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test_generate_proposals_v2_op.py
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test_generate_proposals_v2_op.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import sys
import math
import paddle
import paddle.fluid as fluid
from op_test import OpTest
from test_anchor_generator_op import anchor_generator_in_python
import copy
from test_generate_proposals_op import clip_tiled_boxes, box_coder, nms
def generate_proposals_v2_in_python(scores, bbox_deltas, im_shape, anchors,
variances, pre_nms_topN, post_nms_topN,
nms_thresh, min_size, eta, pixel_offset):
all_anchors = anchors.reshape(-1, 4)
rois = np.empty((0, 5), dtype=np.float32)
roi_probs = np.empty((0, 1), dtype=np.float32)
rpn_rois = []
rpn_roi_probs = []
rois_num = []
num_images = scores.shape[0]
for img_idx in range(num_images):
img_i_boxes, img_i_probs = proposal_for_one_image(
im_shape[img_idx, :], all_anchors, variances,
bbox_deltas[img_idx, :, :, :], scores[img_idx, :, :, :],
pre_nms_topN, post_nms_topN, nms_thresh, min_size, eta,
pixel_offset)
rois_num.append(img_i_probs.shape[0])
rpn_rois.append(img_i_boxes)
rpn_roi_probs.append(img_i_probs)
return rpn_rois, rpn_roi_probs, rois_num
def proposal_for_one_image(im_shape, all_anchors, variances, bbox_deltas,
scores, pre_nms_topN, post_nms_topN, nms_thresh,
min_size, eta, pixel_offset):
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
# - bbox deltas will be (4 * A, H, W) format from conv output
# - transpose to (H, W, 4 * A)
# - reshape to (H * W * A, 4) where rows are ordered by (H, W, A)
# in slowest to fastest order to match the enumerated anchors
bbox_deltas = bbox_deltas.transpose((1, 2, 0)).reshape(-1, 4)
all_anchors = all_anchors.reshape(-1, 4)
variances = variances.reshape(-1, 4)
# Same story for the scores:
# - scores are (A, H, W) format from conv output
# - transpose to (H, W, A)
# - reshape to (H * W * A, 1) where rows are ordered by (H, W, A)
# to match the order of anchors and bbox_deltas
scores = scores.transpose((1, 2, 0)).reshape(-1, 1)
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN (e.g. 6000)
if pre_nms_topN <= 0 or pre_nms_topN >= len(scores):
order = np.argsort(-scores.squeeze())
else:
# Avoid sorting possibly large arrays;
# First partition to get top K unsorted
# and then sort just those
inds = np.argpartition(-scores.squeeze(), pre_nms_topN)[:pre_nms_topN]
order = np.argsort(-scores[inds].squeeze())
order = inds[order]
scores = scores[order, :]
bbox_deltas = bbox_deltas[order, :]
all_anchors = all_anchors[order, :]
proposals = box_coder(all_anchors, bbox_deltas, variances, pixel_offset)
# clip proposals to image (may result in proposals with zero area
# that will be removed in the next step)
proposals = clip_tiled_boxes(proposals, im_shape, pixel_offset)
# remove predicted boxes with height or width < min_size
keep = filter_boxes(proposals, min_size, im_shape, pixel_offset)
if len(keep) == 0:
proposals = np.zeros((1, 4)).astype('float32')
scores = np.zeros((1, 1)).astype('float32')
return proposals, scores
proposals = proposals[keep, :]
scores = scores[keep, :]
# apply loose nms (e.g. threshold = 0.7)
# take post_nms_topN (e.g. 1000)
# return the top proposals
if nms_thresh > 0:
keep = nms(boxes=proposals,
scores=scores,
nms_threshold=nms_thresh,
eta=eta,
pixel_offset=pixel_offset)
if post_nms_topN > 0 and post_nms_topN < len(keep):
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep, :]
return proposals, scores
def filter_boxes(boxes, min_size, im_shape, pixel_offset=True):
"""Only keep boxes with both sides >= min_size and center within the image.
