diff --git a/README.md b/README.md
index b445919..04f8eeb 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,25 @@
-# animegan2-pytorch
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+### PyTorch Implementation of [AnimeGANv2](https://github.com/TachibanaYoshino/AnimeGANv2)
+
+
+**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)
+
+
+
+
+
+**Note:** Training code not included / Results looks slightly blurrier than the original ones.
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diff --git a/convert_weights.py b/convert_weights.py
new file mode 100644
index 0000000..e064709
--- /dev/null
+++ b/convert_weights.py
@@ -0,0 +1,140 @@
+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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diff --git a/model.py b/model.py
new file mode 100644
index 0000000..14cd552
--- /dev/null
+++ b/model.py
@@ -0,0 +1,106 @@
+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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diff --git a/test.py b/test.py
new file mode 100644
index 0000000..f1ad1cc
--- /dev/null
+++ b/test.py
@@ -0,0 +1,80 @@
+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)
+