Update 12/2 2

how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/neural_style.py

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+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+# Original source: https://github.com/pytorch/examples/blob/master/fast_neural_style/neural_style/neural_style.py
+import argparse
+import os
+import sys
+import re
+
+from PIL import Image
+import torch
+from torchvision import transforms
+
+
+def load_image(filename, size=None, scale=None):
+    img = Image.open(filename)
+    if size is not None:
+        img = img.resize((size, size), Image.ANTIALIAS)
+    elif scale is not None:
+        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
+    return img
+
+
+def save_image(filename, data):
+    img = data.clone().clamp(0, 255).numpy()
+    img = img.transpose(1, 2, 0).astype("uint8")
+    img = Image.fromarray(img)
+    img.save(filename)
+
+
+class TransformerNet(torch.nn.Module):
+    def __init__(self):
+        super(TransformerNet, self).__init__()
+        # Initial convolution layers
+        self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
+        self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
+        self.conv2 = ConvLayer(32, 64, kernel_size=3, stride=2)
+        self.in2 = torch.nn.InstanceNorm2d(64, affine=True)
+        self.conv3 = ConvLayer(64, 128, kernel_size=3, stride=2)
+        self.in3 = torch.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, kernel_size=3, stride=1, upsample=2)
+        self.in4 = torch.nn.InstanceNorm2d(64, affine=True)
+        self.deconv2 = UpsampleConvLayer(64, 32, kernel_size=3, stride=1, upsample=2)
+        self.in5 = torch.nn.InstanceNorm2d(32, affine=True)
+        self.deconv3 = ConvLayer(32, 3, kernel_size=9, stride=1)
+        # Non-linearities
+        self.relu = torch.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
+
+
+class ConvLayer(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride):
+        super(ConvLayer, self).__init__()
+        reflection_padding = kernel_size // 2
+        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
+        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
+
+    def forward(self, x):
+        out = self.reflection_pad(x)
+        out = self.conv2d(out)
+        return out
+
+
+class ResidualBlock(torch.nn.Module):
+    """ResidualBlock
+    introduced in: https://arxiv.org/abs/1512.03385
+    recommended architecture: http://torch.ch/blog/2016/02/04/resnets.html
+    """
+
+    def __init__(self, channels):
+        super(ResidualBlock, self).__init__()
+        self.conv1 = ConvLayer(channels, channels, kernel_size=3, stride=1)
+        self.in1 = torch.nn.InstanceNorm2d(channels, affine=True)
+        self.conv2 = ConvLayer(channels, channels, kernel_size=3, stride=1)
+        self.in2 = torch.nn.InstanceNorm2d(channels, affine=True)
+        self.relu = torch.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(torch.nn.Module):
+    """UpsampleConvLayer
+    Upsamples the input and then does a convolution. This method gives better results
+    compared to ConvTranspose2d.
+    ref: http://distill.pub/2016/deconv-checkerboard/
+    """
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride, upsample=None):
+        super(UpsampleConvLayer, self).__init__()
+        self.upsample = upsample
+        if upsample:
+            self.upsample_layer = torch.nn.Upsample(mode='nearest', scale_factor=upsample)
+        reflection_padding = kernel_size // 2
+        self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
+        self.conv2d = torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride)
+
+    def forward(self, x):
+        x_in = x
+        if self.upsample:
+            x_in = self.upsample_layer(x_in)
+        out = self.reflection_pad(x_in)
+        out = self.conv2d(out)
+        return out
+    
+
+def stylize(args):
+    device = torch.device("cuda" if args.cuda else "cpu")
+    with torch.no_grad():
+        style_model = TransformerNet()
+        state_dict = torch.load(os.path.join(args.model_dir, args.style+".pth"))
+        # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
+        for k in list(state_dict.keys()):
+            if re.search(r'in\d+\.running_(mean|var)$', k):
+                del state_dict[k]
+        style_model.load_state_dict(state_dict)
+        style_model.to(device)
+
+        filenames = os.listdir(args.content_dir)
+
+        for filename in filenames:
+            print("Processing {}".format(filename))
+            full_path = os.path.join(args.content_dir, filename)
+            content_image = load_image(full_path, scale=args.content_scale)
+            content_transform = transforms.Compose([
+                transforms.ToTensor(),
+                transforms.Lambda(lambda x: x.mul(255))
+            ])
+            content_image = content_transform(content_image)
+            content_image = content_image.unsqueeze(0).to(device)
+
+            output = style_model(content_image).cpu()
+
+            output_path = os.path.join(args.output_dir, filename)
+            save_image(output_path, output[0])
+
+def main():
+    arg_parser = argparse.ArgumentParser(description="parser for fast-neural-style")
+
+    arg_parser.add_argument("--content-scale", type=float, default=None,
+                                 help="factor for scaling down the content image")
+    arg_parser.add_argument("--model-dir", type=str, required=True,
+                                 help="saved model to be used for stylizing the image.")
+    arg_parser.add_argument("--cuda", type=int, required=True,
+                                 help="set it to 1 for running on GPU, 0 for CPU")
+    arg_parser.add_argument("--style", type=str,
+                                 help="style name")
+
+    arg_parser.add_argument("--content-dir", type=str, required=True,
+            help="directory holding the images")
+    arg_parser.add_argument("--output-dir", type=str, required=True,
+            help="directory holding the output images")
+    args = arg_parser.parse_args()
+
+    if args.cuda and not torch.cuda.is_available():
+        print("ERROR: cuda is not available, try running on CPU")
+        sys.exit(1)
+    os.makedirs(args.output_dir, exist_ok=True)
+    stylize(args)
+
+
+if __name__ == "__main__":
+    main()