Delete src

src/audio2exp_models/audio2exp.py

@@ -1,41 +0,0 @@
-from tqdm import tqdm
-import torch
-from torch import nn
-
-
-class Audio2Exp(nn.Module):
-    def __init__(self, netG, cfg, device, prepare_training_loss=False):
-        super(Audio2Exp, self).__init__()
-        self.cfg = cfg
-        self.device = device
-        self.netG = netG.to(device)
-
-    def test(self, batch):
-
-        mel_input = batch['indiv_mels']                         # bs T 1 80 16
-        bs = mel_input.shape[0]
-        T = mel_input.shape[1]
-
-        exp_coeff_pred = []
-
-        for i in tqdm(range(0, T, 10),'audio2exp:'): # every 10 frames
-            
-            current_mel_input = mel_input[:,i:i+10]
-
-            #ref = batch['ref'][:, :, :64].repeat((1,current_mel_input.shape[1],1))           #bs T 64
-            ref = batch['ref'][:, :, :64][:, i:i+10]
-            ratio = batch['ratio_gt'][:, i:i+10]                               #bs T
-
-            audiox = current_mel_input.view(-1, 1, 80, 16)                  # bs*T 1 80 16
-
-            curr_exp_coeff_pred  = self.netG(audiox, ref, ratio)         # bs T 64 
-
-            exp_coeff_pred += [curr_exp_coeff_pred]
-
-        # BS x T x 64
-        results_dict = {
-            'exp_coeff_pred': torch.cat(exp_coeff_pred, axis=1)
-            }
-        return results_dict
-
-

src/audio2exp_models/networks.py

@@ -1,74 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-class Conv2d(nn.Module):
-    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, use_act = True, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.conv_block = nn.Sequential(
-                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
-                            nn.BatchNorm2d(cout)
-                            )
-        self.act = nn.ReLU()
-        self.residual = residual
-        self.use_act = use_act
-
-    def forward(self, x):
-        out = self.conv_block(x)
-        if self.residual:
-            out += x
-        
-        if self.use_act:
-            return self.act(out)
-        else:
-            return out
-
-class SimpleWrapperV2(nn.Module):
-    def __init__(self) -> None:
-        super().__init__()
-        self.audio_encoder = nn.Sequential(
-            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
-            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
-            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),
-            )
-
-        #### load the pre-trained audio_encoder 
-        #self.audio_encoder = self.audio_encoder.to(device)  
-        '''
-        wav2lip_state_dict = torch.load('/apdcephfs_cq2/share_1290939/wenxuazhang/checkpoints/wav2lip.pth')['state_dict']
-        state_dict = self.audio_encoder.state_dict()
-
-        for k,v in wav2lip_state_dict.items():
-            if 'audio_encoder' in k:
-                print('init:', k)
-                state_dict[k.replace('module.audio_encoder.', '')] = v
-        self.audio_encoder.load_state_dict(state_dict)
-        '''
-
-        self.mapping1 = nn.Linear(512+64+1, 64)
-        #self.mapping2 = nn.Linear(30, 64)
-        #nn.init.constant_(self.mapping1.weight, 0.)
-        nn.init.constant_(self.mapping1.bias, 0.)
-
-    def forward(self, x, ref, ratio):
-        x = self.audio_encoder(x).view(x.size(0), -1)
-        ref_reshape = ref.reshape(x.size(0), -1)
-        ratio = ratio.reshape(x.size(0), -1)
-        
-        y = self.mapping1(torch.cat([x, ref_reshape, ratio], dim=1)) 
-        out = y.reshape(ref.shape[0], ref.shape[1], -1) #+ ref # resudial
-        return out

src/audio2pose_models/audio2pose.py

@@ -1,94 +0,0 @@
-import torch
-from torch import nn
-from src.audio2pose_models.cvae import CVAE
-from src.audio2pose_models.discriminator import PoseSequenceDiscriminator
-from src.audio2pose_models.audio_encoder import AudioEncoder
-
-class Audio2Pose(nn.Module):
-    def __init__(self, cfg, wav2lip_checkpoint, device='cuda'):
-        super().__init__()
-        self.cfg = cfg
-        self.seq_len = cfg.MODEL.CVAE.SEQ_LEN
-        self.latent_dim = cfg.MODEL.CVAE.LATENT_SIZE
-        self.device = device
-
-        self.audio_encoder = AudioEncoder(wav2lip_checkpoint, device)
-        self.audio_encoder.eval()
-        for param in self.audio_encoder.parameters():
-            param.requires_grad = False
-
-        self.netG = CVAE(cfg)
-        self.netD_motion = PoseSequenceDiscriminator(cfg)
-        
-        
-    def forward(self, x):
-
-        batch = {}
-        coeff_gt = x['gt'].cuda().squeeze(0)           #bs frame_len+1 73
-        batch['pose_motion_gt'] = coeff_gt[:, 1:, -9:-3] - coeff_gt[:, :1, -9:-3] #bs frame_len 6
-        batch['ref'] = coeff_gt[:, 0, -9:-3]  #bs  6
-        batch['class'] = x['class'].squeeze(0).cuda() # bs
-        indiv_mels= x['indiv_mels'].cuda().squeeze(0) # bs seq_len+1 80 16
-
-        # forward
-        audio_emb_list = []
-        audio_emb = self.audio_encoder(indiv_mels[:, 1:, :, :].unsqueeze(2)) #bs seq_len 512
-        batch['audio_emb'] = audio_emb
-        batch = self.netG(batch)
-
-        pose_motion_pred = batch['pose_motion_pred']           # bs frame_len 6
-        pose_gt = coeff_gt[:, 1:, -9:-3].clone()               # bs frame_len 6
-        pose_pred = coeff_gt[:, :1, -9:-3] + pose_motion_pred  # bs frame_len 6
-
-        batch['pose_pred'] = pose_pred
-        batch['pose_gt'] = pose_gt
-
-        return batch
-
-    def test(self, x):
-
-        batch = {}
-        ref = x['ref']                            #bs 1 70
-        batch['ref'] = x['ref'][:,0,-6:]  
-        batch['class'] = x['class']  
-        bs = ref.shape[0]
-        
-        indiv_mels= x['indiv_mels']               # bs T 1 80 16
-        indiv_mels_use = indiv_mels[:, 1:]        # we regard the ref as the first frame
-        num_frames = x['num_frames']
-        num_frames = int(num_frames) - 1
-
-        #  
-        div = num_frames//self.seq_len
-        re = num_frames%self.seq_len
-        audio_emb_list = []
-        pose_motion_pred_list = [torch.zeros(batch['ref'].unsqueeze(1).shape, dtype=batch['ref'].dtype, 
-                                                device=batch['ref'].device)]
-
-        for i in range(div):
-            z = torch.randn(bs, self.latent_dim).to(ref.device)
-            batch['z'] = z
-            audio_emb = self.audio_encoder(indiv_mels_use[:, i*self.seq_len:(i+1)*self.seq_len,:,:,:]) #bs seq_len 512
-            batch['audio_emb'] = audio_emb
-            batch = self.netG.test(batch)
-            pose_motion_pred_list.append(batch['pose_motion_pred'])  #list of bs seq_len 6
-        
-        if re != 0:
-            z = torch.randn(bs, self.latent_dim).to(ref.device)
-            batch['z'] = z
-            audio_emb = self.audio_encoder(indiv_mels_use[:, -1*self.seq_len:,:,:,:]) #bs seq_len  512
-            if audio_emb.shape[1] != self.seq_len:
-                pad_dim = self.seq_len-audio_emb.shape[1]
-                pad_audio_emb = audio_emb[:, :1].repeat(1, pad_dim, 1) 
-                audio_emb = torch.cat([pad_audio_emb, audio_emb], 1) 
-            batch['audio_emb'] = audio_emb
-            batch = self.netG.test(batch)
-            pose_motion_pred_list.append(batch['pose_motion_pred'][:,-1*re:,:])   
-        
-        pose_motion_pred = torch.cat(pose_motion_pred_list, dim = 1)
-        batch['pose_motion_pred'] = pose_motion_pred
-
-        pose_pred = ref[:, :1, -6:] + pose_motion_pred  # bs T 6
-
-        batch['pose_pred'] = pose_pred
-        return batch

src/audio2pose_models/audio_encoder.py

@@ -1,64 +0,0 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-class Conv2d(nn.Module):
-    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.conv_block = nn.Sequential(
-                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
-                            nn.BatchNorm2d(cout)
-                            )
-        self.act = nn.ReLU()
-        self.residual = residual
-
-    def forward(self, x):
-        out = self.conv_block(x)
-        if self.residual:
-            out += x
-        return self.act(out)
-
-class AudioEncoder(nn.Module):
-    def __init__(self, wav2lip_checkpoint, device):
-        super(AudioEncoder, self).__init__()
-
-        self.audio_encoder = nn.Sequential(
-            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
-            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
-
-            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
-            Conv2d(512, 512, kernel_size=1, stride=1, padding=0),)
-
-        #### load the pre-trained audio_encoder
-        wav2lip_state_dict = torch.load(wav2lip_checkpoint, map_location=torch.device(device))['state_dict']
-        state_dict = self.audio_encoder.state_dict()
-
-        for k,v in wav2lip_state_dict.items():
-            if 'audio_encoder' in k:
-                state_dict[k.replace('module.audio_encoder.', '')] = v
-        self.audio_encoder.load_state_dict(state_dict)
-
-
-    def forward(self, audio_sequences):
-        # audio_sequences = (B, T, 1, 80, 16)
-        B = audio_sequences.size(0)
-
-        audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
-
-        audio_embedding = self.audio_encoder(audio_sequences) # B, 512, 1, 1
-        dim = audio_embedding.shape[1]
-        audio_embedding = audio_embedding.reshape((B, -1, dim, 1, 1))
-
-        return audio_embedding.squeeze(-1).squeeze(-1) #B seq_len+1 512 

