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/bbox_transform_test.py (12258B)

from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np

# Reference implementation from detectron/lib/utils/boxes.py
def bbox_transform(boxes, deltas, weights=(1.0, 1.0, 1.0, 1.0)):
    """Forward transform that maps proposal boxes to predicted ground-truth
    boxes using bounding-box regression deltas. See bbox_transform_inv for a
    description of the weights argument.
    """
    if boxes.shape[0] == 0:
        return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)

boxes = boxes.astype(deltas.dtype, copy=False)

widths = boxes[:, 2] - boxes[:, 0] + 1.0
    heights = boxes[:, 3] - boxes[:, 1] + 1.0
    ctr_x = boxes[:, 0] + 0.5 * widths
    ctr_y = boxes[:, 1] + 0.5 * heights

wx, wy, ww, wh = weights
    dx = deltas[:, 0::4] / wx
    dy = deltas[:, 1::4] / wy
    dw = deltas[:, 2::4] / ww
    dh = deltas[:, 3::4] / wh

# Prevent sending too large values into np.exp()
    BBOX_XFORM_CLIP = np.log(1000. / 16.)
    dw = np.minimum(dw, BBOX_XFORM_CLIP)
    dh = np.minimum(dh, BBOX_XFORM_CLIP)

pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
    pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
    pred_w = np.exp(dw) * widths[:, np.newaxis]
    pred_h = np.exp(dh) * heights[:, np.newaxis]

pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
    # x1
    pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
    # y1
    pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
    # x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
    # y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1

return pred_boxes

# Reference implementation from detectron/lib/utils/boxes.py
def clip_tiled_boxes(boxes, im_shape):
    """Clip boxes to image boundaries. im_shape is [height, width] and boxes
    has shape (N, 4 * num_tiled_boxes)."""
    assert (
        boxes.shape[1] % 4 == 0
    ), "boxes.shape[1] is {:d}, but must be divisible by 4.".format(
        boxes.shape[1]
    )
    # x1 >= 0
    boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
    # y1 >= 0
    boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
    # x2 < im_shape[1]
    boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
    # y2 < im_shape[0]
    boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
    return boxes

def generate_rois(roi_counts, im_dims):
    assert len(roi_counts) == len(im_dims)
    all_rois = []
    for i, num_rois in enumerate(roi_counts):
        if num_rois == 0:
            continue
        # [batch_idx, x1, y1, x2, y2]
        rois = np.random.uniform(0, im_dims[i], size=(roi_counts[i], 5)).astype(
            np.float32
        )
        rois[:, 0] = i  # batch_idx
        # Swap (x1, x2) if x1 > x2
        rois[:, 1], rois[:, 3] = (
            np.minimum(rois[:, 1], rois[:, 3]),
            np.maximum(rois[:, 1], rois[:, 3]),
        )
        # Swap (y1, y2) if y1 > y2
        rois[:, 2], rois[:, 4] = (
            np.minimum(rois[:, 2], rois[:, 4]),
            np.maximum(rois[:, 2], rois[:, 4]),
        )
        all_rois.append(rois)
    if len(all_rois) > 0:
        return np.vstack(all_rois)
    return np.empty((0, 5)).astype(np.float32)

def bbox_transform_rotated(
    boxes,
    deltas,
    weights=(1.0, 1.0, 1.0, 1.0),
    angle_bound_on=True,
    angle_bound_lo=-90,
    angle_bound_hi=90,
):
    """
    Similar to bbox_transform but for rotated boxes with angle info.
    """
    if boxes.shape[0] == 0:
        return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)

boxes = boxes.astype(deltas.dtype, copy=False)

ctr_x = boxes[:, 0]
    ctr_y = boxes[:, 1]
    widths = boxes[:, 2]
    heights = boxes[:, 3]
    angles = boxes[:, 4]

wx, wy, ww, wh = weights
    dx = deltas[:, 0::5] / wx
    dy = deltas[:, 1::5] / wy
    dw = deltas[:, 2::5] / ww
    dh = deltas[:, 3::5] / wh
    da = deltas[:, 4::5] * 180.0 / np.pi

