!6383 Add modelzoo CNNCTC Network.

Merge pull request !6383 from linqingke/fasterrcnn

model_zoo/official/cv/cnnctc/eval.py

@@ -0,0 +1,109 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""cnnctc eval"""
+
+import argparse
+import time
+import numpy as np
+
+from mindspore import Tensor, context
+import mindspore.common.dtype as mstype
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.dataset import GeneratorDataset
+
+from src.util import CTCLabelConverter, AverageMeter
+from src.config import Config_CNNCTC
+from src.dataset import IIIT_Generator_batch
+from src.cnn_ctc import CNNCTC_Model
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False,
+                    save_graphs_path=".", enable_auto_mixed_precision=False)
+
+def test_dataset_creator():
+    ds = GeneratorDataset(IIIT_Generator_batch, ['img', 'label_indices', 'text', 'sequence_length', 'label_str'])
+    return ds
+
+
+def test(config):
+    ds = test_dataset_creator()
+
+    net = CNNCTC_Model(config.NUM_CLASS, config.HIDDEN_SIZE, config.FINAL_FEATURE_WIDTH)
+
+    ckpt_path = config.CKPT_PATH
+    param_dict = load_checkpoint(ckpt_path)
+    load_param_into_net(net, param_dict)
+    print('parameters loaded! from: ', ckpt_path)
+
+    converter = CTCLabelConverter(config.CHARACTER)
+
+    model_run_time = AverageMeter()
+    npu_to_cpu_time = AverageMeter()
+    postprocess_time = AverageMeter()
+
+    count = 0
+    correct_count = 0
+    for data in ds.create_tuple_iterator():
+        img, _, text, _, length = data
+
+        img_tensor = Tensor(img, mstype.float32)
+
+        model_run_begin = time.time()
+        model_predict = net(img_tensor)
+        model_run_end = time.time()
+        model_run_time.update(model_run_end - model_run_begin)
+
+        npu_to_cpu_begin = time.time()
+        model_predict = np.squeeze(model_predict.asnumpy())
+        npu_to_cpu_end = time.time()
+        npu_to_cpu_time.update(npu_to_cpu_end - npu_to_cpu_begin)
+
+        postprocess_begin = time.time()
+        preds_size = np.array([model_predict.shape[1]] * config.TEST_BATCH_SIZE)
+        preds_index = np.argmax(model_predict, 2)
+        preds_index = np.reshape(preds_index, [-1])
+        preds_str = converter.decode(preds_index, preds_size)
+        postprocess_end = time.time()
+        postprocess_time.update(postprocess_end - postprocess_begin)
+
+        label_str = converter.reverse_encode(text.asnumpy(), length.asnumpy())
+
+        if count == 0:
+            model_run_time.reset()
+            npu_to_cpu_time.reset()
+            postprocess_time.reset()
+        else:
+            print('---------model run time--------', model_run_time.avg)
+            print('---------npu_to_cpu run time--------', npu_to_cpu_time.avg)
+            print('---------postprocess run time--------', postprocess_time.avg)
+
+        print("Prediction samples: \n", preds_str[:5])
+        print("Ground truth: \n", label_str[:5])
+        for pred, label in zip(preds_str, label_str):
+            if pred == label:
+                correct_count += 1
+            count += 1
+
+    print('accuracy: ', correct_count / count)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="FasterRcnn training")
+    parser.add_argument('--device_id', type=int, default=0, help="Device id, default is 0.")
+    parser.add_argument("--ckpt_path", type=str, default="", help="trained file path.")
+    args_opt = parser.parse_args()
+
+    cfg = Config_CNNCTC()
+    if args_opt.ckpt_path != "":
+        cfg.CKPT_PATH = args_opt.ckpt_path
+    test(cfg)

