forked from mindspore-Ecosystem/mindspore
!1622 change mobilenet file struct.
Merge pull request !1622 from SanjayChan/r0.3
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@ -13,7 +13,7 @@ The overall network architecture of MobileNetV2 is show below:
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# Dataset
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Dataset used: [imagenet](http://www.image-net.org/)
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Dataset used: imagenet
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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@ -60,8 +60,8 @@ Dataset used: [imagenet](http://www.image-net.org/)
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### Usage
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- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
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- Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]
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- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]
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### Launch
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@ -42,6 +42,7 @@ run_ascend()
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--server_id=$3 \
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--training_script=${BASEPATH}/../train.py \
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--dataset_path=$5 \
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--pre_trained=$6 \
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--platform=$1 &> ../train.log & # dataset train folder
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}
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@ -73,14 +74,15 @@ run_gpu()
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python ${BASEPATH}/../train.py \
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--dataset_path=$4 \
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--platform=$1 \
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--pre_trained=$5 \
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&> ../train.log & # dataset train folder
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}
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if [ $# -gt 5 ] || [ $# -lt 4 ]
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if [ $# -gt 6 ] || [ $# -lt 4 ]
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then
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echo "Usage:\n \
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Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
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GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
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Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
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GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
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"
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exit 1
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fi
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@ -33,11 +33,11 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.communication.management import init, get_group_size
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from mindspore.model_zoo.mobilenetV2 import mobilenet_v2
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import mindspore.dataset.engine as de
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from src.dataset import create_dataset
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from src.lr_generator import get_lr
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from src.config import config_gpu, config_ascend
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from src.mobilenetV2 import mobilenet_v2
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random.seed(1)
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np.random.seed(1)
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@ -13,7 +13,7 @@ The overall network architecture of MobileNetV3 is show below:
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# Dataset
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Dataset used: [imagenet](http://www.image-net.org/)
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Dataset used: imagenet
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- Dataset size: ~125G, 1.2W colorful images in 1000 classes
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- Train: 120G, 1.2W images
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@ -67,8 +67,8 @@ Dataset used: [imagenet](http://www.image-net.org/)
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```
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# training example
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Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
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GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
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Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/ mobilenet_199.ckpt
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GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/ mobilenet_199.ckpt
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```
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### Result
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@ -41,6 +41,7 @@ run_ascend()
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--server_id=$3 \
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--training_script=${BASEPATH}/../train.py \
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--dataset_path=$5 \
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--pre_trained=$6 \
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--platform=$1 &> ../train.log & # dataset train folder
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}
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@ -72,14 +73,15 @@ run_gpu()
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python ${BASEPATH}/../train.py \
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--dataset_path=$4 \
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--platform=$1 \
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--pre_trained=$5 \
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&> ../train.log & # dataset train folder
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}
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if [ $# -gt 5 ] || [ $# -lt 4 ]
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if [ $# -gt 6 ] || [ $# -lt 4 ]
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then
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echo "Usage:\n \
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Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
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GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
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Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
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GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
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"
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exit 1
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fi
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@ -34,10 +34,10 @@ from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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import mindspore.dataset.engine as de
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from mindspore.communication.management import init, get_group_size
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from mindspore.model_zoo.mobilenetV3 import mobilenet_v3_large
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from src.dataset import create_dataset
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from src.lr_generator import get_lr
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from src.config import config_gpu, config_ascend
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from src.mobilenetV3 import mobilenet_v3_large
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random.seed(1)
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np.random.seed(1)
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@ -1,285 +0,0 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""MobileNetV2 model define"""
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import numpy as np
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore.ops.operations import TensorAdd
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from mindspore import Parameter, Tensor
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from mindspore.common.initializer import initializer
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__all__ = ['MobileNetV2', 'mobilenet_v2']
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def _make_divisible(v, divisor, min_value=None):
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"""
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This function is taken from the original tf repo.
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It ensures that all layers have a channel number that is divisible by 8
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It can be seen here:
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https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
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:param v:
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:param divisor:
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:param min_value:
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:return:
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"""
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class GlobalAvgPooling(nn.Cell):
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"""
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Global avg pooling definition.
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Args:
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Returns:
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Tensor, output tensor.
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Examples:
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>>> GlobalAvgPooling()
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"""
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def __init__(self):
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super(GlobalAvgPooling, self).__init__()
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self.mean = P.ReduceMean(keep_dims=False)
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def construct(self, x):
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x = self.mean(x, (2, 3))
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return x
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class DepthwiseConv(nn.Cell):
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"""
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Depthwise Convolution warpper definition.
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Args:
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in_planes (int): Input channel.
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kernel_size (int): Input kernel size.
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stride (int): Stride size.
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pad_mode (str): pad mode in (pad, same, valid)
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channel_multiplier (int): Output channel multiplier
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has_bias (bool): has bias or not
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Returns:
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Tensor, output tensor.
