def forward(self, x):
for name, module in self.base._modules.items():
if name == 'avgpool':
break
x = module(x)
if self.cut_at_pooling:
return x
x = F.avg_pool2d(x, x.size()[2:])
x = x.view(x.size(0), -1)
if self.has_embedding:
x = self.feat(x)
x = self.feat_bn(x)
if self.norm:
x = F.normalize(x)
elif self.has_embedding:
x = F.relu(x)
if self.dropout > 0:
x = self.drop(x)
if self.num_classes > 0:
x = self.classifier(x)
return x
python类avg_pool2d()的实例源码
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, x):
# save for combining later with output
residual = x
if self.combine == 'concat':
residual = F.avg_pool2d(residual, kernel_size=3,
stride=2, padding=1)
out = self.g_conv_1x1_compress(x)
out = channel_shuffle(out, self.groups)
out = self.depthwise_conv3x3(out)
out = self.bn_after_depthwise(out)
out = self.g_conv_1x1_expand(out)
out = self._combine_func(residual, out)
return F.relu(out)
def forward(self, x):
x = self.conv1(x)
x = self.maxpool(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
# global average pooling layer
x = F.avg_pool2d(x, x.data.size()[-2:])
# flatten for input to fully-connected layer
x = x.view(x.size(0), -1)
x = self.fc(x)
return F.log_softmax(x, dim=1)
def forward(self, x):
x = F.relu(self.conv11(x))
x = self.bn11(x)
x = F.relu(self.conv12(x))
x = self.bn12(x)
x = F.relu(self.conv21(x))
x = self.bn21(x)
x = F.relu(self.conv22(x))
x = self.bn22(x)
x = F.avg_pool2d(x, kernel_size=[x.size(2), x.size(3)])
x = self.fc(x.view(x.size()[:2]))#
x = F.softmax(x)
return x
def forward(self, x):
x = F.relu(self.conv11(x))
x = self.bn11(x)
x = self.offset12(x)
x = F.relu(self.conv12(x))
x = self.bn12(x)
x = self.offset21(x)
x = F.relu(self.conv21(x))
x = self.bn21(x)
x = self.offset22(x)
x = F.relu(self.conv22(x))
x = self.bn22(x)
x = F.avg_pool2d(x, kernel_size=[x.size(2), x.size(3)])
x = self.fc(x.view(x.size()[:2]))
x = F.softmax(x)
return x
def forward(self, x):
avg = F.avg_pool2d(x,kernel_size=7, stride=1, padding=3)
x1_1 = torch.cat([x,avg],1)
x1_1 = F.relu(self.conv1_1(x1_1))
x1_2 = F.avg_pool2d(x1_1,kernel_size=2, stride=2)
x1_2 = F.relu(self.conv1_2(x1_2))
x1_4 = F.avg_pool2d(x1_2,kernel_size=2, stride=2)
x1_4 = F.relu(self.conv1_3(x1_4))
x1_2_ = F.upsample_nearest(x1_4, scale_factor=2)
x1_2 = torch.cat([x1_2,x1_2_],1)
x1_2 = F.relu(self.conv1_4(x1_2))
x1_1_ = F.upsample_nearest(x1_2, scale_factor=2)
x1_1 = torch.cat([x1_1,x1_1_],1)
px = F.relu(self.conv1_5(x1_1))
px = torch.cat([px,px,px],1)
px = 1-px/16
return px*x+(1-px)*avg
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.bn1(x)
x = self.conv2(x)
x = self.bn2(x)
x = F.max_pool2d(x, kernel_size=(1,2), stride=(1,2), padding=(0,1))
x = F.relu(x)
x = self.conv3(x)
x = self.bn3(x)
x = F.max_pool2d(x, kernel_size=(1,2), stride=(1,2), padding=(0,1))
x = F.relu(x)
x = self.conv4(x)
x = self.bn4(x)
x = F.avg_pool2d(x, kernel_size=(1,2), stride=(1,2), padding=0)
x = F.relu(x)
x = x.view(-1,192)
return x
def forward(self, x):
x = self.conv2d_1a(x)
x = self.conv2d_2a(x)
x = self.conv2d_2b(x)
x = self.maxpool_3a(x)
x = self.conv2d_3b(x)
x = self.conv2d_4a(x)
x = self.maxpool_5a(x)
x = self.mixed_5b(x)
x = self.repeat(x)
x = self.mixed_6a(x)
x = self.repeat_1(x)
x = self.mixed_7a(x)
x = self.repeat_2(x)
x = self.block8(x)
x = self.conv2d_7b(x)
#x = F.avg_pool2d(x, 8, count_include_pad=False)]
x = adaptive_avgmax_pool2d(x, self.global_pool, count_include_pad=False)
x = x.view(x.size(0), -1)
if self.drop_rate > 0:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.classif(x)
return x
def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avgmaxc':
x = torch.cat([
F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad),
F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
], dim=1)
elif pool_type == 'avgmax':
x_avg = F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
x_max = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
x = 0.5 * (x_avg + x_max)
elif pool_type == 'max':
x = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
else:
if pool_type != 'avg':
print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
x = F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
return x
def forward(self, x):
x = F.relu(self.conv11(x))
x = self.bn11(x)
x = F.relu(self.conv12(x))
x = self.bn12(x)
x = F.relu(self.conv21(x))
x = self.bn21(x)
x = F.relu(self.conv22(x))
x = self.bn22(x)
x = F.avg_pool2d(x, kernel_size=[x.size(2), x.size(3)])
x = self.fc(x.view(x.size()[:2]))#
x = F.softmax(x)
return x
def forward(self, x):
x = F.relu(self.conv11(x))
x = self.bn11(x)
x = self.offset12(x)
x = F.relu(self.conv12(x))
x = self.bn12(x)
x = self.offset21(x)
x = F.relu(self.conv21(x))
x = self.bn21(x)
x = self.offset22(x)
x = F.relu(self.conv22(x))
x = self.bn22(x)
x = F.avg_pool2d(x, kernel_size=[x.size(2), x.size(3)])
x = self.fc(x.view(x.size()[:2]))
x = F.softmax(x)
return x
def modify_densenets(model):
# Modify attributs
model.last_linear = model.classifier
del model.classifier
def logits(self, features):
x = F.relu(features, inplace=True)
x = F.avg_pool2d(x, kernel_size=7, stride=1)
x = x.view(x.size(0), -1)
x = self.last_linear(x)
return x
def forward(self, input):
x = self.features(input)
x = self.logits(x)
return x
# Modify methods
setattr(model.__class__, 'logits', logits)
setattr(model.__class__, 'forward', forward)
return model
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, inputs):
H = self.conv1(inputs)
if not self.pre_act:
H = self.bn1(H)
H = F.relu(H)
for section_index in range(self.num_sections):
H = getattr(self, f'section_{section_index}')(H)
if self.pre_act:
H = self.bn1(H)
H = F.relu(H)
H = F.avg_pool2d(H, H.size()[2:])
H = H.view(H.size(0), -1)
outputs = self.fc(H)
return outputs
def adaptive_avgmax_pool2d(x, pool_type='avg', padding=0, count_include_pad=False):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avgmaxc':
x = torch.cat([
F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad),
F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
], dim=1)
elif pool_type == 'avgmax':
x_avg = F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
x_max = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
x = 0.5 * (x_avg + x_max)
elif pool_type == 'max':
x = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding)
else:
if pool_type != 'avg':
print('Invalid pool type %s specified. Defaulting to average pooling.' % pool_type)
x = F.avg_pool2d(
x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad)
return x
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch7x7 = self.branch7x7_1(x)
branch7x7 = self.branch7x7_2(branch7x7)
branch7x7 = self.branch7x7_3(branch7x7)
branch7x7dbl = self.branch7x7dbl_1(x)
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [
self.branch3x3_2a(branch3x3),
self.branch3x3_2b(branch3x3),
]
branch3x3 = torch.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = torch.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return torch.cat(outputs, 1)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
# out = self.layer4(out)
out = F.avg_pool2d(out, 8)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def forward(self, color, sketch):
color = F.avg_pool2d(color, 16, 16)
sketch = self.model(sketch)
out = self.prototype(torch.cat([sketch, color], 1))
return self.out(out.view(color.size(0), -1))
def forward(self, color, sketch):
color = F.avg_pool2d(color, 16, 16)
sketch = self.model(sketch)
out = self.prototype(torch.cat([sketch, color], 1))
return self.out(out.view(color.size(0), -1))
def forward(self, color, sketch):
color = F.avg_pool2d(color, 16, 16)
sketch = self.model(sketch)
out = self.prototype(torch.cat([sketch, color], 1))
return self.out(out.view(color.size(0), -1))
def forward(self, color, sketch):
color = F.avg_pool2d(color, 16, 16)
sketch = self.model(sketch)
out = self.prototype(torch.cat([sketch, color], 1))
return self.out(out.view(color.size(0), -1))
def forward(self, color, sketch):
color = F.avg_pool2d(color, 16, 16)
sketch = self.model(sketch)
out = self.prototype(torch.cat([sketch, color], 1))
return self.out(out.view(color.size(0), -1))
def forward(self, x):
out = self.conv1(F.relu(self.bn1(x)))
out = F.avg_pool2d(out, 2)
return out
def forward(self, x):
out = self.conv1(x)
out = self.trans1(self.dense1(out))
out = self.trans2(self.dense2(out))
out = self.dense3(out)
out = torch.squeeze(F.avg_pool2d(F.relu(self.bn1(out)), 8))
out = F.log_softmax(self.fc(out))
return out
