build: vendorize facexlib

had too many unused sub-dependencies

also monkeypatch the download mechanism to use our standard download function

Makefile

@@ -222,6 +222,18 @@ vendorize_refiners:
     find ./imaginairy/vendored/refiners/ -type f -name "*.py" -exec sed -i '' 's/import refiners/import imaginairy.vendored.refiners/g' {} + &&\
 	make af
 
+vendorize_facexlib:
+	export REPO=git@github.com:xinntao/facexlib.git PKG=facexlib COMMIT=260620ae93990a300f4b16448df9bb459f1caba9 && \
+	make download_repo REPO=$$REPO PKG=$$PKG COMMIT=$$COMMIT && \
+	mkdir -p ./imaginairy/vendored/$$PKG && \
+	rm -rf ./imaginairy/vendored/$$PKG/* && \
+	cp -R ./downloads/$$PKG/facexlib/* ./imaginairy/vendored/$$PKG/ && \
+	rm -rf ./imaginairy/vendored/$$PKG/weights && \
+	cp ./downloads/$$PKG/LICENSE ./imaginairy/vendored/$$PKG/ && \
+	echo "vendored from $$REPO @ $$COMMIT" | tee ./imaginairy/vendored/$$PKG/readme.txt
+	find ./imaginairy/vendored/facexlib/ -type f -name "*.py" -exec sed -i '' 's/from facexlib/from imaginairy.vendored.facexlib/g' {} + &&\
+	sed -i '' '/from \.version import __gitsha__, __version__/d' ./imaginairy/vendored/facexlib/__init__.py
+	make af
 
 vendorize:  ## vendorize a github repo.  `make vendorize REPO=git@github.com:openai/CLIP.git PKG=clip`
 	mkdir -p ./downloads

imaginairy/enhancers/face_restoration_codeformer.py

@@ -5,20 +5,62 @@ from functools import lru_cache
 
 import numpy as np
 import torch
-from facexlib.utils.face_restoration_helper import FaceRestoreHelper
 from PIL import Image
 from torchvision.transforms.functional import normalize
 
 from imaginairy.utils.model_manager import get_cached_url_path
 from imaginairy.vendored.basicsr.img_util import img2tensor, tensor2img
 from imaginairy.vendored.codeformer.codeformer_arch import CodeFormer
+from imaginairy.vendored.facexlib.utils.face_restoration_helper import FaceRestoreHelper
 
 logger = logging.getLogger(__name__)
 
+
 face_restore_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 half_mode = face_restore_device == "cuda"
 
 
+def load_file_from_url(
+    url, model_dir=None, progress=True, file_name=None, save_dir=None
+):
+    return get_cached_url_path(url, category="facexlib")
+
+
+@lru_cache(maxsize=1)
+def patch_download_function_in_facexlib_modules():
+    """Replaces the custom weights downloaded with the standard imaginairy one."""
+    import imaginairy.vendored.facexlib.utils.misc
+    from imaginairy.vendored.facexlib import (
+        alignment,
+        assessment,
+        detection,
+        headpose,
+        matting,
+        parsing,
+        recognition,
+        tracking,
+        visualization,
+    )
+
+    modules = [
+        alignment,
+        assessment,
+        detection,
+        headpose,
+        matting,
+        parsing,
+        recognition,
+        tracking,
+        visualization,
+        imaginairy.vendored.facexlib.utils.misc,
+    ]
+    for m in modules:
+        m.load_file_from_url = load_file_from_url
+
+
+patch_download_function_in_facexlib_modules()
+
+
 @lru_cache
 def codeformer_model():
     model = CodeFormer(

imaginairy/img_processors/openpose.py

@@ -5,7 +5,6 @@ from collections import OrderedDict
 from functools import lru_cache
 
 import cv2
-import matplotlib as mpl
 import numpy as np
 import torch
 from scipy.ndimage.filters import gaussian_filter
@@ -125,6 +124,8 @@ def draw_bodypose(canvas, candidate, subset):
 
 # image drawed by opencv is not good.
 def draw_handpose(canvas, all_hand_peaks, show_number=False):
+    import matplotlib as mpl
+
     edges = [
         [0, 1],
         [1, 2],

imaginairy/modules/sgm/autoencoding/losses/discriminator_loss.py

@@ -7,7 +7,6 @@ import torch
 import torch.nn as nn
 import torchvision
 from einops import rearrange
-from matplotlib import colormaps, pyplot as plt
 
 from imaginairy.modules.sgm.autoencoding.lpips.loss.lpips import LPIPS
 from imaginairy.modules.sgm.autoencoding.lpips.model.model import weights_init
@@ -98,6 +97,8 @@ class GeneralLPIPSWithDiscriminator(nn.Module):
     def log_images(
         self, inputs: torch.Tensor, reconstructions: torch.Tensor
     ) -> Dict[str, torch.Tensor]:
+        from matplotlib import colormaps, pyplot as plt
+
         # calc logits of real/fake
         logits_real = self.discriminator(inputs.contiguous().detach())
         if len(logits_real.shape) < 4:

imaginairy/vendored/facexlib/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Xintao Wang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

imaginairy/vendored/facexlib/__init__.py

@@ -0,0 +1,7 @@
+# flake8: noqa
+from .alignment import *
+from .detection import *
+from .recognition import *
+from .tracking import *
+from .utils import *
+from .visualization import *

imaginairy/vendored/facexlib/alignment/README.md

@@ -0,0 +1,20 @@
+
+## Landmarks
+
+- 5 landmarks
+
+<p align="center">
+  <img src="../../assets/landmarks_5.jpg", height="300">
+</p>
+
+- 68 landmarks
+
+<p align="center">
+  <img src="../../assets/landmarks_68.png", height="400">
+</p>
+
+- 98 landmarks
+
+<p align="center">
+  <img src="../../assets/landmarks_98.png", height="500">
+</p>

imaginairy/vendored/facexlib/alignment/__init__.py

@@ -0,0 +1,22 @@
+import torch
+
+from imaginairy.vendored.facexlib.utils import load_file_from_url
+from .awing_arch import FAN
+from .convert_98_to_68_landmarks import landmark_98_to_68
+
+__all__ = ['FAN', 'landmark_98_to_68']
+
+
+def init_alignment_model(model_name, half=False, device='cuda', model_rootpath=None):
+    if model_name == 'awing_fan':
+        model = FAN(num_modules=4, num_landmarks=98, device=device)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/alignment_WFLW_4HG.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+    model.load_state_dict(torch.load(model_path)['state_dict'], strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

imaginairy/vendored/facexlib/alignment/awing_arch.py

@@ -0,0 +1,378 @@
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def calculate_points(heatmaps):
+    # change heatmaps to landmarks
+    B, N, H, W = heatmaps.shape
+    HW = H * W
+    BN_range = np.arange(B * N)
+
+    heatline = heatmaps.reshape(B, N, HW)
+    indexes = np.argmax(heatline, axis=2)
+
+    preds = np.stack((indexes % W, indexes // W), axis=2)
+    preds = preds.astype(np.float, copy=False)
+
+    inr = indexes.ravel()
+
+    heatline = heatline.reshape(B * N, HW)
+    x_up = heatline[BN_range, inr + 1]
+    x_down = heatline[BN_range, inr - 1]
+    # y_up = heatline[BN_range, inr + W]
+
+    if any((inr + W) >= 4096):
+        y_up = heatline[BN_range, 4095]
+    else:
+        y_up = heatline[BN_range, inr + W]
+    if any((inr - W) <= 0):
+        y_down = heatline[BN_range, 0]
+    else:
+        y_down = heatline[BN_range, inr - W]
+
+    think_diff = np.sign(np.stack((x_up - x_down, y_up - y_down), axis=1))
+    think_diff *= .25
+
+    preds += think_diff.reshape(B, N, 2)
+    preds += .5
+    return preds
+
+
+class AddCoordsTh(nn.Module):
+
+    def __init__(self, x_dim=64, y_dim=64, with_r=False, with_boundary=False):
+        super(AddCoordsTh, self).__init__()
+        self.x_dim = x_dim
+        self.y_dim = y_dim
+        self.with_r = with_r
+        self.with_boundary = with_boundary
+
+    def forward(self, input_tensor, heatmap=None):
+        """
+        input_tensor: (batch, c, x_dim, y_dim)
+        """
+        batch_size_tensor = input_tensor.shape[0]
+
+        xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32, device=input_tensor.device)
+        xx_ones = xx_ones.unsqueeze(-1)
+
+        xx_range = torch.arange(self.x_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0)
+        xx_range = xx_range.unsqueeze(1)
+
+        xx_channel = torch.matmul(xx_ones.float(), xx_range.float())
+        xx_channel = xx_channel.unsqueeze(-1)
+
+        yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32, device=input_tensor.device)
+        yy_ones = yy_ones.unsqueeze(1)
+
+        yy_range = torch.arange(self.y_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0)
+        yy_range = yy_range.unsqueeze(-1)
+
+        yy_channel = torch.matmul(yy_range.float(), yy_ones.float())
+        yy_channel = yy_channel.unsqueeze(-1)
+
+        xx_channel = xx_channel.permute(0, 3, 2, 1)
+        yy_channel = yy_channel.permute(0, 3, 2, 1)
+
+        xx_channel = xx_channel / (self.x_dim - 1)
+        yy_channel = yy_channel / (self.y_dim - 1)
+
+        xx_channel = xx_channel * 2 - 1
+        yy_channel = yy_channel * 2 - 1
+
+        xx_channel = xx_channel.repeat(batch_size_tensor, 1, 1, 1)
+        yy_channel = yy_channel.repeat(batch_size_tensor, 1, 1, 1)
+
+        if self.with_boundary and heatmap is not None:
+            boundary_channel = torch.clamp(heatmap[:, -1:, :, :], 0.0, 1.0)
+
+            zero_tensor = torch.zeros_like(xx_channel)
+            xx_boundary_channel = torch.where(boundary_channel > 0.05, xx_channel, zero_tensor)
+            yy_boundary_channel = torch.where(boundary_channel > 0.05, yy_channel, zero_tensor)
+        if self.with_boundary and heatmap is not None:
+            xx_boundary_channel = xx_boundary_channel.to(input_tensor.device)
+            yy_boundary_channel = yy_boundary_channel.to(input_tensor.device)
+        ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1)
+
+        if self.with_r:
+            rr = torch.sqrt(torch.pow(xx_channel, 2) + torch.pow(yy_channel, 2))
+            rr = rr / torch.max(rr)
+            ret = torch.cat([ret, rr], dim=1)
+
+        if self.with_boundary and heatmap is not None:
+            ret = torch.cat([ret, xx_boundary_channel, yy_boundary_channel], dim=1)
+        return ret
+
+
+class CoordConvTh(nn.Module):
+    """CoordConv layer as in the paper."""
+
+    def __init__(self, x_dim, y_dim, with_r, with_boundary, in_channels, first_one=False, *args, **kwargs):
+        super(CoordConvTh, self).__init__()
+        self.addcoords = AddCoordsTh(x_dim=x_dim, y_dim=y_dim, with_r=with_r, with_boundary=with_boundary)
+        in_channels += 2
+        if with_r:
+            in_channels += 1
+        if with_boundary and not first_one:
+            in_channels += 2
+        self.conv = nn.Conv2d(in_channels=in_channels, *args, **kwargs)
+
+    def forward(self, input_tensor, heatmap=None):
+        ret = self.addcoords(input_tensor, heatmap)
+        last_channel = ret[:, -2:, :, :]
+        ret = self.conv(ret)
+        return ret, last_channel
+
+
+def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False, dilation=1):
+    '3x3 convolution with padding'
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd, padding=padding, bias=bias, dilation=dilation)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        # self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        # self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ConvBlock(nn.Module):
+
+    def __init__(self, in_planes, out_planes):
+        super(ConvBlock, self).__init__()
+        self.bn1 = nn.BatchNorm2d(in_planes)
+        self.conv1 = conv3x3(in_planes, int(out_planes / 2))
+        self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
+        self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4), padding=1, dilation=1)
+        self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
+        self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4), padding=1, dilation=1)
+
+        if in_planes != out_planes:
+            self.downsample = nn.Sequential(
+                nn.BatchNorm2d(in_planes),
+                nn.ReLU(True),
+                nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False),
+            )
+        else:
+            self.downsample = None
+
+    def forward(self, x):
+        residual = x
+
+        out1 = self.bn1(x)
+        out1 = F.relu(out1, True)
+        out1 = self.conv1(out1)
+
+        out2 = self.bn2(out1)
+        out2 = F.relu(out2, True)
+        out2 = self.conv2(out2)
+
+        out3 = self.bn3(out2)
+        out3 = F.relu(out3, True)
+        out3 = self.conv3(out3)
+
+        out3 = torch.cat((out1, out2, out3), 1)
+
+        if self.downsample is not None:
+            residual = self.downsample(residual)
+
+        out3 += residual
+
+        return out3
+
+
+class HourGlass(nn.Module):
+
+    def __init__(self, num_modules, depth, num_features, first_one=False):
+        super(HourGlass, self).__init__()
+        self.num_modules = num_modules
+        self.depth = depth
+        self.features = num_features
+        self.coordconv = CoordConvTh(
+            x_dim=64,
+            y_dim=64,
+            with_r=True,
+            with_boundary=True,
+            in_channels=256,
+            first_one=first_one,
+            out_channels=256,
+            kernel_size=1,
+            stride=1,
+            padding=0)
+        self._generate_network(self.depth)
+
+    def _generate_network(self, level):
+        self.add_module('b1_' + str(level), ConvBlock(256, 256))
+
+        self.add_module('b2_' + str(level), ConvBlock(256, 256))
+
+        if level > 1:
+            self._generate_network(level - 1)
+        else:
+            self.add_module('b2_plus_' + str(level), ConvBlock(256, 256))
+
+        self.add_module('b3_' + str(level), ConvBlock(256, 256))
+
+    def _forward(self, level, inp):
+        # Upper branch
+        up1 = inp
+        up1 = self._modules['b1_' + str(level)](up1)
+
+        # Lower branch
+        low1 = F.avg_pool2d(inp, 2, stride=2)
+        low1 = self._modules['b2_' + str(level)](low1)
+
+        if level > 1:
+            low2 = self._forward(level - 1, low1)
+        else:
+            low2 = low1
+            low2 = self._modules['b2_plus_' + str(level)](low2)
+
+        low3 = low2
+        low3 = self._modules['b3_' + str(level)](low3)
+
+        up2 = F.interpolate(low3, scale_factor=2, mode='nearest')
+
+        return up1 + up2
+
+    def forward(self, x, heatmap):
+        x, last_channel = self.coordconv(x, heatmap)
+        return self._forward(self.depth, x), last_channel
+
+
+class FAN(nn.Module):
+
+    def __init__(self, num_modules=1, end_relu=False, gray_scale=False, num_landmarks=68, device='cuda'):
+        super(FAN, self).__init__()
+        self.device = device
+        self.num_modules = num_modules
+        self.gray_scale = gray_scale
+        self.end_relu = end_relu
+        self.num_landmarks = num_landmarks
+
+        # Base part
+        if self.gray_scale:
+            self.conv1 = CoordConvTh(
+                x_dim=256,
+                y_dim=256,
+                with_r=True,
+                with_boundary=False,
+                in_channels=3,
+                out_channels=64,
+                kernel_size=7,
+                stride=2,
+                padding=3)
+        else:
+            self.conv1 = CoordConvTh(
+                x_dim=256,
+                y_dim=256,
+                with_r=True,
+                with_boundary=False,
+                in_channels=3,
+                out_channels=64,
+                kernel_size=7,
+                stride=2,
+                padding=3)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.conv2 = ConvBlock(64, 128)
+        self.conv3 = ConvBlock(128, 128)
+        self.conv4 = ConvBlock(128, 256)
+
+        # Stacking part
+        for hg_module in range(self.num_modules):
+            if hg_module == 0:
+                first_one = True
+            else:
+                first_one = False
+            self.add_module('m' + str(hg_module), HourGlass(1, 4, 256, first_one))
+            self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256))
+            self.add_module('conv_last' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+            self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256))
+            self.add_module('l' + str(hg_module), nn.Conv2d(256, num_landmarks + 1, kernel_size=1, stride=1, padding=0))
+
+            if hg_module < self.num_modules - 1:
+                self.add_module('bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0))
+                self.add_module('al' + str(hg_module),
+                                nn.Conv2d(num_landmarks + 1, 256, kernel_size=1, stride=1, padding=0))
+
+    def forward(self, x):
+        x, _ = self.conv1(x)
+        x = F.relu(self.bn1(x), True)
+        # x = F.relu(self.bn1(self.conv1(x)), True)
+        x = F.avg_pool2d(self.conv2(x), 2, stride=2)
+        x = self.conv3(x)
+        x = self.conv4(x)
+
+        previous = x
+
+        outputs = []
+        boundary_channels = []
+        tmp_out = None
+        for i in range(self.num_modules):
+            hg, boundary_channel = self._modules['m' + str(i)](previous, tmp_out)
+
+            ll = hg
+            ll = self._modules['top_m_' + str(i)](ll)
+
+            ll = F.relu(self._modules['bn_end' + str(i)](self._modules['conv_last' + str(i)](ll)), True)
+
+            # Predict heatmaps
+            tmp_out = self._modules['l' + str(i)](ll)
+            if self.end_relu:
+                tmp_out = F.relu(tmp_out)  # HACK: Added relu
+            outputs.append(tmp_out)
+            boundary_channels.append(boundary_channel)
+
+            if i < self.num_modules - 1:
+                ll = self._modules['bl' + str(i)](ll)
+                tmp_out_ = self._modules['al' + str(i)](tmp_out)
+                previous = previous + ll + tmp_out_
+
+        return outputs, boundary_channels
+
+    def get_landmarks(self, img):
+        H, W, _ = img.shape
+        offset = W / 64, H / 64, 0, 0
+
+        img = cv2.resize(img, (256, 256))
+        inp = img[..., ::-1]
+        inp = torch.from_numpy(np.ascontiguousarray(inp.transpose((2, 0, 1)))).float()
+        inp = inp.to(self.device)
+        inp.div_(255.0).unsqueeze_(0)
+
+        outputs, _ = self.forward(inp)
+        out = outputs[-1][:, :-1, :, :]
+        heatmaps = out.detach().cpu().numpy()
+
+        pred = calculate_points(heatmaps).reshape(-1, 2)
+
+        pred *= offset[:2]
+        pred += offset[-2:]
+
+        return pred

imaginairy/vendored/facexlib/alignment/convert_98_to_68_landmarks.py

@@ -0,0 +1,82 @@
+import numpy as np
+
+
+def load_txt_file(file_path):
+    """Load data or string from txt file."""
+
+    with open(file_path, 'r') as cfile:
+        content = cfile.readlines()
+    cfile.close()
+    content = [x.strip() for x in content]
+    num_lines = len(content)
+    return content, num_lines
+
+
+def anno_parser(anno_path, num_pts, line_offset=0):
+    """Parse the annotation.
+    Args:
+        anno_path: path of anno file (suffix .txt)
+        num_pts: number of landmarks.
+        line_offset: first point starts, default: 0.
+
+    Returns:
+        pts: num_pts x 2 (x, y)
+    """
+
+    data, _ = load_txt_file(anno_path)
+    n_points = num_pts
+    # read points coordinate.
+    pts = np.zeros((n_points, 2), dtype='float32')
+    for point_index in range(n_points):
+        try:
+            pts_list = data[point_index + line_offset].split(',')
+            pts[point_index, 0] = float(pts_list[0])
+            pts[point_index, 1] = float(pts_list[1])
+        except ValueError:
+            print(f'Error in loading points in {anno_path}')
+    return pts
+
+
+def landmark_98_to_68(landmark_98):
+    """Transfer 98 landmark positions to 68 landmark positions.
+    Args:
+        landmark_98(numpy array): Polar coordinates of 98 landmarks, (98, 2)
+    Returns:
+        landmark_68(numpy array): Polar coordinates of 98 landmarks, (68, 2)
+    """
+
+    landmark_68 = np.zeros((68, 2), dtype='float32')
+    # cheek
+    for i in range(0, 33):
+        if i % 2 == 0:
+            landmark_68[int(i / 2), :] = landmark_98[i, :]
+    # nose
+    for i in range(51, 60):
+        landmark_68[i - 24, :] = landmark_98[i, :]
+    # mouth
+    for i in range(76, 96):
+        landmark_68[i - 28, :] = landmark_98[i, :]
+    # left eyebrow
+    landmark_68[17, :] = landmark_98[33, :]
+    landmark_68[18, :] = (landmark_98[34, :] + landmark_98[41, :]) / 2
+    landmark_68[19, :] = (landmark_98[35, :] + landmark_98[40, :]) / 2
+    landmark_68[20, :] = (landmark_98[36, :] + landmark_98[39, :]) / 2
+    landmark_68[21, :] = (landmark_98[37, :] + landmark_98[38, :]) / 2
+    # right eyebrow
+    landmark_68[22, :] = (landmark_98[42, :] + landmark_98[50, :]) / 2
+    landmark_68[23, :] = (landmark_98[43, :] + landmark_98[49, :]) / 2
+    landmark_68[24, :] = (landmark_98[44, :] + landmark_98[48, :]) / 2
+    landmark_68[25, :] = (landmark_98[45, :] + landmark_98[47, :]) / 2
+    landmark_68[26, :] = landmark_98[46, :]
+    # left eye
+    LUT_landmark_68_left_eye = [36, 37, 38, 39, 40, 41]
+    LUT_landmark_98_left_eye = [60, 61, 63, 64, 65, 67]
+    for idx, landmark_98_index in enumerate(LUT_landmark_98_left_eye):
+        landmark_68[LUT_landmark_68_left_eye[idx], :] = landmark_98[landmark_98_index, :]
+    # right eye
+    LUT_landmark_68_right_eye = [42, 43, 44, 45, 46, 47]
+    LUT_landmark_98_right_eye = [68, 69, 71, 72, 73, 75]
+    for idx, landmark_98_index in enumerate(LUT_landmark_98_right_eye):
+        landmark_68[LUT_landmark_68_right_eye[idx], :] = landmark_98[landmark_98_index, :]
+
+    return landmark_68

imaginairy/vendored/facexlib/assessment/__init__.py

@@ -0,0 +1,20 @@
+import torch
+
+from imaginairy.vendored.facexlib.utils import load_file_from_url
+from .hyperiqa_net import HyperIQA
+
+
+def init_assessment_model(model_name, half=False, device='cuda', model_rootpath=None):
+    if model_name == 'hypernet':
+        model = HyperIQA(16, 112, 224, 112, 56, 28, 14, 7)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/assessment_hyperIQA.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    # load the pre-trained hypernet model
+    hypernet_model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+    model.hypernet.load_state_dict((torch.load(hypernet_model_path, map_location=lambda storage, loc: storage)))
+    model = model.eval()
+    model = model.to(device)
+    return model

imaginairy/vendored/facexlib/assessment/hyperiqa_net.py

@@ -0,0 +1,298 @@
+import torch as torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class HyperIQA(nn.Module):
+    """
+    Combine the hypernet and target network within a network.
+    """
+
+    def __init__(self, *args):
+        super(HyperIQA, self).__init__()
+        self.hypernet = HyperNet(*args)
+
+    def forward(self, img):
+        net_params = self.hypernet(img)
+        # build the target network
+        target_net = TargetNet(net_params)
+        for param in target_net.parameters():
+            param.requires_grad = False
+        # predict the face quality
+        pred = target_net(net_params['target_in_vec'])
+        return pred
+
+
+class HyperNet(nn.Module):
+    """
+    Hyper network for learning perceptual rules.
+    Args:
+        lda_out_channels: local distortion aware module output size.
+        hyper_in_channels: input feature channels for hyper network.
+        target_in_size: input vector size for target network.
+        target_fc(i)_size: fully connection layer size of target network.
+        feature_size: input feature map width/height for hyper network.
+    Note:
+        For size match, input args must satisfy: 'target_fc(i)_size * target_fc(i+1)_size' is divisible by 'feature_size ^ 2'. # noqa E501
+    """
+
+    def __init__(self, lda_out_channels, hyper_in_channels, target_in_size, target_fc1_size, target_fc2_size,
+                 target_fc3_size, target_fc4_size, feature_size):
+        super(HyperNet, self).__init__()
+
+        self.hyperInChn = hyper_in_channels
+        self.target_in_size = target_in_size
+        self.f1 = target_fc1_size
+        self.f2 = target_fc2_size
+        self.f3 = target_fc3_size
+        self.f4 = target_fc4_size
+        self.feature_size = feature_size
+
+        self.res = resnet50_backbone(lda_out_channels, target_in_size)
+
+        self.pool = nn.AdaptiveAvgPool2d((1, 1))
+
+        # Conv layers for resnet output features
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(2048, 1024, 1, padding=(0, 0)), nn.ReLU(inplace=True), nn.Conv2d(1024, 512, 1, padding=(0, 0)),
+            nn.ReLU(inplace=True), nn.Conv2d(512, self.hyperInChn, 1, padding=(0, 0)), nn.ReLU(inplace=True))
+
+        # Hyper network part, conv for generating target fc weights, fc for generating target fc biases
+        self.fc1w_conv = nn.Conv2d(
+            self.hyperInChn, int(self.target_in_size * self.f1 / feature_size**2), 3, padding=(1, 1))
+        self.fc1b_fc = nn.Linear(self.hyperInChn, self.f1)
+
+        self.fc2w_conv = nn.Conv2d(self.hyperInChn, int(self.f1 * self.f2 / feature_size**2), 3, padding=(1, 1))
+        self.fc2b_fc = nn.Linear(self.hyperInChn, self.f2)
+
+        self.fc3w_conv = nn.Conv2d(self.hyperInChn, int(self.f2 * self.f3 / feature_size**2), 3, padding=(1, 1))
+        self.fc3b_fc = nn.Linear(self.hyperInChn, self.f3)
+
+        self.fc4w_conv = nn.Conv2d(self.hyperInChn, int(self.f3 * self.f4 / feature_size**2), 3, padding=(1, 1))
+        self.fc4b_fc = nn.Linear(self.hyperInChn, self.f4)
+
+        self.fc5w_fc = nn.Linear(self.hyperInChn, self.f4)
+        self.fc5b_fc = nn.Linear(self.hyperInChn, 1)
+
+    def forward(self, img):
+        feature_size = self.feature_size
+
+        res_out = self.res(img)
+
+        # input vector for target net
+        target_in_vec = res_out['target_in_vec'].view(-1, self.target_in_size, 1, 1)
+
+        # input features for hyper net
+        hyper_in_feat = self.conv1(res_out['hyper_in_feat']).view(-1, self.hyperInChn, feature_size, feature_size)
+
+        # generating target net weights & biases
+        target_fc1w = self.fc1w_conv(hyper_in_feat).view(-1, self.f1, self.target_in_size, 1, 1)
+        target_fc1b = self.fc1b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f1)
+
+        target_fc2w = self.fc2w_conv(hyper_in_feat).view(-1, self.f2, self.f1, 1, 1)
+        target_fc2b = self.fc2b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f2)
+
+        target_fc3w = self.fc3w_conv(hyper_in_feat).view(-1, self.f3, self.f2, 1, 1)
+        target_fc3b = self.fc3b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f3)
+
+        target_fc4w = self.fc4w_conv(hyper_in_feat).view(-1, self.f4, self.f3, 1, 1)
+        target_fc4b = self.fc4b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, self.f4)
+
+        target_fc5w = self.fc5w_fc(self.pool(hyper_in_feat).squeeze()).view(-1, 1, self.f4, 1, 1)
+        target_fc5b = self.fc5b_fc(self.pool(hyper_in_feat).squeeze()).view(-1, 1)
+
+        out = {}
+        out['target_in_vec'] = target_in_vec
+        out['target_fc1w'] = target_fc1w
+        out['target_fc1b'] = target_fc1b
+        out['target_fc2w'] = target_fc2w
+        out['target_fc2b'] = target_fc2b
+        out['target_fc3w'] = target_fc3w
+        out['target_fc3b'] = target_fc3b
+        out['target_fc4w'] = target_fc4w
+        out['target_fc4b'] = target_fc4b
+        out['target_fc5w'] = target_fc5w
+        out['target_fc5b'] = target_fc5b
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * 4)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNetBackbone(nn.Module):
+
+    def __init__(self, lda_out_channels, in_chn, block, layers, num_classes=1000):
+        super(ResNetBackbone, self).__init__()
+        self.inplanes = 64
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+
+        # local distortion aware module
+        self.lda1_pool = nn.Sequential(
+            nn.Conv2d(256, 16, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.AvgPool2d(7, stride=7),
+        )
+        self.lda1_fc = nn.Linear(16 * 64, lda_out_channels)
+
+        self.lda2_pool = nn.Sequential(
+            nn.Conv2d(512, 32, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.AvgPool2d(7, stride=7),
+        )
+        self.lda2_fc = nn.Linear(32 * 16, lda_out_channels)
+
+        self.lda3_pool = nn.Sequential(
+            nn.Conv2d(1024, 64, kernel_size=1, stride=1, padding=0, bias=False),
+            nn.AvgPool2d(7, stride=7),
+        )
+        self.lda3_fc = nn.Linear(64 * 4, lda_out_channels)
+
+        self.lda4_pool = nn.AvgPool2d(7, stride=7)
+        self.lda4_fc = nn.Linear(2048, in_chn - lda_out_channels * 3)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+        x = self.layer1(x)
+
+        # the same effect as lda operation in the paper, but save much more memory
+        lda_1 = self.lda1_fc(self.lda1_pool(x).view(x.size(0), -1))
+        x = self.layer2(x)
+        lda_2 = self.lda2_fc(self.lda2_pool(x).view(x.size(0), -1))
+        x = self.layer3(x)
+        lda_3 = self.lda3_fc(self.lda3_pool(x).view(x.size(0), -1))
+        x = self.layer4(x)
+        lda_4 = self.lda4_fc(self.lda4_pool(x).view(x.size(0), -1))
+
+        vec = torch.cat((lda_1, lda_2, lda_3, lda_4), 1)
+
+        out = {}
+        out['hyper_in_feat'] = x
+        out['target_in_vec'] = vec
+
+        return out
+
+
+def resnet50_backbone(lda_out_channels, in_chn, **kwargs):
+    """Constructs a ResNet-50 model_hyper."""
+    model = ResNetBackbone(lda_out_channels, in_chn, Bottleneck, [3, 4, 6, 3], **kwargs)
+    return model
+
+
+class TargetNet(nn.Module):
+    """
+    Target network for quality prediction.
+    """
+
+    def __init__(self, paras):
+        super(TargetNet, self).__init__()
+        self.l1 = nn.Sequential(
+            TargetFC(paras['target_fc1w'], paras['target_fc1b']),
+            nn.Sigmoid(),
+        )
+        self.l2 = nn.Sequential(
+            TargetFC(paras['target_fc2w'], paras['target_fc2b']),
+            nn.Sigmoid(),
+        )
+
+        self.l3 = nn.Sequential(
+            TargetFC(paras['target_fc3w'], paras['target_fc3b']),
+            nn.Sigmoid(),
+        )
+
+        self.l4 = nn.Sequential(
+            TargetFC(paras['target_fc4w'], paras['target_fc4b']),
+            nn.Sigmoid(),
+            TargetFC(paras['target_fc5w'], paras['target_fc5b']),
+        )
+
+    def forward(self, x):
+        q = self.l1(x)
+        # q = F.dropout(q)
+        q = self.l2(q)
+        q = self.l3(q)
+        q = self.l4(q).squeeze()
+        return q
+
+
+class TargetFC(nn.Module):
+    """
+    Fully connection operations for target net
+    Note:
+        Weights & biases are different for different images in a batch,
+        thus here we use group convolution for calculating images in a batch with individual weights & biases.
+    """
+
+    def __init__(self, weight, bias):
+        super(TargetFC, self).__init__()
+        self.weight = weight
+        self.bias = bias
+
+    def forward(self, input_):
+
+        input_re = input_.view(-1, input_.shape[0] * input_.shape[1], input_.shape[2], input_.shape[3])
+        weight_re = self.weight.view(self.weight.shape[0] * self.weight.shape[1], self.weight.shape[2],
+                                     self.weight.shape[3], self.weight.shape[4])
+        bias_re = self.bias.view(self.bias.shape[0] * self.bias.shape[1])
+        out = F.conv2d(input=input_re, weight=weight_re, bias=bias_re, groups=self.weight.shape[0])
+
+        return out.view(input_.shape[0], self.weight.shape[1], input_.shape[2], input_.shape[3])

