├── R2CNN_HEAD_FPN_Tensorflow ├── __init__.py ├── data │ ├── __init__.py │ ├── io │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── read_tfrecord.pyc │ │ ├── image_preprocess.pyc │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── read_tfrecord.cpython-35.pyc │ │ │ ├── image_preprocess.cpython-35.pyc │ │ │ └── image_preprocess.cpython-36.pyc │ │ ├── tif2jpg.py │ │ ├── divide_data.py │ │ ├── read_tfrecord.py │ │ ├── convert_data_to_tfrecord.py │ │ └── image_preprocess.py │ ├── __init__.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ └── __init__.cpython-36.pyc │ ├── pretrained_weights │ │ └── README.md │ └── tfrecords │ │ └── README.md ├── libs │ ├── __init__.py │ ├── rpn │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── build_rpn.pyc │ │ └── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── build_rpn.cpython-35.pyc │ │ │ └── build_rpn.cpython-36.pyc │ ├── box_utils │ │ ├── __init__.py │ │ ├── 1.jpg │ │ ├── iou.pyc │ │ ├── nms.pyc │ │ ├── __init__.pyc │ │ ├── iou_cpu.so │ │ ├── iou_rotate.pyc │ │ ├── nms_rotate.pyc │ │ ├── boxes_utils.pyc │ │ ├── make_anchor.pyc │ │ ├── rbbox_overlaps.hpp │ │ ├── rbbox_overlaps.so │ │ ├── coordinate_convert.pyc │ │ ├── encode_and_decode.pyc │ │ ├── rotate_polygon_nms.so │ │ ├── show_box_in_tensor.pyc │ │ ├── anchor_utils_pyfunc.pyc │ │ ├── __pycache__ │ │ │ ├── iou.cpython-35.pyc │ │ │ ├── iou.cpython-36.pyc │ │ │ ├── nms.cpython-35.pyc │ │ │ ├── nms.cpython-36.pyc │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── nms_rotate.cpython-35.pyc │ │ │ ├── nms_rotate.cpython-36.pyc │ │ │ ├── boxes_utils.cpython-35.pyc │ │ │ ├── boxes_utils.cpython-36.pyc │ │ │ ├── make_anchor.cpython-35.pyc │ │ │ ├── make_anchor.cpython-36.pyc │ │ │ ├── visualization.cpython-36.pyc │ │ │ ├── encode_and_decode.cpython-35.pyc │ │ │ ├── encode_and_decode.cpython-36.pyc │ │ │ ├── anchor_utils_pyfunc.cpython-35.pyc │ │ │ ├── anchor_utils_pyfunc.cpython-36.pyc │ │ │ ├── coordinate_convert.cpython-35.pyc │ │ │ ├── coordinate_convert.cpython-36.pyc │ │ │ ├── show_box_in_tensor.cpython-35.pyc │ │ │ └── show_box_in_tensor.cpython-36.pyc │ │ ├── build │ │ │ └── temp.linux-x86_64-3.6 │ │ │ │ ├── iou_cpu.o │ │ │ │ ├── rbbox_overlaps.o │ │ │ │ ├── rotate_polygon_nms.o │ │ │ │ ├── rbbox_overlaps_kernel.o │ │ │ │ └── rotate_polygon_nms_kernel.o │ │ ├── iou_cpu.cpython-36m-x86_64-linux-gnu.so │ │ ├── rotate_gpu_nms.hpp │ │ ├── rotate_polygon_nms.hpp │ │ ├── rbbox_overlaps.cpython-36m-x86_64-linux-gnu.so │ │ ├── rotate_polygon_nms.cpython-36m-x86_64-linux-gnu.so │ │ ├── nms.py │ │ ├── rbbox_overlaps.pyx │ │ ├── rotate_polygon_nms.pyx │ │ ├── iou.py │ │ ├── visualization.py │ │ ├── iou_cpu.pyx │ │ ├── iou_rotate.py │ │ ├── coordinate_convert.py │ │ ├── make_anchor.py │ │ ├── anchor_utils_pyfunc.py │ │ ├── nms_rotate.py │ │ └── boxes_utils.py │ ├── configs │ │ ├── __init__.py │ │ ├── cfgs.pyc │ │ ├── __init__.pyc │ │ ├── __pycache__ │ │ │ ├── cfgs.cpython-35.pyc │ │ │ ├── cfgs.cpython-36.pyc │ │ │ ├── __init__.cpython-35.pyc │ │ │ └── __init__.cpython-36.pyc │ │ ├── v1_ship_head.py │ │ ├── v1_UAV_rotate.py │ │ └── cfgs.py │ ├── fast_rcnn │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ └── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── build_fast_rcnn.cpython-35.pyc │ │ │ └── build_fast_rcnn.cpython-36.pyc │ ├── losses │ │ ├── __init__.py │ │ ├── losses.pyc │ │ ├── __init__.pyc │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── losses.cpython-35.pyc │ │ │ └── losses.cpython-36.pyc │ │ └── losses.py │ ├── networks │ │ ├── __init__.py │ │ ├── nets │ │ │ └── __init__.py │ │ ├── slim_nets │ │ │ ├── __init__.py │ │ │ ├── vgg.pyc │ │ │ ├── __init__.pyc │ │ │ ├── resnet_v1.pyc │ │ │ ├── mobilenet_v1.png │ │ │ ├── mobilenet_v1.pyc │ │ │ ├── resnet_utils.pyc │ │ │ ├── inception_resnet_v2.pyc │ │ │ ├── __pycache__ │ │ │ │ ├── vgg.cpython-35.pyc │ │ │ │ ├── vgg.cpython-36.pyc │ │ │ │ ├── __init__.cpython-35.pyc │ │ │ │ ├── __init__.cpython-36.pyc │ │ │ │ ├── resnet_v1.cpython-35.pyc │ │ │ │ ├── resnet_v1.cpython-36.pyc │ │ │ │ ├── mobilenet_v1.cpython-35.pyc │ │ │ │ ├── mobilenet_v1.cpython-36.pyc │ │ │ │ ├── resnet_utils.cpython-35.pyc │ │ │ │ ├── resnet_utils.cpython-36.pyc │ │ │ │ ├── inception_resnet_v2.cpython-35.pyc │ │ │ │ └── inception_resnet_v2.cpython-36.pyc │ │ │ ├── inception.py │ │ │ ├── nets_factory_test.py │ │ │ ├── inception_utils.py │ │ │ ├── lenet.py │ │ │ ├── mobilenet_v1.md │ │ │ ├── cifarnet.py │ │ │ ├── nets_factory.py │ │ │ ├── overfeat.py │ │ │ ├── alexnet.py │ │ │ ├── overfeat_test.py │ │ │ └── alexnet_test.py │ │ ├── __init__.pyc │ │ ├── network_factory.pyc │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── network_factory.cpython-35.pyc │ │ │ └── network_factory.cpython-36.pyc │ │ └── network_factory.py │ ├── label_name_dict │ │ ├── __init__.py │ │ ├── __init__.pyc │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-35.pyc │ │ │ ├── __init__.cpython-36.pyc │ │ │ ├── label_dict.cpython-35.pyc │ │ │ └── label_dict.cpython-36.pyc │ │ └── label_dict.py │ ├── __init__.pyc │ └── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ └── __init__.cpython-36.pyc ├── tools │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ └── restore_model.cpython-35.pyc │ ├── restore_model.py │ └── test_rotate.py ├── configs │ ├── __init__.py │ ├── __init__.pyc │ ├── config_vgg16.pyc │ ├── config_res101.pyc │ ├── config_resnet_50.pyc │ ├── config_inception_resnet.pyc │ ├── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ ├── __init__.cpython-36.pyc │ │ ├── config_res101.cpython-35.pyc │ │ ├── config_res101.cpython-36.pyc │ │ └── config_resnet_50.cpython-35.pyc │ ├── config_resnet_50.py │ ├── config_res101.py │ ├── config_vgg16.py │ └── config_inception_resnet.py ├── help_utils │ ├── __init__.py │ ├── tools.pyc │ ├── __init__.pyc │ ├── help_utils.pyc │ ├── __pycache__ │ │ ├── tools.cpython-35.pyc │ │ ├── tools.cpython-36.pyc │ │ ├── __init__.cpython-35.pyc │ │ ├── __init__.cpython-36.pyc │ │ ├── help_utils.cpython-35.pyc │ │ └── help_utils.cpython-36.pyc │ └── tools.py ├── HEAD ├── config ├── description ├── .directory ├── graph.png ├── sample.tif ├── output │ └── res101_summary │ │ ├── rpn_loss.bmp │ │ ├── total_loss.bmp │ │ ├── fast_rcnn_loss.bmp │ │ └── README.md ├── .idea │ ├── vcs.xml │ ├── misc.xml │ ├── modules.xml │ ├── deployment.xml │ ├── FPN_v1.iml │ └── webServers.xml ├── hooks │ ├── post-update.sample │ ├── pre-applypatch.sample │ ├── applypatch-msg.sample │ ├── commit-msg.sample │ ├── prepare-commit-msg.sample │ ├── pre-push.sample │ ├── pre-commit.sample │ ├── update.sample │ └── pre-rebase.sample ├── info │ └── exclude ├── sh.exe.stackdump ├── sample.xml └── README.md └── README.md /R2CNN_HEAD_FPN_Tensorflow/__init__.py: 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-------------------------------------------------------------------------------- 1 | # git ls-files --others --exclude-from=.git/info/exclude 2 | # Lines that start with '#' are comments. 3 | # For a project mostly in C, the following would be a good set of 4 | # exclude patterns (uncomment them if you want to use them): 5 | # *.[oa] 6 | # *~ 7 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/data/pretrained_weights/README.md: -------------------------------------------------------------------------------- 1 | # Pre-training weights stored here (ResNet101 or ResNet50) 2 | 3 | download pretrain weight([resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) or [resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)) from [here](https://github.com/yangxue0827/models/tree/master/slim), then extract to here. 4 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/data/tfrecords/README.md: -------------------------------------------------------------------------------- 1 | # Store tfrecord here 2 | The data is VOC format, reference [here](sample.xml) 3 | data path format 4 | VOCdevkit 5 | >VOCdevkit_train 6 | >>Annotation 7 | >>JPEGImages 8 | 9 | >VOCdevkit_test 10 | >>Annotation 11 | >>JPEGImages 12 | 13 | python ./data/io/convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.tif' --dataset='ship' 14 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/pre-applypatch.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to verify what is about to be committed 4 | # by applypatch from an e-mail message. 5 | # 6 | # The hook should exit with non-zero status after issuing an 7 | # appropriate message if it wants to stop the commit. 8 | # 9 | # To enable this hook, rename this file to "pre-applypatch". 10 | 11 | . git-sh-setup 12 | test -x "$GIT_DIR/hooks/pre-commit" && 13 | exec "$GIT_DIR/hooks/pre-commit" ${1+"$@"} 14 | : 15 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/data/io/tif2jpg.py: -------------------------------------------------------------------------------- 1 | # -*-encoding:utf-8-*- 2 | import os 3 | import cv2 4 | 5 | input_path = '/yangxue/data_ship_clean/VOCdevkit/TIFImages/' 6 | output_path = '/yangxue/data_ship_clean/VOCdevkit/JPEGImages/' 7 | 8 | images = os.listdir(input_path) 9 | 10 | for count, i in enumerate(images): 11 | img = cv2.imread(os.path.join(input_path, str(i))) 12 | cv2.imwrite(os.path.join(output_path, i.replace('.tif', '.jpg')), img) 13 | if count % 1000 == 0: 14 | print(count) 15 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/.idea/deployment.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 15 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/applypatch-msg.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to check the commit log message taken by 4 | # applypatch from an e-mail message. 5 | # 6 | # The hook should exit with non-zero status after issuing an 7 | # appropriate message if it wants to stop the commit. The hook is 8 | # allowed to edit the commit message file. 9 | # 10 | # To enable this hook, rename this file to "applypatch-msg". 11 | 12 | . git-sh-setup 13 | test -x "$GIT_DIR/hooks/commit-msg" && 14 | exec "$GIT_DIR/hooks/commit-msg" ${1+"$@"} 15 | : 16 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/help_utils/tools.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division, print_function, absolute_import 3 | import math 4 | import sys 5 | import os 6 | 7 | 8 | def view_bar(message, num, total): 9 | rate = num / total 10 | rate_num = int(rate * 40) 11 | rate_nums = math.ceil(rate * 100) 12 | r = '\r%s:[%s%s]%d%%\t%d/%d' % (message, ">" * rate_num, " " * (40 - rate_num), rate_nums, num, total,) 13 | sys.stdout.write(r) 14 | sys.stdout.flush() 15 | 16 | 17 | def mkdir(path): 18 | if not os.path.exists(path): 19 | os.makedirs(path) -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/.idea/FPN_v1.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 12 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/sh.exe.stackdump: -------------------------------------------------------------------------------- 1 | MSYS-1.0.12 Build:2012-07-05 14:56 2 | Exception: STATUS_ACCESS_VIOLATION at eip=02A37968 3 | eax=00000001 ebx=0069FA7C ecx=02AB4C5C edx=026F0000 esi=0069FA80 edi=00000000 4 | ebp=0069FA54 esp=0069F8DC program=C:\Users\yangxue\AppData\Local\GitHub\PortableGit_c2ba306e536fdf878271f7fe636a147ff37326ad\bin\sh.exe 5 | cs=0023 ds=002B es=002B fs=0053 gs=002B ss=002B 6 | Stack trace: 7 | Frame Function Args 8 | 0069FA54 02A37968 (0055005C, 00650073, 00730072, 0079005C) 9 | 72238 [main] sh 8740 handle_exceptions: Error while dumping state (probably corrupted stack) 10 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/nms.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import tensorflow as tf 8 | 9 | 10 | def non_maximal_suppression(boxes, scores, iou_threshold, max_output_size, name='non_maximal_suppression'): 11 | with tf.variable_scope(name): 12 | nms_index = tf.image.non_max_suppression( 13 | boxes=boxes, 14 | scores=scores, 15 | max_output_size=max_output_size, 16 | iou_threshold=iou_threshold, 17 | name=name 18 | ) 19 | return nms_index -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/.idea/webServers.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 14 | 15 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/rbbox_overlaps.pyx: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | cimport numpy as np 3 | 4 | cdef extern from "rbbox_overlaps.hpp": 5 | void _overlaps(np.float32_t*, np.float32_t*, np.float32_t*, int, int, int) 6 | 7 | def rbbx_overlaps (np.ndarray[np.float32_t, ndim=2] boxes, np.ndarray[np.float32_t, ndim=2] query_boxes, np.int32_t device_id=0): 8 | # boxes: [x, y, w, h, theta] 9 | cdef int N = boxes.shape[0] 10 | cdef int K = query_boxes.shape[0] 11 | cdef np.ndarray[np.float32_t, ndim=2] overlaps = np.zeros((N, K), dtype = np.float32) 12 | _overlaps(&overlaps[0, 0], &boxes[0, 0], &query_boxes[0, 0], N, K, device_id) 13 | return overlaps 14 | 15 | 16 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/commit-msg.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to check the commit log message. 4 | # Called by "git commit" with one argument, the name of the file 5 | # that has the commit message. The hook should exit with non-zero 6 | # status after issuing an appropriate message if it wants to stop the 7 | # commit. The hook is allowed to edit the commit message file. 8 | # 9 | # To enable this hook, rename this file to "commit-msg". 10 | 11 | # Uncomment the below to add a Signed-off-by line to the message. 12 | # Doing this in a hook is a bad idea in general, but the prepare-commit-msg 13 | # hook is more suited to it. 14 | # 15 | # SOB=$(git var GIT_AUTHOR_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') 16 | # grep -qs "^$SOB" "$1" || echo "$SOB" >> "$1" 17 | 18 | # This example catches duplicate Signed-off-by lines. 19 | 20 | test "" = "$(grep '^Signed-off-by: ' "$1" | 21 | sort | uniq -c | sed -e '/^[ ]*1[ ]/d')" || { 22 | echo >&2 Duplicate Signed-off-by lines. 23 | exit 1 24 | } 25 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/rotate_polygon_nms.pyx: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | cimport numpy as np 3 | 4 | assert sizeof(int) == sizeof(np.int32_t) 5 | 6 | cdef extern from "rotate_gpu_nms.hpp": 7 | void _rotate_nms(np.int32_t*, int*, np.float32_t*, int, int, float, int) 8 | 9 | def rotate_gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float_t thresh, np.int32_t device_id=0): 10 | cdef int boxes_num = dets.shape[0] 11 | cdef int boxes_dim = dets.shape[1] 12 | cdef int num_out 13 | cdef np.ndarray[np.int32_t, ndim=1] \ 14 | keep = np.zeros(boxes_num, dtype=np.int32) 15 | cdef np.ndarray[np.float32_t, ndim=1] \ 16 | scores = dets[:, 5] 17 | cdef np.ndarray[np.int_t, ndim=1] \ 18 | order = scores.argsort()[::-1] 19 | cdef np.ndarray[np.float32_t, ndim=2] \ 20 | sorted_dets = dets[order, :] 21 | thresh = thresh 22 | _rotate_nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id) 23 | keep = keep[:num_out] 24 | return order[keep] 25 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/iou.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | 8 | import tensorflow as tf 9 | 10 | 11 | def iou_calculate(boxes_1, boxes_2): 12 | ''' 13 | 14 | :param boxes_1: [N, 4] [ymin, xmin, ymax, xmax] 15 | :param boxes_2: [M, 4] [ymin, xmin. ymax, xmax] 16 | :return: 17 | ''' 18 | with tf.name_scope('iou_caculate'): 19 | 20 | ymin_1, xmin_1, ymax_1, xmax_1 = tf.split(boxes_1, 4, axis=1) # ymin_1 shape is [N, 1].. 21 | 22 | ymin_2, xmin_2, ymax_2, xmax_2 = tf.unstack(boxes_2, axis=1) # ymin_2 shape is [M, ].. 23 | 24 | max_xmin = tf.maximum(xmin_1, xmin_2) 25 | min_xmax = tf.minimum(xmax_1, xmax_2) 26 | 27 | max_ymin = tf.maximum(ymin_1, ymin_2) 28 | min_ymax = tf.minimum(ymax_1, ymax_2) 29 | 30 | overlap_h = tf.maximum(0., min_ymax - max_ymin) # avoid h < 0 31 | overlap_w = tf.maximum(0., min_xmax - max_xmin) 32 | 33 | overlaps = overlap_h * overlap_w 34 | 35 | area_1 = (xmax_1 - xmin_1) * (ymax_1 - ymin_1) # [N, 1] 36 | area_2 = (xmax_2 - xmin_2) * (ymax_2 - ymin_2) # [M, ] 37 | 38 | iou = overlaps / (area_1 + area_2 - overlaps) 39 | 40 | return iou 41 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/prepare-commit-msg.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to prepare the commit log message. 4 | # Called by "git commit" with the name of the file that has the 5 | # commit message, followed by the description of the commit 6 | # message's source. The hook's purpose is to edit the commit 7 | # message file. If the hook fails with a non-zero status, 8 | # the commit is aborted. 9 | # 10 | # To enable this hook, rename this file to "prepare-commit-msg". 11 | 12 | # This hook includes three examples. The first comments out the 13 | # "Conflicts:" part of a merge commit. 14 | # 15 | # The second includes the output of "git diff --name-status -r" 16 | # into the message, just before the "git status" output. It is 17 | # commented because it doesn't cope with --amend or with squashed 18 | # commits. 19 | # 20 | # The third example adds a Signed-off-by line to the message, that can 21 | # still be edited. This is rarely a good idea. 22 | 23 | case "$2,$3" in 24 | merge,) 25 | /usr/bin/perl -i.bak -ne 's/^/# /, s/^# #/#/ if /^Conflicts/ .. /#/; print' "$1" ;; 26 | 27 | # ,|template,) 28 | # /usr/bin/perl -i.bak -pe ' 29 | # print "\n" . `git diff --cached --name-status -r` 30 | # if /^#/ && $first++ == 0' "$1" ;; 31 | 32 | *) ;; 33 | esac 34 | 35 | # SOB=$(git var GIT_AUTHOR_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') 36 | # grep -qs "^$SOB" "$1" || echo "$SOB" >> "$1" 37 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/pre-push.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | # An example hook script to verify what is about to be pushed. Called by "git 4 | # push" after it has checked the remote status, but before anything has been 5 | # pushed. If this script exits with a non-zero status nothing will be pushed. 6 | # 7 | # This hook is called with the following parameters: 8 | # 9 | # $1 -- Name of the remote to which the push is being done 10 | # $2 -- URL to which the push is being done 11 | # 12 | # If pushing without using a named remote those arguments will be equal. 13 | # 14 | # Information about the commits which are being pushed is supplied as lines to 15 | # the standard input in the form: 16 | # 17 | # 18 | # 19 | # This sample shows how to prevent push of commits where the log message starts 20 | # with "WIP" (work in progress). 21 | 22 | remote="$1" 23 | url="$2" 24 | 25 | z40=0000000000000000000000000000000000000000 26 | 27 | IFS=' ' 28 | while read local_ref local_sha remote_ref remote_sha 29 | do 30 | if [ "$local_sha" = $z40 ] 31 | then 32 | # Handle delete 33 | else 34 | if [ "$remote_sha" = $z40 ] 35 | then 36 | # New branch, examine all commits 37 | range="$local_sha" 38 | else 39 | # Update to existing branch, examine new commits 40 | range="$remote_sha..