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- # Copyright 2021 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.
- # ============================================================================
- """
- Image classifiation.
- """
- import math
- import mindspore.nn as nn
- import mindspore.common.dtype as mstype
- from mindspore.common import initializer as init
- from mindspore.common.initializer import initializer
- from src.utils.var_init import KaimingNormal
-
-
- def _make_layer(base, args, batch_norm):
- """Make stage network of VGG."""
- layers = []
- in_channels = 3
- for v in base:
- if v == 'M':
- layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
- else:
- weight = 'ones'
- if args.initialize_mode == "XavierUniform":
- weight_shape = (v, in_channels, 3, 3)
- weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32)
-
- conv2d = nn.Conv2d(in_channels=in_channels,
- out_channels=v,
- kernel_size=3,
- padding=args.padding,
- pad_mode=args.pad_mode,
- has_bias=args.has_bias,
- weight_init=weight)
- if batch_norm:
- layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
- else:
- layers += [conv2d, nn.ReLU()]
- in_channels = v
- return nn.SequentialCell(layers)
-
-
- class Vgg(nn.Cell):
- """
- VGG network definition.
-
- Args:
- base (list): Configuration for different layers, mainly the channel number of Conv layer.
- num_classes (int): Class numbers. Default: 1000.
- batch_norm (bool): Whether to do the batchnorm. Default: False.
- batch_size (int): Batch size. Default: 1.
- include_top(bool): Whether to include the 3 fully-connected layers at the top of the network. Default: True.
-
- Returns:
- Tensor, infer output tensor.
-
- Examples:
- >>> Vgg([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
- >>> num_classes=1000, batch_norm=False, batch_size=1)
- """
-
- def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1, args=None, include_top=True):
- super(Vgg, self).__init__()
- _ = batch_size
- self.layers = _make_layer(base, args, batch_norm=batch_norm)
- self.include_top = include_top
- self.flatten = nn.Flatten()
- dropout_ratio = 0.5
- self.classifier = nn.SequentialCell([
- nn.Dense(512 * 7 * 7, 4096),
- nn.ReLU(),
- nn.Dropout(dropout_ratio),
- nn.Dense(4096, 4096),
- nn.ReLU(),
- nn.Dropout(dropout_ratio),
- nn.Dense(4096, num_classes)])
-
- def construct(self, x):
- """construct"""
- x = self.layers(x)
- if self.include_top:
- x = self.flatten(x)
- x = self.classifier(x)
- return x
-
- def custom_init_weight(self):
- """
- Init the weight of Conv2d and Dense in the net.
- """
- for _, cell in self.cells_and_names():
- if isinstance(cell, nn.Conv2d):
- cell.weight.set_data(init.initializer(
- KaimingNormal(a=math.sqrt(5), mode='fan_out', nonlinearity='relu'),
- cell.weight.shape, cell.weight.dtype))
- if cell.bias is not None:
- cell.bias.set_data(init.initializer(
- 'zeros', cell.bias.shape, cell.bias.dtype))
- elif isinstance(cell, nn.Dense):
- cell.weight.set_data(init.initializer(
- init.Normal(0.01), cell.weight.shape, cell.weight.dtype))
- if cell.bias is not None:
- cell.bias.set_data(init.initializer(
- 'zeros', cell.bias.shape, cell.bias.dtype))
-
-
- cfg = {
- '11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
- '13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
- '16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
- '19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
- }
-
-
- def vgg16(num_classes=1000, args=None, **kwargs):
- """
- Get Vgg16 neural network with batch normalization.
-
- Args:
- num_classes (int): Class numbers. Default: 1000.
- args(namespace): param for net init.
- phase(str): train or test mode.
-
- Returns:
- Cell, cell instance of Vgg16 neural network with batch normalization.
-
- Examples:
- >>> vgg16(num_classes=1000, args=args, **kwargs)
- """
-
- if args is None:
- from src.config import cifar_cfg
- args = cifar_cfg
- net = Vgg(cfg['16'], num_classes=num_classes, args=args, batch_norm=args.batch_norm, **kwargs)
- return net
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