!1622 change mobilenet file struct.

Merge pull request !1622 from SanjayChan/r0.3

mindspore/model_zoo/mobilenetv2/Readme.md → example/mobilenetv2_imagenet/Readme.md

@@ -13,7 +13,7 @@ The overall network architecture of MobileNetV2 is show below:
 
 # Dataset
 
-Dataset used: [imagenet](http://www.image-net.org/)
+Dataset used: imagenet
 
 - Dataset size: ~125G, 1.2W colorful images in 1000 classes
 	- Train: 120G, 1.2W images
@@ -60,8 +60,8 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ### Usage
 
-- 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]
-- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]
+- 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]
+- GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]
 
 ### Launch
 

mindspore/model_zoo/mobilenetv2/scripts/run_train.sh → example/mobilenetv2_imagenet/scripts/run_train.sh

@@ -42,6 +42,7 @@ run_ascend()
             --server_id=$3 \
             --training_script=${BASEPATH}/../train.py \
             --dataset_path=$5 \
+            --pre_trained=$6 \
             --platform=$1 &> ../train.log &  # dataset train folder
 }
 
@@ -73,14 +74,15 @@ run_gpu()
     python ${BASEPATH}/../train.py \
         --dataset_path=$4 \
         --platform=$1 \
+        --pre_trained=$5 \
         &> ../train.log &  # dataset train folder
 }
 
-if [ $# -gt 5 ] || [ $# -lt 4 ]
+if [ $# -gt 6 ] || [ $# -lt 4 ]
 then
     echo "Usage:\n \
-          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 \
-          GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
+          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 \
+          GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
           "
 exit 1
 fi

mindspore/model_zoo/mobilenetv2/train.py → example/mobilenetv2_imagenet/train.py

@@ -33,11 +33,11 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.communication.management import init, get_group_size
+from mindspore.model_zoo.mobilenetV2 import mobilenet_v2
 import mindspore.dataset.engine as de
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
 from src.config import config_gpu, config_ascend
-from src.mobilenetV2 import mobilenet_v2
 
 random.seed(1)
 np.random.seed(1)

mindspore/model_zoo/mobilenetv3/Readme.md → example/mobilenetv3_imagenet/Readme.md

@@ -13,7 +13,7 @@ The overall network architecture of MobileNetV3 is show below:
 
 # Dataset
 
-Dataset used: [imagenet](http://www.image-net.org/)
+Dataset used: imagenet
 
 - Dataset size: ~125G, 1.2W colorful images in 1000 classes
 	- Train: 120G, 1.2W images
@@ -67,8 +67,8 @@ Dataset used: [imagenet](http://www.image-net.org/)
 
 ``` 
 # training example
-  Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/
-  GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
+  Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/ mobilenet_199.ckpt
+  GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/ mobilenet_199.ckpt
 ```
 
 ### Result

mindspore/model_zoo/mobilenetv3/scripts/run_train.sh → example/mobilenetv3_imagenet/scripts/run_train.sh

@@ -41,6 +41,7 @@ run_ascend()
             --server_id=$3 \
             --training_script=${BASEPATH}/../train.py \
             --dataset_path=$5 \
+            --pre_trained=$6 \
             --platform=$1 &> ../train.log &  # dataset train folder
 }
 
@@ -72,14 +73,15 @@ run_gpu()
     python ${BASEPATH}/../train.py \
         --dataset_path=$4 \
         --platform=$1 \
+        --pre_trained=$5 \
         &> ../train.log &  # dataset train folder
 }
 
-if [ $# -gt 5 ] || [ $# -lt 4 ]
+if [ $# -gt 6 ] || [ $# -lt 4 ]
 then
     echo "Usage:\n \
-          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 \
-          GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH]\n \
+          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 \
+          GPU: sh run_train.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH]\n \
           "
 exit 1
 fi

mindspore/model_zoo/mobilenetv3/train.py → example/mobilenetv3_imagenet/train.py

@@ -34,10 +34,10 @@ from mindspore.train.loss_scale_manager import FixedLossScaleManager
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 import mindspore.dataset.engine as de
 from mindspore.communication.management import init, get_group_size
+from mindspore.model_zoo.mobilenetV3 import mobilenet_v3_large
 from src.dataset import create_dataset
 from src.lr_generator import get_lr
 from src.config import config_gpu, config_ascend
-from src.mobilenetV3 import mobilenet_v3_large
 
