python max((1、2、3)*2)_Python functional.max_pool2d方法代码示例
本文整理匯總了Python中torch.nn.functional.max_pool2d方法的典型用法代碼示例。如果您正苦于以下問(wèn)題：Python functional.max_pool2d方法的具體用法？Python functional.max_pool2d怎么用？Python functional.max_pool2d使用的例子？那么恭喜您, 這里精選的方法代碼示例或許可以為您提供幫助。您也可以進(jìn)一步了解該方法所在模塊torch.nn.functional的用法示例。
在下文中一共展示了functional.max_pool2d方法的14個(gè)代碼示例，這些例子默認(rèn)根據(jù)受歡迎程度排序。您可以為喜歡或者感覺(jué)有用的代碼點(diǎn)贊，您的評(píng)價(jià)將有助于我們的系統(tǒng)推薦出更棒的Python代碼示例。
示例1: test_resize_methods
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def test_resize_methods():
inputs_x = torch.randn([2, 256, 128, 128])
target_resize_sizes = [(128, 128), (256, 256)]
resize_methods_list = ['nearest', 'bilinear']
for method in resize_methods_list:
merge_cell = BaseMergeCell(upsample_mode=method)
for target_size in target_resize_sizes:
merge_cell_out = merge_cell._resize(inputs_x, target_size)
gt_out = F.interpolate(inputs_x, size=target_size, mode=method)
assert merge_cell_out.equal(gt_out)
target_size = (64, 64) # resize to a smaller size
merge_cell = BaseMergeCell()
merge_cell_out = merge_cell._resize(inputs_x, target_size)
kernel_size = inputs_x.shape[-1] // target_size[-1]
gt_out = F.max_pool2d(
inputs_x, kernel_size=kernel_size, stride=kernel_size)
assert (merge_cell_out == gt_out).all()
開(kāi)發(fā)者ID:open-mmlab，項(xiàng)目名稱(chēng):mmdetection，代碼行數(shù):21，
示例2: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
out = F.relu(self.conv1(x))
out = self.bnm1(out)
out = F.relu(self.conv2(out))
out = self.bnm2(out)
out = F.max_pool2d(out, 2)
out = F.relu(self.conv3(out))
out = self.bnm3(out)
out = F.relu(self.conv4(out))
out = self.bnm4(out)
out = F.max_pool2d(out, 2)
out = out.view(out.size(0), -1)
#out = self.dropout1(out)
out = F.relu(self.fc1(out))
#out = self.dropout2(out)
out = self.bnm5(out)
out = F.relu(self.fc2(out))
#out = self.dropout3(out)
out = self.bnm6(out)
out = self.fc3(out)
return (out)
開(kāi)發(fā)者ID:StephanZheng，項(xiàng)目名稱(chēng):neural-fingerprinting，代碼行數(shù):23，
示例3: apply
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def apply(features: Tensor, proposal_bboxes: Tensor, proposal_batch_indices: Tensor, mode: Mode) -> Tensor:
_, _, feature_map_height, feature_map_width = features.shape
scale = 1 / 16
output_size = (7 * 2, 7 * 2)
if mode == Pooler.Mode.POOLING:
pool = []
for (proposal_bbox, proposal_batch_index) in zip(proposal_bboxes, proposal_batch_indices):
start_x = max(min(round(proposal_bbox[0].item() * scale), feature_map_width - 1), 0) # [0, feature_map_width)
start_y = max(min(round(proposal_bbox[1].item() * scale), feature_map_height - 1), 0) # (0, feature_map_height]
end_x = max(min(round(proposal_bbox[2].item() * scale) + 1, feature_map_width), 1) # [0, feature_map_width)
end_y = max(min(round(proposal_bbox[3].item() * scale) + 1, feature_map_height), 1) # (0, feature_map_height]
roi_feature_map = features[proposal_batch_index, :, start_y:end_y, start_x:end_x]
pool.append(F.adaptive_max_pool2d(input=roi_feature_map, output_size=output_size))
