本文整理汇总了Python中torch.flatten方法的典型用法代码示例。如果您正苦于以下问题：Python torch.flatten方法的具体用法？Python torch.flatten怎么用？Python torch.flatten使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch的用法示例。
在下文中一共展示了torch.flatten方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
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)
x = self.avgpool(x)
#x = x.view(x.size(0), -1)
x = torch.flatten(x, 1)
if self.drop:
x = self.drop(x)
x = self.fc(x)
return x 示例2: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def forward(self, x):
x = self.model(x)
x = torch.flatten(x, start_dim=1) # Flattens layers without losing batches
x = self.full_conn1(x)
x = self.norm1(x)
x = F.relu(x)
x = F.dropout(x)
x = self.full_conn2(x)
x = F.relu(x)
x = F.dropout(x)
x = self.full_conn3(x)
return x 示例3: _forward_impl
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def _forward_impl(self, x):
# See note [TorchScript super()]
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)
x = self.avgpool(x)
# EDIT(momohatt): Add 'start_dim='
x = torch.flatten(x, start_dim=1)
x = self.fc(x)
return x 示例4: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, start_dim=1) # EDIT(momohatt): Add 'start_dim='
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
# Example input 示例5: protobuf_tensor_serializer
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def protobuf_tensor_serializer(worker: AbstractWorker, tensor: torch.Tensor) -> TensorDataPB:
"""Strategy to serialize a tensor using Protobuf"""
dtype = TORCH_DTYPE_STR[tensor.dtype]
protobuf_tensor = TensorDataPB()
if tensor.is_quantized:
protobuf_tensor.is_quantized = True
protobuf_tensor.scale = tensor.q_scale()
protobuf_tensor.zero_point = tensor.q_zero_point()
data = torch.flatten(tensor).int_repr().tolist()
else:
data = torch.flatten(tensor).tolist()
protobuf_tensor.dtype = dtype
protobuf_tensor.shape.dims.extend(tensor.size())
getattr(protobuf_tensor, "contents_" + dtype).extend(data)
return protobuf_tensor 示例6: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x, layer1_sum = self.layer1(x)
x, layer2_sum = self.layer2(x)
x, layer3_sum = self.layer3(x)
x, layer4_sum = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x, layer1_sum + layer2_sum + layer3_sum + layer4_sum 示例7: _forward_impl
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def _forward_impl(self, x):
# See note [TorchScript super()]
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)
x = self.bn5(x)
x = self.relu(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
#x = self.fc(x)
return x 示例8: _forward_impl
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def _forward_impl(self, x):
# See note [TorchScript super()]
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)
x = self.bn5(x)
x = self.relu(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
#x = self.fc(x)
return x 示例9: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def forward(self, x):
x = x.reshape(280, 280, 4)
x = torch.narrow(x, dim=2, start=3, length=1)
x = x.reshape(1, 1, 280, 280)
x = F.avg_pool2d(x, 10, stride=10)
x = x / 255
x = (x - MEAN) / STANDARD_DEVIATION
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.softmax(x, dim=1)
return output 示例10: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
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)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x 示例11: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
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)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x 示例12: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def forward(self, x):
x = self.conv1(x) # batch*32*20*20
x = self.bn1(x)
x = F.relu(x)
x = F.max_pool2d(x, 2) # batch*32*10*10
x = self.conv2(x) # batch*64*8*8
x = self.bn2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2) # batch*64*4*4
x = self.conv3(x) # batch*128*2*2
x = self.bn3(x)
x = F.relu(x)
x = torch.flatten(x, 1) # batch*512
x = self.fc1(x) # batch*128
x = F.relu(x)
x = self.fc2(x) # batch*55
x = F.log_softmax(x, dim=1)
return x 示例13: findwordlist
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def findwordlist(template, closewordind, vocab, numwords=10, addeos=False):
"""
Based on a template sentence, find the candidate word list.
Input:
template: source sentence.
closewordind: precalculated 100 closest word indices (using character embeddings). torch.LongTensor.
vocab: full vocabulary.
numwords: number of closest words per word in the template.
addeos: whether to include '<eos>' in the candidate word list.
"""
if isinstance(template, str):
template = template.split()
templateind = closewordind.new_tensor([vocab.stoi[w] for w in template])
# subvocab = closewordind[templateind, :numwords].flatten().cpu() # torch.flatten() only exists from PyTorch 0.4.1
subvocab = closewordind[templateind, :numwords].view(-1).cpu()
if addeos:
subvocab = torch.cat([subvocab, torch.LongTensor([vocab.stoi['<eos>']])])
subvocab = subvocab.unique(sorted=True)
word_list = [vocab.itos[i] for i in subvocab]
return word_list, subvocab 示例14: _forward_impl
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def _forward_impl(self, x):
# See note [TorchScript super()]
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)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x 示例15: forward
# 需要导入模块: import torch [as 别名]
# 或者: from torch import flatten [as 别名]
def forward(self, x):
# N x 768 x 17 x 17
x = F.avg_pool2d(x, kernel_size=5, stride=3)
# N x 768 x 5 x 5
x = self.conv0(x)
# N x 128 x 5 x 5
x = self.conv1(x)
# N x 768 x 1 x 1
# Adaptive average pooling
x = F.adaptive_avg_pool2d(x, (1, 1))
# N x 768 x 1 x 1
x = torch.flatten(x, 1)
# N x 768
x = self.fc(x)
# N x 1000
return x