diff --git a/dubhe_data_process/common/data/__init__.py b/dubhe_data_process/common/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dubhe_data_process/entrance/__init__.py b/dubhe_data_process/entrance/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dubhe_data_process/entrance/algorithm-annotation.py b/dubhe_data_process/entrance/algorithm-annotation.py new file mode 100644 index 0000000..7142ffc --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-annotation.py @@ -0,0 +1,61 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 +import threading + +import time +import sys +sys.path.append("../") +import common.RedisUtil as f +from common import config as config +from entrance.executor import annotation as annotation, taskexecutor +import luascript.starttaskscript as start_script +import logging +from common import select_gpu as gpu + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +if __name__ == '__main__': + """Automatic annotation algorithm entry.""" + gpu.select_gpu() + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=taskexecutor.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + annotation._init() + while 1: + try: + if config.loadJsonData(config.sign) == 0: + logging.info('not to execute new task') + time.sleep(1) + else: + logging.info('get one task') + element = redisClient.eval(start_script.startTaskLua, 1, config.queue, + config.annotationStartQueue, int(time.time())) + if len(element) > 0: + taskexecutor.annotationExecutor(redisClient, element[0]); + else: + logging.info('task queue is empty.') + time.sleep(1) + except Exception as e: + logging.error('except:', e) + time.sleep(1) diff --git a/dubhe_data_process/entrance/algorithm-imagenet.py b/dubhe_data_process/entrance/algorithm-imagenet.py new file mode 100644 index 0000000..60048a2 --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-imagenet.py @@ -0,0 +1,69 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 + +import json +import threading +import time +import sys +sys.path.append("../") +from entrance.executor import imagenet as imagenet +import common.RedisUtil as f +import common.config as config +import luascript.starttaskscript as start_script +import logging +import common.select_gpu as gpu + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +if __name__ == '__main__': + """Imagenet algorithm entry.""" + gpu.select_gpu() + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=imagenet.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + imagenet._init() + while 1: + try: + if config.loadJsonData(config.sign) == 0: + logging.info('not to execute new task') + time.sleep(1) + else: + logging.info('get one task') + element = redisClient.eval(start_script.startTaskLua, 1, config.imagenetTaskQueue, + config.imagenetStartQueue, int(time.time())) + if len(element) > 0: + key = element[0].decode() + jsonStr = f.getByKey(redisClient, key.replace('"', '')); + result = imagenet.process(jsonStr, element[0]) + logging.info("result:", result) + + logging.info('save result to redis') + f.pushToQueue(redisClient, config.imagenetFinishQueue, json.dumps(result)) + redisClient.zrem(config.imagenetStartQueue, element[0]) + else: + logging.info('task queue is empty.') + time.sleep(2) + except Exception as e: + logging.error('except:', e) + time.sleep(1) diff --git a/dubhe_data_process/entrance/algorithm-imgprocess.py b/dubhe_data_process/entrance/algorithm-imgprocess.py new file mode 100644 index 0000000..012ab73 --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-imgprocess.py @@ -0,0 +1,55 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +import threading +import time +import sys +sys.path.append("../") +import common.RedisUtil as f +import luascript.starttaskscript as start_script +import common.config as config +import logging +from entrance.executor import imgprocess + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +if __name__ == '__main__': + """Enhancement algorithm entry.""" + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=imgprocess.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + while 1: + try: + if config.loadJsonData(config.sign) == 0: + logging.info('not to execute new task') + time.sleep(5) + else: + enhanceTaskId = redisClient.eval(start_script.startTaskLua, 1, config.imgProcessTaskQueue, + config.imgProcessStartQueue, int(time.time())) + if len(enhanceTaskId) > 0: + imgprocess.start_enhance_task(enhanceTaskId, redisClient) + else: + logging.info('task queue is empty.') + time.sleep(5) + except Exception as e: + logging.error('except:', e) + time.sleep(1) diff --git a/dubhe_data_process/entrance/algorithm-lungsegmenatation.py b/dubhe_data_process/entrance/algorithm-lungsegmenatation.py new file mode 100644 index 0000000..89c4d4a --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-lungsegmenatation.py @@ -0,0 +1,63 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +import json +import threading +from datetime import datetime +import time +import sys +sys.path.append("../") +import common.RedisUtil as f +import luascript.starttaskscript as start_script +import common.config as config +import logging +from entrance.executor import lungsegmentation as lungseg +import redis + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', level=logging.DEBUG) + +if __name__ == '__main__': + """Lung segmentation algorithm based on CT image dcm entry.""" + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=lungseg.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + while 1: + try: + # if config.loadJsonData(config.sign) == 0: + # logging.info('not to execute new task') + # time.sleep(5) + # else: + logging.info("read redis:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + lungTask = redisClient.eval(start_script.startTaskLua, 1, config.dcmTaskQueue, config.dcmStartQueue, int(time.time())) + if len(lungTask) > 0: + logging.info("start process.") + key = lungTask[0].decode() + jsonStr = f.getByKey(redisClient, key.replace('"', '')) + if lungseg.process(jsonStr, lungTask[0]): + f.pushToQueue(redisClient, config.dcmFinishQueue, key) + redisClient.zrem(config.dcmStartQueue, lungTask[0]) + logging.info('success.') + else: + logging.info('task queue is empty.') + time.sleep(1) + except Exception as e: + logging.error('except:', e) + time.sleep(1) diff --git a/dubhe_data_process/entrance/algorithm-ofrecord.py b/dubhe_data_process/entrance/algorithm-ofrecord.py new file mode 100644 index 0000000..3d831be --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-ofrecord.py @@ -0,0 +1,80 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 + +import os +import json +import threading +import time +import sys +sys.path.append("../") +import common.RedisUtil as f +import common.config as config +import luascript.starttaskscript as start_script +import logging +import traceback +from entrance.executor import ofrecord + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s',level=logging.DEBUG) + +basePath = '/nfs/' +descPath = 'ofrecord/train' + +if __name__ == '__main__': + """Ofrecord algorithm entry.""" + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=ofrecord.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + while 1: + try: + if config.loadJsonData(config.sign) == 0: + logging.info('not to execute new task') + time.sleep(1) + else: + element = redisClient.eval(start_script.startTaskLua, 1, config.ofrecordTaskQueue, + config.ofrecordStartQueue, int(time.time())) + if len(element) > 0: + key = element[0].decode() + detail = f.getByKey(redisClient, key.replace('"', '')) + jsonStr = json.loads(detail.decode()) + label_map = {} + index = 0 + for item in jsonStr["datasetLabels"].keys(): + if index >= 0 and item != '@type': + label_map[item] = jsonStr["datasetLabels"][item] + index += 1 + ofrecord.execute(os.path.join(basePath, jsonStr["datasetPath"]), + os.path.join(basePath, jsonStr["datasetPath"], descPath), + label_map, + jsonStr["files"], + jsonStr["partNum"], + element[0]) + logging.info('save result to redis') + f.pushToQueue(redisClient, config.ofrecordFinishQueue, key) + redisClient.zrem(config.ofrecordStartQueue, element[0]) + else: + logging.info('task queue is empty.') + time.sleep(2) + except Exception as e: + logging.error('except:', e) + redisClient.zrem(config.ofrecordStartQueue, element[0]) + time.sleep(1) diff --git a/dubhe_data_process/entrance/algorithm-text-classification.py b/dubhe_data_process/entrance/algorithm-text-classification.py new file mode 100644 index 0000000..05e6e10 --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-text-classification.py @@ -0,0 +1,58 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 +import sys +sys.path.append("../") +import threading +import time +import common.RedisUtil as f +import common.config as config +from entrance.executor import text_classification as classify, text_taskexecutor +import luascript.starttaskscript as start_script +import logging + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +if __name__ == '__main__': + """Automatic text classification algorithm entry.""" + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=text_taskexecutor.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + classify._init() # + while 1: + try: + if config.loadJsonData(config.sign) == 0: + logging.info('not to execute new task') + time.sleep(1) + else: + logging.info('get one task') + element = redisClient.eval(start_script.startTaskLua, 1, config.textClassificationQueue, + config.textClassificationStartQueue, int(time.time())) + if len(element) > 0: + text_taskexecutor.textClassificationExecutor(redisClient, element[0]) + else: + logging.info('task queue is empty.') + time.sleep(1) + except Exception as e: + logging.error('except:', e) + time.sleep(1) diff --git a/dubhe_data_process/entrance/algorithm-track.py b/dubhe_data_process/entrance/algorithm-track.py new file mode 100644 index 0000000..a174aac --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-track.py @@ -0,0 +1,66 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 +import threading +import time +import sys +sys.path.append("../") +import common.RedisUtil as f +import common.config as config +import luascript.starttaskscript as start_script +import logging +from entrance.executor import track + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +if __name__ == '__main__': + """Track algorithm entry.""" + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=track.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + while 1: + try: + if config.loadJsonData(config.sign) == 0: + logging.info('not to execute new task') + time.sleep(1) + else: + logging.info('get one task') + element = redisClient.eval(start_script.startTaskLua, 1, config.trackTaskQueue, + config.trackStartQueue, int(time.time())) + if len(element) > 0: + key = element[0].decode() + jsonStr = f.getByKey(redisClient, key.replace('"', '')); + if track.trackProcess(jsonStr, element[0]): + f.pushToQueue(redisClient, config.trackFinishQueue, key) + redisClient.zrem(config.trackStartQueue, element[0]) + logging.info('success') + else: + f.pushToQueue(redisClient, config.trackFailedQueue, key) + redisClient.zrem(config.trackStartQueue, element[0]) + logging.info('failed') + else: + logging.info('task queue is empty.') + time.sleep(1) + except Exception as e: + logging.error('except:', e) + time.sleep(1) diff --git a/dubhe_data_process/entrance/algorithm-videosample.py b/dubhe_data_process/entrance/algorithm-videosample.py new file mode 100644 index 0000000..300a12d --- /dev/null +++ b/dubhe_data_process/entrance/algorithm-videosample.py @@ -0,0 +1,63 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +import json +import threading +from datetime import datetime +import time +import sys +sys.path.append("../") +import common.RedisUtil as f +import luascript.starttaskscript as start_script +import common.config as config +import logging +from entrance.executor import videosample + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +if __name__ == '__main__': + """VideoSample algorithm entry.""" + jsonData = config.loadJsonData(config.configPath) + redisClient = f.getRedisConnection(jsonData["ip"], jsonData["port"], jsonData["database"], jsonData["password"]) + logging.info('init redis client %s', redisClient) + t = threading.Thread(target=videosample.delayKeyThread, args=(redisClient,)) + t.setDaemon(True) + t.start() + while 1: + try: + if config.loadJsonData(config.sign) == 0: + logging.info('not to execute new task') + time.sleep(5) + else: + logging.info("read redis:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + sampleTask = redisClient.eval(start_script.startTaskLua, 1, config.videoPendingQueue, + config.videoStartQueue, int(time.time())) + logging.info(int(time.time())) + if len(sampleTask) > 0: + datasetId = json.loads(sampleTask[0])['datasetIdKey'] + taskParameters = json.loads(redisClient.get("videoSample:" + str(datasetId))) + path = taskParameters['path'] + frameList = taskParameters['frames'] + videosample.sampleProcess(datasetId, path, frameList, redisClient) + else: + logging.info('task queue is empty.') + time.sleep(5) + except Exception as e: + logging.error('except:', e) + time.sleep(1) diff --git a/dubhe_data_process/entrance/executor/__init__.py b/dubhe_data_process/entrance/executor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/dubhe_data_process/entrance/executor/annotation.py b/dubhe_data_process/entrance/executor/annotation.py new file mode 100644 index 0000000..7dfbda6 --- /dev/null +++ b/dubhe_data_process/entrance/executor/annotation.py @@ -0,0 +1,46 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 +import time +import sys +sys.path.append("../../") +from entrance.executor import predict_with_print_box as yolo_demo +from common.log_config import setup_log + + +label_log = setup_log('dev', 'label.log') + + +def _init(): + print('init yolo_obj') + global yolo_obj + yolo_obj = yolo_demo.YoloInference(label_log) + + +def _annotation(type_, image_path_list, id_list, annotation_url_list, label_list, coco_flag=0): + """Perform automatic annotation task.""" + image_num = len(image_path_list) + if image_num < 16: + for i in range(16 - image_num): + image_path_list.append(image_path_list[0]) + id_list.append(id_list[0]) + annotation_url_list.append(annotation_url_list[0]) + image_num = len(image_path_list) + annotations = yolo_obj.yolo_inference(type_, id_list, annotation_url_list, image_path_list, label_list, coco_flag) + return annotations[0:image_num] diff --git a/dubhe_data_process/entrance/executor/classify_by_textcnn.py b/dubhe_data_process/entrance/executor/classify_by_textcnn.py new file mode 100644 index 0000000..2971530 --- /dev/null +++ b/dubhe_data_process/entrance/executor/classify_by_textcnn.py @@ -0,0 +1,246 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +import json +import re +import six +import numpy as np +from typing import Tuple +# import requests # 在 nfs 没有挂载 时使用 url 访问 +import sys +sys.path.append("../../") +import oneflow as flow +import oneflow.typing as tp + +BATCH_SIZE = 16 + + +class TextCNN: + def __init__(self, emb_sz, emb_dim, ksize_list, n_filters_list, n_classes, dropout): + self.initializer = flow.random_normal_initializer(stddev=0.1) + self.emb_sz = emb_sz + self.emb_dim = emb_dim + self.ksize_list = ksize_list + self.n_filters_list = n_filters_list + self.n_classes = n_classes + self.dropout = dropout + self.total_n_filters = sum(self.n_filters_list) + + def get_logits(self, inputs, is_train): + emb_weight = flow.get_variable( + 'embedding-weight', + shape=(self.emb_sz, self.emb_dim), + dtype=flow.float32, + trainable=is_train, + reuse=False, + initializer=self.initializer, + ) + data = flow.gather(emb_weight, inputs, axis=0) + data = flow.transpose(data, [0, 2, 1]) # BLH -> BHL + data = flow.reshape(data, list(data.shape) + [1]) + seq_length = data.shape[2] + pooled_list = [] + for i in range(len(self.n_filters_list)): + ksz = self.ksize_list[i] + n_filters = self.n_filters_list[i] + conv = flow.layers.conv2d(data, n_filters, [ksz, 1], data_format="NCHW", + kernel_initializer=self.initializer, name='conv-{}'.format(i)) # NCHW + # conv = flow.layers.layer_norm(conv, name='ln-{}'.format(i)) + conv = flow.nn.relu(conv) + pooled = flow.nn.max_pool2d(conv, [seq_length - ksz + 1, 1], strides=1, padding='VALID', data_format="NCHW") + pooled_list.append(pooled) + pooled = flow.concat(pooled_list, 3) + pooled = flow.reshape(pooled, [-1, self.total_n_filters]) + + if is_train: + pooled = flow.nn.dropout(pooled, rate=self.dropout) + + pooled = flow.layers.dense(pooled, self.total_n_filters, use_bias=True, + kernel_initializer=self.initializer, name='dense-1') + pooled = flow.nn.relu(pooled) + logits = flow.layers.dense(pooled, self.n_classes, use_bias=True, + kernel_initializer=self.initializer, name='dense-2') + return logits + + +def get_eval_config(): + config = flow.function_config() + config.default_data_type(flow.float) + return config + + +def pad_sequences(sequences, maxlen=None, dtype='int32', + padding='pre', truncating='pre', value=0.): + """Pads sequences to the same length. + + This function transforms a list of + `num_samples` sequences (lists of integers) + into a 2D Numpy array of shape `(num_samples, num_timesteps)`. + `num_timesteps` is either the `maxlen` argument if provided, + or the length of the longest sequence otherwise. + + Sequences that are shorter than `num_timesteps` + are padded with `value` at the beginning or the end + if padding='post. + + Sequences longer than `num_timesteps` are truncated + so that they fit the desired length. + The position where padding or truncation happens is determined by + the arguments `padding` and `truncating`, respectively. + + Pre-padding is the default. + + # Arguments + sequences: List of lists, where each element is a sequence. + maxlen: Int, maximum length of all sequences. + dtype: Type of the output sequences. + To pad sequences with variable length strings, you can use `object`. + padding: String, 'pre' or 'post': + pad either before or after each sequence. + truncating: String, 'pre' or 'post': + remove values from sequences larger than + `maxlen`, either at the beginning or at the end of the sequences. + value: Float or String, padding value. + + # Returns + x: Numpy array with shape `(len(sequences), maxlen)` + + # Raises + ValueError: In case of invalid values for `truncating` or `padding`, + or in case of invalid shape for a `sequences` entry. + """ + if not hasattr(sequences, '__len__'): + raise ValueError('`sequences` must be iterable.') + num_samples = len(sequences) + + lengths = [] + sample_shape = () + flag = True + + # take the sample shape from the first non empty sequence + # checking for consistency in the main loop below. + + for x in sequences: + try: + lengths.append(len(x)) + if flag and len(x): + sample_shape = np.asarray(x).shape[1:] + flag = False + except TypeError: + raise ValueError('`sequences` must be a list of iterables. ' + 'Found non-iterable: ' + str(x)) + + if maxlen is None: + maxlen = np.max(lengths) + + is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(dtype, np.unicode_) + if isinstance(value, six.string_types) and dtype != object and not is_dtype_str: + raise ValueError("`dtype` {} is not compatible with `value`'s type: {}\n" + "You should set `dtype=object` for variable length strings." + .format(dtype, type(value))) + + x = np.full((num_samples, maxlen) + sample_shape, value, dtype=dtype) + for idx, s in enumerate(sequences): + if not len(s): + continue # empty list/array was found + if truncating == 'pre': + trunc = s[-maxlen:] + elif truncating == 'post': + trunc = s[:maxlen] + else: + raise ValueError('Truncating type "%s" ' + 'not understood' % truncating) + + # check `trunc` has expected shape + trunc = np.asarray(trunc, dtype=dtype) + if trunc.shape[1:] != sample_shape: + raise ValueError('Shape of sample %s of sequence at position %s ' + 'is different from expected shape %s' % + (trunc.shape[1:], idx, sample_shape)) + + if padding == 'post': + x[idx, :len(trunc)] = trunc + elif padding == 'pre': + x[idx, -len(trunc):] = trunc + else: + raise ValueError('Padding type "%s" not understood' % padding) + return x + + +@flow.global_function('predict', get_eval_config()) +def predict_job(text: tp.Numpy.Placeholder((BATCH_SIZE, 150), dtype=flow.int32), + ) -> Tuple[tp.Numpy, tp.Numpy]: + with flow.scope.placement("gpu", "0:0"): + model = TextCNN(50000, 100, ksize_list=[2, 3, 4, 5], n_filters_list=[100] * 4, n_classes=2, dropout=0.5) + logits = model.get_logits(text, is_train=False) + logits = flow.nn.softmax(logits) + label = flow.math.argmax(logits) + return label, logits + + +class TextCNNClassifier: + + def __init__(self): + model_load_dir = "../of_model/textcnn_imdb_of_best_model/" + word_index_dir = "../of_model/imdb_word_index/imdb_word_index.json" + + checkpoint = flow.train.CheckPoint() + checkpoint.init() + checkpoint.load(model_load_dir) + + with open(word_index_dir) as f: + word_index = json.load(f) + word_index = {k: (v + 2) for k, v in word_index.items()} + word_index[""] = 0 + word_index[""] = 1 + word_index[""] = 2 + self.word_index = word_index + + def inference(self, text_path_list, id_list, label_list): + print("infer") + classifications = [] + batch_text = [] + for i, text_path in enumerate(text_path_list): + text = open('/nfs/' + text_path, "r").read() + """ + # 在 nfs 没有挂载 时使用 url 访问 MinIO 进行测试 + url = "http://10.5.29.100:9000/" + text_path + print(url) + text = requests.get(url).text # .encode('utf-8').decode('utf-8') + """ + text = re.sub("[^a-zA-Z']", " ", text) + text = list(map(lambda x: x.lower(), text.split())) + text.insert(0, "") + batch_text.append( + list(map(lambda x: self.word_index[x] if x in self.word_index else self.word_index[""], text)) + ) + + if i % BATCH_SIZE == BATCH_SIZE - 1: + text = pad_sequences(batch_text, value=self.word_index[""], padding='post', maxlen=150) + text = np.array(text, dtype=np.int32) + label, logits = predict_job(text) + label = label.tolist() + logits = logits.tolist() + for k in range(BATCH_SIZE): + classifications.append({ + 'id': id_list[i - BATCH_SIZE + 1 + k], + 'annotation': json.dumps( + [{'category_id': label_list[label[k]], 'score': round(logits[k][label[k]], 4)}]) + }) + batch_text = [] + return classifications diff --git a/dubhe_data_process/entrance/executor/imagenet.py b/dubhe_data_process/entrance/executor/imagenet.py new file mode 100644 index 0000000..3cdc1cb --- /dev/null +++ b/dubhe_data_process/entrance/executor/imagenet.py @@ -0,0 +1,103 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import os +import sched +import sys + +sys.path.append("../../common") +import logging +import time +import json +import common.of_cnn_resnet as of_cnn_resnet +import numpy as np +import luascript.delaytaskscript as delay_script +import common.config as config +from datetime import datetime + +schedule = sched.scheduler(time.time, time.sleep) + +base_path = "/nfs/" +delayId = "" + + +def _init(): + of_cnn_resnet.init_resnet() + logging.info('env init finished') + + +def process(task_dict, key): + """Imagenet task method. + Args: + task_dict: imagenet task details. + key: imagenet task key. + """ + global delayId + delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" + task_dict = json.loads(task_dict) + id_list = [] + image_path_list = [] + annotation_path_list = [] + for file in task_dict["files"]: + id_list.append(file["id"]) + image_path = base_path + file["url"] + image_path_list.append(image_path) + annotation_url = image_path.replace("origin/", "annotation/") + annotation_path_list.append(os.path.splitext(annotation_url)[0]) + isExists = os.path.exists(os.path.dirname(annotation_url)) + if not isExists: + os.makedirs(os.path.dirname(annotation_url)) + label_list = task_dict["labels"] + image_num = len(image_path_list) + annotations = [] + for inds in range(len(image_path_list)): + temp = {} + temp['id'] = id_list[inds] + score, ca_id = of_cnn_resnet.resnet_inf(image_path_list[inds]) + temp['annotation'] = [{'category_id': int(ca_id), 'score': np.float(score)}] + temp['annotation'] = json.dumps(temp['annotation']) + annotations.append(temp) + with open(annotation_path_list[inds], 'w') as w: + w.write(temp['annotation']) + result = {"annotations": annotations, "task": key.decode()} + return result + + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + redisClient.eval(delay_script.delayTaskLua, 1, config.imagenetStartQueue, delayId, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() diff --git a/dubhe_data_process/entrance/executor/imgprocess.py b/dubhe_data_process/entrance/executor/imgprocess.py new file mode 100644 index 0000000..a461439 --- /dev/null +++ b/dubhe_data_process/entrance/executor/imgprocess.py @@ -0,0 +1,189 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# !