utils.py

import math
import numpy as np
import pandas as pd

import gradio as gr
from huggingface_hub import from_pretrained_fastai
from fastai.vision.all import *
from torchvision.models import vgg19, vgg16

pascal_source = '.'
EXAMPLES_PATH = Path('/content/examples')
repo_id = "hugginglearners/fastai-style-transfer"

def get_stl_fs(fs): return fs[:-1]

def style_loss(inp:Tensor, out_feat:Tensor):
  "Calculate style loss, assumes we have `im_grams`"
  # Get batch size
  bs = inp[0].shape[0]
  loss = []
  # For every item in our inputs
  for y, f in zip(*map(get_stl_fs, [im_grams, inp])):
    # Calculate MSE
    loss.append(F.mse_loss(y.repeat(bs, 1, 1), gram(f)))
  # Multiply their sum by 30000
  return 3e5 * sum(loss)

class FeatureLoss(Module):
  "Combines two losses and features into a useable loss function"
  def __init__(self, feats, style_loss, act_loss, hooks, feat_net):
    store_attr()
    self.hooks = hooks
    self.feat_net = feat_net
    self.reset_metrics()

  def forward(self, pred, targ):
    # First get the features of our prediction and target
    pred_feat, targ_feat = self.feats(self.feat_net, self.hooks, pred), self.feats(self.feat_net, self.hooks, targ)
    # Calculate style and activation loss
    style_loss = self.style_loss(pred_feat, targ_feat)
    act_loss = self.act_loss(pred_feat, targ_feat)
    # Store the loss
    self._add_loss(style_loss, act_loss)
    # Return the sum
    return style_loss + act_loss

  def reset_metrics(self):
    # Generates a blank metric
    self.metrics = dict(style = [], content = [])

  def _add_loss(self, style_loss, act_loss):
    # Add to our metrics
    self.metrics['style'].append(style_loss)
    self.metrics['content'].append(act_loss)

def act_loss(inp:Tensor, targ:Tensor):
  "Calculate the MSE loss of the activation layers"
  return F.mse_loss(inp[-1], targ[-1])

class ReflectionLayer(Module):
    "A series of Reflection Padding followed by a ConvLayer"
    def __init__(self, in_channels, out_channels, ks=3, stride=2):
        reflection_padding = ks // 2
        self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
        self.conv2d = nn.Conv2d(in_channels, out_channels, ks, stride)

    def forward(self, x):
        out = self.reflection_pad(x)
        out = self.conv2d(out)
        return out

class ResidualBlock(Module):
    "Two reflection layers and an added activation function with residual"
    def __init__(self, channels):
          self.conv1 = ReflectionLayer(channels, channels, ks=3, stride=1)
          self.in1 = nn.InstanceNorm2d(channels, affine=True)
          self.conv2 = ReflectionLayer(channels, channels, ks=3, stride=1)
          self.in2 = nn.InstanceNorm2d(channels, affine=True)
          self.relu = nn.ReLU()

    def forward(self, x):
          residual = x
          out = self.relu(self.in1(self.conv1(x)))
          out = self.in2(self.conv2(out))
          out = out + residual
          return out

class UpsampleConvLayer(Module):
    "Upsample with a ReflectionLayer"
    def __init__(self, in_channels, out_channels, ks=3, stride=1, upsample=None):
        self.upsample = upsample
        reflection_padding = ks // 2
        self.reflection_pad = nn.ReflectionPad2d(reflection_padding)
        self.conv2d = nn.Conv2d(in_channels, out_channels, ks, stride)

    def forward(self, x):
        x_in = x
        if self.upsample:
            x_in = torch.nn.functional.interpolate(x_in, mode='nearest', scale_factor=self.upsample)
        out = self.reflection_pad(x_in)
        out = self.conv2d(out)
        return out

class TransformerNet(Module):
    "A simple network for style transfer"
    def __init__(self):
        # Initial convolution layers
        self.conv1 = ReflectionLayer(3, 32, ks=9, stride=1)
        self.in1 = nn.InstanceNorm2d(32, affine=True)
        self.conv2 = ReflectionLayer(32, 64, ks=3, stride=2)
        self.in2 = nn.InstanceNorm2d(64, affine=True)
        self.conv3 = ReflectionLayer(64, 128, ks=3, stride=2)
        self.in3 = nn.InstanceNorm2d(128, affine=True)
        # Residual layers
        self.res1 = ResidualBlock(128)
        self.res2 = ResidualBlock(128)
        self.res3 = ResidualBlock(128)
        self.res4 = ResidualBlock(128)
        self.res5 = ResidualBlock(128)
        # Upsampling Layers
        self.deconv1 = UpsampleConvLayer(128, 64, ks=3, stride=1, upsample=2)
        self.in4 = nn.InstanceNorm2d(64, affine=True)
        self.deconv2 = UpsampleConvLayer(64, 32, ks=3, stride=1, upsample=2)
        self.in5 = nn.InstanceNorm2d(32, affine=True)
        self.deconv3 = ReflectionLayer(32, 3, ks=9, stride=1)
        # Non-linearities
        self.relu = nn.ReLU()

    def forward(self, X):
        y = self.relu(self.in1(self.conv1(X)))
        y = self.relu(self.in2(self.conv2(y)))
        y = self.relu(self.in3(self.conv3(y)))
        y = self.res1(y)
        y = self.res2(y)
        y = self.res3(y)
        y = self.res4(y)
        y = self.res5(y)
        y = self.relu(self.in4(self.deconv1(y)))
        y = self.relu(self.in5(self.deconv2(y)))
        y = self.deconv3(y)
        return y