src/audio2pose_models/cvae.py

@@ -1,149 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-from src.audio2pose_models.res_unet import ResUnet
-
-def class2onehot(idx, class_num):
-
-    assert torch.max(idx).item() < class_num
-    onehot = torch.zeros(idx.size(0), class_num).to(idx.device)
-    onehot.scatter_(1, idx, 1)
-    return onehot
-
-class CVAE(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        encoder_layer_sizes = cfg.MODEL.CVAE.ENCODER_LAYER_SIZES
-        decoder_layer_sizes = cfg.MODEL.CVAE.DECODER_LAYER_SIZES
-        latent_size = cfg.MODEL.CVAE.LATENT_SIZE
-        num_classes = cfg.DATASET.NUM_CLASSES
-        audio_emb_in_size = cfg.MODEL.CVAE.AUDIO_EMB_IN_SIZE
-        audio_emb_out_size = cfg.MODEL.CVAE.AUDIO_EMB_OUT_SIZE
-        seq_len = cfg.MODEL.CVAE.SEQ_LEN
-
-        self.latent_size = latent_size
-
-        self.encoder = ENCODER(encoder_layer_sizes, latent_size, num_classes,
-                                audio_emb_in_size, audio_emb_out_size, seq_len)
-        self.decoder = DECODER(decoder_layer_sizes, latent_size, num_classes,
-                                audio_emb_in_size, audio_emb_out_size, seq_len)
-    def reparameterize(self, mu, logvar):
-        std = torch.exp(0.5 * logvar)
-        eps = torch.randn_like(std)
-        return mu + eps * std
-
-    def forward(self, batch):
-        batch = self.encoder(batch)
-        mu = batch['mu']
-        logvar = batch['logvar']
-        z = self.reparameterize(mu, logvar)
-        batch['z'] = z
-        return self.decoder(batch)
-
-    def test(self, batch):
-        '''
-        class_id = batch['class']
-        z = torch.randn([class_id.size(0), self.latent_size]).to(class_id.device)
-        batch['z'] = z
-        '''
-        return self.decoder(batch)
-
-class ENCODER(nn.Module):
-    def __init__(self, layer_sizes, latent_size, num_classes, 
-                audio_emb_in_size, audio_emb_out_size, seq_len):
-        super().__init__()
-
-        self.resunet = ResUnet()
-        self.num_classes = num_classes
-        self.seq_len = seq_len
-
-        self.MLP = nn.Sequential()
-        layer_sizes[0] += latent_size + seq_len*audio_emb_out_size + 6
-        for i, (in_size, out_size) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
-            self.MLP.add_module(
-                name="L{:d}".format(i), module=nn.Linear(in_size, out_size))
-            self.MLP.add_module(name="A{:d}".format(i), module=nn.ReLU())
-
-        self.linear_means = nn.Linear(layer_sizes[-1], latent_size)
-        self.linear_logvar = nn.Linear(layer_sizes[-1], latent_size)
-        self.linear_audio = nn.Linear(audio_emb_in_size, audio_emb_out_size)
-
-        self.classbias = nn.Parameter(torch.randn(self.num_classes, latent_size))
-
-    def forward(self, batch):
-        class_id = batch['class']
-        pose_motion_gt = batch['pose_motion_gt']                             #bs seq_len 6
-        ref = batch['ref']                             #bs 6
-        bs = pose_motion_gt.shape[0]
-        audio_in = batch['audio_emb']                          # bs seq_len audio_emb_in_size
-
-        #pose encode
-        pose_emb = self.resunet(pose_motion_gt.unsqueeze(1))          #bs 1 seq_len 6 
-        pose_emb = pose_emb.reshape(bs, -1)                    #bs seq_len*6
-
-        #audio mapping
-        print(audio_in.shape)
-        audio_out = self.linear_audio(audio_in)                # bs seq_len audio_emb_out_size
-        audio_out = audio_out.reshape(bs, -1)
-
-        class_bias = self.classbias[class_id]                  #bs latent_size
-        x_in = torch.cat([ref, pose_emb, audio_out, class_bias], dim=-1) #bs seq_len*(audio_emb_out_size+6)+latent_size
-        x_out = self.MLP(x_in)
-
-        mu = self.linear_means(x_out)
-        logvar = self.linear_means(x_out)                      #bs latent_size 
-
-        batch.update({'mu':mu, 'logvar':logvar})
-        return batch
-
-class DECODER(nn.Module):
-    def __init__(self, layer_sizes, latent_size, num_classes, 
-                audio_emb_in_size, audio_emb_out_size, seq_len):
-        super().__init__()
-
-        self.resunet = ResUnet()
-        self.num_classes = num_classes
-        self.seq_len = seq_len
-
-        self.MLP = nn.Sequential()
-        input_size = latent_size + seq_len*audio_emb_out_size + 6
-        for i, (in_size, out_size) in enumerate(zip([input_size]+layer_sizes[:-1], layer_sizes)):
-            self.MLP.add_module(
-                name="L{:d}".format(i), module=nn.Linear(in_size, out_size))
-            if i+1 < len(layer_sizes):
-                self.MLP.add_module(name="A{:d}".format(i), module=nn.ReLU())
-            else:
-                self.MLP.add_module(name="sigmoid", module=nn.Sigmoid())
-        
-        self.pose_linear = nn.Linear(6, 6)
-        self.linear_audio = nn.Linear(audio_emb_in_size, audio_emb_out_size)
-
-        self.classbias = nn.Parameter(torch.randn(self.num_classes, latent_size))
-
-    def forward(self, batch):
-
-        z = batch['z']                                          #bs latent_size
-        bs = z.shape[0]
-        class_id = batch['class']
-        ref = batch['ref']                             #bs 6
-        audio_in = batch['audio_emb']                           # bs seq_len audio_emb_in_size
-        #print('audio_in: ', audio_in[:, :, :10])
-
-        audio_out = self.linear_audio(audio_in)                 # bs seq_len audio_emb_out_size
-        #print('audio_out: ', audio_out[:, :, :10])
-        audio_out = audio_out.reshape([bs, -1])                 # bs seq_len*audio_emb_out_size
-        class_bias = self.classbias[class_id]                   #bs latent_size
-
-        z = z + class_bias
-        x_in = torch.cat([ref, z, audio_out], dim=-1)
-        x_out = self.MLP(x_in)                                  # bs layer_sizes[-1]
-        x_out = x_out.reshape((bs, self.seq_len, -1))
-
-        #print('x_out: ', x_out)
-
-        pose_emb = self.resunet(x_out.unsqueeze(1))             #bs 1 seq_len 6
-
-        pose_motion_pred = self.pose_linear(pose_emb.squeeze(1))       #bs seq_len 6
-
-        batch.update({'pose_motion_pred':pose_motion_pred})
-        return batch

src/audio2pose_models/discriminator.py

@@ -1,76 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-class ConvNormRelu(nn.Module):
-    def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
-                 kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
-        super().__init__()
-        if kernel_size is None:
-            if downsample:
-                kernel_size, stride, padding = 4, 2, 1
-            else:
-                kernel_size, stride, padding = 3, 1, 1
-
-        if conv_type == '2d':
-            self.conv = nn.Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride,
-                padding,
-                bias=False,
-            )
-            if norm == 'BN':
-                self.norm = nn.BatchNorm2d(out_channels)
-            elif norm == 'IN':
-                self.norm = nn.InstanceNorm2d(out_channels)
-            else:
-                raise NotImplementedError
-        elif conv_type == '1d':
-            self.conv = nn.Conv1d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride,
-                padding,
-                bias=False,
-            )
-            if norm == 'BN':
-                self.norm = nn.BatchNorm1d(out_channels)
-            elif norm == 'IN':
-                self.norm = nn.InstanceNorm1d(out_channels)
-            else:
-                raise NotImplementedError
-        nn.init.kaiming_normal_(self.conv.weight)
-
-        self.act = nn.LeakyReLU(negative_slope=0.2, inplace=False) if leaky else nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.conv(x)
-        if isinstance(self.norm, nn.InstanceNorm1d):
-            x = self.norm(x.permute((0, 2, 1))).permute((0, 2, 1))  # normalize on [C]
-        else:
-            x = self.norm(x)
-        x = self.act(x)
-        return x
-
-
-class PoseSequenceDiscriminator(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        self.cfg = cfg
-        leaky = self.cfg.MODEL.DISCRIMINATOR.LEAKY_RELU
-
-        self.seq = nn.Sequential(
-            ConvNormRelu('1d', cfg.MODEL.DISCRIMINATOR.INPUT_CHANNELS, 256, downsample=True, leaky=leaky),  # B, 256, 64
-            ConvNormRelu('1d', 256, 512, downsample=True, leaky=leaky),  # B, 512, 32
-            ConvNormRelu('1d', 512, 1024, kernel_size=3, stride=1, padding=1, leaky=leaky),  # B, 1024, 16
-            nn.Conv1d(1024, 1, kernel_size=3, stride=1, padding=1, bias=True)  # B, 1, 16
-        )
-
-    def forward(self, x):
-        x = x.reshape(x.size(0), x.size(1), -1).transpose(1, 2)
-        x = self.seq(x)
-        x = x.squeeze(1)
-        return x

src/audio2pose_models/networks.py

@@ -1,140 +0,0 @@
-import torch.nn as nn
-import torch
-
-
-class ResidualConv(nn.Module):
-    def __init__(self, input_dim, output_dim, stride, padding):
-        super(ResidualConv, self).__init__()
-
-        self.conv_block = nn.Sequential(
-            nn.BatchNorm2d(input_dim),
-            nn.ReLU(),
-            nn.Conv2d(
-                input_dim, output_dim, kernel_size=3, stride=stride, padding=padding
-            ),
-            nn.BatchNorm2d(output_dim),
-            nn.ReLU(),
-            nn.Conv2d(output_dim, output_dim, kernel_size=3, padding=1),
-        )
-        self.conv_skip = nn.Sequential(
-            nn.Conv2d(input_dim, output_dim, kernel_size=3, stride=stride, padding=1),
-            nn.BatchNorm2d(output_dim),
-        )
-
-    def forward(self, x):
-
-        return self.conv_block(x) + self.conv_skip(x)
-
-
-class Upsample(nn.Module):
-    def __init__(self, input_dim, output_dim, kernel, stride):
-        super(Upsample, self).__init__()
-
-        self.upsample = nn.ConvTranspose2d(
-            input_dim, output_dim, kernel_size=kernel, stride=stride
-        )
-
-    def forward(self, x):
-        return self.upsample(x)
-
-
-class Squeeze_Excite_Block(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(Squeeze_Excite_Block, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y.expand_as(x)
-
-
-class ASPP(nn.Module):
-    def __init__(self, in_dims, out_dims, rate=[6, 12, 18]):
-        super(ASPP, self).__init__()
-
-        self.aspp_block1 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[0], dilation=rate[0]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-        self.aspp_block2 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[1], dilation=rate[1]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-        self.aspp_block3 = nn.Sequential(
-            nn.Conv2d(
-                in_dims, out_dims, 3, stride=1, padding=rate[2], dilation=rate[2]
-            ),
-            nn.ReLU(inplace=True),
-            nn.BatchNorm2d(out_dims),
-        )
-
-        self.output = nn.Conv2d(len(rate) * out_dims, out_dims, 1)
-        self._init_weights()
-
-    def forward(self, x):
-        x1 = self.aspp_block1(x)
-        x2 = self.aspp_block2(x)
-        x3 = self.aspp_block3(x)
-        out = torch.cat([x1, x2, x3], dim=1)
-        return self.output(out)
-
-    def _init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight)
-            elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-
-
-class Upsample_(nn.Module):
-    def __init__(self, scale=2):
-        super(Upsample_, self).__init__()
-
-        self.upsample = nn.Upsample(mode="bilinear", scale_factor=scale)
-
-    def forward(self, x):
-        return self.upsample(x)
-
-
-class AttentionBlock(nn.Module):
-    def __init__(self, input_encoder, input_decoder, output_dim):
-        super(AttentionBlock, self).__init__()
-
-        self.conv_encoder = nn.Sequential(
-            nn.BatchNorm2d(input_encoder),
-            nn.ReLU(),
-            nn.Conv2d(input_encoder, output_dim, 3, padding=1),
-            nn.MaxPool2d(2, 2),
-        )
-
-        self.conv_decoder = nn.Sequential(
-            nn.BatchNorm2d(input_decoder),
-            nn.ReLU(),
-            nn.Conv2d(input_decoder, output_dim, 3, padding=1),
-        )
-
-        self.conv_attn = nn.Sequential(
-            nn.BatchNorm2d(output_dim),
-            nn.ReLU(),
-            nn.Conv2d(output_dim, 1, 1),
-        )
-
-    def forward(self, x1, x2):
-        out = self.conv_encoder(x1) + self.conv_decoder(x2)
-        out = self.conv_attn(out)
-        return out * x2

src/audio2pose_models/res_unet.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-from src.audio2pose_models.networks import ResidualConv, Upsample
-
-
-class ResUnet(nn.Module):
-    def __init__(self, channel=1, filters=[32, 64, 128, 256]):
-        super(ResUnet, self).__init__()
-
-        self.input_layer = nn.Sequential(
-            nn.Conv2d(channel, filters[0], kernel_size=3, padding=1),
-            nn.BatchNorm2d(filters[0]),
-            nn.ReLU(),
-            nn.Conv2d(filters[0], filters[0], kernel_size=3, padding=1),
-        )
-        self.input_skip = nn.Sequential(
-            nn.Conv2d(channel, filters[0], kernel_size=3, padding=1)
-        )
-
-        self.residual_conv_1 = ResidualConv(filters[0], filters[1], stride=(2,1), padding=1)
-        self.residual_conv_2 = ResidualConv(filters[1], filters[2], stride=(2,1), padding=1)
-
-        self.bridge = ResidualConv(filters[2], filters[3], stride=(2,1), padding=1)
-
-        self.upsample_1 = Upsample(filters[3], filters[3], kernel=(2,1), stride=(2,1))
-        self.up_residual_conv1 = ResidualConv(filters[3] + filters[2], filters[2], stride=1, padding=1)
-
-        self.upsample_2 = Upsample(filters[2], filters[2], kernel=(2,1), stride=(2,1))
-        self.up_residual_conv2 = ResidualConv(filters[2] + filters[1], filters[1], stride=1, padding=1)
-
-        self.upsample_3 = Upsample(filters[1], filters[1], kernel=(2,1), stride=(2,1))
-        self.up_residual_conv3 = ResidualConv(filters[1] + filters[0], filters[0], stride=1, padding=1)
-
-        self.output_layer = nn.Sequential(
-            nn.Conv2d(filters[0], 1, 1, 1),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        # Encode
-        x1 = self.input_layer(x) + self.input_skip(x)
-        x2 = self.residual_conv_1(x1)
-        x3 = self.residual_conv_2(x2)
-        # Bridge
-        x4 = self.bridge(x3)
-
-        # Decode
-        x4 = self.upsample_1(x4)
-        x5 = torch.cat([x4, x3], dim=1)
-
-        x6 = self.up_residual_conv1(x5)
-
-        x6 = self.upsample_2(x6)
-        x7 = torch.cat([x6, x2], dim=1)
-
-        x8 = self.up_residual_conv2(x7)
-
-        x8 = self.upsample_3(x8)
-        x9 = torch.cat([x8, x1], dim=1)
-
-        x10 = self.up_residual_conv3(x9)
-
-        output = self.output_layer(x10)
-
-        return output

src/config/auido2exp.yaml

@@ -1,58 +0,0 @@
-DATASET:
-  TRAIN_FILE_LIST: /apdcephfs_cq2/share_1290939/wenxuazhang/code/file_list/train.txt
-  EVAL_FILE_LIST: /apdcephfs_cq2/share_1290939/wenxuazhang/code/file_list/val.txt
-  TRAIN_BATCH_SIZE: 32
-  EVAL_BATCH_SIZE: 32
-  EXP: True
-  EXP_DIM: 64
-  FRAME_LEN: 32
-  COEFF_LEN: 73
-  NUM_CLASSES: 46
-  AUDIO_ROOT_PATH: /apdcephfs_cq2/share_1290939/wenxuazhang/voxceleb1/wav
-  COEFF_ROOT_PATH: /apdcephfs_cq2/share_1290939/wenxuazhang/voxceleb1/wav2lip_3dmm
-  LMDB_PATH: /apdcephfs_cq2/share_1290939/shadowcun/datasets/VoxCeleb/v1/imdb
-  DEBUG: True
-  NUM_REPEATS: 2
-  T: 40
-  
-
-MODEL:
-  FRAMEWORK: V2
-  AUDIOENCODER:
-    LEAKY_RELU: True
-    NORM: 'IN'
-  DISCRIMINATOR:
-    LEAKY_RELU: False
-    INPUT_CHANNELS: 6
-  CVAE:
-    AUDIO_EMB_IN_SIZE: 512
-    AUDIO_EMB_OUT_SIZE: 128
-    SEQ_LEN: 32
-    LATENT_SIZE: 256
-    ENCODER_LAYER_SIZES: [192, 1024]
-    DECODER_LAYER_SIZES: [1024, 192]
-    
-
-TRAIN:
-  MAX_EPOCH: 300
-  GENERATOR:
-    LR: 2.0e-5
-  DISCRIMINATOR:
-    LR: 1.0e-5
-  LOSS:
-    W_FEAT: 0
-    W_COEFF_EXP: 2
-    W_LM: 1.0e-2
-    W_LM_MOUTH: 0
-    W_REG: 0
-    W_SYNC: 0
-    W_COLOR: 0
-    W_EXPRESSION: 0
-    W_LIPREADING: 0.01
-    W_LIPREADING_VV: 0
-    W_EYE_BLINK: 4
-
-TAG:
-  NAME:  small_dataset
-
-

src/config/auido2pose.yaml

@@ -1,49 +0,0 @@
-DATASET:
-  TRAIN_FILE_LIST: /apdcephfs_cq2/share_1290939/wenxuazhang/code/audio2pose_unet_noAudio/dataset/train_33.txt
-  EVAL_FILE_LIST: /apdcephfs_cq2/share_1290939/wenxuazhang/code/audio2pose_unet_noAudio/dataset/val.txt
-  TRAIN_BATCH_SIZE: 64
-  EVAL_BATCH_SIZE: 1
-  EXP: True
-  EXP_DIM: 64
-  FRAME_LEN: 32
-  COEFF_LEN: 73
-  NUM_CLASSES: 46
-  AUDIO_ROOT_PATH: /apdcephfs_cq2/share_1290939/wenxuazhang/voxceleb1/wav
-  COEFF_ROOT_PATH: /apdcephfs_cq2/share_1290939/shadowcun/datasets/VoxCeleb/v1/imdb
-  DEBUG: True
-  
-
-MODEL:
-  AUDIOENCODER:
-    LEAKY_RELU: True
-    NORM: 'IN'
-  DISCRIMINATOR:
-    LEAKY_RELU: False
-    INPUT_CHANNELS: 6
-  CVAE:
-    AUDIO_EMB_IN_SIZE: 512
-    AUDIO_EMB_OUT_SIZE: 6
-    SEQ_LEN: 32
-    LATENT_SIZE: 64
-    ENCODER_LAYER_SIZES: [192, 128]
-    DECODER_LAYER_SIZES: [128, 192]
-    
-
-TRAIN:
-  MAX_EPOCH: 150
-  GENERATOR:
-    LR: 1.0e-4
-  DISCRIMINATOR:
-    LR: 1.0e-4
-  LOSS:
-    LAMBDA_REG: 1
-    LAMBDA_LANDMARKS: 0
-    LAMBDA_VERTICES: 0
-    LAMBDA_GAN_MOTION: 0.7
-    LAMBDA_GAN_COEFF: 0
-    LAMBDA_KL: 1
-
-TAG:
-  NAME: cvae_UNET_useAudio_usewav2lipAudioEncoder
-
-