"""
# Scale min_size to match image scale
min_size = max(min_size, 1.0)
offset = 1 if pixel_offset else 0
ws = boxes[:, 2] - boxes[:, 0] + offset
hs = boxes[:, 3] - boxes[:, 1] + offset
if pixel_offset:
x_ctr = boxes[:, 0] + ws / 2.
y_ctr = boxes[:, 1] + hs / 2.
keep = np.where((ws >= min_size) & (hs >= min_size)
& (x_ctr < im_shape[1]) & (y_ctr < im_shape[0]))[0]
else:
keep = np.where((ws >= min_size) & (hs >= min_size))[0]
return keep
class TestGenerateProposalsV2Op(OpTest):
def set_data(self):
self.init_test_params()
self.init_test_input()
self.init_test_output()
self.inputs = {
'Scores': self.scores,
'BboxDeltas': self.bbox_deltas,
'ImShape': self.im_shape.astype(np.float32),
'Anchors': self.anchors,
'Variances': self.variances
}
self.attrs = {
'pre_nms_topN': self.pre_nms_topN,
'post_nms_topN': self.post_nms_topN,
'nms_thresh': self.nms_thresh,
'min_size': self.min_size,
'eta': self.eta,
'pixel_offset': self.pixel_offset,
}
self.outputs = {
'RpnRois': (self.rpn_rois[0], [self.rois_num]),
'RpnRoiProbs': (self.rpn_roi_probs[0], [self.rois_num]),
}
def test_check_output(self):
self.check_output()
def setUp(self):
self.op_type = "generate_proposals_v2"
self.set_data()
def init_test_params(self):
self.pre_nms_topN = 12000 # train 12000, test 2000
self.post_nms_topN = 5000 # train 6000, test 1000
self.nms_thresh = 0.7
self.min_size = 3.0
self.eta = 1.
self.pixel_offset = True
def init_test_input(self):
batch_size = 1
input_channels = 20
layer_h = 16
layer_w = 16
input_feat = np.random.random(
(batch_size, input_channels, layer_h, layer_w)).astype('float32')
self.anchors, self.variances = anchor_generator_in_python(
input_feat=input_feat,
anchor_sizes=[16., 32.],
aspect_ratios=[0.5, 1.0],
variances=[1.0, 1.0, 1.0, 1.0],
stride=[16.0, 16.0],
offset=0.5)
self.im_shape = np.array([[64, 64]]).astype('float32')
num_anchors = self.anchors.shape[2]
self.scores = np.random.random(
(batch_size, num_anchors, layer_h, layer_w)).astype('float32')
self.bbox_deltas = np.random.random(
(batch_size, num_anchors * 4, layer_h, layer_w)).astype('float32')
def init_test_output(self):
self.rpn_rois, self.rpn_roi_probs, self.rois_num = generate_proposals_v2_in_python(
self.scores, self.bbox_deltas, self.im_shape, self.anchors,
self.variances, self.pre_nms_topN, self.post_nms_topN,
self.nms_thresh, self.min_size, self.eta, self.pixel_offset)
class TestGenerateProposalsV2OutLodOp(TestGenerateProposalsV2Op):
def set_data(self):
self.init_test_params()
self.init_test_input()
self.init_test_output()
self.inputs = {
'Scores': self.scores,
'BboxDeltas': self.bbox_deltas,
'ImShape': self.im_shape.astype(np.float32),
'Anchors': self.anchors,
'Variances': self.variances
}
self.attrs = {
'pre_nms_topN': self.pre_nms_topN,
'post_nms_topN': self.post_nms_topN,
'nms_thresh': self.nms_thresh,
'min_size': self.min_size,
'eta': self.eta,
'return_rois_num': True
}
self.outputs = {
'RpnRois': (self.rpn_rois[0], [self.rois_num]),
'RpnRoiProbs': (self.rpn_roi_probs[0], [self.rois_num]),
'RpnRoisNum': (np.asarray(self.rois_num, dtype=np.int32))
}
class TestGenerateProposalsV2OpNoBoxLeft(TestGenerateProposalsV2Op):
def init_test_params(self):
self.pre_nms_topN = 12000 # train 12000, test 2000
self.post_nms_topN = 5000 # train 6000, test 1000
self.nms_thresh = 0.7
self.min_size = 1000.0
self.eta = 1.
