From 27c3b6d5b37eee0933f8474aceaf8cf029165596 Mon Sep 17 00:00:00 2001 From: chenzomi Date: Fri, 19 Jun 2020 20:33:31 +0800 Subject: [PATCH] add auto create graph for aware quantization training demo --- model_zoo/lenet_quant/README.md | 61 ++++++++++++++++++++ model_zoo/lenet_quant/eval.py | 64 +++++++++++++++++++++ model_zoo/lenet_quant/eval_quant.py | 69 ++++++++++++++++++++++ model_zoo/lenet_quant/src/config.py | 31 ++++++++++ model_zoo/lenet_quant/src/dataset.py | 60 +++++++++++++++++++ model_zoo/lenet_quant/src/lenet.py | 60 +++++++++++++++++++ model_zoo/lenet_quant/src/lenet_fusion.py | 57 ++++++++++++++++++ model_zoo/lenet_quant/train.py | 61 ++++++++++++++++++++ model_zoo/lenet_quant/train_quant.py | 70 +++++++++++++++++++++++ 9 files changed, 533 insertions(+) create mode 100644 model_zoo/lenet_quant/README.md create mode 100644 model_zoo/lenet_quant/eval.py create mode 100644 model_zoo/lenet_quant/eval_quant.py create mode 100644 model_zoo/lenet_quant/src/config.py create mode 100644 model_zoo/lenet_quant/src/dataset.py create mode 100644 model_zoo/lenet_quant/src/lenet.py create mode 100644 model_zoo/lenet_quant/src/lenet_fusion.py create mode 100644 model_zoo/lenet_quant/train.py create mode 100644 model_zoo/lenet_quant/train_quant.py diff --git a/model_zoo/lenet_quant/README.md b/model_zoo/lenet_quant/README.md new file mode 100644 index 000000000..dc2f17346 --- /dev/null +++ b/model_zoo/lenet_quant/README.md @@ -0,0 +1,61 @@ +# LeNet Quantization Example + +## Description + +Training LeNet with MNIST dataset in MindSpore with aware quantization trainging. + +This is the simple and basic tutorial for constructing a network in MindSpore with quantization. + +## Requirements + +- Install [MindSpore](https://www.mindspore.cn/install/en). + +- Download the MNIST dataset, the directory structure is as follows: + +``` +└─MNIST_Data + ├─test + │ t10k-images.idx3-ubyte + │ t10k-labels.idx1-ubyte + └─train + train-images.idx3-ubyte + train-labels.idx1-ubyte +``` + +## Running the example + +```python +# train LeNet, hyperparameter setting in config.py +python train.py --data_path MNIST_Data +``` + +You will get the loss value of each step as following: + +```bash +Epoch: [ 1/ 10] step: [ 1 / 900], loss: [2.3040/2.5234], time: [1.300234] +... +Epoch: [ 10/ 10] step: [887 / 900], loss: [0.0113/0.0223], time: [1.300234] +Epoch: [ 10/ 10] step: [888 / 900], loss: [0.0334/0.0223], time: [1.300234] +Epoch: [ 10/ 10] step: [889 / 900], loss: [0.0233/0.0223], time: [1.300234] +... +``` + +Then, evaluate LeNet according to network model + +```python +python eval.py --data_path MNIST_Data --ckpt_path checkpoint_lenet-1_1875.ckpt +``` + +## Note +Here are some optional parameters: + +```bash +--device_target {Ascend,GPU,CPU} + device where the code will be implemented (default: Ascend) +--data_path DATA_PATH + path where the dataset is saved +--dataset_sink_mode DATASET_SINK_MODE + dataset_sink_mode is False or True +``` + +You can run ```python train.py -h``` or ```python eval.py -h``` to get more information. diff --git a/model_zoo/lenet_quant/eval.py b/model_zoo/lenet_quant/eval.py new file mode 100644 index 000000000..c1e3a5fd8 --- /dev/null +++ b/model_zoo/lenet_quant/eval.py @@ -0,0 +1,64 @@ +# 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. +# ============================================================================ +""" +######################## eval lenet example ######################## +eval lenet according to model file: +python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt +""" + +import os +import argparse +import mindspore.nn as nn +from mindspore import context +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy +from src.dataset import create_dataset +from src.config import mnist_cfg as cfg +from src.lenet_fusion import LeNet5 as LeNet5Fusion + +parser = argparse.ArgumentParser(description='MindSpore MNIST Example') +parser.add_argument('--device_target', type=str, default="Ascend", + choices=['Ascend', 'GPU', 'CPU'], + help='device where the code will be implemented (default: Ascend)') +parser.add_argument('--data_path', type=str, default="./MNIST_Data", + help='path where the dataset is saved') +parser.add_argument('--ckpt_path', type=str, default="", + help='if mode is test, must provide path