resnet.py

 
"""
This module implements the ResNet generator model.
"""
 
import torch
import torch.nn as nn
import torch.nn.functional as F
 
 
 
class ResidualBlock(nn.Module):
    """Residual block layer."""
 
    
    def __init__(self, ninput, noutput, upsample=True):
        super().__init__()
        self.upsampleupsample = upsample
        # shortcut branch
        self.convconv = None
        if upsample or (ninput != noutput):
            self.convconv = nn.Conv2d(ninput, noutput, 1, 1, 0)
        # residual branch
        self.norm1norm1 = nn.BatchNorm2d(ninput)
        self.conv1conv1 = nn.Conv2d(ninput, noutput, 3, 1, 1)
        self.norm2norm2 = nn.BatchNorm2d(noutput)
        self.conv2conv2 = nn.Conv2d(noutput, noutput, 3, 1, 1)
 
    
    def forward(self, x):
        """Compute the layer output for a given input."""
        # residual branch
        h = x
        h = self.norm1norm1(h)
        h.relu_()
        if self.upsampleupsample:
            h = F.interpolate(h, mode="nearest", scale_factor=2)
        h = self.conv1conv1(h)
        h = self.norm2norm2(h)
        h.relu_()
        h = self.conv2conv2(h)
        # shortcut branch
        if self.upsampleupsample:
            x = F.interpolate(x, mode="nearest", scale_factor=2)
        if self.convconv:
            x = self.convconv(x)
        # return sum of both
        return h + x
 
    
 
    
 
    
 
    
 
    
 
    
 
 
 
class Model(nn.Module):
    """ResNet generator model."""
 
    
    def __init__(self):
        super().__init__()
        # fully-connected inputs
        self.fcfc = nn.Linear(96, 49152)
        # stack of residual blocks
        self.blocksblocks = nn.ModuleList(
            [
                # (256, 8, 24)
                ResidualBlock(256, 256),
                # (256, 16, 48)
                ResidualBlock(256, 128),
                # (128, 32, 96)
                ResidualBlock(128, 64),
                # (64, 64, 192)
                ResidualBlock(64, 32),
                # (32, 128, 384)
                ResidualBlock(32, 16),
                # (16, 256, 768)
            ]
        )
        # norm and conv outputs
        self.normnorm = nn.BatchNorm2d(16)
        self.convconv = nn.Conv2d(16, 1, 3, 1, 1)
 
    
    def forward(self, z):
        """Compute the model output for a given input."""
        z = self.fcfc(z)
        z = z.view(-1, 256, 8, 24)
        for block in self.blocksblocks:
            z = block(z)
        z = self.normnorm(z)
        z.relu_()
        z = self.convconv(z)
        return z.tanh_()
 
    
 
    
 
    
 
    
 
 
 
def generate(model):
    """Produce one pseudo-random image for each PXD module
    using the ResNet generator model.
    """
    # infer the device that is in use
    device = next(model.parameters()).device
    # without computing gradients
    with torch.no_grad():
        # initialize the model input(s)
        z = torch.randn(40, 96, device=device)
        # evaluate the model output and crop
        x = model(z)[:, 0, 3:-3, :]
        # delete the reference(s) to model input(s)
        del z
        # always transfer to the CPU
        x = x.cpu()
        # apply the inverse logarithmic transformation
        x = x.mul_(0.5).add_(0.5).clamp_(0.0, 1.0)
        x = torch.pow(256.0, x).sub_(1.0).clamp_(0.0, 255.0)
        # convert to unsigned 8-bit integer data type
        return x.to(torch.uint8)