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Add the autograd implementation of xceptionnet
Add the autograd implementation of xceptionnet
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# 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. | ||
# ============================================================================= | ||
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from singa import autograd | ||
from singa import tensor | ||
from singa import device | ||
from singa import layer | ||
from singa import opt | ||
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import numpy as np | ||
from tqdm import trange | ||
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# the code is modified from | ||
# https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py | ||
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class Block(layer.Layer): | ||
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def __init__(self, | ||
in_filters, | ||
out_filters, | ||
reps, | ||
strides=1, | ||
padding=0, | ||
start_with_relu=True, | ||
grow_first=True): | ||
super(Block, self).__init__() | ||
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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 | ||
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self.layers = [] | ||
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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 | ||
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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)) | ||
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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)) | ||
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if not start_with_relu: | ||
self.layers = self.layers[1:] | ||
else: | ||
self.layers[0] = layer.ReLU() | ||
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if strides != 1: | ||
self.layers.append(layer.MaxPool2d(3, strides, padding + 1)) | ||
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self.register_layers(*self.layers) | ||
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self.add = layer.Add() | ||
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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) | ||
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if self.skip is not None: | ||
skip = self.skip(x) | ||
skip = self.skipbn(skip) | ||
else: | ||
skip = x | ||
y = self.add(y, skip) | ||
return y | ||
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__all__ = ['Xception'] | ||
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class Xception(layer.Layer): | ||
""" | ||
Xception optimized for the ImageNet dataset, as specified in | ||
https://arxiv.org/pdf/1610.02357.pdf | ||
""" | ||
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def __init__(self, num_classes=1000): | ||
""" Constructor | ||
Args: | ||
num_classes: number of classes | ||
""" | ||
super(Xception, self).__init__() | ||
self.num_classes = num_classes | ||
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self.conv1 = layer.Conv2d(3, 32, 3, 2, 0, bias=False) | ||
self.bn1 = layer.BatchNorm2d(32) | ||
self.relu1 = layer.ReLU() | ||
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self.conv2 = layer.Conv2d(32, 64, 3, 1, 1, bias=False) | ||
self.bn2 = layer.BatchNorm2d(64) | ||
self.relu2 = layer.ReLU() | ||
# do relu here | ||
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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) | ||
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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) | ||
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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) | ||
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self.block12 = Block(728, | ||
1024, | ||
2, | ||
2, | ||
start_with_relu=True, | ||
grow_first=False) | ||
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self.conv3 = layer.SeparableConv2d(1024, 1536, 3, 1, 1) | ||
self.bn3 = layer.BatchNorm2d(1536) | ||
self.relu3 = layer.ReLU() | ||
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# Relu Layer | ||
self.conv4 = layer.SeparableConv2d(1536, 2048, 3, 1, 1) | ||
self.bn4 = layer.BatchNorm2d(2048) | ||
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self.relu4 = layer.ReLU() | ||
self.globalpooling = layer.MaxPool2d(10, 1) | ||
self.flatten = layer.Flatten() | ||
self.fc = layer.Linear(2048, num_classes) | ||
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def features(self, input): | ||
x = self.conv1(input) | ||
x = self.bn1(x) | ||
x = self.relu1(x) | ||
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x = self.conv2(x) | ||
x = self.bn2(x) | ||
x = self.relu2(x) | ||
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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) | ||
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x = self.conv3(x) | ||
x = self.bn3(x) | ||
x = self.relu3(x) | ||
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x = self.conv4(x) | ||
x = self.bn4(x) | ||
return x | ||
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def logits(self, features): | ||
x = self.relu4(features) | ||
x = self.globalpooling(x) | ||
x = self.flatten(x) | ||
x = self.fc(x) | ||
return x | ||
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def forward(self, input): | ||
x = self.features(input) | ||
x = self.logits(x) | ||
return x | ||
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if __name__ == '__main__': | ||
model = Xception(num_classes=1000) | ||
print('Start intialization............') | ||
dev = device.create_cuda_gpu_on(0) | ||
#dev = device.create_cuda_gpu() | ||
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niters = 20 | ||
batch_size = 16 | ||
IMG_SIZE = 299 | ||
sgd = opt.SGD(lr=0.1, momentum=0.9, weight_decay=1e-5) | ||
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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) | ||
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with trange(niters) as t: | ||
for _ in t: | ||
x = model(tx) | ||
loss = autograd.softmax_cross_entropy(x, ty) | ||
sgd(loss) |