imaginairy/vendored/facexlib/detection/__init__.py

@@ -0,0 +1,31 @@
+import torch
+from copy import deepcopy
+
+from imaginairy.vendored.facexlib.utils import load_file_from_url
+from .retinaface import RetinaFace
+
+
+def init_detection_model(model_name, half=False, device='cuda', model_rootpath=None):
+    if model_name == 'retinaface_resnet50':
+        model = RetinaFace(network_name='resnet50', half=half, device=device)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
+    elif model_name == 'retinaface_mobile0.25':
+        model = RetinaFace(network_name='mobile0.25', half=half, device=device)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+
+    # TODO: clean pretrained model
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+    # remove unnecessary 'module.'
+    for k, v in deepcopy(load_net).items():
+        if k.startswith('module.'):
+            load_net[k[7:]] = v
+            load_net.pop(k)
+    model.load_state_dict(load_net, strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

imaginairy/vendored/facexlib/detection/align_trans.py

@@ -0,0 +1,219 @@
+import cv2
+import numpy as np
+
+from .matlab_cp2tform import get_similarity_transform_for_cv2
+
+# reference facial points, a list of coordinates (x,y)
+REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278],
+                           [33.54930115, 92.3655014], [62.72990036, 92.20410156]]
+
+DEFAULT_CROP_SIZE = (96, 112)
+
+
+class FaceWarpException(Exception):
+
+    def __str__(self):
+        return 'In File {}:{}'.format(__file__, super.__str__(self))
+
+
+def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False):
+    """
+    Function:
+    ----------
+        get reference 5 key points according to crop settings:
+        0. Set default crop_size:
+            if default_square:
+                crop_size = (112, 112)
+            else:
+                crop_size = (96, 112)
+        1. Pad the crop_size by inner_padding_factor in each side;
+        2. Resize crop_size into (output_size - outer_padding*2),
+            pad into output_size with outer_padding;
+        3. Output reference_5point;
+    Parameters:
+    ----------
+        @output_size: (w, h) or None
+            size of aligned face image
+        @inner_padding_factor: (w_factor, h_factor)
+            padding factor for inner (w, h)
+        @outer_padding: (w_pad, h_pad)
+            each row is a pair of coordinates (x, y)
+        @default_square: True or False
+            if True:
+                default crop_size = (112, 112)
+            else:
+                default crop_size = (96, 112);
+        !!! make sure, if output_size is not None:
+                (output_size - outer_padding)
+                = some_scale * (default crop_size * (1.0 +
+                inner_padding_factor))
+    Returns:
+    ----------
+        @reference_5point: 5x2 np.array
+            each row is a pair of transformed coordinates (x, y)
+    """
+
+    tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
+    tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
+
+    # 0) make the inner region a square
+    if default_square:
+        size_diff = max(tmp_crop_size) - tmp_crop_size
+        tmp_5pts += size_diff / 2
+        tmp_crop_size += size_diff
+
+    if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]):
+
+        return tmp_5pts
+
+    if (inner_padding_factor == 0 and outer_padding == (0, 0)):
+        if output_size is None:
+            return tmp_5pts
+        else:
+            raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size))
+
+    # check output size
+    if not (0 <= inner_padding_factor <= 1.0):
+        raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
+
+    if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None):
+        output_size = tmp_crop_size * \
+            (1 + inner_padding_factor * 2).astype(np.int32)
+        output_size += np.array(outer_padding)
+    if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]):
+        raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])')
+
+    # 1) pad the inner region according inner_padding_factor
+    if inner_padding_factor > 0:
+        size_diff = tmp_crop_size * inner_padding_factor * 2
+        tmp_5pts += size_diff / 2
+        tmp_crop_size += np.round(size_diff).astype(np.int32)
+
+    # 2) resize the padded inner region
+    size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
+
+    if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]:
+        raise FaceWarpException('Must have (output_size - outer_padding)'
+                                '= some_scale * (crop_size * (1.0 + inner_padding_factor)')
+
+    scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
+    tmp_5pts = tmp_5pts * scale_factor
+    #    size_diff = tmp_crop_size * (scale_factor - min(scale_factor))
+    #    tmp_5pts = tmp_5pts + size_diff / 2
+    tmp_crop_size = size_bf_outer_pad
+
+    # 3) add outer_padding to make output_size
+    reference_5point = tmp_5pts + np.array(outer_padding)
+    tmp_crop_size = output_size
+
+    return reference_5point
+
+
+def get_affine_transform_matrix(src_pts, dst_pts):
+    """
+    Function:
+    ----------
+        get affine transform matrix 'tfm' from src_pts to dst_pts
+    Parameters:
+    ----------
+        @src_pts: Kx2 np.array
+            source points matrix, each row is a pair of coordinates (x, y)
+        @dst_pts: Kx2 np.array
+            destination points matrix, each row is a pair of coordinates (x, y)
+    Returns:
+    ----------
+        @tfm: 2x3 np.array
+            transform matrix from src_pts to dst_pts
+    """
+
+    tfm = np.float32([[1, 0, 0], [0, 1, 0]])
+    n_pts = src_pts.shape[0]
+    ones = np.ones((n_pts, 1), src_pts.dtype)
+    src_pts_ = np.hstack([src_pts, ones])
+    dst_pts_ = np.hstack([dst_pts, ones])
+
+    A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
+
+    if rank == 3:
+        tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]])
+    elif rank == 2:
+        tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
+
+    return tfm
+
+
+def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'):
+    """
+    Function:
+    ----------
+        apply affine transform 'trans' to uv
+    Parameters:
+    ----------
+        @src_img: 3x3 np.array
+            input image
+        @facial_pts: could be
+            1)a list of K coordinates (x,y)
+        or
+            2) Kx2 or 2xK np.array
+            each row or col is a pair of coordinates (x, y)
+        @reference_pts: could be
+            1) a list of K coordinates (x,y)
+        or
+            2) Kx2 or 2xK np.array
+            each row or col is a pair of coordinates (x, y)
+        or
+            3) None
+            if None, use default reference facial points
+        @crop_size: (w, h)
+            output face image size
+        @align_type: transform type, could be one of
+            1) 'similarity': use similarity transform
+            2) 'cv2_affine': use the first 3 points to do affine transform,
+                    by calling cv2.getAffineTransform()
+            3) 'affine': use all points to do affine transform
+    Returns:
+    ----------
+        @face_img: output face image with size (w, h) = @crop_size
+    """
+
+    if reference_pts is None:
+        if crop_size[0] == 96 and crop_size[1] == 112:
+            reference_pts = REFERENCE_FACIAL_POINTS
+        else:
+            default_square = False
+            inner_padding_factor = 0
+            outer_padding = (0, 0)
+            output_size = crop_size
+
+            reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding,
+                                                        default_square)
+
+    ref_pts = np.float32(reference_pts)
+    ref_pts_shp = ref_pts.shape
+    if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
+        raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2')
+
+    if ref_pts_shp[0] == 2:
+        ref_pts = ref_pts.T
+
+    src_pts = np.float32(facial_pts)
+    src_pts_shp = src_pts.shape
+    if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
+        raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2')
+
+    if src_pts_shp[0] == 2:
+        src_pts = src_pts.T
+
+    if src_pts.shape != ref_pts.shape:
+        raise FaceWarpException('facial_pts and reference_pts must have the same shape')
+
+    if align_type == 'cv2_affine':
+        tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3])
+    elif align_type == 'affine':
+        tfm = get_affine_transform_matrix(src_pts, ref_pts)
+    else:
+        tfm = get_similarity_transform_for_cv2(src_pts, ref_pts)
+
+    face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1]))
+
+    return face_img

imaginairy/vendored/facexlib/detection/matlab_cp2tform.py

@@ -0,0 +1,317 @@
+import numpy as np
+from numpy.linalg import inv, lstsq
+from numpy.linalg import matrix_rank as rank
+from numpy.linalg import norm
+
+
+class MatlabCp2tormException(Exception):
+
+    def __str__(self):
+        return 'In File {}:{}'.format(__file__, super.__str__(self))
+
+
+def tformfwd(trans, uv):
+    """
+    Function:
+    ----------
+        apply affine transform 'trans' to uv
+
+    Parameters:
+    ----------
+        @trans: 3x3 np.array
+            transform matrix
+        @uv: Kx2 np.array
+            each row is a pair of coordinates (x, y)
+
+    Returns:
+    ----------
+        @xy: Kx2 np.array
+            each row is a pair of transformed coordinates (x, y)
+    """
+    uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
+    xy = np.dot(uv, trans)
+    xy = xy[:, 0:-1]
+    return xy
+
+
+def tforminv(trans, uv):
+    """
+    Function:
+    ----------
+        apply the inverse of affine transform 'trans' to uv
+
+    Parameters:
+    ----------
+        @trans: 3x3 np.array
+            transform matrix
+        @uv: Kx2 np.array
+            each row is a pair of coordinates (x, y)
+
+    Returns:
+    ----------
+        @xy: Kx2 np.array
+            each row is a pair of inverse-transformed coordinates (x, y)
+    """
+    Tinv = inv(trans)
+    xy = tformfwd(Tinv, uv)
+    return xy
+
+
+def findNonreflectiveSimilarity(uv, xy, options=None):
+    options = {'K': 2}
+
+    K = options['K']
+    M = xy.shape[0]
+    x = xy[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
+    y = xy[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
+
+    tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
+    tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
+    X = np.vstack((tmp1, tmp2))
+
+    u = uv[:, 0].reshape((-1, 1))  # use reshape to keep a column vector
+    v = uv[:, 1].reshape((-1, 1))  # use reshape to keep a column vector
+    U = np.vstack((u, v))
+
+    # We know that X * r = U
+    if rank(X) >= 2 * K:
+        r, _, _, _ = lstsq(X, U, rcond=-1)
+        r = np.squeeze(r)
+    else:
+        raise Exception('cp2tform:twoUniquePointsReq')
+    sc = r[0]
+    ss = r[1]
+    tx = r[2]
+    ty = r[3]
+
+    Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
+    T = inv(Tinv)
+    T[:, 2] = np.array([0, 0, 1])
+
+    return T, Tinv
+
+
+def findSimilarity(uv, xy, options=None):
+    options = {'K': 2}
+
+    #    uv = np.array(uv)
+    #    xy = np.array(xy)
+
+    # Solve for trans1
+    trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
+
+    # Solve for trans2
+
+    # manually reflect the xy data across the Y-axis
+    xyR = xy
+    xyR[:, 0] = -1 * xyR[:, 0]
+
+    trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
+
+    # manually reflect the tform to undo the reflection done on xyR
+    TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
+
+    trans2 = np.dot(trans2r, TreflectY)
+
+    # Figure out if trans1 or trans2 is better
+    xy1 = tformfwd(trans1, uv)
+    norm1 = norm(xy1 - xy)
+
+    xy2 = tformfwd(trans2, uv)
+    norm2 = norm(xy2 - xy)
+
+    if norm1 <= norm2:
+        return trans1, trans1_inv
+    else:
+        trans2_inv = inv(trans2)
+        return trans2, trans2_inv
+
+
+def get_similarity_transform(src_pts, dst_pts, reflective=True):
+    """
+    Function:
+    ----------
+        Find Similarity Transform Matrix 'trans':
+            u = src_pts[:, 0]
+            v = src_pts[:, 1]
+            x = dst_pts[:, 0]
+            y = dst_pts[:, 1]
+            [x, y, 1] = [u, v, 1] * trans
+
+    Parameters:
+    ----------
+        @src_pts: Kx2 np.array
+            source points, each row is a pair of coordinates (x, y)
+        @dst_pts: Kx2 np.array
+            destination points, each row is a pair of transformed
+            coordinates (x, y)
+        @reflective: True or False
+            if True:
+                use reflective similarity transform
+            else:
+                use non-reflective similarity transform
+
+    Returns:
+    ----------
+       @trans: 3x3 np.array
+            transform matrix from uv to xy
+        trans_inv: 3x3 np.array
+            inverse of trans, transform matrix from xy to uv
+    """
+
+    if reflective:
+        trans, trans_inv = findSimilarity(src_pts, dst_pts)
+    else:
+        trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
+
+    return trans, trans_inv
+
+
+def cvt_tform_mat_for_cv2(trans):
+    """
+    Function:
+    ----------
+        Convert Transform Matrix 'trans' into 'cv2_trans' which could be
+        directly used by cv2.warpAffine():
+            u = src_pts[:, 0]
+            v = src_pts[:, 1]
+            x = dst_pts[:, 0]
+            y = dst_pts[:, 1]
+            [x, y].T = cv_trans * [u, v, 1].T
+
+    Parameters:
+    ----------
+        @trans: 3x3 np.array
+            transform matrix from uv to xy
+
+    Returns:
+    ----------
+        @cv2_trans: 2x3 np.array
+            transform matrix from src_pts to dst_pts, could be directly used
+            for cv2.warpAffine()
+    """
+    cv2_trans = trans[:, 0:2].T
+
+    return cv2_trans
+
+
+def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
+    """
+    Function:
+    ----------
+        Find Similarity Transform Matrix 'cv2_trans' which could be
+        directly used by cv2.warpAffine():
+            u = src_pts[:, 0]
+            v = src_pts[:, 1]
+            x = dst_pts[:, 0]
+            y = dst_pts[:, 1]
+            [x, y].T = cv_trans * [u, v, 1].T
+
+    Parameters:
+    ----------
+        @src_pts: Kx2 np.array
+            source points, each row is a pair of coordinates (x, y)
+        @dst_pts: Kx2 np.array
+            destination points, each row is a pair of transformed
+            coordinates (x, y)
+        reflective: True or False
+            if True:
+                use reflective similarity transform
+            else:
+                use non-reflective similarity transform
+
+    Returns:
+    ----------
+        @cv2_trans: 2x3 np.array
+            transform matrix from src_pts to dst_pts, could be directly used
+            for cv2.warpAffine()
+    """
+    trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
+    cv2_trans = cvt_tform_mat_for_cv2(trans)
+
+    return cv2_trans
+
+
+if __name__ == '__main__':
+    """
+    u = [0, 6, -2]
+    v = [0, 3, 5]
+    x = [-1, 0, 4]
+    y = [-1, -10, 4]
+
+    # In Matlab, run:
+    #
+    #   uv = [u'; v'];
+    #   xy = [x'; y'];
+    #   tform_sim=cp2tform(uv,xy,'similarity');
+    #
+    #   trans = tform_sim.tdata.T
+    #   ans =
+    #       -0.0764   -1.6190         0
+    #        1.6190   -0.0764         0
+    #       -3.2156    0.0290    1.0000
+    #   trans_inv = tform_sim.tdata.Tinv
+    #    ans =
+    #
+    #       -0.0291    0.6163         0
+    #       -0.6163   -0.0291         0
+    #       -0.0756    1.9826    1.0000
+    #    xy_m=tformfwd(tform_sim, u,v)
+    #
+    #    xy_m =
+    #
+    #       -3.2156    0.0290
+    #        1.1833   -9.9143
+    #        5.0323    2.8853
+    #    uv_m=tforminv(tform_sim, x,y)
+    #
+    #    uv_m =
+    #
+    #        0.5698    1.3953
+    #        6.0872    2.2733
+    #       -2.6570    4.3314
+    """
+    u = [0, 6, -2]
+    v = [0, 3, 5]
+    x = [-1, 0, 4]
+    y = [-1, -10, 4]
+
+    uv = np.array((u, v)).T
+    xy = np.array((x, y)).T
+
+    print('\n--->uv:')
+    print(uv)
+    print('\n--->xy:')
+    print(xy)
+
+    trans, trans_inv = get_similarity_transform(uv, xy)
+
+    print('\n--->trans matrix:')
+    print(trans)
+
+    print('\n--->trans_inv matrix:')
+    print(trans_inv)
+
+    print('\n---> apply transform to uv')
+    print('\nxy_m = uv_augmented * trans')
+    uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
+    xy_m = np.dot(uv_aug, trans)
+    print(xy_m)
+
+    print('\nxy_m = tformfwd(trans, uv)')
+    xy_m = tformfwd(trans, uv)
+    print(xy_m)
+
+    print('\n---> apply inverse transform to xy')
+    print('\nuv_m = xy_augmented * trans_inv')
+    xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
+    uv_m = np.dot(xy_aug, trans_inv)
+    print(uv_m)
+
+    print('\nuv_m = tformfwd(trans_inv, xy)')
+    uv_m = tformfwd(trans_inv, xy)
+    print(uv_m)
+
+    uv_m = tforminv(trans, xy)
+    print('\nuv_m = tforminv(trans, xy)')
+    print(uv_m)

