def updateStack(self, blocks, indexes, overlapping=0, block_size=64): """ Update the self.stack with the divided volumes. @blocks (list) The divided volumes. @indexes (list) The list containing index numbers corresponding to the blocks. @overlapping (integer) A overlapping pixel size. Should be the same value used in the block_separator function. @block_size (integer) A block size determining the divided volume size. Should be the same value used in the block_separator function. """ self.checkStack() for block, index in zip(blocks, indexes): self.stack[index[0]:index[0]+block_size, index[1]:index[1]+block_size, index[2]:index[2]+block_size] = block[overlapping:-overlapping, overlapping:-overlapping, overlapping:-overlapping] return请详细解释每一行代码的意思

def filterNormalization(self, block_size=64, all_at_once = False): """ Normalize signal intensity. @block_size (integer) A block size determining the divided volume size. This argument is passed to the block_separator function. @all_at_once (bool) A flag determining all-at-onec processing. This argument is passed to the block_separator function. """ print("Intensity normalization") if self.peak_air == None: raise Exception('Call the calculateNormalizationParam in ahead.') maxid = [self.peak_air, self.peak_soil] maxid = [i-self.hist_x[0] for i in maxid] plt.figure() plt.plot(self.hist_x, self.hist_y) plt.plot(self.hist_x[maxid], self.hist_y[maxid],'ro') plt.xlabel('intensity') plt.ylabel('count') plt.pause(.01) i_block = self.block_separator(overlapping = 1, block_size = block_size, all_at_once = all_at_once) for blocks, indexes in i_block: blocks = tqdm_multiprocessing(functools.partial(normalizeIntensity, peak_air=self.peak_air, peak_soil=self.peak_soil), blocks) self.updateStack(blocks, indexes, overlapping = 1, block_size = block_size) return请完整详细解释每一行的代码意思

def filterNormalization(self, block_size=64, all_at_once=False): """ Normalize signal intensity. @block_size (integer): A block size determining the divided volume size. This argument is passed...

def __update_volume(self, blocks, indexes, overlapping=0, block_size=64): for block, index in zip(blocks, indexes): block = block[ overlapping:-overlapping, overlapping:-overlapping, overlapping:-overlapping, ] s = [slice(index[i], index[i] + block_size) for i in range(3)] self.__volume[s[0], s[1], s[2]] = block请详细解释每一行代码的意思

1. def __update_volume(self, blocks, indexes, overlapping=0, block_size=64): 这是一个方法定义。它的名称是“__update_volume”，括号内的参数是“self”（表示类实例本身），以及三个其他参数（“blocks”，...

def block_separator(self, overlapping=0, block_size=64, all_at_once = False): """ Generator of the CT images. The CT volume is divided into smaller ones. @overlapping (integer) A overlapping pixel size. Be sure that the value should be enough large if you apply filters later. @block_size (integer) A block size determining the divided volume size. If the overlapping is zero, CT volume is divided into smaller ones with a side of block size. @all_at_once (bool) A flag determining all-at-onec processing. If the all_at_once is True, this function returns an iterator yielding the list containing all divided volumes. If False, this function returns an iterator yielding the lists containing portions of divided volumes. """ self.checkStack() buf = np.pad(self.stack, overlapping, mode = 'symmetric') blocks = [] indexes = [] for zi in range(0, self.shape[0], block_size): for yi in range(0, self.shape[1], block_size): for xi in range(0, self.shape[2], block_size): blocks.append(buf[zi:zi+block_size+overlapping2, yi:yi+block_size+overlapping2, xi:xi+block_size+overlapping*2]) indexes.append([zi, yi, xi]) if not all_at_once: yield (blocks, indexes) blocks = [] indexes = [] if blocks: yield (blocks, indexes) return详细解释每一行代码的意思

def block_separator(self, overlapping=0, block_size=64, all_at_once = False): 这是函数定义开始。函数名为 block_separator，括号中有三个参数。self 是一个对象，在这里是指 CTVolume 对象，这个对象包含了一...