/usr/bin/env python3 +# -*- coding: utf-8 -*- +from datetime import datetime +import sched +import os +import cv2 +import numpy as np +import logging +import time +import json +import argparse +import sys +import codecs +import shutil + +import luascript.delaytaskscript as delay_script +import common.config as config + +from common.augment_utils.ACE import ACE_color +from common.augment_utils.dehaze import deHaze, addHaze +from common.augment_utils.hist_equalize import adaptive_hist_equalize +from common.log_config import setup_log + +schedule = sched.scheduler(time.time, time.sleep) + +delayId = "" +finish_key = {} +re_task_id = {} +sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) + +# task url suffix +img_pro_url = 'api/data/datasets/' + +# arguments +parser = argparse.ArgumentParser(description="config for image augmentation server") +parser.add_argument("-m", "--mode", type=str, default="test", required=False) +args = parser.parse_args() + +# url concat(ip + port + suffix) +url_json = '../common/config/url.json' +with open(url_json) as f: + url_dict = json.loads(f.read()) +img_pro_url = url_dict[args.mode] + img_pro_url + +# creat task quene +base_path = "/nfs/" + +# create log path and file +des_folder = os.path.join('./log', args.mode) +if not os.path.exists(des_folder): + os.makedirs(des_folder) + +logging = setup_log(args.mode, 'enhance-' + args.mode + '.log') +enhanceTaskId = "" + + +def start_enhance_task(enhanceTaskId, redisClient): + """Enhance task method. + Args: + enhanceTaskId: enhance task id. + redisClient: redis client. + """ + global delayId + detailKey = 'imgProcess:' + eval(str(enhanceTaskId[0], encoding="utf-8")) + delayId = "\"" + eval(str(enhanceTaskId[0], encoding="utf-8")) + "\"" + print(detailKey) + taskParameters = json.loads(redisClient.get(detailKey).decode()) + dataset_id = taskParameters['id'] + img_save_path = taskParameters['enhanceFilePath'] + ann_save_path = taskParameters["enhanceAnnotationPath"] + file_list = taskParameters['fileDtos'] + nums_, img_path_list, ann_path_list = img_ann_list_gen(file_list) + process_type = taskParameters['type'] + re_task_id = eval(str(enhanceTaskId[0], encoding="utf-8")) + img_process_config = [dataset_id, img_save_path, + ann_save_path, img_path_list, + ann_path_list, process_type, re_task_id] + image_enhance_process(img_process_config, redisClient) + logging.info(str(nums_) + ' images for augment') + + +def img_ann_list_gen(file_list): + """Analyze the json request and convert to list""" + nums_ = len(file_list) + img_list = [] + ann_list = [] + for i in range(nums_): + img_list.append(file_list[i]['filePath']) + ann_list.append(file_list[i]['annotationPath']) + return nums_, img_list, ann_list + + +def image_enhance_process(img_task, redisClient): + """The implementation of image augmentation thread""" + global img_pro_url + global finish_key + global re_task_id + logging.info('img_process server start'.center(66, '-')) + logging.info(img_pro_url) + try: + dataset_id = img_task[0] + img_save_path = img_task[1] + ann_save_path = img_task[2] + img_list = img_task[3] + ann_list = img_task[4] + method = img_task[5] + re_task_id = img_task[6] + suffix = '_enchanced_' + re_task_id + logging.info("dataset_id " + str(dataset_id)) + + finish_key = {"processKey": re_task_id} + finish_data = {"id": re_task_id, + "suffix": suffix} + for j in range(len(ann_list)): + img_path = img_list[j] + ann_path = ann_list[j] + img_process(suffix, img_path, ann_path, + img_save_path, ann_save_path, method) + + redisClient.set("imgProcess:finished:" + re_task_id, json.dumps(finish_data)) + redisClient.zrem(config.imgProcessStartQueue, "\"" + re_task_id + "\"") + redisClient.lpush(config.imgProcessFinishQueue, json.dumps(finish_key, separators=(',', ':'))) + logging.info('suffix:' + suffix) + logging.info("End img_process of dataset:" + str(dataset_id)) + + except Exception as e: + redisClient.lpush(config.imgProcessFailedQueue, json.dumps(finish_key, separators=(',', ':'))) + redisClient.zrem(config.imgProcessStartQueue, "\"" + re_task_id + "\"") + logging.info(img_pro_url) + logging.error("Error imgProcess") + logging.error(e) + time.sleep(0.01) + + +def img_process(suffix, img_path, ann_path, img_save_path, ann_save_path, method_ind): + """Process images and save in specified path""" + inds2method = {1: deHaze, 2: addHaze, 3: ACE_color, 4: adaptive_hist_equalize} + method = inds2method[method_ind] + img_raw = cv2.imdecode(np.fromfile(img_path.encode('utf-8'), dtype=np.uint8), 1) + img_suffix = os.path.splitext(img_path)[-1] + ann_name = os.path.basename(ann_path) + if method_ind <= 3: + processed_img = method(img_raw / 255.0) * 255 + else: + processed_img = method(img_raw) + cv2.imwrite(img_save_path + "/" + ann_name + suffix + img_suffix, + processed_img.astype(np.uint8)) + shutil.copyfile(ann_path.encode('utf-8'), (ann_path + suffix).encode('utf-8')) + + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + logging.info("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S") + ":" + delayId) + redisClient.eval(delay_script.delayTaskLua, 1, config.imgProcessStartQueue, delayId, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() diff --git a/dubhe_data_process/entrance/executor/lungsegmentation.py b/dubhe_data_process/entrance/executor/lungsegmentation.py new file mode 100644 index 0000000..115cb14 --- /dev/null +++ b/dubhe_data_process/entrance/executor/lungsegmentation.py @@ -0,0 +1,175 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +import sched +import sys + +sys.path.append("../../") +import logging +import time +import json +import numpy as np +import luascript.delaytaskscript as delay_script +import common.config as config +from datetime import datetime +from skimage.morphology import disk, binary_erosion, binary_closing +from skimage.measure import label,regionprops, find_contours +from skimage.filters import roberts +from scipy import ndimage as ndi +from skimage.segmentation import clear_border +import pydicom as dicom +import os +import logging + +schedule = sched.scheduler(time.time, time.sleep) + +base_path = "/nfs/" +delayId = "" + + +def process(task_dict, key): + """Lung segmentation based on dcm task method. + Args: + task_dict: imagenet task details. + key: imagenet task key. + """ + global delayId + delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" + task_dict = json.loads(task_dict) + base_path = task_dict["annotationPath"] + if not os.path.exists(base_path): + logging.info("make annotation path.") + os.makedirs(base_path) + + for dcm in task_dict["dcms"]: + image, image_path = preprocesss_dcm_image(dcm) + # segmentation and wirte coutours to result_path + result_path = os.path.join(base_path, image_path) + contour(segmentation(image), result_path) + + logging.info("all dcms in one task are processed.") + return True + +def preprocesss_dcm_image(path): + """Load and preprocesss dcm image. + Args: + path: dcm file path. + """ + # result_path = os.path.basename(path).split(".", 1)[0] + ".json" + result_path = ".".join(os.path.basename(path).split(".")