Python torch.nn.functional.max_unpool2d() Examples
The following are 10
code examples of torch.nn.functional.max_unpool2d().
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Example #1
| Source File: network.py From piwise with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def forward(self, x):
x1 = self.dec1(x)
d1, m1 = F.max_pool2d(x1, kernel_size=2, stride=2, return_indices=True)
x2 = self.dec2(d1)
d2, m2 = F.max_pool2d(x2, kernel_size=2, stride=2, return_indices=True)
x3 = self.dec3(d2)
d3, m3 = F.max_pool2d(x3, kernel_size=2, stride=2, return_indices=True)
x4 = self.dec4(d3)
d4, m4 = F.max_pool2d(x4, kernel_size=2, stride=2, return_indices=True)
x5 = self.dec5(d4)
d5, m5 = F.max_pool2d(x5, kernel_size=2, stride=2, return_indices=True)
def upsample(d):
e5 = self.enc5(F.max_unpool2d(d, m5, kernel_size=2, stride=2, output_size=x5.size()))
e4 = self.enc4(F.max_unpool2d(e5, m4, kernel_size=2, stride=2, output_size=x4.size()))
e3 = self.enc3(F.max_unpool2d(e4, m3, kernel_size=2, stride=2, output_size=x3.size()))
e2 = self.enc2(F.max_unpool2d(e3, m2, kernel_size=2, stride=2, output_size=x2.size()))
e1 = F.max_unpool2d(e2, m1, kernel_size=2, stride=2, output_size=x1.size())
return e1
e = upsample(d5)
return self.final(e)
Example #2
| Source File: segnet.py From binseg_pytoch with Apache License 2.0 | 6 votes |
|
def forward(self,x):
## Assumion X of size 240x320x3
pool1, indices1 = F.max_pool2d(self.conv1(x), 2, 2, return_indices=True) ## 120x160x64
pool2, indices2 = F.max_pool2d(self.conv2(pool1), 2,2,return_indices=True) ## 60x80x128
pool3, indices3 = F.max_pool2d(self.conv3(pool2), 2,2, return_indices=True) ## 30x40x256
pool4, indices4 = F.max_pool2d(self.conv4(pool3), 2, 2, return_indices=True) ## 15x20x512
pool5, indices5 = F.max_pool2d(self.conv5(pool4), 2, 2, return_indices=True) ## 7x10x512
dec1 = self.dec512(F.max_unpool2d(pool5,indices5,2, 2, output_size=pool4.size())) #15x20x512
dec2 = self.dec256(F.max_unpool2d(dec1, indices4,2,2)) ## 30x40x256
dec3 = self.dec128(F.max_unpool2d(dec2, indices3, 2, 2)) ## 60x80x128
dec4 = self.dec64(F.max_unpool2d(dec3, indices2, 2, 2)) ## 120x160x64
dec5 = self.final(F.max_unpool2d(dec4, indices1, 2, 2)) ## 240x320x1
return self.activation(dec5)
Example #3
| Source File: deep_lift.py From captum with BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def maxpool2d(
module: Module,
inputs: Tensor,
outputs: Tensor,
grad_input: Tensor,
grad_output: Tensor,
eps: float = 1e-10,
):
return maxpool(
module,
F.max_pool2d,
F.max_unpool2d,
inputs,
outputs,
grad_input,
grad_output,
eps=eps,
)
Example #4
| Source File: segnet.py From pytorch-unet-segnet with MIT License | 5 votes |
|
def forward(self, x, indices, size):
unpooled = F.max_unpool2d(x, indices, 2, 2, 0, size)
return self.features(unpooled)
Example #5
| Source File: modsegnet.py From pytorch-unet-segnet with MIT License | 5 votes |
|
def forward(self, x, feat_encoder, indices, size):
unpooled = F.max_unpool2d(x, indices, 2, 2, 0, size)
feat = torch.cat([unpooled, feat_encoder], 1)
feat = self.encoder(feat)
return feat
Example #6
| Source File: segnet.py From pytorch_segmentation with MIT License | 5 votes |
|
def forward(self, x):
inputsize = x.size()
# Encoder
x, indices = self.first_conv(x)
x = self.encoder(x)
# Decoder
x = self.decoder(x)
h_diff = ceil((x.size()[2] - indices.size()[2]) / 2)
w_diff = ceil((x.size()[3] - indices.size()[3]) / 2)
