!10222 lstm D

From: @ttudu
Reviewed-by: 
Signed-off-by:

model_zoo/official/nlp/lstm/eval.py

@@ -20,8 +20,9 @@ import os
 
 import numpy as np
 
-from src.config import lstm_cfg as cfg
+from src.config import lstm_cfg as cfg, lstm_cfg_ascend
 from src.dataset import lstm_create_dataset, convert_to_mindrecord
+from src.lr_schedule import get_lr
 from src.lstm import SentimentNet
 from mindspore import Tensor, nn, Model, context
 from mindspore.nn import Accuracy
@@ -40,8 +41,8 @@ if __name__ == '__main__':
                         help='path where the pre-process data is stored.')
     parser.add_argument('--ckpt_path', type=str, default=None,
                         help='the checkpoint file path used to evaluate model.')
-    parser.add_argument('--device_target', type=str, default="GPU", choices=['GPU', 'CPU'],
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
-                        help='the target device to run, support "GPU", "CPU". Default: "GPU".')
+                        help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
     args = parser.parse_args()
 
     context.set_context(
@@ -49,11 +50,24 @@ if __name__ == '__main__':
         save_graphs=False,
         device_target=args.device_target)
 
+    if args.device_target == 'Ascend':
+        cfg = lstm_cfg_ascend
+    else:
+        cfg = lstm_cfg
+
     if args.preprocess == "true":
         print("============== Starting Data Pre-processing ==============")
         convert_to_mindrecord(cfg.embed_size, args.aclimdb_path, args.preprocess_path, args.glove_path)
 
     embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
+    # DynamicRNN in this network on Ascend platform only support the condition that the shape of input_size
+    # and hiddle_size is multiples of 16, this problem will be solved later.
+    if args.device_target == 'Ascend':
+        pad_num = int(np.ceil(cfg.embed_size / 16) * 16 - cfg.embed_size)
+        if pad_num > 0:
+            embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant')
+        cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16)
+
     network = SentimentNet(vocab_size=embedding_table.shape[0],
                            embed_size=cfg.embed_size,
                            num_hiddens=cfg.num_hiddens,
@@ -64,13 +78,23 @@ if __name__ == '__main__':
                            batch_size=cfg.batch_size)
 
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-    opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
+    ds_eval = lstm_create_dataset(args.preprocess_path, cfg.batch_size, training=False)
+    if cfg.dynamic_lr:
+        lr = Tensor(get_lr(global_step=cfg.global_step,
+                           lr_init=cfg.lr_init, lr_end=cfg.lr_end, lr_max=cfg.lr_max,
+                           warmup_epochs=cfg.warmup_epochs,
+                           total_epochs=cfg.num_epochs,
+                           steps_per_epoch=ds_eval.get_dataset_size(),
+                           lr_adjust_epoch=cfg.lr_adjust_epoch))
+    else:
+        lr = cfg.learning_rate
+
+    opt = nn.Momentum(network.trainable_params(), lr, cfg.momentum)
     loss_cb = LossMonitor()
 
     model = Model(network, loss, opt, {'acc': Accuracy()})
 
     print("============== Starting Testing ==============")
-    ds_eval = lstm_create_dataset(args.preprocess_path, cfg.batch_size, training=False)
     param_dict = load_checkpoint(args.ckpt_path)
     load_param_into_net(network, param_dict)
     if args.device_target == "CPU":

model_zoo/official/nlp/lstm/script/run_eval_ascend.sh

@@ -0,0 +1,39 @@
+#!/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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash run_eval_ascend.sh DEVICE_ID PREPROCESS_DIR CKPT_FILE"
+echo "for example: bash run_eval_ascend.sh 0 ./preprocess lstm-20_390.ckpt"
+echo "=============================================================================================================="
+
+DEVICE_ID=$1
+PREPROCESS_DIR=$2
+CKPT_FILE=$3
+
+rm -rf eval
+mkdir -p eval
+cd eval
+mkdir -p ms_log
+CUR_DIR=`pwd`
+export GLOG_log_dir=${CUR_DIR}/ms_log
+export GLOG_logtostderr=0
+export DEVICE_ID=$DEVICE_ID
+python ../../eval.py  \
+    --device_target="Ascend" \
+    --preprocess=false  \
+    --preprocess_path=$PREPROCESS_DIR \
+    --ckpt_path=$CKPT_FILE > log.txt 2>&1 &