model_zoo/official/cv/cnnctc/scripts/run_distribute_train_ascend.sh

@@ -0,0 +1,57 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+current_exec_path=$(pwd)
+echo ${current_exec_path}
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+echo $PATH1
+
+PATH2=$(get_real_path $2)
+echo $PATH2
+
+python ${current_exec_path}/src/generate_hccn_file.py --rank_file=$PATH1
+export RANK_TABLE_FILE=$PATH1
+export RANK_SIZE=8
+ulimit -u unlimited
+for((i=0;i<$RANK_SIZE;i++));
+do
+    rm ./train_parallel_$i/ -rf
+    mkdir ./train_parallel_$i
+    cp ./*.py ./train_parallel_$i
+    cp ./scripts/*.sh ./train_parallel_$i
+    cp -r ./src ./train_parallel_$i
+    cd ./train_parallel_$i || exit
+    export RANK_ID=$i
+    export DEVICE_ID=$i
+    echo "start training for rank $RANK_ID, device $DEVICE_ID"
+    if [ -f $PATH2 ]
+    then
+      python train.py --device_id=$i --ckpt_path=$PATH2 --run_distribute=True >log_$i.log 2>&1 &
+    else
+      python train.py --device_id=$i --run_distribute=True >log_$i.log 2>&1 &
+    fi
+    cd .. || exit
+done
+

model_zoo/official/cv/cnnctc/scripts/run_eval_ascend.sh

@@ -0,0 +1,54 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+if [ $# -ne 1 ]
+then
+    echo "Usage: sh run_eval_ascend.sh [TRAINED_CKPT]"
+exit 1
+fi
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+
+PATH1=$(get_real_path $1)
+echo $PATH1
+if [ ! -f $PATH1 ]
+then
+    echo "error: TRAINED_CKPT=$PATH1 is not a file"
+exit 1
+fi
+
+ulimit -u unlimited
+export DEVICE_ID=0
+
+if [ -d "eval" ];
+then
+    rm -rf ./eval
+fi
+mkdir ./eval
+cp ./*.py ./eval
+cp ./scripts/*.sh ./eval
+cp -r ./src ./eval
+cd ./eval || exit
+echo "start infering for device $DEVICE_ID"
+env > env.log
+python eval.py --device_id=$DEVICE_ID --ckpt_path=$PATH1 &> log &
+cd .. || exit

model_zoo/official/cv/cnnctc/scripts/run_standalone_train_ascend.sh

@@ -0,0 +1,45 @@
+#!/bin/bash
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
+PATH1=$(get_real_path $1)
+
+ulimit -u unlimited
+
+if [ -d "train" ];
+then
+    rm -rf ./train
+fi
+mkdir ./train
+cp ./*.py ./train
+cp ./scripts/*.sh ./train
+cp -r ./src ./train
+cd ./train || exit
+echo "start training for device $DEVICE_ID"
+env > env.log
+if [ -f $PATH1 ]
+then
+  python train.py --device_id=$DEVICE_ID --ckpt_path=$PATH1 --run_distribute=False &> log &
+else
+  python train.py --device_id=$DEVICE_ID --run_distribute=False &> log &
+fi
+cd .. || exit

model_zoo/official/cv/cnnctc/src/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""src init file"""

model_zoo/official/cv/cnnctc/src/callback.py

@@ -0,0 +1,71 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""loss callback"""
+
+import time
+from mindspore.train.callback import Callback
+from .util import AverageMeter
+
+class LossCallBack(Callback):
+    """
+    Monitor the loss in training.
+
+    If the loss is NAN or INF terminating training.
+
+    Note:
+        If per_print_times is 0 do not print loss.
+
+    Args:
+        per_print_times (int): Print loss every times. Default: 1.
+    """
+
+    def __init__(self, per_print_times=1):
+        super(LossCallBack, self).__init__()
+        if not isinstance(per_print_times, int) or per_print_times < 0:
+            raise ValueError("print_step must be int and >= 0.")
+        self._per_print_times = per_print_times
+        self.loss_avg = AverageMeter()
+        self.timer = AverageMeter()
+        self.start_time = time.time()
+
+    def step_end(self, run_context):
+        cb_params = run_context.original_args()
+
+        loss = cb_params.net_outputs.asnumpy()
+
+        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
+        cur_num = cb_params.cur_step_num
+
+        if cur_step_in_epoch % 2000 == 1:
+            self.loss_avg = AverageMeter()
+            self.timer = AverageMeter()
+            self.start_time = time.time()
+        else:
+            self.timer.update(time.time() - self.start_time)
+            self.start_time = time.time()
+
+        self.loss_avg.update(loss)
+
+        if self._per_print_times != 0 and cur_num % self._per_print_times == 0:
+            loss_file = open("./loss.log", "a+")
+            loss_file.write("epoch: %s step: %s , loss is %s, average time per step is %s" % (
+                cb_params.cur_epoch_num, cur_step_in_epoch,
+                self.loss_avg.avg, self.timer.avg))
+            loss_file.write("\n")
+            loss_file.close()
+
+            print("epoch: %s step: %s , loss is %s, average time per step is %s" % (
+                cb_params.cur_epoch_num, cur_step_in_epoch,
+                self.loss_avg.avg, self.timer.avg))