if indices.size()[2] % 2 == 1:
x = x[:, :, h_diff:x.size()[2]-(h_diff-1), w_diff: x.size()[3]-(w_diff-1)]
else:
x = x[:, :, h_diff:x.size()[2]-h_diff, w_diff: x.size()[3]-w_diff]
x = F.max_unpool2d(x, indices, kernel_size=2, stride=2)
x = self.last_conv(x)
if inputsize != x.size():
h_diff = (x.size()[2] - inputsize[2]) // 2
w_diff = (x.size()[3] - inputsize[3]) // 2
x = x[:, :, h_diff:x.size()[2]-h_diff, w_diff: x.size()[3]-w_diff]
if h_diff % 2 != 0: x = x[:, :, :-1, :]
if w_diff % 2 != 0: x = x[:, :, :, :-1]
return x
Example #7
| Source File: test_pyprof_nvtx.py From apex with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_max_unpool2d(self):
inp = torch.randn(1, 16, 32, 32, device='cuda', dtype=self.dtype)
output, indices = F.max_pool2d(inp, kernel_size=5, stride=2, padding=2, return_indices=True, ceil_mode=True)
output = F.max_unpool2d(output, indices, kernel_size=2, stride=2, padding=2)
Example #8
| Source File: segnet.py From DenseFusion with MIT License | 4 votes |
|
def forward(self, x):
x11 = F.relu(self.bn11(self.conv11(x)))
x12 = F.relu(self.bn12(self.conv12(x11)))
x1p, id1 = F.max_pool2d(x12,kernel_size=2, stride=2,return_indices=True)
x21 = F.relu(self.bn21(self.conv21(x1p)))
x22 = F.relu(self.bn22(self.conv22(x21)))
x2p, id2 = F.max_pool2d(x22,kernel_size=2, stride=2,return_indices=True)
x31 = F.relu(self.bn31(self.conv31(x2p)))
x32 = F.relu(self.bn32(self.conv32(x31)))
x33 = F.relu(self.bn33(self.conv33(x32)))
x3p, id3 = F.max_pool2d(x33,kernel_size=2, stride=2,return_indices=True)
x41 = F.relu(self.bn41(self.conv41(x3p)))
x42 = F.relu(self.bn42(self.conv42(x41)))
x43 = F.relu(self.bn43(self.conv43(x42)))
x4p, id4 = F.max_pool2d(x43,kernel_size=2, stride=2,return_indices=True)
x51 = F.relu(self.bn51(self.conv51(x4p)))
x52 = F.relu(self.bn52(self.conv52(x51)))
x53 = F.relu(self.bn53(self.conv53(x52)))
x5p, id5 = F.max_pool2d(x53,kernel_size=2, stride=2,return_indices=True)
x5d = F.max_unpool2d(x5p, id5, kernel_size=2, stride=2)
x53d = F.relu(self.bn53d(self.conv53d(x5d)))
x52d = F.relu(self.bn52d(self.conv52d(x53d)))
x51d = F.relu(self.bn51d(self.conv51d(x52d)))
x4d = F.max_unpool2d(x51d, id4, kernel_size=2, stride=2)
x43d = F.relu(self.bn43d(self.conv43d(x4d)))
x42d = F.relu(self.bn42d(self.conv42d(x43d)))
x41d = F.relu(self.bn41d(self.conv41d(x42d)))
x3d = F.max_unpool2d(x41d, id3, kernel_size=2, stride=2)
x33d = F.relu(self.bn33d(self.conv33d(x3d)))
x32d = F.relu(self.bn32d(self.conv32d(x33d)))
x31d = F.relu(self.bn31d(self.conv31d(x32d)))
x2d = F.max_unpool2d(x31d, id2, kernel_size=2, stride=2)
x22d = F.relu(self.bn22d(self.conv22d(x2d)))
x21d = F.relu(self.bn21d(self.conv21d(x22d)))
x1d = F.max_unpool2d(x21d, id1, kernel_size=2, stride=2)
x12d = F.relu(self.bn12d(self.conv12d(x1d)))
x11d = self.conv11d(x12d)
return x11d
Example #9
| Source File: segnet.py From segnet_pytorch with GNU General Public License v3.0 | 4 votes |
|
def forward(self, x):
# Stage 1
x11 = F.relu(self.bn11(self.conv11(x)))
x12 = F.relu(self.bn12(self.conv12(x11)))
x1p, id1 = F.max_pool2d(x12,kernel_size=2, stride=2,return_indices=True)
# Stage 2
x21 = F.relu(self.bn21(self.conv21(x1p)))
x22 = F.relu(self.bn22(self.conv22(x21)))
x2p, id2 = F.max_pool2d(x22,kernel_size=2, stride=2,return_indices=True)
# Stage 3
x31 = F.relu(self.bn31(self.conv31(x2p)))
x32 = F.relu(self.bn32(self.conv32(x31)))
x33 = F.relu(self.bn33(self.conv33(x32)))
x3p, id3 = F.max_pool2d(x33,kernel_size=2, stride=2,return_indices=True)
# Stage 4
x41 = F.relu(self.bn41(self.conv41(x3p)))
x42 = F.relu(self.bn42(self.conv42(x41)))
x43 = F.relu(self.bn43(self.conv43(x42)))
x4p, id4 = F.max_pool2d(x43,kernel_size=2, stride=2,return_indices=True)
# Stage 5
x51 = F.relu(self.bn51(self.conv51(x4p)))
x52 = F.relu(self.bn52(self.conv52(x51)))
x53 = F.relu(self.bn53(self.conv53(x52)))
x5p, id5 = F.max_pool2d(x53,kernel_size=2, stride=2,return_indices=True)
# Stage 5d
x5d = F.max_unpool2d(x5p, id5, kernel_size=2, stride=2)
x53d = F.relu(self.bn53d(self.conv53d(x5d)))
x52d = F.relu(self.bn52d(self.conv52d(x53d)))
x51d = F.relu(self.bn51d(self.conv51d(x52d)))
# Stage 4d
x4d = F.max_unpool2d(x51d, id4, kernel_size=2, stride=2)
x43d = F.relu(self.bn43d(self.conv43d(x4d)))
x42d = F.relu(self.bn42d(self.conv42d(x43d)))
x41d = F.relu(self.bn41d(self.conv41d(x42d)))
# Stage 3d
x3d = F.max_unpool2d(x41d, id3, kernel_size=2, stride=2)
x33d = F.relu(self.bn33d(self.conv33d(x3d)))
x32d = F.relu(self.bn32d(self.conv32d(x33d)))
x31d = F.relu(self.bn31d(self.conv31d(x32d)))
# Stage 2d
x2d = F.max_unpool2d(x31d, id2, kernel_size=2, stride=2)
x22d = F.relu(self.bn22d(self.conv22d(x2d)))
x21d = F.relu(self.bn21d(self.conv21d(x22d)))
# Stage 1d
x1d = F.max_unpool2d(x21d, id1, kernel_size=2, stride=2)
x12d = F.relu(self.bn12d(self.conv12d(x1d)))
x11d = self.conv11d(x12d)
return x11d
Example #10
| Source File: SegNet.py From CGNet with MIT License | 4 votes |
|
def forward(self, x):
# Stage 1
x11 = F.relu(self.bn11(self.conv11(x)))
x12 = F.relu(self.bn12(self.conv12(x11)))
x1p, id1 = F.max_pool2d(x12,kernel_size=2, stride=2,return_indices=True)
# Stage 2
x21 = F.relu(self.bn21(self.conv21(x1p)))
x22 = F.relu(self.bn22(self.conv22(x21)))
x2p, id2 = F.max_pool2d(x22,kernel_size=2, stride=2,return_indices=True)
# Stage 3
x31 = F.relu(self.bn31(self.conv31(x2p)))
x32 = F.relu(self.bn32(self.conv32(x31)))
x33 = F.relu(self.bn33(self.conv33(x32)))
x3p, id3 = F.max_pool2d(x33,kernel_size=2, stride=2,return_indices=True)
# Stage 4
x41 = F.relu(self.bn41(self.conv41(x3p)))
x42 = F.relu(self.bn42(self.conv42(x41)))
x43 = F.relu(self.bn43(self.conv43(x42)))
x4p, id4 = F.max_pool2d(x43,kernel_size=2, stride=2,return_indices=True)
# Stage 5
x51 = F.relu(self.bn51(self.conv51(x4p)))
x52 = F.relu(self.bn52(self.conv52(x51)))
x53 = F.relu(self.bn53(self.conv53(x52)))
x5p, id5 = F.max_pool2d(x53,kernel_size=2, stride=2,return_indices=True)
# Stage 5d
x5d = F.max_unpool2d(x5p, id5, kernel_size=2, stride=2)
x53d = F.relu(self.bn53d(self.conv53d(x5d)))
x52d = F.relu(self.bn52d(self.conv52d(x53d)))
x51d = F.relu(self.bn51d(self.conv51d(x52d)))
# Stage 4d
x4d = F.max_unpool2d(x51d, id4, kernel_size=2, stride=2)
x43d = F.relu(self.bn43d(self.conv43d(x4d)))
x42d = F.relu(self.bn42d(self.conv42d(x43d)))
x41d = F.relu(self.bn41d(self.conv41d(x42d)))
# Stage 3d
x3d = F.max_unpool2d(x41d, id3, kernel_size=2, stride=2)
x33d = F.relu(self.bn33d(self.conv33d(x3d)))
x32d = F.relu(self.bn32d(self.conv32d(x33d)))
x31d = F.relu(self.bn31d(self.conv31d(x32d)))
# Stage 2d
x2d = F.max_unpool2d(x31d, id2, kernel_size=2, stride=2)
x22d = F.relu(self.bn22d(self.conv22d(x2d)))
x21d = F.relu(self.bn21d(self.conv21d(x22d)))
# Stage 1d
x1d = F.max_unpool2d(x21d, id1, kernel_size=2, stride=2)
x12d = F.relu(self.bn12d(self.conv12d(x1d)))
x11d = self.conv11d(x12d)
return x11d