src/config/facerender.yaml

@@ -1,45 +0,0 @@
-model_params:
-  common_params:
-    num_kp: 15 
-    image_channel: 3                    
-    feature_channel: 32
-    estimate_jacobian: False   # True
-  kp_detector_params:
-     temperature: 0.1
-     block_expansion: 32            
-     max_features: 1024
-     scale_factor: 0.25         # 0.25
-     num_blocks: 5
-     reshape_channel: 16384  # 16384 = 1024 * 16
-     reshape_depth: 16
-  he_estimator_params:
-     block_expansion: 64            
-     max_features: 2048
-     num_bins: 66
-  generator_params:
-    block_expansion: 64
-    max_features: 512
-    num_down_blocks: 2
-    reshape_channel: 32
-    reshape_depth: 16         # 512 = 32 * 16
-    num_resblocks: 6
-    estimate_occlusion_map: True
-    dense_motion_params:
-      block_expansion: 32
-      max_features: 1024
-      num_blocks: 5
-      reshape_depth: 16
-      compress: 4
-  discriminator_params:
-    scales: [1]
-    block_expansion: 32                 
-    max_features: 512
-    num_blocks: 4
-    sn: True
-  mapping_params:
-      coeff_nc: 70
-      descriptor_nc: 1024
-      layer: 3
-      num_kp: 15
-      num_bins: 66
-

src/config/facerender_still.yaml

@@ -1,45 +0,0 @@
-model_params:
-  common_params:
-    num_kp: 15 
-    image_channel: 3                    
-    feature_channel: 32
-    estimate_jacobian: False   # True
-  kp_detector_params:
-     temperature: 0.1
-     block_expansion: 32            
-     max_features: 1024
-     scale_factor: 0.25         # 0.25
-     num_blocks: 5
-     reshape_channel: 16384  # 16384 = 1024 * 16
-     reshape_depth: 16
-  he_estimator_params:
-     block_expansion: 64            
-     max_features: 2048
-     num_bins: 66
-  generator_params:
-    block_expansion: 64
-    max_features: 512
-    num_down_blocks: 2
-    reshape_channel: 32
-    reshape_depth: 16         # 512 = 32 * 16
-    num_resblocks: 6
-    estimate_occlusion_map: True
-    dense_motion_params:
-      block_expansion: 32
-      max_features: 1024
-      num_blocks: 5
-      reshape_depth: 16
-      compress: 4
-  discriminator_params:
-    scales: [1]
-    block_expansion: 32                 
-    max_features: 512
-    num_blocks: 4
-    sn: True
-  mapping_params:
-      coeff_nc: 73
-      descriptor_nc: 1024
-      layer: 3
-      num_kp: 15
-      num_bins: 66
-

src/face3d/data/__init__.py

@@ -1,116 +0,0 @@
-"""This package includes all the modules related to data loading and preprocessing
-
- To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
- You need to implement four functions:
-    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
-    -- <__len__>:                       return the size of dataset.
-    -- <__getitem__>:                   get a data point from data loader.
-    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
-
-Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
-See our template dataset class 'template_dataset.py' for more details.
-"""
-import numpy as np
-import importlib
-import torch.utils.data
-from face3d.data.base_dataset import BaseDataset
-
-
-def find_dataset_using_name(dataset_name):
-    """Import the module "data/[dataset_name]_dataset.py".
-
-    In the file, the class called DatasetNameDataset() will
-    be instantiated. It has to be a subclass of BaseDataset,
-    and it is case-insensitive.
-    """
-    dataset_filename = "data." + dataset_name + "_dataset"
-    datasetlib = importlib.import_module(dataset_filename)
-
-    dataset = None
-    target_dataset_name = dataset_name.replace('_', '') + 'dataset'
-    for name, cls in datasetlib.__dict__.items():
-        if name.lower() == target_dataset_name.lower() \
-           and issubclass(cls, BaseDataset):
-            dataset = cls
-
-    if dataset is None:
-        raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
-
-    return dataset
-
-
-def get_option_setter(dataset_name):
-    """Return the static method <modify_commandline_options> of the dataset class."""
-    dataset_class = find_dataset_using_name(dataset_name)
-    return dataset_class.modify_commandline_options
-
-
-def create_dataset(opt, rank=0):
-    """Create a dataset given the option.
-
-    This function wraps the class CustomDatasetDataLoader.
-        This is the main interface between this package and 'train.py'/'test.py'
-
-    Example:
-        >>> from data import create_dataset
-        >>> dataset = create_dataset(opt)
-    """
-    data_loader = CustomDatasetDataLoader(opt, rank=rank)
-    dataset = data_loader.load_data()
-    return dataset
-
-class CustomDatasetDataLoader():
-    """Wrapper class of Dataset class that performs multi-threaded data loading"""
-
-    def __init__(self, opt, rank=0):
-        """Initialize this class
-
-        Step 1: create a dataset instance given the name [dataset_mode]
-        Step 2: create a multi-threaded data loader.
-        """
-        self.opt = opt
-        dataset_class = find_dataset_using_name(opt.dataset_mode)
-        self.dataset = dataset_class(opt)
-        self.sampler = None
-        print("rank %d %s dataset [%s] was created" % (rank, self.dataset.name, type(self.dataset).__name__))
-        if opt.use_ddp and opt.isTrain:
-            world_size = opt.world_size
-            self.sampler = torch.utils.data.distributed.DistributedSampler(
-                    self.dataset,
-                    num_replicas=world_size,
-                    rank=rank,
-                    shuffle=not opt.serial_batches
-                )
-            self.dataloader = torch.utils.data.DataLoader(
-                        self.dataset,
-                        sampler=self.sampler,
-                        num_workers=int(opt.num_threads / world_size), 
-                        batch_size=int(opt.batch_size / world_size), 
-                        drop_last=True)
-        else:
-            self.dataloader = torch.utils.data.DataLoader(
-                self.dataset,
-                batch_size=opt.batch_size,
-                shuffle=(not opt.serial_batches) and opt.isTrain,
-                num_workers=int(opt.num_threads),
-                drop_last=True
-            )
-
-    def set_epoch(self, epoch):
-        self.dataset.current_epoch = epoch
-        if self.sampler is not None:
-            self.sampler.set_epoch(epoch)
-
-    def load_data(self):
-        return self
-
-    def __len__(self):
-        """Return the number of data in the dataset"""
-        return min(len(self.dataset), self.opt.max_dataset_size)
-
-    def __iter__(self):
-        """Return a batch of data"""
-        for i, data in enumerate(self.dataloader):
-            if i * self.opt.batch_size >= self.opt.max_dataset_size:
-                break
-            yield data

src/face3d/data/base_dataset.py

@@ -1,125 +0,0 @@
-"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
-
-It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
-"""
-import random
-import numpy as np
-import torch.utils.data as data
-from PIL import Image
-import torchvision.transforms as transforms
-from abc import ABC, abstractmethod
-
-
-class BaseDataset(data.Dataset, ABC):
-    """This class is an abstract base class (ABC) for datasets.
-
-    To create a subclass, you need to implement the following four functions:
-    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
-    -- <__len__>:                       return the size of dataset.
-    -- <__getitem__>:                   get a data point.
-    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
-    """
-
-    def __init__(self, opt):
-        """Initialize the class; save the options in the class
-
-        Parameters:
-            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        self.opt = opt
-        # self.root = opt.dataroot
-        self.current_epoch = 0
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        return parser
-
-    @abstractmethod
-    def __len__(self):
-        """Return the total number of images in the dataset."""
-        return 0
-
-    @abstractmethod
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index - - a random integer for data indexing
-
-        Returns:
-            a dictionary of data with their names. It ususally contains the data itself and its metadata information.
-        """
-        pass
-
-
-def get_transform(grayscale=False):
-    transform_list = []
-    if grayscale:
-        transform_list.append(transforms.Grayscale(1))
-    transform_list += [transforms.ToTensor()]
-    return transforms.Compose(transform_list)
-
-def get_affine_mat(opt, size):
-    shift_x, shift_y, scale, rot_angle, flip = 0., 0., 1., 0., False
-    w, h = size
-
-    if 'shift' in opt.preprocess:
-        shift_pixs = int(opt.shift_pixs)
-        shift_x = random.randint(-shift_pixs, shift_pixs)
-        shift_y = random.randint(-shift_pixs, shift_pixs)
-    if 'scale' in opt.preprocess:
-        scale = 1 + opt.scale_delta * (2 * random.random() - 1)
-    if 'rot' in opt.preprocess:
-        rot_angle = opt.rot_angle * (2 * random.random() - 1)
-        rot_rad = -rot_angle * np.pi/180
-    if 'flip' in opt.preprocess:
-        flip = random.random() > 0.5
-
-    shift_to_origin = np.array([1, 0, -w//2, 0, 1, -h//2, 0, 0, 1]).reshape([3, 3])
-    flip_mat = np.array([-1 if flip else 1, 0, 0, 0, 1, 0, 0, 0, 1]).reshape([3, 3])
-    shift_mat = np.array([1, 0, shift_x, 0, 1, shift_y, 0, 0, 1]).reshape([3, 3])
-    rot_mat = np.array([np.cos(rot_rad), np.sin(rot_rad), 0, -np.sin(rot_rad), np.cos(rot_rad), 0, 0, 0, 1]).reshape([3, 3])
-    scale_mat = np.array([scale, 0, 0, 0, scale, 0, 0, 0, 1]).reshape([3, 3])
-    shift_to_center = np.array([1, 0, w//2, 0, 1, h//2, 0, 0, 1]).reshape([3, 3])
-    
-    affine = shift_to_center @ scale_mat @ rot_mat @ shift_mat @ flip_mat @ shift_to_origin    
-    affine_inv = np.linalg.inv(affine)
-    return affine, affine_inv, flip
-
-def apply_img_affine(img, affine_inv, method=Image.BICUBIC):
-    return img.transform(img.size, Image.AFFINE, data=affine_inv.flatten()[:6], resample=Image.BICUBIC)
-
-def apply_lm_affine(landmark, affine, flip, size):
-    _, h = size
-    lm = landmark.copy()
-    lm[:, 1] = h - 1 - lm[:, 1]
-    lm = np.concatenate((lm, np.ones([lm.shape[0], 1])), -1)
-    lm = lm @ np.transpose(affine)
-    lm[:, :2] = lm[:, :2] / lm[:, 2:]
-    lm = lm[:, :2]
-    lm[:, 1] = h - 1 - lm[:, 1]
-    if flip:
-        lm_ = lm.copy()
-        lm_[:17] = lm[16::-1]
-        lm_[17:22] = lm[26:21:-1]
-        lm_[22:27] = lm[21:16:-1]
-        lm_[31:36] = lm[35:30:-1]
-        lm_[36:40] = lm[45:41:-1]
-        lm_[40:42] = lm[47:45:-1]
-        lm_[42:46] = lm[39:35:-1]
-        lm_[46:48] = lm[41:39:-1]
-        lm_[48:55] = lm[54:47:-1]
-        lm_[55:60] = lm[59:54:-1]
-        lm_[60:65] = lm[64:59:-1]
-        lm_[65:68] = lm[67:64:-1]
-        lm = lm_
-    return lm