model_zoo/official/cv/cnnctc/src/config.py

@@ -0,0 +1,43 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""network config setting, will be used in train.py and eval.py"""
+
+class Config_CNNCTC():
+    # model config
+    CHARACTER = '0123456789abcdefghijklmnopqrstuvwxyz'
+    NUM_CLASS = len(CHARACTER) + 1
+    HIDDEN_SIZE = 512
+    FINAL_FEATURE_WIDTH = 26
+
+    # dataset config
+    IMG_H = 32
+    IMG_W = 100
+    TRAIN_DATASET_PATH = 'CNNCTC_Data/ST_MJ/'
+    TRAIN_DATASET_INDEX_PATH = 'CNNCTC_Data/st_mj_fixed_length_index_list.pkl'
+    TRAIN_BATCH_SIZE = 192
+    TEST_DATASET_PATH = 'CNNCTC_Data/IIIT5k_3000'
+    TEST_BATCH_SIZE = 256
+    TEST_DATASET_SIZE = 2976
+    TRAIN_EPOCHS = 3
+
+    # training config
+    CKPT_PATH = ''
+    SAVE_PATH = './'
+    LR = 1e-4
+    LR_PARA = 5e-4
+    MOMENTUM = 0.8
+    LOSS_SCALE = 8096
+    SAVE_CKPT_PER_N_STEP = 2000
+    KEEP_CKPT_MAX_NUM = 5

model_zoo/official/cv/cnnctc/src/generate_hccn_file.py

@@ -0,0 +1,88 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""generate ascend rank file"""
+
+import os
+import socket
+import argparse
+
+parser = argparse.ArgumentParser(description="ascend distribute rank.")
+parser.add_argument("--rank_file", type=str, default="scripts/rank_table_8p.json", help="rank_tabel_file_path.")
+
+def main(rank_table_file):
+    nproc_per_node = 8
+
+    visible_devices = ['0', '1', '2', '3', '4', '5', '6', '7']
+
+    server_id = socket.gethostbyname(socket.gethostname())
+
+    hccn_configs = open('/etc/hccn.conf', 'r').readlines()
+    device_ips = {}
+    for hccn_item in hccn_configs:
+        hccn_item = hccn_item.strip()
+        if hccn_item.startswith('address_'):
+            device_id, device_ip = hccn_item.split('=')
+            device_id = device_id.split('_')[1]
+            device_ips[device_id] = device_ip
+            print('device_id:{}, device_ip:{}'.format(device_id, device_ip))
+
+    hccn_table = {}
+    hccn_table['board_id'] = '0x002f'  # A+K
+    # hccn_table['board_id'] = '0x0000' # A+X
+
+    hccn_table['chip_info'] = '910'
+    hccn_table['deploy_mode'] = 'lab'
+    hccn_table['group_count'] = '1'
+    hccn_table['group_list'] = []
+    instance_list = []
+    for instance_id in range(nproc_per_node):
+        instance = {}
+        instance['devices'] = []
+        device_id = visible_devices[instance_id]
+        device_ip = device_ips[device_id]
+        instance['devices'].append({
+            'device_id': device_id,
+            'device_ip': device_ip,
+        })
+        instance['rank_id'] = str(instance_id)
+        instance['server_id'] = server_id
+        instance_list.append(instance)
+    hccn_table['group_list'].append({
+        'device_num': str(nproc_per_node),
+        'server_num': '1',
+        'group_name': '',
+        'instance_count': str(nproc_per_node),
+        'instance_list': instance_list,
+    })
+    hccn_table['para_plane_nic_location'] = 'device'
+    hccn_table['para_plane_nic_name'] = []
+    for instance_id in range(nproc_per_node):
+        eth_id = visible_devices[instance_id]
+        hccn_table['para_plane_nic_name'].append('eth{}'.format(eth_id))
+    hccn_table['para_plane_nic_num'] = str(nproc_per_node)
+    hccn_table['status'] = 'completed'
+    import json
+    with open(rank_table_file, 'w') as table_fp:
+        json.dump(hccn_table, table_fp, indent=4)
+
+if __name__ == '__main__':
+    args_opt = parser.parse_args()
+    rank_table = args_opt.rank_file
+    if os.path.exists(rank_table):
+        print('Rank table file exists.')
+    else:
+        print('Generating rank table file.')
+        main(rank_table)
+        print('Rank table file generated')