[0:-1]) + ".json" + dcm = dicom.dcmread(path) + image = dcm.pixel_array.astype(np.int16) + + # Set outside-of-scan pixels to 0. + image[image == -2000] = 0 + + # Convert to Hounsfield units (HU) + intercept = dcm.RescaleIntercept + slope = dcm.RescaleSlope + + if slope != 1: + image = slope * image.astype(np.float64) + image = image.astype(np.int16) + + image += np.int16(intercept) + logging.info("preprocesss_dcm_image done.") + return np.array(image, dtype=np.int16), result_path + +def segmentation(image): + """Segments the lung from the given 2D slice. + Args: + image: single image in one dcm. + """ + # Step 1: Convert into a binary image. + binary = image < -350 + + # Step 2: Remove the blobs connected to the border of the image. + cleared = clear_border(binary) + + # Step 3: Label the image. + label_image = label(cleared) + + # Step 4: Keep the labels with 2 largest areas. + areas = [r.area for r in regionprops(label_image)] + areas.sort() + if len(areas) > 2: + for region in regionprops(label_image): + if region.area < areas[-2]: + for coordinates in region.coords: + label_image[coordinates[0], coordinates[1]] = 0 + binary = label_image > 0 + + # Step 5: Erosion operation with a disk of radius 2. This operation is seperate the lung nodules attached to the blood vessels. + selem = disk(1) + binary = binary_erosion(binary, selem) + + # Step 6: Closure operation with a disk of radius 10. This operation is to keep nodules attached to the lung wall. + selem = disk(16) + binary = binary_closing(binary, selem) + + # Step 7: Fill in the small holes inside the binary mask of lungs. + for _ in range(3): + edges = roberts(binary) + binary = ndi.binary_fill_holes(edges) + logging.info("lung segmentation done.") + return binary + +def contour(image, path): + """Get contours of segmentation. + Args: + seg: segmentation of lung. + """ + result = [] + contours = find_contours(image, 0.5) + if len(contours) > 2: + contours.sort(key = lambda x: int(x.shape[0])) + contours = contours[-2:] + + for n, contour in enumerate(contours): + # result.append({"type":n, "annotation":contour.tolist()}) + result.append({"type":n, "annotation":np.flip(contour, 1).tolist()}) + + # write json + with open(path, 'w') as f: + json.dump(result, f) + logging.info("write {} done.".format(path)) + + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + redisClient.eval(delay_script.delayTaskLua, 1, config.dcmStartQueue, delayId, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() diff --git a/dubhe_data_process/entrance/executor/ofrecord.py b/dubhe_data_process/entrance/executor/ofrecord.py new file mode 100644 index 0000000..5bf95c5 --- /dev/null +++ b/dubhe_data_process/entrance/executor/ofrecord.py @@ -0,0 +1,181 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# -*- coding: utf-8 -*- + +import logging +import json +import os +import struct +import cv2 +import sched +import numpy as np +import oneflow.core.record.record_pb2 as of_record +import luascript.delaytaskscript as delay_script +import time +import common.config as config +from datetime import datetime + +schedule = sched.scheduler(time.time, time.sleep) + +delayId = "" + +class ImageCoder(object): + """Helper class that provides image coding utilities.""" + + def __init__(self, size=None): + self.size = size + + def _resize(self, image_data): + if self.size is not None and image_data.shape[:2] != self.size: + return cv2.resize(image_data, self.size) + return image_data + + def image_to_jpeg(self, image_data): + image_data = cv2.imdecode(np.frombuffer(image_data, np.uint8), 1) + image_data = self._resize(image_data) + return cv2.imencode(".jpg", image_data)[1].tobytes( + ), image_data.shape[0], image_data.shape[1] + + +def _process_image(filename, coder): + """Process a single image file. + Args: + filename: string, path to an image file e.g., '/path/to/example.JPG'. + coder: instance of ImageCoder to provide image coding utils. + Returns: + image_buffer: string, JPEG encoding of RGB image. + height: integer, image height in pixels. + width: integer, image width in pixels. + """ + # Read the image file. + with open(filename, 'rb') as f: + image_data = f.read() + image_data, height, width = coder.image_to_jpeg(image_data) + + return image_data, height, width + + +def _bytes_feature(value): + """Wrapper for inserting bytes features into Example proto.""" + return of_record.Feature(bytes_list=of_record.BytesList(value=[value])) + + +def dense_to_one_hot(labels_dense, num_classes): + """Convert class labels from scalars to one-hot vectors.""" + num_labels = labels_dense.shape[0] + index_offset = np.arange(num_labels) * num_classes + labels_one_hot = np.zeros((num_labels, num_classes)) + labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 + return labels_one_hot + + +def extract_img_label(names, path): + """Extract the images and labels into np array [index]. + Args: + f: A file object that contain images and annotations. + Returns: + data: A 4D uint8 np array [index, h, w, depth]. + labels: a 1D uint8 np array. + num_img: the number of images. + """ + train_img = os.path.join(path, 'origin/') + train_label = os.path.join(path, 'annotation/') + num_imgs = len(names) + data = [] + labels = [] + print('^^^^^^^^^^ start img_set for sycle') + for i in names: + name = os.path.splitext(i)[0] + print(name) + coder = ImageCoder((224, 224)) + image_buffer, height, width = _process_image( + os.path.join(train_img, i), coder) + + data += [image_buffer] + + if os.path.exists(os.path.join(train_label, name)): + + with open(os.path.join(train_label, name), "r", encoding='utf-8') as jsonFile: + la = json.load(jsonFile) + if la: + labels += [la[0]['category_id']] + else: + data.pop() + num_imgs -= 1 + else: + print('File is not found') + print('^^^^^^^^^ img_set for end') + data = np.array(data) + labels = np.array(labels) + print(data.shape, labels.shape) + return num_imgs, data, labels + + +def execute(src_path, desc, label_map, files, part_id, key): + """Execute ofrecord task method.""" + global delayId + delayId = delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" + logging.info(part_id) + num_imgs, images, labels = extract_img_label(files, src_path) + keys = sorted(list(map(int, label_map.keys()))) + for i in range(len(keys)): + label_map[str(keys[i])] = i + if not num_imgs: + return False, 0, 0 + try: + os.makedirs(desc) + except Exception as e: + print('{} exists.'.format(desc)) + filename = 'part-{}'.format(part_id) + filename = os.path.join(desc, filename) + f = open(filename, 'wb') + print(filename) + for i in range(num_imgs): + img = images[i] + label = label_map[str(labels[i])] + sample = of_record.OFRecord(feature={ + 'class/label': of_record.Feature(int32_list=of_record.Int32List(value=[label])), + 'encoded': _bytes_feature(img) + }) + size = sample.ByteSize() + f.write(struct.pack("q", size)) + f.write(sample.SerializeToString()) + if f: + f.close() + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + redisClient.eval(delay_script.delayTaskLua, 1, config.ofrecordStartQueue, delayId, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() \ No newline at end of file diff --git a/dubhe_data_process/entrance/executor/predict_with_print_box.py b/dubhe_data_process/entrance/executor/predict_with_print_box.py new file mode 100644 index 0000000..2b0bef2 --- /dev/null +++ b/dubhe_data_process/entrance/executor/predict_with_print_box.py @@ -0,0 +1,283 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +import json +import time + +import cv2 +import numpy as np +import oneflow_yolov3 +import sys +sys.path.append("../../") +from common.yolo_net import YoloPredictNet + +import oneflow as flow + + +'''Init oneflow config''' +model_load_dir = "../of_model/yolov3_model_python/" +label_to_name_file = "../common/data/coco.names" +use_tensorrt = 0 +gpu_num_per_node = 1 +batch_size = 16 +image_height = 608 +image_width = 608 +flow.config.load_library(oneflow_yolov3.lib_path()) +func_config = flow.FunctionConfig() +func_config.default_distribute_strategy(flow.distribute.consistent_strategy()) +func_config.default_data_type(flow.float) +if use_tensorrt != 0: + func_config.use_tensorrt(True) +label_2_name = [] +with open(label_to_name_file, 'r') as f: + label_2_name = f.readlines() +nms = True +print("nms:", nms) +input_blob_def_dict = { + "images": flow.FixedTensorDef((batch_size, 3, image_height, image_width), dtype=flow.float), + "origin_image_info": flow.FixedTensorDef((batch_size, 2), dtype=flow.int32), +} + + +def xywh_2_x1y1x2y2(x, y, w, h, origin_image): + """The format of box transform""" + x1 = (x - w / 2.) * origin_image[1] + x2 = (x + w / 2.) * origin_image[1] + y1 = (y - h / 2.) * origin_image[0] + y2 = (y + h / 2.) * origin_image[0] + return x1, y1, x2, y2 + + +def batch_boxes(positions, probs, origin_image_info): + """The images postprocessing""" + batch_size = positions.shape[0] + batch_list = [] + if nms == True: + for k in range(batch_size): + box_list = [] + for i in range(1, 81): + for j in range(positions.shape[2]): + if positions[k][i][j][2] != 0 and positions[k][i][j][3] != 0 and probs[k][i][j] != 0: + x1, y1, x2, y2 = xywh_2_x1y1x2y2(positions[k][i][j][0], positions[k][i][j][1], + positions[k][i][j][2], positions[k][i][j][3], + origin_image_info[k]) + bbox = [i - 1, x1, y1, x2, y2, probs[k][i][j]] + box_list.append(bbox) + batch_list.append(np.asarray(box_list)) + else: + for k in range(batch_size): + box_list = [] + for j in range(positions.shape[1]): + for i in range(1, 81): + if positions[k][j][2] != 0 and positions[k][j][3] != 0 and probs[k][j][i] != 0: + x1, y1, x2, y2 = xywh_2_x1y1x2y2(positions[k][j][0], positions[k][j][1], positions[k][j][2], + positions[k][j][3], origin_image_info[k]) + bbox = [i - 1, x1, y1, x2, y2, probs[k][j][i]] + box_list.append(bbox) + batch_list.append(np.asarray(box_list)) + return batch_list + + +@flow.function(func_config) +def yolo_user_op_eval_job(images=input_blob_def_dict["images"], + origin_image_info=input_blob_def_dict["origin_image_info"]): + """The model inference""" + yolo_pos_result, yolo_prob_result = YoloPredictNet(images, origin_image_info, trainable=False) + yolo_pos_result = flow.identity(yolo_pos_result, name="yolo_pos_result_end") + yolo_prob_result = flow.identity(yolo_prob_result, name="yolo_prob_result_end") + return yolo_pos_result, yolo_prob_result, origin_image_info + + +def yolo_show(image_path_list, batch_list): + """Debug the result of Yolov3""" + font = cv2.FONT_HERSHEY_SIMPLEX + for img_path, batch in zip(image_path_list, batch_list): + result_list = batch.tolist() + img = cv2.imread(img_path) + for result in result_list: + cls = int(result[0]) + bbox = result[1:-1] + score = result[-1] + print('img_file:', img_path) + print('cls:', cls) + print('bbox:', bbox) + c = ((int(bbox[0]) + int(bbox[2])) / 2, (int(bbox[1] + int(bbox[3])) / 2)) + cv2.rectangle(img, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (0, 255, 255), 1) + cv2.putText(img, str(cls), (int(c[0]), int(c[1])), font, 1, (0, 0, 255), 1) + result_name = img_path.split('/')[-1] + cv2.imwrite("data/results/" + result_name, img) + + +def resize_image(img, origin_h, origin_w, image_height, image_width): + """The resize of image preprocessing""" + w = image_width + h = image_height + resized = np.zeros((3, image_height, image_width), dtype=np.float32) + part = np.zeros((3, origin_h, image_width), dtype=np.float32) + w_scale = (float)(origin_w - 1) / (w - 1) + h_scale = (float)(origin_h - 1) / (h - 1) + + for c in range(w): + if c == w - 1 or origin_w == 1: + val = img[:, :, origin_w - 1] + else: + sx = c * w_scale + ix = int(sx) + dx = sx - ix + val = (1 - dx) * img[:, :, ix] + dx * img[:, :, ix + 1] + part[:, :, c] = val + for r in range(h): + sy = r * h_scale + iy = int(sy) + dy = sy - iy + val = (1 - dy) * part[:, iy, :] + resized[:, r, :] = val + if r == h - 1 or origin_h == 1: + continue + resized[:, r, :] = resized[:, r, :] + dy * part[:, iy + 1, :] + return resized + + +def batch_image_preprocess_v2(img_path_list, image_height, image_width): + """The images preprocessing""" + result_list = [] + origin_info_list = [] + for img_path in img_path_list: + img = cv2.imread(img_path, cv2.IMREAD_COLOR) + img = img.transpose(2, 0, 1).astype(np.float32) # hwc->chw + img = img / 255 # /255 + img[[0, 1, 2], :, :] = img[[2, 1, 0], :, :] # bgr2rgb + + w = image_width + h = image_height + origin_h = img.shape[1] + origin_w = img.shape[2] + new_w = origin_w + new_h = origin_h + if w / origin_w < h / origin_h: + new_w = w + new_h = origin_h * w // origin_w + else: + new_h = h + new_w = origin_w * h // origin_h + resize_img = resize_image(img, origin_h, origin_w, new_h, new_w) + + dw = (w - new_w) // 2 + dh = (h - new_h) // 2 + + padh_before = int(dh) + padh_after = int(h - new_h - padh_before) + padw_before = int(dw) + padw_after = int(w - new_w - padw_before) + result = np.pad(resize_img, pad_width=((0, 0), (padh_before, padh_after), (padw_before, padw_after)), + mode='constant', constant_values=0.5) + origin_image_info = [origin_h, origin_w] + result_list.append(result) + origin_info_list.append(origin_image_info) + results = np.asarray(result_list).astype(np.float32) + origin_image_infos = np.asarray(origin_info_list).astype(np.int32) + return results, origin_image_infos + + +def coco_format(type_, id_list, annotation_url_list, file_list, result_list, label_list, coco_flag=0): + """Transform the annotations to coco format""" + annotations = [] + for i, result in enumerate(result_list): + temp = {} + annotation_url = annotation_url_list[i] + file_path = file_list[i] + temp['id'] = id_list[i] + temp['annotation'] = [] + im = cv2.imread(file_path) + height, width, _ = im.shape + if result.shape[0] == 0: + temp['annotation'] = json.dumps(temp['annotation']) + annotations.append(temp) + with open(annotation_url, 'w') as w: + w.write(temp['annotation']) + continue + else: + for j in range(result.shape[0]): + cls_id = int(result[j][0]) + 1 + coco_flag + x1 = result[j][1] + x2 = result[j][3] + y1 = result[j][2] + y2 = result[j][4] + score = result[j][5] + width = max(0, x2 - x1) + height = max(0, y2 - y1) + if cls_id in label_list: + temp['annotation'].append({ + 'area': width * height, + 'bbox': [x1, y1, width, height], + 'category_id': cls_id, + 'iscrowd': 0, + 'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]], + 'score': score + }) + if type_ == 2 and len(temp['annotation']) > 0: + temp['annotation'] = [temp['annotation'][0]] + temp['annotation'][0].pop('area') + temp['annotation'][0].pop('bbox') + temp['annotation'][0].pop('iscrowd') + temp['annotation'][0].pop('segmentation') + temp['annotation'] = json.dumps(temp['annotation']) + annotations.append(temp) + with open(annotation_url, 'w') as wr: + wr.write(temp['annotation']) + return annotations + + +class YoloInference(object): + """Yolov3 detection inference""" + + def __init__(self, label_log): + self.label_log = label_log + flow.config.gpu_device_num(gpu_num_per_node) + flow.env.ctrl_port(9789) + + check_point = flow.train.CheckPoint() + if not model_load_dir: + check_point.init() + else: + check_point.load(model_load_dir) + print("Load check_point success") + self.label_log.info("Load check_point success") + + def yolo_inference(self, type_, id_list, annotation_url_list, image_path_list, label_list, coco_flag=0): + annotations = [] + try: + if len(image_path_list) == 16: + t0 = time.time() + images, origin_image_info = batch_image_preprocess_v2(image_path_list, image_height, image_width) + yolo_pos, yolo_prob, origin_image_info = yolo_user_op_eval_job(images, origin_image_info).get() + batch_list = batch_boxes(yolo_pos, yolo_prob, origin_image_info) + annotations = coco_format(type_, id_list, annotation_url_list, image_path_list, batch_list, label_list, coco_flag) + t1 = time.time() + print('t1-t0:', t1 - t0) + except: + print("Forward Error") + self.label_log.error("Forward Error") + for i, image_path in enumerate(image_path_list): + temp = {} + id_name = id_list[i] + temp['id'] = id_name + temp['annotation'] = [] + temp['annotation'] = json.dumps(temp['annotation']) + annotations.append(temp) + return annotations diff --git a/dubhe_data_process/entrance/executor/taskexecutor.py b/dubhe_data_process/entrance/executor/taskexecutor.py new file mode 100644 index 0000000..7cf9831 --- /dev/null +++ b/dubhe_data_process/entrance/executor/taskexecutor.py @@ -0,0 +1,108 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 + +import codecs +import os +import sched +import sys +import json +import logging +import time +import common.RedisUtil as f +import common.config as config +from entrance.executor import annotation as annotation +from datetime import datetime +import luascript.delaytaskscript as delay_script + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +schedule = sched.scheduler(time.time, time.sleep) +sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) + +delayId = "" + + +def annotationExecutor(redisClient, key): + """Annotation task method. + Args: + redisClient: redis client. + key: annotation task key. + """ + global delayId + print('-------------process one-----------------') + try: + delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" + logging.info('get element is {0}'.format(key)) + key = key.decode() + jsonStr = f.getByKey(redisClient, key.replace('"', '')); + print(jsonStr) + jsonObject = json.loads(jsonStr.decode('utf-8')); + image_path_list = [] + id_list = [] + annotation_url_list = [] + label_list = [] + label_list = jsonObject['labels'] + for fileObject in jsonObject['files']: + pic_url = '/nfs/' + fileObject['url'] + image_path_list.append(pic_url) + annotation_url = pic_url.replace("origin/", "annotation/") + annotation_url_list.append(os.path.splitext(annotation_url)[0]) + isExists = os.path.exists(os.path.dirname(annotation_url)) + if not isExists: + os.makedirs(os.path.dirname(annotation_url)) + id_list.append(fileObject['id']) + print(image_path_list) + print(annotation_url_list) + print(label_list) + coco_flag = 0 + if "labelType" in jsonObject: + label_type = jsonObject['labelType'] + if label_type == 3: + coco_flag = 80 + annotations = annotation._annotation(0, image_path_list, id_list, annotation_url_list, label_list, coco_flag); + result = {"task": key, "annotations": annotations} + f.pushToQueue(redisClient, config.annotationFinishQueue, json.dumps(result)) + redisClient.zrem(config.annotationStartQueue, key) + except Exception as e: + print(e) + + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + redisClient.eval(delay_script.delayTaskLua, 1, config.annotationStartQueue, delayId, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() diff --git a/dubhe_data_process/entrance/executor/text_classification.py b/dubhe_data_process/entrance/executor/text_classification.py new file mode 100644 index 0000000..1a73711 --- /dev/null +++ b/dubhe_data_process/entrance/executor/text_classification.py @@ -0,0 +1,45 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +from entrance.executor import classify_by_textcnn as classify + + +def _init(): + print('init classify_obj') + global classify_obj + classify_obj = classify.TextCNNClassifier() # label_log + + +def _classification(text_path_list, id_list, label_list): + """Perform automatic text classification task.""" + textnum = len(text_path_list) + batched_num = ((textnum - 1) // classify.BATCH_SIZE + 1) * classify.BATCH_SIZE + for i in range(batched_num - textnum): + text_path_list.append(text_path_list[0]) + id_list.append(id_list[0]) + annotations = classify_obj.inference(text_path_list, id_list, label_list) # + return annotations[0:textnum] + + +if __name__ == "__main__": + test_len = 22 + _init() + ans = _classification(["dubhe-dev/dataset/2738/origin/32_3_ts1607326726114630.txt"] * test_len, [1] * test_len, + [111, 112]) + print(ans) + print(len(ans)) diff --git a/dubhe_data_process/entrance/executor/text_taskexecutor.py b/dubhe_data_process/entrance/executor/text_taskexecutor.py new file mode 100644 index 0000000..16e987c --- /dev/null +++ b/dubhe_data_process/entrance/executor/text_taskexecutor.py @@ -0,0 +1,94 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# coding:utf-8 + +import codecs +import sched +import sys +import json +import logging +import time +import common.RedisUtil as f +import common.config as config +from entrance.executor import text_classification as text_classification +from datetime import datetime +import luascript.delaytaskscript as delay_script + +logging.basicConfig(format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + level=logging.DEBUG) + +schedule = sched.scheduler(time.time, time.sleep) +sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) + +delayId = "" + + +def textClassificationExecutor(redisClient, key): + """Annotation task method. + Args: + redisClient: redis client. + key: annotation task key. + """ + global delayId + print('-------------process one-----------------') + try: + delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" + logging.info('get element is {0}'.format(key)) + key = key.decode() + jsonStr = f.getByKey(redisClient, key.replace('"', '')) + print(jsonStr) + jsonObject = json.loads(jsonStr.decode('utf-8')) + text_path_list = [] + id_list = [] + label_list = jsonObject['labels'] + for fileObject in jsonObject['files']: + text_path_list.append(fileObject['url']) + id_list.append(fileObject['id']) + print(text_path_list) + print(id_list) + print(label_list) + classifications = text_classification._classification(text_path_list, id_list, label_list) # -------------- + result = {"task": key, "classifications": classifications} # -------------- + f.pushToQueue(redisClient, config.textClassificationFinishQueue, json.dumps(result)) + redisClient.zrem(config.textClassificationStartQueue, key) + except Exception as e: + print(e) + + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + redisClient.eval(delay_script.delayTaskLua, 1, config.textClassificationStartQueue, delayId, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() diff --git a/dubhe_data_process/entrance/executor/track.py b/dubhe_data_process/entrance/executor/track.py new file mode 100644 index 0000000..c247f84 --- /dev/null +++ b/dubhe_data_process/entrance/executor/track.py @@ -0,0 +1,93 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +# -*- coding: utf-8 -*- +import sched + +import common.config as config +import luascript.delaytaskscript as delay_script +from track_only.hog_track import * + +schedule = sched.scheduler(time.time, time.sleep) + +delayId = "" + + +def trackProcess(task, key): + """Track task method. + Args: + task: dataset id. + key: video file path. + Returns: + True: track success + False: track failed + """ + global delayId + delayId = "\"" + eval(str(key, encoding="utf-8")) + "\"" + task = json.loads(task.decode('utf-8')) + image_list = [] + label_list = [] + images_data = task['images'] + path = task['path'] + + for file in images_data: + filePath = path + "/origin/" + file + annotationPath = path + "/annotation/" + file.split('.')