imaginairy/vendored/facexlib/detection/retinaface.py

@@ -0,0 +1,366 @@
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from PIL import Image
+from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter
+
+from imaginairy.vendored.facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face
+from imaginairy.vendored.facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head
+from imaginairy.vendored.facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm,
+                                                 py_cpu_nms)
+
+
+def generate_config(network_name):
+
+    cfg_mnet = {
+        'name': 'mobilenet0.25',
+        'min_sizes': [[16, 32], [64, 128], [256, 512]],
+        'steps': [8, 16, 32],
+        'variance': [0.1, 0.2],
+        'clip': False,
+        'loc_weight': 2.0,
+        'gpu_train': True,
+        'batch_size': 32,
+        'ngpu': 1,
+        'epoch': 250,
+        'decay1': 190,
+        'decay2': 220,
+        'image_size': 640,
+        'return_layers': {
+            'stage1': 1,
+            'stage2': 2,
+            'stage3': 3
+        },
+        'in_channel': 32,
+        'out_channel': 64
+    }
+
+    cfg_re50 = {
+        'name': 'Resnet50',
+        'min_sizes': [[16, 32], [64, 128], [256, 512]],
+        'steps': [8, 16, 32],
+        'variance': [0.1, 0.2],
+        'clip': False,
+        'loc_weight': 2.0,
+        'gpu_train': True,
+        'batch_size': 24,
+        'ngpu': 4,
+        'epoch': 100,
+        'decay1': 70,
+        'decay2': 90,
+        'image_size': 840,
+        'return_layers': {
+            'layer2': 1,
+            'layer3': 2,
+            'layer4': 3
+        },
+        'in_channel': 256,
+        'out_channel': 256
+    }
+
+    if network_name == 'mobile0.25':
+        return cfg_mnet
+    elif network_name == 'resnet50':
+        return cfg_re50
+    else:
+        raise NotImplementedError(f'network_name={network_name}')
+
+
+class RetinaFace(nn.Module):
+
+    def __init__(self, network_name='resnet50', half=False, phase='test', device=None):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device
+
+        super(RetinaFace, self).__init__()
+        self.half_inference = half
+        cfg = generate_config(network_name)
+        self.backbone = cfg['name']
+
+        self.model_name = f'retinaface_{network_name}'
+        self.cfg = cfg
+        self.phase = phase
+        self.target_size, self.max_size = 1600, 2150
+        self.resize, self.scale, self.scale1 = 1., None, None
+        self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]], device=self.device)
+        self.reference = get_reference_facial_points(default_square=True)
+        # Build network.
+        backbone = None
+        if cfg['name'] == 'mobilenet0.25':
+            backbone = MobileNetV1()
+            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+        elif cfg['name'] == 'Resnet50':
+            import torchvision.models as models
+            backbone = models.resnet50(pretrained=False)
+            self.body = IntermediateLayerGetter(backbone, cfg['return_layers'])
+
+        in_channels_stage2 = cfg['in_channel']
+        in_channels_list = [
+            in_channels_stage2 * 2,
+            in_channels_stage2 * 4,
+            in_channels_stage2 * 8,
+        ]
+
+        out_channels = cfg['out_channel']
+        self.fpn = FPN(in_channels_list, out_channels)
+        self.ssh1 = SSH(out_channels, out_channels)
+        self.ssh2 = SSH(out_channels, out_channels)
+        self.ssh3 = SSH(out_channels, out_channels)
+
+        self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel'])
+        self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel'])
+
+        self.to(self.device)
+        self.eval()
+        if self.half_inference:
+            self.half()
+
+    def forward(self, inputs):
+        out = self.body(inputs)
+
+        if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50':
+            out = list(out.values())
+        # FPN
+        fpn = self.fpn(out)
+
+        # SSH
+        feature1 = self.ssh1(fpn[0])
+        feature2 = self.ssh2(fpn[1])
+        feature3 = self.ssh3(fpn[2])
+        features = [feature1, feature2, feature3]
+
+        bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1)
+        classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1)
+        tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)]
+        ldm_regressions = (torch.cat(tmp, dim=1))
+
+        if self.phase == 'train':
+            output = (bbox_regressions, classifications, ldm_regressions)
+        else:
+            output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions)
+        return output
+
+    def __detect_faces(self, inputs):
+        # get scale
+        height, width = inputs.shape[2:]
+        self.scale = torch.tensor([width, height, width, height], dtype=torch.float32, device=self.device)
+        tmp = [width, height, width, height, width, height, width, height, width, height]
+        self.scale1 = torch.tensor(tmp, dtype=torch.float32, device=self.device)
+
+        # forawrd
+        inputs = inputs.to(self.device)
+        if self.half_inference:
+            inputs = inputs.half()
+        loc, conf, landmarks = self(inputs)
+
+        # get priorbox
+        priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:])
+        priors = priorbox.forward().to(self.device)
+
+        return loc, conf, landmarks, priors
+
+    # single image detection
+    def transform(self, image, use_origin_size):
+        # convert to opencv format
+        if isinstance(image, Image.Image):
+            image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        image = image.astype(np.float32)
+
+        # testing scale
+        im_size_min = np.min(image.shape[0:2])
+        im_size_max = np.max(image.shape[0:2])
+        resize = float(self.target_size) / float(im_size_min)
+
+        # prevent bigger axis from being more than max_size
+        if np.round(resize * im_size_max) > self.max_size:
+            resize = float(self.max_size) / float(im_size_max)
+        resize = 1 if use_origin_size else resize
+
+        # resize
+        if resize != 1:
+            image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+
+        # convert to torch.tensor format
+        # image -= (104, 117, 123)
+        image = image.transpose(2, 0, 1)
+        image = torch.from_numpy(image).unsqueeze(0)
+
+        return image, resize
+
+    def detect_faces(
+        self,
+        image,
+        conf_threshold=0.8,
+        nms_threshold=0.4,
+        use_origin_size=True,
+    ):
+        image, self.resize = self.transform(image, use_origin_size)
+        image = image.to(self.device)
+        if self.half_inference:
+            image = image.half()
+        image = image - self.mean_tensor
+
+        loc, conf, landmarks, priors = self.__detect_faces(image)
+
+        boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance'])
+        boxes = boxes * self.scale / self.resize
+        boxes = boxes.cpu().numpy()
+
+        scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
+
+        landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance'])
+        landmarks = landmarks * self.scale1 / self.resize
+        landmarks = landmarks.cpu().numpy()
+
+        # ignore low scores
+        inds = np.where(scores > conf_threshold)[0]
+        boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds]
+
+        # sort
+        order = scores.argsort()[::-1]
+        boxes, landmarks, scores = boxes[order], landmarks[order], scores[order]
+
+        # do NMS
+        bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(bounding_boxes, nms_threshold)
+        bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep]
+        # self.t['forward_pass'].toc()
+        # print(self.t['forward_pass'].average_time)
+        # import sys
+        # sys.stdout.flush()
+        return np.concatenate((bounding_boxes, landmarks), axis=1)
+
+    def __align_multi(self, image, boxes, landmarks, limit=None):
+
+        if len(boxes) < 1:
+            return [], []
+
+        if limit:
+            boxes = boxes[:limit]
+            landmarks = landmarks[:limit]
+
+        faces = []
+        for landmark in landmarks:
+            facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)]
+
+            warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112))
+            faces.append(warped_face)
+
+        return np.concatenate((boxes, landmarks), axis=1), faces
+
+    def align_multi(self, img, conf_threshold=0.8, limit=None):
+
+        rlt = self.detect_faces(img, conf_threshold=conf_threshold)
+        boxes, landmarks = rlt[:, 0:5], rlt[:, 5:]
+
+        return self.__align_multi(img, boxes, landmarks, limit)
+
+    # batched detection
+    def batched_transform(self, frames, use_origin_size):
+        """
+        Arguments:
+            frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c],
+                type=np.float32, BGR format).
+            use_origin_size: whether to use origin size.
+        """
+        from_PIL = True if isinstance(frames[0], Image.Image) else False
+
+        # convert to opencv format
+        if from_PIL:
+            frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames]
+            frames = np.asarray(frames, dtype=np.float32)
+
+        # testing scale
+        im_size_min = np.min(frames[0].shape[0:2])
+        im_size_max = np.max(frames[0].shape[0:2])
+        resize = float(self.target_size) / float(im_size_min)
+
+        # prevent bigger axis from being more than max_size
+        if np.round(resize * im_size_max) > self.max_size:
+            resize = float(self.max_size) / float(im_size_max)
+        resize = 1 if use_origin_size else resize
+
+        # resize
+        if resize != 1:
+            if not from_PIL:
+                frames = F.interpolate(frames, scale_factor=resize)
+            else:
+                frames = [
+                    cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR)
+                    for frame in frames
+                ]
+
+        # convert to torch.tensor format
+        if not from_PIL:
+            frames = frames.transpose(1, 2).transpose(1, 3).contiguous()
+        else:
+            frames = frames.transpose((0, 3, 1, 2))
+            frames = torch.from_numpy(frames)
+
+        return frames, resize
+
+    def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True):
+        """
+        Arguments:
+            frames: a list of PIL.Image, or np.array(shape=[n, h, w, c],
+                type=np.uint8, BGR format).
+            conf_threshold: confidence threshold.
+            nms_threshold: nms threshold.
+            use_origin_size: whether to use origin size.
+        Returns:
+            final_bounding_boxes: list of np.array ([n_boxes, 5],
+                type=np.float32).
+            final_landmarks: list of np.array ([n_boxes, 10], type=np.float32).
+        """
+        # self.t['forward_pass'].tic()
+        frames, self.resize = self.batched_transform(frames, use_origin_size)
+        frames = frames.to(self.device)
+        frames = frames - self.mean_tensor
+
+        b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames)
+
+        final_bounding_boxes, final_landmarks = [], []
+
+        # decode
+        priors = priors.unsqueeze(0)
+        b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize
+        b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize
+        b_conf = b_conf[:, :, 1]
+
+        # index for selection
+        b_indice = b_conf > conf_threshold
+
+        # concat
+        b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float()
+
+        for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice):
+
+            # ignore low scores
+            pred, landm = pred[inds, :], landm[inds, :]
+            if pred.shape[0] == 0:
+                final_bounding_boxes.append(np.array([], dtype=np.float32))
+                final_landmarks.append(np.array([], dtype=np.float32))
+                continue
+
+            # sort
+            # order = score.argsort(descending=True)
+            # box, landm, score = box[order], landm[order], score[order]
+
+            # to CPU
+            bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy()
+
+            # NMS
+            keep = py_cpu_nms(bounding_boxes, nms_threshold)
+            bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep]
+
+            # append
+            final_bounding_boxes.append(bounding_boxes)
+            final_landmarks.append(landmarks)
+        # self.t['forward_pass'].toc(average=True)
+        # self.batch_time += self.t['forward_pass'].diff
+        # self.total_frame += len(frames)
+        # print(self.batch_time / self.total_frame)
+
+        return final_bounding_boxes, final_landmarks

imaginairy/vendored/facexlib/detection/retinaface_net.py

@@ -0,0 +1,196 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv_bn(inp, oup, stride=1, leaky=0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_bn_no_relu(inp, oup, stride):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+        nn.BatchNorm2d(oup),
+    )
+
+
+def conv_bn1X1(inp, oup, stride, leaky=0):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_dw(inp, oup, stride, leaky=0.1):
+    return nn.Sequential(
+        nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
+        nn.BatchNorm2d(inp),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True),
+        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.LeakyReLU(negative_slope=leaky, inplace=True),
+    )
+
+
+class SSH(nn.Module):
+
+    def __init__(self, in_channel, out_channel):
+        super(SSH, self).__init__()
+        assert out_channel % 4 == 0
+        leaky = 0
+        if (out_channel <= 64):
+            leaky = 0.1
+        self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)
+
+        self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)
+        self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+        self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)
+        self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+    def forward(self, input):
+        conv3X3 = self.conv3X3(input)
+
+        conv5X5_1 = self.conv5X5_1(input)
+        conv5X5 = self.conv5X5_2(conv5X5_1)
+
+        conv7X7_2 = self.conv7X7_2(conv5X5_1)
+        conv7X7 = self.conv7x7_3(conv7X7_2)
+
+        out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
+        out = F.relu(out)
+        return out
+
+
+class FPN(nn.Module):
+
+    def __init__(self, in_channels_list, out_channels):
+        super(FPN, self).__init__()
+        leaky = 0
+        if (out_channels <= 64):
+            leaky = 0.1
+        self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)
+        self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)
+        self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)
+
+        self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
+        self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
+
+    def forward(self, input):
+        # names = list(input.keys())
+        # input = list(input.values())
+
+        output1 = self.output1(input[0])
+        output2 = self.output2(input[1])
+        output3 = self.output3(input[2])
+
+        up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')
+        output2 = output2 + up3
+        output2 = self.merge2(output2)
+
+        up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')
+        output1 = output1 + up2
+        output1 = self.merge1(output1)
+
+        out = [output1, output2, output3]
+        return out
+
+
+class MobileNetV1(nn.Module):
+
+    def __init__(self):
+        super(MobileNetV1, self).__init__()
+        self.stage1 = nn.Sequential(
+            conv_bn(3, 8, 2, leaky=0.1),  # 3
+            conv_dw(8, 16, 1),  # 7
+            conv_dw(16, 32, 2),  # 11
+            conv_dw(32, 32, 1),  # 19
+            conv_dw(32, 64, 2),  # 27
+            conv_dw(64, 64, 1),  # 43
+        )
+        self.stage2 = nn.Sequential(
+            conv_dw(64, 128, 2),  # 43 + 16 = 59
+            conv_dw(128, 128, 1),  # 59 + 32 = 91
+            conv_dw(128, 128, 1),  # 91 + 32 = 123
+            conv_dw(128, 128, 1),  # 123 + 32 = 155
+            conv_dw(128, 128, 1),  # 155 + 32 = 187
+            conv_dw(128, 128, 1),  # 187 + 32 = 219
+        )
+        self.stage3 = nn.Sequential(
+            conv_dw(128, 256, 2),  # 219 +3 2 = 241
+            conv_dw(256, 256, 1),  # 241 + 64 = 301
+        )
+        self.avg = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(256, 1000)
+
+    def forward(self, x):
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.avg(x)
+        # x = self.model(x)
+        x = x.view(-1, 256)
+        x = self.fc(x)
+        return x
+
+
+class ClassHead(nn.Module):
+
+    def __init__(self, inchannels=512, num_anchors=3):
+        super(ClassHead, self).__init__()
+        self.num_anchors = num_anchors
+        self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)
+
+    def forward(self, x):
+        out = self.conv1x1(x)
+        out = out.permute(0, 2, 3, 1).contiguous()
+
+        return out.view(out.shape[0], -1, 2)
+
+
+class BboxHead(nn.Module):
+
+    def __init__(self, inchannels=512, num_anchors=3):
+        super(BboxHead, self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)
+
+    def forward(self, x):
+        out = self.conv1x1(x)
+        out = out.permute(0, 2, 3, 1).contiguous()
+
+        return out.view(out.shape[0], -1, 4)
+
+
+class LandmarkHead(nn.Module):
+
+    def __init__(self, inchannels=512, num_anchors=3):
+        super(LandmarkHead, self).__init__()
+        self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)
+
+    def forward(self, x):
+        out = self.conv1x1(x)
+        out = out.permute(0, 2, 3, 1).contiguous()
+
+        return out.view(out.shape[0], -1, 10)
+
+
+def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
+    classhead = nn.ModuleList()
+    for i in range(fpn_num):
+        classhead.append(ClassHead(inchannels, anchor_num))
+    return classhead
+
+
+def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
+    bboxhead = nn.ModuleList()
+    for i in range(fpn_num):
+        bboxhead.append(BboxHead(inchannels, anchor_num))
+    return bboxhead
+
+
+def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):
+    landmarkhead = nn.ModuleList()
+    for i in range(fpn_num):
+        landmarkhead.append(LandmarkHead(inchannels, anchor_num))
+    return landmarkhead