$local_sha" 41 | fi 42 | 43 | # Check for WIP commit 44 | commit=`git rev-list -n 1 --grep '^WIP' "$range"` 45 | if [ -n "$commit" ] 46 | then 47 | echo "Found WIP commit in $local_ref, not pushing" 48 | exit 1 49 | fi 50 | fi 51 | done 52 | 53 | exit 0 54 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/visualization.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import tensorflow as tf 8 | 9 | 10 | def roi_visualize(img, img_h, img_w, roi_box, rois): 11 | with tf.variable_scope('roi_visualize'): 12 | ymin, xmin, ymax, xmax = tf.unstack(roi_box, axis=1) 13 | 14 | normalize_ymin = ymin / img_h 15 | normalize_xmin = xmin / img_w 16 | normalize_ymax = ymax / img_h 17 | normalize_xmax = xmax / img_w 18 | 19 | tmp_img = tf.squeeze(img) + tf.constant([103.939, 116.779, 123.68], dtype=tf.float32) 20 | tmp_img = tf.cast(tmp_img * 225 / tf.reduce_max(tmp_img), dtype=tf.uint8) 21 | tmp_img = tf.expand_dims(tmp_img, axis=0) 22 | target = tf.image.crop_and_resize(tmp_img, 23 | boxes=tf.transpose(tf.stack([normalize_ymin, normalize_xmin, 24 | normalize_ymax, normalize_xmax])), 25 | box_ind=tf.zeros(shape=[tf.shape(roi_box)[0], ], 26 | dtype=tf.int32), 27 | crop_size=[56, 56], 28 | name='crop_img_object' 29 | ) 30 | 31 | rois = tf.image.resize_bilinear(rois, size=[56, 56]) 32 | rois_mean = tf.reduce_mean(rois, axis=3) 33 | tf.summary.image('target', target[:, :, :, ::-1]) 34 | tf.summary.image('rois', tf.expand_dims(rois_mean, axis=3)) 35 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/data/io/divide_data.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division, print_function, absolute_import 3 | import sys 4 | sys.path.append('../../') 5 | import shutil 6 | import os 7 | import random 8 | import math 9 | 10 | 11 | def mkdir(path): 12 | if not os.path.exists(path): 13 | os.makedirs(path) 14 | 15 | 16 | divide_rate = 0.9 17 | 18 | root_path = '/yangxue/shapan' 19 | 20 | image_path = root_path + '/VOCdevkit/JPEGImages' 21 | xml_path = root_path + '/VOCdevkit/Annotations' 22 | 23 | image_list = os.listdir(image_path) 24 | 25 | image_name = [n.split('.')[0] for n in image_list] 26 | 27 | random.shuffle(image_name) 28 | 29 | train_image = image_name[:int(math.ceil(len(image_name)) * divide_rate)] 30 | test_image = image_name[int(math.ceil(len(image_name)) * divide_rate):] 31 | 32 | image_output_train = os.path.join(root_path, 'VOCdevkit_train/JPEGImages') 33 | mkdir(image_output_train) 34 | image_output_test = os.path.join(root_path, 'VOCdevkit_test/JPEGImages') 35 | mkdir(image_output_test) 36 | 37 | xml_train = os.path.join(root_path, 'VOCdevkit_train/Annotations') 38 | mkdir(xml_train) 39 | xml_test = os.path.join(root_path, 'VOCdevkit_test/Annotations') 40 | mkdir(xml_test) 41 | 42 | 43 | count = 0 44 | for i in train_image: 45 | shutil.copy(os.path.join(image_path, i + '.jpg'), image_output_train) 46 | shutil.copy(os.path.join(xml_path, i + '.xml'), xml_train) 47 | if count % 1000 == 0: 48 | print("process step {}".format(count)) 49 | count += 1 50 | 51 | for i in test_image: 52 | shutil.copy(os.path.join(image_path, i + '.jpg'), image_output_test) 53 | shutil.copy(os.path.join(xml_path, i + '.xml'), xml_test) 54 | if count % 1000 == 0: 55 | print("process step {}".format(count)) 56 | count += 1 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/inception.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Brings all inception models under one namespace.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | # pylint: disable=unused-import 22 | from nets.inception_resnet_v2 import inception_resnet_v2 23 | from nets.inception_resnet_v2 import inception_resnet_v2_arg_scope 24 | from nets.inception_resnet_v2 import inception_resnet_v2_base 25 | from nets.inception_v1 import inception_v1 26 | from nets.inception_v1 import inception_v1_arg_scope 27 | from nets.inception_v1 import inception_v1_base 28 | from nets.inception_v2 import inception_v2 29 | from nets.inception_v2 import inception_v2_arg_scope 30 | from nets.inception_v2 import inception_v2_base 31 | from nets.inception_v3 import inception_v3 32 | from nets.inception_v3 import inception_v3_arg_scope 33 | from nets.inception_v3 import inception_v3_base 34 | from nets.inception_v4 import inception_v4 35 | from nets.inception_v4 import inception_v4_arg_scope 36 | from nets.inception_v4 import inception_v4_base 37 | # pylint: enable=unused-import 38 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/tools/restore_model.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import print_function 5 | from __future__ import division 6 | 7 | import tensorflow as tf 8 | import tensorflow.contrib.slim as slim 9 | import os 10 | 11 | from libs.configs import cfgs 12 | from libs.networks.network_factory import get_flags_byname 13 | 14 | 15 | RESTORE_FROM_RPN = False 16 | FLAGS = get_flags_byname(cfgs.NET_NAME) 17 | os.environ["CUDA_VISIBLE_DEVICES"] = "6" 18 | 19 | 20 | def get_restorer(): 21 | 22 | checkpoint_path = tf.train.latest_checkpoint(os.path.join(FLAGS.trained_checkpoint, cfgs.VERSION)) 23 | 24 | if checkpoint_path != None: 25 | if RESTORE_FROM_RPN: 26 | print('___restore from rpn___') 27 | model_variables = slim.get_model_variables() 28 | restore_variables = [var for var in model_variables if not var.name.startswith('Fast_Rcnn')] + [slim.get_or_create_global_step()] 29 | for var in restore_variables: 30 | print(var.name) 31 | restorer = tf.train.Saver(restore_variables) 32 | else: 33 | restorer = tf.train.Saver() 34 | print("model restore from :", checkpoint_path) 35 | else: 36 | checkpoint_path = FLAGS.pretrained_model_path 37 | print("model restore from pretrained mode, path is :", checkpoint_path) 38 | 39 | model_variables = slim.get_model_variables() 40 | 41 | restore_variables = [var for var in model_variables 42 | if (var.name.startswith(cfgs.NET_NAME) 43 | and not var.name.startswith('{}/logits'.format(cfgs.NET_NAME)))] 44 | for var in restore_variables: 45 | print(var.name) 46 | restorer = tf.train.Saver(restore_variables) 47 | return restorer, checkpoint_path -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/configs/v1_ship_head.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division, print_function, absolute_import 3 | import os 4 | 5 | # root path 6 | ROO_PATH = os.path.abspath('/yangxue/FPN_v21') 7 | 8 | # pretrain weights path 9 | MODEL_PATH = ROO_PATH + '/output/model' 10 | SUMMARY_PATH = ROO_PATH + '/output/summary' 11 | 12 | TEST_SAVE_PATH = ROO_PATH + '/tools/test_result' 13 | INFERENCE_IMAGE_PATH = ROO_PATH + '/tools/inference_image' 14 | INFERENCE_SAVE_PATH = ROO_PATH + '/tools/inference_result' 15 | 16 | NET_NAME = 'resnet_v1_101' 17 | VERSION = 'v1_ship_head' 18 | CLASS_NUM = 1 19 | LEVEL = ['P2', 'P3', 'P4', 'P5', 'P6'] 20 | BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] 21 | STRIDE = [4, 8, 16, 32, 64] 22 | ANCHOR_SCALES = [1.] 23 | ANCHOR_RATIOS = [1 / 2., 1 / 3., 1., 3., 2.] 24 | SCALE_FACTORS = [10., 10., 5., 5., 10.] 25 | OUTPUT_STRIDE = 16 26 | SHORT_SIDE_LEN = 600 27 | DATASET_NAME = 'ship' 28 | 29 | BATCH_SIZE = 1 30 | WEIGHT_DECAY = {'vggnet16': 0.0005, 'resnet_v1_50': 0.0001, 'resnet_v1_101': 0.0001} 31 | EPSILON = 1e-5 32 | MOMENTUM = 0.9 33 | MAX_ITERATION = 40000 34 | GPU_GROUP = "1" 35 | 36 | # rpn 37 | SHARE_HEAD = False 38 | RPN_NMS_IOU_THRESHOLD = 0.7 39 | MAX_PROPOSAL_NUM = 300 40 | RPN_IOU_POSITIVE_THRESHOLD = 0.6 41 | RPN_IOU_NEGATIVE_THRESHOLD = 0.25 42 | RPN_MINIBATCH_SIZE = 256 43 | RPN_POSITIVE_RATE = 0.5 44 | IS_FILTER_OUTSIDE_BOXES = True 45 | RPN_TOP_K_NMS = 3000 46 | FEATURE_PYRAMID_MODE = 0 # {0: 'feature_pyramid', 1: 'dense_feature_pyramid'} 47 | 48 | # fast rcnn 49 | ROTATE_NMS_USE_GPU = True 50 | ROI_SIZE = 14 51 | ROI_POOL_KERNEL_SIZE = 2 52 | USE_DROPOUT = False 53 | KEEP_PROB = 0.5 54 | FAST_RCNN_NMS_IOU_THRESHOLD = 0.15 55 | FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 56 | FINAL_SCORE_THRESHOLD = 0.8 57 | FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 58 | FAST_RCNN_MINIBATCH_SIZE = 128 59 | FAST_RCNN_POSITIVE_RATE = 0.5 60 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/configs/v1_UAV_rotate.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division, print_function, absolute_import 3 | import os 4 | 5 | # root path 6 | ROO_PATH = os.path.abspath('/yangxue/FPN_v21') 7 | 8 | # pretrain weights path 9 | MODEL_PATH = ROO_PATH + '/output/model' 10 | SUMMARY_PATH = ROO_PATH + '/output/summary' 11 | 12 | TEST_SAVE_PATH = ROO_PATH + '/tools/test_result' 13 | INFERENCE_IMAGE_PATH = ROO_PATH + '/tools/inference_image' 14 | INFERENCE_SAVE_PATH = ROO_PATH + '/tools/inference_result' 15 | 16 | NET_NAME = 'resnet_v1_101' 17 | VERSION = 'v1_UAV_rotate' 18 | CLASS_NUM = 3 19 | LEVEL = ['P2', 'P3', 'P4', 'P5', 'P6'] 20 | BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] 21 | STRIDE = [4, 8, 16, 32, 64] 22 | ANCHOR_SCALES = [1.] 23 | ANCHOR_RATIOS = [1, 0.5, 2, 1 / 3., 3., 1.5, 1 / 1.5] 24 | SCALE_FACTORS = [10., 10., 5., 5., 5.] 25 | OUTPUT_STRIDE = 16 26 | SHORT_SIDE_LEN = 600 27 | DATASET_NAME = 'UAV' 28 | 29 | BATCH_SIZE = 1 30 | WEIGHT_DECAY = {'vggnet16': 0.0005, 'resnet_v1_50': 0.0001, 'resnet_v1_101': 0.0001} 31 | EPSILON = 1e-5 32 | MOMENTUM = 0.9 33 | MAX_ITERATION = 50000 34 | GPU_GROUP = "1" 35 | 36 | # rpn 37 | SHARE_HEAD = False 38 | RPN_NMS_IOU_THRESHOLD = 0.6 39 | MAX_PROPOSAL_NUM = 300 40 | RPN_IOU_POSITIVE_THRESHOLD = 0.5 41 | RPN_IOU_NEGATIVE_THRESHOLD = 0.2 42 | RPN_MINIBATCH_SIZE = 256 43 | RPN_POSITIVE_RATE = 0.5 44 | IS_FILTER_OUTSIDE_BOXES = True 45 | RPN_TOP_K_NMS = 3000 46 | FEATURE_PYRAMID_MODE = 0 # {0: 'feature_pyramid', 1: 'dense_feature_pyramid'} 47 | 48 | # fast rcnn 49 | FAST_RCNN_MODE = 'build_fast_rcnn1' 50 | ROI_SIZE = 14 51 | ROI_POOL_KERNEL_SIZE = 2 52 | USE_DROPOUT = False 53 | KEEP_PROB = 0.5 54 | FAST_RCNN_NMS_IOU_THRESHOLD = 0.2 55 | FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 56 | FINAL_SCORE_THRESHOLD = 0.7 57 | FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.45 58 | FAST_RCNN_MINIBATCH_SIZE = 512 59 | FAST_RCNN_POSITIVE_RATE = 0.25 60 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/pre-commit.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to verify what is about to be committed. 4 | # Called by "git commit" with no arguments. The hook should 5 | # exit with non-zero status after issuing an appropriate message if 6 | # it wants to stop the commit. 7 | # 8 | # To enable this hook, rename this file to "pre-commit". 9 | 10 | if git rev-parse --verify HEAD >/dev/null 2>&1 11 | then 12 | against=HEAD 13 | else 14 | # Initial commit: diff against an empty tree object 15 | against=4b825dc642cb6eb9a060e54bf8d69288fbee4904 16 | fi 17 | 18 | # If you want to allow non-ascii filenames set this variable to true. 19 | allownonascii=$(git config hooks.allownonascii) 20 | 21 | # Redirect output to stderr. 22 | exec 1>&2 23 | 24 | # Cross platform projects tend to avoid non-ascii filenames; prevent 25 | # them from being added to the repository. We exploit the fact that the 26 | # printable range starts at the space character and ends with tilde. 27 | if [ "$allownonascii" != "true" ] && 28 | # Note that the use of brackets around a tr range is ok here, (it's 29 | # even required, for portability to Solaris 10's /usr/bin/tr), since 30 | # the square bracket bytes happen to fall in the designated range. 31 | test $(git diff --cached --name-only --diff-filter=A -z $against | 32 | LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0 33 | then 34 | echo "Error: Attempt to add a non-ascii file name." 35 | echo 36 | echo "This can cause problems if you want to work" 37 | echo "with people on other platforms." 38 | echo 39 | echo "To be portable it is advisable to rename the file ..." 40 | echo 41 | echo "If you know what you are doing you can disable this" 42 | echo "check using:" 43 | echo 44 | echo " git config hooks.allownonascii true" 45 | echo 46 | exit 1 47 | fi 48 | 49 | # If there are whitespace errors, print the offending file names and fail. 50 | exec git diff-index --check --cached $against -- 51 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/configs/config_resnet_50.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import tensorflow as tf 6 | 7 | tf.app.flags.DEFINE_string( 8 | 'dataset_tfrecord', 9 | '../data/tfrecords', 10 | 'tfrecord of fruits dataset' 11 | ) 12 | tf.app.flags.DEFINE_integer( 13 | 'shortside_size', 14 | 600, 15 | 'the value of new height and new width, new_height = new_width' 16 | ) 17 | 18 | ########################### 19 | # data batch 20 | ########################## 21 | tf.app.flags.DEFINE_integer( 22 | 'num_classes', 23 | 20, 24 | 'num of classes' 25 | ) 26 | tf.app.flags.DEFINE_integer( 27 | 'batch_size', 28 | 1, #64 29 | 'num of imgs in a batch' 30 | ) 31 | 32 | ############################### 33 | # optimizer-- MomentumOptimizer 34 | ############################### 35 | tf.app.flags.DEFINE_float( 36 | 'momentum', 37 | 0.9, 38 | 'accumulation = momentum * accumulation + gradient' 39 | ) 40 | 41 | ############################ 42 | # train 43 | ######################## 44 | tf.app.flags.DEFINE_integer( 45 | 'max_steps', 46 | 900000, 47 | 'max iterate steps' 48 | ) 49 | 50 | tf.app.flags.DEFINE_string( 51 | 'pretrained_model_path', 52 | '../data/pretrained_weights/resnet_50.ckpt', 53 | 'the path of pretrained weights' 54 | ) 55 | tf.app.flags.DEFINE_float( 56 | 'weight_decay', 57 | 0.0001, 58 | 'weight_decay in regulation' 59 | ) 60 | ################################ 61 | # summary and save_weights_checkpoint 62 | ################################## 63 | tf.app.flags.DEFINE_string( 64 | 'summary_path', 65 | '../output/resnet_summary', 66 | 'the path of summary write to ' 67 | ) 68 | tf.app.flags.DEFINE_string( 69 | 'trained_checkpoint', 70 | '../output/resnet_trained_weights', 71 | 'the path to save trained_weights' 72 | ) 73 | FLAGS = tf.app.flags.FLAGS -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/configs/cfgs.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division, print_function, absolute_import 3 | import os 4 | 5 | # root path 6 | ROO_PATH = os.path.abspath('/home/jzchen/WorkingSpace/R2CNN_HEAD_FPN_Tensorflow') 7 | 8 | # pretrain weights path 9 | MODEL_PATH = ROO_PATH + '/output/model' 10 | SUMMARY_PATH = ROO_PATH + '/output/summary' 11 | 12 | TEST_SAVE_PATH = ROO_PATH + '/tools/test_result' 13 | # INFERENCE_IMAGE_PATH = ROO_PATH + '/tools/inference_image' 14 | INFERENCE_IMAGE_PATH = '/home/jzchen/data/RemoteSensing/ships/HRSC2016/HRSC2016/Train/AllImages' 15 | INFERENCE_SAVE_PATH = ROO_PATH + '/tools/inference_result' 16 | 17 | # Evaluation save path 18 | EVALUATE_R_DIR = ROO_PATH + '/tools/evaluation' 19 | 20 | NET_NAME = 'resnet_v1_101' 21 | VERSION = 'v1_ship_head' 22 | # CLASS_NUM = 1 23 | CLASS_NUM = 22 24 | LEVEL = ['P2', 'P3', 'P4', 'P5', 'P6'] 25 | BASE_ANCHOR_SIZE_LIST = [32, 64, 128, 256, 512] 26 | STRIDE = [4, 8, 16, 32, 64] 27 | ANCHOR_SCALES = [1.] 28 | ANCHOR_RATIOS = [1 / 2., 1 / 3., 1., 3., 2.] 29 | SCALE_FACTORS = [10., 10., 5., 5., 10.] 30 | OUTPUT_STRIDE = 16 31 | SHORT_SIDE_LEN = 600 32 | DATASET_NAME = 'HRSC2016' 33 | 34 | BATCH_SIZE = 1 35 | WEIGHT_DECAY = {'vggnet16': 0.0005, 'resnet_v1_50': 0.0001, 'resnet_v1_101': 0.0001} 36 | EPSILON = 1e-5 37 | MOMENTUM = 0.9 38 | MAX_ITERATION = 80000 39 | GPU_GROUP = "0" 40 | 41 | # rpn 42 | SHARE_HEAD = False 43 | RPN_NMS_IOU_THRESHOLD = 0.7 44 | MAX_PROPOSAL_NUM = 300 45 | RPN_IOU_POSITIVE_THRESHOLD = 0.6 46 | RPN_IOU_NEGATIVE_THRESHOLD = 0.25 47 | RPN_MINIBATCH_SIZE = 256 48 | RPN_POSITIVE_RATE = 0.5 49 | IS_FILTER_OUTSIDE_BOXES = True 50 | RPN_TOP_K_NMS = 3000 51 | FEATURE_PYRAMID_MODE = 0 # {0: 'feature_pyramid', 1: 'dense_feature_pyramid'} 52 | 53 | # fast rcnn 54 | ROTATE_NMS_USE_GPU = True 55 | ROI_SIZE = 14 56 | ROI_POOL_KERNEL_SIZE = 2 57 | USE_DROPOUT = False 58 | KEEP_PROB = 0.5 59 | FAST_RCNN_NMS_IOU_THRESHOLD = 0.15 60 | FAST_RCNN_NMS_MAX_BOXES_PER_CLASS = 100 61 | # FINAL_SCORE_THRESHOLD = 0.8 62 | FINAL_SCORE_THRESHOLD = 0.4 63 | FAST_RCNN_IOU_POSITIVE_THRESHOLD = 0.5 64 | FAST_RCNN_MINIBATCH_SIZE = 128 65 | FAST_RCNN_POSITIVE_RATE = 0.5 66 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/iou_cpu.pyx: -------------------------------------------------------------------------------- 1 | # written by yjr 2 | 3 | cimport cython 4 | import numpy as np 5 | cimport numpy as np 6 | import cv2 7 | import time 8 | 9 | DTYPE = np.float32 10 | ctypedef np.float32_t DTYPE_t 11 | ctypedef bint BOOL 12 | 13 | cdef DTYPE_t two_boxes_iou(np.ndarray[DTYPE_t, ndim=1] rectangle_1, np.ndarray[DTYPE_t, ndim=1] rectangle_2): 14 | 15 | """ 16 | calu rectangle_1 and rectangle_2 iou 17 | :param rectangle_1: [x, y, w, h, theta]. shape: (5, ) 18 | :param rectangle_2: 19 | :return: 20 | """ 21 | cdef DTYPE_t area1 = rectangle_1[2] * rectangle_1[3] 22 | cdef DTYPE_t area2 = rectangle_2[2] * rectangle_2[3] 23 | 24 | rect_1 = ((rectangle_1[0], rectangle_1[1]), (rectangle_1[3], rectangle_1[2]), rectangle_1[-1]) 25 | rect_2 = ((rectangle_2[0], rectangle_2[1]), (rectangle_2[3], rectangle_2[2]), rectangle_2[-1]) 26 | 27 | inter_points = cv2.rotatedRectangleIntersection(rect_1, rect_2)[1] 28 | 29 | cdef np.ndarray[DTYPE_t, ndim=3] order_points 30 | cdef float inter_area, iou 31 | if inter_points is not None: 32 | order_points = cv2.convexHull(inter_points, returnPoints=True) 33 | 34 | inter_area = cv2.contourArea(order_points) 35 | if area1 + area2 == inter_area: 36 | print ("area1-->", area1) 37 | print ("area2-->", area2) 38 | print ("inter_area-->", inter_area) 39 | iou = inter_area *1.0 / (area1 + area2 - inter_area) 40 | return iou 41 | else: 42 | return 0.0 43 | 44 | cpdef np.ndarray[DTYPE_t, ndim=2] get_iou_matrix( 45 | np.ndarray[DTYPE_t, ndim=2] boxes1, # (N, 5) 46 | np.ndarray[DTYPE_t, ndim=2] boxes2): # (M, 5) 47 | 48 | cdef unsigned int num_of_boxes1 = boxes1.shape[0] 49 | cdef unsigned int num_of_boxes2 = boxes2.shape[0] 50 | 51 | cdef np.ndarray[DTYPE_t, ndim=2] iou_matrix = np.zeros((num_of_boxes1, num_of_boxes2), dtype=DTYPE) 52 | # cdef DTYPE_t box_iou 53 | cdef unsigned int n, m 54 | # st = time.time() 55 | for n in range(num_of_boxes1): 56 | for m in range(num_of_boxes2): 57 | 58 | iou_matrix[n, m] = two_boxes_iou(boxes1[n], boxes2[m]) 59 | # print "iou_matrix cost time: ", time.time() - st 60 | return iou_matrix 61 | 62 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/nets_factory_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Google Inc. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | 16 | """Tests for slim.inception.""" 