pool = torch.stack(pool, dim=0)
elif mode == Pooler.Mode.ALIGN:
pool = ROIAlign(output_size, spatial_scale=scale, sampling_ratio=0)(
features,
torch.cat([proposal_batch_indices.view(-1, 1).float(), proposal_bboxes], dim=1)
)
else:
raise ValueError
pool = F.max_pool2d(input=pool, kernel_size=2, stride=2)
return pool
開(kāi)發(fā)者ID:potterhsu，項(xiàng)目名稱(chēng):easy-faster-rcnn.pytorch，代碼行數(shù):27，
示例4: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, X):
h = F.relu(self.conv1_1(X))
h = F.relu(self.conv1_2(h))
relu1_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv2_1(h))
h = F.relu(self.conv2_2(h))
relu2_2 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv3_1(h))
h = F.relu(self.conv3_2(h))
h = F.relu(self.conv3_3(h))
relu3_3 = h
h = F.max_pool2d(h, kernel_size=2, stride=2)
h = F.relu(self.conv4_1(h))
h = F.relu(self.conv4_2(h))
h = F.relu(self.conv4_3(h))
relu4_3 = h
return [relu1_2, relu2_2, relu3_3, relu4_3]
## Weights init function
開(kāi)發(fā)者ID:AlexiaJM，項(xiàng)目名稱(chēng):Deep-learning-with-cats，代碼行數(shù):26，
示例5: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, inputs, y=None):
# Apply convs
theta = self.theta(inputs)
phi = F.max_pool2d(self.phi(inputs), [2, 2])
g = F.max_pool2d(self.g(inputs), [2, 2])
# Perform reshapes
theta = theta.view(-1, self.channels // self.heads, inputs.shape[2] * inputs.shape[3])
phi = phi.view(-1, self.channels // self.heads, inputs.shape[2] * inputs.shape[3] // 4)
g = g.view(-1, self.channels // 2, inputs.shape[2] * inputs.shape[3] // 4)
# Matmul and softmax to get attention maps
beta = F.softmax(torch.bmm(theta.transpose(1, 2), phi), -1)
# Attention map times g path
o = self.o(torch.bmm(g, beta.transpose(1, 2)).view(-1, self.channels // 2, inputs.shape[2],
inputs.shape[3]))
outputs = self.gamma * o + inputs
return outputs
開(kāi)發(fā)者ID:bayesiains，項(xiàng)目名稱(chēng):nsf，代碼行數(shù):18，
示例6: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
x = F.relu(self.bn1_a(self.conv1_a(x)))
x_pool1b = F.max_pool2d(F.relu(self.bn1_b(self.conv1_b(x))),2, stride=2)
x = self.layer1(x_pool1b)
x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))),2, stride=2)
x = self.layer2(x)
x_pool3 = F.max_pool2d(F.relu(self.bn3(self.conv3(x))),2, stride=2)
x = self.layer3(x_pool3)
x = F.max_pool2d(F.relu(self.bn4(self.conv4(x))),2, stride=2)
x = self.layer4(x)
x = x.view(-1, self.num_flat_features(x))
x = self.fc5_new(x)
# x1 = x1.view(1,-1,512)
# x1, hn1 = self.lstm1(x1, (self.h1, self.c1))
x = self.fc8_final(x)
return x
開(kāi)發(fā)者ID:XiaoYee，項(xiàng)目名稱(chēng):emotion_classification，代碼行數(shù):23，
示例7: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.max_pool2d(x, 2)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.normalize(x)
return x
開(kāi)發(fā)者ID:peisuke，項(xiàng)目名稱(chēng):MomentumContrast.pytorch，代碼行數(shù):11，
示例8: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
"""Forward input images through the network to generate heatmaps."""