imaginairy/vendored/facexlib/detection/retinaface_utils.py

@@ -0,0 +1,421 @@
+import numpy as np
+import torch
+import torchvision
+from itertools import product as product
+from math import ceil
+
+
+class PriorBox(object):
+
+    def __init__(self, cfg, image_size=None, phase='train'):
+        super(PriorBox, self).__init__()
+        self.min_sizes = cfg['min_sizes']
+        self.steps = cfg['steps']
+        self.clip = cfg['clip']
+        self.image_size = image_size
+        self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps]
+        self.name = 's'
+
+    def forward(self):
+        anchors = []
+        for k, f in enumerate(self.feature_maps):
+            min_sizes = self.min_sizes[k]
+            for i, j in product(range(f[0]), range(f[1])):
+                for min_size in min_sizes:
+                    s_kx = min_size / self.image_size[1]
+                    s_ky = min_size / self.image_size[0]
+                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
+                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
+                    for cy, cx in product(dense_cy, dense_cx):
+                        anchors += [cx, cy, s_kx, s_ky]
+
+        # back to torch land
+        output = torch.Tensor(anchors).view(-1, 4)
+        if self.clip:
+            output.clamp_(max=1, min=0)
+        return output
+
+
+def py_cpu_nms(dets, thresh):
+    """Pure Python NMS baseline."""
+    keep = torchvision.ops.nms(
+        boxes=torch.Tensor(dets[:, :4]),
+        scores=torch.Tensor(dets[:, 4]),
+        iou_threshold=thresh,
+    )
+
+    return list(keep)
+
+
+def point_form(boxes):
+    """ Convert prior_boxes to (xmin, ymin, xmax, ymax)
+    representation for comparison to point form ground truth data.
+    Args:
+        boxes: (tensor) center-size default boxes from priorbox layers.
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat(
+        (
+            boxes[:, :2] - boxes[:, 2:] / 2,  # xmin, ymin
+            boxes[:, :2] + boxes[:, 2:] / 2),
+        1)  # xmax, ymax
+
+
+def center_size(boxes):
+    """ Convert prior_boxes to (cx, cy, w, h)
+    representation for comparison to center-size form ground truth data.
+    Args:
+        boxes: (tensor) point_form boxes
+    Return:
+        boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes.
+    """
+    return torch.cat(
+        (boxes[:, 2:] + boxes[:, :2]) / 2,  # cx, cy
+        boxes[:, 2:] - boxes[:, :2],
+        1)  # w, h
+
+
+def intersect(box_a, box_b):
+    """ We resize both tensors to [A,B,2] without new malloc:
+    [A,2] -> [A,1,2] -> [A,B,2]
+    [B,2] -> [1,B,2] -> [A,B,2]
+    Then we compute the area of intersect between box_a and box_b.
+    Args:
+      box_a: (tensor) bounding boxes, Shape: [A,4].
+      box_b: (tensor) bounding boxes, Shape: [B,4].
+    Return:
+      (tensor) intersection area, Shape: [A,B].
+    """
+    A = box_a.size(0)
+    B = box_b.size(0)
+    max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2))
+    min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2))
+    inter = torch.clamp((max_xy - min_xy), min=0)
+    return inter[:, :, 0] * inter[:, :, 1]
+
+
+def jaccard(box_a, box_b):
+    """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
+    is simply the intersection over union of two boxes.  Here we operate on
+    ground truth boxes and default boxes.
+    E.g.:
+        A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
+    Args:
+        box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4]
+        box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4]
+    Return:
+        jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)]
+    """
+    inter = intersect(box_a, box_b)
+    area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter)  # [A,B]
+    area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter)  # [A,B]
+    union = area_a + area_b - inter
+    return inter / union  # [A,B]
+
+
+def matrix_iou(a, b):
+    """
+    return iou of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
+    return area_i / (area_a[:, np.newaxis] + area_b - area_i)
+
+
+def matrix_iof(a, b):
+    """
+    return iof of a and b, numpy version for data augenmentation
+    """
+    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
+    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])
+
+    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
+    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
+    return area_i / np.maximum(area_a[:, np.newaxis], 1)
+
+
+def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx):
+    """Match each prior box with the ground truth box of the highest jaccard
+    overlap, encode the bounding boxes, then return the matched indices
+    corresponding to both confidence and location preds.
+    Args:
+        threshold: (float) The overlap threshold used when matching boxes.
+        truths: (tensor) Ground truth boxes, Shape: [num_obj, 4].
+        priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4].
+        variances: (tensor) Variances corresponding to each prior coord,
+            Shape: [num_priors, 4].
+        labels: (tensor) All the class labels for the image, Shape: [num_obj].
+        landms: (tensor) Ground truth landms, Shape [num_obj, 10].
+        loc_t: (tensor) Tensor to be filled w/ encoded location targets.
+        conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds.
+        landm_t: (tensor) Tensor to be filled w/ encoded landm targets.
+        idx: (int) current batch index
+    Return:
+        The matched indices corresponding to 1)location 2)confidence
+        3)landm preds.
+    """
+    # jaccard index
+    overlaps = jaccard(truths, point_form(priors))
+    # (Bipartite Matching)
+    # [1,num_objects] best prior for each ground truth
+    best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True)
+
+    # ignore hard gt
+    valid_gt_idx = best_prior_overlap[:, 0] >= 0.2
+    best_prior_idx_filter = best_prior_idx[valid_gt_idx, :]
+    if best_prior_idx_filter.shape[0] <= 0:
+        loc_t[idx] = 0
+        conf_t[idx] = 0
+        return
+
+    # [1,num_priors] best ground truth for each prior
+    best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True)
+    best_truth_idx.squeeze_(0)
+    best_truth_overlap.squeeze_(0)
+    best_prior_idx.squeeze_(1)
+    best_prior_idx_filter.squeeze_(1)
+    best_prior_overlap.squeeze_(1)
+    best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2)  # ensure best prior
+    # TODO refactor: index  best_prior_idx with long tensor
+    # ensure every gt matches with its prior of max overlap
+    for j in range(best_prior_idx.size(0)):  # 判别此anchor是预测哪一个boxes
+        best_truth_idx[best_prior_idx[j]] = j
+    matches = truths[best_truth_idx]  # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来
+    conf = labels[best_truth_idx]  # Shape: [num_priors]      此处为每一个anchor对应的label取出来
+    conf[best_truth_overlap < threshold] = 0  # label as background   overlap<0.35的全部作为负样本
+    loc = encode(matches, priors, variances)
+
+    matches_landm = landms[best_truth_idx]
+    landm = encode_landm(matches_landm, priors, variances)
+    loc_t[idx] = loc  # [num_priors,4] encoded offsets to learn
+    conf_t[idx] = conf  # [num_priors] top class label for each prior
+    landm_t[idx] = landm
+
+
+def encode(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 4].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded boxes (tensor), Shape: [num_priors, 4]
+    """
+
+    # dist b/t match center and prior's center
+    g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, 2:])
+    # match wh / prior wh
+    g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:]
+    g_wh = torch.log(g_wh) / variances[1]
+    # return target for smooth_l1_loss
+    return torch.cat([g_cxcy, g_wh], 1)  # [num_priors,4]
+
+
+def encode_landm(matched, priors, variances):
+    """Encode the variances from the priorbox layers into the ground truth boxes
+    we have matched (based on jaccard overlap) with the prior boxes.
+    Args:
+        matched: (tensor) Coords of ground truth for each prior in point-form
+            Shape: [num_priors, 10].
+        priors: (tensor) Prior boxes in center-offset form
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        encoded landm (tensor), Shape: [num_priors, 10]
+    """
+
+    # dist b/t match center and prior's center
+    matched = torch.reshape(matched, (matched.size(0), 5, 2))
+    priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2)
+    priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2)
+    g_cxcy = matched[:, :, :2] - priors[:, :, :2]
+    # encode variance
+    g_cxcy /= (variances[0] * priors[:, :, 2:])
+    # g_cxcy /= priors[:, :, 2:]
+    g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1)
+    # return target for smooth_l1_loss
+    return g_cxcy
+
+
+# Adapted from https://github.com/Hakuyume/chainer-ssd
+def decode(loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        loc (tensor): location predictions for loc layers,
+            Shape: [num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+
+    boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:],
+                       priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1)
+    boxes[:, :2] -= boxes[:, 2:] / 2
+    boxes[:, 2:] += boxes[:, :2]
+    return boxes
+
+
+def decode_landm(pre, priors, variances):
+    """Decode landm from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        pre (tensor): landm predictions for loc layers,
+            Shape: [num_priors,10]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded landm predictions
+    """
+    tmp = (
+        priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:],
+        priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:],
+    )
+    landms = torch.cat(tmp, dim=1)
+    return landms
+
+
+def batched_decode(b_loc, priors, variances):
+    """Decode locations from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        b_loc (tensor): location predictions for loc layers,
+            Shape: [num_batches,num_priors,4]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [1,num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded bounding box predictions
+    """
+    boxes = (
+        priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]),
+    )
+    boxes = torch.cat(boxes, dim=2)
+
+    boxes[:, :, :2] -= boxes[:, :, 2:] / 2
+    boxes[:, :, 2:] += boxes[:, :, :2]
+    return boxes
+
+
+def batched_decode_landm(pre, priors, variances):
+    """Decode landm from predictions using priors to undo
+    the encoding we did for offset regression at train time.
+    Args:
+        pre (tensor): landm predictions for loc layers,
+            Shape: [num_batches,num_priors,10]
+        priors (tensor): Prior boxes in center-offset form.
+            Shape: [1,num_priors,4].
+        variances: (list[float]) Variances of priorboxes
+    Return:
+        decoded landm predictions
+    """
+    landms = (
+        priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:],
+        priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:],
+    )
+    landms = torch.cat(landms, dim=2)
+    return landms
+
+
+def log_sum_exp(x):
+    """Utility function for computing log_sum_exp while determining
+    This will be used to determine unaveraged confidence loss across
+    all examples in a batch.
+    Args:
+        x (Variable(tensor)): conf_preds from conf layers
+    """
+    x_max = x.data.max()
+    return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max
+
+
+# Original author: Francisco Massa:
+# https://github.com/fmassa/object-detection.torch
+# Ported to PyTorch by Max deGroot (02/01/2017)
+def nms(boxes, scores, overlap=0.5, top_k=200):
+    """Apply non-maximum suppression at test time to avoid detecting too many
+    overlapping bounding boxes for a given object.
+    Args:
+        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
+        scores: (tensor) The class predscores for the img, Shape:[num_priors].
+        overlap: (float) The overlap thresh for suppressing unnecessary boxes.
+        top_k: (int) The Maximum number of box preds to consider.
+    Return:
+        The indices of the kept boxes with respect to num_priors.
+    """
+
+    keep = torch.Tensor(scores.size(0)).fill_(0).long()
+    if boxes.numel() == 0:
+        return keep
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    area = torch.mul(x2 - x1, y2 - y1)
+    v, idx = scores.sort(0)  # sort in ascending order
+    # I = I[v >= 0.01]
+    idx = idx[-top_k:]  # indices of the top-k largest vals
+    xx1 = boxes.new()
+    yy1 = boxes.new()
+    xx2 = boxes.new()
+    yy2 = boxes.new()
+    w = boxes.new()
+    h = boxes.new()
+
+    # keep = torch.Tensor()
+    count = 0
+    while idx.numel() > 0:
+        i = idx[-1]  # index of current largest val
+        # keep.append(i)
+        keep[count] = i
+        count += 1
+        if idx.size(0) == 1:
+            break
+        idx = idx[:-1]  # remove kept element from view
+        # load bboxes of next highest vals
+        torch.index_select(x1, 0, idx, out=xx1)
+        torch.index_select(y1, 0, idx, out=yy1)
+        torch.index_select(x2, 0, idx, out=xx2)
+        torch.index_select(y2, 0, idx, out=yy2)
+        # store element-wise max with next highest score
+        xx1 = torch.clamp(xx1, min=x1[i])
+        yy1 = torch.clamp(yy1, min=y1[i])
+        xx2 = torch.clamp(xx2, max=x2[i])
+        yy2 = torch.clamp(yy2, max=y2[i])
+        w.resize_as_(xx2)
+        h.resize_as_(yy2)
+        w = xx2 - xx1
+        h = yy2 - yy1
+        # check sizes of xx1 and xx2.. after each iteration
+        w = torch.clamp(w, min=0.0)
+        h = torch.clamp(h, min=0.0)
+        inter = w * h
+        # IoU = i / (area(a) + area(b) - i)
+        rem_areas = torch.index_select(area, 0, idx)  # load remaining areas)
+        union = (rem_areas - inter) + area[i]
+        IoU = inter / union  # store result in iou
+        # keep only elements with an IoU <= overlap
+        idx = idx[IoU.le(overlap)]
+    return keep, count

imaginairy/vendored/facexlib/headpose/__init__.py

@@ -0,0 +1,20 @@
+import torch
+
+from imaginairy.vendored.facexlib.utils import load_file_from_url
+from .hopenet_arch import HopeNet
+
+
+def init_headpose_model(model_name, half=False, device='cuda', model_rootpath=None):
+    if model_name == 'hopenet':
+        model = HopeNet('resnet', [3, 4, 6, 3], 66)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/headpose_hopenet.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)['params']
+    model.load_state_dict(load_net, strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

imaginairy/vendored/facexlib/headpose/hopenet_arch.py

@@ -0,0 +1,72 @@
+import torch
+import torch.nn as nn
+import torchvision
+
+
+class HopeNet(nn.Module):
+    # Hopenet with 3 output layers for yaw, pitch and roll
+    # Predicts Euler angles by binning and regression with the expected value
+    def __init__(self, block, layers, num_bins):
+        super(HopeNet, self).__init__()
+        if block == 'resnet':
+            block = torchvision.models.resnet.Bottleneck
+        self.inplanes = 64
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AvgPool2d(7)
+        self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)
+        self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
+        self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
+
+        self.idx_tensor = torch.arange(66).float()
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+        return nn.Sequential(*layers)
+
+    @staticmethod
+    def softmax_temperature(tensor, temperature):
+        result = torch.exp(tensor / temperature)
+        result = torch.div(result, torch.sum(result, 1).unsqueeze(1).expand_as(result))
+        return result
+
+    def bin2degree(self, predict):
+        predict = self.softmax_temperature(predict, 1)
+        return torch.sum(predict * self.idx_tensor.type_as(predict), 1) * 3 - 99
+
+    def forward(self, x):
+        x = self.relu(self.bn1(self.conv1(x)))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        pre_yaw = self.fc_yaw(x)
+        pre_pitch = self.fc_pitch(x)
+        pre_roll = self.fc_roll(x)
+
+        yaw = self.bin2degree(pre_yaw)
+        pitch = self.bin2degree(pre_pitch)
+        roll = self.bin2degree(pre_roll)
+        return yaw, pitch, roll

imaginairy/vendored/facexlib/matting/__init__.py

@@ -0,0 +1,27 @@
+import torch
+from copy import deepcopy
+
+from imaginairy.vendored.facexlib.utils import load_file_from_url
+from .modnet import MODNet
+
+
+def init_matting_model(model_name='modnet', half=False, device='cuda', model_rootpath=None):
+    if model_name == 'modnet':
+        model = MODNet(backbone_pretrained=False)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/matting_modnet_portrait.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+    # TODO: clean pretrained model
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+    # remove unnecessary 'module.'
+    for k, v in deepcopy(load_net).items():
+        if k.startswith('module.'):
+            load_net[k[7:]] = v
+            load_net.pop(k)
+    model.load_state_dict(load_net, strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

imaginairy/vendored/facexlib/matting/backbone.py

@@ -0,0 +1,80 @@
+import os
+import torch
+import torch.nn as nn
+
+from .mobilenetv2 import MobileNetV2
+
+
+class BaseBackbone(nn.Module):
+    """ Superclass of Replaceable Backbone Model for Semantic Estimation
+    """
+
+    def __init__(self, in_channels):
+        super(BaseBackbone, self).__init__()
+        self.in_channels = in_channels
+
+        self.model = None
+        self.enc_channels = []
+
+    def forward(self, x):
+        raise NotImplementedError
+
+    def load_pretrained_ckpt(self):
+        raise NotImplementedError
+
+
+class MobileNetV2Backbone(BaseBackbone):
+    """ MobileNetV2 Backbone
+    """
+
+    def __init__(self, in_channels):
+        super(MobileNetV2Backbone, self).__init__(in_channels)
+
+        self.model = MobileNetV2(self.in_channels, alpha=1.0, expansion=6, num_classes=None)
+        self.enc_channels = [16, 24, 32, 96, 1280]
+
+    def forward(self, x):
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(0, 2)), x)
+        x = self.model.features[0](x)
+        x = self.model.features[1](x)
+        enc2x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(2, 4)), x)
+        x = self.model.features[2](x)
+        x = self.model.features[3](x)
+        enc4x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(4, 7)), x)
+        x = self.model.features[4](x)
+        x = self.model.features[5](x)
+        x = self.model.features[6](x)
+        enc8x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(7, 14)), x)
+        x = self.model.features[7](x)
+        x = self.model.features[8](x)
+        x = self.model.features[9](x)
+        x = self.model.features[10](x)
+        x = self.model.features[11](x)
+        x = self.model.features[12](x)
+        x = self.model.features[13](x)
+        enc16x = x
+
+        # x = reduce(lambda x, n: self.model.features[n](x), list(range(14, 19)), x)
+        x = self.model.features[14](x)
+        x = self.model.features[15](x)
+        x = self.model.features[16](x)
+        x = self.model.features[17](x)
+        x = self.model.features[18](x)
+        enc32x = x
+        return [enc2x, enc4x, enc8x, enc16x, enc32x]
+
+    def load_pretrained_ckpt(self):
+        # the pre-trained model is provided by https://github.com/thuyngch/Human-Segmentation-PyTorch
+        ckpt_path = './pretrained/mobilenetv2_human_seg.ckpt'
+        if not os.path.exists(ckpt_path):
+            print('cannot find the pretrained mobilenetv2 backbone')
+            exit()
+
+        ckpt = torch.load(ckpt_path)
+        self.model.load_state_dict(ckpt)

imaginairy/vendored/facexlib/matting/mobilenetv2.py

@@ -0,0 +1,192 @@
+""" This file is adapted from https://github.com/thuyngch/Human-Segmentation-PyTorch"""
+
+import math
+import torch
+from torch import nn
+
+# ------------------------------------------------------------------------------
+#  Useful functions
+# ------------------------------------------------------------------------------
+
+
+def _make_divisible(v, divisor, min_value=None):
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+def conv_bn(inp, oup, stride):
+    return nn.Sequential(nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True))
+
+
+def conv_1x1_bn(inp, oup):
+    return nn.Sequential(nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU6(inplace=True))
+
+
+# ------------------------------------------------------------------------------
+#  Class of Inverted Residual block
+# ------------------------------------------------------------------------------
+
+
+class InvertedResidual(nn.Module):
+
+    def __init__(self, inp, oup, stride, expansion, dilation=1):
+        super(InvertedResidual, self).__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = round(inp * expansion)
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        if expansion == 1:
+            self.conv = nn.Sequential(
+                # dw
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
+                nn.BatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                # pw-linear
+                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+                nn.BatchNorm2d(oup),
+            )
+        else:
+            self.conv = nn.Sequential(
+                # pw
+                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
+                nn.BatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                # dw
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
+                nn.BatchNorm2d(hidden_dim),
+                nn.ReLU6(inplace=True),
+                # pw-linear
+                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
+                nn.BatchNorm2d(oup),
+            )
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        else:
+            return self.conv(x)
+
+
+# ------------------------------------------------------------------------------
+#  Class of MobileNetV2
+# ------------------------------------------------------------------------------
+
+
+class MobileNetV2(nn.Module):
+
+    def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000):
+        super(MobileNetV2, self).__init__()
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        input_channel = 32
+        last_channel = 1280
+        interverted_residual_setting = [
+            # t, c, n, s
+            [1, 16, 1, 1],
+            [expansion, 24, 2, 2],
+            [expansion, 32, 3, 2],
+            [expansion, 64, 4, 2],
+            [expansion, 96, 3, 1],
+            [expansion, 160, 3, 2],
+            [expansion, 320, 1, 1],
+        ]
+
+        # building first layer
+        input_channel = _make_divisible(input_channel * alpha, 8)
+        self.last_channel = _make_divisible(last_channel * alpha, 8) if alpha > 1.0 else last_channel
+        self.features = [conv_bn(self.in_channels, input_channel, 2)]
+
+        # building inverted residual blocks
+        for t, c, n, s in interverted_residual_setting:
+            output_channel = _make_divisible(int(c * alpha), 8)
+            for i in range(n):
+                if i == 0:
+                    self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t))
+                else:
+                    self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=t))
+                input_channel = output_channel
+
+        # building last several layers
+        self.features.append(conv_1x1_bn(input_channel, self.last_channel))
+
+        # make it nn.Sequential
+        self.features = nn.Sequential(*self.features)
+
+        # building classifier
+        if self.num_classes is not None:
+            self.classifier = nn.Sequential(
+                nn.Dropout(0.2),
+                nn.Linear(self.last_channel, num_classes),
+            )
+
+        # Initialize weights
+        self._init_weights()
+
+    def forward(self, x):
+        # Stage1
+        x = self.features[0](x)
+        x = self.features[1](x)
+        # Stage2
+        x = self.features[2](x)
+        x = self.features[3](x)
+        # Stage3
+        x = self.features[4](x)
+        x = self.features[5](x)
+        x = self.features[6](x)
+        # Stage4
+        x = self.features[7](x)
+        x = self.features[8](x)
+        x = self.features[9](x)
+        x = self.features[10](x)
+        x = self.features[11](x)
+        x = self.features[12](x)
+        x = self.features[13](x)
+        # Stage5
+        x = self.features[14](x)
+        x = self.features[15](x)
+        x = self.features[16](x)
+        x = self.features[17](x)
+        x = self.features[18](x)
+
+        # Classification
+        if self.num_classes is not None:
+            x = x.mean(dim=(2, 3))
+            x = self.classifier(x)
+
+        # Output
+        return x
+
+    def _load_pretrained_model(self, pretrained_file):
+        pretrain_dict = torch.load(pretrained_file, map_location='cpu')
+        model_dict = {}
+        state_dict = self.state_dict()
+        print('[MobileNetV2] Loading pretrained model...')
+        for k, v in pretrain_dict.items():
+            if k in state_dict:
+                model_dict[k] = v
+            else:
+                print(k, 'is ignored')
+        state_dict.update(model_dict)
+        self.load_state_dict(state_dict)
+
+    def _init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                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):
+                n = m.weight.size(1)
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()

imaginairy/vendored/facexlib/matting/modnet.py

@@ -0,0 +1,267 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .backbone import MobileNetV2Backbone
+
+# ------------------------------------------------------------------------------
+#  MODNet Basic Modules
+# ------------------------------------------------------------------------------
+
+
+class IBNorm(nn.Module):
+    """ Combine Instance Norm and Batch Norm into One Layer
+    """
+
+    def __init__(self, in_channels):
+        super(IBNorm, self).__init__()
+        in_channels = in_channels
+        self.bnorm_channels = int(in_channels / 2)
+        self.inorm_channels = in_channels - self.bnorm_channels
+
+        self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True)
+        self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False)
+
+    def forward(self, x):
+        bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous())
+        in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous())
+
+        return torch.cat((bn_x, in_x), 1)
+
+
+class Conv2dIBNormRelu(nn.Module):
+    """ Convolution + IBNorm + ReLu
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 bias=True,
+                 with_ibn=True,
+                 with_relu=True):
+        super(Conv2dIBNormRelu, self).__init__()
+
+        layers = [
+            nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                stride=stride,
+                padding=padding,
+                dilation=dilation,
+                groups=groups,
+                bias=bias)
+        ]
+
+        if with_ibn:
+            layers.append(IBNorm(out_channels))
+        if with_relu:
+            layers.append(nn.ReLU(inplace=True))
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class SEBlock(nn.Module):
+    """ SE Block Proposed in https://arxiv.org/pdf/1709.01507.pdf
+    """
+
+    def __init__(self, in_channels, out_channels, reduction=1):
+        super(SEBlock, self).__init__()
+        self.pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Sequential(
+            nn.Linear(in_channels, int(in_channels // reduction), bias=False), nn.ReLU(inplace=True),
+            nn.Linear(int(in_channels // reduction), out_channels, bias=False), nn.Sigmoid())
+
+    def forward(self, x):
+        b, c, _, _ = x.size()
+        w = self.pool(x).view(b, c)
+        w = self.fc(w).view(b, c, 1, 1)
+
+        return x * w.expand_as(x)
+
+
+# ------------------------------------------------------------------------------
+#  MODNet Branches
+# ------------------------------------------------------------------------------
+
+
+class LRBranch(nn.Module):
+    """ Low Resolution Branch of MODNet
+    """
+
+    def __init__(self, backbone):
+        super(LRBranch, self).__init__()
+
+        enc_channels = backbone.enc_channels
+
+        self.backbone = backbone
+        self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4)
+        self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2)
+        self.conv_lr8x = Conv2dIBNormRelu(enc_channels[3], enc_channels[2], 5, stride=1, padding=2)
+        self.conv_lr = Conv2dIBNormRelu(
+            enc_channels[2], 1, kernel_size=3, stride=2, padding=1, with_ibn=False, with_relu=False)
+
+    def forward(self, img, inference):
+        enc_features = self.backbone.forward(img)
+        enc2x, enc4x, enc32x = enc_features[0], enc_features[1], enc_features[4]
+
+        enc32x = self.se_block(enc32x)
+        lr16x = F.interpolate(enc32x, scale_factor=2, mode='bilinear', align_corners=False)
+        lr16x = self.conv_lr16x(lr16x)
+        lr8x = F.interpolate(lr16x, scale_factor=2, mode='bilinear', align_corners=False)
+        lr8x = self.conv_lr8x(lr8x)
+
+        pred_semantic = None
+        if not inference:
+            lr = self.conv_lr(lr8x)
+            pred_semantic = torch.sigmoid(lr)
+
+        return pred_semantic, lr8x, [enc2x, enc4x]
+
+
+class HRBranch(nn.Module):
+    """ High Resolution Branch of MODNet
+    """
+
+    def __init__(self, hr_channels, enc_channels):
+        super(HRBranch, self).__init__()
+
+        self.tohr_enc2x = Conv2dIBNormRelu(enc_channels[0], hr_channels, 1, stride=1, padding=0)
+        self.conv_enc2x = Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=2, padding=1)
+
+        self.tohr_enc4x = Conv2dIBNormRelu(enc_channels[1], hr_channels, 1, stride=1, padding=0)
+        self.conv_enc4x = Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1)
+
+        self.conv_hr4x = nn.Sequential(
+            Conv2dIBNormRelu(3 * hr_channels + 3, 2 * hr_channels, 3, stride=1, padding=1),
+            Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1),
+            Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1),
+        )
+
+        self.conv_hr2x = nn.Sequential(
+            Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1),
+            Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1),
+            Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1),
+            Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1),
+        )
+
+        self.conv_hr = nn.Sequential(
+            Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=1, padding=1),
+            Conv2dIBNormRelu(hr_channels, 1, kernel_size=1, stride=1, padding=0, with_ibn=False, with_relu=False),
+        )
+
+    def forward(self, img, enc2x, enc4x, lr8x, inference):
+        img2x = F.interpolate(img, scale_factor=1 / 2, mode='bilinear', align_corners=False)
+        img4x = F.interpolate(img, scale_factor=1 / 4, mode='bilinear', align_corners=False)
+
+        enc2x = self.tohr_enc2x(enc2x)
+        hr4x = self.conv_enc2x(torch.cat((img2x, enc2x), dim=1))
+
+        enc4x = self.tohr_enc4x(enc4x)
+        hr4x = self.conv_enc4x(torch.cat((hr4x, enc4x), dim=1))
+
+        lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False)
+        hr4x = self.conv_hr4x(torch.cat((hr4x, lr4x, img4x), dim=1))
+
+        hr2x = F.interpolate(hr4x, scale_factor=2, mode='bilinear', align_corners=False)
+        hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1))
+
+        pred_detail = None
+        if not inference:
+            hr = F.interpolate(hr2x, scale_factor=2, mode='bilinear', align_corners=False)
+            hr = self.conv_hr(torch.cat((hr, img), dim=1))
+            pred_detail = torch.sigmoid(hr)
+
+        return pred_detail, hr2x
+
+
+class FusionBranch(nn.Module):
+    """ Fusion Branch of MODNet
+    """
+
+    def __init__(self, hr_channels, enc_channels):
+        super(FusionBranch, self).__init__()
+        self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2)
+
+        self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1)
+        self.conv_f = nn.Sequential(
+            Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1),
+            Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False),
+        )
+
+    def forward(self, img, lr8x, hr2x):
+        lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False)
+        lr4x = self.conv_lr4x(lr4x)
+        lr2x = F.interpolate(lr4x, scale_factor=2, mode='bilinear', align_corners=False)
+
+        f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1))
+        f = F.interpolate(f2x, scale_factor=2, mode='bilinear', align_corners=False)
+        f = self.conv_f(torch.cat((f, img), dim=1))
+        pred_matte = torch.sigmoid(f)
+
+        return pred_matte
+
+
+# ------------------------------------------------------------------------------
+#  MODNet
+# ------------------------------------------------------------------------------
+
+
+class MODNet(nn.Module):
+    """ Architecture of MODNet
+    """
+
+    def __init__(self, in_channels=3, hr_channels=32, backbone_pretrained=True):
+        super(MODNet, self).__init__()
+
+        self.in_channels = in_channels
+        self.hr_channels = hr_channels
+        self.backbone_pretrained = backbone_pretrained
+
+        self.backbone = MobileNetV2Backbone(self.in_channels)
+
+        self.lr_branch = LRBranch(self.backbone)
+        self.hr_branch = HRBranch(self.hr_channels, self.backbone.enc_channels)
+        self.f_branch = FusionBranch(self.hr_channels, self.backbone.enc_channels)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                self._init_conv(m)
+            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.InstanceNorm2d):
+                self._init_norm(m)
+
+        if self.backbone_pretrained:
+            self.backbone.load_pretrained_ckpt()
+
+    def forward(self, img, inference):
+        pred_semantic, lr8x, [enc2x, enc4x] = self.lr_branch(img, inference)
+        pred_detail, hr2x = self.hr_branch(img, enc2x, enc4x, lr8x, inference)
+        pred_matte = self.f_branch(img, lr8x, hr2x)
+
+        return pred_semantic, pred_detail, pred_matte
+
+    def freeze_norm(self):
+        norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d]
+        for m in self.modules():
+            for n in norm_types:
+                if isinstance(m, n):
+                    m.eval()
+                    continue
+
+    def _init_conv(self, conv):
+        nn.init.kaiming_uniform_(conv.weight, a=0, mode='fan_in', nonlinearity='relu')
+        if conv.bias is not None:
+            nn.init.constant_(conv.bias, 0)
+
+    def _init_norm(self, norm):
+        if norm.weight is not None:
+            nn.init.constant_(norm.weight, 1)
+            nn.init.constant_(norm.bias, 0)

imaginairy/vendored/facexlib/parsing/__init__.py

@@ -0,0 +1,24 @@
+import torch
+
+from imaginairy.vendored.facexlib.utils import load_file_from_url
+from .bisenet import BiSeNet
+from .parsenet import ParseNet
+
+
+def init_parsing_model(model_name='bisenet', half=False, device='cuda', model_rootpath=None):
+    if model_name == 'bisenet':
+        model = BiSeNet(num_class=19)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth'
+    elif model_name == 'parsenet':
+        model = ParseNet(in_size=512, out_size=512, parsing_ch=19)
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
+    model.load_state_dict(load_net, strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