src/face3d/data/flist_dataset.py

@@ -1,125 +0,0 @@
-"""This script defines the custom dataset for Deep3DFaceRecon_pytorch
-"""
-
-import os.path
-from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
-from data.image_folder import make_dataset
-from PIL import Image
-import random
-import util.util as util
-import numpy as np
-import json
-import torch
-from scipy.io import loadmat, savemat
-import pickle
-from util.preprocess import align_img, estimate_norm
-from util.load_mats import load_lm3d
-
-
-def default_flist_reader(flist):
-    """
-    flist format: impath label\nimpath label\n ...(same to caffe's filelist)
-    """
-    imlist = []
-    with open(flist, 'r') as rf:
-        for line in rf.readlines():
-            impath = line.strip()
-            imlist.append(impath)
-
-    return imlist
-
-def jason_flist_reader(flist):
-    with open(flist, 'r') as fp:
-        info = json.load(fp)
-    return info
-
-def parse_label(label):
-    return torch.tensor(np.array(label).astype(np.float32))
-
-
-class FlistDataset(BaseDataset):
-    """
-    It requires one directories to host training images '/path/to/data/train'
-    You can train the model with the dataset flag '--dataroot /path/to/data'.
-    """
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        BaseDataset.__init__(self, opt)
-        
-        self.lm3d_std = load_lm3d(opt.bfm_folder)
-        
-        msk_names = default_flist_reader(opt.flist)
-        self.msk_paths = [os.path.join(opt.data_root, i) for i in msk_names]
-
-        self.size = len(self.msk_paths) 
-        self.opt = opt
-        
-        self.name = 'train' if opt.isTrain else 'val'
-        if '_' in opt.flist:
-            self.name += '_' + opt.flist.split(os.sep)[-1].split('_')[0]
-        
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index (int)      -- a random integer for data indexing
-
-        Returns a dictionary that contains A, B, A_paths and B_paths
-            img (tensor)       -- an image in the input domain
-            msk (tensor)       -- its corresponding attention mask
-            lm  (tensor)       -- its corresponding 3d landmarks
-            im_paths (str)     -- image paths
-            aug_flag (bool)    -- a flag used to tell whether its raw or augmented
-        """
-        msk_path = self.msk_paths[index % self.size]  # make sure index is within then range
-        img_path = msk_path.replace('mask/', '')
-        lm_path = '.'.join(msk_path.replace('mask', 'landmarks').split('.')[:-1]) + '.txt'
-
-        raw_img = Image.open(img_path).convert('RGB')
-        raw_msk = Image.open(msk_path).convert('RGB')
-        raw_lm = np.loadtxt(lm_path).astype(np.float32)
-
-        _, img, lm, msk = align_img(raw_img, raw_lm, self.lm3d_std, raw_msk)
-        
-        aug_flag = self.opt.use_aug and self.opt.isTrain
-        if aug_flag:
-            img, lm, msk = self._augmentation(img, lm, self.opt, msk)
-        
-        _, H = img.size
-        M = estimate_norm(lm, H)
-        transform = get_transform()
-        img_tensor = transform(img)
-        msk_tensor = transform(msk)[:1, ...]
-        lm_tensor = parse_label(lm)
-        M_tensor = parse_label(M)
-
-
-        return {'imgs': img_tensor, 
-                'lms': lm_tensor, 
-                'msks': msk_tensor, 
-                'M': M_tensor,
-                'im_paths': img_path, 
-                'aug_flag': aug_flag,
-                'dataset': self.name}
-
-    def _augmentation(self, img, lm, opt, msk=None):
-        affine, affine_inv, flip = get_affine_mat(opt, img.size)
-        img = apply_img_affine(img, affine_inv)
-        lm = apply_lm_affine(lm, affine, flip, img.size)
-        if msk is not None:
-            msk = apply_img_affine(msk, affine_inv, method=Image.BILINEAR)
-        return img, lm, msk
-    
-
-
-
-    def __len__(self):
-        """Return the total number of images in the dataset.
-        """
-        return self.size

src/face3d/data/image_folder.py

@@ -1,66 +0,0 @@
-"""A modified image folder class
-
-We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
-so that this class can load images from both current directory and its subdirectories.
-"""
-import numpy as np
-import torch.utils.data as data
-
-from PIL import Image
-import os
-import os.path
-
-IMG_EXTENSIONS = [
-    '.jpg', '.JPG', '.jpeg', '.JPEG',
-    '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
-    '.tif', '.TIF', '.tiff', '.TIFF',
-]
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def make_dataset(dir, max_dataset_size=float("inf")):
-    images = []
-    assert os.path.isdir(dir) or os.path.islink(dir), '%s is not a valid directory' % dir
-
-    for root, _, fnames in sorted(os.walk(dir, followlinks=True)):
-        for fname in fnames:
-            if is_image_file(fname):
-                path = os.path.join(root, fname)
-                images.append(path)
-    return images[:min(max_dataset_size, len(images))]
-
-
-def default_loader(path):
-    return Image.open(path).convert('RGB')
-
-
-class ImageFolder(data.Dataset):
-
-    def __init__(self, root, transform=None, return_paths=False,
-                 loader=default_loader):
-        imgs = make_dataset(root)
-        if len(imgs) == 0:
-            raise(RuntimeError("Found 0 images in: " + root + "\n"
-                               "Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
-
-        self.root = root
-        self.imgs = imgs
-        self.transform = transform
-        self.return_paths = return_paths
-        self.loader = loader
-
-    def __getitem__(self, index):
-        path = self.imgs[index]
-        img = self.loader(path)
-        if self.transform is not None:
-            img = self.transform(img)
-        if self.return_paths:
-            return img, path
-        else:
-            return img
-
-    def __len__(self):
-        return len(self.imgs)

src/face3d/data/template_dataset.py

@@ -1,75 +0,0 @@
-"""Dataset class template
-
-This module provides a template for users to implement custom datasets.
-You can specify '--dataset_mode template' to use this dataset.
-The class name should be consistent with both the filename and its dataset_mode option.
-The filename should be <dataset_mode>_dataset.py
-The class name should be <Dataset_mode>Dataset.py
-You need to implement the following functions:
-    -- <modify_commandline_options>:　Add dataset-specific options and rewrite default values for existing options.
-    -- <__init__>: Initialize this dataset class.
-    -- <__getitem__>: Return a data point and its metadata information.
-    -- <__len__>: Return the number of images.
-"""
-from data.base_dataset import BaseDataset, get_transform
-# from data.image_folder import make_dataset
-# from PIL import Image
-
-
-class TemplateDataset(BaseDataset):
-    """A template dataset class for you to implement custom datasets."""
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new dataset-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        parser.add_argument('--new_dataset_option', type=float, default=1.0, help='new dataset option')
-        parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0)  # specify dataset-specific default values
-        return parser
-
-    def __init__(self, opt):
-        """Initialize this dataset class.
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        A few things can be done here.
-        - save the options (have been done in BaseDataset)
-        - get image paths and meta information of the dataset.
-        - define the image transformation.
-        """
-        # save the option and dataset root
-        BaseDataset.__init__(self, opt)
-        # get the image paths of your dataset;
-        self.image_paths = []  # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
-        # define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
-        self.transform = get_transform(opt)
-
-    def __getitem__(self, index):
-        """Return a data point and its metadata information.
-
-        Parameters:
-            index -- a random integer for data indexing
-
-        Returns:
-            a dictionary of data with their names. It usually contains the data itself and its metadata information.
-
-        Step 1: get a random image path: e.g., path = self.image_paths[index]
-        Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB').
-        Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image)
-        Step 4: return a data point as a dictionary.
-        """
-        path = 'temp'    # needs to be a string
-        data_A = None    # needs to be a tensor
-        data_B = None    # needs to be a tensor
-        return {'data_A': data_A, 'data_B': data_B, 'path': path}
-
-    def __len__(self):
-        """Return the total number of images."""
-        return len(self.image_paths)