x = F.max_pool2d(F.relu(self.bn1(self.conv1(x))), 2)
x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))), 2)
x = F.max_pool2d(F.relu(self.bn3(self.conv3(x))), 2)
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = F.relu(self.bn6(self.conv6(x)))
x = F.sigmoid(self.conv7(x))
return x
開(kāi)發(fā)者ID:aleju，項(xiàng)目名稱(chēng):cat-bbs，代碼行數(shù):12，
示例9: _crop_pool_layer
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def _crop_pool_layer(self, bottom, rois, max_pool=True): # done
# implement it using stn
# box to affine
# input (x1,y1,x2,y2)
"""
[ x2-x1 x1 + x2 - W + 1 ]
[ ----- 0 --------------- ]
[ W - 1 W - 1 ]
[ ]
[ y2-y1 y1 + y2 - H + 1 ]
[ 0 ----- --------------- ]
[ H - 1 H - 1 ]
"""
rois = rois.detach()
x1 = rois[:, 1::4] / 16.0
y1 = rois[:, 2::4] / 16.0
x2 = rois[:, 3::4] / 16.0
y2 = rois[:, 4::4] / 16.0
height = bottom.size(2)
width = bottom.size(3)
# affine theta
theta = Variable(rois.data.new(rois.size(0), 2, 3).zero_())
theta[:, 0, 0] = (x2 - x1) / (width - 1)
theta[:, 0 ,2] = (x1 + x2 - width + 1) / (width - 1)
theta[:, 1, 1] = (y2 - y1) / (height - 1)
theta[:, 1, 2] = (y1 + y2 - height + 1) / (height - 1)
if max_pool:
pre_pool_size = cfg.POOLING_SIZE * 2
grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, pre_pool_size, pre_pool_size)))
crops = F.grid_sample(bottom.expand(rois.size(0), bottom.size(1), bottom.size(2), bottom.size(3)), grid)
crops = F.max_pool2d(crops, 2, 2)
else:
grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, cfg.POOLING_SIZE, cfg.POOLING_SIZE)))
crops = F.grid_sample(bottom.expand(rois.size(0), bottom.size(1), bottom.size(2), bottom.size(3)), grid)
return crops
開(kāi)發(fā)者ID:Sunarker，項(xiàng)目名稱(chēng):Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數(shù):42，
示例10: _resize
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def _resize(self, x, size):
if x.shape[-2:] == size:
return x
elif x.shape[-2:] < size:
return F.interpolate(x, size=size, mode=self.upsample_mode)
else:
assert x.shape[-2] % size[-2] == 0 and x.shape[-1] % size[-1] == 0
kernel_size = x.shape[-1] // size[-1]
x = F.max_pool2d(x, kernel_size=kernel_size, stride=kernel_size)
return x
開(kāi)發(fā)者ID:open-mmlab，項(xiàng)目名稱(chēng):mmdetection，代碼行數(shù):12，
示例11: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
# Left branch
y1 = self.sep_conv1(x)
y2 = self.sep_conv2(x)
# Right branch
y3 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)
if self.stride==2:
y3 = self.bn1(self.conv1(y3))
y4 = self.sep_conv3(x)
# Concat & reduce channels
b1 = F.relu(y1+y2)
b2 = F.relu(y3+y4)
y = torch.cat([b1,b2], 1)
return F.relu(self.bn2(self.conv2(y)))
開(kāi)發(fā)者ID:StephanZheng，項(xiàng)目名稱(chēng):neural-fingerprinting，代碼行數(shù):16，
示例12: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
out = F.relu(self.conv1(x))
out = F.max_pool2d(out, 2)
out = F.relu(self.conv2(out))
out = F.max_pool2d(out, 2)
out = out.view(out.size(0), -1)
out = F.relu(self.fc1(out))
out = F.relu(self.fc2(out))
out = self.fc3(out)
return (out,F.log_softmax(out))
開(kāi)發(fā)者ID:StephanZheng，項(xiàng)目名稱(chēng):neural-fingerprinting，代碼行數(shù):12，
示例13: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
開(kāi)發(fā)者ID:StephanZheng，項(xiàng)目名稱(chēng):neural-fingerprinting，代碼行數(shù):10，
示例14: forward
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# 需要導(dǎo)入模塊: from torch.nn import functional [as 別名]
# 或者: from torch.nn.functional import max_pool2d [as 別名]
def forward(self, x):
y1 = self.sep_conv1(x)
y2 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)
if self.stride==2:
y2 = self.bn1(self.conv1(y2))
return F.relu(y1+y2)
開(kāi)發(fā)者ID:StephanZheng，項(xiàng)目名稱(chēng):neural-fingerprinting，代碼行數(shù):8，
注：本文中的torch.nn.functional.max_pool2d方法示例整理自Github/MSDocs等源碼及文檔管理平臺(tái)，相關(guān)代碼片段篩選自各路編程大神貢獻(xiàn)的開(kāi)源項(xiàng)目，源碼版權(quán)歸原作者所有，傳播和使用請(qǐng)參考對(duì)應(yīng)項(xiàng)目的License；未經(jīng)允許，請(qǐng)勿轉(zhuǎn)載。
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