FM / operator

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/

/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
activation_ops_test.py	9691	0644	edit dl rm
adadelta_test.py	7932	0644	edit dl rm
adagrad_test.py	7586	0644	edit dl rm
adagrad_test_helper.py	5181	0644	edit dl rm
adam_test.py	21559	0644	edit dl rm
affine_channel_op_test.py	3784	0644	edit dl rm
alias_with_name_test.py	938	0644	edit dl rm
apmeter_test.py	2738	0644	edit dl rm
arg_ops_test.py	1917	0644	edit dl rm
assert_test.py	797	0644	edit dl rm
async_net_barrier_test.py	946	0644	edit dl rm
atomic_ops_test.py	4104	0644	edit dl rm
basic_rnn_test.py	4720	0644	edit dl rm
batch_box_cox_test.py	5080	0644	edit dl rm
batch_bucketize_op_test.py	3730	0644	edit dl rm
batch_moments_op_test.py	2795	0644	edit dl rm
batch_sparse_to_dense_op_test.py	4199	0644	edit dl rm
bbox_transform_test.py	12258	0644	edit dl rm
bisect_percentile_op_test.py	6227	0644	edit dl rm
blobs_queue_db_test.py	3240	0644	edit dl rm
boolean_mask_test.py	16389	0644	edit dl rm
boolean_unmask_test.py	1711	0644	edit dl rm
box_with_nms_limit_op_test.py	8750	0644	edit dl rm
bucketize_op_test.py	930	0644	edit dl rm
cast_op_test.py	1600	0644	edit dl rm
ceil_op_test.py	888	0644	edit dl rm
channel_backprop_stats_op_test.py	2131	0644	edit dl rm
channel_shuffle_test.py	1794	0644	edit dl rm
channel_stats_op_test.py	2639	0644	edit dl rm
checkpoint_test.py	1500	0644	edit dl rm
clip_op_test.py	1984	0644	edit dl rm
clip_tensor_op_test.py	2076	0644	edit dl rm
collect_and_distribute_fpn_rpn_proposals_op_test.py	11269	0644	edit dl rm
concat_op_cost_test.py	2858	0644	edit dl rm
concat_split_op_test.py	7266	0644	edit dl rm
conditional_test.py	995	0644	edit dl rm
conftest.py	1446	0644	edit dl rm
conv_test.py	32473	0644	edit dl rm
conv_transpose_test.py	15945	0644	edit dl rm
copy_ops_test.py	7374	0644	edit dl rm
copy_rows_to_tensor_op_test.py	2526	0644	edit dl rm
cosine_embedding_criterion_op_test.py	1953	0644	edit dl rm
counter_ops_test.py	3348	0644	edit dl rm
crf_test.py	5315	0644	edit dl rm
cross_entropy_ops_test.py	10085	0644	edit dl rm
ctc_beam_search_decoder_op_test.py	5197	0644	edit dl rm
ctc_greedy_decoder_op_test.py	4743	0644	edit dl rm
cudnn_recurrent_test.py	5817	0644	edit dl rm
dataset_ops_test.py	23847	0644	edit dl rm
data_couple_op_test.py	858	0644	edit dl rm
decay_adagrad_test.py	2694	0644	edit dl rm
deform_conv_test.py	19276	0644	edit dl rm
dense_vector_to_id_list_op_test.py	2044	0644	edit dl rm
depthwise_3x3_conv_test.py	1863	0644	edit dl rm
detectron_keypoints.py	7973	0644	edit dl rm
distance_op_test.py	4351	0644	edit dl rm
dropout_op_test.py	2971	0644	edit dl rm
duplicate_operands_test.py	734	0644	edit dl rm
elementwise_linear_op_test.py	1382	0644	edit dl rm
elementwise_logical_ops_test.py	4617	0644	edit dl rm
elementwise_ops_test.py	33354	0644	edit dl rm
elementwise_op_broadcast_test.py	17466	0644	edit dl rm
emptysample_ops_test.py	1977	0644	edit dl rm
enforce_finite_op_test.py	1286	0644	edit dl rm
ensure_clipped_test.py	1505	0644	edit dl rm