model_zoo/official/nlp/lstm/script/run_eval_cpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_eval_cpu.sh ACLIMDB_DIR GLOVE_DIR CKPT_FILE"
 echo "for example: bash run_eval_cpu.sh ./aclimdb ./glove_dir lstm-20_390.ckpt"
 echo "=============================================================================================================="

model_zoo/official/nlp/lstm/script/run_eval_gpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_train_gpu.sh DEVICE_ID ACLIMDB_DIR GLOVE_DIR CKPT_FILE"
 echo "for example: bash run_train_gpu.sh 0 ./aclimdb ./glove_dir lstm-20_390.ckpt"
 echo "=============================================================================================================="

model_zoo/official/nlp/lstm/script/run_train_ascend.sh

@@ -0,0 +1,39 @@
+#!/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.
+# ============================================================================
+
+echo "=============================================================================================================="
+echo "Please run the script as: "
+echo "bash run_train_ascend.sh DEVICE_ID ACLIMDB_DIR GLOVE_DIR"
+echo "for example: bash run_train_ascend.sh 0 ./aclimdb ./glove_dir"
+echo "=============================================================================================================="
+
+DEVICE_ID=$1
+ACLIMDB_DIR=$2
+GLOVE_DIR=$3
+
+mkdir -p train
+cd train
+mkdir -p ms_log
+CUR_DIR=`pwd`
+export GLOG_log_dir=${CUR_DIR}/ms_log
+export GLOG_logtostderr=0
+export DEVICE_ID=$DEVICE_ID
+python ../../train.py  \
+    --device_target="Ascend" \
+    --aclimdb_path=$ACLIMDB_DIR \
+    --glove_path=$GLOVE_DIR \
+    --preprocess=true  \
+    --preprocess_path=./preprocess > log.txt 2>&1 &

model_zoo/official/nlp/lstm/script/run_train_cpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_train_cpu.sh ACLIMDB_DIR GLOVE_DIR"
 echo "for example: bash run_train_gpu.sh ./aclimdb ./glove_dir"
 echo "=============================================================================================================="

model_zoo/official/nlp/lstm/script/run_train_gpu.sh

@@ -15,7 +15,7 @@
 # ============================================================================
 
 echo "=============================================================================================================="
-echo "Please run the scipt as: "
+echo "Please run the script as: "
 echo "bash run_train_gpu.sh DEVICE_ID ACLIMDB_DIR GLOVE_DIR"
 echo "for example: bash run_train_gpu.sh 0 ./aclimdb ./glove_dir"
 echo "=============================================================================================================="

model_zoo/official/nlp/lstm/src/config.py

@@ -20,6 +20,7 @@ from easydict import EasyDict as edict
 # LSTM CONFIG
 lstm_cfg = edict({
     'num_classes': 2,
+    'dynamic_lr': False,
     'learning_rate': 0.1,
     'momentum': 0.9,
     'num_epochs': 20,
@@ -31,3 +32,24 @@ lstm_cfg = edict({
     'save_checkpoint_steps': 390,
     'keep_checkpoint_max': 10
 })
+
+# LSTM CONFIG IN ASCEND
+lstm_cfg_ascend = edict({
+    'num_classes': 2,
+    'momentum': 0.9,
+    'num_epochs': 20,
+    'batch_size': 64,
+    'embed_size': 300,
+    'num_hiddens': 128,
+    'num_layers': 2,
+    'bidirectional': True,
+    'save_checkpoint_steps': 7800,
+    'keep_checkpoint_max': 10,
+    'dynamic_lr': True,
+    'lr_init': 0.05,
+    'lr_end': 0.01,
+    'lr_max': 0.1,
+    'lr_adjust_epoch': 6,
+    'warmup_epochs': 1,
+    'global_step': 0
+})