#256个block memory_size = 256 #pid进程号 class Process: def init(self, pid, block, duration): self.block = block self.duration = duration self.pid = pid self.memory = None @property def pid(self): return self.pid @property def block(self): return self.block @property def duration(self): return self.__duration def set_memory(self, memory_start, memory_end): self.__memory = (memory_start, memory_end) def get_memory(self): return self.memory class MemoryAllocator: def init__(self, memory_size): self.__memory_blocks = [None] * memory_size def memory_view(self): '''return the array of the use of memory blocks.''' return tuple(self.__memory_blocks) def allocate_memory(self, block_start, length, process): for block_id in range(block_start, block_start+length): assert self.__memory_blocks[block_id] is None, 'tend to allocate occupied blocks' self.__memory_blocks[block_id] = process process.set_memory(block_start, length) def free_memory(self, process): assert process.get_memory() is not None, 'process should already hold memory blocks' block_start, length = process.get_memory() for block_id in range(block_start, block_start+length): assert self.__memory_blocks[block_id] == process, 'the orresponding memory blocks should be assigned to the process' self.__memory_blocks[block_id] = None

self.__memory_blocks = [False] * memory_size def find_free_memory(self, length): for i in range(len(self.__memory_blocks) - length + 1): if all(not self.__memory_blocks[j] for j in range(i, i + ...

使用二叉树改写class MemoryManager: def init(self, allocator): self.allocator = allocator def allocate(self, process, request_size): memory_view = self.allocator.memory_view() best_fit_block = None best_fit_size = float('inf') for i in range(len(memory_view)): if memory_view[i] is None: block_size = 0 j = i while j < len(memory_view) and memory_view[j] is None: block_size += 1 j += 1 if block_size >= request_size and block_size < best_fit_size: best_fit_block = i best_fit_size = block_size if best_fit_block is not None: block_start = best_fit_block self.allocator.allocate_memory(block_start, request_size, process) else: raise AssertionError('allocation failed')

self.free_blocks.remove(best_fit_block) if best_fit_block.size > request_size: # 如果该内存块剩余空间大于请求大小，则将剩余空间加入空闲内存块列表中 remaining_block = MemoryBlock(start=block_start ...

优化代码class MemoryManager: def init(self, allocator): self.allocator = allocator def allocate(self, process, request_size): memory_view = self.allocator.memory_view() block_start = None for i, block in enumerate(memory_view): if block is None: if block_start is None: block_start = i if i - block_start + 1 == request_size: self.allocator.allocate_memory(block_start, request_size, process) return block_start else: block_start = None return None

在这个代码中，可以考虑将 enumerate(memory_view) 替换为 enumerate(self.allocator.get_free_blocks(request_size)) 来避免遍历整个内存块。这里假设 get_free_blocks 方法会返回所有可以容纳请求大小的内存...

优化代码class MemoryManager: def init(self, allocator): self.allocator = allocator def allocate(self, process, request_size): memory_view = self.allocator.memory_view() block_start = None for i, block in enumerate(memory_view): if block is None: if block_start is None: block_start = i if i - block_start + 1 == request_size: self.allocator.allocate_memory(block_start, request_size, process) return block_start else: block_start = None return None

在这个代码中，可以考虑将 enumerate(memory_view) 替换为 enumerate(self.allocator.get_free_blocks(request_size)) 来避免遍历整个内存块。这里假设 get_free_blocks 方法会返回所有可以容纳请求大小的内存...

import torch import vega import torch.nn as nn class SimpleCnn(nn.Module): """Simple CNN network.""" def init(self): """Initialize.""" super(SimpleCnn, self).init() self.num_class = 11 # nas best hyperparameter self.blocks = 3 self.channel1 = 32 self.channel2 = 64 self.b1 = nn.Sequential( nn.Conv2d(1, self.channel1, padding=(0, 4), kernel_size=(1, 9), stride=(1, 1)), nn.BatchNorm2d(self.channel1), nn.ReLU(inplace=True) ) self.b2 = nn.Sequential( nn.Conv2d(self.channel1, self.channel2, padding=(0, 1), kernel_size=(2, 3), stride=(1, 2)), nn.BatchNorm2d(self.channel2), nn.ReLU(inplace=True) ) self.blocks = self._blocks(self.channel2, self.channel2) self.fc = nn.Linear(64self.channel2, self.num_class) def _blocks(self, in_channels, out_channels): blocks = nn.ModuleList([None] self.blocks) for i in range(self.blocks): blocks[i] = nn.Sequential( nn.Conv2d(in_channels, out_channels, padding=(0,1), kernel_size=(1,3)), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), ) in_channels = out_channels return blocks def forward(self, x): """Forward.""" x = self.b1(x) x = self.b2(x) for block in self.blocks: x = block(x) x = x.view(x.size(0), -1) x = self.fc(x) return x if name == "main": model = SimpleCnn() model.load_state_dict(torch.load('nas_results/model_12.pth')) model.eval()将这段代码从基于pytorch改为基于mindspore

self.blocks = self._blocks(self.channel2, self.channel2) self.fc = nn.Dense(64*self.channel2, self.num_class) def _blocks(self, in_channels, out_channels): blocks = nn.CellList([None] * self....