[0] + if not os.path.exists(filePath): + continue + if not os.path.exists(annotationPath): + continue + image_list.append(filePath) + label_list.append(annotationPath) + image_num = len(label_list) + track_det = Detector( + 'xxx.avi', + min_confidence=0.35, + max_cosine_distance=0.2, + max_iou_distance=0.7, + max_age=30, + out_dir='results/') + track_det.write_img = False + RET = track_det.run_track(image_list, label_list) + if RET == 'OK': + return True + else: + return False + + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + redisClient.eval(delay_script.delayTaskLua, 1, config.trackStartQueue, delayId, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() diff --git a/dubhe_data_process/entrance/executor/videosample.py b/dubhe_data_process/entrance/executor/videosample.py new file mode 100644 index 0000000..b54bd82 --- /dev/null +++ b/dubhe_data_process/entrance/executor/videosample.py @@ -0,0 +1,100 @@ +""" +/** +* Copyright 2020 Tianshu AI Platform. All Rights Reserved. +* +* Licensed under the Apache License, Version 2.0 (the "License"); +* you may not use this file except in compliance with the License. +* You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +* ============================================================= +*/ +""" +import json +import os +import sched +import time +from datetime import datetime + +import luascript.finishtaskscript as finish_script +import luascript.failedtaskscript as failed_script +import luascript.delaytaskscript as delay_script +import common.config as config + +import cv2 + +schedule = sched.scheduler(time.time, time.sleep) + +datasetIdKey = "" + + +def sampleProcess(datasetId, path, frameList, redisClient): + """Video sampling method. + Args: + datasetId: dataset id. + path: video file path. + frameList: picture frame number list. + redisClient: redis client. + """ + global datasetIdKey + datasetIdJson = {'datasetIdKey': datasetId} + datasetIdKey = json.dumps(datasetIdJson, separators=(',', ':')) + try: + videoName = path.split('/')[-1] + save_path = path.split(videoName)[0].replace("video", "origin") + is_exists = os.path.exists(save_path) + if not is_exists: + os.makedirs(save_path) + print('path of %s is build' % save_path) + else: + print('path of %s already exist and start' % save_path) + cap = cv2.VideoCapture(path) + for i in frameList: + cap.set(cv2.CAP_PROP_POS_FRAMES, i) + success, video_capture = cap.read() + # 保存图片 + if success is True and video_capture is not None: + save_name = save_path + videoName.split('.')[0] + '_' + str(i) + '.jpg' + cv2.imwrite(save_name, video_capture) + redisClient.lpush("videoSample_pictures:" + datasetId, + '{' + '\"pictureName\":' + "\"" + save_name + "\"" + '}') + print('image of %s is saved' % save_name) + print('video is all read') + redisClient.eval(finish_script.finishTaskLua, 3, config.videoStartQueue, config.videoFinishQueue, + "videoSample:" + str(datasetId), + datasetIdKey, str(datasetIdKey)) + except Exception as e: + print(e) + redisClient.eval(failed_script.failedTaskLua, 4, config.videoStartQueue, config.videoFailedQueue, + "videoSample_pictures:" + datasetId, + "videoSample:" + str(datasetId), + datasetIdKey, str(datasetIdKey)) + + +def delaySchduled(inc, redisClient): + """Delay task method. + Args: + inc: scheduled task time. + redisClient: redis client. + """ + try: + print("delay:" + datetime.now().strftime("B%Y-%m-%d %H:%M:%S")) + redisClient.eval(delay_script.delayTaskLua, 1, config.videoStartQueue, datasetIdKey, int(time.time())) + schedule.enter(inc, 0, delaySchduled, (inc, redisClient)) + except Exception as e: + print("delay error" + e) + + +def delayKeyThread(redisClient): + """Delay task thread. + Args: + redisClient: redis client. + """ + schedule.enter(0, 0, delaySchduled, (5, redisClient)) + schedule.run() diff --git a/dubhe_data_process/entrance/log/dev/.gitkeep b/dubhe_data_process/entrance/log/dev/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/dubhe_data_process/of_model/imdb_word_index/imdb_word_index.json b/dubhe_data_process/of_model/imdb_word_index/imdb_word_index.json new file mode 100644 index 0000000..bb1e965 --- /dev/null +++ b/dubhe_data_process/of_model/imdb_word_index/imdb_word_index.json @@ -0,0 +1 @@ +{"fawn": 34701, "tsukino": 52006, "nunnery": 52007, "sonja": 16816, "vani": 63951, "woods": 1408, "spiders": 16115, "hanging": 2345, "woody": 2289, "trawling": 52008, "hold's": 52009, "comically": 11307, "localized": 40830, "disobeying": 30568, "'royale": 52010, "harpo's": 40831, "canet": 52011, "aileen": 19313, "acurately": 52012, "diplomat's": 52013, "rickman": 25242, "arranged": 6746, "rumbustious": 52014, "familiarness": 52015, "spider'": 52016, "hahahah": 68804, "wood'": 52017, "transvestism": 40833, "hangin'": 34702, "bringing": 2338, "seamier": 40834, "wooded": 34703, "bravora": 52018, "grueling": 16817, "wooden": 1636, "wednesday": 16818, "'prix": 52019, "altagracia": 34704, "circuitry": 52020, "crotch": 11585, "busybody": 57766, "tart'n'tangy": 52021, "burgade": 14129, "thrace": 52023, "tom's": 11038, "snuggles": 52025, "francesco": 29114, "complainers": 52027, "templarios": 52125, "272": 40835, "273": 52028, "zaniacs": 52130, "275": 34706, "consenting": 27631, "snuggled": 40836, "inanimate": 15492, "uality": 52030, "bronte": 11926, "errors": 4010, "dialogs": 3230, "yomada's": 52031, "madman's": 34707, "dialoge": 30585, "usenet": 52033, "videodrome": 40837, "kid'": 26338, "pawed": 52034, "'girlfriend'": 30569, "'pleasure": 52035, "'reloaded'": 52036, "kazakos'": 40839, "rocque": 52037, "mailings": 52038, "brainwashed": 11927, "mcanally": 16819, "tom''": 52039, "kurupt": 25243, "affiliated": 21905, "babaganoosh": 52040, "noe's": 40840, "quart": 40841, "kids": 359, "uplifting": 5034, "controversy": 7093, "kida": 21906, "kidd": 23379, "error'": 52041, "neurologist": 52042, "spotty": 18510, "cobblers": 30570, "projection": 9878, "fastforwarding": 40842, "sters": 52043, "eggar's": 52044, "etherything": 52045, "gateshead": 40843, "airball": 34708, "unsinkable": 25244, "stern": 7180, "cervi's": 52046, "dnd": 40844, "dna": 11586, "insecurity": 20598, "'reboot'": 52047, "trelkovsky": 11037, "jaekel": 52048, "sidebars": 52049, "sforza's": 52050, "distortions": 17633, "mutinies": 52051, "sermons": 30602, "7ft": 40846, "boobage": 52052, "o'bannon's": 52053, "populations": 23380, "chulak": 52054, "mesmerize": 27633, "quinnell": 52055, "yahoo": 10307, "meteorologist": 52057, "beswick": 42577, "boorman": 15493, "voicework": 40847, "ster'": 52058, "blustering": 22922, "hj": 52059, "intake": 27634, "morally": 5621, "jumbling": 40849, "bowersock": 52060, "'porky's'": 52061, "gershon": 16821, "ludicrosity": 40850, "coprophilia": 52062, "expressively": 40851, "india's": 19500, "post's": 34710, "wana": 52063, "wang": 5283, "wand": 30571, "wane": 25245, "edgeways": 52321, "titanium": 34711, "pinta": 40852, "want": 178, "pinto": 30572, "whoopdedoodles": 52065, "tchaikovsky": 21908, "travel": 2103, "'victory'": 52066, "copious": 11928, "gouge": 22433, "chapters'": 52067, "barbra": 6702, "uselessness": 30573, "wan'": 52068, "assimilated": 27635, "petiot": 16116, "most\u0085and": 52069, "dinosaurs": 3930, "wrong": 352, "seda": 52070, "stollen": 52071, "sentencing": 34712, "ouroboros": 40853, "assimilates": 40854, "colorfully": 40855, "glenne": 27636, "dongen": 52072, "subplots": 4760, "kiloton": 52073, "chandon": 23381, "effect'": 34713, "snugly": 27637, "kuei": 40856, "welcomed": 9092, "dishonor": 30071, "concurrence": 52075, "stoicism": 23382, "guys'": 14896, "beroemd'": 52077, "butcher": 6703, "melfi's": 40857, "aargh": 30623, "playhouse": 20599, "wickedly": 11308, "fit": 1180, "labratory": 52078, "lifeline": 40859, "screaming": 1927, "fix": 4287, "cineliterate": 52079, "fic": 52080, "fia": 52081, "fig": 34714, "fmvs": 52082, "fie": 52083, "reentered": 52084, "fin": 30574, "doctresses": 52085, "fil": 52086, "zucker": 12606, "ached": 31931, "counsil": 52088, "paterfamilias": 52089, "songwriter": 13885, "shivam": 34715, "hurting": 9654, "effects": 299, "slauther": 52090, "'flame'": 52091, "sommerset": 52092, "interwhined": 52093, "whacking": 27638, "bartok": 52094, "barton": 8775, "frewer": 21909, "fi'": 52095, "ingrid": 6192, "stribor": 30575, "approporiately": 52096, "wobblyhand": 52097, "tantalisingly": 52098, "ankylosaurus": 52099, "parasites": 17634, "childen": 52100, "jenkins'": 52101, "metafiction": 52102, "golem": 17635, "indiscretion": 40860, "reeves'": 23383, "inamorata's": 57781, "brittannica": 52104, "adapt": 7916, "russo's": 30576, "guitarists": 48246, "abbott": 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