17 | 18 | from __future__ import absolute_import 19 | from __future__ import division 20 | from __future__ import print_function 21 | 22 | import tensorflow as tf 23 | 24 | from nets import nets_factory 25 | 26 | slim = tf.contrib.slim 27 | 28 | 29 | class NetworksTest(tf.test.TestCase): 30 | 31 | def testGetNetworkFn(self): 32 | batch_size = 5 33 | num_classes = 1000 34 | for net in nets_factory.networks_map: 35 | with self.test_session(): 36 | net_fn = nets_factory.get_network_fn(net, num_classes) 37 | # Most networks use 224 as their default_image_size 38 | image_size = getattr(net_fn, 'default_image_size', 224) 39 | inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) 40 | logits, end_points = net_fn(inputs) 41 | self.assertTrue(isinstance(logits, tf.Tensor)) 42 | self.assertTrue(isinstance(end_points, dict)) 43 | self.assertEqual(logits.get_shape().as_list()[0], batch_size) 44 | self.assertEqual(logits.get_shape().as_list()[-1], num_classes) 45 | 46 | def testGetNetworkFnArgScope(self): 47 | batch_size = 5 48 | num_classes = 10 49 | net = 'cifarnet' 50 | with self.test_session(use_gpu=True): 51 | net_fn = nets_factory.get_network_fn(net, num_classes) 52 | image_size = getattr(net_fn, 'default_image_size', 224) 53 | with slim.arg_scope([slim.model_variable, slim.variable], 54 | device='/CPU:0'): 55 | inputs = tf.random_uniform((batch_size, image_size, image_size, 3)) 56 | net_fn(inputs) 57 | weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, 'CifarNet/conv1')[0] 58 | self.assertDeviceEqual('/CPU:0', weights.device) 59 | 60 | if __name__ == '__main__': 61 | tf.test.main() 62 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/configs/config_res101.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import tensorflow as tf 6 | 7 | ###################### 8 | # data set 9 | #################### 10 | tf.app.flags.DEFINE_string( 11 | 'dataset_tfrecord', 12 | '../data/tfrecords', 13 | 'tfrecord of fruits dataset' 14 | ) 15 | tf.app.flags.DEFINE_integer( 16 | 'new_img_size', 17 | 224, 18 | 'the value of new height and new width, new_height = new_width' 19 | ) 20 | 21 | ########################### 22 | # data batch 23 | ########################## 24 | tf.app.flags.DEFINE_integer( 25 | 'num_classes', 26 | 134, 27 | 'num of classes' 28 | ) 29 | tf.app.flags.DEFINE_integer( 30 | 'batch_size', 31 | 64, #64 32 | 'num of imgs in a batch' 33 | ) 34 | tf.app.flags.DEFINE_integer( 35 | 'val_batch_size', 36 | 32, 37 | 'val or test batch' 38 | ) 39 | ########################### 40 | ## learning rate 41 | ######################### 42 | tf.app.flags.DEFINE_float( 43 | 'lr_begin', 44 | 0.001, # 0.01 # 0.001 for without prepocess 45 | 'the value of learning rate start with' 46 | ) 47 | tf.app.flags.DEFINE_integer( 48 | 'decay_steps', 49 | 20000, # 5000 50 | "after 'decay_steps' steps, learning rate begin decay" 51 | ) 52 | tf.app.flags.DEFINE_float( 53 | 'decay_rate', 54 | 0.1, 55 | 'decay rate' 56 | ) 57 | 58 | ############################### 59 | # optimizer-- MomentumOptimizer 60 | ############################### 61 | tf.app.flags.DEFINE_float( 62 | 'momentum', 63 | 0.9, 64 | 'accumulation = momentum * accumulation + gradient' 65 | ) 66 | 67 | ############################ 68 | # train 69 | ######################## 70 | tf.app.flags.DEFINE_integer( 71 | 'max_steps', 72 | 4003, 73 | 'max iterate steps' 74 | ) 75 | 76 | tf.app.flags.DEFINE_string( 77 | 'pretrained_model_path', 78 | '../data/pretrained_weights/resnet_v1_101.ckpt', 79 | 'the path of pretrained weights' 80 | ) 81 | tf.app.flags.DEFINE_float( 82 | 'weight_decay', 83 | 0.0001, 84 | 'weight_decay in regulation' 85 | ) 86 | ################################ 87 | # summary and save_weights_checkpoint 88 | ################################## 89 | tf.app.flags.DEFINE_string( 90 | 'summary_path', 91 | '../output/res101_summary', 92 | 'the path of summary write to ' 93 | ) 94 | tf.app.flags.DEFINE_string( 95 | 'trained_checkpoint', 96 | '../output/res101_trained_weights', 97 | 'the path to save trained_weights' 98 | ) 99 | FLAGS = tf.app.flags.FLAGS -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/configs/config_vgg16.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import tensorflow as tf 6 | 7 | ###################### 8 | # data set 9 | #################### 10 | 11 | tf.app.flags.DEFINE_string( 12 | 'dataset_tfrecord', 13 | '../data/tfrecords', 14 | 'tfrecord of dog dataset' 15 | ) 16 | tf.app.flags.DEFINE_integer( 17 | 'new_img_size', 18 | 224, 19 | 'the value of new height and new width, new_height = new_width' 20 | ) 21 | 22 | ########################### 23 | # data batch 24 | ########################## 25 | tf.app.flags.DEFINE_integer( 26 | 'num_classes', 27 | 134, 28 | 'num of classes' 29 | ) 30 | tf.app.flags.DEFINE_integer( 31 | 'batch_size', 32 | 32, #64 33 | 'num of imgs in a batch' 34 | ) 35 | tf.app.flags.DEFINE_integer( 36 | 'val_batch_size', 37 | 8, 38 | 'val or test batch' 39 | ) 40 | ########################### 41 | ## learning rate 42 | ######################### 43 | tf.app.flags.DEFINE_float( 44 | 'lr_begin', 45 | 0.001, # 0.01 # 0.001 for without prepocess # 0.01 for inception 46 | 'the value of learning rate start with' 47 | ) 48 | tf.app.flags.DEFINE_integer( 49 | 'decay_steps', 50 | 3000, # 5000 51 | "after 'decay_steps' steps, learning rate begin decay" 52 | ) 53 | tf.app.flags.DEFINE_float( 54 | 'decay_rate', 55 | 0.1, 56 | 'decay rate' 57 | ) 58 | 59 | ############################### 60 | # optimizer-- MomentumOptimizer 61 | ############################### 62 | tf.app.flags.DEFINE_float( 63 | 'momentum', 64 | 0.9, 65 | 'accumulation = momentum * accumulation + gradient' 66 | ) 67 | 68 | ############################ 69 | # train 70 | ######################## 71 | tf.app.flags.DEFINE_integer( 72 | 'max_steps', 73 | 20010, 74 | 'max iterate steps' 75 | ) 76 | 77 | tf.app.flags.DEFINE_string( 78 | 'pretrained_model_path', 79 | '../data/pretrained_weights/vgg_16.ckpt', 80 | 'the path of pretrained weights' 81 | ) 82 | tf.app.flags.DEFINE_float( 83 | 'weight_decay', 84 | 0.0005, 85 | 'weight_decay in regulation' 86 | ) 87 | ################################ 88 | # summary and save_weights_checkpoint 89 | ################################## 90 | tf.app.flags.DEFINE_string( 91 | 'summary_path', 92 | '../output/vgg16_summary', 93 | 'the path of summary write to ' 94 | ) 95 | tf.app.flags.DEFINE_string( 96 | 'trained_checkpoint', 97 | '../output/vgg16_trainedweights', 98 | 'the path to save trained_weights' 99 | ) 100 | FLAGS = tf.app.flags.FLAGS -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/configs/config_inception_resnet.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import tensorflow as tf 6 | 7 | ###################### 8 | # data set 9 | #################### 10 | 11 | tf.app.flags.DEFINE_string( 12 | 'dataset_tfrecord', 13 | '../data/tfrecords', 14 | 'tfrecord of fruits dataset' 15 | ) 16 | tf.app.flags.DEFINE_integer( 17 | 'new_img_size', 18 | 224, 19 | 'the value of new height and new width, new_height = new_width' 20 | ) 21 | 22 | ########################### 23 | # data batch 24 | ########################## 25 | tf.app.flags.DEFINE_integer( 26 | 'num_classes', 27 | 100, 28 | 'num of classes' 29 | ) 30 | tf.app.flags.DEFINE_integer( 31 | 'batch_size', 32 | 32, #64 33 | 'num of imgs in a batch' 34 | ) 35 | tf.app.flags.DEFINE_integer( 36 | 'val_batch_size', 37 | 16, 38 | 'val or test batch' 39 | ) 40 | ########################### 41 | ## learning rate 42 | ######################### 43 | tf.app.flags.DEFINE_float( 44 | 'lr_begin', 45 | 0.0001, # 0.01 # 0.001 for without prepocess 46 | 'the value of learning rate start with' 47 | ) 48 | tf.app.flags.DEFINE_integer( 49 | 'decay_steps', 50 | 2000, # 5000 51 | "after 'decay_steps' steps, learning rate begin decay" 52 | ) 53 | tf.app.flags.DEFINE_float( 54 | 'decay_rate', 55 | 0.1, 56 | 'decay rate' 57 | ) 58 | 59 | ############################### 60 | # optimizer-- MomentumOptimizer 61 | ############################### 62 | tf.app.flags.DEFINE_float( 63 | 'momentum', 64 | 0.9, 65 | 'accumulation = momentum * accumulation + gradient' 66 | ) 67 | 68 | ############################ 69 | # train 70 | ######################## 71 | tf.app.flags.DEFINE_integer( 72 | 'max_steps', 73 | 30050, 74 | 'max iterate steps' 75 | ) 76 | 77 | tf.app.flags.DEFINE_string( 78 | 'pretrained_model_path', 79 | '../data/pretrained_weights/inception_resnet_v2_2016_08_30.ckpt', 80 | 'the path of pretrained weights' 81 | ) 82 | tf.app.flags.DEFINE_float( 83 | 'weight_decay', 84 | 0.00004, 85 | 'weight_decay in regulation' 86 | ) 87 | ################################ 88 | # summary and save_weights_checkpoint 89 | ################################## 90 | tf.app.flags.DEFINE_string( 91 | 'summary_path', 92 | '../output/inception_res_summary', 93 | 'the path of summary write to ' 94 | ) 95 | tf.app.flags.DEFINE_string( 96 | 'trained_checkpoint', 97 | '../output/inception_res_trainedweights', 98 | 'the path to save trained_weights' 99 | ) 100 | FLAGS = tf.app.flags.FLAGS -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/inception_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains common code shared by all inception models. 16 | 17 | Usage of arg scope: 18 | with slim.arg_scope(inception_arg_scope()): 19 | logits, end_points = inception.inception_v3(images, num_classes, 20 | is_training=is_training) 21 | 22 | """ 23 | from __future__ import absolute_import 24 | from __future__ import division 25 | from __future__ import print_function 26 | 27 | import tensorflow as tf 28 | 29 | slim = tf.contrib.slim 30 | 31 | 32 | def inception_arg_scope(weight_decay=0.00004, 33 | use_batch_norm=True, 34 | batch_norm_decay=0.9997, 35 | batch_norm_epsilon=0.001): 36 | """Defines the default arg scope for inception models. 37 | 38 | Args: 39 | weight_decay: The weight decay to use for regularizing the model. 40 | use_batch_norm: "If `True`, batch_norm is applied after each convolution. 41 | batch_norm_decay: Decay for batch norm moving average. 42 | batch_norm_epsilon: Small float added to variance to avoid dividing by zero 43 | in batch norm. 44 | 45 | Returns: 46 | An `arg_scope` to use for the inception models. 47 | """ 48 | batch_norm_params = { 49 | # Decay for the moving averages. 50 | 'decay': batch_norm_decay, 51 | # epsilon to prevent 0s in variance. 52 | 'epsilon': batch_norm_epsilon, 53 | # collection containing update_ops. 54 | 'updates_collections': tf.GraphKeys.UPDATE_OPS, 55 | } 56 | if use_batch_norm: 57 | normalizer_fn = slim.batch_norm 58 | normalizer_params = batch_norm_params 59 | else: 60 | normalizer_fn = None 61 | normalizer_params = {} 62 | # Set weight_decay for weights in Conv and FC layers. 63 | with slim.arg_scope([slim.conv2d, slim.fully_connected], 64 | weights_regularizer=slim.l2_regularizer(weight_decay)): 65 | with slim.arg_scope( 66 | [slim.conv2d], 67 | weights_initializer=slim.variance_scaling_initializer(), 68 | activation_fn=tf.nn.relu, 69 | normalizer_fn=normalizer_fn, 70 | normalizer_params=normalizer_params) as sc: 71 | return sc 72 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/iou_rotate.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import numpy as np 8 | import cv2 9 | import time 10 | from libs.box_utils.rbbox_overlaps import rbbx_overlaps 11 | from libs.box_utils.iou_cpu import get_iou_matrix 12 | import tensorflow as tf 13 | from libs.box_utils.coordinate_convert import * 14 | 15 | 16 | def iou_rotate_calculate(boxes1, boxes2, use_gpu=True, gpu_id=0): 17 | ''' 18 | 19 | :param boxes_list1:[N, 8] tensor 20 | :param boxes_list2: [M, 8] tensor 21 | :return: 22 | ''' 23 | 24 | boxes1 = tf.cast(boxes1, tf.float32) 25 | boxes2 = tf.cast(boxes2, tf.float32) 26 | if use_gpu: 27 | 28 | iou_matrix = tf.py_func(rbbx_overlaps, 29 | inp=[boxes1, boxes2, gpu_id], 30 | Tout=tf.float32) 31 | else: 32 | iou_matrix = tf.py_func(get_iou_matrix, inp=[boxes1, boxes2], 33 | Tout=tf.float32) 34 | 35 | iou_matrix = tf.reshape(iou_matrix, [tf.shape(boxes1)[0], tf.shape(boxes2)[0]]) 36 | 37 | return iou_matrix 38 | 39 | 40 | def iou_rotate_calculate1(boxes1, boxes2, use_gpu=True, gpu_id=0): 41 | 42 | # start = time.time() 43 | if use_gpu: 44 | ious = rbbx_overlaps(boxes1, boxes2, gpu_id) 45 | else: 46 | area1 = boxes1[:, 2] * boxes1[:, 3] 47 | area2 = boxes2[:, 2] * boxes2[:, 3] 48 | ious = [] 49 | for i, box1 in enumerate(boxes1): 50 | temp_ious = [] 51 | r1 = ((box1[0], box1[1]), (box1[2], box1[3]), box1[4]) 52 | for j, box2 in enumerate(boxes2): 53 | r2 = ((box2[0], box2[1]), (box2[2], box2[3]), box2[4]) 54 | 55 | int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1] 56 | if int_pts is not None: 57 | order_pts = cv2.convexHull(int_pts, returnPoints=True) 58 | 59 | int_area = cv2.contourArea(order_pts) 60 | 61 | inter = int_area * 1.0 / (area1[i] + area2[j] - int_area) 62 | temp_ious.append(inter) 63 | else: 64 | temp_ious.append(0.0) 65 | ious.append(temp_ious) 66 | 67 | # print('{}s'.format(time.time() - start)) 68 | 69 | return np.array(ious, dtype=np.float32) 70 | 71 | 72 | if __name__ == '__main__': 73 | import os 74 | os.environ["CUDA_VISIBLE_DEVICES"] = '13' 75 | boxes1 = np.array([[50, 50, 100, 300, 0], 76 | [60, 60, 100, 200, 0]], np.float32) 77 | 78 | boxes2 = np.array([[50, 50, 100, 300, -45.], 79 | [200, 200, 100, 200, 0.]], np.float32) 80 | 81 | start = time.time() 82 | with tf.Session() as sess: 83 | ious = iou_rotate_calculate1(boxes1, boxes2, use_gpu=False) 84 | print(sess.run(ious)) 85 | print('{}s'.format(time.time() - start)) 86 | 87 | # start = time.time() 88 | # for _ in range(10): 89 | # ious = rbbox_overlaps.rbbx_overlaps(boxes1, boxes2) 90 | # print('{}s'.format(time.time() - start)) 91 | # print(ious) 92 | 93 | # print(ovr) 94 | 95 | 96 | 97 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Ship-Detection 2 | Ship Detection based on FPN, horizontal bounding box, rotated bounding box, and head prediction. 3 | 4 | This work is based on Yang xue, etc ([Github](https://github.com/yangxue0827/R2CNN_HEAD_FPN_Tensorflow)). Thanks to their great work. Based on their work, I have made following modifications: 5 | 6 | ## Focal loss 7 | 8 | You need to lower the "FINAL_SCORE_THRESHOLD" value (in the cfgs.py) to see more prediction results. 9 | 10 | ## Randomly ratotation of data augmentation 11 | 12 | When each time we train a image (batch size = 1), we randomly rotate the image by a angle within [0,90]. The expriments reveal that it is an efficient way as regularization. 13 | 14 | Attention: the loss of ship head regression is multiplied by 10 and added into the total loss. 15 | 16 | ## Correction of eval.py (eval_deprecated.py) to EVAL_TEST.py for evaluation 17 | 18 | Since their original evaluation code (eval.py) is not recommended, I have upgraded this as EVAL_TEST.py, in which it includes following concerns: 19 | 20 | ### Inconsitent Coordinate 21 | 22 | 1. Format of coordinates of predicted bounding boxes and ground truth bounding boxes: 23 | fast_rcnn_decode_boxes/gtbox_minAreaRectangle: [ymin, xmin, ymax, xmax] 24 | fast_rcnn_decode_boxes_rotate/gtbox: [y_c, x_c, h, w, theta] 25 | 2. However, when caculating IoUs on horizontal and rotated bounding boxes (EVAL_test.py and iou_rotate.py), it requires following format: 26 | Rotated: [x_c, y_c, w, h, theta] 27 | Horizontal: [xmin, ymin, xmax, ymax] 28 | 29 | ### Calculating the interpolation performed in all points 30 | 31 | In some images there are more than one detection overlapping a ground truth. For those cases the detection with the highest IOU is taken, discarding the other detections. This rule is applied by the PASCAL VOC 2012 metric. However, in my upgraded implementatioin ([Reference](https://github.com/DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow)), the detection with the highest condidence is taken. I assume it is a tradeoff between classification and regression. 32 | 33 | ### When evaluating, you need to set the number of images needed to be evaluated manually. 34 | 35 | ### Need to further verification 36 | 37 | ## Change to class-agnostic regression 38 | 39 | Originally, regression and NMS are committed for each class. Due to limited size of training data, I change the multi-classes regression to class-agnostic regression. Especially, the scores used for NMS are selected according to the highest confidence in all classes corresponding to each instance (each row). However, the values of fast_rcnn_loc_loss and fast_rcnn_loc_rotate_loss are too small, and it may causes unnormal gradient behavior. Therefore, you need to lower the learning rate to continue the training process and further analyze such phenomenon according to related works and Tensorflow debugger. 40 | 41 | ## Dataset 42 | 43 | [HRSC 2016](https://sites.google.com/site/hrsc2016/home) 44 | 45 | ## Reference 46 | 47 | ### [Metrics for object detection](https://github.com/MonsterZhZh/Object-Detection-Metrics) 48 | 49 | ### [Oriented Evaluation Server provided by DOTA](https://github.com/CAPTAIN-WHU/DOTA_devkit) 50 | 51 | ### [Tensorflow debugger(from tensorflow.python import debug as tf_debug)](https://blog.csdn.net/qq_22291287/article/details/82712050) 52 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/label_name_dict/label_dict.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division, print_function, absolute_import 3 | 4 | from libs.configs import cfgs 5 | 6 | if cfgs.DATASET_NAME == 'HRSC2016': 7 | # NAME_LABEL_MAP = { 8 | # 'back_ground': 0, 9 | # 'ship': 1 10 | # } 11 | # Class_ID distribution on the HRSC_2016 Train set: 12 | # 100000001:386 13 | # 100000002:1 14 | # 100000003:44 15 | # 100000004:1 16 | 17 | # 100000005:50 18 | # 100000006:20 19 | # 100000007:266 20 | # 100000008:66 21 | # 100000009:132 22 | # 100000010:30 23 | # 100000011:160 24 | # Without 100000012 25 | # 100000013:8 26 | # Without 100000014 27 | # 100000015:51 28 | # 100000016:99 29 | # Without 100000017 30 | # 100000018:34 31 | # 100000019:47 32 | # 100000020:15 33 | # 100000022:55 34 | # 100000024:8 35 | # 100000025:167 36 | # 100000026:6 37 | # 100000027:44 38 | # 100000028:5 39 | # 100000029:15 40 | # 100000030:25 41 | # Without 100000031 42 | # 100000032:13 43 | # Without 100000033 44 | 45 | # Total Instances: 1748 46 | # Total classes: 26 47 | NAME_LABEL_MAP = { 48 | 'back_ground': 0, 49 | '100000005': 1, 50 | '100000006' : 2, 51 | '100000007' : 3, 52 | '100000008' : 4, 53 | '100000009' : 5, 54 | '100000010' : 6, 55 | '100000011' : 7, 56 | # 'Kitty' : 8, 57 | '100000013' : 8, 58 | # 'Abukuma' : 10, 59 | '100000015' : 9, 60 | '100000016' : 10, 61 | # 'USS' : 13, 62 | '100000018' : 11, 63 | '100000019' : 12, 64 | '100000020' : 13, 65 | '100000022' : 14, 66 | '100000024' : 15, 67 | '100000025' : 16, 68 | '100000026' : 17, 69 | '100000027' : 18, 70 | '100000028' : 19, 71 | '100000029' : 20, 72 | '100000030' : 21, 73 | # 'Ford_class' : 25, 74 | '100000032' : 22, 75 | # 'Invincible_class' : 27 76 | } 77 | elif cfgs.DATASET_NAME == 'UAV': 78 | NAME_LABEL_MAP = { 79 | 'back_ground': 0, 80 | 'M41': 1, 81 | 'M603A': 2, 82 | 'M48H': 3, 83 | } 84 | elif cfgs.DATASET_NAME == 'airplane': 85 | NAME_LABEL_MAP = { 86 | 'back_ground': 0, 87 | 'airplane': 1 88 | } 89 | elif cfgs.DATASET_NAME == 'pascal': 90 | NAME_LABEL_MAP = { 91 | 'back_ground': 0, 92 | 'aeroplane': 1, 93 | 'bicycle': 2, 94 | 'bird': 3, 95 | 'boat': 4, 96 | 'bottle': 5, 97 | 'bus': 6, 98 | 'car': 7, 99 | 'cat': 8, 100 | 'chair': 9, 101 | 'cow': 10, 102 | 'diningtable': 11, 103 | 'dog': 12, 104 | 'horse': 13, 105 | 'motorbike': 14, 106 | 'person': 15, 107 | 'pottedplant': 16, 108 | 'sheep': 17, 109 | 'sofa': 18, 110 | 'train': 19, 111 | 'tvmonitor': 20 112 | } 113 | else: 114 | assert 'please set label dict!' 115 | 116 | 117 | def get_label_name_map(): 118 | reverse_dict = {} 119 | for name, label in NAME_LABEL_MAP.items(): 120 | reverse_dict[label] = name 121 | return reverse_dict 122 | 123 | LABEl_NAME_MAP = get_label_name_map() -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/coordinate_convert.