优化class MemoryManager: def init(self, allocator): self.allocator = allocator self.free_blocks = [(0, allocator)] def allocate(self, process, request_size): memory_view = self.allocator.memory_view() block_start = None for i in range(len(memory_view)): if memory_view[i] is None: if block_start is None: block_start = i if i - block_start + 1 == request_size: for j in range(block_start, i + 1): memory_view = list(memory_view) memory_view[j] = process self.allocator.allocate_memory(block_start, request_size, process) # 调用分配函数 return memory_view else: block_start = None return None

这段代码中可以进行的优化有： 1. 使用列表推导式代替 for 循环 ... self.free_blocks.append((start + request_size, end)) memory_view = tuple(memory_view) return memory_view return None

class GraphSAGE(nn.Module): def init(self, in_feats, hidden_feats, out_feats, num_layers, activation): super(GraphSAGE, self).init() self.num_layers = num_layers self.conv1 = SAGEConv(in_feats, hidden_feats, aggregator_type='mean') self.convs = nn.ModuleList() for i in range(num_layers - 2): self.convs.append(SAGEConv(hidden_feats, hidden_feats, aggregator_type='mean')) self.conv_last = SAGEConv(hidden_feats, out_feats, aggregator_type='mean') self.activation = activation def forward(self, blocks, x): h = x for i, block in enumerate(blocks): h_dst = h[:block.number_of_dst_nodes()] h = self.convs[i](block, (h, h_dst)) if i != self.num_layers - 2: h = self.activation(h) h = self.conv_last(blocks[-1], (h, h_dst)) return h改写一下，让它适用于异质图

def forward(self, blocks, x_dict): h = {k: v for k, v in x_dict.items()} for i, block in enumerate(blocks): edge_type = block.edata['type'] h_dst = h[str(edge_type)][block.dstdata[dgl.NID]] h = ...

class BasicBlock2D(nn.Module): expansion = 1 def init(self, in_channels, out_channels, stride=1): super(BasicBlock2D, self).init() self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(out_channels) self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(out_channels) self.shortcut = nn.Sequential() if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = nn.Sequential( nn.Conv2d(in_channels, self.expansion * out_channels, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(self.expansion * out_channels) ) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.bn2(self.conv2(out)) out += self.shortcut(x) out = F.relu(out) return out # 定义二维ResNet-18模型 class ResNet18_2D(nn.Module): def init(self, num_classes=1000): super(ResNet18_2D, self).init() self.in_channels = 64 self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(64) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(BasicBlock2D, 64, 2, stride=1) self.layer2 = self._make_layer(BasicBlock2D, 128, 2, stride=2) self.layer3 = self._make_layer(BasicBlock2D, 256, 2, stride=2) self.layer4 = self._make_layer(BasicBlock2D, 512, 2, stride=2) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 , 512) def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in range(1, num_blocks): layers.append(block(self.in_channels, out_channels)) return nn.Sequential(*layers) def forward(self, x): out = F.relu(self.bn1(self.conv1(x))) out = self.maxpool(out) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = self.avgpool(out) # print(out.shape) out = out.view(out.size(0), -1) out = self.fc(out) return out改为用稀疏表示替换全连接层

def _make_layer(self, block, out_channels, num_blocks, stride): layers = [] layers.append(block(self.in_channels, out_channels, stride)) self.in_channels = out_channels * block.expansion for _ in...