where the trained ckpt file') +parser.add_argument('--dataset_sink_mode', type=bool, default=True, + help='dataset_sink_mode is False or True') +args = parser.parse_args() + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) + ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1) + step_size = ds_eval.get_dataset_size() + + network = LeNet5Fusion(cfg.num_classes) + net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + repeat_size = cfg.epoch_size + net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) + config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) + + param_dict = load_checkpoint(args.ckpt_path) + load_param_into_net(network, param_dict) + + print("============== Starting Testing ==============") + acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode) + print("============== {} ==============".format(acc)) diff --git a/model_zoo/lenet_quant/eval_quant.py b/model_zoo/lenet_quant/eval_quant.py new file mode 100644 index 000000000..0ff943f8c --- /dev/null +++ b/model_zoo/lenet_quant/eval_quant.py @@ -0,0 +1,69 @@ +# 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. +# ============================================================================ +""" +######################## eval lenet example ######################## +eval lenet according to model file: +python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt +""" + +import os +import argparse +import mindspore.nn as nn +from mindspore import context +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy +from mindspore.train.quant import quant +from src.dataset import create_dataset +from src.config import mnist_cfg as cfg +from src.lenet_fusion import LeNet5 as LeNet5Fusion + +parser = argparse.ArgumentParser(description='MindSpore MNIST Example') +parser.add_argument('--device_target', type=str, default="Ascend", + choices=['Ascend', 'GPU', 'CPU'], + help='device where the code will be implemented (default: Ascend)') +parser.add_argument('--data_path', type=str, default="./MNIST_Data", + help='path where the dataset is saved') +parser.add_argument('--ckpt_path', type=str, default="", + help='if mode is test, must provide path where the trained ckpt file') +parser.add_argument('--dataset_sink_mode', type=bool, default=True, + help='dataset_sink_mode is False or True') +args = parser.parse_args() + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) + ds_eval = create_dataset(os.path.join(args.data_path, "test"), cfg.batch_size, 1) + step_size = ds_eval.get_dataset_size() + + # define funsion network + network = LeNet5Fusion(cfg.num_classes) + # convert funsion netwrok to aware quantizaiton network + network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000) + + net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) + config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) + + # load aware quantizaiton network checkpoint + param_dict = load_checkpoint(args.ckpt_path) + load_param_into_net(network, param_dict) + + print("============== Starting Testing ==============") + acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode) + print("============== {} ==============".format(acc)) diff --git a/model_zoo/lenet_quant/src/config.py b/model_zoo/lenet_quant/src/config.py new file mode 100644 index 000000000..ab4b2e408 --- /dev/null +++ b/model_zoo/lenet_quant/src/config.py @@ -0,0 +1,31 @@ +# 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 +""" + +from easydict import EasyDict as edict + +mnist_cfg = edict({ + 'num_classes': 10, + 'lr': 0.01, + 'momentum': 0.9, + 'epoch_size': 10, + 'batch_size': 64, + 'buffer_size': 1000, + 'image_height': 32, + 'image_width': 32, + 'keep_checkpoint_max': 10, +}) diff --git a/model_zoo/lenet_quant/src/dataset.py b/model_zoo/lenet_quant/src/dataset.py new file mode 100644 index 000000000..cef697348 --- /dev/null +++ b/model_zoo/lenet_quant/src/dataset.