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import cv2 8 | import numpy as np 9 | 10 | 11 | def forward_convert(coordinate, with_label=True): 12 | """ 13 | :param coordinate: format [y_c, x_c, h, w, theta] 14 | :return: format [y1, x1, y2, x2, y3, x3, y4, x4] 15 | """ 16 | boxes = [] 17 | if with_label: 18 | for rect in coordinate: 19 | box = cv2.boxPoints(((rect[1], rect[0]), (rect[3], rect[2]), rect[4])) 20 | box = np.reshape(box, [-1, ]) 21 | boxes.append([box[1], box[0], box[3], box[2], box[5], box[4], box[7], box[6], rect[5]]) 22 | else: 23 | for rect in coordinate: 24 | box = cv2.boxPoints(((rect[1], rect[0]), (rect[3], rect[2]), rect[4])) 25 | box = np.reshape(box, [-1, ]) 26 | boxes.append([box[1], box[0], box[3], box[2], box[5], box[4], box[7], box[6]]) 27 | 28 | return np.array(boxes, dtype=np.float32) 29 | 30 | 31 | def back_forward_convert(coordinate, with_label=True): 32 | """ 33 | :param coordinate: format [x1, y1, x2, y2, x3, y3, x4, y4, (label)] 34 | :param with_label: default True 35 | :return: format [y_c, x_c, h, w, theta, (label)] 36 | """ 37 | 38 | boxes = [] 39 | if with_label: 40 | for rect in coordinate: 41 | box = np.int0(rect[:-1]) 42 | box = box.reshape([4, 2]) 43 | rect1 = cv2.minAreaRect(box) 44 | 45 | x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2] 46 | boxes.append([y, x, h, w, theta, rect[-1]]) 47 | 48 | else: 49 | for rect in coordinate: 50 | box = np.int0(rect) 51 | box = box.reshape([4, 2]) 52 | rect1 = cv2.minAreaRect(box) 53 | 54 | x, y, w, h, theta = rect1[0][0], rect1[0][1], rect1[1][0], rect1[1][1], rect1[2] 55 | boxes.append([y, x, h, w, theta]) 56 | 57 | return np.array(boxes, dtype=np.float32) 58 | 59 | 60 | def get_head_quadrant(head, gtbox): 61 | """ 62 | :param head: [head_x, head_y] 63 | :param gtbox: [y_c, x_c, h, w, theta, label] 64 | :return: head quadrant 0/1/2/3 65 | """ 66 | head_quadrant = [] 67 | for i, box in enumerate(gtbox): 68 | detla_x = head[i][0] - box[1] 69 | detla_y = head[i][1] - box[0] 70 | if (detla_x >= 0) and (detla_y >= 0): 71 | head_quadrant.append(0) 72 | elif (detla_x >= 0) and (detla_y <= 0): 73 | head_quadrant.append(1) 74 | elif (detla_x <= 0) and (detla_y <= 0): 75 | head_quadrant.append(2) 76 | else: 77 | head_quadrant.append(3) 78 | return np.array(head_quadrant, np.float32) 79 | 80 | if __name__ == '__main__': 81 | coord = np.array([[150, 150, 50, 100, -90, 1], 82 | [150, 150, 100, 50, -90, 1], 83 | [150, 150, 50, 100, -45, 1], 84 | [150, 150, 100, 50, -45, 1]]) 85 | 86 | coord1 = np.array([[150, 150, 100, 50, 0], 87 | [150, 150, 100, 50, -90], 88 | [150, 150, 100, 50, 45], 89 | [150, 150, 100, 50, -45]]) 90 | 91 | coord2 = forward_convert(coord) 92 | # coord3 = forward_convert(coord1, mode=-1) 93 | print(coord2) 94 | 95 | head_quadrant = get_head_quadrant([[0, 200], 96 | [200, 200], 97 | [200, 100], 98 | [100, 100]], coord) 99 | print(head_quadrant) 100 | 101 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/sample.xml: -------------------------------------------------------------------------------- 1 | 2 | xxx 3 | 17%4800%7000.tif 4 | 5 | xxxx 6 | IECAS 7 | IECAS 8 | 0 9 | 10 | 11 | IECAS 12 | IECAS 13 | 14 | 15 | 1000 16 | 600 17 | 3 18 | 19 | 0 20 | 21 | ship 22 | Unknown 23 | 1 24 | 0 25 | 26 | 281 27 | 587 28 | 266 29 | 570 30 | 353 31 | 494 32 | 368 33 | 511 34 | 275 35 | 580 36 | 37 | 38 | 39 | ship 40 | Unknown 41 | 1 42 | 0 43 | 44 | 471 45 | 250 46 | 464 47 | 228 48 | 566 49 | 193 50 | 574 51 | 215 52 | 468 53 | 239 54 | 55 | 56 | 57 | ship 58 | Unknown 59 | 1 60 | 0 61 | 62 | 470 63 | 415 64 | 454 65 | 397 66 | 537 67 | 325 68 | 554 69 | 344 70 | 462 71 | 406 72 | 73 | 74 | 75 | ship 76 | Unknown 77 | 1 78 | 0 79 | 80 | 184 81 | 384 82 | 174 83 | 362 84 | 289 85 | 306 86 | 299 87 | 329 88 | 180 89 | 374 90 | 91 | 92 | 93 | ship 94 | Unknown 95 | 1 96 | 0 97 | 98 | 686 99 | 191 100 | 671 101 | 179 102 | 731 103 | 107 104 | 747 105 | 119 106 | 678 107 | 185 108 | 109 | 110 | 111 | ship 112 | Unknown 113 | 1 114 | 0 115 | 116 | 464 117 | 132 118 | 448 119 | 108 120 | 577 121 | 21 122 | 592 123 | 44 124 | 456 125 | 120 126 | 127 | 128 | 129 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/tools/test_rotate.py: -------------------------------------------------------------------------------- 1 | # -*- coding:utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import print_function 5 | from __future__ import division 6 | import sys 7 | sys.path.append('../') 8 | 9 | import tensorflow.contrib.slim as slim 10 | import os 11 | import time 12 | from data.io.read_tfrecord import next_batch 13 | from libs.configs import cfgs 14 | from help_utils import tools 15 | from libs.box_utils.show_box_in_tensor import * 16 | from libs.box_utils.coordinate_convert import back_forward_convert, get_head_quadrant 17 | from libs.box_utils.boxes_utils import get_horizen_minAreaRectangle, get_head 18 | 19 | import numpy as np 20 | 21 | 22 | os.environ["CUDA_VISIBLE_DEVICES"] = cfgs.GPU_GROUP 23 | 24 | 25 | def test_rotate(): 26 | with tf.Graph().as_default(): 27 | with tf.name_scope('get_batch'): 28 | img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \ 29 | next_batch(dataset_name=cfgs.DATASET_NAME, 30 | batch_size=cfgs.BATCH_SIZE, 31 | shortside_len=cfgs.SHORT_SIDE_LEN, 32 | is_training=True) 33 | gtboxes_and_label, head = get_head(tf.squeeze(gtboxes_and_label_batch, 0)) 34 | gtboxes_and_label = tf.py_func(back_forward_convert, 35 | inp=[gtboxes_and_label], 36 | Tout=tf.float32) 37 | gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 6]) 38 | head_quadrant = tf.py_func(get_head_quadrant, 39 | inp=[head, gtboxes_and_label], 40 | Tout=tf.float32) 41 | head_quadrant = tf.reshape(head_quadrant, [-1, 1]) 42 | 43 | gtboxes_and_label_minAreaRectangle = get_horizen_minAreaRectangle(gtboxes_and_label) 44 | 45 | gtboxes_and_label_minAreaRectangle = tf.reshape(gtboxes_and_label_minAreaRectangle, [-1, 5]) 46 | 47 | with tf.name_scope('draw_gtboxes'): 48 | gtboxes_in_img = draw_box_with_color(img_batch, tf.reshape(gtboxes_and_label_minAreaRectangle, [-1, 5])[:, :-1], 49 | text=tf.shape(gtboxes_and_label_minAreaRectangle)[0]) 50 | 51 | gtboxes_rotate_in_img = draw_box_with_color_rotate(img_batch, tf.reshape(gtboxes_and_label, [-1, 6])[:, :-1], 52 | text=tf.shape(gtboxes_and_label)[0], 53 | head=head_quadrant) 54 | 55 | init_op = tf.group( 56 | tf.global_variables_initializer(), 57 | tf.local_variables_initializer() 58 | ) 59 | 60 | config = tf.ConfigProto() 61 | # config.gpu_options.per_process_gpu_memory_fraction = 0.5 62 | config.gpu_options.allow_growth = True 63 | with tf.Session(config=config) as sess: 64 | sess.run(init_op) 65 | coord = tf.train.Coordinator() 66 | threads = tf.train.start_queue_runners(sess, coord) 67 | 68 | for i in range(650): 69 | img_gtboxes, img_gtboxes_rotate, img_name = sess.run([gtboxes_in_img, gtboxes_rotate_in_img, img_name_batch]) 70 | img_gtboxes = np.squeeze(img_gtboxes, axis=0) 71 | img_gtboxes_rotate = np.squeeze(img_gtboxes_rotate, axis=0) 72 | 73 | print(i) 74 | cv2.imwrite(cfgs.INFERENCE_SAVE_PATH + '/{}_horizontal_fpn.jpg'.format(str(img_name[0])), img_gtboxes) 75 | cv2.imwrite(cfgs.INFERENCE_SAVE_PATH + '/{}_rotate_fpn.jpg'.format(str(img_name[0])), img_gtboxes_rotate) 76 | 77 | coord.request_stop() 78 | coord.join(threads) 79 | 80 | 81 | if __name__ == '__main__': 82 | 83 | test_rotate() -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/lenet.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains a variant of the LeNet model definition.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import tensorflow as tf 22 | 23 | slim = tf.contrib.slim 24 | 25 | 26 | def lenet(images, num_classes=10, is_training=False, 27 | dropout_keep_prob=0.5, 28 | prediction_fn=slim.softmax, 29 | scope='LeNet'): 30 | """Creates a variant of the LeNet model. 31 | 32 | Note that since the output is a set of 'logits', the values fall in the 33 | interval of (-infinity, infinity). Consequently, to convert the outputs to a 34 | probability distribution over the characters, one will need to convert them 35 | using the softmax function: 36 | 37 | logits = lenet.lenet(images, is_training=False) 38 | probabilities = tf.nn.softmax(logits) 39 | predictions = tf.argmax(logits, 1) 40 | 41 | Args: 42 | images: A batch of `Tensors` of size [batch_size, height, width, channels]. 43 | num_classes: the number of classes in the dataset. 44 | is_training: specifies whether or not we're currently training the model. 45 | This variable will determine the behaviour of the dropout layer. 46 | dropout_keep_prob: the percentage of activation values that are retained. 47 | prediction_fn: a function to get predictions out of logits. 48 | scope: Optional variable_scope. 49 | 50 | Returns: 51 | logits: the pre-softmax activations, a tensor of size 52 | [batch_size, `num_classes`] 53 | end_points: a dictionary from components of the network to the corresponding 54 | activation. 55 | """ 56 | end_points = {} 57 | 58 | with tf.variable_scope(scope, 'LeNet', [images, num_classes]): 59 | net = slim.conv2d(images, 32, [5, 5], scope='conv1') 60 | net = slim.max_pool2d(net, [2, 2], 2, scope='pool1') 61 | net = slim.conv2d(net, 64, [5, 5], scope='conv2') 62 | net = slim.max_pool2d(net, [2, 2], 2, scope='pool2') 63 | net = slim.flatten(net) 64 | end_points['Flatten'] = net 65 | 66 | net = slim.fully_connected(net, 1024, scope='fc3') 67 | net = slim.dropout(net, dropout_keep_prob, is_training=is_training, 68 | scope='dropout3') 69 | logits = slim.fully_connected(net, num_classes, activation_fn=None, 70 | scope='fc4') 71 | 72 | end_points['Logits'] = logits 73 | end_points['Predictions'] = prediction_fn(logits, scope='Predictions') 74 | 75 | return logits, end_points 76 | lenet.default_image_size = 28 77 | 78 | 79 | def lenet_arg_scope(weight_decay=0.0): 80 | """Defines the default lenet argument scope. 81 | 82 | Args: 83 | weight_decay: The weight decay to use for regularizing the model. 84 | 85 | Returns: 86 | An `arg_scope` to use for the inception v3 model. 87 | """ 88 | with slim.arg_scope( 89 | [slim.conv2d, slim.fully_connected], 90 | weights_regularizer=slim.l2_regularizer(weight_decay), 91 | weights_initializer=tf.truncated_normal_initializer(stddev=0.1), 92 | activation_fn=tf.nn.relu) as sc: 93 | return sc 94 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/make_anchor.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import tensorflow as tf 8 | 9 | 10 | def enum_scales(base_anchor, anchor_scales, name='enum_scales'): 11 | 12 | ''' 13 | :param base_anchor: [y_center, x_center, h, w] 14 | :param anchor_scales: different scales, like [0.5, 1., 2.0] 15 | :return: return base anchors in different scales. 16 | Example:[[0, 0, 128, 128],[0, 0, 256, 256],[0, 0, 512, 512]] 17 | ''' 18 | with tf.variable_scope(name): 19 | anchor_scales = tf.reshape(anchor_scales, [-1, 1]) 20 | 21 | return base_anchor * anchor_scales 22 | 23 | 24 | def enum_ratios(anchors, anchor_ratios, name='enum_ratios'): 25 | 26 | ''' 27 | :param anchors: base anchors in different scales 28 | :param anchor_ratios: ratio = h / w 29 | :return: base anchors in different scales and ratios 30 | ''' 31 | 32 | with tf.variable_scope(name): 33 | _, _, hs, ws = tf.unstack(anchors, axis=1) # for base anchor, w == h 34 | sqrt_ratios = tf.sqrt(anchor_ratios) 35 | sqrt_ratios = tf.expand_dims(sqrt_ratios, axis=1) 36 | ws = tf.reshape(ws / sqrt_ratios, [-1]) 37 | hs = tf.reshape(hs * sqrt_ratios, [-1]) 38 | # assert tf.shape(ws) == tf.shape(hs), 'h shape is not equal w shape' 39 | 40 | num_anchors_per_location = tf.shape(ws)[0] 41 | 42 | return tf.transpose(tf.stack([tf.zeros([num_anchors_per_location, ]), 43 | tf.zeros([num_anchors_per_location,]), 44 | ws, hs])) 45 | 46 | 47 | def make_anchors(base_anchor_size, anchor_scales, anchor_ratios, featuremaps_height, 48 | featuremaps_width, stride, name='make_anchors'): 49 | 50 | ''' 51 | :param base_anchor_size: base anchor size in different scales 52 | :param anchor_scales: anchor scales 53 | :param anchor_ratios: anchor ratios 54 | :param featuremaps_width: width of featuremaps 55 | :param featuremaps_height: height of featuremaps 56 | :return: anchors of shape [w * h * len(anchor_scales) * len(anchor_ratios), 4] 57 | ''' 58 | 59 | with tf.variable_scope(name): 60 | # [y_center, x_center, h, w] 61 | base_anchor = tf.constant([0, 0, base_anchor_size, base_anchor_size], dtype=tf.float32) 62 | base_anchors = enum_ratios(enum_scales(base_anchor, anchor_scales), anchor_ratios) 63 | 64 | _, _, ws, hs = tf.unstack(base_anchors, axis=1) 65 | 66 | x_centers = tf.range(tf.cast(featuremaps_width, tf.float32), dtype=tf.float32) * stride 67 | y_centers = tf.range(tf.cast(featuremaps_height, tf.float32), dtype=tf.float32) * stride 68 | 69 | x_centers, y_centers = tf.meshgrid(x_centers, y_centers) 70 | 71 | ws, x_centers = tf.meshgrid(ws, x_centers) 72 | hs, y_centers = tf.meshgrid(hs, y_centers) 73 | 74 | box_centers = tf.stack([y_centers, x_centers], axis=2) 75 | box_centers = tf.reshape(box_centers, [-1, 2]) 76 | 77 | box_sizes = tf.stack([hs, ws], axis=2) 78 | box_sizes = tf.reshape(box_sizes, [-1, 2]) 79 | final_anchors = tf.concat([box_centers - 0.5*box_sizes, box_centers+0.5*box_sizes], axis=1) 80 | return final_anchors 81 | 82 | if __name__ == '__main__': 83 | base_anchor = tf.constant([256], dtype=tf.float32) 84 | anchor_scales = tf.constant([1.0], dtype=tf.float32) 85 | anchor_ratios = tf.constant([0.5, 1.0, 2.0], dtype=tf.float32) 86 | # print(enum_scales(base_anchor, anchor_scales)) 87 | sess = tf.Session() 88 | # print(sess.run(enum_ratios(enum_scales(base_anchor, anchor_scales), anchor_ratios))) 89 | anchors = make_anchors(256, anchor_scales, anchor_ratios, 90 | featuremaps_height=38, 91 | featuremaps_width=50, stride=16) 92 | 93 | _anchors = sess.run(anchors) 94 | print(_anchors) 95 | 96 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/mobilenet_v1.md: -------------------------------------------------------------------------------- 1 | # MobileNet_v1 2 | 3 | [MobileNets](https://arxiv.org/abs/1704.04861) are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices with [TensorFlow Mobile](https://www.tensorflow.org/mobile/). 4 | 5 | MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. 6 | 7 | ![alt text](mobilenet_v1.png "MobileNet Graph") 8 | 9 | # Pre-trained Models 10 | 11 | Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates (MACs) which measures the number of fused Multiplication and Addition operations. These MobileNet models have been trained on the 12 | [ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/) 13 | image classification dataset. Accuracies were computed by evaluating using a single image crop. 14 | 15 | Model Checkpoint | Million MACs | Million Parameters | Top-1 Accuracy| Top-5 Accuracy | 16 | :----:|:------------:|:----------:|:-------:|:-------:| 17 | [MobileNet_v1_1.0_224](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz)|569|4.24|70.7|89.5| 18 | [MobileNet_v1_1.0_192](http://download.tensorflow.org/models/mobilenet_v1_1.0_192_2017_06_14.tar.gz)|418|4.24|69.3|88.9| 19 | [MobileNet_v1_1.0_160](http://download.tensorflow.org/models/mobilenet_v1_1.0_160_2017_06_14.tar.gz)|291|4.24|67.2|87.5| 20 | [MobileNet_v1_1.0_128](http://download.tensorflow.org/models/mobilenet_v1_1.0_128_2017_06_14.tar.gz)|186|4.24|64.1|85.3| 21 | [MobileNet_v1_0.75_224](http://download.tensorflow.org/models/mobilenet_v1_0.75_224_2017_06_14.tar.gz)|317|2.59|68.4|88.2| 22 | [MobileNet_v1_0.75_192](http://download.tensorflow.org/models/mobilenet_v1_0.75_192_2017_06_14.tar.gz)|233|2.59|67.4|87.3| 23 | [MobileNet_v1_0.75_160](http://download.tensorflow.org/models/mobilenet_v1_0.75_160_2017_06_14.tar.gz)|162|2.59|65.2|86.1| 24 | [MobileNet_v1_0.75_128](http://download.tensorflow.org/models/mobilenet_v1_0.75_128_2017_06_14.tar.gz)|104|2.59|61.8|83.6| 25 | [MobileNet_v1_0.50_224](http://download.tensorflow.org/models/mobilenet_v1_0.50_224_2017_06_14.tar.gz)|150|1.34|64.0|85.4| 26 | [MobileNet_v1_0.50_192](http://download.tensorflow.org/models/mobilenet_v1_0.50_192_2017_06_14.tar.gz)|110|1.34|62.1|84.0| 27 | [MobileNet_v1_0.50_160](http://download.tensorflow.org/models/mobilenet_v1_0.50_160_2017_06_14.tar.gz)|77|1.34|59.9|82.5| 28 | [MobileNet_v1_0.50_128](http://download.tensorflow.org/models/mobilenet_v1_0.50_128_2017_06_14.tar.gz)|49|1.34|56.2|79.6| 29 | [MobileNet_v1_0.25_224](http://download.tensorflow.org/models/mobilenet_v1_0.25_224_2017_06_14.tar.gz)|41|0.47|50.6|75.0| 30 | [MobileNet_v1_0.25_192](http://download.tensorflow.org/models/mobilenet_v1_0.25_192_2017_06_14.tar.gz)|34|0.47|49.0|73.6| 31 | [MobileNet_v1_0.25_160](http://download.tensorflow.org/models/mobilenet_v1_0.25_160_2017_06_14.tar.gz)|21|0.47|46.0|70.7| 32 | [MobileNet_v1_0.25_128](http://download.tensorflow.org/models/mobilenet_v1_0.25_128_2017_06_14.tar.gz)|14|0.47|41.3|66.2| 33 | 34 | 35 | Here is an example of how to download the MobileNet_v1_1.0_224 checkpoint: 36 | 37 | ```shell 38 | $ CHECKPOINT_DIR=/tmp/checkpoints 39 | $ mkdir ${CHECKPOINT_DIR} 40 | $ wget http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz 41 | $ tar -xvf mobilenet_v1_1.0_224_2017_06_14.tar.gz 42 | $ mv mobilenet_v1_1.0_224.ckpt.* ${CHECKPOINT_DIR} 43 | $ rm mobilenet_v1_1.0_224_2017_06_14.tar.gz 44 | ``` 45 | More information on integrating MobileNets into your project can be found at the [TF-Slim Image Classification Library](https://github.com/tensorflow/models/blob/master/slim/README.md). 46 | 47 | To get started running models on-device go to [TensorFlow Mobile](https://www.tensorflow.org/mobile/). 48 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/data/io/read_tfrecord.