src/face3d/extract_kp_videos.py

@@ -1,108 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import face_alignment
-import numpy as np
-from PIL import Image
-from tqdm import tqdm
-from itertools import cycle
-
-from torch.multiprocessing import Pool, Process, set_start_method
-
-class KeypointExtractor():
-    def __init__(self, device):
-        self.detector = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, 
-                                                     device=device)   
-
-    def extract_keypoint(self, images, name=None, info=True):
-        if isinstance(images, list):
-            keypoints = []
-            if info:
-                i_range = tqdm(images,desc='landmark Det:')
-            else:
-                i_range = images
-
-            for image in i_range:
-                current_kp = self.extract_keypoint(image)
-                if np.mean(current_kp) == -1 and keypoints:
-                    keypoints.append(keypoints[-1])
-                else:
-                    keypoints.append(current_kp[None])
-
-            keypoints = np.concatenate(keypoints, 0)
-            np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-        else:
-            while True:
-                try:
-                    keypoints = self.detector.get_landmarks_from_image(np.array(images))[0]
-                    break
-                except RuntimeError as e:
-                    if str(e).startswith('CUDA'):
-                        print("Warning: out of memory, sleep for 1s")
-                        time.sleep(1)
-                    else:
-                        print(e)
-                        break    
-                except TypeError:
-                    print('No face detected in this image')
-                    shape = [68, 2]
-                    keypoints = -1. * np.ones(shape)                    
-                    break
-            if name is not None:
-                np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-
-def read_video(filename):
-    frames = []
-    cap = cv2.VideoCapture(filename)
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if ret:
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            frame = Image.fromarray(frame)
-            frames.append(frame)
-        else:
-            break
-    cap.release()
-    return frames
-
-def run(data):
-    filename, opt, device = data
-    os.environ['CUDA_VISIBLE_DEVICES'] = device
-    kp_extractor = KeypointExtractor()
-    images = read_video(filename)
-    name = filename.split('/')[-2:]
-    os.makedirs(os.path.join(opt.output_dir, name[-2]), exist_ok=True)
-    kp_extractor.extract_keypoint(
-        images, 
-        name=os.path.join(opt.output_dir, name[-2], name[-1])
-    )
-
-if __name__ == '__main__':
-    set_start_method('spawn')
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-    parser.add_argument('--output_dir', type=str, help='the folder of the output files')
-    parser.add_argument('--device_ids', type=str, default='0,1')
-    parser.add_argument('--workers', type=int, default=4)
-
-    opt = parser.parse_args()
-    filenames = list()
-    VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
-    VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
-    extensions = VIDEO_EXTENSIONS
-    
-    for ext in extensions:
-        os.listdir(f'{opt.input_dir}')
-        print(f'{opt.input_dir}/*.{ext}')
-        filenames = sorted(glob.glob(f'{opt.input_dir}/*.{ext}'))
-    print('Total number of videos:', len(filenames))
-    pool = Pool(opt.workers)
-    args_list = cycle([opt])
-    device_ids = opt.device_ids.split(",")
-    device_ids = cycle(device_ids)
-    for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
-        None

src/face3d/extract_kp_videos_safe.py

@@ -1,138 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import numpy as np
-from PIL import Image
-import torch
-from tqdm import tqdm
-from itertools import cycle
-from facexlib.alignment import init_alignment_model, landmark_98_to_68
-from facexlib.detection import init_detection_model
-from torch.multiprocessing import Pool, Process, set_start_method
-
-
-class KeypointExtractor():
-    def __init__(self, device='cuda'):
-
-        ### gfpgan/weights
-        try:
-            import webui  # in webui
-            root_path = 'extensions/SadTalker/gfpgan/weights' 
-
-        except:
-            root_path = 'gfpgan/weights'
-
-        self.detector = init_alignment_model('awing_fan',device=device, model_rootpath=root_path)   
-        self.det_net = init_detection_model('retinaface_resnet50', half=False,device=device, model_rootpath=root_path)
-
-    def extract_keypoint(self, images, name=None, info=True):
-        if isinstance(images, list):
-            keypoints = []
-            if info:
-                i_range = tqdm(images,desc='landmark Det:')
-            else:
-                i_range = images
-
-            for image in i_range:
-                current_kp = self.extract_keypoint(image)
-                # current_kp = self.detector.get_landmarks(np.array(image))
-                if np.mean(current_kp) == -1 and keypoints:
-                    keypoints.append(keypoints[-1])
-                else:
-                    keypoints.append(current_kp[None])
-
-            keypoints = np.concatenate(keypoints, 0)
-            np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-        else:
-            while True:
-                try:
-                    with torch.no_grad():
-                        # face detection -> face alignment.
-                        img = np.array(images)
-                        bboxes = self.det_net.detect_faces(images, 0.97)
-                        
-                        bboxes = bboxes[0]
-
-                        # bboxes[0] -= 100
-                        # bboxes[1] -= 100
-                        # bboxes[2] += 100
-                        # bboxes[3] += 100
-                        img = img[int(bboxes[1]):int(bboxes[3]), int(bboxes[0]):int(bboxes[2]), :]
-
-                        keypoints = landmark_98_to_68(self.detector.get_landmarks(img)) # [0]
-
-                        #### keypoints to the original location
-                        keypoints[:,0] += int(bboxes[0])
-                        keypoints[:,1] += int(bboxes[1])
-
-                        break
-                except RuntimeError as e:
-                    if str(e).startswith('CUDA'):
-                        print("Warning: out of memory, sleep for 1s")
-                        time.sleep(1)
-                    else:
-                        print(e)
-                        break    
-                except TypeError:
-                    print('No face detected in this image')
-                    shape = [68, 2]
-                    keypoints = -1. * np.ones(shape)                    
-                    break
-            if name is not None:
-                np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-
-def read_video(filename):
-    frames = []
-    cap = cv2.VideoCapture(filename)
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if ret:
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            frame = Image.fromarray(frame)
-            frames.append(frame)
-        else:
-            break
-    cap.release()
-    return frames
-
-def run(data):
-    filename, opt, device = data
-    os.environ['CUDA_VISIBLE_DEVICES'] = device
-    kp_extractor = KeypointExtractor()
-    images = read_video(filename)
-    name = filename.split('/')[-2:]
-    os.makedirs(os.path.join(opt.output_dir, name[-2]), exist_ok=True)
-    kp_extractor.extract_keypoint(
-        images, 
-        name=os.path.join(opt.output_dir, name[-2], name[-1])
-    )
-
-if __name__ == '__main__':
-    set_start_method('spawn')
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-    parser.add_argument('--output_dir', type=str, help='the folder of the output files')
-    parser.add_argument('--device_ids', type=str, default='0,1')
-    parser.add_argument('--workers', type=int, default=4)
-
-    opt = parser.parse_args()
-    filenames = list()
-    VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
-    VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
-    extensions = VIDEO_EXTENSIONS
-    
-    for ext in extensions:
-        os.listdir(f'{opt.input_dir}')
-        print(f'{opt.input_dir}/*.{ext}')
-        filenames = sorted(glob.glob(f'{opt.input_dir}/*.{ext}'))
-    print('Total number of videos:', len(filenames))
-    pool = Pool(opt.workers)
-    args_list = cycle([opt])
-    device_ids = opt.device_ids.split(",")
-    device_ids = cycle(device_ids)
-    for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
-        None

src/face3d/models/__init__.py

@@ -1,67 +0,0 @@
-"""This package contains modules related to objective functions, optimizations, and network architectures.
-
-To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
-You need to implement the following five functions:
-    -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
-    -- <set_input>:                     unpack data from dataset and apply preprocessing.
-    -- <forward>:                       produce intermediate results.
-    -- <optimize_parameters>:           calculate loss, gradients, and update network weights.
-    -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
-
-In the function <__init__>, you need to define four lists:
-    -- self.loss_names (str list):          specify the training losses that you want to plot and save.
-    -- self.model_names (str list):         define networks used in our training.
-    -- self.visual_names (str list):        specify the images that you want to display and save.
-    -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
-
-Now you can use the model class by specifying flag '--model dummy'.
-See our template model class 'template_model.py' for more details.
-"""
-
-import importlib
-from src.face3d.models.base_model import BaseModel
-
-
-def find_model_using_name(model_name):
-    """Import the module "models/[model_name]_model.py".
-
-    In the file, the class called DatasetNameModel() will
-    be instantiated. It has to be a subclass of BaseModel,
-    and it is case-insensitive.
-    """
-    model_filename = "face3d.models." + model_name + "_model"
-    modellib = importlib.import_module(model_filename)
-    model = None
-    target_model_name = model_name.replace('_', '') + 'model'
-    for name, cls in modellib.__dict__.items():
-        if name.lower() == target_model_name.lower() \
-           and issubclass(cls, BaseModel):
-            model = cls
-
-    if model is None:
-        print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
-        exit(0)
-
-    return model
-
-
-def get_option_setter(model_name):
-    """Return the static method <modify_commandline_options> of the model class."""
-    model_class = find_model_using_name(model_name)
-    return model_class.modify_commandline_options
-
-
-def create_model(opt):
-    """Create a model given the option.
-
-    This function warps the class CustomDatasetDataLoader.
-    This is the main interface between this package and 'train.py'/'test.py'
-
-    Example:
-        >>> from models import create_model
-        >>> model = create_model(opt)
-    """
-    model = find_model_using_name(opt.model)
-    instance = model(opt)
-    print("model [%s] was created" % type(instance).__name__)
-    return instance