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. +# ============================================================================ +""" +Produce the dataset +""" + +import mindspore.dataset as ds +import mindspore.dataset.transforms.vision.c_transforms as CV +import mindspore.dataset.transforms.c_transforms as C +from mindspore.dataset.transforms.vision import Inter +from mindspore.common import dtype as mstype + + +def create_dataset(data_path, batch_size=32, repeat_size=1, + num_parallel_workers=1): + """ + create dataset for train or test + """ + # define dataset + mnist_ds = ds.MnistDataset(data_path) + + resize_height, resize_width = 32, 32 + rescale = 1.0 / 255.0 + shift = 0.0 + rescale_nml = 1 / 0.3081 + shift_nml = -1 * 0.1307 / 0.3081 + + # define map operations + resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) # Bilinear mode + rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) + rescale_op = CV.Rescale(rescale, shift) + hwc2chw_op = CV.HWC2CHW() + type_cast_op = C.TypeCast(mstype.int32) + + # apply map operations on images + mnist_ds = mnist_ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=resize_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=rescale_nml_op, num_parallel_workers=num_parallel_workers) + mnist_ds = mnist_ds.map(input_columns="image", operations=hwc2chw_op, num_parallel_workers=num_parallel_workers) + + # apply DatasetOps + buffer_size = 10000 + mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) # 10000 as in LeNet train script + mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) + mnist_ds = mnist_ds.repeat(repeat_size) + + return mnist_ds diff --git a/model_zoo/lenet_quant/src/lenet.py b/model_zoo/lenet_quant/src/lenet.py new file mode 100644 index 000000000..026f1e8df --- /dev/null +++ b/model_zoo/lenet_quant/src/lenet.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. +# ============================================================================ +"""LeNet.""" +import mindspore.nn as nn + + +class LeNet5(nn.Cell): + """ + Lenet network + + Args: + num_class (int): Num classes. Default: 10. + + Returns: + Tensor, output tensor + Examples: + >>> LeNet(num_class=10) + + """ + + def __init__(self, num_class=10, channel=1): + super(LeNet5, self).__init__() + self.num_class = num_class + + self.conv1 = nn.Conv2d(channel, 6, 5) + self.conv2 = nn.Conv2d(6, 16, 5) + self.fc1 = nn.Dense(16 * 5 * 5, 120) + self.fc2 = nn.Dense(120, 84) + self.fc3 = nn.Dense(84, self.num_class) + + self.relu = nn.ReLU() + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.conv1(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.relu(x) + x = self.max_pool2d(x) + x = self.flatten(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.relu(x) + x = self.fc3(x) + return x diff --git a/model_zoo/lenet_quant/src/lenet_fusion.py b/model_zoo/lenet_quant/src/lenet_fusion.py new file mode 100644 index 000000000..809276a48 --- /dev/null +++ b/model_zoo/lenet_quant/src/lenet_fusion.py @@ -0,0 +1,57 @@ +# 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. +# ============================================================================ +"""LeNet.""" +import mindspore.nn as nn + + +class LeNet5(nn.Cell): + """ + Lenet network + + Args: + num_class (int): Num classes. Default: 10. + + Returns: + Tensor, output tensor + Examples: + >>> LeNet(num_class=10) + + """ + + def __init__(self, num_class=10, channel=1): + super(LeNet5, self).__init__() + self.num_class = num_class + + # change `nn.Conv2d` to `nn.Conv2dBnAct` + self.conv1 = nn.Conv2dBnAct(channel, 6, 5, activation='relu') + self.conv2 = nn.Conv2dBnAct(6, 16, 5, activation='relu') + # change `nn.Dense` to `nn.DenseBnAct` + self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu') + self.fc2 = nn.DenseBnAct(120, 84, activation='relu') + self.fc3 = nn.DenseBnAct(84, self.num_class) + + self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) + self.flatten = nn.Flatten() + + def construct(self, x): + x = self.conv1(x) + x = self.max_pool2d(x) + x = self.conv2(x) + x = self.max_pool2d(x) + x = self.flatten(x) + x = self.fc1(x) + x = self.fc2(x) + x = self.fc3(x) + return x diff --git a/model_zoo/lenet_quant/train.py b/model_zoo/lenet_quant/train.py new file mode 100644 index 000000000..6e7a46fb3 --- /dev/null +++ b/model_zoo/lenet_quant/train.py @@ -0,0 +1,61 @@ +# 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. +# ============================================================================ +""" +######################## train lenet example ######################## +train lenet and get network model files(.ckpt) : +python train.py --data_path /YourDataPath +""" + +import os +import argparse +import mindspore.nn as nn +from mindspore import context +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy +from src.dataset import create_dataset +from src.config import mnist_cfg as cfg +from src.lenet_fusion import LeNet5 as LeNet5Fusion + +parser = argparse.ArgumentParser(description='MindSpore MNIST Example') +parser.add_argument('--device_target', type=str, default="Ascend", + choices=['Ascend', 'GPU', 'CPU'], + help='device where the code will be implemented (default: Ascend)') +parser.add_argument('--data_path', type=str, default="./MNIST_Data", + help='path where the dataset is saved') +parser.add_argument('--ckpt_path', type=str, default="", + help='if mode is test, must provide path where the trained ckpt file') +parser.add_argument('--dataset_sink_mode', type=bool, default=True, + help='dataset_sink_mode is False or True') +args = parser.parse_args() + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) + ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, cfg.epoch_size) + step_size = ds_train.get_dataset_size() + + network = LeNet5Fusion(cfg.num_classes) + net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) + time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) + config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) + + print("============== Starting Training ==============") + model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()], + dataset_sink_mode=args.dataset_sink_mode) + print("============== End Training ==============") diff --git a/model_zoo/lenet_quant/train_quant.py b/model_zoo/lenet_quant/train_quant.py new file mode 100644 index 000000000..3de700af7 --- /dev/null +++ b/model_zoo/lenet_quant/train_quant.py @@ -0,0 +1,70 @@ +# 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. +# ============================================================================ +""" +######################## train lenet example ######################## +train lenet and get network model files(.ckpt) : +python train.py --data_path /YourDataPath +""" + +import os +import argparse +import mindspore.nn as nn +from mindspore import context +from mindspore.train.serialization import load_checkpoint, load_param_into_net +from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor +from mindspore.train import Model +from mindspore.nn.metrics import Accuracy +from mindspore.train.quant import quant +from src.dataset import create_dataset +from src.config import mnist_cfg as cfg +from src.lenet_fusion import LeNet5 as LeNet5Fusion + +parser = argparse.ArgumentParser(description='MindSpore MNIST Example') +parser.add_argument('--device_target', type=str, default="Ascend", + choices=['Ascend', 'GPU', 'CPU'], + help='device where the code will be implemented (default: Ascend)') +parser.add_argument('--data_path', type=str, default="./MNIST_Data", + help='path where the dataset is saved') +parser.add_argument('--ckpt_path', type=str, default="", + help='if mode is test, must provide path where the trained ckpt file') +parser.add_argument('--dataset_sink_mode', type=bool, default=True, + help='dataset_sink_mode is False or True') +args = parser.parse_args() + +if __name__ == "__main__": + context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) + ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, cfg.epoch_size) + step_size = ds_train.get_dataset_size() + + # define funsion network + network = LeNet5Fusion(cfg.num_classes) + # load aware quantizaiton network checkpoint + param_dict = load_checkpoint(args.ckpt_path) + load_param_into_net(network, param_dict) + # convert funsion netwrok to aware quantizaiton network + network = quant.convert_quant_network(network, quant_delay=0, bn_fold=False, freeze_bn=10000) + + net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean") + net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum) + time_cb = TimeMonitor(data_size=ds_train.get_dataset_size()) + config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size, + keep_checkpoint_max=cfg.keep_checkpoint_max) + ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck) + model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) + + print("============== Starting Training ==============") + model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()], + dataset_sink_mode=args.dataset_sink_mode) + print("============== End Training ==============") -- GitLab