 random.seed(1)
 np.random.seed(1)

mindspore/model_zoo/mobilenet.py

-# 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.
-# ============================================================================
-"""MobileNetV2 model define"""
-import numpy as np
-import mindspore.nn as nn
-from mindspore.ops import operations as P
-from mindspore.ops.operations import TensorAdd
-from mindspore import Parameter, Tensor
-from mindspore.common.initializer import initializer
-
-__all__ = ['MobileNetV2', 'mobilenet_v2']
-
-
-def _make_divisible(v, divisor, min_value=None):
-    """
-    This function is taken from the original tf repo.
-    It ensures that all layers have a channel number that is divisible by 8
-    It can be seen here:
-    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
-    :param v:
-    :param divisor:
-    :param min_value:
-    :return:
-    """
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-
-
-class GlobalAvgPooling(nn.Cell):
-    """
-    Global avg pooling definition.
-
-    Args:
-
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> GlobalAvgPooling()
-    """
-    def __init__(self):
-        super(GlobalAvgPooling, self).__init__()
-        self.mean = P.ReduceMean(keep_dims=False)
-
-    def construct(self, x):
-        x = self.mean(x, (2, 3))
-        return x
-
-
-class DepthwiseConv(nn.Cell):
-    """
-    Depthwise Convolution warpper definition.
-
-    Args:
-        in_planes (int): Input channel.
-        kernel_size (int): Input kernel size.
-        stride (int): Stride size.
-        pad_mode (str): pad mode in (pad, same, valid)
-        channel_multiplier (int): Output channel multiplier
-        has_bias (bool): has bias or not
-
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> DepthwiseConv(16, 3, 1, 'pad', 1, channel_multiplier=1)
-    """
-    def __init__(self, in_planes, kernel_size, stride, pad_mode, pad, channel_multiplier=1, has_bias=False):
-        super(DepthwiseConv, self).__init__()
-        self.has_bias = has_bias
-        self.in_channels = in_planes
-        self.channel_multiplier = channel_multiplier
-        self.out_channels = in_planes * channel_multiplier
-        self.kernel_size = (kernel_size, kernel_size)
-        self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=channel_multiplier,
-                                                      kernel_size=self.kernel_size,
-                                                      stride=stride, pad_mode=pad_mode, pad=pad)
-        self.bias_add = P.BiasAdd()
-        weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
-        self.weight = Parameter(initializer('ones', weight_shape), name='weight')
-
-        if has_bias:
-            bias_shape = [channel_multiplier * in_planes]
-            self.bias = Parameter(initializer('zeros', bias_shape), name='bias')
-        else:
-            self.bias = None
-
-    def construct(self, x):
-        output = self.depthwise_conv(x, self.weight)
-        if self.has_bias:
-            output = self.bias_add(output, self.bias)
-        return output
-
-
-class ConvBNReLU(nn.Cell):
-    """
-    Convolution/Depthwise fused with Batchnorm and ReLU block definition.
-
-    Args:
-        in_planes (int): Input channel.
-        out_planes (int): Output channel.
-        kernel_size (int): Input kernel size.
-        stride (int): Stride size for the first convolutional layer. Default: 1.
-        groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.
-
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)
-    """
-    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):
-        super(ConvBNReLU, self).__init__()
-        padding = (kernel_size - 1) // 2
-        if groups == 1:
-            conv = nn.Conv2d(in_planes, out_planes, kernel_size, stride, pad_mode='pad',
-                             padding=padding)
-        else:
-            conv = DepthwiseConv(in_planes, kernel_size, stride, pad_mode='pad', pad=padding)
-        layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]
-        self.features = nn.SequentialCell(layers)
-
-    def construct(self, x):
-        output = self.features(x)
-        return output
-
-
-class InvertedResidual(nn.Cell):
-    """
-    Mobilenetv2 residual block definition.
-
-    Args:
-        inp (int): Input channel.
-        oup (int): Output channel.
-        stride (int): Stride size for the first convolutional layer. Default: 1.
-        expand_ratio (int): expand ration of input channel
-
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> ResidualBlock(3, 256, 1, 1)
-    """
-    def __init__(self, inp, oup, stride, expand_ratio):
-        super(InvertedResidual, self).__init__()