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Examples:
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>>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
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"""
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def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
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super(DepthwiseConv, self).__init__()
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self.has_bias = has_bias
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self.in_channels = in_planes
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self.channel_multiplier = channel_multiplier
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self.out_channels = in_planes * channel_multiplier
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self.kernel_size = (kernel_size, kernel_size)
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self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
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kernel_size=self.kernel_size,
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stride=stride, pad_mode=pad_mode, pad=pad)
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self.bias_add = P.BiasAdd()
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weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
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self.weight = Parameter(initializer('ones', weight_shape), name='weight')
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if has_bias:
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bias_shape = [channel_multiplier * in_planes]
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self.bias = Parameter(initializer('zeros', bias_shape), name='bias')
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else:
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self.bias = None
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def construct(self, x):
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output = self.depthwise_conv(x, self.weight)
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if self.has_bias:
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output = self.bias_add(output, self.bias)
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return output
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class ConvBNReLU(nn.Cell):
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"""
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Convolution/Depthwise fused with Batchnorm and ReLU block definition.
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Args:
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in_planes (int): Input channel.
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out_planes (int): Output channel.
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kernel_size (int): Input kernel size.
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stride (int): Stride size for the first convolutional layer. Default: 1.
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groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
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Returns:
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Tensor, output tensor.
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Examples:
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>>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
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"""
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def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
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super(ConvBNReLU, self).__init__()
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padding = (kernel_size - 1) // 2
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if groups == 1:
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conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad',
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padding=padding)
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else:
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conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
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layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
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self.features = nn.SequentialCell(layers)
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def construct(self, x):
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output = self.features(x)
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return output
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class InvertedResidual(nn.Cell):
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"""
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Mobilenetv2 residual block definition.
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Args:
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inp (int): Input channel.
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oup (int): Output channel.
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stride (int): Stride size for the first convolutional layer. Default: 1.
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expand_ratio (int): expand ration of input channel
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Returns:
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Tensor, output tensor.
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Examples:
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>>> ResidualBlock(3, 256, 1, 1)
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"""
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def __init__(self, inp, oup, stride, expand_ratio):
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super(InvertedResidual, self).__init__()
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assert stride in [1, 2]
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hidden_dim = int(round(inp * expand_ratio))
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self.use_res_connect = stride == 1 and inp == oup
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layers = []
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if expand_ratio != 1:
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layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
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layers.extend([
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# dw
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ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
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# pw-linear
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nn.Conv2d(hidden_dim, oup, kernel_size=1, stride=1, has_bias=False),
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nn.BatchNorm2d(oup),
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])
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self.conv = nn.SequentialCell(layers)
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self.add = TensorAdd()
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self.cast = P.Cast()
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def construct(self, x):
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identity = x
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x = self.conv(x)
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if self.use_res_connect:
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return self.add(identity, x)
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return x
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class MobileNetV2(nn.Cell):
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"""
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MobileNetV2 architecture.
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Args:
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class_num (Cell): number of classes.
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width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
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has_dropout (bool): Is dropout used. Default is false
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inverted_residual_setting (list): Inverted residual settings. Default is None
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round_nearest (list): Channel round to . Default is 8
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Returns:
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Tensor, output tensor.
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Examples:
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>>> MobileNetV2(num_classes=1000)
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"""
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def __init__(self, num_classes=1000, width_mult=1.,
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has_dropout=False, inverted_residual_setting=None, round_nearest=8):
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super(MobileNetV2, self).__init__()
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block = InvertedResidual
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input_channel = 32
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last_channel = 1280
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# setting of inverted residual blocks
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self.cfgs = inverted_residual_setting
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if inverted_residual_setting is None:
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self.cfgs = [
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# t, c, n, s
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[1, 16, 1, 1],
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[6, 24, 2, 2],
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[6, 32, 3, 2],
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[6, 64, 4, 2],
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[6, 96, 3, 1],
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[6, 160, 3, 2],
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[6, 320, 1, 1],
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]
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# building first layer
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input_channel = _make_divisible(input_channel * width_mult, round_nearest)
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self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
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features = [ConvBNReLU(3, input_channel, stride=2)]
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# building inverted residual blocks
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for t, c, n, s in self.cfgs:
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output_channel = _make_divisible(c * width_mult, round_nearest)
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for i in range(n):
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stride = s if i == 0 else 1
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features.append(block(input_channel, output_channel, stride, expand_ratio=t))
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input_channel = output_channel
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# building last several layers
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features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
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# make it nn.CellList
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self.features = nn.SequentialCell(features)
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# mobilenet head
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head = ([GlobalAvgPooling(), nn.Dense(self.out_channels, num_classes, has_bias=True)] if not has_dropout else
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[GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(self.out_channels, num_classes, has_bias=True)])
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self.head = nn.SequentialCell(head)
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self._initialize_weights()
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def construct(self, x):
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x = self.features(x)
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x = self.head(x)
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return x
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def _initialize_weights(self):
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"""
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Initialize weights.
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Args:
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Returns:
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None.
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Examples:
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>>> _initialize_weights()
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"""
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for _, m in self.cells_and_names():
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if isinstance(m, (nn.Conv2d, DepthwiseConv)):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
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m.weight.data.shape()).astype("float32")))
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if m.bias is not None:
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m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
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elif isinstance(m, nn.BatchNorm2d):
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m.gamma.set_parameter_data(Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
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m.beta.set_parameter_data(Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
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elif isinstance(m, nn.Dense):
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m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape()).astype("float32")))
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if m.bias is not None:
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m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
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def mobilenet_v2(**kwargs):
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"""
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Constructs a MobileNet V2 model
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"""
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return MobileNetV2(**kwargs)
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