] + ([] if weights is None else [weights])
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=inputs,
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reference=label_softmax_crossent_spatial,
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)
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self.assertGradientChecks(
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gc, op, inputs, 0, [1], stepsize=1e-4, threshold=1e-2)
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@unittest.skipIf(not workspace.has_gpu_support, "No gpu support")
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def test_compare_cpugpu(self):
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'''
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Additional test that checks CPU and GPU returns same values
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with larger examples. This is mainly to test the more complex
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GPU implementation is correct.
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'''
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from caffe2.proto import caffe2_pb2
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for j in range(3):
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gpuop = core.CreateOperator(
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"SoftmaxWithLoss",
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["X_gpu", "label_gpu"],
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["probs_gpu", "avgloss_gpu"],
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spatial=1,
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device_option=core.DeviceOption(caffe2_pb2.CUDA, 0)
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)
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cpuop = core.CreateOperator(
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"SoftmaxWithLoss",
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["X_cpu", "label_cpu"],
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["probs_cpu", "avgloss_cpu"],
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spatial=1,
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device_option=core.DeviceOption(caffe2_pb2.CPU)
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)
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n = 8
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D = 4
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W = 64 + int(np.random.rand(1) * 1024)
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H = 64 + int(np.random.rand(1) * 1024)
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print("W: {} H: {}".format(W, H))
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X = np.random.rand(n, D, H, W).astype(np.float32)
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X = X + 1e-2
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# Initialize label. Some of the labels are (-1), i.e "DONT CARE"
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label = (np.random.rand(n, H, W) * (D + 1)).astype(np.int32) - 1
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gpu0 = core.DeviceOption(caffe2_pb2.CUDA, 0)
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workspace.FeedBlob("X_cpu", X)
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workspace.FeedBlob("label_cpu", label)
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workspace.FeedBlob("X_gpu", X, device_option=gpu0)
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workspace.FeedBlob("label_gpu", label, device_option=gpu0)
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workspace.RunOperatorOnce(gpuop)
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workspace.RunOperatorOnce(cpuop)
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probs_gpu = workspace.FetchBlob("probs_gpu")
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probs_cpu = workspace.FetchBlob("probs_cpu")
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loss_gpu = workspace.FetchBlob("avgloss_gpu")
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loss_cpu = workspace.FetchBlob("avgloss_cpu")
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np.testing.assert_allclose(probs_gpu, probs_cpu, rtol=1e-4)
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np.testing.assert_allclose(loss_gpu, loss_cpu, rtol=1e-1)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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