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import print_function 5 | from __future__ import division 6 | 7 | import tensorflow as tf 8 | import os 9 | from data.io import image_preprocess 10 | 11 | 12 | def read_single_example_and_decode(filename_queue): 13 | 14 | # tfrecord_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) 15 | # reader = tf.TFRecordReader(options=tfrecord_options) 16 | reader = tf.TFRecordReader() 17 | 18 | _, serialized_example = reader.read(filename_queue) 19 | 20 | features = tf.parse_single_example( 21 | serialized=serialized_example, 22 | features={ 23 | 'img_name': tf.FixedLenFeature([], tf.string), 24 | 'img_height': tf.FixedLenFeature([], tf.int64), 25 | 'img_width': tf.FixedLenFeature([], tf.int64), 26 | 'img': tf.FixedLenFeature([], tf.string), 27 | 'gtboxes_and_label': tf.FixedLenFeature([], tf.string), 28 | 'num_objects': tf.FixedLenFeature([], tf.int64) 29 | } 30 | ) 31 | img_name = features['img_name'] 32 | img_height = tf.cast(features['img_height'], tf.int32) 33 | img_width = tf.cast(features['img_width'], tf.int32) 34 | img = tf.decode_raw(features['img'], tf.uint8) 35 | 36 | img = tf.reshape(img, shape=[img_height, img_width, 3]) 37 | 38 | gtboxes_and_label = tf.decode_raw(features['gtboxes_and_label'], tf.int32) 39 | gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 11]) 40 | 41 | num_objects = tf.cast(features['num_objects'], tf.int32) 42 | return img_name, img, gtboxes_and_label, num_objects 43 | 44 | 45 | def read_and_prepocess_single_img(filename_queue, shortside_len, is_training): 46 | 47 | img_name, img, gtboxes_and_label, num_objects = read_single_example_and_decode(filename_queue) 48 | # img = tf.image.per_image_standardization(img) 49 | img = tf.cast(img, tf.float32) 50 | img = img - tf.constant([103.939, 116.779, 123.68]) 51 | if is_training: 52 | img, gtboxes_and_label = image_preprocess.rotate_point(img_tensor=img, gtboxes_and_label=gtboxes_and_label) 53 | img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label, 54 | target_shortside_len=shortside_len) 55 | img, gtboxes_and_label = image_preprocess.random_flip_left_right(img_tensor=img, gtboxes_and_label=gtboxes_and_label) 56 | 57 | else: 58 | img, gtboxes_and_label = image_preprocess.short_side_resize(img_tensor=img, gtboxes_and_label=gtboxes_and_label, 59 | target_shortside_len=shortside_len) 60 | 61 | return img_name, img, gtboxes_and_label, num_objects 62 | 63 | 64 | def next_batch(dataset_name, batch_size, shortside_len, is_training): 65 | if dataset_name not in ['UAV', 'shapan', 'airplane', 'SHIP', 'HRSC2016', 'spacenet', 'pascal', 'coco']: 66 | raise ValueError('dataSet name must be in pascal or coco') 67 | 68 | if is_training: 69 | pattern = os.path.join('../data/tfrecords', dataset_name + '_train*') 70 | else: 71 | pattern = os.path.join('../data/tfrecords', dataset_name + '_test*') 72 | 73 | print('tfrecord path is -->', os.path.abspath(pattern)) 74 | filename_tensorlist = tf.train.match_filenames_once(pattern) 75 | 76 | filename_queue = tf.train.string_input_producer(filename_tensorlist) 77 | 78 | img_name, img, gtboxes_and_label, num_obs = read_and_prepocess_single_img(filename_queue, shortside_len, 79 | is_training=is_training) 80 | img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch = \ 81 | tf.train.batch( 82 | [img_name, img, gtboxes_and_label, num_obs], 83 | batch_size=batch_size, 84 | capacity=100, 85 | num_threads=16, 86 | dynamic_pad=True) 87 | return img_name_batch, img_batch, gtboxes_and_label_batch, num_obs_batch 88 | 89 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/update.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # An example hook script to blocks unannotated tags from entering. 4 | # Called by "git receive-pack" with arguments: refname sha1-old sha1-new 5 | # 6 | # To enable this hook, rename this file to "update". 7 | # 8 | # Config 9 | # ------ 10 | # hooks.allowunannotated 11 | # This boolean sets whether unannotated tags will be allowed into the 12 | # repository. By default they won't be. 13 | # hooks.allowdeletetag 14 | # This boolean sets whether deleting tags will be allowed in the 15 | # repository. By default they won't be. 16 | # hooks.allowmodifytag 17 | # This boolean sets whether a tag may be modified after creation. By default 18 | # it won't be. 19 | # hooks.allowdeletebranch 20 | # This boolean sets whether deleting branches will be allowed in the 21 | # repository. By default they won't be. 22 | # hooks.denycreatebranch 23 | # This boolean sets whether remotely creating branches will be denied 24 | # in the repository. By default this is allowed. 25 | # 26 | 27 | # --- Command line 28 | refname="$1" 29 | oldrev="$2" 30 | newrev="$3" 31 | 32 | # --- Safety check 33 | if [ -z "$GIT_DIR" ]; then 34 | echo "Don't run this script from the command line." >&2 35 | echo " (if you want, you could supply GIT_DIR then run" >&2 36 | echo " $0 )" >&2 37 | exit 1 38 | fi 39 | 40 | if [ -z "$refname" -o -z "$oldrev" -o -z "$newrev" ]; then 41 | echo "usage: $0 " >&2 42 | exit 1 43 | fi 44 | 45 | # --- Config 46 | allowunannotated=$(git config --bool hooks.allowunannotated) 47 | allowdeletebranch=$(git config --bool hooks.allowdeletebranch) 48 | denycreatebranch=$(git config --bool hooks.denycreatebranch) 49 | allowdeletetag=$(git config --bool hooks.allowdeletetag) 50 | allowmodifytag=$(git config --bool hooks.allowmodifytag) 51 | 52 | # check for no description 53 | projectdesc=$(sed -e '1q' "$GIT_DIR/description") 54 | case "$projectdesc" in 55 | "Unnamed repository"* | "") 56 | echo "*** Project description file hasn't been set" >&2 57 | exit 1 58 | ;; 59 | esac 60 | 61 | # --- Check types 62 | # if $newrev is 0000...0000, it's a commit to delete a ref. 63 | zero="0000000000000000000000000000000000000000" 64 | if [ "$newrev" = "$zero" ]; then 65 | newrev_type=delete 66 | else 67 | newrev_type=$(git cat-file -t $newrev) 68 | fi 69 | 70 | case "$refname","$newrev_type" in 71 | refs/tags/*,commit) 72 | # un-annotated tag 73 | short_refname=${refname##refs/tags/} 74 | if [ "$allowunannotated" != "true" ]; then 75 | echo "*** The un-annotated tag, $short_refname, is not allowed in this repository" >&2 76 | echo "*** Use 'git tag [ -a | -s ]' for tags you want to propagate." >&2 77 | exit 1 78 | fi 79 | ;; 80 | refs/tags/*,delete) 81 | # delete tag 82 | if [ "$allowdeletetag" != "true" ]; then 83 | echo "*** Deleting a tag is not allowed in this repository" >&2 84 | exit 1 85 | fi 86 | ;; 87 | refs/tags/*,tag) 88 | # annotated tag 89 | if [ "$allowmodifytag" != "true" ] && git rev-parse $refname > /dev/null 2>&1 90 | then 91 | echo "*** Tag '$refname' already exists." >&2 92 | echo "*** Modifying a tag is not allowed in this repository." >&2 93 | exit 1 94 | fi 95 | ;; 96 | refs/heads/*,commit) 97 | # branch 98 | if [ "$oldrev" = "$zero" -a "$denycreatebranch" = "true" ]; then 99 | echo "*** Creating a branch is not allowed in this repository" >&2 100 | exit 1 101 | fi 102 | ;; 103 | refs/heads/*,delete) 104 | # delete branch 105 | if [ "$allowdeletebranch" != "true" ]; then 106 | echo "*** Deleting a branch is not allowed in this repository" >&2 107 | exit 1 108 | fi 109 | ;; 110 | refs/remotes/*,commit) 111 | # tracking branch 112 | ;; 113 | refs/remotes/*,delete) 114 | # delete tracking branch 115 | if [ "$allowdeletebranch" != "true" ]; then 116 | echo "*** Deleting a tracking branch is not allowed in this repository" >&2 117 | exit 1 118 | fi 119 | ;; 120 | *) 121 | # Anything else (is there anything else?) 122 | echo "*** Update hook: unknown type of update to ref $refname of type $newrev_type" >&2 123 | exit 1 124 | ;; 125 | esac 126 | 127 | # --- Finished 128 | exit 0 129 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/network_factory.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | from __future__ import print_function 3 | from __future__ import absolute_import 4 | 5 | import tensorflow as tf 6 | import tensorflow.contrib.slim as slim 7 | 8 | import os,sys 9 | sys.path.insert(0, '../../') 10 | 11 | from libs.networks.slim_nets import resnet_v1 12 | from libs.networks.slim_nets import mobilenet_v1 13 | from libs.networks.slim_nets import inception_resnet_v2 14 | from libs.networks.slim_nets import vgg 15 | 16 | # FLAGS = get_flags_byname() 17 | 18 | 19 | def get_flags_byname(net_name): 20 | if net_name not in ['resnet_v1_50', 'mobilenet_224', 'inception_resnet', 'vgg16', 'resnet_v1_101']: 21 | raise ValueError("not include network: {}, we allow resnet_50, mobilenet_224, inception_resnet," 22 | " vgg16, resnet_v1_101" 23 | "") 24 | 25 | if net_name == 'resnet_v1_50': 26 | from configs import config_resnet_50 27 | return config_resnet_50.FLAGS 28 | if net_name == 'mobilenet_224': 29 | from configs import config_mobilenet_224 30 | return config_mobilenet_224.FLAGS 31 | if net_name == 'inception_resnet': 32 | from configs import config_inception_resnet 33 | return config_inception_resnet.FLAGS 34 | if net_name == 'vgg16': 35 | from configs import config_vgg16 36 | return config_vgg16.FLAGS 37 | if net_name == 'resnet_v1_101': 38 | from configs import config_res101 39 | return config_res101.FLAGS 40 | 41 | 42 | def get_network_byname(net_name, 43 | inputs, 44 | num_classes=None, 45 | is_training=True, 46 | global_pool=True, 47 | output_stride=None, 48 | spatial_squeeze=True): 49 | if net_name == 'resnet_v1_50': 50 | FLAGS = get_flags_byname(net_name) 51 | with slim.arg_scope(resnet_v1.resnet_arg_scope(weight_decay=FLAGS.weight_decay)): 52 | logits, end_points = resnet_v1.resnet_v1_50(inputs=inputs, 53 | num_classes=num_classes, 54 | is_training=is_training, 55 | global_pool=global_pool, 56 | output_stride=output_stride, 57 | spatial_squeeze=spatial_squeeze 58 | ) 59 | 60 | return logits, end_points 61 | if net_name == 'resnet_v1_101': 62 | FLAGS = get_flags_byname(net_name) 63 | with slim.arg_scope(resnet_v1.resnet_arg_scope(weight_decay=FLAGS.weight_decay)): 64 | logits, end_points = resnet_v1.resnet_v1_101(inputs=inputs, 65 | num_classes=num_classes, 66 | is_training=is_training, 67 | global_pool=global_pool, 68 | output_stride=output_stride, 69 | spatial_squeeze=spatial_squeeze 70 | ) 71 | return logits, end_points 72 | 73 | # if net_name == 'inception_resnet': 74 | # FLAGS = get_flags_byname(net_name) 75 | # arg_sc = inception_resnet_v2.inception_resnet_v2_arg_scope(weight_decay=FLAGS.weight_decay) 76 | # with slim.arg_scope(arg_sc): 77 | # logits, end_points = inception_resnet_v2.inception_resnet_v2(inputs=inputs, 78 | # num_classes=num_classes, 79 | # is_training=is_training) 80 | # 81 | # return logits, end_points 82 | # 83 | # if net_name == 'vgg16': 84 | # FLAGS = get_flags_byname(net_name) 85 | # arg_sc = vgg.vgg_arg_scope(weight_decay=FLAGS.weight_decay) 86 | # with slim.arg_scope(arg_sc): 87 | # logits, end_points = vgg.vgg_16(inputs=inputs, 88 | # num_classes=num_classes, 89 | # is_training=is_training) 90 | # return logits, end_points 91 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/anchor_utils_pyfunc.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import numpy as np 8 | import cv2 9 | from help_utils.help_utils import show_boxes_in_img 10 | 11 | 12 | def make_anchors(base_anchor_size, anchor_scales, anchor_ratios, featuremaps_height, 13 | featuremaps_width, stride 14 | ): 15 | ''' 16 | :param base_anchor_size: 17 | :param anchor_scales: 18 | :param anchor_ratios: 19 | :param featuremaps_width: 20 | :param featuremaps_height: 21 | :param stride 22 | :return: anchors of shape: [w*h*9, 4] 23 | ''' 24 | base_anchor = [0, 0, base_anchor_size, base_anchor_size] # [y_center, x_center, h, w] 25 | per_location_anchors = enum_ratios(enum_scales(base_anchor, anchor_scales), 26 | anchor_ratios) 27 | 28 | ws, hs = per_location_anchors[:, 2], per_location_anchors[:, 3] 29 | 30 | x_centers = np.arange(featuremaps_width) * stride 31 | y_centers = np.arange(featuremaps_height) * stride 32 | 33 | x_centers, y_centers = np.meshgrid(x_centers, y_centers) 34 | 35 | ws, x_centers = np.meshgrid(ws, x_centers) 36 | hs, y_centers = np.meshgrid(hs, y_centers) 37 | 38 | box_centers = np.stack([y_centers, x_centers], axis=2) 39 | box_centers = np.reshape(box_centers, [-1, 2]) 40 | 41 | box_sizes = np.stack([hs, ws], axis=2) 42 | box_sizes = np.reshape(box_sizes, [-1, 2]) 43 | final_anchors = np.concatenate([box_centers - 0.5*box_sizes, box_centers+0.5*box_sizes], axis=1) 44 | final_anchors = final_anchors.astype(dtype=np.float32) 45 | return final_anchors 46 | 47 | 48 | def enum_scales(base_anchor, anchor_scales): 49 | ''' 50 | for baseanchor : center point is zero 51 | :param base_anchor: [y_center, x_center, h, w] -->may[0, 0, 256, 256] 52 | :param anchor_scales: maybe [0.5, 1., 2.0] 53 | :return: 54 | ''' 55 | 56 | base_anchor = np.array(base_anchor) 57 | anchor_scales = np.array(anchor_scales).reshape(len(anchor_scales), 1) 58 | 59 | return base_anchor * anchor_scales 60 | 61 | 62 | def enum_ratios(anchors, anchor_ratios): 63 | ''' 64 | h / w = ratio 65 | :param anchors: 66 | :param anchor_ratios: 67 | :return: 68 | ''' 69 | 70 | ws = anchors[:, 3] # for base anchor, w == h 71 | hs = anchors[:, 2] 72 | sqrt_ratios = np.sqrt(np.array(anchor_ratios)) 73 | ws = np.reshape(ws / sqrt_ratios[:, np.newaxis], [-1]) 74 | hs = np.reshape(hs * sqrt_ratios[:, np.newaxis], [-1]) 75 | assert ws.shape == hs.shape, 'h shape is not equal w shape' 76 | 77 | num_anchors_per_location = ws.shape[0] 78 | 79 | return np.hstack([np.zeros((num_anchors_per_location, 1)), 80 | np.zeros((num_anchors_per_location, 1)), 81 | ws[:, np.newaxis], 82 | hs[:, np.newaxis]]) 83 | 84 | 85 | def filter_outside_boxes(anchors, img_h, img_w): 86 | ''' 87 | 88 | :param anchors:[-1, 4] ... [ymin, xmin, ymax, xmax] 89 | :param img_h: 90 | :param img_w: 91 | :return: 92 | ''' 93 | 94 | index = (anchors[:, 0] > 0) & (anchors[:, 0] < img_h) & \ 95 | (anchors[:, 1] > 0) & (anchors[:, 1] < img_w) & \ 96 | (anchors[:, 2] < img_h) & (anchors[:, 2] > 0) & \ 97 | (anchors[:, 3] < img_w) & (anchors[:, 3] > 0) 98 | 99 | valid_indices = np.where(index == True)[0] 100 | 101 | return valid_indices 102 | 103 | 104 | def show_anchors_in_img(anchors): 105 | img = cv2.imread('1.jpg') 106 | img = cv2.resize(img, (800, 600), interpolation=cv2.INTER_AREA) 107 | img = show_boxes_in_img(img, anchors) 108 | 109 | cv2.imshow('resize_img', img) 110 | cv2.waitKey(0) 111 | 112 | if __name__ == '__main__': 113 | print(enum_scales([0, 0, 256, 256], [0.5, 1.0, 2.0])) 114 | print("_______________") 115 | anchors = make_anchors(256, 116 | [1.0], 117 | [0.5, 1.0, 2.0], 118 | featuremaps_height=38, 119 | featuremaps_width=50, 120 | stride=16) 121 | indices = filter_outside_boxes(anchors, img_h=600, img_w=800) 122 | show_anchors_in_img(np.column_stack([anchors[indices], np.ones(shape=(anchors[indices].shape[0], 1))])) 123 | 124 | 125 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/losses/losses.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import tensorflow as tf 8 | from tensorflow.python.ops import array_ops 9 | 10 | 11 | def l1_smooth_losses(predict_boxes, gtboxes, object_weights, classes_weights=None): 12 | ''' 13 | 14 | :param predict_boxes: [minibatch_size, -1] 15 | :param gtboxes: [minibatch_size, -1] 16 | :param object_weights: [minibatch_size, ]. 1.0 represent object, 0.0 represent others(ignored or background) 17 | :return: 18 | ''' 19 | 20 | diff = predict_boxes - gtboxes 21 | abs_diff = tf.cast(tf.abs(diff), tf.float32) 22 | 23 | if classes_weights is None: 24 | ''' 25 | first_stage: 26 | predict_boxes :[minibatch_size, 4] 27 | gtboxes: [minibatchs_size, 4] 28 | ''' 29 | anchorwise_smooth_l1norm = tf.reduce_sum( 30 | tf.where(tf.less(abs_diff, 1), 0.5 * tf.square(abs_diff), abs_diff - 0.5), 31 | axis=1) * object_weights 32 | else: 33 | ''' 34 | fast_rcnn: 35 | predict_boxes: [minibatch_size, 4*num_classes] 36 | gtboxes: [minibatch_size, 4*num_classes] 37 | classes_weights : [minibatch_size, 4*num_classes] 38 | ''' 39 | anchorwise_smooth_l1norm = tf.reduce_sum( 40 | tf.where(tf.less(abs_diff, 1), 0.5*tf.square(abs_diff)*classes_weights, 41 | (abs_diff - 0.5)*classes_weights), 42 | axis=1)*object_weights 43 | return tf.reduce_mean(anchorwise_smooth_l1norm, axis=0) # reduce mean 44 | 45 | 46 | def weighted_softmax_cross_entropy_loss(predictions, labels, weights): 47 | ''' 48 | 49 | :param predictions: 50 | :param labels: 51 | :param weights: [N, ] 1 -> should be sampled , 0-> not should be sampled 52 | :return: 53 | # ''' 54 | per_row_cross_ent = tf.nn.softmax_cross_entropy_with_logits(logits=predictions, 55 | labels=labels) 56 | 57 | weighted_cross_ent = tf.reduce_sum(per_row_cross_ent * weights) 58 | return weighted_cross_ent / tf.reduce_sum(weights) 59 | 60 | def focal_loss(prediction_tensor, target_tensor, weights=None, alpha=0.25, gamma=2): 61 | """Compute focal loss for predictions. 62 | Multi-labels Focal loss formula: 63 | FL = -alpha * (z-p)^gamma * log(p) -(1-alpha) * p^gamma * log(1-p) 64 | ,which alpha = 0.25, gamma = 2, p = sigmoid(x), z = target_tensor. 65 | Args: 66 | prediction_tensor: A float tensor of shape [batch_size, num_anchors, 67 | num_classes] representing the predicted logits for each class 68 | target_tensor: A float tensor of shape [batch_size, num_anchors, 69 | num_classes] representing one-hot encoded classification targets 70 | weights: A float tensor of shape [batch_size, num_anchors] 71 | alpha: A scalar tensor for focal loss alpha hyper-parameter 72 | gamma: A scalar tensor for focal loss gamma hyper-parameter 73 | Returns: 74 | loss: A (scalar) tensor representing the value of the loss function 75 | """ 76 | sigmoid_p = tf.nn.sigmoid(prediction_tensor) 77 | zeros = array_ops.zeros_like(sigmoid_p, dtype=sigmoid_p.dtype) 78 | 79 | # For poitive prediction, only need consider front part loss, back part is 0; 80 | # target_tensor > zeros <=> z=1, so poitive coefficient = z - p. 81 | pos_p_sub = array_ops.where(target_tensor > zeros, target_tensor - sigmoid_p, zeros) 82 | 83 | # For negative prediction, only need consider back part loss, front part is 0; 84 | # target_tensor > zeros <=> z=1, so negative coefficient = 0. 85 | neg_p_sub = array_ops.where(target_tensor > zeros, zeros, sigmoid_p) 86 | per_entry_cross_ent = - alpha * (pos_p_sub ** gamma) * tf.log(tf.clip_by_value(sigmoid_p, 1e-8, 1.0)) \ 87 | - (1 - alpha) * (neg_p_sub ** gamma) * tf.log(tf.clip_by_value(1.0 - sigmoid_p, 1e-8, 1.0)) 88 | return tf.reduce_sum(per_entry_cross_ent) 89 | 90 | def test_smoothl1(): 91 | 92 | predict_boxes = tf.constant([[1, 1, 2, 2], 93 | [2, 2, 2, 2], 94 | [3, 3, 3, 3]]) 95 | gtboxes = tf.constant([[1, 1, 1, 1], 96 | [2, 1, 1, 1], 97 | [3, 3, 2, 1]]) 98 | 99 | loss = l1_smooth_losses(predict_boxes, gtboxes, [1, 1, 1]) 100 | 101 | with tf.Session() as sess: 102 | print(sess.run(loss)) 103 | 104 | if __name__ == '__main__': 105 | test_smoothl1() 106 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/nms_rotate.