self.pixel_offset = True
class TestGenerateProposalsV2OpNoOffset(TestGenerateProposalsV2Op):
def init_test_params(self):
self.pre_nms_topN = 12000 # train 12000, test 2000
self.post_nms_topN = 5000 # train 6000, test 1000
self.nms_thresh = 0.7
self.min_size = 3.0
self.eta = 1.
self.pixel_offset = False
class testGenerateProposalsAPI(unittest.TestCase):
def setUp(self):
np.random.seed(678)
self.scores_np = np.random.rand(2, 3, 4, 4).astype('float32')
self.bbox_deltas_np = np.random.rand(2, 12, 4, 4).astype('float32')
self.img_size_np = np.array([[8, 8], [6, 6]]).astype('float32')
self.anchors_np = np.reshape(np.arange(4 * 4 * 3 * 4),
[4, 4, 3, 4]).astype('float32')
self.variances_np = np.ones((4, 4, 3, 4)).astype('float32')
self.roi_expected, self.roi_probs_expected, self.rois_num_expected = generate_proposals_v2_in_python(
self.scores_np,
self.bbox_deltas_np,
self.img_size_np,
self.anchors_np,
self.variances_np,
pre_nms_topN=10,
post_nms_topN=5,
nms_thresh=0.5,
min_size=0.1,
eta=1.0,
pixel_offset=False)
self.roi_expected = np.array(self.roi_expected).squeeze(1)
self.roi_probs_expected = np.array(self.roi_probs_expected).squeeze(1)
self.rois_num_expected = np.array(self.rois_num_expected)
def test_dynamic(self):
paddle.disable_static()
scores = paddle.to_tensor(self.scores_np)
bbox_deltas = paddle.to_tensor(self.bbox_deltas_np)
img_size = paddle.to_tensor(self.img_size_np)
anchors = paddle.to_tensor(self.anchors_np)
variances = paddle.to_tensor(self.variances_np)
rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals(
scores,
bbox_deltas,
img_size,
anchors,
variances,
pre_nms_top_n=10,
post_nms_top_n=5,
return_rois_num=True)
self.assertTrue(np.allclose(self.roi_expected, rois.numpy()))
self.assertTrue(np.allclose(self.roi_probs_expected, roi_probs.numpy()))
self.assertTrue(np.allclose(self.rois_num_expected, rois_num.numpy()))
def test_static(self):
paddle.enable_static()
scores = paddle.static.data(name='scores',
shape=[2, 3, 4, 4],
dtype='float32')
bbox_deltas = paddle.static.data(name='bbox_deltas',
shape=[2, 12, 4, 4],
dtype='float32')
img_size = paddle.static.data(name='img_size',
shape=[2, 2],
dtype='float32')
anchors = paddle.static.data(name='anchors',
shape=[4, 4, 3, 4],
dtype='float32')
variances = paddle.static.data(name='variances',
shape=[4, 4, 3, 4],
dtype='float32')
rois, roi_probs, rois_num = paddle.vision.ops.generate_proposals(
scores,
bbox_deltas,
img_size,
anchors,
variances,
pre_nms_top_n=10,
post_nms_top_n=5,
return_rois_num=True)
exe = paddle.static.Executor()
rois, roi_probs, rois_num = exe.run(
paddle.static.default_main_program(),
feed={
'scores': self.scores_np,
'bbox_deltas': self.bbox_deltas_np,
'img_size': self.img_size_np,
'anchors': self.anchors_np,
'variances': self.variances_np,
},
fetch_list=[rois.name, roi_probs.name, rois_num.name],
return_numpy=False)
self.assertTrue(np.allclose(self.roi_expected, np.array(rois)))
self.assertTrue(
np.allclose(self.roi_probs_expected, np.array(roi_probs)))
self.assertTrue(np.allclose(self.rois_num_expected, np.array(rois_num)))
if __name__ == '__main__':
paddle.enable_static()
unittest.main()