为以下的每句代码做注释：class ResNet(nn.Module): def init(self, block, blocks_num, num_classes=1000, include_top=True): super(ResNet, self).init() self.include_top = include_top self.in_channel = 64 self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = nn.BatchNorm2d(self.in_channel) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, blocks_num[0]) self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2) self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2) self.layer4 = self.make_layer(block, 512, blocks_num[3], stride=2) if self.include_top: self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal(m.weight, mode='fan_out', nonlinearity='relu') def _make_layer(self, block, channel, block_num, stride=1): downsample = None if stride != 1 or self.in_channel != channel * block.expansion: downsample = nn.Sequential( nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(channel * block.expansion)) layers = [] layers.append(block(self.in_channel, channel, downsample=downsample, stride=stride)) self.in_channel = channel * block.expansion for _ in range(1, block_num): layers.append(block(self.in_channel, channel)) return nn.Sequential(*layers) def forward(self, x): x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) if self.include_top: x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x

- self.layer1 = self._make_layer(block, 64, blocks_num[0]) 定义 ResNet 中的第一个残差块，包含 blocks_num[0] 个基础模块，每个基础模块的输出通道数为 64。 - self.layer2 = self._make_layer(block, 128, ...

优化class MemoryManager: def init(self, allocator): self.allocator = allocator def allocate(self, process, request_size): memory_view = self.allocator.memory_view() block_start = None for i in range(len(memory_view)): if memory_view[i] is None: if block_start is None: block_start = i if i - block_start + 1 == request_size: for j in range(block_start, i + 1): memory_view = list(memory_view) memory_view[j] = process self.allocator.allocate_memory(block_start, request_size, process) # 调用分配函数 return memory_view else: block_start = None return None

这段代码中可以进行的优化有： 1. 避免重复的列表转换与之前相同，将 memory_... self.free_blocks.append((start + request_size, end)) memory_view = tuple(memory_view) return memory_view return None

def init(self): super().init() self._initial = nn.Sequential( nn.Conv2d(3, 32, kernel_size=9, stride=1, padding=4, padding_mode='reflect'), nn.InstanceNorm2d(32, affine=True), nn.ReLU(inplace=True), ) self._down_blocks = nn.Sequential( DownBlock(32, 64, kernel_size=3), DownBlock(64, 128, kernel_size=3), ) self._residual_blocks = nn.Sequential( *[ResidualBlock(128, kernel_size=3) for _ in range(5)] ) self._up_blocks = nn.Sequential( UpBlock(128, 64, kernel_size=3), UpBlock(64, 32, kernel_size=3), ) self._final = nn.Conv2d(32, 3, kernel_size=9, stride=1, padding=4, padding_mode='reflect') def forward(self, x: Tensor) -> Tensor: x = self._initial(x) x = self._down_blocks(x) x = self._residual_blocks(x) x = self._up_blocks(x) x = self._final(x) x = torch.sigmoid(x) return x

这段代码是一个基于深度学习的图像处理模型，使用了卷积神经网络和残差网络来实现图像的去噪和修复。其中包含了初始卷积层、下采样卷积层、残差块和上采样卷积层，...最后通过 sigmoid 函数将像素值限制在 0~1 范围内。

def init(self, block, blocks_num, num_classes=1000, include_top=True, groups=1, width_per_group=64): super(ResNet, self).init()

- block：表示ResNet模型中使用的基本块的类型，例如ResNet-50中使用的基本块是Bottleneck。 - blocks_num：表示每个阶段中使用的基本块的数量，例如ResNet-50中第1个阶段使用3个基本块，第2个阶段使用4个基本块，...

class Generator(nn.Module): def init(self, n_residual_blocks, upsample_factor): super(Generator, self).init() self.n_residual_blocks = n_residual_blocks self.upsample_factor = upsample_factor self.conv1 = nn.Conv2d(3, 64, 9, stride=1, padding=4) for i in range(self.n_residual_blocks): self.add_module('residual_block' + str(i+1), residualBlock()) self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1) self.bn2 = nn.BatchNorm2d(64) for i in range(self.upsample_factor/2): self.add_module('upsample' + str(i+1), upsampleBlock(64, 256)) self.conv3 = nn.Conv2d(64, 3, 9, stride=1, padding=4) def forward(self, x): x = swish(self.conv1(x)) y = x.clone() for i in range(self.n_residual_blocks): y = self.getattr('residual_block' + str(i+1))(y) x = self.bn2(self.conv2(y)) + x for i in range(self.upsample_factor/2): x = self.getattr('upsample' + str(i+1))(x) return self.conv3(x)

然后使用循环为模型添加了n_residual_blocks个残差块（residualBlock）。接着是第二个卷积层（conv2），再加上批归一化层（bn2）。随后，使用循环为模型添加了upsample_factor/2个上采样块（upsampleBlock）。最后，...

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def init(self, block, blocks_num, num_classes=1000, include_top=True, groups=1, width_per_group=64): super(ResNet, self).init()