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import numpy as np 8 | import cv2 9 | from libs.configs import cfgs 10 | import tensorflow as tf 11 | if cfgs.ROTATE_NMS_USE_GPU: 12 | from libs.box_utils.rotate_polygon_nms import rotate_gpu_nms 13 | 14 | 15 | def nms_rotate(decode_boxes, scores, iou_threshold, max_output_size, 16 | use_angle_condition=False, angle_threshold=0, use_gpu=True, gpu_id=0): 17 | """ 18 | :param boxes: format [x_c, y_c, w, h, theta] 19 | :param scores: scores of boxes 20 | :param threshold: iou threshold (0.7 or 0.5) 21 | :param max_output_size: max number of output 22 | :return: the remaining index of boxes 23 | """ 24 | 25 | if use_gpu: 26 | keep = nms_rotate_gpu(boxes_list=decode_boxes, 27 | scores=scores, 28 | iou_threshold=iou_threshold, 29 | angle_gap_threshold=angle_threshold, 30 | use_angle_condition=use_angle_condition, 31 | device_id=gpu_id) 32 | 33 | keep = tf.cond( 34 | tf.greater(tf.shape(keep)[0], max_output_size), 35 | true_fn=lambda: tf.slice(keep, [0], [max_output_size]), 36 | false_fn=lambda: keep) 37 | 38 | else: 39 | keep = tf.py_func(nms_rotate_cpu, 40 | inp=[decode_boxes, scores, iou_threshold, max_output_size], 41 | Tout=tf.int64) 42 | return keep 43 | 44 | 45 | def nms_rotate_cpu(boxes, scores, iou_threshold, max_output_size): 46 | 47 | keep = [] 48 | 49 | order = scores.argsort()[::-1] 50 | num = boxes.shape[0] 51 | 52 | suppressed = np.zeros((num), dtype=np.int) 53 | 54 | for _i in range(num): 55 | if len(keep) >= max_output_size: 56 | break 57 | 58 | i = order[_i] 59 | if suppressed[i] == 1: 60 | continue 61 | keep.append(i) 62 | r1 = ((boxes[i, 1], boxes[i, 0]), (boxes[i, 3], boxes[i, 2]), boxes[i, 4]) 63 | area_r1 = boxes[i, 2] * boxes[i, 3] 64 | for _j in range(_i + 1, num): 65 | j = order[_j] 66 | if suppressed[i] == 1: 67 | continue 68 | r2 = ((boxes[j, 1], boxes[j, 0]), (boxes[j, 3], boxes[j, 2]), boxes[j, 4]) 69 | area_r2 = boxes[j, 2] * boxes[j, 3] 70 | inter = 0.0 71 | 72 | int_pts = cv2.rotatedRectangleIntersection(r1, r2)[1] 73 | if int_pts is not None: 74 | order_pts = cv2.convexHull(int_pts, returnPoints=True) 75 | 76 | int_area = cv2.contourArea(order_pts) 77 | 78 | inter = int_area * 1.0 / (area_r1 + area_r2 - int_area + cfgs.EPSILON) 79 | 80 | if inter >= iou_threshold: 81 | suppressed[j] = 1 82 | 83 | return np.array(keep, np.int64) 84 | 85 | 86 | def nms_rotate_gpu(boxes_list, scores, iou_threshold, use_angle_condition=False, angle_gap_threshold=0, device_id=0): 87 | if use_angle_condition: 88 | y_c, x_c, h, w, theta = tf.unstack(boxes_list, axis=1) 89 | boxes_list = tf.transpose(tf.stack([x_c, y_c, w, h, theta])) 90 | det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1) 91 | keep = tf.py_func(rotate_gpu_nms, 92 | inp=[det_tensor, iou_threshold, device_id], 93 | Tout=tf.int64) 94 | return keep 95 | else: 96 | y_c, x_c, h, w, theta = tf.unstack(boxes_list, axis=1) 97 | boxes_list = tf.transpose(tf.stack([x_c, y_c, w, h, theta])) 98 | det_tensor = tf.concat([boxes_list, tf.expand_dims(scores, axis=1)], axis=1) 99 | keep = tf.py_func(rotate_gpu_nms, 100 | inp=[det_tensor, iou_threshold, device_id], 101 | Tout=tf.int64) 102 | keep = tf.reshape(keep, [-1]) 103 | return keep 104 | 105 | 106 | if __name__ == '__main__': 107 | boxes = np.array([[50, 50, 100, 100, 0], 108 | [60, 60, 100, 100, 0], 109 | [50, 50, 100, 100, -45.], 110 | [200, 200, 100, 100, 0.]]) 111 | 112 | scores = np.array([0.99, 0.88, 0.66, 0.77]) 113 | 114 | keep = nms_rotate(tf.convert_to_tensor(boxes, dtype=tf.float32), tf.convert_to_tensor(scores, dtype=tf.float32), 115 | 0.7, 5) 116 | 117 | import os 118 | os.environ["CUDA_VISIBLE_DEVICES"] = '0' 119 | with tf.Session() as sess: 120 | print(sess.run(keep)) 121 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/cifarnet.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains a variant of the CIFAR-10 model definition.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | 21 | import tensorflow as tf 22 | 23 | slim = tf.contrib.slim 24 | 25 | trunc_normal = lambda stddev: tf.truncated_normal_initializer(stddev=stddev) 26 | 27 | 28 | def cifarnet(images, num_classes=10, is_training=False, 29 | dropout_keep_prob=0.5, 30 | prediction_fn=slim.softmax, 31 | scope='CifarNet'): 32 | """Creates a variant of the CifarNet model. 33 | 34 | Note that since the output is a set of 'logits', the values fall in the 35 | interval of (-infinity, infinity). Consequently, to convert the outputs to a 36 | probability distribution over the characters, one will need to convert them 37 | using the softmax function: 38 | 39 | logits = cifarnet.cifarnet(images, is_training=False) 40 | probabilities = tf.nn.softmax(logits) 41 | predictions = tf.argmax(logits, 1) 42 | 43 | Args: 44 | images: A batch of `Tensors` of size [batch_size, height, width, channels]. 45 | num_classes: the number of classes in the dataset. 46 | is_training: specifies whether or not we're currently training the model. 47 | This variable will determine the behaviour of the dropout layer. 48 | dropout_keep_prob: the percentage of activation values that are retained. 49 | prediction_fn: a function to get predictions out of logits. 50 | scope: Optional variable_scope. 51 | 52 | Returns: 53 | logits: the pre-softmax activations, a tensor of size 54 | [batch_size, `num_classes`] 55 | end_points: a dictionary from components of the network to the corresponding 56 | activation. 57 | """ 58 | end_points = {} 59 | 60 | with tf.variable_scope(scope, 'CifarNet', [images, num_classes]): 61 | net = slim.conv2d(images, 64, [5, 5], scope='conv1') 62 | end_points['conv1'] = net 63 | net = slim.max_pool2d(net, [2, 2], 2, scope='pool1') 64 | end_points['pool1'] = net 65 | net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1') 66 | net = slim.conv2d(net, 64, [5, 5], scope='conv2') 67 | end_points['conv2'] = net 68 | net = tf.nn.lrn(net, 4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm2') 69 | net = slim.max_pool2d(net, [2, 2], 2, scope='pool2') 70 | end_points['pool2'] = net 71 | net = slim.flatten(net) 72 | end_points['Flatten'] = net 73 | net = slim.fully_connected(net, 384, scope='fc3') 74 | end_points['fc3'] = net 75 | net = slim.dropout(net, dropout_keep_prob, is_training=is_training, 76 | scope='dropout3') 77 | net = slim.fully_connected(net, 192, scope='fc4') 78 | end_points['fc4'] = net 79 | logits = slim.fully_connected(net, num_classes, 80 | biases_initializer=tf.zeros_initializer(), 81 | weights_initializer=trunc_normal(1/192.0), 82 | weights_regularizer=None, 83 | activation_fn=None, 84 | scope='logits') 85 | 86 | end_points['Logits'] = logits 87 | end_points['Predictions'] = prediction_fn(logits, scope='Predictions') 88 | 89 | return logits, end_points 90 | cifarnet.default_image_size = 32 91 | 92 | 93 | def cifarnet_arg_scope(weight_decay=0.004): 94 | """Defines the default cifarnet argument scope. 95 | 96 | Args: 97 | weight_decay: The weight decay to use for regularizing the model. 98 | 99 | Returns: 100 | An `arg_scope` to use for the inception v3 model. 101 | """ 102 | with slim.arg_scope( 103 | [slim.conv2d], 104 | weights_initializer=tf.truncated_normal_initializer(stddev=5e-2), 105 | activation_fn=tf.nn.relu): 106 | with slim.arg_scope( 107 | [slim.fully_connected], 108 | biases_initializer=tf.constant_initializer(0.1), 109 | weights_initializer=trunc_normal(0.04), 110 | weights_regularizer=slim.l2_regularizer(weight_decay), 111 | activation_fn=tf.nn.relu) as sc: 112 | return sc 113 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/nets_factory.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains a factory for building various models.""" 16 | 17 | from __future__ import absolute_import 18 | from __future__ import division 19 | from __future__ import print_function 20 | import functools 21 | 22 | import tensorflow as tf 23 | 24 | from nets import alexnet 25 | from nets import cifarnet 26 | from nets import inception 27 | from nets import lenet 28 | from nets import mobilenet_v1 29 | from nets import overfeat 30 | from nets import resnet_v1 31 | from nets import resnet_v2 32 | from nets import vgg 33 | 34 | slim = tf.contrib.slim 35 | 36 | networks_map = {'alexnet_v2': alexnet.alexnet_v2, 37 | 'cifarnet': cifarnet.cifarnet, 38 | 'overfeat': overfeat.overfeat, 39 | 'vgg_a': vgg.vgg_a, 40 | 'vgg_16': vgg.vgg_16, 41 | 'vgg_19': vgg.vgg_19, 42 | 'inception_v1': inception.inception_v1, 43 | 'inception_v2': inception.inception_v2, 44 | 'inception_v3': inception.inception_v3, 45 | 'inception_v4': inception.inception_v4, 46 | 'inception_resnet_v2': inception.inception_resnet_v2, 47 | 'lenet': lenet.lenet, 48 | 'resnet_v1_50': resnet_v1.resnet_v1_50, 49 | 'resnet_v1_101': resnet_v1.resnet_v1_101, 50 | 'resnet_v1_152': resnet_v1.resnet_v1_152, 51 | 'resnet_v1_200': resnet_v1.resnet_v1_200, 52 | 'resnet_v2_50': resnet_v2.resnet_v2_50, 53 | 'resnet_v2_101': resnet_v2.resnet_v2_101, 54 | 'resnet_v2_152': resnet_v2.resnet_v2_152, 55 | 'resnet_v2_200': resnet_v2.resnet_v2_200, 56 | 'mobilenet_v1': mobilenet_v1.mobilenet_v1, 57 | } 58 | 59 | arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope, 60 | 'cifarnet': cifarnet.cifarnet_arg_scope, 61 | 'overfeat': overfeat.overfeat_arg_scope, 62 | 'vgg_a': vgg.vgg_arg_scope, 63 | 'vgg_16': vgg.vgg_arg_scope, 64 | 'vgg_19': vgg.vgg_arg_scope, 65 | 'inception_v1': inception.inception_v3_arg_scope, 66 | 'inception_v2': inception.inception_v3_arg_scope, 67 | 'inception_v3': inception.inception_v3_arg_scope, 68 | 'inception_v4': inception.inception_v4_arg_scope, 69 | 'inception_resnet_v2': 70 | inception.inception_resnet_v2_arg_scope, 71 | 'lenet': lenet.lenet_arg_scope, 72 | 'resnet_v1_50': resnet_v1.resnet_arg_scope, 73 | 'resnet_v1_101': resnet_v1.resnet_arg_scope, 74 | 'resnet_v1_152': resnet_v1.resnet_arg_scope, 75 | 'resnet_v1_200': resnet_v1.resnet_arg_scope, 76 | 'resnet_v2_50': resnet_v2.resnet_arg_scope, 77 | 'resnet_v2_101': resnet_v2.resnet_arg_scope, 78 | 'resnet_v2_152': resnet_v2.resnet_arg_scope, 79 | 'resnet_v2_200': resnet_v2.resnet_arg_scope, 80 | 'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope, 81 | } 82 | 83 | 84 | def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False): 85 | """Returns a network_fn such as `logits, end_points = network_fn(images)`. 86 | 87 | Args: 88 | name: The name of the network. 89 | num_classes: The number of classes to use for classification. 90 | weight_decay: The l2 coefficient for the model weights. 91 | is_training: `True` if the model is being used for training and `False` 92 | otherwise. 93 | 94 | Returns: 95 | network_fn: A function that applies the model to a batch of images. It has 96 | the following signature: 97 | logits, end_points = network_fn(images) 98 | Raises: 99 | ValueError: If network `name` is not recognized. 100 | """ 101 | if name not in networks_map: 102 | raise ValueError('Name of network unknown %s' % name) 103 | arg_scope = arg_scopes_map[name](weight_decay=weight_decay) 104 | func = networks_map[name] 105 | @functools.wraps(func) 106 | def network_fn(images): 107 | with slim.arg_scope(arg_scope): 108 | return func(images, num_classes, is_training=is_training) 109 | if hasattr(func, 'default_image_size'): 110 | network_fn.default_image_size = func.default_image_size 111 | 112 | return network_fn 113 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/data/io/convert_data_to_tfrecord.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division, print_function, absolute_import 3 | import sys 4 | sys.path.append('../../') 5 | 6 | import xml.etree.cElementTree as ET 7 | from libs.configs import cfgs 8 | import numpy as np 9 | import tensorflow as tf 10 | import glob 11 | import cv2 12 | from libs.label_name_dict.label_dict import * 13 | from help_utils.tools import * 14 | 15 | tf.app.flags.DEFINE_string('VOC_dir', '/home/jzchen/data/RemoteSensing/ships/HRSC2016/HRSC2016/Train/', 'Voc dir') 16 | tf.app.flags.DEFINE_string('xml_dir', 'Annotations', 'xml dir') 17 | tf.app.flags.DEFINE_string('image_dir', 'AllImages', 'image dir') 18 | tf.app.flags.DEFINE_string('save_name', 'train', 'save name') 19 | tf.app.flags.DEFINE_string('save_dir', cfgs.ROO_PATH + '/data/tfrecords/', 'save name') 20 | tf.app.flags.DEFINE_string('img_format', '.bmp', 'format of image') 21 | tf.app.flags.DEFINE_string('dataset', 'ship', 'dataset') 22 | FLAGS = tf.app.flags.FLAGS 23 | 24 | 25 | def _int64_feature(value): 26 | return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) 27 | 28 | 29 | def _bytes_feature(value): 30 | value = tf.compat.as_bytes(value) 31 | return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) 32 | 33 | 34 | def read_xml_gtbox_and_label(xml_path): 35 | """ 36 | :param xml_path: the path of voc xml 37 | :return: a list contains gtboxes and labels, shape is [num_of_gtboxes, 9], 38 | and has [x1, y1, x2, y2, x3, y3, x4, y4, label] in a per row 39 | """ 40 | 41 | tree = ET.parse(xml_path) 42 | root = tree.getroot() 43 | img_width = None 44 | img_height = None 45 | box_list = [] 46 | for child_of_root in root: 47 | # if child_of_root.tag == 'filename': 48 | # assert child_of_root.text == xml_path.split('/')[-1].split('.')[0] \ 49 | # + FLAGS.img_format, 'xml_name and img_name cannot match' 50 | 51 | if child_of_root.tag == 'size': 52 | for child_item in child_of_root: 53 | if child_item.tag == 'width': 54 | img_width = int(child_item.text) 55 | if child_item.tag == 'height': 56 | img_height = int(child_item.text) 57 | 58 | if child_of_root.tag == 'object': 59 | label = None 60 | for child_item in child_of_root: 61 | if child_item.tag == 'name': 62 | label = NAME_LABEL_MAP[child_item.text] 63 | if child_item.tag == 'bndbox': 64 | tmp_box = [] 65 | for node in child_item: 66 | tmp_box.append(int(node.text)) 67 | assert label is not None, 'label is none, error' 68 | tmp_box.append(label) 69 | box_list.append(tmp_box) 70 | 71 | gtbox_label = np.array(box_list, dtype=np.int32) 72 | 73 | return img_height, img_width, gtbox_label 74 | 75 | 76 | def convert_pascal_to_tfrecord(): 77 | xml_path = '/home/jzchen/WorkingSpace/R2CNN_HEAD_FPN_Tensorflow/' + FLAGS.xml_dir 78 | image_path = FLAGS.VOC_dir + FLAGS.image_dir 79 | save_path = FLAGS.save_dir + FLAGS.dataset + '_' + FLAGS.save_name + '.tfrecord' 80 | mkdir(FLAGS.save_dir) 81 | 82 | # writer_options = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB) 83 | # writer = tf.python_io.TFRecordWriter(path=save_path, options=writer_options) 84 | writer = tf.python_io.TFRecordWriter(path=save_path) 85 | 86 | for count, xml in enumerate(glob.glob(xml_path + '/*.xml')): 87 | # to avoid path error in different development platform 88 | xml = xml.replace('\\', '/') 89 | 90 | img_name = xml.split('/')[-1].split('.')[0] + FLAGS.img_format 91 | img_path = image_path + '/' + img_name 92 | 93 | if not os.path.exists(img_path): 94 | print('{} is not exist!'.format(img_path)) 95 | continue 96 | 97 | img_height, img_width, gtbox_label = read_xml_gtbox_and_label(xml) 98 | 99 | # img = np.array(Image.open(img_path)) 100 | img = cv2.imread(img_path) 101 | 102 | feature = tf.train.Features(feature={ 103 | # do not need encode() in linux 104 | # 'img_name': _bytes_feature(img_name.encode()), 105 | 'img_name': _bytes_feature(img_name), 106 | 'img_height': _int64_feature(img_height), 107 | 'img_width': _int64_feature(img_width), 108 | 'img': _bytes_feature(img.tostring()), 109 | 'gtboxes_and_label': _bytes_feature(gtbox_label.tostring()), 110 | 'num_objects': _int64_feature(gtbox_label.shape[0]) 111 | }) 112 | 113 | example = tf.train.Example(features=feature) 114 | 115 | writer.write(example.SerializeToString()) 116 | 117 | view_bar('Conversion progress', count + 1, len(glob.glob(xml_path + '/*.xml'))) 118 | 119 | print('\nConversion is complete!') 120 | 121 | 122 | if __name__ == '__main__': 123 | # xml_path = '../data/dataset/VOCdevkit/VOC2007/Annotations/000005.xml' 124 | # read_xml_gtbox_and_label(xml_path) 125 | 126 | convert_pascal_to_tfrecord() 127 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/README.md: -------------------------------------------------------------------------------- 1 | # R2CNN_HEAD (The paper is under review.): Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multiscale Rotation Region Convolutional Neural Network 2 | 3 | ## Recommend improved code： https://github.com/DetectionTeamUCAS 4 | 5 | A Tensorflow implementation of FPN or R2CNN detection framework based on [FPN](https://github.com/yangxue0827/FPN_Tensorflow). 6 | You can refer to the papers [R2CNN Rotational Region CNN for Orientation Robust Scene Text Detection](https://arxiv.org/abs/1706.09579) or [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144) 7 | Other rotation detection method reference [R-DFPN](https://github.com/yangxue0827/R-DFPN_FPN_Tensorflow), [RRPN](https://github.com/yangJirui/RRPN_FPN_Tensorflow) and [R2CNN](https://github.com/yangxue0827/R2CNN_FPN_Tensorflow) 8 | 9 | If useful to you, please star to support my work. Thanks. 10 | 11 | 12 | ## Citation 13 | Some relevant achievements based on this code. 14 | 15 | @article{[yang2018position](https://ieeexplore.ieee.org/document/8464244), 16 | title={Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network}, 17 | author={Yang, Xue and Sun, Hao and Sun, Xian and Yan, Menglong and Guo, Zhi and Fu, Kun}, 18 | journal={IEEE Access}, 19 | volume={6}, 20 | pages={50839-50849}, 21 | year={2018}, 22 | publisher={IEEE} 23 | } 24 | 25 | @article{[yang2018r-dfpn](http://www.mdpi.com/2072-4292/10/1/132), 26 | title={Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks}, 27 | author={Yang, Xue and Sun, Hao and Fu, Kun and Yang, Jirui and Sun, Xian and Yan, Menglong and Guo, Zhi}, 28 | journal={Remote Sensing}, 29 | volume={10}, 30 | number={1}, 31 | pages={132}, 32 | year={2018}, 33 | publisher={Multidisciplinary Digital Publishing Institute} 34 | } 35 | 36 | ## Configuration Environment 37 | ubuntu(Encoding problems may occur on windows) + python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080 38 | If you want to use cpu, you need to modify the parameters of NMS and IOU functions use_gpu = False in cfgs.py 39 | You can also use docker environment, command: docker pull yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0 40 | 41 | ## Installation 42 | Clone the repository 43 | ```Shell 44 | git clone https://github.com/yangxue0827/R2CNN_HEAD_FPN_Tensorflow.git 45 | ``` 46 | 47 | ## Make tfrecord 48 | The data is VOC format, reference [here](sample.xml) 49 | data path format ($R2CNN_HEAD_ROOT/data/io/divide_data.py) 50 | ``` 51 | ├── VOCdevkit 52 | │   ├── VOCdevkit_train 53 | │   ├── Annotation 54 | │   ├── JPEGImages 55 | │ ├── VOCdevkit_test 56 | │   ├── Annotation 57 | │   ├── JPEGImages 58 | ``` 59 | 60 | Clone the repository 61 | ```Shell 62 | cd $R2CNN_HEAD_ROOT/data/io/ 63 | python convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.jpg' --dataset='ship' 64 | 65 | ``` 66 | 67 | ## Compile 68 | ``` 69 | cd $PATH_ROOT/libs/box_utils/ 70 | python setup.py build_ext --inplace 71 | ``` 72 | 73 | ## Demo 74 | 1、Unzip the weight $R2CNN_HEAD_ROOT/output/res101_trained_weights/*.rar 75 | 2、put images in $R2CNN_HEAD_ROOT/tools/inference_image 76 | 3、Configure parameters in $R2CNN_HEAD_ROOT/libs/configs/cfgs.py and modify the project's root directory 77 | 4、 78 | ```Shell 79 | cd $R2CNN_HEAD_ROOT/tools 80 | ``` 81 | 5、image slice 82 | ```Shell 83 | python inference.py 84 | ``` 85 | 86 | 6、big image 87 | ```Shell 88 | cd $FPN_ROOT/tools 89 | python demo.py --src_folder=.