forked from mindspore-Ecosystem/mindspore
!2418 add pretrain for lstm & vgg16 and remove lstm/vgg16/googlenet from directory 'mindspore/model_zoo'
Merge pull request !2418 from caojian05/ms_master_dev
This commit is contained in:
Gitee
commit
73f440a54d
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@ -1,143 +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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"""GoogleNet"""
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import mindspore.nn as nn
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from mindspore.common.initializer import TruncatedNormal
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from mindspore.ops import operations as P
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def weight_variable():
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"""Weight variable."""
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return TruncatedNormal(0.02)
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class Conv2dBlock(nn.Cell):
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"""
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Basic convolutional block
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Args:
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in_channles (int): Input channel.
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out_channels (int): Output channel.
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kernel_size (int): Input kernel size. Default: 1
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stride (int): Stride size for the first convolutional layer. Default: 1.
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padding (int): Implicit paddings on both sides of the input. Default: 0.
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pad_mode (str): Padding mode. Optional values are "same", "valid", "pad". Default: "same".
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Returns:
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Tensor, output tensor.
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"""
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def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, pad_mode="same"):
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super(Conv2dBlock, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
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padding=padding, pad_mode=pad_mode, weight_init=weight_variable(),
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bias_init=False)
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self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
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self.relu = nn.ReLU()
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def construct(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.relu(x)
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return x
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class Inception(nn.Cell):
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"""
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Inception Block
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"""
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def __init__(self, in_channels, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
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super(Inception, self).__init__()
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self.b1 = Conv2dBlock(in_channels, n1x1, kernel_size=1)
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self.b2 = nn.SequentialCell([Conv2dBlock(in_channels, n3x3red, kernel_size=1),
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Conv2dBlock(n3x3red, n3x3, kernel_size=3, padding=0)])
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self.b3 = nn.SequentialCell([Conv2dBlock(in_channels, n5x5red, kernel_size=1),
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Conv2dBlock(n5x5red, n5x5, kernel_size=3, padding=0)])
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self.maxpool = P.MaxPoolWithArgmax(ksize=3, strides=1, padding="same")
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self.b4 = Conv2dBlock(in_channels, pool_planes, kernel_size=1)
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self.concat = P.Concat(axis=1)
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def construct(self, x):
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branch1 = self.b1(x)
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branch2 = self.b2(x)
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branch3 = self.b3(x)
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cell, argmax = self.maxpool(x)
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branch4 = self.b4(cell)
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_ = argmax
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return self.concat((branch1, branch2, branch3, branch4))
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class GooGLeNet(nn.Cell):
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"""
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Googlenet architecture
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"""
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def __init__(self, num_classes):
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super(GooGLeNet, self).__init__()
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self.conv1 = Conv2dBlock(3, 64, kernel_size=7, stride=2, padding=0)
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self.maxpool1 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
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self.conv2 = Conv2dBlock(64, 64, kernel_size=1)
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self.conv3 = Conv2dBlock(64, 192, kernel_size=3, padding=0)
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self.maxpool2 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
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self.block3a = Inception(192, 64, 96, 128, 16, 32, 32)
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self.block3b = Inception(256, 128, 128, 192, 32, 96, 64)
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self.maxpool3 = P.MaxPoolWithArgmax(ksize=3, strides=2, padding="same")
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self.block4a = Inception(480, 192, 96, 208, 16, 48, 64)
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self.block4b = Inception(512, 160, 112, 224, 24, 64, 64)
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self.block4c = Inception(512, 128, 128, 256, 24, 64, 64)
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self.block4d = Inception(512, 112, 144, 288, 32, 64, 64)
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self.block4e = Inception(528, 256, 160, 320, 32, 128, 128)
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self.maxpool4 = P.MaxPoolWithArgmax(ksize=2, strides=2, padding="same")
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self.block5a = Inception(832, 256, 160, 320, 32, 128, 128)
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self.block5b = Inception(832, 384, 192, 384, 48, 128, 128)
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self.mean = P.ReduceMean(keep_dims=True)
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self.dropout = nn.Dropout(keep_prob=0.8)
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self.flatten = nn.Flatten()
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self.classifier = nn.Dense(1024, num_classes, weight_init=weight_variable(),
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bias_init=weight_variable())
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def construct(self, x):
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x = self.conv1(x)
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x, argmax = self.maxpool1(x)
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x = self.conv2(x)
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x = self.conv3(x)
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x, argmax = self.maxpool2(x)
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x = self.block3a(x)
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x = self.block3b(x)
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x, argmax = self.maxpool3(x)
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x = self.block4a(x)
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x = self.block4b(x)
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x = self.block4c(x)
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x = self.block4d(x)
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x = self.block4e(x)
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x, argmax = self.maxpool4(x)
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x = self.block5a(x)
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x = self.block5b(x)
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x = self.mean(x, (2, 3))
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x = self.flatten(x)
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x = self.classifier(x)
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_ = argmax
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return x
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@ -1,93 +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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"""LSTM."""
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import numpy as np
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from mindspore import Tensor, nn, context
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from mindspore.ops import operations as P
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# Initialize short-term memory (h) and long-term memory (c) to 0
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def lstm_default_state(batch_size, hidden_size, num_layers, bidirectional):
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"""init default input."""
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num_directions = 1
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if bidirectional:
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num_directions = 2
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if context.get_context("device_target") == "CPU":
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h_list = []
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c_list = []
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i = 0
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while i < num_layers:
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hi = Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32))
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h_list.append(hi)
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ci = Tensor(np.zeros((num_directions, batch_size, hidden_size)).astype(np.float32))
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c_list.append(ci)
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i = i + 1
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h = tuple(h_list)
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c = tuple(c_list)
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return h, c
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h = Tensor(
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np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
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c = Tensor(
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np.zeros((num_layers * num_directions, batch_size, hidden_size)).astype(np.float32))
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return h, c
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class SentimentNet(nn.Cell):
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"""Sentiment network structure."""
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def __init__(self,
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vocab_size,
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embed_size,
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num_hiddens,
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num_layers,
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bidirectional,
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num_classes,
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weight,
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batch_size):
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super(SentimentNet, self).__init__()
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# Mapp words to vectors
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self.embedding = nn.Embedding(vocab_size,
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embed_size,
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embedding_table=weight)
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self.embedding.embedding_table.requires_grad = False
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self.trans = P.Transpose()
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self.perm = (1, 0, 2)
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self.encoder = nn.LSTM(input_size=embed_size,
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hidden_size=num_hiddens,
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num_layers=num_layers,
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has_bias=True,
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bidirectional=bidirectional,
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dropout=0.0)
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self.h, self.c = lstm_default_state(batch_size, num_hiddens, num_layers, bidirectional)
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self.concat = P.Concat(1)
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if bidirectional:
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self.decoder = nn.Dense(num_hiddens * 4, num_classes)
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else:
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self.decoder = nn.Dense(num_hiddens * 2, num_classes)
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def construct(self, inputs):
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# input：(64,500,300)
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embeddings = self.embedding(inputs)
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embeddings = self.trans(embeddings, self.perm)
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output, _ = self.encoder(embeddings, (self.h, self.c))
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# states[i] size(64,200) -> encoding.size(64,400)
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encoding = self.concat((output[0], output[499]))
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outputs = self.decoder(encoding)
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return outputs
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@ -1,104 +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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"""VGG."""
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import mindspore.nn as nn
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from mindspore.common.initializer import initializer
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import mindspore.common.dtype as mstype
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def _make_layer(base, batch_norm):
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"""Make stage network of VGG."""
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layers = []
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in_channels = 3
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for v in base:
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if v == 'M':
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layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
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else:
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weight_shape = (v, in_channels, 3, 3)
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weight = initializer('XavierUniform', shape=weight_shape, dtype=mstype.float32).to_tensor()
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conv2d = nn.Conv2d(in_channels=in_channels,
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out_channels=v,
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kernel_size=3,
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padding=0,
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pad_mode='same',
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weight_init=weight)
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if batch_norm:
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layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU()]
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else:
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layers += [conv2d, nn.ReLU()]
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in_channels = v
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return nn.SequentialCell(layers)
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class Vgg(nn.Cell):
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"""
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VGG network definition.
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Args:
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base (list): Configuration for different layers, mainly the channel number of Conv layer.
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num_classes (int): Class numbers. Default: 1000.
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batch_norm (bool): Whether to do the batchnorm. Default: False.
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batch_size (int): Batch size. Default: 1.
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Returns:
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Tensor, infer output tensor.
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Examples:
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>>> Vgg([64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
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>>> num_classes=1000, batch_norm=False, batch_size=1)
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"""
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def __init__(self, base, num_classes=1000, batch_norm=False, batch_size=1):
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super(Vgg, self).__init__()
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_ = batch_size
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self.layers = _make_layer(base, batch_norm=batch_norm)
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self.flatten = nn.Flatten()
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self.classifier = nn.SequentialCell([
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nn.Dense(512 * 7 * 7, 4096),
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nn.ReLU(),
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nn.Dense(4096, 4096),
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nn.ReLU(),
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nn.Dense(4096, num_classes)])
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def construct(self, x):
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x = self.layers(x)
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x = self.flatten(x)
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x = self.classifier(x)
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return x
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cfg = {
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'11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
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'16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
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'19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
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}
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def vgg16(num_classes=1000):
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"""
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Get Vgg16 neural network with batch normalization.
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Args:
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num_classes (int): Class numbers. Default: 1000.
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Returns:
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Cell, cell instance of Vgg16 neural network with batch normalization.
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Examples:
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>>> vgg16(num_classes=1000)
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"""
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net = Vgg(cfg['16'], num_classes=num_classes, batch_norm=True)
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return net
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@ -72,7 +72,8 @@ result: {'acc': 0.83}
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```
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usage: train.py [--preprocess {true,false}] [--aclimdb_path ACLIMDB_PATH]
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[--glove_path GLOVE_PATH] [--preprocess_path PREPROCESS_PATH]
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[--ckpt_path CKPT_PATH] [--device_target {GPU,CPU}]
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[--ckpt_path CKPT_PATH] [--pre_trained PRE_TRAINED]
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[--device_target {GPU,CPU}]
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parameters/options:
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--preprocess whether to preprocess data.
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@ -80,6 +81,7 @@ parameters/options:
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--glove_path path where the GloVe is stored.
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--preprocess_path path where the pre-process data is stored.
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--ckpt_path the path to save the checkpoint file.
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--pre_trained the pretrained checkpoint file path.
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--device_target the target device to run, support "GPU", "CPU".
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```
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@ -28,6 +28,7 @@ from mindspore import Tensor, nn, Model, context
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from mindspore.model_zoo.lstm import SentimentNet
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from mindspore.nn import Accuracy
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from mindspore.train.callback import LossMonitor, CheckpointConfig, ModelCheckpoint, TimeMonitor
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from mindspore.train.serialization import load_param_into_net, load_checkpoint
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='MindSpore LSTM Example')
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@ -41,6 +42,8 @@ if __name__ == '__main__':
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help='path where the pre-process data is stored.')
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parser.add_argument('--ckpt_path', type=str, default="./",
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help='the path to save the checkpoint file.')
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parser.add_argument('--pre_trained', type=str, default=None,
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help='the pretrained checkpoint file path.')
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parser.add_argument('--device_target', type=str, default="GPU", choices=['GPU', 'CPU'],
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help='the target device to run, support "GPU", "CPU". Default: "GPU".')
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args = parser.parse_args()
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@ -63,6 +66,9 @@ if __name__ == '__main__':
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num_classes=cfg.num_classes,
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weight=Tensor(embedding_table),
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batch_size=cfg.batch_size)
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# pre_trained
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if args.pre_trained:
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load_param_into_net(network, load_checkpoint(args.pre_trained))
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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opt = nn.Momentum(network.trainable_params(), cfg.learning_rate, cfg.momentum)
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|
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@ -73,12 +73,13 @@ train_parallel1/log:epcoh: 2 step: 97, loss is 1.7133579
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### Training
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||||
```
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usage: train.py [--device_target TARGET][--data_path DATA_PATH]
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[--device_id DEVICE_ID]
|
||||
[--device_id DEVICE_ID][--pre_trained PRE_TRAINED]
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||||
parameters/options:
|
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--device_target the training backend type, default is Ascend.
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--data_path the storage path of dataset
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||||
--device_id the device which used to train model.
|
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--pre_trained the pretrained checkpoint file path.
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||||
```
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|
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@ -29,6 +29,7 @@ from mindspore.communication.management import init
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from mindspore.nn.optim.momentum import Momentum
|
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
|
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from mindspore.train.model import Model, ParallelMode
|
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from mindspore.train.serialization import load_param_into_net, load_checkpoint
|
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from src.config import cifar_cfg as cfg
|
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from src.dataset import vgg_create_dataset
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from src.vgg import vgg16
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@ -64,6 +65,7 @@ if __name__ == '__main__':
|
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help='device where the code will be implemented. (Default: Ascend)')
|
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parser.add_argument('--data_path', type=str, default='./cifar', help='path where the dataset is saved')
|
||||
parser.add_argument('--device_id', type=int, default=None, help='device id of GPU or Ascend. (Default: None)')
|
||||
parser.add_argument('--pre_trained', type=str, default=None, help='the pretrained checkpoint file path.')
|
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args_opt = parser.parse_args()
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
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|
|
@ -80,6 +82,10 @@ if __name__ == '__main__':
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batch_num = dataset.get_dataset_size()
|
||||
|
||||
net = vgg16(num_classes=cfg.num_classes)
|
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# pre_trained
|
||||
if args_opt.pre_trained:
|
||||
load_param_into_net(net, load_checkpoint(args_opt.pre_trained))
|
||||
|
||||
lr = lr_steps(0, lr_max=cfg.lr_init, total_epochs=cfg.epoch_size, steps_per_epoch=batch_num)
|
||||
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), Tensor(lr), cfg.momentum,
|
||||
weight_decay=cfg.weight_decay)
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.model_zoo.vgg import vgg16
|
||||
from model_zoo.vgg16.src.vgg import vgg16
|
||||
from ..ut_filter import non_graph_engine
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue