3f9e92e897b00162556361412c68ad1da474f716,tests/classifiersFrameworks/test_pytorch.py,,test_class_gradient,#Any#Any#Any#,178
Before Change
classifier_logits, _ = get_image_classifier_list(one_classifier=True, from_logits=True)
expected_gradients_1_all_labels = np.asarray(
[
-0.00367321,
-0.0002892,
0.00037825,
-0.00053344,
0.00192121,
0.00112047,
0.0023135,
0.0,
0.0,
-0.00391743,
-0.0002264,
0.00238103,
-0.00073711,
0.00270405,
0.00389043,
0.00440818,
-0.00412769,
-0.00441795,
0.00081916,
-0.00091284,
0.00119645,
-0.00849089,
0.00547925,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
expected_gradients_2_all_labels = np.asarray(
[
-1.0557442e-03,
-1.0079540e-03,
-7.7426381e-04,
1.7387437e-03,
2.1773505e-03,
5.0880131e-05,
1.6497375e-03,
2.6113102e-03,
6.0904315e-03,
4.1080985e-04,
2.5268074e-03,
-3.6661496e-04,
-3.0568994e-03,
-1.1665225e-03,
3.8904310e-03,
3.1726388e-04,
1.3203262e-03,
-1.1720933e-04,
-1.4315107e-03,
-4.7676827e-04,
9.7251305e-04,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
]
)
expected_gradients_1_label5 = np.asarray(
[
-0.00367321,
-0.0002892,
0.00037825,
-0.00053344,
0.00192121,
0.00112047,
0.0023135,
0.0,
0.0,
-0.00391743,
-0.0002264,
0.00238103,
-0.00073711,
0.00270405,
0.00389043,
0.00440818,
-0.00412769,
-0.00441795,
0.00081916,
-0.00091284,
0.00119645,
-0.00849089,
0.00547925,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
expected_gradients_2_label5 = np.asarray(
[
-1.0557442e-03,
-1.0079540e-03,
-7.7426381e-04,
1.7387437e-03,
2.1773505e-03,
5.0880131e-05,
1.6497375e-03,
2.6113102e-03,
6.0904315e-03,
4.1080985e-04,
2.5268074e-03,
-3.6661496e-04,
-3.0568994e-03,
-1.1665225e-03,
3.8904310e-03,
3.1726388e-04,
1.3203262e-03,
-1.1720933e-04,
-1.4315107e-03,
-4.7676827e-04,
9.7251305e-04,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
]
)
expected_gradients_1_label_array = np.asarray(
[
-0.00195835,
-0.00134457,
-0.00307221,
-0.00340564,
0.00175022,
-0.00239714,
-0.00122619,
0.0,
0.0,
-0.00520899,
-0.00046105,
0.00414874,
-0.00171095,
0.00429184,
0.0075138,
0.00792443,
0.0019566,
0.00035517,
0.00504575,
-0.00037397,
0.00022343,
-0.00530035,
0.0020528,
0.0,
0.0,
0.0,
0.0,
0.0,
]
)
expected_gradients_2_label_array = np.asarray(
[
5.0867130e-03,
4.8564533e-03,
6.1040395e-03,
8.6531248e-03,
-6.0958802e-03,
-1.4114541e-02,
-7.1085966e-04,
-5.0330797e-04,
1.2943064e-02,
8.2416134e-03,
-1.9859453e-04,
-9.8110031e-05,
-3.8902226e-03,
-1.2945874e-03,
7.5138002e-03,
1.7720887e-03,
3.1399354e-04,
2.3657191e-04,
-3.0891625e-03,
-1.0211228e-03,
2.0828887e-03,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
0.0000000e00,
]
)
decimal_precision = 4
expected_values = {
"expected_gradients_1_all_labels": ExpectedValue(
expected_gradients_1_all_labels,
decimal_precision,
),
"expected_gradients_2_all_labels": ExpectedValue(
expected_gradients_2_all_labels,
decimal_precision,
),
"expected_gradients_1_label5": ExpectedValue(
expected_gradients_1_label5,
decimal_precision,
),
"expected_gradients_2_label5": ExpectedValue(
expected_gradients_2_label5,
decimal_precision,
),
"expected_gradients_1_labelArray": ExpectedValue(
expected_gradients_1_label_array,
decimal_precision,
),
"expected_gradients_2_labelArray": ExpectedValue(
expected_gradients_2_label_array,
decimal_precision,
),
}
labels = np.random.randint(5, size=x_test_mnist.shape[0])
