VQVAE

codes/models/vqvae/vqvae.py

@@ -0,0 +1,249 @@
+# Copyright 2018 The Sonnet Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or  implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+
+# Borrowed from https://github.com/rosinality/vq-vae-2-pytorch
+# Which was itself orrowed from https://github.com/deepmind/sonnet
+
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+import torch.distributed as distributed
+
+from trainer.networks import register_model
+from utils.util import checkpoint, opt_get
+
+
+class Quantize(nn.Module):
+    def __init__(self, dim, n_embed, decay=0.99, eps=1e-5):
+        super().__init__()
+
+        self.dim = dim
+        self.n_embed = n_embed
+        self.decay = decay
+        self.eps = eps
+
+        embed = torch.randn(dim, n_embed)
+        self.register_buffer("embed", embed)
+        self.register_buffer("cluster_size", torch.zeros(n_embed))
+        self.register_buffer("embed_avg", embed.clone())
+
+    def forward(self, input):
+        flatten = input.reshape(-1, self.dim)
+        dist = (
+            flatten.pow(2).sum(1, keepdim=True)
+            - 2 * flatten @ self.embed
+            + self.embed.pow(2).sum(0, keepdim=True)
+        )
+        _, embed_ind = (-dist).max(1)
+        embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype)
+        embed_ind = embed_ind.view(*input.shape[:-1])
+        quantize = self.embed_code(embed_ind)
+
+        if self.training:
+            embed_onehot_sum = embed_onehot.sum(0)
+            embed_sum = flatten.transpose(0, 1) @ embed_onehot
+
+            if distributed.is_initialized() and distributed.get_world_size() > 1:
+                distributed.all_reduce(embed_onehot_sum)
+                distributed.all_reduce(embed_sum)
+
+            self.cluster_size.data.mul_(self.decay).add_(
+                embed_onehot_sum, alpha=1 - self.decay
+            )
+            self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay)
+            n = self.cluster_size.sum()
+            cluster_size = (
+                (self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n
+            )
+            embed_normalized = self.embed_avg / cluster_size.unsqueeze(0)
+            self.embed.data.copy_(embed_normalized)
+
+        diff = (quantize.detach() - input).pow(2).mean()
+        quantize = input + (quantize - input).detach()
+
+        return quantize, diff, embed_ind
+
+    def embed_code(self, embed_id):
+        return F.embedding(embed_id, self.embed.transpose(0, 1))
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channel, channel):
+        super().__init__()
+
+        self.conv = nn.Sequential(
+            nn.ReLU(inplace=True),
+            nn.Conv2d(in_channel, channel, 3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(channel, in_channel, 1),
+        )
+
+    def forward(self, input):
+        out = self.conv(input)
+        out += input
+
+        return out
+
+
+class Encoder(nn.Module):
+    def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride):
+        super().__init__()
+
+        if stride == 4:
+            blocks = [
+                nn.Conv2d(in_channel, channel // 2, 4, stride=2, padding=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(channel // 2, channel, 4, stride=2, padding=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(channel, channel, 3, padding=1),
+            ]
+
+        elif stride == 2:
+            blocks = [
+                nn.Conv2d(in_channel, channel // 2, 4, stride=2, padding=1),
+                nn.ReLU(inplace=True),
+                nn.Conv2d(channel // 2, channel, 3, padding=1),
+            ]
+
+        for i in range(n_res_block):
+            blocks.append(ResBlock(channel, n_res_channel))
+
+        blocks.append(nn.ReLU(inplace=True))
+
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, input):
+        return self.blocks(input)
+
+
+class Decoder(nn.Module):
+    def __init__(
+        self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride
+    ):
+        super().__init__()
+
+        blocks = [nn.Conv2d(in_channel, channel, 3, padding=1)]
+
+        for i in range(n_res_block):
+            blocks.append(ResBlock(channel, n_res_channel))
+
+        blocks.append(nn.ReLU(inplace=True))
+
+        if stride == 4:
+            blocks.extend(
+                [
+                    nn.ConvTranspose2d(channel, channel // 2, 4, stride=2, padding=1),
+                    nn.ReLU(inplace=True),
+                    nn.ConvTranspose2d(
+                        channel // 2, out_channel, 4, stride=2, padding=1
+                    ),
+                ]
+            )
+
+        elif stride == 2:
+            blocks.append(
+                nn.ConvTranspose2d(channel, out_channel, 4, stride=2, padding=1)
+            )
+
+        self.blocks = nn.Sequential(*blocks)
+
+    def forward(self, input):
+        return self.blocks(input)
+
+
+class VQVAE(nn.Module):
+    def __init__(
+        self,
+        in_channel=3,
+        channel=128,
+        n_res_block=2,
+        n_res_channel=32,
+        codebook_dim=64,
+        codebook_size=512,
+        decay=0.99,
+    ):
+        super().__init__()
+
+        self.enc_b = Encoder(in_channel, channel, n_res_block, n_res_channel, stride=4)
+        self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2)
+        self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1)
+        self.quantize_t = Quantize(codebook_dim, codebook_size)
+        self.dec_t = Decoder(
+            codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2
+        )
+        self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1)
+        self.quantize_b = Quantize(codebook_dim, codebook_size)
+        self.upsample_t = nn.ConvTranspose2d(
+            codebook_dim, codebook_dim, 4, stride=2, padding=1
+        )
+        self.dec = Decoder(
+            codebook_dim + codebook_dim,
+            in_channel,
+            channel,
+            n_res_block,
+            n_res_channel,
+            stride=4,
+        )
+
+    def forward(self, input):
+        quant_t, quant_b, diff, _, _ = self.encode(input)
+        dec = self.decode(quant_t, quant_b)
+
+        return dec, diff
+
+    def encode(self, input):
+        enc_b = checkpoint(self.enc_b, input)
+        enc_t = checkpoint(self.enc_t, enc_b)
+
+        quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1)
+        quant_t, diff_t, id_t = self.quantize_t(quant_t)
+        quant_t = quant_t.permute(0, 3, 1, 2)
+        diff_t = diff_t.unsqueeze(0)
+
+        dec_t = checkpoint(self.dec_t, quant_t)
+        enc_b = torch.cat([dec_t, enc_b], 1)
+
+        quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1)
+        quant_b, diff_b, id_b = self.quantize_b(quant_b)
+        quant_b = quant_b.permute(0, 3, 1, 2)
+        diff_b = diff_b.unsqueeze(0)
+
+        return quant_t, quant_b, diff_t + diff_b, id_t, id_b
+
+    def decode(self, quant_t, quant_b):
+        upsample_t = self.upsample_t(quant_t)
+        quant = torch.cat([upsample_t, quant_b], 1)
+        dec = checkpoint(self.dec, quant)
+
+        return dec
+
+    def decode_code(self, code_t, code_b):
+        quant_t = self.quantize_t.embed_code(code_t)
+        quant_t = quant_t.permute(0, 3, 1, 2)
+        quant_b = self.quantize_b.embed_code(code_b)
+        quant_b = quant_b.permute(0, 3, 1, 2)
+
+        dec = self.decode(quant_t, quant_b)
+
+        return dec
+
+
+@register_model
+def register_vqvae(opt_net, opt):
+    kw = opt_get(opt_net, ['kwargs'], {})
+    return VQVAE(**kw)

codes/scripts/extract_square_images.py

@@ -14,14 +14,14 @@ def main():
     split_img = False
     opt = {}
     opt['n_thread'] = 4
-    opt['compression_level'] = 90  # JPEG compression quality rating.
+    opt['compression_level'] = 98  # JPEG compression quality rating.
     # CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
     # compression time. If read raw images during training, use 0 for faster IO speed.
 
     opt['dest'] = 'file'
-    opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\vixen\\vix_cropped']
-    opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\video_512_cropped'
-    opt['imgsize'] = 512
+    opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new']
+    opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_256_full'
+    opt['imgsize'] = 256
     #opt['bottom_crop'] = 120
 
     save_folder = opt['save_folder']

codes/train.py

@@ -295,7 +295,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_pixpro_resnet.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_stylesr.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()