| # 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. | |
| # ============================================================================= | |
| # the code is modified from | |
| # https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py | |
| from singa import layer | |
| from singa import model | |
| 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 | |
| class Xception(model.Model): | |
| """ | |
| Xception optimized for the ImageNet dataset, as specified in | |
| https://arxiv.org/pdf/1610.02357.pdf | |
| """ | |
| def __init__(self, num_classes=10, num_channels=3): | |
| """ Constructor | |
| Args: | |
| num_classes: number of classes | |
| """ | |
| super(Xception, self).__init__() | |
| self.num_classes = num_classes | |
| self.input_size = 299 | |
| self.dimension = 4 | |
| self.conv1 = layer.Conv2d(num_channels, 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(num_classes) | |
| self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() | |
| 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, x): | |
| x = self.features(x) | |
| x = self.logits(x) | |
| return x | |
| def train_one_batch(self, x, y, dist_option, spars): | |
| out = self.forward(x) | |
| loss = self.softmax_cross_entropy(out, y) | |
| if dist_option == 'plain': | |
| self.optimizer(loss) | |
| elif dist_option == 'half': | |
| self.optimizer.backward_and_update_half(loss) | |
| elif dist_option == 'partialUpdate': | |
| self.optimizer.backward_and_partial_update(loss) | |
| elif dist_option == 'sparseTopK': | |
| self.optimizer.backward_and_sparse_update(loss, | |
| topK=True, | |
| spars=spars) | |
| elif dist_option == 'sparseThreshold': | |
| self.optimizer.backward_and_sparse_update(loss, | |
| topK=False, | |
| spars=spars) | |
| return out, loss | |
| def set_optimizer(self, optimizer): | |
| self.optimizer = optimizer | |
| def create_model(pretrained=False, **kwargs): | |
| """Constructs a Xceptionnet model. | |
| Args: | |
| pretrained (bool): If True, returns a pre-trained model. | |
| Returns: | |
| The created Xceptionnet model. | |
| """ | |
| model = Xception(**kwargs) | |
| return model | |
| __all__ = ['Xception', 'create_model'] |