# Prevent sending too large values into np.exp()
    BBOX_XFORM_CLIP = np.log(1000. / 16.)
    dw = np.minimum(dw, BBOX_XFORM_CLIP)
    dh = np.minimum(dh, BBOX_XFORM_CLIP)

pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
    pred_boxes[:, 0::5] = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
    pred_boxes[:, 1::5] = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
    pred_boxes[:, 2::5] = np.exp(dw) * widths[:, np.newaxis]
    pred_boxes[:, 3::5] = np.exp(dh) * heights[:, np.newaxis]

pred_angle = da + angles[:, np.newaxis]
    if angle_bound_on:
        period = angle_bound_hi - angle_bound_lo
        assert period % 180 == 0
        pred_angle[np.where(pred_angle < angle_bound_lo)] += period
        pred_angle[np.where(pred_angle > angle_bound_hi)] -= period
    pred_boxes[:, 4::5] = pred_angle

return pred_boxes

def clip_tiled_boxes_rotated(boxes, im_shape, angle_thresh=1.0):
    """
    Similar to clip_tiled_boxes but for rotated boxes with angle info.
    Only clips almost horizontal boxes within angle_thresh. The rest are
    left unchanged.
    """
    assert (
        boxes.shape[1] % 5 == 0
    ), "boxes.shape[1] is {:d}, but must be divisible by 5.".format(
        boxes.shape[1]
    )

(H, W) = im_shape[:2]

# Filter boxes that are almost upright within angle_thresh tolerance
    idx = np.where(np.abs(boxes[:, 4::5]) <= angle_thresh)
    idx5 = idx[1] * 5
    # convert to (x1, y1, x2, y2)
    x1 = boxes[idx[0], idx5] - (boxes[idx[0], idx5 + 2] - 1) / 2.0
    y1 = boxes[idx[0], idx5 + 1] - (boxes[idx[0], idx5 + 3] - 1) / 2.0
    x2 = boxes[idx[0], idx5] + (boxes[idx[0], idx5 + 2] - 1) / 2.0
    y2 = boxes[idx[0], idx5 + 1] + (boxes[idx[0], idx5 + 3] - 1) / 2.0
    # clip
    x1 = np.maximum(np.minimum(x1, W - 1), 0)
    y1 = np.maximum(np.minimum(y1, H - 1), 0)
    x2 = np.maximum(np.minimum(x2, W - 1), 0)
    y2 = np.maximum(np.minimum(y2, H - 1), 0)
    # convert back to (xc, yc, w, h)
    boxes[idx[0], idx5] = (x1 + x2) / 2.0
    boxes[idx[0], idx5 + 1] = (y1 + y2) / 2.0
    boxes[idx[0], idx5 + 2] = x2 - x1 + 1
    boxes[idx[0], idx5 + 3] = y2 - y1 + 1

return boxes

def generate_rois_rotated(roi_counts, im_dims):
    rois = generate_rois(roi_counts, im_dims)
    # [batch_id, ctr_x, ctr_y, w, h, angle]
    rotated_rois = np.empty((rois.shape[0], 6)).astype(np.float32)
    rotated_rois[:, 0] = rois[:, 0]  # batch_id
    rotated_rois[:, 1] = (rois[:, 1] + rois[:, 3]) / 2.  # ctr_x = (x1 + x2) / 2
    rotated_rois[:, 2] = (rois[:, 2] + rois[:, 4]) / 2.  # ctr_y = (y1 + y2) / 2
    rotated_rois[:, 3] = rois[:, 3] - rois[:, 1] + 1.0  # w = x2 - x1 + 1
    rotated_rois[:, 4] = rois[:, 4] - rois[:, 2] + 1.0  # h = y2 - y1 + 1
    rotated_rois[:, 5] = np.random.uniform(-90.0, 90.0)  # angle in degrees
    return rotated_rois

class TestBBoxTransformOp(serial.SerializedTestCase):
    @given(
        num_rois=st.integers(1, 10),
        num_classes=st.integers(1, 10),
        im_dim=st.integers(100, 600),
        skip_batch_id=st.booleans(),
        rotated=st.booleans(),
        angle_bound_on=st.booleans(),
        clip_angle_thresh=st.sampled_from([-1.0, 1.0]),
        **hu.gcs_cpu_only
    )
    @settings(deadline=10000)
    def test_bbox_transform(
        self,
        num_rois,
        num_classes,
        im_dim,
        skip_batch_id,
        rotated,
        angle_bound_on,
        clip_angle_thresh,
        gc,
        dc,
    ):
        """
        Test with all rois belonging to a single image per run.
        """
        rois = (
            generate_rois_rotated([num_rois], [im_dim])
            if rotated
            else generate_rois([num_rois], [im_dim])
        )
        box_dim = 5 if rotated else 4
        if skip_batch_id:
            rois = rois[:, 1:]
        deltas = np.random.randn(num_rois, box_dim * num_classes).astype(np.float32)
        im_info = np.array([im_dim, im_dim, 1.0]).astype(np.float32).reshape(1, 3)

def bbox_transform_ref(rois, deltas, im_info):
            boxes = rois if rois.shape[1] == box_dim else rois[:, 1:]
            im_shape = im_info[0, 0:2]
            if rotated:
                box_out = bbox_transform_rotated(
                    boxes, deltas, angle_bound_on=angle_bound_on
                )
                box_out = clip_tiled_boxes_rotated(
                    box_out, im_shape, angle_thresh=clip_angle_thresh
                )
            else:
                box_out = bbox_transform(boxes, deltas)
                box_out = clip_tiled_boxes(box_out, im_shape)
            return [box_out]

op = core.CreateOperator(
            "BBoxTransform",
            ["rois", "deltas", "im_info"],
            ["box_out"],
            apply_scale=False,
            correct_transform_coords=True,
            rotated=rotated,
            angle_bound_on=angle_bound_on,
            clip_angle_thresh=clip_angle_thresh,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[rois, deltas, im_info],
            reference=bbox_transform_ref,
        )

@given(
        roi_counts=st.lists(st.integers(0, 5), min_size=1, max_size=10),
        num_classes=st.integers(1, 10),
        rotated=st.booleans(),
        angle_bound_on=st.booleans(),
        clip_angle_thresh=st.sampled_from([-1.0, 1.0]),
        **hu.gcs_cpu_only
    )
    @settings(deadline=10000)
    def test_bbox_transform_batch(
        self,
        roi_counts,
        num_classes,
        rotated,
        angle_bound_on,
        clip_angle_thresh,
        gc,
        dc,
    ):
        """
        Test with rois for multiple images in a batch
        """
        batch_size = len(roi_counts)
        total_rois = sum(roi_counts)
        im_dims = np.random.randint(100, 600, batch_size)
        rois = (
            generate_rois_rotated(roi_counts, im_dims)
            if rotated
            else generate_rois(roi_counts, im_dims)
        )
        box_dim = 5 if rotated else 4
        deltas = np.random.randn(total_rois, box_dim * num_classes).astype(np.float32)
        im_info = np.zeros((batch_size, 3)).astype(np.float32)
        im_info[:, 0] = im_dims
        im_info[:, 1] = im_dims
        im_info[:, 2] = 1.0

def bbox_transform_ref(rois, deltas, im_info):
            box_out = []
            offset = 0
            for i, num_rois in enumerate(roi_counts):
                if num_rois == 0:
                    continue
                cur_boxes = rois[offset : offset + num_rois, 1:]
                cur_deltas = deltas[offset : offset + num_rois]
                im_shape = im_info[i, 0:2]
                if rotated:
                    cur_box_out = bbox_transform_rotated(
                        cur_boxes, cur_deltas, angle_bound_on=angle_bound_on
                    )
                    cur_box_out = clip_tiled_boxes_rotated(
                        cur_box_out, im_shape, angle_thresh=clip_angle_thresh
                    )
                else:
                    cur_box_out = bbox_transform(cur_boxes, cur_deltas)
                    cur_box_out = clip_tiled_boxes(cur_box_out, im_shape)
                box_out.append(cur_box_out)
                offset += num_rois

if len(box_out) > 0:
                box_out = np.vstack(box_out)
            else:
                box_out = np.empty(deltas.shape).astype(np.float32)
            return [box_out, roi_counts]

op = core.CreateOperator(
            "BBoxTransform",
            ["rois", "deltas", "im_info"],
            ["box_out", "roi_batch_splits"],
            apply_scale=False,
            correct_transform_coords=True,
            rotated=rotated,
            angle_bound_on=angle_bound_on,
            clip_angle_thresh=clip_angle_thresh,
        )

self.assertReferenceChecks(
            device_option=gc,
            op=op,
            inputs=[rois, deltas, im_info],
            reference=bbox_transform_ref,
        )

cmd: