How to use PNASNet5 as encoder in Unet in pytorch

I want use PNASNet5Large as encoder for my Unet here is my wrong aproach for the PNASNet5Large but working for resnet:

class UNetResNet(nn.Module):
def __init__(self, encoder_depth, num_classes, num_filters=32, dropout_2d=0.2,
pretrained=False, is_deconv=False):
super().__init__()
self.num_classes = num_classes
self.dropout_2d = dropout_2d

if encoder_depth == 34:
self.encoder = torchvision.models.resnet34(pretrained=pretrained)
bottom_channel_nr = 512
elif encoder_depth == 101:
self.encoder = torchvision.models.resnet101(pretrained=pretrained)
bottom_channel_nr = 2048
elif encoder_depth == 152: #this works
self.encoder = torchvision.models.resnet152(pretrained=pretrained)
bottom_channel_nr = 2048
elif encoder_depth == 777: #coded version for the pnasnet
self.encoder = PNASNet5Large()
bottom_channel_nr = 4320 #this unknown for me as well


self.pool = nn.MaxPool2d(2, 2)
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Sequential(self.encoder.conv1,
self.encoder.bn1,
self.encoder.relu,
self.pool)

self.conv2 = self.encoder.layer1 #PNASNet5Large doesn't have such layers
self.conv3 = self.encoder.layer2
self.conv4 = self.encoder.layer3
self.conv5 = self.encoder.layer4
self.center = DecoderCenter(bottom_channel_nr, num_filters * 8 *2, num_filters * 8, False)

self.dec5 = DecoderBlock(bottom_channel_nr + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
self.dec4 = DecoderBlock(bottom_channel_nr // 2 + num_filters * 8, num_filters * 8 * 2, num_filters * 8, is_deconv)
self.dec3 = DecoderBlock(bottom_channel_nr // 4 + num_filters * 8, num_filters * 4 * 2, num_filters * 2, is_deconv)
self.dec2 = DecoderBlock(bottom_channel_nr // 8 + num_filters * 2, num_filters * 2 * 2, num_filters * 2 * 2,
is_deconv)
self.dec1 = DecoderBlock(num_filters * 2 * 2, num_filters * 2 * 2, num_filters, is_deconv)
self.dec0 = ConvRelu(num_filters, num_filters)
self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)

def forward(self, x):
conv1 = self.conv1(x)
conv2 = self.conv2(conv1)
conv3 = self.conv3(conv2)
conv4 = self.conv4(conv3)
conv5 = self.conv5(conv4)
center = self.center(conv5)
dec5 = self.dec5(torch.cat([center, conv5], 1))
dec4 = self.dec4(torch.cat([dec5, conv4], 1))
dec3 = self.dec3(torch.cat([dec4, conv3], 1))
dec2 = self.dec2(torch.cat([dec3, conv2], 1))
dec1 = self.dec1(dec2)
dec0 = self.dec0(dec1)
return self.final(F.dropout2d(dec0, p=self.dropout_2d))

1) How to get how many bottom channels pnasnet has. It ends up following way:

```
...
self.cell_11 = Cell(in_channels_left=4320, out_channels_left=864,
in_channels_right=4320, out_channels_right=864)
self.relu = nn.ReLU()
self.avg_pool = nn.AvgPool2d(11, stride=1, padding=0)
self.dropout = nn.Dropout(0.5)
self.last_linear = nn.Linear(4320, num_classes)
```

Is `4320` the answer or not, `in_channels_left` and `out_channels_left` - something new for me

2) Resnet has somekind of 4 big layers which I use and encoders in my Unet arch, how get similar layer from pnasnet

I'm using pytorch 3.1 and this is the link to the [Pnasnet directory][1]

3) AttributeError: 'PNASNet5Large' object has no attribute 'conv1' - so doesn't have conv1 as well

Start with the PNASNet directory and the PNASNet5Large implementation, then compare its cell structure and forward outputs with the UNetResNet encoder stages shown in the issue. Determine which intermediate features and channel sizes can serve as skip connections, and verify the resulting tensor shapes through the UNet path. Done means a documented, working way to use PNASNet5Large as the encoder.

Written by the indexing model from the issue text.