imaginairy/vendored/facexlib/parsing/bisenet.py

@@ -0,0 +1,140 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .resnet import ResNet18
+
+
+class ConvBNReLU(nn.Module):
+
+    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):
+        super(ConvBNReLU, self).__init__()
+        self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False)
+        self.bn = nn.BatchNorm2d(out_chan)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(self.bn(x))
+        return x
+
+
+class BiSeNetOutput(nn.Module):
+
+    def __init__(self, in_chan, mid_chan, num_class):
+        super(BiSeNetOutput, self).__init__()
+        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
+        self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        feat = self.conv(x)
+        out = self.conv_out(feat)
+        return out, feat
+
+
+class AttentionRefinementModule(nn.Module):
+
+    def __init__(self, in_chan, out_chan):
+        super(AttentionRefinementModule, self).__init__()
+        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
+        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
+        self.bn_atten = nn.BatchNorm2d(out_chan)
+        self.sigmoid_atten = nn.Sigmoid()
+
+    def forward(self, x):
+        feat = self.conv(x)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv_atten(atten)
+        atten = self.bn_atten(atten)
+        atten = self.sigmoid_atten(atten)
+        out = torch.mul(feat, atten)
+        return out
+
+
+class ContextPath(nn.Module):
+
+    def __init__(self):
+        super(ContextPath, self).__init__()
+        self.resnet = ResNet18()
+        self.arm16 = AttentionRefinementModule(256, 128)
+        self.arm32 = AttentionRefinementModule(512, 128)
+        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
+        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
+
+    def forward(self, x):
+        feat8, feat16, feat32 = self.resnet(x)
+        h8, w8 = feat8.size()[2:]
+        h16, w16 = feat16.size()[2:]
+        h32, w32 = feat32.size()[2:]
+
+        avg = F.avg_pool2d(feat32, feat32.size()[2:])
+        avg = self.conv_avg(avg)
+        avg_up = F.interpolate(avg, (h32, w32), mode='nearest')
+
+        feat32_arm = self.arm32(feat32)
+        feat32_sum = feat32_arm + avg_up
+        feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest')
+        feat32_up = self.conv_head32(feat32_up)
+
+        feat16_arm = self.arm16(feat16)
+        feat16_sum = feat16_arm + feat32_up
+        feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest')
+        feat16_up = self.conv_head16(feat16_up)
+
+        return feat8, feat16_up, feat32_up  # x8, x8, x16
+
+
+class FeatureFusionModule(nn.Module):
+
+    def __init__(self, in_chan, out_chan):
+        super(FeatureFusionModule, self).__init__()
+        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
+        self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False)
+        self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False)
+        self.relu = nn.ReLU(inplace=True)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, fsp, fcp):
+        fcat = torch.cat([fsp, fcp], dim=1)
+        feat = self.convblk(fcat)
+        atten = F.avg_pool2d(feat, feat.size()[2:])
+        atten = self.conv1(atten)
+        atten = self.relu(atten)
+        atten = self.conv2(atten)
+        atten = self.sigmoid(atten)
+        feat_atten = torch.mul(feat, atten)
+        feat_out = feat_atten + feat
+        return feat_out
+
+
+class BiSeNet(nn.Module):
+
+    def __init__(self, num_class):
+        super(BiSeNet, self).__init__()
+        self.cp = ContextPath()
+        self.ffm = FeatureFusionModule(256, 256)
+        self.conv_out = BiSeNetOutput(256, 256, num_class)
+        self.conv_out16 = BiSeNetOutput(128, 64, num_class)
+        self.conv_out32 = BiSeNetOutput(128, 64, num_class)
+
+    def forward(self, x, return_feat=False):
+        h, w = x.size()[2:]
+        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # return res3b1 feature
+        feat_sp = feat_res8  # replace spatial path feature with res3b1 feature
+        feat_fuse = self.ffm(feat_sp, feat_cp8)
+
+        out, feat = self.conv_out(feat_fuse)
+        out16, feat16 = self.conv_out16(feat_cp8)
+        out32, feat32 = self.conv_out32(feat_cp16)
+
+        out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True)
+        out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True)
+        out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True)
+
+        if return_feat:
+            feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True)
+            feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True)
+            feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True)
+            return out, out16, out32, feat, feat16, feat32
+        else:
+            return out, out16, out32

imaginairy/vendored/facexlib/parsing/parsenet.py

@@ -0,0 +1,194 @@
+"""Modified from https://github.com/chaofengc/PSFRGAN
+"""
+import numpy as np
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class NormLayer(nn.Module):
+    """Normalization Layers.
+
+    Args:
+        channels: input channels, for batch norm and instance norm.
+        input_size: input shape without batch size, for layer norm.
+    """
+
+    def __init__(self, channels, normalize_shape=None, norm_type='bn'):
+        super(NormLayer, self).__init__()
+        norm_type = norm_type.lower()
+        self.norm_type = norm_type
+        if norm_type == 'bn':
+            self.norm = nn.BatchNorm2d(channels, affine=True)
+        elif norm_type == 'in':
+            self.norm = nn.InstanceNorm2d(channels, affine=False)
+        elif norm_type == 'gn':
+            self.norm = nn.GroupNorm(32, channels, affine=True)
+        elif norm_type == 'pixel':
+            self.norm = lambda x: F.normalize(x, p=2, dim=1)
+        elif norm_type == 'layer':
+            self.norm = nn.LayerNorm(normalize_shape)
+        elif norm_type == 'none':
+            self.norm = lambda x: x * 1.0
+        else:
+            assert 1 == 0, f'Norm type {norm_type} not support.'
+
+    def forward(self, x, ref=None):
+        if self.norm_type == 'spade':
+            return self.norm(x, ref)
+        else:
+            return self.norm(x)
+
+
+class ReluLayer(nn.Module):
+    """Relu Layer.
+
+    Args:
+        relu type: type of relu layer, candidates are
+            - ReLU
+            - LeakyReLU: default relu slope 0.2
+            - PRelu
+            - SELU
+            - none: direct pass
+    """
+
+    def __init__(self, channels, relu_type='relu'):
+        super(ReluLayer, self).__init__()
+        relu_type = relu_type.lower()
+        if relu_type == 'relu':
+            self.func = nn.ReLU(True)
+        elif relu_type == 'leakyrelu':
+            self.func = nn.LeakyReLU(0.2, inplace=True)
+        elif relu_type == 'prelu':
+            self.func = nn.PReLU(channels)
+        elif relu_type == 'selu':
+            self.func = nn.SELU(True)
+        elif relu_type == 'none':
+            self.func = lambda x: x * 1.0
+        else:
+            assert 1 == 0, f'Relu type {relu_type} not support.'
+
+    def forward(self, x):
+        return self.func(x)
+
+
+class ConvLayer(nn.Module):
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 scale='none',
+                 norm_type='none',
+                 relu_type='none',
+                 use_pad=True,
+                 bias=True):
+        super(ConvLayer, self).__init__()
+        self.use_pad = use_pad
+        self.norm_type = norm_type
+        if norm_type in ['bn']:
+            bias = False
+
+        stride = 2 if scale == 'down' else 1
+
+        self.scale_func = lambda x: x
+        if scale == 'up':
+            self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
+
+        self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))
+        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
+
+        self.relu = ReluLayer(out_channels, relu_type)
+        self.norm = NormLayer(out_channels, norm_type=norm_type)
+
+    def forward(self, x):
+        out = self.scale_func(x)
+        if self.use_pad:
+            out = self.reflection_pad(out)
+        out = self.conv2d(out)
+        out = self.norm(out)
+        out = self.relu(out)
+        return out
+
+
+class ResidualBlock(nn.Module):
+    """
+    Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
+    """
+
+    def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
+        super(ResidualBlock, self).__init__()
+
+        if scale == 'none' and c_in == c_out:
+            self.shortcut_func = lambda x: x
+        else:
+            self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
+
+        scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
+        scale_conf = scale_config_dict[scale]
+
+        self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)
+        self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
+
+    def forward(self, x):
+        identity = self.shortcut_func(x)
+
+        res = self.conv1(x)
+        res = self.conv2(res)
+        return identity + res
+
+
+class ParseNet(nn.Module):
+
+    def __init__(self,
+                 in_size=128,
+                 out_size=128,
+                 min_feat_size=32,
+                 base_ch=64,
+                 parsing_ch=19,
+                 res_depth=10,
+                 relu_type='LeakyReLU',
+                 norm_type='bn',
+                 ch_range=[32, 256]):
+        super().__init__()
+        self.res_depth = res_depth
+        act_args = {'norm_type': norm_type, 'relu_type': relu_type}
+        min_ch, max_ch = ch_range
+
+        ch_clip = lambda x: max(min_ch, min(x, max_ch))  # noqa: E731
+        min_feat_size = min(in_size, min_feat_size)
+
+        down_steps = int(np.log2(in_size // min_feat_size))
+        up_steps = int(np.log2(out_size // min_feat_size))
+
+        # =============== define encoder-body-decoder ====================
+        self.encoder = []
+        self.encoder.append(ConvLayer(3, base_ch, 3, 1))
+        head_ch = base_ch
+        for i in range(down_steps):
+            cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
+            self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
+            head_ch = head_ch * 2
+
+        self.body = []
+        for i in range(res_depth):
+            self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
+
+        self.decoder = []
+        for i in range(up_steps):
+            cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
+            self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
+            head_ch = head_ch // 2
+
+        self.encoder = nn.Sequential(*self.encoder)
+        self.body = nn.Sequential(*self.body)
+        self.decoder = nn.Sequential(*self.decoder)
+        self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
+        self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
+
+    def forward(self, x):
+        feat = self.encoder(x)
+        x = feat + self.body(feat)
+        x = self.decoder(x)
+        out_img = self.out_img_conv(x)
+        out_mask = self.out_mask_conv(x)
+        return out_mask, out_img

imaginairy/vendored/facexlib/parsing/resnet.py

@@ -0,0 +1,69 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+
+    def __init__(self, in_chan, out_chan, stride=1):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(in_chan, out_chan, stride)
+        self.bn1 = nn.BatchNorm2d(out_chan)
+        self.conv2 = conv3x3(out_chan, out_chan)
+        self.bn2 = nn.BatchNorm2d(out_chan)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = None
+        if in_chan != out_chan or stride != 1:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(out_chan),
+            )
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = F.relu(self.bn1(residual))
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+
+        shortcut = x
+        if self.downsample is not None:
+            shortcut = self.downsample(x)
+
+        out = shortcut + residual
+        out = self.relu(out)
+        return out
+
+
+def create_layer_basic(in_chan, out_chan, bnum, stride=1):
+    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
+    for i in range(bnum - 1):
+        layers.append(BasicBlock(out_chan, out_chan, stride=1))
+    return nn.Sequential(*layers)
+
+
+class ResNet18(nn.Module):
+
+    def __init__(self):
+        super(ResNet18, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
+        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
+        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
+        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = F.relu(self.bn1(x))
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        feat8 = self.layer2(x)  # 1/8
+        feat16 = self.layer3(feat8)  # 1/16
+        feat32 = self.layer4(feat16)  # 1/32
+        return feat8, feat16, feat32

imaginairy/vendored/facexlib/readme.txt

@@ -0,0 +1 @@
+vendored from git@github.com:xinntao/facexlib.git @ 260620ae93990a300f4b16448df9bb459f1caba9

imaginairy/vendored/facexlib/recognition/__init__.py

@@ -0,0 +1,19 @@
+import torch
+
+from imaginairy.vendored.facexlib.utils import load_file_from_url
+from .arcface_arch import Backbone
+
+
+def init_recognition_model(model_name, half=False, device='cuda', model_rootpath=None):
+    if model_name == 'arcface':
+        model = Backbone(num_layers=50, drop_ratio=0.6, mode='ir_se').to('cuda').eval()
+        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/recognition_arcface_ir_se50.pth'
+    else:
+        raise NotImplementedError(f'{model_name} is not implemented.')
+
+    model_path = load_file_from_url(
+        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
+    model.load_state_dict(torch.load(model_path), strict=True)
+    model.eval()
+    model = model.to(device)
+    return model

imaginairy/vendored/facexlib/recognition/arcface_arch.py

@@ -0,0 +1,238 @@
+import torch
+from collections import namedtuple
+from torch.nn import (AdaptiveAvgPool2d, BatchNorm1d, BatchNorm2d, Conv2d, Dropout, Linear, MaxPool2d, Module, PReLU,
+                      ReLU, Sequential, Sigmoid)
+
+# Original Arcface Model
+
+
+class Flatten(Module):
+
+    def forward(self, input):
+        return input.view(input.size(0), -1)
+
+
+def l2_norm(input, axis=1):
+    norm = torch.norm(input, 2, axis, True)
+    output = torch.div(input, norm)
+    return output
+
+
+class SEModule(Module):
+
+    def __init__(self, channels, reduction):
+        super(SEModule, self).__init__()
+        self.avg_pool = AdaptiveAvgPool2d(1)
+        self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False)
+        self.relu = ReLU(inplace=True)
+        self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False)
+        self.sigmoid = Sigmoid()
+
+    def forward(self, x):
+        module_input = x
+        x = self.avg_pool(x)
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        x = self.sigmoid(x)
+        return module_input * x
+
+
+class bottleneck_IR(Module):
+
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+        return res + shortcut
+
+
+class bottleneck_IR_SE(Module):
+
+    def __init__(self, in_channel, depth, stride):
+        super(bottleneck_IR_SE, self).__init__()
+        if in_channel == depth:
+            self.shortcut_layer = MaxPool2d(1, stride)
+        else:
+            self.shortcut_layer = Sequential(Conv2d(in_channel, depth, (1, 1), stride, bias=False), BatchNorm2d(depth))
+        self.res_layer = Sequential(
+            BatchNorm2d(in_channel), Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth),
+            Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth), SEModule(depth, 16))
+
+    def forward(self, x):
+        shortcut = self.shortcut_layer(x)
+        res = self.res_layer(x)
+        return res + shortcut
+
+
+class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])):
+    '''A named tuple describing a ResNet block.'''
+
+
+def get_block(in_channel, depth, num_units, stride=2):
+    return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)]
+
+
+def get_blocks(num_layers):
+    if num_layers == 50:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=4),
+            get_block(in_channel=128, depth=256, num_units=14),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 100:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=13),
+            get_block(in_channel=128, depth=256, num_units=30),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    elif num_layers == 152:
+        blocks = [
+            get_block(in_channel=64, depth=64, num_units=3),
+            get_block(in_channel=64, depth=128, num_units=8),
+            get_block(in_channel=128, depth=256, num_units=36),
+            get_block(in_channel=256, depth=512, num_units=3)
+        ]
+    return blocks
+
+
+class Backbone(Module):
+
+    def __init__(self, num_layers, drop_ratio, mode='ir'):
+        super(Backbone, self).__init__()
+        assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152'
+        assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se'
+        blocks = get_blocks(num_layers)
+        if mode == 'ir':
+            unit_module = bottleneck_IR
+        elif mode == 'ir_se':
+            unit_module = bottleneck_IR_SE
+        self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), BatchNorm2d(64), PReLU(64))
+        self.output_layer = Sequential(
+            BatchNorm2d(512), Dropout(drop_ratio), Flatten(), Linear(512 * 7 * 7, 512), BatchNorm1d(512))
+        modules = []
+        for block in blocks:
+            for bottleneck in block:
+                modules.append(unit_module(bottleneck.in_channel, bottleneck.depth, bottleneck.stride))
+        self.body = Sequential(*modules)
+
+    def forward(self, x):
+        x = self.input_layer(x)
+        x = self.body(x)
+        x = self.output_layer(x)
+        return l2_norm(x)
+
+
+# MobileFaceNet
+
+
+class Conv_block(Module):
+
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(Conv_block, self).__init__()
+        self.conv = Conv2d(
+            in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
+        self.bn = BatchNorm2d(out_c)
+        self.prelu = PReLU(out_c)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.prelu(x)
+        return x
+
+
+class Linear_block(Module):
+
+    def __init__(self, in_c, out_c, kernel=(1, 1), stride=(1, 1), padding=(0, 0), groups=1):
+        super(Linear_block, self).__init__()
+        self.conv = Conv2d(
+            in_c, out_channels=out_c, kernel_size=kernel, groups=groups, stride=stride, padding=padding, bias=False)
+        self.bn = BatchNorm2d(out_c)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class Depth_Wise(Module):
+
+    def __init__(self, in_c, out_c, residual=False, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=1):
+        super(Depth_Wise, self).__init__()
+        self.conv = Conv_block(in_c, out_c=groups, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
+        self.conv_dw = Conv_block(groups, groups, groups=groups, kernel=kernel, padding=padding, stride=stride)
+        self.project = Linear_block(groups, out_c, kernel=(1, 1), padding=(0, 0), stride=(1, 1))
+        self.residual = residual
+
+    def forward(self, x):
+        if self.residual:
+            short_cut = x
+        x = self.conv(x)
+        x = self.conv_dw(x)
+        x = self.project(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(
+                Depth_Wise(c, c, residual=True, kernel=kernel, padding=padding, stride=stride, groups=groups))
+        self.model = Sequential(*modules)
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class MobileFaceNet(Module):
+
+    def __init__(self, embedding_size):
+        super(MobileFaceNet, self).__init__()
+        self.conv1 = Conv_block(3, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1))
+        self.conv2_dw = Conv_block(64, 64, kernel=(3, 3), stride=(1, 1), padding=(1, 1), groups=64)
+        self.conv_23 = Depth_Wise(64, 64, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=128)
+        self.conv_3 = Residual(64, num_block=4, groups=128, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.conv_34 = Depth_Wise(64, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=256)
+        self.conv_4 = Residual(128, num_block=6, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.conv_45 = Depth_Wise(128, 128, kernel=(3, 3), stride=(2, 2), padding=(1, 1), groups=512)
+        self.conv_5 = Residual(128, num_block=2, groups=256, kernel=(3, 3), stride=(1, 1), padding=(1, 1))
+        self.conv_6_sep = Conv_block(128, 512, kernel=(1, 1), stride=(1, 1), padding=(0, 0))
+        self.conv_6_dw = Linear_block(512, 512, groups=512, kernel=(7, 7), stride=(1, 1), padding=(0, 0))
+        self.conv_6_flatten = Flatten()
+        self.linear = Linear(512, embedding_size, bias=False)
+        self.bn = BatchNorm1d(embedding_size)
+
+    def forward(self, x):
+        out = self.conv1(x)
+        out = self.conv2_dw(out)
+        out = self.conv_23(out)
+        out = self.conv_3(out)
+        out = self.conv_34(out)
+        out = self.conv_4(out)
+        out = self.conv_45(out)
+        out = self.conv_5(out)
+        out = self.conv_6_sep(out)
+        out = self.conv_6_dw(out)
+        out = self.conv_6_flatten(out)
+        out = self.linear(out)
+        out = self.bn(out)
+        return l2_norm(out)