ensure_cpu_output_op_test.py	1244	0644	edit dl rm
erf_op_test.py	749	0644	edit dl rm
expand_op_test.py	2109	0644	edit dl rm
fc_operator_test.py	3720	0644	edit dl rm
feature_maps_ops_test.py	21492	0644	edit dl rm
filler_ops_test.py	8476	0644	edit dl rm
find_op_test.py	1316	0644	edit dl rm
flatten_op_test.py	922	0644	edit dl rm
flexible_top_k_test.py	2609	0644	edit dl rm
floor_op_test.py	894	0644	edit dl rm
fused_nbit_rowwise_conversion_ops_test.py	14077	0644	edit dl rm
fused_nbit_rowwise_test_helper.py	2693	0644	edit dl rm
gather_ops_test.py	9216	0644	edit dl rm
gather_ranges_op_test.py	9125	0644	edit dl rm
given_tensor_byte_string_to_uint8_fill_op_test.py	1392	0644	edit dl rm
given_tensor_fill_op_test.py	1503	0644	edit dl rm
glu_op_test.py	1212	0644	edit dl rm
group_conv_test.py	2870	0644	edit dl rm
group_norm_op_test.py	5252	0644	edit dl rm
gru_test.py	12932	0644	edit dl rm
heatmap_max_keypoint_op_test.py	4770	0644	edit dl rm
histogram_test.py	3097	0644	edit dl rm
hsm_test.py	9456	0644	edit dl rm
hyperbolic_ops_test.py	1472	0644	edit dl rm
im2col_col2im_test.py	4311	0644	edit dl rm
image_input_op_test.py	17345	0644	edit dl rm
index_hash_ops_test.py	2885	0644	edit dl rm
index_ops_test.py	4597	0644	edit dl rm
instance_norm_test.py	9917	0644	edit dl rm
integral_image_ops_test.py	3419	0644	edit dl rm
jsd_ops_test.py	1044	0644	edit dl rm
key_split_ops_test.py	1289	0644	edit dl rm
lars_test.py	1354	0644	edit dl rm
layer_norm_op_test.py	14983	0644	edit dl rm
leaky_relu_test.py	5639	0644	edit dl rm
learning_rate_adaption_op_test.py	2837	0644	edit dl rm
learning_rate_op_test.py	8652	0644	edit dl rm
lengths_pad_op_test.py	1625	0644	edit dl rm
lengths_reducer_fused_nbit_rowwise_ops_test.py	15495	0644	edit dl rm
lengths_tile_op_test.py	1332	0644	edit dl rm
lengths_top_k_ops_test.py	2371	0644	edit dl rm
length_split_op_test.py	4868	0644	edit dl rm
listwise_l2r_operator_test.py	8740	0644	edit dl rm
load_save_test.py	33241	0644	edit dl rm
locally_connected_op_test.py	7761	0644	edit dl rm
loss_ops_test.py	902	0644	edit dl rm
lpnorm_op_test.py	2725	0644	edit dl rm
map_ops_test.py	2249	0644	edit dl rm
margin_ranking_criterion_op_test.py	1816	0644	edit dl rm
math_ops_test.py	1603	0644	edit dl rm
matmul_op_test.py	10096	0644	edit dl rm
mean_op_test.py	1469	0644	edit dl rm
merge_id_lists_op_test.py	2989	0644	edit dl rm
mkl_conv_op_test.py	1547	0644	edit dl rm
mkl_packed_fc_op_test.py	2647	0644	edit dl rm
mod_op_test.py	1459	0644	edit dl rm
moments_op_test.py	1722	0644	edit dl rm
momentum_sgd_test.py	6480	0644	edit dl rm
mpi_test.py	8154	0644	edit dl rm
mul_gradient_benchmark.py	1509	0644	edit dl rm
negate_gradient_op_test.py	1518	0644	edit dl rm
ngram_ops_test.py	2327	0644	edit dl rm
normalize_op_test.py	1679	0644	edit dl rm
numpy_tile_op_test.py	1924	0644	edit dl rm
one_hot_ops_test.py	7478	0644	edit dl rm
onnx_while_test.py	3070	0644	edit dl rm
order_switch_test.py	1306	0644	edit dl rm
pack_ops_test.py	12634	0644	edit dl rm
pack_rnn_sequence_op_test.py	2891	0644	edit dl rm
pad_test.py	1377	0644	edit dl rm