\demo_src --des_folder=.\demo_des 90 | ``` 91 | 92 | ## Train 93 | 1、Modify $R2CNN_HEAD_ROOT/libs/lable_name_dict/***_dict.py, corresponding to the number of categories in the configuration file 94 | 2、download pretrain weight([resnet_v1_101_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) or [resnet_v1_50_2016_08_28.tar.gz](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz)) from [here](https://github.com/yangxue0827/models/tree/master/slim), then extract to folder $R2CNN_HEAD_ROOT/data/pretrained_weights 95 | 3、 96 | ```Shell 97 | cd $R2CNN_HEAD_ROOT/tools 98 | python train.py 99 | ``` 100 | 101 | ## Test tfrecord 102 | ```Shell 103 | cd $R2CNN_HEAD_ROOT/tools 104 | python test.py 105 | ``` 106 | 107 | ## eval(Not recommended, Please refer [here](https://github.com/DetectionTeamUCAS)) 108 | ```Shell 109 | cd $R2CNN_HEAD_ROOT/tools 110 | python eval.py 111 | ``` 112 | 113 | ## Summary 114 | ```Shell 115 | tensorboard --logdir=$R2CNN_HEAD_ROOT/output/res101_summary/ 116 | ``` 117 | ![01](output/res101_summary/fast_rcnn_loss.bmp) 118 | ![02](output/res101_summary/rpn_loss.bmp) 119 | ![03](output/res101_summary/total_loss.bmp) 120 | 121 | ## Graph 122 | ![04](graph.png) 123 | 124 | ## Test results 125 | ![11](tools/test_result/01_horizontal_gt.jpg) 126 | ![12](tools/test_result/01_horizontal_fpn.jpg) 127 | 128 | ![13](tools/test_result/01_rotate_gt.jpg) 129 | ![14](tools/test_result/01_rotate_fpn.jpg) 130 | 131 | ![15](tools/test_result/08_horizontal_gt.jpg) 132 | ![16](tools/test_result/08_horizontal_fpn.jpg) 133 | 134 | ![17](tools/test_result/08_rotate_gt.jpg) 135 | ![18](tools/test_result/08_rotate_fpn.jpg) -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/overfeat.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains the model definition for the OverFeat network. 16 | 17 | The definition for the network was obtained from: 18 | OverFeat: Integrated Recognition, Localization and Detection using 19 | Convolutional Networks 20 | Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and 21 | Yann LeCun, 2014 22 | http://arxiv.org/abs/1312.6229 23 | 24 | Usage: 25 | with slim.arg_scope(overfeat.overfeat_arg_scope()): 26 | outputs, end_points = overfeat.overfeat(inputs) 27 | 28 | @@overfeat 29 | """ 30 | from __future__ import absolute_import 31 | from __future__ import division 32 | from __future__ import print_function 33 | 34 | import tensorflow as tf 35 | 36 | slim = tf.contrib.slim 37 | trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) 38 | 39 | 40 | def overfeat_arg_scope(weight_decay=0.0005): 41 | with slim.arg_scope([slim.conv2d, slim.fully_connected], 42 | activation_fn=tf.nn.relu, 43 | weights_regularizer=slim.l2_regularizer(weight_decay), 44 | biases_initializer=tf.zeros_initializer()): 45 | with slim.arg_scope([slim.conv2d], padding='SAME'): 46 | with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: 47 | return arg_sc 48 | 49 | 50 | def overfeat(inputs, 51 | num_classes=1000, 52 | is_training=True, 53 | dropout_keep_prob=0.5, 54 | spatial_squeeze=True, 55 | scope='overfeat'): 56 | """Contains the model definition for the OverFeat network. 57 | 58 | The definition for the network was obtained from: 59 | OverFeat: Integrated Recognition, Localization and Detection using 60 | Convolutional Networks 61 | Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and 62 | Yann LeCun, 2014 63 | http://arxiv.org/abs/1312.6229 64 | 65 | Note: All the fully_connected layers have been transformed to conv2d layers. 66 | To use in classification mode, resize input to 231x231. To use in fully 67 | convolutional mode, set spatial_squeeze to false. 68 | 69 | Args: 70 | inputs: a tensor of size [batch_size, height, width, channels]. 71 | num_classes: number of predicted classes. 72 | is_training: whether or not the model is being trained. 73 | dropout_keep_prob: the probability that activations are kept in the dropout 74 | layers during training. 75 | spatial_squeeze: whether or not should squeeze the spatial dimensions of the 76 | outputs. Useful to remove unnecessary dimensions for classification. 77 | scope: Optional scope for the variables. 78 | 79 | Returns: 80 | the last op containing the log predictions and end_points dict. 81 | 82 | """ 83 | with tf.variable_scope(scope, 'overfeat', [inputs]) as sc: 84 | end_points_collection = sc.name + '_end_points' 85 | # Collect outputs for conv2d, fully_connected and max_pool2d 86 | with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], 87 | outputs_collections=end_points_collection): 88 | net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', 89 | scope='conv1') 90 | net = slim.max_pool2d(net, [2, 2], scope='pool1') 91 | net = slim.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2') 92 | net = slim.max_pool2d(net, [2, 2], scope='pool2') 93 | net = slim.conv2d(net, 512, [3, 3], scope='conv3') 94 | net = slim.conv2d(net, 1024, [3, 3], scope='conv4') 95 | net = slim.conv2d(net, 1024, [3, 3], scope='conv5') 96 | net = slim.max_pool2d(net, [2, 2], scope='pool5') 97 | with slim.arg_scope([slim.conv2d], 98 | weights_initializer=trunc_normal(0.005), 99 | biases_initializer=tf.constant_initializer(0.1)): 100 | # Use conv2d instead of fully_connected layers. 101 | net = slim.conv2d(net, 3072, [6, 6], padding='VALID', scope='fc6') 102 | net = slim.dropout(net, dropout_keep_prob, is_training=is_training, 103 | scope='dropout6') 104 | net = slim.conv2d(net, 4096, [1, 1], scope='fc7') 105 | net = slim.dropout(net, dropout_keep_prob, is_training=is_training, 106 | scope='dropout7') 107 | net = slim.conv2d(net, num_classes, [1, 1], 108 | activation_fn=None, 109 | normalizer_fn=None, 110 | biases_initializer=tf.zeros_initializer(), 111 | scope='fc8') 112 | # Convert end_points_collection into a end_point dict. 113 | end_points = slim.utils.convert_collection_to_dict(end_points_collection) 114 | if spatial_squeeze: 115 | net = tf.squeeze(net, [1, 2], name='fc8/squeezed') 116 | end_points[sc.name + '/fc8'] = net 117 | return net, end_points 118 | overfeat.default_image_size = 231 119 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/hooks/pre-rebase.sample: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | # 3 | # Copyright (c) 2006, 2008 Junio C Hamano 4 | # 5 | # The "pre-rebase" hook is run just before "git rebase" starts doing 6 | # its job, and can prevent the command from running by exiting with 7 | # non-zero status. 8 | # 9 | # The hook is called with the following parameters: 10 | # 11 | # $1 -- the upstream the series was forked from. 12 | # $2 -- the branch being rebased (or empty when rebasing the current branch). 13 | # 14 | # This sample shows how to prevent topic branches that are already 15 | # merged to 'next' branch from getting rebased, because allowing it 16 | # would result in rebasing already published history. 17 | 18 | publish=next 19 | basebranch="$1" 20 | if test "$#" = 2 21 | then 22 | topic="refs/heads/$2" 23 | else 24 | topic=`git symbolic-ref HEAD` || 25 | exit 0 ;# we do not interrupt rebasing detached HEAD 26 | fi 27 | 28 | case "$topic" in 29 | refs/heads/??/*) 30 | ;; 31 | *) 32 | exit 0 ;# we do not interrupt others. 33 | ;; 34 | esac 35 | 36 | # Now we are dealing with a topic branch being rebased 37 | # on top of master. Is it OK to rebase it? 38 | 39 | # Does the topic really exist? 40 | git show-ref -q "$topic" || { 41 | echo >&2 "No such branch $topic" 42 | exit 1 43 | } 44 | 45 | # Is topic fully merged to master? 46 | not_in_master=`git rev-list --pretty=oneline ^master "$topic"` 47 | if test -z "$not_in_master" 48 | then 49 | echo >&2 "$topic is fully merged to master; better remove it." 50 | exit 1 ;# we could allow it, but there is no point. 51 | fi 52 | 53 | # Is topic ever merged to next? If so you should not be rebasing it. 54 | only_next_1=`git rev-list ^master "^$topic" ${publish} | sort` 55 | only_next_2=`git rev-list ^master ${publish} | sort` 56 | if test "$only_next_1" = "$only_next_2" 57 | then 58 | not_in_topic=`git rev-list "^$topic" master` 59 | if test -z "$not_in_topic" 60 | then 61 | echo >&2 "$topic is already up-to-date with master" 62 | exit 1 ;# we could allow it, but there is no point. 63 | else 64 | exit 0 65 | fi 66 | else 67 | not_in_next=`git rev-list --pretty=oneline ^${publish} "$topic"` 68 | /usr/bin/perl -e ' 69 | my $topic = $ARGV[0]; 70 | my $msg = "* $topic has commits already merged to public branch:\n"; 71 | my (%not_in_next) = map { 72 | /^([0-9a-f]+) /; 73 | ($1 => 1); 74 | } split(/\n/, $ARGV[1]); 75 | for my $elem (map { 76 | /^([0-9a-f]+) (.*)$/; 77 | [$1 => $2]; 78 | } split(/\n/, $ARGV[2])) { 79 | if (!exists $not_in_next{$elem->[0]}) { 80 | if ($msg) { 81 | print STDERR $msg; 82 | undef $msg; 83 | } 84 | print STDERR " $elem->[1]\n"; 85 | } 86 | } 87 | ' "$topic" "$not_in_next" "$not_in_master" 88 | exit 1 89 | fi 90 | 91 | exit 0 92 | 93 | ################################################################ 94 | 95 | This sample hook safeguards topic branches that have been 96 | published from being rewound. 97 | 98 | The workflow assumed here is: 99 | 100 | * Once a topic branch forks from "master", "master" is never 101 | merged into it again (either directly or indirectly). 102 | 103 | * Once a topic branch is fully cooked and merged into "master", 104 | it is deleted. If you need to build on top of it to correct 105 | earlier mistakes, a new topic branch is created by forking at 106 | the tip of the "master". This is not strictly necessary, but 107 | it makes it easier to keep your history simple. 108 | 109 | * Whenever you need to test or publish your changes to topic 110 | branches, merge them into "next" branch. 111 | 112 | The script, being an example, hardcodes the publish branch name 113 | to be "next", but it is trivial to make it configurable via 114 | $GIT_DIR/config mechanism. 115 | 116 | With this workflow, you would want to know: 117 | 118 | (1) ... if a topic branch has ever been merged to "next". Young 119 | topic branches can have stupid mistakes you would rather 120 | clean up before publishing, and things that have not been 121 | merged into other branches can be easily rebased without 122 | affecting other people. But once it is published, you would 123 | not want to rewind it. 124 | 125 | (2) ... if a topic branch has been fully merged to "master". 126 | Then you can delete it. More importantly, you should not 127 | build on top of it -- other people may already want to 128 | change things related to the topic as patches against your 129 | "master", so if you need further changes, it is better to 130 | fork the topic (perhaps with the same name) afresh from the 131 | tip of "master". 132 | 133 | Let's look at this example: 134 | 135 | o---o---o---o---o---o---o---o---o---o "next" 136 | / / / / 137 | / a---a---b A / / 138 | / / / / 139 | / / c---c---c---c B / 140 | / / / \ / 141 | / / / b---b C \ / 142 | / / / / \ / 143 | ---o---o---o---o---o---o---o---o---o---o---o "master" 144 | 145 | 146 | A, B and C are topic branches. 147 | 148 | * A has one fix since it was merged up to "next". 149 | 150 | * B has finished. It has been fully merged up to "master" and "next", 151 | and is ready to be deleted. 152 | 153 | * C has not merged to "next" at all. 154 | 155 | We would want to allow C to be rebased, refuse A, and encourage 156 | B to be deleted. 157 | 158 | To compute (1): 159 | 160 | git rev-list ^master ^topic next 161 | git rev-list ^master next 162 | 163 | if these match, topic has not merged in next at all. 164 | 165 | To compute (2): 166 | 167 | git rev-list master..topic 168 | 169 | if this is empty, it is fully merged to "master". 170 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/alexnet.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Contains a model definition for AlexNet. 16 | 17 | This work was first described in: 18 | ImageNet Classification with Deep Convolutional Neural Networks 19 | Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton 20 | 21 | and later refined in: 22 | One weird trick for parallelizing convolutional neural networks 23 | Alex Krizhevsky, 2014 24 | 25 | Here we provide the implementation proposed in "One weird trick" and not 26 | "ImageNet Classification", as per the paper, the LRN layers have been removed. 27 | 28 | Usage: 29 | with slim.arg_scope(alexnet.alexnet_v2_arg_scope()): 30 | outputs, end_points = alexnet.alexnet_v2(inputs) 31 | 32 | @@alexnet_v2 33 | """ 34 | 35 | from __future__ import absolute_import 36 | from __future__ import division 37 | from __future__ import print_function 38 | 39 | import tensorflow as tf 40 | 41 | slim = tf.contrib.slim 42 | trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) 43 | 44 | 45 | def alexnet_v2_arg_scope(weight_decay=0.0005): 46 | with slim.arg_scope([slim.conv2d, slim.fully_connected], 47 | activation_fn=tf.nn.relu, 48 | biases_initializer=tf.constant_initializer(0.1), 49 | weights_regularizer=slim.l2_regularizer(weight_decay)): 50 | with slim.arg_scope([slim.conv2d], padding='SAME'): 51 | with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc: 52 | return arg_sc 53 | 54 | 55 | def alexnet_v2(inputs, 56 | num_classes=1000, 57 | is_training=True, 58 | dropout_keep_prob=0.5, 59 | spatial_squeeze=True, 60 | scope='alexnet_v2'): 61 | """AlexNet version 2. 62 | 63 | Described in: http://arxiv.org/pdf/1404.5997v2.pdf 64 | Parameters from: 65 | github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ 66 | layers-imagenet-1gpu.cfg 67 | 68 | Note: All the fully_connected layers have been transformed to conv2d layers. 69 | To use in classification mode, resize input to 224x224. To use in fully 70 | convolutional mode, set spatial_squeeze to false. 71 | The LRN layers have been removed and change the initializers from 72 | random_normal_initializer to xavier_initializer. 73 | 74 | Args: 75 | inputs: a tensor of size [batch_size, height, width, channels]. 76 | num_classes: number of predicted classes. 77 | is_training: whether or not the model is being trained. 78 | dropout_keep_prob: the probability that activations are kept in the dropout 79 | layers during training. 80 | spatial_squeeze: whether or not should squeeze the spatial dimensions of the 81 | outputs. Useful to remove unnecessary dimensions for classification. 82 | scope: Optional scope for the variables. 83 | 84 | Returns: 85 | the last op containing the log predictions and end_points dict. 86 | """ 87 | with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc: 88 | end_points_collection = sc.name + '_end_points' 89 | # Collect outputs for conv2d, fully_connected and max_pool2d. 90 | with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], 91 | outputs_collections=[end_points_collection]): 92 | net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', 93 | scope='conv1') 94 | net = slim.max_pool2d(net, [3, 3], 2, scope='pool1') 95 | net = slim.conv2d(net, 192, [5, 5], scope='conv2') 96 | net = slim.max_pool2d(net, [3, 3], 2, scope='pool2') 97 | net = slim.conv2d(net, 384, [3, 3], scope='conv3') 98 | net = slim.conv2d(net, 384, [3, 3], scope='conv4') 99 | net = slim.conv2d(net, 256, [3, 3], scope='conv5') 100 | net = slim.max_pool2d(net, [3, 3], 2, scope='pool5') 101 | 102 | # Use conv2d instead of fully_connected layers. 103 | with slim.arg_scope([slim.conv2d], 104 | weights_initializer=trunc_normal(0.005), 105 | biases_initializer=tf.constant_initializer(0.1)): 106 | net = slim.conv2d(net, 4096, [5, 5], padding='VALID', 107 | scope='fc6') 108 | net = slim.dropout(net, dropout_keep_prob, is_training=is_training, 109 | scope='dropout6') 110 | net = slim.conv2d(net, 4096, [1, 1], scope='fc7') 111 | net = slim.dropout(net, dropout_keep_prob, is_training=is_training, 112 | scope='dropout7') 113 | net = slim.conv2d(net, num_classes, [1, 1], 114 | activation_fn=None, 115 | normalizer_fn=None, 116 | biases_initializer=tf.zeros_initializer(), 117 | scope='fc8') 118 | 119 | # Convert end_points_collection into a end_point dict. 120 | end_points = slim.utils.convert_collection_to_dict(end_points_collection) 121 | if spatial_squeeze: 122 | net = tf.squeeze(net, [1, 2], name='fc8/squeezed') 123 | end_points[sc.name + '/fc8'] = net 124 | return net, end_points 125 | alexnet_v2.default_image_size = 224 126 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/box_utils/boxes_utils.