backend_test_class_gradient(framework, get_default_mnist_subset, classifier_logits, expected_values, labels)
After Change
// ),
// }
expected_values = {
"expected_gradients_1_all_labels": ExpectedValue(
np.asarray(
[
-0.03347399,
-0.03195872,
-0.02650188,
0.04111874,
0.08676253,
0.03339913,
0.06925241,
0.09387045,
0.15184258,
-0.00684002,
0.05070481,
0.01409407,
-0.03632583,
0.00151133,
0.05102589,
0.00766463,
-0.00898967,
0.00232938,
-0.00617045,
-0.00201032,
0.00410065,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
),
4,
),
"expected_gradients_2_all_labels": ExpectedValue(
np.asarray(
[
-0.09723657,
-0.00240533,
0.02445251,
-0.00035474,
0.04765627,
0.04286841,
0.07209076,
0.0,
0.0,
-0.07938144,
-0.00142567,
0.02882954,
-0.00049514,
0.04170151,
0.05102589,
0.09544909,
-0.04401167,
-0.06158172,
0.03359772,
-0.00838454,
0.01722163,
-0.13376027,
0.08206709,
0.0,
0.0,
0.0,
0.0,
0.0,
]
),
4,
),
"expected_gradients_1_label5": ExpectedValue(
np.asarray(
[
-0.03347399,
-0.03195872,
-0.02650188,
0.04111874,
0.08676253,
0.03339913,
0.06925241,
0.09387045,
0.15184258,
-0.00684002,
0.05070481,
0.01409407,
-0.03632583,
0.00151133,
0.05102589,
0.00766463,
-0.00898967,
0.00232938,
-0.00617045,
-0.00201032,
0.00410065,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
),
4,
),
"expected_gradients_2_label5": ExpectedValue(
np.asarray(
[
-0.09723657,
-0.00240533,
0.02445251,
-0.00035474,
0.04765627,
0.04286841,
0.07209076,
0.0,
0.0,
-0.07938144,
-0.00142567,
0.02882954,
-0.00049514,
0.04170151,
0.05102589,
0.09544909,
-0.04401167,
-0.06158172,
0.03359772,
-0.00838454,
0.01722163,
-0.13376027,
0.08206709,
0.0,
0.0,
0.0,
0.0,
0.0,
]
),
4,
),
"expected_gradients_1_labelArray": ExpectedValue(
np.asarray(
[
0.06860766,
0.065502,
0.08539103,
0.13868105,
-0.05520725,
-0.18788849,
0.02264893,
0.02980516,
0.2226511,
0.11288887,
-0.00678776,
0.02045561,
-0.03120914,
0.00642691,
0.08449504,
0.02848018,
-0.03251382,
0.00854315,
-0.02354656,
-0.00767687,
0.01565931,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
),
4,
),
"expected_gradients_2_labelArray": ExpectedValue(
np.asarray(
[
-0.0487146,
-0.0171556,
-0.03161772,
-0.0420007,
0.03360246,
-0.01864819,
0.00315916,
0.0,
0.0,
-0.07631349,
-0.00374462,
0.04229517,
-0.01131879,
0.05044588,
0.08449504,
0.12417868,
0.07536847,
0.03906382,
0.09467953,
0.00543209,
-0.00504872,
-0.03366479,
-0.00385999,
0.0,
0.0,
0.0,
0.0,
0.0,
]
),
4,
),
}
labels = np.random.randint(5, size=x_test_mnist.shape[0])
backend_test_class_gradient(framework, get_default_mnist_subset, classifier_logits, expected_values, labels)
In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 7
Instances
Project Name: IBM/adversarial-robustness-toolbox
Commit Name: 3f9e92e897b00162556361412c68ad1da474f716
Time: 2020-07-06
Author: killian.levacher@ibm.com
File Name: tests/classifiersFrameworks/test_pytorch.py
Class Name:
Method Name: test_class_gradient
Project Name: deepmind/dm_control
Commit Name: 904598fd329014cd06e4daa9d6a892d1042f4fca
Time: 2019-02-21
Author: alimuldal@google.com
File Name: dm_control/locomotion/soccer/boxhead.py
Class Name:
Method Name: _asset_png_with_background_rgba_bytes
Project Name: nipy/dipy
Commit Name: 4f3da275037d4fe3108039fdbfc03b2f81fbee8c
Time: 2015-10-15
Author: dimrozakis@gmail.com
File Name: dipy/reconst/dti.py
Class Name:
Method Name: ols_fit_tensor