-        assert stride in [1, 2]
-
-        hidden_dim = int(round(inp * expand_ratio))
-        self.use_res_connect = stride == 1 and inp == oup
-
-        layers = []
-        if expand_ratio != 1:
-            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
-        layers.extend([
-            # dw
-            ConvBNReLU(hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
-            # pw-linear
-            nn.Conv2d(hidden_dim, oup, kernel_size=1, stride=1, has_bias=False),
-            nn.BatchNorm2d(oup),
-        ])
-        self.conv = nn.SequentialCell(layers)
-        self.add = TensorAdd()
-        self.cast = P.Cast()
-
-    def construct(self, x):
-        identity = x
-        x = self.conv(x)
-        if self.use_res_connect:
-            return self.add(identity, x)
-        return x
-
-
-class MobileNetV2(nn.Cell):
-    """
-    MobileNetV2 architecture.
-
-    Args:
-        class_num (Cell): number of classes.
-        width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.
-        has_dropout (bool): Is dropout used. Default is false
-        inverted_residual_setting (list): Inverted residual settings. Default is None
-        round_nearest (list): Channel round to . Default is 8
-    Returns:
-        Tensor, output tensor.
-
-    Examples:
-        >>> MobileNetV2(num_classes=1000)
-    """
-    def __init__(self, num_classes=1000, width_mult=1.,
-                 has_dropout=False, inverted_residual_setting=None, round_nearest=8):
-        super(MobileNetV2, self).__init__()
-        block = InvertedResidual
-        input_channel = 32
-        last_channel = 1280
-        # setting of inverted residual blocks
-        self.cfgs = inverted_residual_setting
-        if inverted_residual_setting is None:
-            self.cfgs = [
-                # t, c, n, s
-                [1, 16, 1, 1],
-                [6, 24, 2, 2],
-                [6, 32, 3, 2],
-                [6, 64, 4, 2],
-                [6, 96, 3, 1],
-                [6, 160, 3, 2],
-                [6, 320, 1, 1],
-            ]
-
-        # building first layer
-        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
-        self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
-        features = [ConvBNReLU(3, input_channel, stride=2)]
-        # building inverted residual blocks
-        for t, c, n, s in self.cfgs:
-            output_channel = _make_divisible(c * width_mult, round_nearest)
-            for i in range(n):
-                stride = s if i == 0 else 1
-                features.append(block(input_channel, output_channel, stride, expand_ratio=t))
-                input_channel = output_channel
-        # building last several layers
-        features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))
-        # make it nn.CellList
-        self.features = nn.SequentialCell(features)
-        # mobilenet head
-        head = ([GlobalAvgPooling(), nn.Dense(self.out_channels, num_classes, has_bias=True)] if not has_dropout else
-                [GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(self.out_channels, num_classes, has_bias=True)])
-        self.head = nn.SequentialCell(head)
-
-        self._initialize_weights()
-
-    def construct(self, x):
-        x = self.features(x)
-        x = self.head(x)
-        return x
-
-    def _initialize_weights(self):
-        """
-        Initialize weights.
-
-        Args:
-
-        Returns:
-            None.
-
-        Examples:
-            >>> _initialize_weights()
-        """
-        for _, m in self.cells_and_names():
-            if isinstance(m, (nn.Conv2d, DepthwiseConv)):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-                m.weight.set_parameter_data(Tensor(np.random.normal(0, np.sqrt(2. / n),
-                                                                    m.weight.data.shape()).astype("float32")))
-                if m.bias is not None:
-                    m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
-            elif isinstance(m, nn.BatchNorm2d):
-                m.gamma.set_parameter_data(Tensor(np.ones(m.gamma.data.shape(), dtype="float32")))
-                m.beta.set_parameter_data(Tensor(np.zeros(m.beta.data.shape(), dtype="float32")))
-            elif isinstance(m, nn.Dense):
-                m.weight.set_parameter_data(Tensor(np.random.normal(0, 0.01, m.weight.data.shape()).astype("float32")))
-                if m.bias is not None:
-                    m.bias.set_parameter_data(Tensor(np.zeros(m.bias.data.shape(), dtype="float32")))
-
-
-def mobilenet_v2(**kwargs):
-    """
-    Constructs a MobileNet V2 model
-    """
-    return MobileNetV2(**kwargs)