init

README.md

-# animegan2-pytorch
+### PyTorch Implementation of [AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)
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+
+
+**Weight Conversion (Optional)**
+```
+git clone https://github.com/TachibanaYoshino/AnimeGANv2
+python convert_weights.py
+
+```
+
+**Inference**
+```
+python test.py --input_dir [image_folder_path]
+
+```
+
+**Results from converted [[Paprika](https://drive.google.com/file/d/1K_xN32uoQKI8XmNYNLTX5gDn1UnQVe5I/view?usp=sharing)] style model**
+
+(input image, original tensorflow result, pytorch result from left to right)
+
+<img src="./samples/compare/1.jpg" width="650"> &nbsp; 
+<img src="./samples/compare/2.jpg" width="650"> &nbsp; 
+<img src="./samples/compare/3.jpg" width="650"> &nbsp; 
+
+**Note:** Training code not included / Results looks slightly blurrier than the original ones.
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convert_weights.py

+import argparse
+
+import numpy as np
+import os
+
+import tensorflow as tf
+from AnimeGANv2.net import generator as tf_generator
+
+import torch
+from model import Generator
+
+
+def load_tf_weights(tf_path):
+    test_real = tf.placeholder(tf.float32, [1, None, None, 3], name='test')
+    with tf.variable_scope("generator", reuse=False):
+        test_generated = tf_generator.G_net(test_real).fake
+
+    saver = tf.train.Saver()
+
+    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, device_count = {'GPU': 0})) as sess:
+        ckpt = tf.train.get_checkpoint_state(tf_path)
+
+        assert ckpt is not None and ckpt.model_checkpoint_path is not None, f"Failed to load checkpoint {checkpoint_dir}"
+
+        saver.restore(sess, ckpt.model_checkpoint_path)
+        print(f"Tensorflow model checkpoint {ckpt.model_checkpoint_path} loaded")
+
+        tf_weights = {}
+        for v in tf.trainable_variables():
+            tf_weights[v.name] = v.eval()
+    
+    return tf_weights
+
+            
+def convert_keys(k):
+
+    # 1. divide tf weight name in three parts [block_idx, layer_idx, weight/bias]
+    # 2. handle each part & merge into a pytorch model keys
+    
+    k = k.replace("Conv/", "Conv_0/").replace("LayerNorm/", "LayerNorm_0/")    
+    keys = k.split("/")[2:]
+    
+    is_dconv = False
+
+    # handle C block..
+    if keys[0] == "C":
+        if keys[1] in ["Conv_1", "LayerNorm_1"]:
+            keys[1] = keys[1].replace("1", "5")
+        
+        if len(keys) == 4:
+            assert "r" in keys[1]
+
+            if keys[1] == keys[2]:
+                is_dconv = True
+                keys[2] = "1.1"
+            
+            block_c_maps = {
+                "1":  "1.2",
+                "Conv_1":  "2",
+                "2":  "3",
+            }
+            if keys[2] in block_c_maps:
+                keys[2] = block_c_maps[keys[2]]
+
+            keys[1] = keys[1].replace("r", "") + ".layers." + keys[2]
+            keys[2] = keys[3]
+            keys.pop(-1)
+    assert len(keys) == 3
+
+    # handle output block
+    if "out" in keys[0]:
+        keys[1] = "0"
+    
+    # first part
+    if keys[0] in ["A", "B", "C", "D", "E"]:
+        keys[0] = "block_" + keys[0].lower()        
+        
+    # second part
+    if "LayerNorm_" in keys[1]:
+        keys[1] = keys[1].replace("LayerNorm_", "") + ".2"
+    if "Conv_" in keys[1]:
+        keys[1] = keys[1].replace("Conv_", "") + ".1"
+        
+    # third part
+    keys[2] = {
+        "weights:0": "weight",
+        "w:0": "weight",
+        "bias:0": "bias",
+        "gamma:0": "weight",
+        "beta:0": "bias",
+    }[keys[2]]
+        
+    return ".".join(keys), is_dconv
+
+
+def convert_and_save(tf_checkpoint_path, save_name):
+
+    tf_weights = load_tf_weights(tf_checkpoint_path)
+    
+    torch_net = Generator()
+    torch_weights = torch_net.state_dict()
+
+    torch_converted_weights = {}
+    for k, v in tf_weights.items():
+        torch_k, is_dconv = convert_keys(k)
+        assert torch_k in torch_weights, f"weight name mismatch: {k}"
+
+        converted_weight = torch.from_numpy(v)
+        if len(converted_weight.shape) == 4:
+            if is_dconv:
+                converted_weight = converted_weight.permute(2, 3, 0, 1)
+            else:
+                converted_weight = converted_weight.permute(3, 2, 0, 1)
+
+        assert torch_weights[torch_k].shape == converted_weight.shape, f"shape mismatch: {k}"
+
+        torch_converted_weights[torch_k] = converted_weight
+
+    assert sorted(list(torch_converted_weights)) == sorted(list(torch_weights)), f"some weights are missing"
+    torch_net.load_state_dict(torch_converted_weights)    
+    torch.save(torch_net.state_dict(), save_name)
+    print(f"PyTorch model saved at {save_name}")
+    
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--tf_checkpoint_path',
+        type=str,
+        default='AnimeGANv2/checkpoint/generator_Paprika_weight',
+    )
+    parser.add_argument(
+        '--save_name', 
+        type=str, 
+        default='pytorch_generator_Paprika.pt',
+    )
+    args = parser.parse_args()
+    
+    convert_and_save(args.tf_checkpoint_path, args.save_name)
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model.py

+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class ConvNormLReLU(nn.Sequential):
+    def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, pad_mode="reflect", groups=1, bias=False):
+        
+        pad_layer = {
+            "zero":    nn.ZeroPad2d,
+            "same":    nn.ReplicationPad2d,
+            "reflect": nn.ReflectionPad2d,
+        }
+        if pad_mode not in pad_layer:
+            raise NotImplementedError
+            
+        super(ConvNormLReLU, self).__init__(
+            pad_layer[pad_mode](padding),
+            nn.Conv2d(in_ch, out_ch, kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=bias),
+            nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True),
+            nn.LeakyReLU(0.2, inplace=True)
+        )
+
+
+class InvertedResBlock(nn.Module):
+    def __init__(self, in_ch, out_ch, expansion_ratio=2):
+        super(InvertedResBlock, self).__init__()
+
+        self.use_res_connect = in_ch == out_ch
+        bottleneck = int(round(in_ch*expansion_ratio))
+        layers = []
+        if expansion_ratio != 1:
+            layers.append(ConvNormLReLU(in_ch, bottleneck, kernel_size=1, padding=0))
+        
+        # dw
+        layers.append(ConvNormLReLU(bottleneck, bottleneck, groups=bottleneck, bias=True))
+        # pw
+        layers.append(nn.Conv2d(bottleneck, out_ch, kernel_size=1, padding=0, bias=False))
+        layers.append(nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True))
+
+        self.layers = nn.Sequential(*layers)
+        
+    def forward(self, input):
+        out = self.layers(input)
+        if self.use_res_connect:
+            out = input + out
+        return out
+
+    
+class Generator(nn.Module):
+    def __init__(self, ):
+        super().__init__()
+        
+        self.block_a = nn.Sequential(
+            ConvNormLReLU(3,  32, kernel_size=7, padding=3),
+            ConvNormLReLU(32, 64, stride=2, padding=(0,1,0,1)),
+            ConvNormLReLU(64, 64)
+        )
+        
+        self.block_b = nn.Sequential(
+            ConvNormLReLU(64,  128, stride=2, padding=(0,1,0,1)),            
+            ConvNormLReLU(128, 128)
+        )
+        
+        self.block_c = nn.Sequential(
+            ConvNormLReLU(128, 128),
+            InvertedResBlock(128, 256, 2),
+            InvertedResBlock(256, 256, 2),
+            InvertedResBlock(256, 256, 2),
+            InvertedResBlock(256, 256, 2),
+            ConvNormLReLU(256, 128),
+        )    
+        
+        self.block_d = nn.Sequential(
+            ConvNormLReLU(128, 128),
+            ConvNormLReLU(128, 128)
+        )
+
+        self.block_e = nn.Sequential(
+            ConvNormLReLU(128, 64),
+            ConvNormLReLU(64,  64),
+            ConvNormLReLU(64,  32, kernel_size=7, padding=3)
+        )
+
+        self.out_layer = nn.Sequential(
+            nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.Tanh()
+        )
+        
+    def forward(self, input):
+        out = self.block_a(input)
+        half_size = out.size()[-2:]
+        out = self.block_b(out)
+        out = self.block_c(out)
+        
+#         out = F.interpolate(out, half_size, mode="bilinear", align_corners=True)
+        out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
+        out = self.block_d(out)
+
+#         out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True)
+        out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False)
+        out = self.block_e(out)
+
+        out = self.out_layer(out)
+        return out
+        
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test.py

+import argparse
+
+import torch
+import cv2
+import numpy as np
+import os
+
+from model import Generator
+
+torch.backends.cudnn.enabled = False
+torch.backends.cudnn.benchmark = False
+torch.backends.cudnn.deterministic = True
+    
+def load_image(image_path):
+    img = cv2.imread(image_path).astype(np.float32)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    h, w = img.shape[:2]
+
+    def to_32s(x):
+        return 256 if x < 256 else x - x%32
+
+    img = cv2.resize(img, (to_32s(w), to_32s(h)))
+    img = torch.from_numpy(img)
+    img = img/127.5 - 1.0
+    return img
+
+
+def test(args):
+    device = args.device
+    
+    net = Generator()
+    net.load_state_dict(torch.load(args.checkpoint, map_location="cpu"))
+    net.to(device).eval()
+    print(f"model loaded: {args.checkpoint}")
+    
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    for image_name in sorted(os.listdir(args.input_dir)):
+        if os.path.splitext(image_name)[-1] not in [".jpg", ".png", ".bmp", ".tiff"]:
+            continue
+            
+        image = load_image(os.path.join(args.input_dir, image_name))
+
+        with torch.no_grad():
+            input = image.permute(2, 0, 1).unsqueeze(0).to(device)
+            out = net(input).squeeze(0).permute(1, 2, 0).cpu().numpy()
+            out = (out + 1)*127.5
+            out = np.clip(out, 0, 255).astype(np.uint8)
+            
+        cv2.imwrite(os.path.join(args.output_dir, image_name), cv2.cvtColor(out, cv2.COLOR_BGR2RGB))
+        print(f"image saved: {image_name}")
+
+    
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--checkpoint',
+        type=str,
+        default='./pytorch_generator_Paprika.pt',
+    )
+    parser.add_argument(
+        '--input_dir', 
+        type=str, 
+        default='./samples/inputs',
+    )
+    parser.add_argument(
+        '--output_dir', 
+        type=str, 
+        default='./samples/results',
+    )
+    parser.add_argument(
+        '--device',
+        type=str,
+        default='cuda:0',
+    )
+    args = parser.parse_args()
+    
+    test(args)
+