src/face3d/models/arcface_torch/README.md

@@ -1,164 +0,0 @@
-# Distributed Arcface Training in Pytorch
-
-This is a deep learning library that makes face recognition efficient, and effective, which can train tens of millions
-identity on a single server.
-
-## Requirements
-
-- Install [pytorch](http://pytorch.org) (torch>=1.6.0), our doc for [install.md](docs/install.md).
-- `pip install -r requirements.txt`.
-- Download the dataset
-  from [https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_](https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_)
-  .
-
-## How to Training
-
-To train a model, run `train.py` with the path to the configs:
-
-### 1. Single node, 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r50
-```
-
-### 2. Multiple nodes, each node 8 GPUs:
-
-Node 0:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-Node 1:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr="ip1" --master_port=1234 train.py train.py configs/ms1mv3_r50
-```
-
-### 3.Training resnet2060 with 8 GPUs:
-
-```shell
-python -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=1234 train.py configs/ms1mv3_r2060.py
-```
-
-## Model Zoo
-
-- The models are available for non-commercial research purposes only.  
-- All models can be found in here.  
-- [Baidu Yun Pan](https://pan.baidu.com/s/1CL-l4zWqsI1oDuEEYVhj-g):   e8pw  
-- [onedrive](https://1drv.ms/u/s!AswpsDO2toNKq0lWY69vN58GR6mw?e=p9Ov5d)
-
-### Performance on [**ICCV2021-MFR**](http://iccv21-mfr.com/)
-
-ICCV2021-MFR testset consists of non-celebrities so we can ensure that it has very few overlap with public available face 
-recognition training set, such as MS1M and CASIA as they mostly collected from online celebrities. 
-As the result, we can evaluate the FAIR performance for different algorithms.  
-
-For **ICCV2021-MFR-ALL** set, TAR is measured on all-to-all 1:1 protocal, with FAR less than 0.000001(e-6). The 
-globalised multi-racial testset contains 242,143 identities and 1,624,305 images. 
-
-For **ICCV2021-MFR-MASK** set, TAR is measured on mask-to-nonmask 1:1 protocal, with FAR less than 0.0001(e-4). 
-Mask testset contains 6,964 identities, 6,964 masked images and 13,928 non-masked images. 
-There are totally 13,928 positive pairs and 96,983,824 negative pairs.
-
-| Datasets | backbone  | Training throughout | Size / MB  | **ICCV2021-MFR-MASK** | **ICCV2021-MFR-ALL** |
-| :---:    | :---      | :---                | :---       |:---                   |:---                  |     
-| MS1MV3    | r18  | -              | 91   | **47.85** | **68.33** |
-| Glint360k | r18  | 8536           | 91   | **53.32** | **72.07** |
-| MS1MV3    | r34  | -              | 130  | **58.72** | **77.36** |
-| Glint360k | r34  | 6344           | 130  | **65.10** | **83.02** |
-| MS1MV3    | r50  | 5500           | 166  | **63.85** | **80.53** |
-| Glint360k | r50  | 5136           | 166  | **70.23** | **87.08** |
-| MS1MV3    | r100 | -              | 248  | **69.09** | **84.31** |
-| Glint360k | r100 | 3332           | 248  | **75.57** | **90.66** |
-| MS1MV3    | mobilefacenet | 12185 | 7.8  | **41.52** | **65.26** |        
-| Glint360k | mobilefacenet | 11197 | 7.8  | **44.52** | **66.48** |  
-
-### Performance on IJB-C and Verification Datasets
-
-|   Datasets | backbone      | IJBC(1e-05) | IJBC(1e-04) | agedb30 | cfp_fp | lfw  |  log    |
-| :---:      |    :---       | :---          | :---  | :---  |:---   |:---    |:---     |  
-| MS1MV3     | r18      | 92.07 | 94.66 | 97.77 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r18_fp16/training.log)|         
-| MS1MV3     | r34      | 94.10 | 95.90 | 98.10 | 98.67 | 99.80 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r34_fp16/training.log)|        
-| MS1MV3     | r50      | 94.79 | 96.46 | 98.35 | 98.96 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r50_fp16/training.log)|         
-| MS1MV3     | r100     | 95.31 | 96.81 | 98.48 | 99.06 | 99.85 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r100_fp16/training.log)|        
-| MS1MV3     | **r2060**| 95.34 | 97.11 | 98.67 | 99.24 | 99.87 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/ms1mv3_arcface_r2060_fp16/training.log)|
-| Glint360k  |r18-0.1   | 93.16 | 95.33 | 97.72 | 97.73 | 99.77 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r18_fp16_0.1/training.log)| 
-| Glint360k  |r34-0.1   | 95.16 | 96.56 | 98.33 | 98.78 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r34_fp16_0.1/training.log)| 
-| Glint360k  |r50-0.1   | 95.61 | 96.97 | 98.38 | 99.20 | 99.83 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r50_fp16_0.1/training.log)| 
-| Glint360k  |r100-0.1  | 95.88 | 97.32 | 98.48 | 99.29 | 99.82 |[log](https://raw.githubusercontent.com/anxiangsir/insightface_arcface_log/master/glint360k_cosface_r100_fp16_0.1/training.log)|
-
-[comment]: <> (More details see [model.md]&#40;docs/modelzoo.md&#41; in docs.)
-
-
-## [Speed Benchmark](docs/speed_benchmark.md)
-
-**Arcface Torch** can train large-scale face recognition training set efficiently and quickly. When the number of
-classes in training sets is greater than 300K and the training is sufficient, partial fc sampling strategy will get same
-accuracy with several times faster training performance and smaller GPU memory. 
-Partial FC is a sparse variant of the model parallel architecture for large sacle  face recognition. Partial FC use a 
-sparse softmax, where each batch dynamicly sample a subset of class centers for training. In each iteration, only a 
-sparse part of the parameters will be updated, which can reduce a lot of GPU memory and calculations. With Partial FC, 
-we can scale trainset of 29 millions identities, the largest to date. Partial FC also supports multi-machine distributed 
-training and mixed precision training.
-
-![Image text](https://github.com/anxiangsir/insightface_arcface_log/blob/master/partial_fc_v2.png)
-
-More details see 
-[speed_benchmark.md](docs/speed_benchmark.md) in docs.
-
-### 1. Training speed of different parallel methods (samples / second), Tesla V100 32GB * 8. (Larger is better)
-
-`-` means training failed because of gpu memory limitations.
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :---    | :--- | :--- | :--- |
-|125000   | 4681         | 4824          | 5004     |
-|1400000  | **1672**     | 3043          | 4738     |
-|5500000  | **-**        | **1389**      | 3975     |
-|8000000  | **-**        | **-**         | 3565     |
-|16000000 | **-**        | **-**         | 2679     |
-|29000000 | **-**        | **-**         | **1855** |
-
-### 2. GPU memory cost of different parallel methods (MB per GPU), Tesla V100 32GB * 8. (Smaller is better)
-
-| Number of Identities in Dataset | Data Parallel | Model Parallel | Partial FC 0.1 |
-| :---    | :---      | :---      | :---  |
-|125000   | 7358      | 5306      | 4868  |
-|1400000  | 32252     | 11178     | 6056  |
-|5500000  | **-**     | 32188     | 9854  |
-|8000000  | **-**     | **-**     | 12310 |
-|16000000 | **-**     | **-**     | 19950 |
-|29000000 | **-**     | **-**     | 32324 |
-
-## Evaluation ICCV2021-MFR and IJB-C
-
-More details see [eval.md](docs/eval.md) in docs.
-
-## Test
-
-We tested many versions of PyTorch. Please create an issue if you are having trouble.  
-
-- [x] torch 1.6.0
-- [x] torch 1.7.1
-- [x] torch 1.8.0
-- [x] torch 1.9.0
-
-## Citation
-
-```
-@inproceedings{deng2019arcface,
-  title={Arcface: Additive angular margin loss for deep face recognition},
-  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  pages={4690--4699},
-  year={2019}
-}
-@inproceedings{an2020partical_fc,
-  title={Partial FC: Training 10 Million Identities on a Single Machine},
-  author={An, Xiang and Zhu, Xuhan and Xiao, Yang and Wu, Lan and Zhang, Ming and Gao, Yuan and Qin, Bin and
-  Zhang, Debing and Fu Ying},
-  booktitle={Arxiv 2010.05222},
-  year={2020}
-}
-```

src/face3d/models/arcface_torch/backbones/__init__.py

@@ -1,25 +0,0 @@
-from .iresnet import iresnet18, iresnet34, iresnet50, iresnet100, iresnet200
-from .mobilefacenet import get_mbf
-
-
-def get_model(name, **kwargs):
-    # resnet
-    if name == "r18":
-        return iresnet18(False, **kwargs)
-    elif name == "r34":
-        return iresnet34(False, **kwargs)
-    elif name == "r50":
-        return iresnet50(False, **kwargs)
-    elif name == "r100":
-        return iresnet100(False, **kwargs)
-    elif name == "r200":
-        return iresnet200(False, **kwargs)
-    elif name == "r2060":
-        from .iresnet2060 import iresnet2060
-        return iresnet2060(False, **kwargs)
-    elif name == "mbf":
-        fp16 = kwargs.get("fp16", False)
-        num_features = kwargs.get("num_features", 512)
-        return get_mbf(fp16=fp16, num_features=num_features)
-    else:
-        raise ValueError()