partition_ops_test.py	6838	0644	edit dl rm
percentile_op_test.py	4427	0644	edit dl rm
piecewise_linear_transform_test.py	6187	0644	edit dl rm
pooling_test.py	16508	0644	edit dl rm
prepend_dim_test.py	1505	0644	edit dl rm
python_op_test.py	1312	0644	edit dl rm
quantile_test.py	3276	0644	edit dl rm
rand_quantization_op_speed_test.py	3128	0644	edit dl rm
rank_loss_operator_test.py	5752	0644	edit dl rm
rebatching_queue_test.py	9047	0644	edit dl rm
record_queue_test.py	3125	0644	edit dl rm
recurrent_network_test.py	14048	0644	edit dl rm
recurrent_net_executor_test.py	10922	0644	edit dl rm
reduce_ops_test.py	17341	0644	edit dl rm
reduction_ops_test.py	4664	0644	edit dl rm
reshape_ops_test.py	8211	0644	edit dl rm
resize_op_test.py	9417	0644	edit dl rm
rmac_regions_op_test.py	3178	0644	edit dl rm
rms_norm_op_test.py	1325	0644	edit dl rm
rnn_cell_test.py	59707	0644	edit dl rm
roi_align_rotated_op_test.py	7567	0644	edit dl rm
rowwise_counter_test.py	2205	0644	edit dl rm
scale_op_test.py	2177	0644	edit dl rm
segment_ops_test.py	25745	0644	edit dl rm
self_binning_histogram_test.py	12915	0644	edit dl rm
selu_op_test.py	3232	0644	edit dl rm
sequence_ops_test.py	16000	0644	edit dl rm
shape_inference_test.py	25708	0644	edit dl rm
sinusoid_position_encoding_op_test.py	2308	0644	edit dl rm
softmax_ops_test.py	23685	0644	edit dl rm
softplus_op_test.py	516	0644	edit dl rm
sparse_dropout_with_replacement_op_test.py	2885	0644	edit dl rm
sparse_gradient_checker_test.py	1294	0644	edit dl rm
sparse_itemwise_dropout_with_replacement_op_test.py	2913	0644	edit dl rm
sparse_lengths_sum_benchmark.py	4159	0644	edit dl rm
sparse_lp_regularizer_test.py	2553	0644	edit dl rm
sparse_normalize_test.py	3136	0644	edit dl rm
sparse_ops_test.py	3469	0644	edit dl rm
sparse_to_dense_mask_op_test.py	3693	0644	edit dl rm
spatial_bn_op_test.py	20182	0644	edit dl rm
specialized_segment_ops_test.py	11775	0644	edit dl rm
split_op_cost_test.py	8645	0644	edit dl rm
square_root_divide_op_test.py	2179	0644	edit dl rm
stats_ops_test.py	1789	0644	edit dl rm
stats_put_ops_test.py	6596	0644	edit dl rm
storm_test.py	6507	0644	edit dl rm
string_ops_test.py	4154	0644	edit dl rm
text_file_reader_test.py	2517	0644	edit dl rm
thresholded_relu_op_test.py	2323	0644	edit dl rm
tile_op_test.py	3887	0644	edit dl rm
top_k_test.py	9113	0644	edit dl rm
torch_integration_test.py	39941	0644	edit dl rm
transpose_op_test.py	2722	0644	edit dl rm
trigonometric_op_test.py	1715	0644	edit dl rm
unique_ops_test.py	2255	0644	edit dl rm
unique_uniform_fill_op_test.py	1335	0644	edit dl rm
unsafe_coalesce_test.py	2940	0644	edit dl rm
upsample_op_test.py	7308	0644	edit dl rm
utility_ops_test.py	15054	0644	edit dl rm
video_input_op_test.py	10503	0644	edit dl rm
weighted_multi_sample_test.py	1997	0644	edit dl rm
weighted_sample_test.py	2739	0644	edit dl rm
weighted_sum_test.py	3052	0644	edit dl rm
weight_scale_test.py	2057	0644	edit dl rm
wngrad_test.py	8279	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/cross_entropy_ops_test.py (10085B)