model_zoo/official/cv/cnnctc/src/preprocess_dataset.py

@@ -0,0 +1,171 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""preprocess dataset"""
+
+import random
+import pickle
+import numpy as np
+import lmdb
+from tqdm import tqdm
+
+def combine_lmdbs(lmdb_paths, lmdb_save_path):
+    max_len = int((26 + 1) // 2)
+    character = '0123456789abcdefghijklmnopqrstuvwxyz'
+
+    env_save = lmdb.open(
+        lmdb_save_path,
+        map_size=1099511627776)
+
+    cnt = 0
+    for lmdb_path in lmdb_paths:
+        env = lmdb.open(lmdb_path, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
+        with env.begin(write=False) as txn:
+            nSamples = int(txn.get('num-samples'.encode()))
+            nSamples = nSamples
+
+            # Filtering
+            for index in tqdm(range(nSamples)):
+                index += 1  # lmdb starts with 1
+                label_key = 'label-%09d'.encode() % index
+                label = txn.get(label_key).decode('utf-8')
+
+                if len(label) > max_len:
+                    continue
+
+                illegal_sample = False
+                for char_item in label.lower():
+                    if char_item not in character:
+                        illegal_sample = True
+                        break
+                if illegal_sample:
+                    continue
+
+                img_key = 'image-%09d'.encode() % index
+                imgbuf = txn.get(img_key)
+
+                with env_save.begin(write=True) as txn_save:
+                    cnt += 1
+
+                    label_key_save = 'label-%09d'.encode() % cnt
+                    label_save = label.encode()
+                    image_key_save = 'image-%09d'.encode() % cnt
+                    image_save = imgbuf
+
+                    txn_save.put(label_key_save, label_save)
+                    txn_save.put(image_key_save, image_save)
+
+    nSamples = cnt
+    with env_save.begin(write=True) as txn_save:
+        txn_save.put('num-samples'.encode(), str(nSamples).encode())
+
+
+def analyze_lmdb_label_length(lmdb_path, batch_size=192, num_of_combinations=1000):
+    label_length_dict = {}
+
+    env = lmdb.open(lmdb_path, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
+    with env.begin(write=False) as txn:
+        nSamples = int(txn.get('num-samples'.encode()))
+        nSamples = nSamples
+
+        for index in tqdm(range(nSamples)):
+            index += 1  # lmdb starts with 1
+            label_key = 'label-%09d'.encode() % index
+            label = txn.get(label_key).decode('utf-8')
+
+            label_length = len(label)
+            if label_length in label_length_dict:
+                label_length_dict[label_length] += 1
+            else:
+                label_length_dict[label_length] = 1
+
+    sorted_label_length = sorted(label_length_dict.items(), key=lambda x: x[1], reverse=True)
+
+    label_length_sum = 0
+    label_num = 0
+    lengths = []
+    p = []
+    for l, num in sorted_label_length:
+        label_length_sum += l * num
+        label_num += num
+        p.append(num)
+        lengths.append(l)
+    for i, _ in enumerate(p):
+        p[i] /= label_num
+
+    average_overall_length = int(label_length_sum / label_num * batch_size)
+
+    def get_combinations_of_fix_length(fix_length, items, p, batch_size):
+        ret = []
+        cur_sum = 0
+        ret = np.random.choice(items, batch_size - 1, True, p)
+        cur_sum = sum(ret)
+        ret = list(ret)
+        if fix_length - cur_sum in items:
+            ret.append(fix_length - cur_sum)
+        else:
+            return None
+        return ret
+
+    result = []
+    while len(result) < num_of_combinations:
+        ret = get_combinations_of_fix_length(average_overall_length, lengths, p, batch_size)
+        if ret is not None:
+            result.append(ret)
+    return result
+
+
+def generate_fix_shape_index_list(lmdb_path, combinations, pkl_save_path, num_of_iters=70000):
+    length_index_dict = {}
+
+    env = lmdb.open(lmdb_path, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
+    with env.begin(write=False) as txn:
+        nSamples = int(txn.get('num-samples'.encode()))
+        nSamples = nSamples
+
+        for index in tqdm(range(nSamples)):
+            index += 1  # lmdb starts with 1
+            label_key = 'label-%09d'.encode() % index
+            label = txn.get(label_key).decode('utf-8')
+
+            label_length = len(label)
+            if label_length in length_index_dict:
+                length_index_dict[label_length].append(index)
+            else:
+                length_index_dict[label_length] = [index]
+
+    ret = []
+    for _ in range(num_of_iters):
+        comb = random.choice(combinations)
+        for l in comb:
+            ret.append(random.choice(length_index_dict[l]))
+
+    with open(pkl_save_path, 'wb') as f:
+        pickle.dump(ret, f, -1)
+
+
+if __name__ == '__main__':
+    # step 1: combine the SynthText dataset and MJSynth dataset into a single lmdb file
+    print('Begin to combine multiple lmdb datasets')
+    combine_lmdbs(['/home/workspace/mindspore_dataset/CNNCTC_Data/1_ST/',
+                   '/home/workspace/mindspore_dataset/CNNCTC_Data/MJ_train/'],
+                  '/home/workspace/mindspore_dataset/CNNCTC_Data/ST_MJ')
+
+    # step 2: generate the order of input data, guarantee that the input batch shape is fixed
+    print('Begin to generate the index order of input data')
+    combination = analyze_lmdb_label_length('/home/workspace/mindspore_dataset/CNNCTC_Data/ST_MJ')
+    generate_fix_shape_index_list('/home/workspace/mindspore_dataset/CNNCTC_Data/ST_MJ', combination,
+                                  '/home/workspace/mindspore_dataset/CNNCTC_Data/st_mj_fixed_length_index_list.pkl')
+
+    print('Done')