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import division 5 | from __future__ import print_function 6 | 7 | import tensorflow as tf 8 | from libs.box_utils.coordinate_convert import forward_convert 9 | 10 | 11 | def clip_boxes_to_img_boundaries(decode_boxes, img_shape): 12 | ''' 13 | 14 | :param decode_boxes: 15 | :return: decode boxes, and already clip to boundaries 16 | ''' 17 | 18 | with tf.name_scope('clip_boxes_to_img_boundaries'): 19 | 20 | ymin, xmin, ymax, xmax = tf.unstack(decode_boxes, axis=1) 21 | img_h, img_w = img_shape[1], img_shape[2] 22 | 23 | xmin = tf.maximum(xmin, 0.0) 24 | xmin = tf.minimum(xmin, tf.cast(img_w, tf.float32)) 25 | 26 | ymin = tf.maximum(ymin, 0.0) 27 | ymin = tf.minimum(ymin, tf.cast(img_h, tf.float32)) # avoid xmin > img_w, ymin > img_h 28 | 29 | xmax = tf.minimum(xmax, tf.cast(img_w, tf.float32)) 30 | ymax = tf.minimum(ymax, tf.cast(img_h, tf.float32)) 31 | 32 | return tf.transpose(tf.stack([ymin, xmin, ymax, xmax])) 33 | 34 | 35 | def filter_outside_boxes(boxes, img_w, img_h): 36 | ''' 37 | :param anchors:boxes with format [xmin, ymin, xmax, ymax] 38 | :param img_h: height of image 39 | :param img_w: width of image 40 | :return: indices of anchors that not outside the image boundary 41 | ''' 42 | 43 | with tf.name_scope('filter_outside_boxes'): 44 | 45 | ymin, xmin, ymax, xmax = tf.unstack(boxes, axis=1) 46 | xmin_index = tf.greater_equal(xmin, 0) 47 | ymin_index = tf.greater_equal(ymin, 0) 48 | xmax_index = tf.less_equal(xmax, img_w) 49 | ymax_index = tf.less_equal(ymax, img_h) 50 | 51 | indices = tf.transpose(tf.stack([ymin_index, xmin_index, ymax_index, xmax_index])) 52 | indices = tf.cast(indices, dtype=tf.int32) 53 | indices = tf.reduce_sum(indices, axis=1) 54 | indices = tf.where(tf.equal(indices, tf.shape(boxes)[1])) 55 | 56 | return tf.reshape(indices, [-1, ]) 57 | 58 | 59 | def nms_boxes(decode_boxes, scores, iou_threshold, max_output_size, name): 60 | ''' 61 | 1) NMS 62 | 2) get maximum num of proposals 63 | :return: valid_indices 64 | ''' 65 | 66 | valid_index = tf.image.non_max_suppression( 67 | boxes=decode_boxes, 68 | scores=scores, 69 | max_output_size=max_output_size, 70 | iou_threshold=iou_threshold, 71 | name=name 72 | ) 73 | 74 | return valid_index 75 | 76 | 77 | def padd_boxes_with_zeros(boxes, scores, max_num_of_boxes): 78 | 79 | ''' 80 | num of boxes less than max num of boxes, so it need to pad with zeros[0, 0, 0, 0] 81 | :param boxes: 82 | :param scores: [-1] 83 | :param max_num_of_boxes: 84 | :return: 85 | ''' 86 | 87 | pad_num = tf.cast(max_num_of_boxes, tf.int32) - tf.shape(boxes)[0] 88 | 89 | zero_boxes = tf.zeros(shape=[pad_num, 4], dtype=boxes.dtype) 90 | zero_scores = tf.zeros(shape=[pad_num], dtype=scores.dtype) 91 | 92 | final_boxes = tf.concat([boxes, zero_boxes], axis=0) 93 | 94 | final_scores = tf.concat([scores, zero_scores], axis=0) 95 | 96 | return final_boxes, final_scores 97 | 98 | 99 | def get_horizen_minAreaRectangle(boxs, with_label=True): 100 | 101 | rpn_proposals_boxes_convert = tf.py_func(forward_convert, 102 | inp=[boxs, with_label], 103 | Tout=tf.float32) 104 | if with_label: 105 | rpn_proposals_boxes_convert = tf.reshape(rpn_proposals_boxes_convert, [-1, 9]) 106 | 107 | boxes_shape = tf.shape(rpn_proposals_boxes_convert) 108 | y_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 0], end=[boxes_shape[0], boxes_shape[1] - 1], 109 | strides=[1, 2]) 110 | x_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 1], end=[boxes_shape[0], boxes_shape[1] - 1], 111 | strides=[1, 2]) 112 | 113 | label = tf.unstack(rpn_proposals_boxes_convert, axis=1)[-1] 114 | 115 | y_max = tf.reduce_max(y_list, axis=1) 116 | y_min = tf.reduce_min(y_list, axis=1) 117 | x_max = tf.reduce_max(x_list, axis=1) 118 | x_min = tf.reduce_min(x_list, axis=1) 119 | return tf.transpose(tf.stack([y_min, x_min, y_max, x_max, label], axis=0)) 120 | else: 121 | rpn_proposals_boxes_convert = tf.reshape(rpn_proposals_boxes_convert, [-1, 8]) 122 | 123 | boxes_shape = tf.shape(rpn_proposals_boxes_convert) 124 | y_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 0], end=[boxes_shape[0], boxes_shape[1]], 125 | strides=[1, 2]) 126 | x_list = tf.strided_slice(rpn_proposals_boxes_convert, begin=[0, 1], end=[boxes_shape[0], boxes_shape[1]], 127 | strides=[1, 2]) 128 | 129 | y_max = tf.reduce_max(y_list, axis=1) 130 | y_min = tf.reduce_min(y_list, axis=1) 131 | x_max = tf.reduce_max(x_list, axis=1) 132 | x_min = tf.reduce_min(x_list, axis=1) 133 | 134 | return tf.transpose(tf.stack([y_min, x_min, y_max, x_max], axis=0)) 135 | 136 | 137 | def get_head(gtboxes_and_label_batch): 138 | """ 139 | :param gtboxes_and_label_batch: [x1, y1, x2, y2, x3, y3, x4, y4, head_x, head_y, label] 140 | :return: [x1, y1, x2, y2, x3, y3, x4, y4, label], [head_x, head_y] 141 | """ 142 | x1, y1, x2, y2, x3, y3, x4, y4, head_x, head_y, label = tf.unstack(gtboxes_and_label_batch, axis=1) 143 | coords_label = tf.transpose(tf.stack([x1, y1, x2, y2, x3, y3, x4, y4, label])) 144 | head = tf.transpose(tf.stack([head_x, head_y])) 145 | 146 | return coords_label, head 147 | 148 | def get_angle(gtboxes_and_label_batch): 149 | 150 | x1, y1, x2, y2, x3, y3, x4, y4, angle, label = tf.unstack(gtboxes_and_label_batch, axis=1) 151 | coords_label = tf.transpose(tf.stack([x1, y1, x2, y2, x3, y3, x4, y4, label])) 152 | angle = tf.transpose(tf.stack([angle])) 153 | 154 | return coords_label, angle 155 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/data/io/image_preprocess.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from __future__ import absolute_import 4 | from __future__ import print_function 5 | from __future__ import division 6 | 7 | import tensorflow as tf 8 | 9 | import numpy as np 10 | import cv2 11 | import math 12 | import random 13 | 14 | 15 | def short_side_resize(img_tensor, gtboxes_and_label, target_shortside_len): 16 | ''' 17 | 18 | :param img_tensor:[h, w, c], gtboxes_and_label:[-1, 9] 19 | :param target_shortside_len: 20 | :return: 21 | ''' 22 | 23 | h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] 24 | 25 | new_h, new_w = tf.cond(tf.less(h, w), 26 | true_fn=lambda: (target_shortside_len, target_shortside_len * w//h), 27 | false_fn=lambda: (target_shortside_len * h//w, target_shortside_len)) 28 | 29 | img_tensor = tf.expand_dims(img_tensor, axis=0) 30 | img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) 31 | 32 | x1, y1, x2, y2, x3, y3, x4, y4, head_x, head_y, label = tf.unstack(gtboxes_and_label, axis=1) 33 | 34 | x1, x2, x3, x4, head_x = x1 * new_w//w, x2 * new_w//w, x3 * new_w//w, x4 * new_w//w, head_x * new_w//w 35 | y1, y2, y3, y4, head_y = y1 * new_h//h, y2 * new_h//h, y3 * new_h//h, y4 * new_h//h, head_y * new_h//h 36 | 37 | img_tensor = tf.squeeze(img_tensor, axis=0) # ensure image tensor rank is 3 38 | return img_tensor, tf.transpose(tf.stack([x1, y1, x2, y2, x3, y3, x4, y4, head_x, head_y, label], axis=0)) 39 | 40 | 41 | def short_side_resize_for_inference_data(img_tensor, target_shortside_len, is_resize=True): 42 | h, w, = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] 43 | 44 | img_tensor = tf.expand_dims(img_tensor, axis=0) 45 | 46 | if is_resize: 47 | new_h, new_w = tf.cond(tf.less(h, w), 48 | true_fn=lambda: (target_shortside_len, target_shortside_len * w // h), 49 | false_fn=lambda: (target_shortside_len * h // w, target_shortside_len)) 50 | img_tensor = tf.image.resize_bilinear(img_tensor, [new_h, new_w]) 51 | 52 | return img_tensor # [1, h, w, c] 53 | 54 | 55 | def flip_left_right(img_tensor, gtboxes_and_label): 56 | h, w = tf.shape(img_tensor)[0], tf.shape(img_tensor)[1] 57 | img_tensor = tf.image.flip_left_right(img_tensor) 58 | 59 | x1, y1, x2, y2, x3, y3, x4, y4, head_x, head_y, label = tf.unstack(gtboxes_and_label, axis=1) 60 | new_x1 = w - x1 61 | new_x2 = w - x2 62 | new_x3 = w - x3 63 | new_x4 = w - x4 64 | new_head_x = w - head_x 65 | return img_tensor, tf.transpose(tf.stack([new_x1, y1, new_x2, y2, new_x3, y3, new_x4, y4, new_head_x, head_y, label], axis=0)) 66 | 67 | 68 | def random_flip_left_right(img_tensor, gtboxes_and_label): 69 | 70 | img_tensor, gtboxes_and_label = tf.cond(tf.less(tf.random_uniform(shape=[], minval=0, maxval=1), 0.5), 71 | lambda: flip_left_right(img_tensor, gtboxes_and_label), 72 | lambda: (img_tensor, gtboxes_and_label)) 73 | 74 | return img_tensor, gtboxes_and_label 75 | 76 | 77 | # ******************************************Rotate img & bounding box by a random angle********************************************** 78 | 79 | def rotate_point(img_tensor, gtboxes_and_label): 80 | 81 | def rotate_point_cv(img, point_label=None, keep_size = False): 82 | ''' 83 | input: 1.img type:numpy_array(np.float32) 2.angle type:int 3.point_label type:numpy_array(np.int32) shape(None, 11), last three represent head coordinates and label 84 | output: 1.rotated_img 2.rotated_point shape(None, 10) 85 | ''' 86 | angle = random.randint(0,90) 87 | 88 | if keep_size: 89 | cols = img.shape[1] 90 | rows = img.shape[0] 91 | M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1) 92 | img = cv2.warpAffine(img, M, (cols, rows)) 93 | a = M[:, :2] ##a.shape (2,2) 94 | b = M[:, 2:] ###b.shape(2,1) 95 | b = np.reshape(b,newshape=(1,2)) 96 | a = np.transpose(a) 97 | point = np.reshape(point_label[:,:-1],newshape=(len(point_label)*5,2)) 98 | point = np.dot(point,a)+b 99 | point = np.reshape(point,newshape=(np.int(len(point)/5),10)) 100 | label = point_label[:,-1] 101 | label = label[:, np.newaxis] 102 | point = np.concatenate((point, label), axis=1) 103 | return img, np.int32(point) 104 | else: 105 | cols = img.shape[1] 106 | rows = img.shape[0] 107 | M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1) 108 | 109 | heightNew = int(cols * math.fabs(math.sin(math.radians(angle))) + rows* math.fabs(math.cos(math.radians(angle)))) 110 | widthNew = int(rows * math.fabs(math.sin(math.radians(angle))) + cols* math.fabs(math.cos(math.radians(angle)))) 111 | M[0, 2] += (widthNew - cols) / 2 112 | M[1, 2] += (heightNew - rows) / 2 113 | 114 | img = cv2.warpAffine(img, M, (widthNew, heightNew)) 115 | a = M[:, :2] ##a.shape (2,2) 116 | b = M[:, 2:] ###b.shape(2,1) 117 | b = np.reshape(b, newshape=(1, 2)) 118 | a = np.transpose(a) 119 | point = np.reshape(point_label[:,:-1], newshape=(len(point_label) * 5, 2)) 120 | point = np.dot(point, a) + b 121 | point = np.reshape(point, newshape=(np.int(len(point) / 5), 10)) 122 | label = point_label[:,-1] 123 | label = label[:, np.newaxis] 124 | point = np.concatenate((point, label), axis=1) 125 | return img, np.int32(point) 126 | 127 | rotated_img_tensor, rotated_gtboxes_and_label = tf.py_func(rotate_point_cv, inp=[img_tensor, gtboxes_and_label], Tout=[tf.float32, tf.int32]) 128 | rotated_gtboxes_and_label.set_shape(gtboxes_and_label.get_shape()) 129 | # rotated_img_tensor = tf.reshape(rotated_img_tensor, [-1,-1,3]) # We only consider RGB three channels now. 130 | rotated_img_tensor.set_shape(tf.TensorShape([None,None,3])) 131 | return rotated_img_tensor, rotated_gtboxes_and_label 132 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/overfeat_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Tests for slim.slim_nets.overfeat.""" 16 | from __future__ import absolute_import 17 | from __future__ import division 18 | from __future__ import print_function 19 | 20 | import tensorflow as tf 21 | 22 | from nets import overfeat 23 | 24 | slim = tf.contrib.slim 25 | 26 | 27 | class OverFeatTest(tf.test.TestCase): 28 | 29 | def testBuild(self): 30 | batch_size = 5 31 | height, width = 231, 231 32 | num_classes = 1000 33 | with self.test_session(): 34 | inputs = tf.random_uniform((batch_size, height, width, 3)) 35 | logits, _ = overfeat.overfeat(inputs, num_classes) 36 | self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed') 37 | self.assertListEqual(logits.get_shape().as_list(), 38 | [batch_size, num_classes]) 39 | 40 | def testFullyConvolutional(self): 41 | batch_size = 1 42 | height, width = 281, 281 43 | num_classes = 1000 44 | with self.test_session(): 45 | inputs = tf.random_uniform((batch_size, height, width, 3)) 46 | logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False) 47 | self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd') 48 | self.assertListEqual(logits.get_shape().as_list(), 49 | [batch_size, 2, 2, num_classes]) 50 | 51 | def testEndPoints(self): 52 | batch_size = 5 53 | height, width = 231, 231 54 | num_classes = 1000 55 | with self.test_session(): 56 | inputs = tf.random_uniform((batch_size, height, width, 3)) 57 | _, end_points = overfeat.overfeat(inputs, num_classes) 58 | expected_names = ['overfeat/conv1', 59 | 'overfeat/pool1', 60 | 'overfeat/conv2', 61 | 'overfeat/pool2', 62 | 'overfeat/conv3', 63 | 'overfeat/conv4', 64 | 'overfeat/conv5', 65 | 'overfeat/pool5', 66 | 'overfeat/fc6', 67 | 'overfeat/fc7', 68 | 'overfeat/fc8' 69 | ] 70 | self.assertSetEqual(set(end_points.keys()), set(expected_names)) 71 | 72 | def testModelVariables(self): 73 | batch_size = 5 74 | height, width = 231, 231 75 | num_classes = 1000 76 | with self.test_session(): 77 | inputs = tf.random_uniform((batch_size, height, width, 3)) 78 | overfeat.overfeat(inputs, num_classes) 79 | expected_names = ['overfeat/conv1/weights', 80 | 'overfeat/conv1/biases', 81 | 'overfeat/conv2/weights', 82 | 'overfeat/conv2/biases', 83 | 'overfeat/conv3/weights', 84 | 'overfeat/conv3/biases', 85 | 'overfeat/conv4/weights', 86 | 'overfeat/conv4/biases', 87 | 'overfeat/conv5/weights', 88 | 'overfeat/conv5/biases', 89 | 'overfeat/fc6/weights', 90 | 'overfeat/fc6/biases', 91 | 'overfeat/fc7/weights', 92 | 'overfeat/fc7/biases', 93 | 'overfeat/fc8/weights', 94 | 'overfeat/fc8/biases', 95 | ] 96 | model_variables = [v.op.name for v in slim.get_model_variables()] 97 | self.assertSetEqual(set(model_variables), set(expected_names)) 98 | 99 | def testEvaluation(self): 100 | batch_size = 2 101 | height, width = 231, 231 102 | num_classes = 1000 103 | with self.test_session(): 104 | eval_inputs = tf.random_uniform((batch_size, height, width, 3)) 105 | logits, _ = overfeat.overfeat(eval_inputs, is_training=False) 106 | self.assertListEqual(logits.get_shape().as_list(), 107 | [batch_size, num_classes]) 108 | predictions = tf.argmax(logits, 1) 109 | self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) 110 | 111 | def testTrainEvalWithReuse(self): 112 | train_batch_size = 2 113 | eval_batch_size = 1 114 | train_height, train_width = 231, 231 115 | eval_height, eval_width = 281, 281 116 | num_classes = 1000 117 | with self.test_session(): 118 | train_inputs = tf.random_uniform( 119 | (train_batch_size, train_height, train_width, 3)) 120 | logits, _ = overfeat.overfeat(train_inputs) 121 | self.assertListEqual(logits.get_shape().as_list(), 122 | [train_batch_size, num_classes]) 123 | tf.get_variable_scope().reuse_variables() 124 | eval_inputs = tf.random_uniform( 125 | (eval_batch_size, eval_height, eval_width, 3)) 126 | logits, _ = overfeat.overfeat(eval_inputs, is_training=False, 127 | spatial_squeeze=False) 128 | self.assertListEqual(logits.get_shape().as_list(), 129 | [eval_batch_size, 2, 2, num_classes]) 130 | logits = tf.reduce_mean(logits, [1, 2]) 131 | predictions = tf.argmax(logits, 1) 132 | self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 133 | 134 | def testForward(self): 135 | batch_size = 1 136 | height, width = 231, 231 137 | with self.test_session() as sess: 138 | inputs = tf.random_uniform((batch_size, height, width, 3)) 139 | logits, _ = overfeat.overfeat(inputs) 140 | sess.run(tf.global_variables_initializer()) 141 | output = sess.run(logits) 142 | self.assertTrue(output.any()) 143 | 144 | if __name__ == '__main__': 145 | tf.test.main() 146 | -------------------------------------------------------------------------------- /R2CNN_HEAD_FPN_Tensorflow/libs/networks/slim_nets/alexnet_test.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | # ============================================================================== 15 | """Tests for slim.slim_nets.alexnet.""" 16 | from __future__ import absolute_import 17 | from __future__ import division 18 | from __future__ import print_function 19 | 20 | import tensorflow as tf 21 | 22 | from nets import alexnet 23 | 24 | slim = tf.contrib.slim 25 | 26 | 27 | class AlexnetV2Test(tf.test.TestCase): 28 | 29 | def testBuild(self): 30 | batch_size = 5 31 | height, width = 224, 224 32 | num_classes = 1000 33 | with self.test_session(): 34 | inputs = tf.random_uniform((batch_size, height, width, 3)) 35 | logits, _ = alexnet.alexnet_v2(inputs, num_classes) 36 | self.assertEquals(logits.op.name, 'alexnet_v2/fc8/squeezed') 37 | self.assertListEqual(logits.get_shape().as_list(), 38 | [batch_size, num_classes]) 39 | 40 | def testFullyConvolutional(self): 41 | batch_size = 1 42 | height, width = 300, 400 43 | num_classes = 1000 44 | with self.test_session(): 45 | inputs = tf.random_uniform((batch_size, height, width, 3)) 46 | logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False) 47 | self.assertEquals(logits.op.name, 'alexnet_v2/fc8/BiasAdd') 48 | self.assertListEqual(logits.get_shape().as_list(), 49 | [batch_size, 4, 7, num_classes]) 50 | 51 | def testEndPoints(self): 52 | batch_size = 5 53 | height, width = 224, 224 54 | num_classes = 1000 55 | with self.test_session(): 56 | inputs = tf.random_uniform((batch_size, height, width, 3)) 57 | _, end_points = alexnet.alexnet_v2(inputs, num_classes) 58 | expected_names = ['alexnet_v2/conv1', 59 | 'alexnet_v2/pool1', 60 | 'alexnet_v2/conv2', 61 | 'alexnet_v2/pool2', 62 | 'alexnet_v2/conv3', 63 | 'alexnet_v2/conv4', 64 | 'alexnet_v2/conv5', 65 | 'alexnet_v2/pool5', 66 | 'alexnet_v2/fc6', 67 | 'alexnet_v2/fc7', 68 | 'alexnet_v2/fc8' 69 | ] 70 | self.assertSetEqual(set(end_points.keys()), set(expected_names)) 71 | 72 | def testModelVariables(self): 73 | batch_size = 5 74 | height, width = 224, 224 75 | num_classes = 1000 76 | with self.test_session(): 77 | inputs = tf.random_uniform((batch_size, height, width, 3)) 78 | alexnet.alexnet_v2(inputs, num_classes) 79 | expected_names = ['alexnet_v2/conv1/weights', 80 | 'alexnet_v2/conv1/biases', 81 | 'alexnet_v2/conv2/weights', 82 | 'alexnet_v2/conv2/biases', 83 | 'alexnet_v2/conv3/weights', 84 | 'alexnet_v2/conv3/biases', 85 | 'alexnet_v2/conv4/weights', 86 | 'alexnet_v2/conv4/biases', 87 | 'alexnet_v2/conv5/weights', 88 | 'alexnet_v2/conv5/biases', 89 | 'alexnet_v2/fc6/weights', 90 | 'alexnet_v2/fc6/biases', 91 | 'alexnet_v2/fc7/weights', 92 | 'alexnet_v2/fc7/biases', 93 | 'alexnet_v2/fc8/weights', 94 | 'alexnet_v2/fc8/biases', 95 | ] 96 | model_variables = [v.op.name for v in slim.get_model_variables()] 97 | self.assertSetEqual(set(model_variables), set(expected_names)) 98 | 99 | def testEvaluation(self): 100 | batch_size = 2 101 | height, width = 224, 224 102 | num_classes = 1000 103 | with self.test_session(): 104 | eval_inputs = tf.random_uniform((batch_size, height, width, 3)) 105 | logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False) 106 | self.assertListEqual(logits.get_shape().as_list(), 107 | [batch_size, num_classes]) 108 | predictions = tf.argmax(logits, 1) 109 | self.assertListEqual(predictions.get_shape().as_list(), [batch_size]) 110 | 111 | def testTrainEvalWithReuse(self): 112 | train_batch_size = 2 113 | eval_batch_size = 1 114 | train_height, train_width = 224, 224 115 | eval_height, eval_width = 300, 400 116 | num_classes = 1000 117 | with self.test_session(): 118 | train_inputs = tf.random_uniform( 119 | (train_batch_size, train_height, train_width, 3)) 120 | logits, _ = alexnet.alexnet_v2(train_inputs) 121 | self.assertListEqual(logits.get_shape().as_list(), 122 | [train_batch_size, num_classes]) 123 | tf.get_variable_scope().reuse_variables() 124 | eval_inputs = tf.random_uniform( 125 | (eval_batch_size, eval_height, eval_width, 3)) 126 | logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False, 127 | spatial_squeeze=False) 128 | self.assertListEqual(logits.get_shape().as_list(), 129 | [eval_batch_size, 4, 7, num_classes]) 130 | logits = tf.reduce_mean(logits, [1, 2]) 131 | predictions = tf.argmax(logits, 1) 132 | self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size]) 133 | 134 | def testForward(self): 135 | batch_size = 1 136 | height, width = 224, 224 137 | with self.test_session() as sess: 138 | inputs = tf.random_uniform((batch_size, height, width, 3)) 139 | logits, _ = alexnet.alexnet_v2(inputs) 140 | sess.run(tf.global_variables_initializer()) 141 | output = sess.run(logits) 142 | self.assertTrue(output.any()) 143 | 144 | if __name__ == '__main__': 145 | tf.test.main() 146 | --------------------------------------------------------------------------------