from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np

import unittest

def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-x))

def sigmoid_cross_entropy_with_logits(x, z):
    return np.maximum(x, 0) - x * z + np.log(1 + np.exp(-np.abs(x)))

def sigmoid_cross_entropy_with_logits_grad(x, z):
    return z - sigmoid(x)

def sigmoid_cross_entropy_with_logits_with_log_D_trick(x, z):
    return -(2 * z - 1.) * np.log(sigmoid(x))

def sigmoid_cross_entropy_with_logits_with_log_D_trick_grad(x, z):
    return (2 * z - 1.) * (1 - sigmoid(x))

def unjoined_sigmoid_cross_entropy(x, z):
    return -z * x + (1. - z) * np.maximum(x, 0) \
        + (1. - z) * np.log(1 + np.exp(-np.abs(x)))

def unjoined_sigmoid_cross_entropy_grad(x, z):
    return z - (1. - z) / (1. + np.exp(-x))

class TestCrossEntropyOps(hu.HypothesisTestCase):
    @given(
        inputs=st.lists(
            elements=st.integers(min_value=1, max_value=5),
            min_size=1,
            max_size=2,
        ).flatmap(
            lambda shape: st.tuples(
                hu.arrays(
                    dims=shape,
                    elements=st.one_of(
                        hu.floats(min_value=-1.0, max_value=-0.1),
                        hu.floats(min_value=0.1, max_value=1.0),
                    )),
                hu.arrays(
                    dims=shape,
                    elements=st.sampled_from([0.0, 1.0]),
                ),
            )
        ),
        options=st.one_of(
            st.tuples(st.just(True), st.just(False)),
            st.tuples(st.just(False), st.just(True)),
            st.tuples(st.just(False), st.just(False))
        ),
        **hu.gcs
    )
    def test_sigmoid_cross_entropy_with_logits(
        self, inputs, options, gc, dc
    ):
        logits, targets = inputs
        log_D_trick, unjoined_lr_loss = options

def sigmoid_xentr_logit_ref(logits, targets):
            if unjoined_lr_loss:
                s = unjoined_sigmoid_cross_entropy(logits, targets)
            else:
                s = (
                    sigmoid_cross_entropy_with_logits(logits, targets)
                    if not log_D_trick else
                    sigmoid_cross_entropy_with_logits_with_log_D_trick(
                        logits, targets
                    )
                )
            m = np.mean(s, axis=len(logits.shape) - 1)
            return (m, )

def sigmoid_xentr_logit_grad_ref(g_out, outputs, fwd_inputs):
            fwd_logits, fwd_targets = fwd_inputs
            inner_size = fwd_logits.shape[-1]
            if unjoined_lr_loss:
                m = unjoined_sigmoid_cross_entropy_grad(logits, targets)
            else:
                m = (
                    sigmoid_cross_entropy_with_logits_grad(fwd_logits, fwd_targets)
                    if not log_D_trick else
                    sigmoid_cross_entropy_with_logits_with_log_D_trick_grad(
                        fwd_logits, fwd_targets
                    )
                )
            # m = fwd_targets - sigmoid(fwd_logits)
            g_in = -np.expand_dims(g_out, axis=-1) * m / inner_size
            return (g_in, None)

op = core.CreateOperator(
            'SigmoidCrossEntropyWithLogits', ['logits', 'targets'],
            ['xentropy'],
            log_D_trick=log_D_trick,
            unjoined_lr_loss=unjoined_lr_loss
        )
        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[logits, targets],
            reference=sigmoid_xentr_logit_ref,
            output_to_grad='xentropy',
            grad_reference=sigmoid_xentr_logit_grad_ref)

@given(
        log_D_trick=st.just(False),
        **hu.gcs_cpu_only
    )
    def test_cross_entropy_and_unjoied_cross_entropy_relation(
        self, log_D_trick, gc, dc
    ):
        logits = np.array([1.4720, 0.3500, -0.6529, -1.1908, 0.8357,
                    -1.0774, -0.3395, -0.2469, 0.6708, -1.8332], dtype='f')
        targets = np.array([1., 1., 1., 1., 1., 1., 0., 0., 0., 0.], dtype='f')
        lr_size = targets.size
        unjoined_lr_loss = False

def sigmoid_xentr_logit_ref(logits, targets):
            if unjoined_lr_loss:
                s = unjoined_sigmoid_cross_entropy(logits, targets)
            else:
                s = sigmoid_cross_entropy_with_logits(logits, targets)
            m = np.mean(s, axis=len(logits.shape) - 1)
            return (m, )

# m = fwd_targets - sigmoid(fwd_logits)
            g_in = -np.expand_dims(g_out, axis=-1) * m / inner_size
            return (g_in, None)

op = core.CreateOperator(
            'SigmoidCrossEntropyWithLogits', ['logits', 'targets'],
            ['xentropy'],
            log_D_trick=log_D_trick,
            unjoined_lr_loss=unjoined_lr_loss
        )
        output_lr = self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[logits, targets],
            reference=sigmoid_xentr_logit_ref,
            output_to_grad='xentropy',
            grad_reference=sigmoid_xentr_logit_grad_ref)