model_zoo/official/cv/cnnctc/src/util.py

@@ -0,0 +1,102 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""util file"""
+
+import numpy as np
+
+class AverageMeter():
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+
+class CTCLabelConverter():
+    """ Convert between text-label and text-index """
+
+    def __init__(self, character):
+        # character (str): set of the possible characters.
+        dict_character = list(character)
+
+        self.dict = {}
+        for i, char in enumerate(dict_character):
+            self.dict[char] = i
+
+        self.character = dict_character + ['[blank]']  # dummy '[blank]' token for CTCLoss (index 0)
+        self.dict['[blank]'] = len(dict_character)
+
+    def encode(self, text):
+        """convert text-label into text-index.
+        input:
+            text: text labels of each image. [batch_size]
+
+        output:
+            text: concatenated text index for CTCLoss.
+                    [sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
+            length: length of each text. [batch_size]
+        """
+        length = [len(s) for s in text]
+        text = ''.join(text)
+        text = [self.dict[char] for char in text]
+
+        return np.array(text), np.array(length)
+
+    def decode(self, text_index, length):
+        """ convert text-index into text-label. """
+        texts = []
+        index = 0
+        for l in length:
+            t = text_index[index:index + l]
+
+            char_list = []
+            for i in range(l):
+                # if t[i] != self.dict['[blank]'] and (not (i > 0 and t[i - 1] == t[i])):  # removing repeated characters and blank.
+                if t[i] != self.dict['[blank]'] and (
+                        not (i > 0 and t[i - 1] == t[i])):  # removing repeated characters and blank.
+                    char_list.append(self.character[t[i]])
+            text = ''.join(char_list)
+
+            texts.append(text)
+            index += l
+        return texts
+
+    def reverse_encode(self, text_index, length):
+        """ convert text-index into text-label. """
+        texts = []
+        index = 0
+        for l in length:
+            t = text_index[index:index + l]
+
+            char_list = []
+            for i in range(l):
+                if t[i] != self.dict['[blank]']:  # removing repeated characters and blank.
+                    char_list.append(self.character[t[i]])
+            text = ''.join(char_list)
+
+            texts.append(text)
+            index += l
+        return texts

model_zoo/official/cv/cnnctc/train.py

@@ -0,0 +1,100 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""cnnctc train"""
+
+import argparse
+import ast
+
+import mindspore
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.dataset import GeneratorDataset
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
+from mindspore.train.model import Model
+from mindspore.communication.management import init
+from mindspore.common import set_seed
+
+from src.config import Config_CNNCTC
+from src.callback import LossCallBack
+from src.dataset import ST_MJ_Generator_batch_fixed_length, ST_MJ_Generator_batch_fixed_length_para
+from src.cnn_ctc import CNNCTC_Model, ctc_loss, WithLossCell
+
+set_seed(1)
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False,
+                    save_graphs_path=".", enable_auto_mixed_precision=False)
+
+
+def dataset_creator(run_distribute):
+    if run_distribute:
+        st_dataset = ST_MJ_Generator_batch_fixed_length_para()
+    else:
+        st_dataset = ST_MJ_Generator_batch_fixed_length()
+
+    ds = GeneratorDataset(st_dataset,
+                          ['img', 'label_indices', 'text', 'sequence_length'],
+                          num_parallel_workers=8)
+
+    return ds
+
+
+def train(args_opt, config):
+    if args_opt.run_distribute:
+        init()
+        context.set_auto_parallel_context(parallel_mode="data_parallel")
+
+    ds = dataset_creator(args_opt.run_distribute)
+
+    net = CNNCTC_Model(config.NUM_CLASS, config.HIDDEN_SIZE, config.FINAL_FEATURE_WIDTH)
+    net.set_train(True)
+
+    if config.CKPT_PATH != '':
+        param_dict = load_checkpoint(config.CKPT_PATH)
+        load_param_into_net(net, param_dict)
+        print('parameters loaded!')
+    else:
+        print('train from scratch...')
+
+    criterion = ctc_loss()
+    opt = mindspore.nn.RMSProp(params=net.trainable_params(), centered=True, learning_rate=config.LR_PARA,
+                               momentum=config.MOMENTUM, loss_scale=config.LOSS_SCALE)
+
+    net = WithLossCell(net, criterion)
+    loss_scale_manager = mindspore.train.loss_scale_manager.FixedLossScaleManager(config.LOSS_SCALE, False)
+    model = Model(net, optimizer=opt, loss_scale_manager=loss_scale_manager, amp_level="O2")
+
+    callback = LossCallBack()
+    config_ck = CheckpointConfig(save_checkpoint_steps=config.SAVE_CKPT_PER_N_STEP,
+                                 keep_checkpoint_max=config.KEEP_CKPT_MAX_NUM)
+    ckpoint_cb = ModelCheckpoint(prefix="CNNCTC", config=config_ck, directory=config.SAVE_PATH)
+
+    if args_opt.device_id == 0:
+        model.train(config.TRAIN_EPOCHS, ds, callbacks=[callback, ckpoint_cb], dataset_sink_mode=False)
+    else:
+        model.train(config.TRAIN_EPOCHS, ds, callbacks=[callback], dataset_sink_mode=False)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='CNNCTC arg')
+    parser.add_argument('--device_id', type=int, default=0, help="Device id, default is 0.")
+    parser.add_argument("--ckpt_path", type=str, default="", help="Pretrain file path.")
+    parser.add_argument("--run_distribute", type=ast.literal_eval, default=False,
+                        help="Run distribute, default is false.")
+    args_cfg = parser.parse_args()
+
+    cfg = Config_CNNCTC()
+    if args_cfg.ckpt_path != "":
+        cfg.CKPT_PATH = args_cfg.ckpt_path
+    train(args_cfg, cfg)

model_zoo/official/cv/faster_rcnn/src/config.py

@@ -11,7 +11,7 @@
 # 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.
-#" :===========================================================================
+# ===========================================================================
 """
 network config setting, will be used in train.py and eval.py
 """