model_zoo/official/nlp/lstm/src/lr_schedule.py

@@ -0,0 +1,60 @@
+# 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.
+# ============================================================================
+
+"""Learning rate schedule"""
+
+import math
+import numpy as np
+
+
+def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch, lr_adjust_epoch):
+    """
+    generate learning rate array
+
+    Args:
+       global_step(int): total steps of the training
+       lr_init(float): init learning rate
+       lr_end(float): end learning rate
+       lr_max(float): max learning rate
+       warmup_epochs(float): number of warmup epochs
+       total_epochs(int): total epoch of training
+       steps_per_epoch(int): steps of one epoch
+       lr_adjust_epoch(int): lr adjust in lr_adjust_epoch, after that, the lr is lr_end
+
+    Returns:
+       np.array, learning rate array
+    """
+    lr_each_step = []
+    total_steps = steps_per_epoch * total_epochs
+    warmup_steps = steps_per_epoch * warmup_epochs
+    adjust_steps = lr_adjust_epoch * steps_per_epoch
+    for i in range(total_steps):
+        if i < warmup_steps:
+            lr = lr_init + (lr_max - lr_init) * i / warmup_steps
+        elif i < adjust_steps:
+            lr = lr_end + \
+                 (lr_max - lr_end) * \
+                 (1. + math.cos(math.pi * (i - warmup_steps) / (adjust_steps - warmup_steps))) / 2.
+        else:
+            lr = lr_end
+        if lr < 0.0:
+            lr = 0.0
+        lr_each_step.append(lr)
+
+    current_step = global_step
+    lr_each_step = np.array(lr_each_step).astype(np.float32)
+    learning_rate = lr_each_step[current_step:]
+
+    return learning_rate

model_zoo/official/nlp/lstm/src/lstm.py

@@ -20,6 +20,8 @@ import numpy as np
 from mindspore import Tensor, nn, context, Parameter, ParameterTuple
 from mindspore.common.initializer import initializer
 from mindspore.ops import operations as P
+import mindspore.ops.functional as F
+import mindspore.common.dtype as mstype
 
 STACK_LSTM_DEVICE = ["CPU"]
 
@@ -44,6 +46,28 @@ def stack_lstm_default_state(batch_size, hidden_size, num_layers, bidirectional)
     h, c = tuple(h_list), tuple(c_list)
     return h, c
 
+def stack_lstm_default_state_ascend(batch_size, hidden_size, num_layers, bidirectional):
+    """init default input."""
+
+    h_list = c_list = []
+    for _ in range(num_layers):
+        h_fw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
+        c_fw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
+        h_i = [h_fw]
+        c_i = [c_fw]
+
+        if bidirectional:
+            h_bw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
+            c_bw = Tensor(np.zeros((1, batch_size, hidden_size)).astype(np.float16))
+            h_i.append(h_bw)
+            c_i.append(c_bw)
+
+        h_list.append(h_i)
+        c_list.append(c_i)
+
+    h, c = tuple(h_list), tuple(c_list)
+    return h, c
+
 
 class StackLSTM(nn.Cell):
     """
@@ -114,6 +138,128 @@ class StackLSTM(nn.Cell):
             x = self.transpose(x, (1, 0, 2))
         return x, (hn, cn)
 
+class LSTM_Ascend(nn.Cell):
+    """ LSTM in Ascend. """
+
+    def __init__(self, bidirectional=False):
+        super(LSTM_Ascend, self).__init__()
+        self.bidirectional = bidirectional
+        self.dynamic_rnn = P.DynamicRNN(forget_bias=0.0)
+        self.reverseV2 = P.ReverseV2(axis=[0])
+        self.concat = P.Concat(2)
+
+    def construct(self, x, h, c, w_f, b_f, w_b=None, b_b=None):
+        """construct"""
+        x = F.cast(x, mstype.float16)
+        if self.bidirectional:
+            y1, h1, c1, _, _, _, _, _ = self.dynamic_rnn(x, w_f, b_f, None, h[0], c[0])
+            r_x = self.reverseV2(x)
+            y2, h2, c2, _, _, _, _, _ = self.dynamic_rnn(r_x, w_b, b_b, None, h[1], c[1])
+            y2 = self.reverseV2(y2)
+
+            output = self.concat((y1, y2))
+            hn = self.concat((h1, h2))
+            cn = self.concat((c1, c2))
+            return output, (hn, cn)
+
+        y1, h1, c1, _, _, _, _, _ = self.dynamic_rnn(x, w_f, b_f, None, h[0], c[0])
+        return y1, (h1, c1)
+
+class StackLSTMAscend(nn.Cell):
+    """ Stack multi-layers LSTM together. """
+
+    def __init__(self,
+                 input_size,
+                 hidden_size,
+                 num_layers=1,
+                 has_bias=True,
+                 batch_first=False,
+                 dropout=0.0,
+                 bidirectional=False):
+        super(StackLSTMAscend, self).__init__()
+        self.num_layers = num_layers
+        self.batch_first = batch_first
+        self.bidirectional = bidirectional
+        self.transpose = P.Transpose()
+
+        # input_size list
+        input_size_list = [input_size]
+        for i in range(num_layers - 1):
+            input_size_list.append(hidden_size * 2)
+
+        #weights, bias and layers init
+        weights_fw = []
+        weights_bw = []
+        bias_fw = []
+        bias_bw = []
+
+        stdv = 1 / math.sqrt(hidden_size)
+        for i in range(num_layers):
+            # forward weight init
+            w_np_fw = np.random.uniform(-stdv,
+                                        stdv,
+                                        (input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float16)
+            w_fw = Parameter(initializer(Tensor(w_np_fw), w_np_fw.shape), name="w_fw_layer" + str(i))
+            weights_fw.append(w_fw)
+            # forward bias init
+            if has_bias:
+                b_fw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float16)
+                b_fw = Parameter(initializer(Tensor(b_fw), b_fw.shape), name="b_fw_layer" + str(i))
+            else:
+                b_fw = np.zeros((hidden_size * 4)).astype(np.float16)
+                b_fw = Parameter(initializer(Tensor(b_fw), b_fw.shape), name="b_fw_layer" + str(i))
+            bias_fw.append(b_fw)
+
+            if bidirectional:
+                # backward weight init
+                w_np_bw = np.random.uniform(-stdv,
+                                            stdv,
+                                            (input_size_list[i] + hidden_size, hidden_size * 4)).astype(np.float16)
+                w_bw = Parameter(initializer(Tensor(w_np_bw), w_np_bw.shape), name="w_bw_layer" + str(i))
+                weights_bw.append(w_bw)
+
+                # backward bias init
+                if has_bias:
+                    b_bw = np.random.uniform(-stdv, stdv, (hidden_size * 4)).astype(np.float16)
+                    b_bw = Parameter(initializer(Tensor(b_bw), b_bw.shape), name="b_bw_layer" + str(i))
+                else:
+                    b_bw = np.zeros((hidden_size * 4)).astype(np.float16)
+                    b_bw = Parameter(initializer(Tensor(b_bw), b_bw.shape), name="b_bw_layer" + str(i))
+                bias_bw.append(b_bw)
+
+        # layer init
+        self.lstm = LSTM_Ascend(bidirectional=bidirectional)
+
+        self.weight_fw = ParameterTuple(tuple(weights_fw))
+        self.weight_bw = ParameterTuple(tuple(weights_bw))
+        self.bias_fw = ParameterTuple(tuple(bias_fw))
+        self.bias_bw = ParameterTuple(tuple(bias_bw))
+
+    def construct(self, x, hx):
+        """construct"""
+        x = F.cast(x, mstype.float16)
+        if self.batch_first:
+            x = self.transpose(x, (1, 0, 2))
+        # stack lstm
+        h, c = hx
+        hn = cn = None
+        for i in range(self.num_layers):
+            if self.bidirectional:
+                x, (hn, cn) = self.lstm(x,
+                                        h[i],
+                                        c[i],
+                                        self.weight_fw[i],
+                                        self.bias_fw[i],
+                                        self.weight_bw[i],
+                                        self.bias_bw[i])
+            else:
+                x, (hn, cn) = self.lstm(x, h[i], c[i], self.weight_fw[i], self.bias_fw[i])
+        if self.batch_first:
+            x = self.transpose(x, (1, 0, 2))
+        x = F.cast(x, mstype.float32)
+        hn = F.cast(x, mstype.float32)
+        cn = F.cast(x, mstype.float32)
+        return x, (hn, cn)
 
 class SentimentNet(nn.Cell):
     """Sentiment network structure."""
@@ -145,7 +291,7 @@ class SentimentNet(nn.Cell):
                                      bidirectional=bidirectional,
                                      dropout=0.0)
             self.h, self.c = stack_lstm_default_state(batch_size, num_hiddens, num_layers, bidirectional)
-        else:
+        elif context.get_context("device_target") == "GPU":
             # standard lstm
             self.encoder = nn.LSTM(input_size=embed_size,
                                    hidden_size=num_hiddens,
@@ -154,8 +300,16 @@ class SentimentNet(nn.Cell):
                                    bidirectional=bidirectional,
                                    dropout=0.0)
             self.h, self.c = lstm_default_state(batch_size, num_hiddens, num_layers, bidirectional)
+        else:
+            self.encoder = StackLSTMAscend(input_size=embed_size,
+                                           hidden_size=num_hiddens,
+                                           num_layers=num_layers,
+                                           has_bias=True,
+                                           bidirectional=bidirectional)
+            self.h, self.c = stack_lstm_default_state_ascend(batch_size, num_hiddens, num_layers, bidirectional)
 
         self.concat = P.Concat(1)
+        self.squeeze = P.Squeeze(axis=0)
         if bidirectional:
             self.decoder = nn.Dense(num_hiddens * 4, num_classes)
         else:
@@ -167,6 +321,6 @@ class SentimentNet(nn.Cell):
         embeddings = self.trans(embeddings, self.perm)
         output, _ = self.encoder(embeddings, (self.h, self.c))
         # states[i] size(64,200)  -> encoding.size(64,400)
-        encoding = self.concat((output[0], output[499]))
+        encoding = self.concat((self.squeeze(output[0:1:1]), self.squeeze(output[499:500:1])))
         outputs = self.decoder(encoding)
         return outputs

model_zoo/official/nlp/lstm/train.py

@@ -20,9 +20,10 @@ import os
 
 import numpy as np
 
-from src.config import lstm_cfg as cfg
+from src.config import lstm_cfg, lstm_cfg_ascend
 from src.dataset import convert_to_mindrecord
 from src.dataset import lstm_create_dataset
+from src.lr_schedule import get_lr
 from src.lstm import SentimentNet
 from mindspore import Tensor, nn, Model, context
 from mindspore.nn import Accuracy
@@ -43,8 +44,8 @@ if __name__ == '__main__':
                         help='the path to save the checkpoint file.')
     parser.add_argument('--pre_trained', type=str, default=None,
                         help='the pretrained checkpoint file path.')
-    parser.add_argument('--device_target', type=str, default="GPU", choices=['GPU', 'CPU'],
+    parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
-                        help='the target device to run, support "GPU", "CPU". Default: "GPU".')
+                        help='the target device to run, support "GPU", "CPU". Default: "Ascend".')
     args = parser.parse_args()
 
     context.set_context(
@@ -52,11 +53,23 @@ if __name__ == '__main__':
         save_graphs=False,
         device_target=args.device_target)
 
+    if args.device_target == 'Ascend':
+        cfg = lstm_cfg_ascend
+    else:
+        cfg = lstm_cfg
+
     if args.preprocess == "true":
         print("============== Starting Data Pre-processing ==============")
         convert_to_mindrecord(cfg.embed_size, args.aclimdb_path, args.preprocess_path, args.glove_path)
 
     embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32)
+    # DynamicRNN in this network on Ascend platform only support the condition that the shape of input_size
+    # and hiddle_size is multiples of 16, this problem will be solved later.
+    if args.device_target == 'Ascend':
+        pad_num = int(np.ceil(cfg.embed_size / 16) * 16 - cfg.embed_size)
+        if pad_num > 0:
+            embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant')
+        cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16)
     network = SentimentNet(vocab_size=embedding_table.shape[0],
                            embed_size=cfg.embed_size,
                            num_hiddens=cfg.num_hiddens,
@@ -69,14 +82,25 @@ if __name__ == '__main__':
     if args.pre_trained:
         load_param_into_net(network, load_checkpoint(args.pre_trained))
 
+    ds_train = lstm_create_dataset(args.preprocess_path, cfg.batch_size, 1)
+
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-    opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
+    if cfg.dynamic_lr:
+        lr = Tensor(get_lr(global_step=cfg.global_step,
+                           lr_init=cfg.lr_init, lr_end=cfg.lr_end, lr_max=cfg.lr_max,
+                           warmup_epochs=cfg.warmup_epochs,
+                           total_epochs=cfg.num_epochs,
+                           steps_per_epoch=ds_train.get_dataset_size(),
+                           lr_adjust_epoch=cfg.lr_adjust_epoch))
+    else:
+        lr = cfg.learning_rate
+
+    opt = nn.Momentum(network.trainable_params(), lr, cfg.momentum)
     loss_cb = LossMonitor()
 
     model = Model(network, loss, opt, {'acc': Accuracy()})
 
     print("============== Starting Training ==============")
-    ds_train = lstm_create_dataset(args.preprocess_path, cfg.batch_size, 1)
     config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                                  keep_checkpoint_max=cfg.keep_checkpoint_max)
     ckpoint_cb = ModelCheckpoint(prefix="lstm", directory=args.ckpt_path, config=config_ck)