# Unjoined dataset where labels change later
        logits = np.array([1.4720, 0.3500, -0.6529, -1.1908, 0.8357,
                    -1.0774, -0.3395, -0.2469, 0.6708, -1.8332, 1.4720, 0.3500,
                    -0.6529, -1.1908, 0.8357, -1.0774], dtype='f')
        targets = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0.,
                            0., 1., 1., 1., 1., 1., 1.], dtype='f')
        unjoined_lr_loss = True
        unjoined_lr_size = targets.size

op = core.CreateOperator(
            'SigmoidCrossEntropyWithLogits', ['logits', 'targets'],
            ['xentropy'],
            log_D_trick=log_D_trick,
            unjoined_lr_loss=unjoined_lr_loss
        )
        outputs_unjoined_lr = self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[logits, targets],
            reference=sigmoid_xentr_logit_ref,
            output_to_grad='xentropy',
            grad_reference=sigmoid_xentr_logit_grad_ref)

self.assertAlmostEqual(
            output_lr[0].item(0) * lr_size / unjoined_lr_size,
            outputs_unjoined_lr[0].item(0),
            delta=0.0001)

@given(
        inputs=st.lists(
            elements=st.integers(min_value=1, max_value=5),
            min_size=1,
            max_size=2,
        ).flatmap(
            lambda shape: st.tuples(
                hu.arrays(
                    dims=shape,
                    elements=st.one_of(
                        hu.floats(min_value=-1.0, max_value=-0.1),
                        hu.floats(min_value=0.1, max_value=1.0),
                    )),
                hu.arrays(
                    dims=shape,
                    elements=st.sampled_from([0.0, 1.0]),
                ),
                hu.arrays(
                    dims=shape,
                    elements=hu.floats(min_value=0.1, max_value=1.0),
                ),
            )
        ),
        **hu.gcs
    )
    def test_weighted_sigmoid_cross_entropy_with_logits(self, inputs, gc, dc):
        logits, targets, weights = inputs

def weighted_sigmoid_xentr_logit_ref(logits, targets, weights):
            s = sigmoid_cross_entropy_with_logits(logits, targets)
            s = np.multiply(s, weights)
            m = np.mean(s, axis=len(logits.shape) - 1)
            return (m, )

def weighted_sigmoid_xentr_logit_grad_ref(g_out, outputs, fwd_inputs):
            fwd_logits, fwd_targets, fwd_weights = fwd_inputs
            inner_size = fwd_logits.shape[-1]
            m = fwd_targets - sigmoid(fwd_logits)
            m = np.multiply(m, weights)
            g_in = -np.expand_dims(g_out, axis=-1) * m / inner_size
            return (g_in, None, None)

op = core.CreateOperator(
            'WeightedSigmoidCrossEntropyWithLogits',
            ['logits', 'targets', 'weights'],
            ['xentropy'])
        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[logits, targets, weights],
            reference=weighted_sigmoid_xentr_logit_ref,
            output_to_grad='xentropy',
            grad_reference=weighted_sigmoid_xentr_logit_grad_ref)

@given(n=st.integers(2, 10),
           b=st.integers(1, 5),
           **hu.gcs_cpu_only)
    def test_soft_label_cross_entropy(self, n, b, gc, dc):
        # Initialize X and add 1e-2 for numerical stability
        X = np.random.rand(b, n).astype(np.float32)
        X = X + 1e-2
        for i in range(b):
            X[i] = X[i] / np.sum(X[i])

# Initialize label
        label = np.random.rand(b, n).astype(np.float32)
        for i in range(b):
            label[i] = label[i] / np.sum(label[i])

# Reference implementation of cross entropy with soft labels
        def soft_label_xentr_ref(X, label):
            xent = [np.sum((-label[j][i] * np.log(max(X[j][i], 1e-20))
                            for i in range(len(X[0])))) for j in range(b)]
            return (xent,)

op = core.CreateOperator("CrossEntropy", ["X", "label"], ["Y"])

# TODO(surya) Once CrossEntropyOp is ported to GPU, add the respective
        # tests to this unit test.
        self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[X, label],
            reference=soft_label_xentr_ref,
        )

self.assertGradientChecks(
            gc, op, [X, label], 0, [0], stepsize=1e-4, threshold=1e-2)

if __name__ == "__main__":
    import unittest
    unittest.main()

cmd: