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Posted to commits@singa.apache.org by zh...@apache.org on 2021/05/21 10:20:57 UTC
[singa] branch dev updated: add autograd for xceptionnet in
cifar_distributed_cnn
This is an automated email from the ASF dual-hosted git repository.
zhaojing pushed a commit to branch dev
in repository https://gitbox.apache.org/repos/asf/singa.git
The following commit(s) were added to refs/heads/dev by this push:
new 00c6fbc add autograd for xceptionnet in cifar_distributed_cnn
new 8259e90 Merge pull request #852 from naili-xing/cifar-distributed-cnn-new-folder
00c6fbc is described below
commit 00c6fbc8e520ece6b58a3d3f2f7e6da95ea475d8
Author: nailixing <xi...@gmail.com>
AuthorDate: Fri May 21 17:04:07 2021 +0800
add autograd for xceptionnet in cifar_distributed_cnn
---
.../cifar_distributed_cnn/autograd/xceptionnet.py | 303 +++++++++++++++++++++
1 file changed, 303 insertions(+)
diff --git a/examples/cifar_distributed_cnn/autograd/xceptionnet.py b/examples/cifar_distributed_cnn/autograd/xceptionnet.py
new file mode 100644
index 0000000..357e47d
--- /dev/null
+++ b/examples/cifar_distributed_cnn/autograd/xceptionnet.py
@@ -0,0 +1,303 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+# =============================================================================
+
+from singa import autograd
+from singa import tensor
+from singa import device
+from singa import layer
+from singa import opt
+
+import numpy as np
+from tqdm import trange
+
+# the code is modified from
+# https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py
+
+
+class Block(layer.Layer):
+
+ def __init__(self,
+ in_filters,
+ out_filters,
+ reps,
+ strides=1,
+ padding=0,
+ start_with_relu=True,
+ grow_first=True):
+ super(Block, self).__init__()
+
+ if out_filters != in_filters or strides != 1:
+ self.skip = layer.Conv2d(in_filters,
+ out_filters,
+ 1,
+ stride=strides,
+ padding=padding,
+ bias=False)
+ self.skipbn = layer.BatchNorm2d(out_filters)
+ else:
+ self.skip = None
+
+ self.layers = []
+
+ filters = in_filters
+ if grow_first:
+ self.layers.append(layer.ReLU())
+ self.layers.append(
+ layer.SeparableConv2d(in_filters,
+ out_filters,
+ 3,
+ stride=1,
+ padding=1,
+ bias=False))
+ self.layers.append(layer.BatchNorm2d(out_filters))
+ filters = out_filters
+
+ for i in range(reps - 1):
+ self.layers.append(layer.ReLU())
+ self.layers.append(
+ layer.SeparableConv2d(filters,
+ filters,
+ 3,
+ stride=1,
+ padding=1,
+ bias=False))
+ self.layers.append(layer.BatchNorm2d(filters))
+
+ if not grow_first:
+ self.layers.append(layer.ReLU())
+ self.layers.append(
+ layer.SeparableConv2d(in_filters,
+ out_filters,
+ 3,
+ stride=1,
+ padding=1,
+ bias=False))
+ self.layers.append(layer.BatchNorm2d(out_filters))
+
+ if not start_with_relu:
+ self.layers = self.layers[1:]
+ else:
+ self.layers[0] = layer.ReLU()
+
+ if strides != 1:
+ self.layers.append(layer.MaxPool2d(3, strides, padding + 1))
+
+ self.register_layers(*self.layers)
+
+ self.add = layer.Add()
+
+ def forward(self, x):
+ y = self.layers[0](x)
+ for layer in self.layers[1:]:
+ if isinstance(y, tuple):
+ y = y[0]
+ y = layer(y)
+
+ if self.skip is not None:
+ skip = self.skip(x)
+ skip = self.skipbn(skip)
+ else:
+ skip = x
+ y = self.add(y, skip)
+ return y
+
+
+__all__ = ['Xception']
+
+
+class Xception(layer.Layer):
+ """
+ Xception optimized for the ImageNet dataset, as specified in
+ https://arxiv.org/pdf/1610.02357.pdf
+ """
+
+ def __init__(self, num_classes=1000):
+ """ Constructor
+ Args:
+ num_classes: number of classes
+ """
+ super(Xception, self).__init__()
+ self.num_classes = num_classes
+
+ self.conv1 = layer.Conv2d(3, 32, 3, 2, 0, bias=False)
+ self.bn1 = layer.BatchNorm2d(32)
+ self.relu1 = layer.ReLU()
+
+ self.conv2 = layer.Conv2d(32, 64, 3, 1, 1, bias=False)
+ self.bn2 = layer.BatchNorm2d(64)
+ self.relu2 = layer.ReLU()
+ # do relu here
+
+ self.block1 = Block(64,
+ 128,
+ 2,
+ 2,
+ padding=0,
+ start_with_relu=False,
+ grow_first=True)
+ self.block2 = Block(128,
+ 256,
+ 2,
+ 2,
+ padding=0,
+ start_with_relu=True,
+ grow_first=True)
+ self.block3 = Block(256,
+ 728,
+ 2,
+ 2,
+ padding=0,
+ start_with_relu=True,
+ grow_first=True)
+
+ self.block4 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+ self.block5 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+ self.block6 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+ self.block7 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+
+ self.block8 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+ self.block9 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+ self.block10 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+ self.block11 = Block(728,
+ 728,
+ 3,
+ 1,
+ start_with_relu=True,
+ grow_first=True)
+
+ self.block12 = Block(728,
+ 1024,
+ 2,
+ 2,
+ start_with_relu=True,
+ grow_first=False)
+
+ self.conv3 = layer.SeparableConv2d(1024, 1536, 3, 1, 1)
+ self.bn3 = layer.BatchNorm2d(1536)
+ self.relu3 = layer.ReLU()
+
+ # do relu here
+ self.conv4 = layer.SeparableConv2d(1536, 2048, 3, 1, 1)
+ self.bn4 = layer.BatchNorm2d(2048)
+
+ self.relu4 = layer.ReLU()
+ self.globalpooling = layer.MaxPool2d(10, 1)
+ self.flatten = layer.Flatten()
+ self.fc = layer.Linear(2048, num_classes)
+
+ def features(self, input):
+ x = self.conv1(input)
+ x = self.bn1(x)
+ x = self.relu1(x)
+
+ x = self.conv2(x)
+ x = self.bn2(x)
+ x = self.relu2(x)
+
+ x = self.block1(x)
+ x = self.block2(x)
+ x = self.block3(x)
+ x = self.block4(x)
+ x = self.block5(x)
+ x = self.block6(x)
+ x = self.block7(x)
+ x = self.block8(x)
+ x = self.block9(x)
+ x = self.block10(x)
+ x = self.block11(x)
+ x = self.block12(x)
+
+ x = self.conv3(x)
+ x = self.bn3(x)
+ x = self.relu3(x)
+
+ x = self.conv4(x)
+ x = self.bn4(x)
+ return x
+
+ def logits(self, features):
+ x = self.relu4(features)
+ x = self.globalpooling(x)
+ x = self.flatten(x)
+ x = self.fc(x)
+ return x
+
+ def forward(self, input):
+ x = self.features(input)
+ x = self.logits(x)
+ return x
+
+
+if __name__ == '__main__':
+ model = Xception(num_classes=1000)
+ print('Start intialization............')
+ dev = device.create_cuda_gpu_on(0)
+ #dev = device.create_cuda_gpu()
+
+ niters = 20
+ batch_size = 16
+ IMG_SIZE = 299
+ sgd = opt.SGD(lr=0.1, momentum=0.9, weight_decay=1e-5)
+
+ tx = tensor.Tensor((batch_size, 3, IMG_SIZE, IMG_SIZE), dev)
+ ty = tensor.Tensor((batch_size,), dev, tensor.int32)
+ autograd.training = True
+ x = np.random.randn(batch_size, 3, IMG_SIZE, IMG_SIZE).astype(np.float32)
+ y = np.random.randint(0, 1000, batch_size, dtype=np.int32)
+ tx.copy_from_numpy(x)
+ ty.copy_from_numpy(y)
+
+ with trange(niters) as t:
+ for _ in t:
+ x = model(tx)
+ loss = autograd.softmax_cross_entropy(x, ty)
+ sgd(loss)