src/face3d/models/arcface_torch/backbones/iresnet.py

@@ -1,187 +0,0 @@
-import torch
-from torch import nn
-
-__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200']
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=3,
-                     stride=stride,
-                     padding=dilation,
-                     groups=groups,
-                     bias=False,
-                     dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=1,
-                     stride=stride,
-                     bias=False)
-
-
-class IBasicBlock(nn.Module):
-    expansion = 1
-    def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 groups=1, base_width=64, dilation=1):
-        super(IBasicBlock, self).__init__()
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
-        self.conv1 = conv3x3(inplanes, planes)
-        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
-        self.prelu = nn.PReLU(planes)
-        self.conv2 = conv3x3(planes, planes, stride)
-        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-        out = self.bn1(x)
-        out = self.conv1(out)
-        out = self.bn2(out)
-        out = self.prelu(out)
-        out = self.conv2(out)
-        out = self.bn3(out)
-        if self.downsample is not None:
-            identity = self.downsample(x)
-        out += identity
-        return out
-
-
-class IResNet(nn.Module):
-    fc_scale = 7 * 7
-    def __init__(self,
-                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
-        super(IResNet, self).__init__()
-        self.fp16 = fp16
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
-        self.prelu = nn.PReLU(self.inplanes)
-        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
-        self.layer2 = self._make_layer(block,
-                                       128,
-                                       layers[1],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block,
-                                       256,
-                                       layers[2],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block,
-                                       512,
-                                       layers[3],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
-        self.dropout = nn.Dropout(p=dropout, inplace=True)
-        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
-        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
-        nn.init.constant_(self.features.weight, 1.0)
-        self.features.weight.requires_grad = False
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.normal_(m.weight, 0, 0.1)
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, IBasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
-            )
-        layers = []
-        layers.append(
-            block(self.inplanes, planes, stride, downsample, self.groups,
-                  self.base_width, previous_dilation))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(
-                block(self.inplanes,
-                      planes,
-                      groups=self.groups,
-                      base_width=self.base_width,
-                      dilation=self.dilation))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        with torch.cuda.amp.autocast(self.fp16):
-            x = self.conv1(x)
-            x = self.bn1(x)
-            x = self.prelu(x)
-            x = self.layer1(x)
-            x = self.layer2(x)
-            x = self.layer3(x)
-            x = self.layer4(x)
-            x = self.bn2(x)
-            x = torch.flatten(x, 1)
-            x = self.dropout(x)
-        x = self.fc(x.float() if self.fp16 else x)
-        x = self.features(x)
-        return x
-
-
-def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
-    model = IResNet(block, layers, **kwargs)
-    if pretrained:
-        raise ValueError()
-    return model
-
-
-def iresnet18(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet34(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet50(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet100(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
-                    progress, **kwargs)
-
-
-def iresnet200(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
-                    progress, **kwargs)
-

src/face3d/models/arcface_torch/backbones/iresnet2060.py

@@ -1,176 +0,0 @@
-import torch
-from torch import nn
-
-assert torch.__version__ >= "1.8.1"
-from torch.utils.checkpoint import checkpoint_sequential
-
-__all__ = ['iresnet2060']
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=3,
-                     stride=stride,
-                     padding=dilation,
-                     groups=groups,
-                     bias=False,
-                     dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes,
-                     out_planes,
-                     kernel_size=1,
-                     stride=stride,
-                     bias=False)
-
-
-class IBasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None,
-                 groups=1, base_width=64, dilation=1):
-        super(IBasicBlock, self).__init__()
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05, )
-        self.conv1 = conv3x3(inplanes, planes)
-        self.bn2 = nn.BatchNorm2d(planes, eps=1e-05, )
-        self.prelu = nn.PReLU(planes)
-        self.conv2 = conv3x3(planes, planes, stride)
-        self.bn3 = nn.BatchNorm2d(planes, eps=1e-05, )
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-        out = self.bn1(x)
-        out = self.conv1(out)
-        out = self.bn2(out)
-        out = self.prelu(out)
-        out = self.conv2(out)
-        out = self.bn3(out)
-        if self.downsample is not None:
-            identity = self.downsample(x)
-        out += identity
-        return out
-
-
-class IResNet(nn.Module):
-    fc_scale = 7 * 7
-
-    def __init__(self,
-                 block, layers, dropout=0, num_features=512, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
-        super(IResNet, self).__init__()
-        self.fp16 = fp16
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
-        self.prelu = nn.PReLU(self.inplanes)
-        self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
-        self.layer2 = self._make_layer(block,
-                                       128,
-                                       layers[1],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block,
-                                       256,
-                                       layers[2],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block,
-                                       512,
-                                       layers[3],
-                                       stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05, )
-        self.dropout = nn.Dropout(p=dropout, inplace=True)
-        self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
-        self.features = nn.BatchNorm1d(num_features, eps=1e-05)
-        nn.init.constant_(self.features.weight, 1.0)
-        self.features.weight.requires_grad = False
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.normal_(m.weight, 0, 0.1)
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, IBasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
-            )
-        layers = []
-        layers.append(
-            block(self.inplanes, planes, stride, downsample, self.groups,
-                  self.base_width, previous_dilation))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(
-                block(self.inplanes,
-                      planes,
-                      groups=self.groups,
-                      base_width=self.base_width,
-                      dilation=self.dilation))
-
-        return nn.Sequential(*layers)
-
-    def checkpoint(self, func, num_seg, x):
-        if self.training:
-            return checkpoint_sequential(func, num_seg, x)
-        else:
-            return func(x)
-
-    def forward(self, x):
-        with torch.cuda.amp.autocast(self.fp16):
-            x = self.conv1(x)
-            x = self.bn1(x)
-            x = self.prelu(x)
-            x = self.layer1(x)
-            x = self.checkpoint(self.layer2, 20, x)
-            x = self.checkpoint(self.layer3, 100, x)
-            x = self.layer4(x)
-            x = self.bn2(x)
-            x = torch.flatten(x, 1)
-            x = self.dropout(x)
-        x = self.fc(x.float() if self.fp16 else x)
-        x = self.features(x)
-        return x
-
-
-def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
-    model = IResNet(block, layers, **kwargs)
-    if pretrained:
-        raise ValueError()
-    return model
-
-
-def iresnet2060(pretrained=False, progress=True, **kwargs):
-    return _iresnet('iresnet2060', IBasicBlock, [3, 128, 1024 - 128, 3], pretrained, progress, **kwargs)

src/face3d/models/arcface_torch/backbones/mobilefacenet.py

@@ -1,130 +0,0 @@
-'''
-Adapted from https://github.com/cavalleria/cavaface.pytorch/blob/master/backbone/mobilefacenet.py
-Original author cavalleria
-'''
-
-import torch.nn as nn
-from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Sequential, Module
-import torch
-
-
-class Flatten(Module):
-    def forward(self, x):
-        return x.view(x.size(0), -1)
-
-
-class ConvBlock(Module):
-    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
-        super(ConvBlock, self).__init__()
-        self.layers = nn.Sequential(
-            Conv2d(in_c, out_c, kernel, groups=groups, stride=stride, padding=padding, bias=False),
-            BatchNorm2d(num_features=out_c),
-            PReLU(num_parameters=out_c)
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-class LinearBlock(Module):
-    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
-        super(LinearBlock, self).__init__()
-        self.layers = nn.Sequential(
-            Conv2d(in_c, out_c, kernel, stride, padding, groups=groups, bias=False),
-            BatchNorm2d(num_features=out_c)
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-class DepthWise(Module):
-    def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
-        super(DepthWise, self).__init__()
-        self.residual = residual
-        self.layers = nn.Sequential(
-            ConvBlock(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1)),
-            ConvBlock(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride),
-            LinearBlock(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
-        )
-
-    def forward(self, x):
-        short_cut = None
-        if self.residual:
-            short_cut = x
-        x = self.layers(x)
-        if self.residual:
-            output = short_cut + x
-        else:
-            output = x
-        return output
-
-
-class Residual(Module):
-    def __init__(self, c, num_block, groups, kernel=(3, 3), stride=(1, 1), padding=(1, 1)):
-        super(Residual, self).__init__()
-        modules = []
-        for _ in range(num_block):
-            modules.append(DepthWise(c, c, True, kernel, stride, padding, groups))
-        self.layers = Sequential(*modules)
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-class GDC(Module):
-    def __init__(self, embedding_size):
-        super(GDC, self).__init__()
-        self.layers = nn.Sequential(
-            LinearBlock(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0)),
-            Flatten(),
-            Linear(512, embedding_size, bias=False),
-            BatchNorm1d(embedding_size))
-
-    def forward(self, x):
-        return self.layers(x)
-
-
-class MobileFaceNet(Module):
-    def __init__(self, fp16=False, num_features=512):
-        super(MobileFaceNet, self).__init__()
-        scale = 2
-        self.fp16 = fp16
-        self.layers = nn.Sequential(
-            ConvBlock(3, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1)),
-            ConvBlock(64 * scale, 64 * scale, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64),
-            DepthWise(64 * scale, 64 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128),
-            Residual(64 * scale, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
-            DepthWise(64 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256),
-            Residual(128 * scale, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
-            DepthWise(128 * scale, 128 * scale, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512),
-            Residual(128 * scale, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1)),
-        )
-        self.conv_sep = ConvBlock(128 * scale, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
-        self.features = GDC(num_features)
-        self._initialize_weights()
-
-    def _initialize_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                if m.bias is not None:
-                    m.bias.data.zero_()
-            elif isinstance(m, nn.BatchNorm2d):
-                m.weight.data.fill_(1)
-                m.bias.data.zero_()
-            elif isinstance(m, nn.Linear):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-                if m.bias is not None:
-                    m.bias.data.zero_()
-
-    def forward(self, x):
-        with torch.cuda.amp.autocast(self.fp16):
-            x = self.layers(x)
-        x = self.conv_sep(x.float() if self.fp16 else x)
-        x = self.features(x)
-        return x
-
-
-def get_mbf(fp16, num_features):
-    return MobileFaceNet(fp16, num_features)

src/face3d/models/arcface_torch/configs/3millions.py

@@ -1,23 +0,0 @@
-from easydict import EasyDict as edict
-
-# configs for test speed
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 1.0
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "synthetic"
-config.num_classes = 300 * 10000
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = []

src/face3d/models/arcface_torch/configs/3millions_pfc.py

@@ -1,23 +0,0 @@
-from easydict import EasyDict as edict
-
-# configs for test speed
-
-config = edict()
-config.loss = "arcface"
-config.network = "r50"
-config.resume = False
-config.output = None
-config.embedding_size = 512
-config.sample_rate = 0.1
-config.fp16 = True
-config.momentum = 0.9
-config.weight_decay = 5e-4
-config.batch_size = 128
-config.lr = 0.1  # batch size is 512
-
-config.rec = "synthetic"
-config.num_classes = 300 * 10000
-config.num_epoch = 30
-config.warmup_epoch = -1
-config.decay_epoch = [10, 16, 22]
-config.val_targets = []