imaginairy/vendored/facexlib/tracking/README.md

@@ -0,0 +1 @@
+https://github.com/abewley/sort

imaginairy/vendored/facexlib/tracking/data_association.py

@@ -0,0 +1,71 @@
+"""
+For each detected item, it computes the intersection over union (IOU) w.r.t.
+each tracked object. (IOU matrix)
+Then, it applies the Hungarian algorithm (via linear_assignment) to assign each
+det. item to the best possible tracked item (i.e. to the one with max IOU)
+"""
+
+import numpy as np
+from numba import jit
+from scipy.optimize import linear_sum_assignment as linear_assignment
+
+
+@jit
+def iou(bb_test, bb_gt):
+    """Computes IOU between two bboxes in the form [x1,y1,x2,y2]
+    """
+    xx1 = np.maximum(bb_test[0], bb_gt[0])
+    yy1 = np.maximum(bb_test[1], bb_gt[1])
+    xx2 = np.minimum(bb_test[2], bb_gt[2])
+    yy2 = np.minimum(bb_test[3], bb_gt[3])
+    w = np.maximum(0., xx2 - xx1)
+    h = np.maximum(0., yy2 - yy1)
+    wh = w * h
+    o = wh / ((bb_test[2] - bb_test[0]) * (bb_test[3] - bb_test[1]) + (bb_gt[2] - bb_gt[0]) *
+              (bb_gt[3] - bb_gt[1]) - wh)
+    return (o)
+
+
+def associate_detections_to_trackers(detections, trackers, iou_threshold=0.25):
+    """Assigns detections to tracked object (both represented as bounding boxes)
+
+    Returns:
+        3 lists of matches, unmatched_detections and unmatched_trackers.
+    """
+    if len(trackers) == 0:
+        return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)
+
+    iou_matrix = np.zeros((len(detections), len(trackers)), dtype=np.float32)
+
+    for d, det in enumerate(detections):
+        for t, trk in enumerate(trackers):
+            iou_matrix[d, t] = iou(det, trk)
+    # The linear assignment module tries to minimize the total assignment cost.
+    # In our case we pass -iou_matrix as we want to maximise the total IOU
+    # between track predictions and the frame detection.
+    row_ind, col_ind = linear_assignment(-iou_matrix)
+
+    unmatched_detections = []
+    for d, det in enumerate(detections):
+        if d not in row_ind:
+            unmatched_detections.append(d)
+    unmatched_trackers = []
+    for t, trk in enumerate(trackers):
+        if t not in col_ind:
+            unmatched_trackers.append(t)
+
+    # filter out matched with low IOU
+    matches = []
+    for row, col in zip(row_ind, col_ind):
+        if iou_matrix[row, col] < iou_threshold:
+            unmatched_detections.append(row)
+            unmatched_trackers.append(col)
+        else:
+            matches.append(np.array([[row, col]]))
+
+    if len(matches) == 0:
+        matches = np.empty((0, 2), dtype=int)
+    else:
+        matches = np.concatenate(matches, axis=0)
+
+    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)

imaginairy/vendored/facexlib/tracking/kalman_tracker.py

@@ -0,0 +1,108 @@
+import numpy as np
+from filterpy.kalman import KalmanFilter
+
+
+def convert_bbox_to_z(bbox):
+    """Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
+    [x,y,s,r] where x,y is the centre of the box and s is the scale/area and
+    r is the aspect ratio
+    """
+    w = bbox[2] - bbox[0]
+    h = bbox[3] - bbox[1]
+    x = bbox[0] + w / 2.
+    y = bbox[1] + h / 2.
+    s = w * h  # scale is just area
+    r = w / float(h)
+    return np.array([x, y, s, r]).reshape((4, 1))
+
+
+def convert_x_to_bbox(x, score=None):
+    """Takes a bounding box in the centre form [x,y,s,r] and returns it in
+    the form [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom
+    right
+    """
+    w = np.sqrt(x[2] * x[3])
+    h = x[2] / w
+    if score is None:
+        return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4))
+    else:
+        return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5))
+
+
+class KalmanBoxTracker(object):
+    """This class represents the internal state of individual tracked objects
+    observed as bbox.
+    doc: https://filterpy.readthedocs.io/en/latest/kalman/KalmanFilter.html
+    """
+    count = 0
+
+    def __init__(self, bbox):
+        """Initialize a tracker using initial bounding box.
+        """
+        # define constant velocity model
+        # TODO: x: what is the meanning of x[4:7], v?
+        self.kf = KalmanFilter(dim_x=7, dim_z=4)
+        # F (dim_x, dim_x): state transition matrix
+        self.kf.F = np.array([[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0,
+                                                                             1], [0, 0, 0, 1, 0, 0, 0],
+                              [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]])
+        # H (dim_z, dim_x): measurement function
+        self.kf.H = np.array([[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0],
+                              [0, 0, 0, 1, 0, 0, 0]])
+        # R (dim_z, dim_z): measurement uncertainty/noise
+        self.kf.R[2:, 2:] *= 10.
+        # P (dim_x, dim_x): covariance matrix
+        # give high uncertainty to the unobservable initial velocities
+        self.kf.P[4:, 4:] *= 1000.
+        self.kf.P *= 10.
+        # Q (dim_x, dim_x): Process uncertainty/noise
+        self.kf.Q[-1, -1] *= 0.01
+        self.kf.Q[4:, 4:] *= 0.01
+        # x (dim_x, 1): filter state estimate
+        self.kf.x[:4] = convert_bbox_to_z(bbox)
+
+        self.time_since_update = 0
+        self.id = KalmanBoxTracker.count
+        KalmanBoxTracker.count += 1
+        self.history = []
+        self.hits = 0
+        self.hit_streak = 0
+        self.age = 0
+
+        # 解决画面中无人脸检测到时而导致的原有追踪器人像预测的漂移bug
+        self.predict_num = 0  # 连续预测的数目
+
+        # additional fields
+        self.face_attributes = []
+
+    def update(self, bbox):
+        """Updates the state vector with observed bbox.
+        """
+        self.time_since_update = 0
+        self.history = []
+        self.hits += 1
+        self.hit_streak += 1  # 连续命中
+        if bbox != []:
+            self.kf.update(convert_bbox_to_z(bbox))
+            self.predict_num = 0
+        else:
+            self.predict_num += 1
+
+    def predict(self):
+        """Advances the state vector and returns the predicted bounding box
+        estimate.
+        """
+
+        if (self.kf.x[6] + self.kf.x[2]) <= 0:
+            self.kf.x[6] *= 0.0
+        self.kf.predict()
+        self.age += 1
+        if self.time_since_update > 0:
+            self.hit_streak = 0
+        self.time_since_update += 1
+        self.history.append(convert_x_to_bbox(self.kf.x))
+        return self.history[-1][0]
+
+    def get_state(self):
+        """Returns the current bounding box estimate."""
+        return convert_x_to_bbox(self.kf.x)[0]

imaginairy/vendored/facexlib/tracking/sort.py

@@ -0,0 +1,92 @@
+import numpy as np
+
+from imaginairy.vendored.facexlib.tracking.data_association import associate_detections_to_trackers
+from imaginairy.vendored.facexlib.tracking.kalman_tracker import KalmanBoxTracker
+
+
+class SORT(object):
+    """SORT: A Simple, Online and Realtime Tracker.
+
+    Ref: https://github.com/abewley/sort
+    """
+
+    def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
+        self.max_age = max_age
+        self.min_hits = min_hits  # 最小的连续命中, 只有满足的才会被返回
+        self.iou_threshold = iou_threshold
+        self.trackers = []
+        self.frame_count = 0
+
+    def update(self, dets, img_size, additional_attr, detect_interval):
+        """This method must be called once for each frame even with
+        empty detections.
+        NOTE:as in practical realtime MOT, the detector doesn't run on every
+        single frame.
+
+        Args:
+            dets (Numpy array): detections in the format
+                [[x0,y0,x1,y1,score], [x0,y0,x1,y1,score], ...]
+
+        Returns:
+             a similar array, where the last column is the object ID.
+        """
+        self.frame_count += 1
+
+        # get predicted locations from existing trackers
+        trks = np.zeros((len(self.trackers), 5))
+        to_del = []  # To be deleted
+        ret = []
+        # predict tracker position using Kalman filter
+        for t, trk in enumerate(trks):
+            pos = self.trackers[t].predict()  # Kalman predict ,very fast ,<1ms
+            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
+            if np.any(np.isnan(pos)):
+                to_del.append(t)
+        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
+        for t in reversed(to_del):
+            self.trackers.pop(t)
+
+        if dets != []:
+            matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(  # noqa: E501
+                dets, trks)
+
+            # update matched trackers with assigned detections
+            for t, trk in enumerate(self.trackers):
+                if t not in unmatched_trks:
+                    d = matched[np.where(matched[:, 1] == t)[0], 0]
+                    trk.update(dets[d, :][0])
+                    trk.face_attributes.append(additional_attr[d[0]])
+
+            # create and initialize new trackers for unmatched detections
+            for i in unmatched_dets:
+                trk = KalmanBoxTracker(dets[i, :])
+                trk.face_attributes.append(additional_attr[i])
+                print(f'New tracker: {trk.id + 1}.')
+                self.trackers.append(trk)
+
+        i = len(self.trackers)
+        for trk in reversed(self.trackers):
+            if dets == []:
+                trk.update([])
+
+            d = trk.get_state()
+            # get return tracklet
+            # 1) time_since_update < 1: detected
+            # 2) i) hit_streak >= min_hits: 最小的连续命中
+            #    ii) frame_count <= min_hits: 最开始的几帧
+            if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
+                ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1))  # +1 as MOT benchmark requires positive
+            i -= 1
+
+            # remove dead tracklet
+            # 1) time_since_update >= max_age: 多久没有更新了
+            # 2) predict_num: 连续预测的帧数
+            # 3) out of image size
+            if (trk.time_since_update >= self.max_age) or (trk.predict_num >= detect_interval) or (
+                    d[2] < 0 or d[3] < 0 or d[0] > img_size[1] or d[1] > img_size[0]):
+                print(f'Remove tracker: {trk.id + 1}')
+                self.trackers.pop(i)
+        if len(ret) > 0:
+            return np.concatenate(ret)
+        else:
+            return np.empty((0, 5))

imaginairy/vendored/facexlib/utils/__init__.py

@@ -0,0 +1,7 @@
+from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back
+from .misc import img2tensor, load_file_from_url, scandir
+
+__all__ = [
+    'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', 'paste_face_back',
+    'img2tensor', 'scandir'
+]

imaginairy/vendored/facexlib/utils/face_restoration_helper.py

@@ -0,0 +1,374 @@
+import cv2
+import numpy as np
+import os
+import torch
+from torchvision.transforms.functional import normalize
+
+from imaginairy.vendored.facexlib.detection import init_detection_model
+from imaginairy.vendored.facexlib.parsing import init_parsing_model
+from imaginairy.vendored.facexlib.utils.misc import img2tensor, imwrite
+
+
+def get_largest_face(det_faces, h, w):
+
+    def get_location(val, length):
+        if val < 0:
+            return 0
+        elif val > length:
+            return length
+        else:
+            return val
+
+    face_areas = []
+    for det_face in det_faces:
+        left = get_location(det_face[0], w)
+        right = get_location(det_face[2], w)
+        top = get_location(det_face[1], h)
+        bottom = get_location(det_face[3], h)
+        face_area = (right - left) * (bottom - top)
+        face_areas.append(face_area)
+    largest_idx = face_areas.index(max(face_areas))
+    return det_faces[largest_idx], largest_idx
+
+
+def get_center_face(det_faces, h=0, w=0, center=None):
+    if center is not None:
+        center = np.array(center)
+    else:
+        center = np.array([w / 2, h / 2])
+    center_dist = []
+    for det_face in det_faces:
+        face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2])
+        dist = np.linalg.norm(face_center - center)
+        center_dist.append(dist)
+    center_idx = center_dist.index(min(center_dist))
+    return det_faces[center_idx], center_idx
+
+
+class FaceRestoreHelper(object):
+    """Helper for the face restoration pipeline (base class)."""
+
+    def __init__(self,
+                 upscale_factor,
+                 face_size=512,
+                 crop_ratio=(1, 1),
+                 det_model='retinaface_resnet50',
+                 save_ext='png',
+                 template_3points=False,
+                 pad_blur=False,
+                 use_parse=False,
+                 device=None,
+                 model_rootpath=None):
+        self.template_3points = template_3points  # improve robustness
+        self.upscale_factor = upscale_factor
+        # the cropped face ratio based on the square face
+        self.crop_ratio = crop_ratio  # (h, w)
+        assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1'
+        self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0]))
+
+        if self.template_3points:
+            self.face_template = np.array([[192, 240], [319, 240], [257, 371]])
+        else:
+            # standard 5 landmarks for FFHQ faces with 512 x 512
+            self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935],
+                                           [201.26117, 371.41043], [313.08905, 371.15118]])
+        self.face_template = self.face_template * (face_size / 512.0)
+        if self.crop_ratio[0] > 1:
+            self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2
+        if self.crop_ratio[1] > 1:
+            self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2
+        self.save_ext = save_ext
+        self.pad_blur = pad_blur
+        if self.pad_blur is True:
+            self.template_3points = False
+
+        self.all_landmarks_5 = []
+        self.det_faces = []
+        self.affine_matrices = []
+        self.inverse_affine_matrices = []
+        self.cropped_faces = []
+        self.restored_faces = []
+        self.pad_input_imgs = []
+
+        if device is None:
+            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        else:
+            self.device = device
+
+        # init face detection model
+        self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath)
+
+        # init face parsing model
+        self.use_parse = use_parse
+        self.face_parse = init_parsing_model(model_name='parsenet', device=self.device, model_rootpath=model_rootpath)
+
+    def set_upscale_factor(self, upscale_factor):
+        self.upscale_factor = upscale_factor
+
+    def read_image(self, img):
+        """img can be image path or cv2 loaded image."""
+        # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255]
+        if isinstance(img, str):
+            img = cv2.imread(img)
+
+        if np.max(img) > 256:  # 16-bit image
+            img = img / 65535 * 255
+        if len(img.shape) == 2:  # gray image
+            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+        elif img.shape[2] == 4:  # RGBA image with alpha channel
+            img = img[:, :, 0:3]
+
+        self.input_img = img
+
+    def get_face_landmarks_5(self,
+                             only_keep_largest=False,
+                             only_center_face=False,
+                             resize=None,
+                             blur_ratio=0.01,
+                             eye_dist_threshold=None):
+        if resize is None:
+            scale = 1
+            input_img = self.input_img
+        else:
+            h, w = self.input_img.shape[0:2]
+            scale = min(h, w) / resize
+            h, w = int(h / scale), int(w / scale)
+            input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4)
+
+        with torch.no_grad():
+            bboxes = self.face_det.detect_faces(input_img, 0.97) * scale
+        for bbox in bboxes:
+            # remove faces with too small eye distance: side faces or too small faces
+            eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]])
+            if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold):
+                continue
+
+            if self.template_3points:
+                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)])
+            else:
+                landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)])
+            self.all_landmarks_5.append(landmark)
+            self.det_faces.append(bbox[0:5])
+        if len(self.det_faces) == 0:
+            return 0
+        if only_keep_largest:
+            h, w, _ = self.input_img.shape
+            self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w)
+            self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]]
+        elif only_center_face:
+            h, w, _ = self.input_img.shape
+            self.det_faces, center_idx = get_center_face(self.det_faces, h, w)
+            self.all_landmarks_5 = [self.all_landmarks_5[center_idx]]
+
+        # pad blurry images
+        if self.pad_blur:
+            self.pad_input_imgs = []
+            for landmarks in self.all_landmarks_5:
+                # get landmarks
+                eye_left = landmarks[0, :]
+                eye_right = landmarks[1, :]
+                eye_avg = (eye_left + eye_right) * 0.5
+                mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5
+                eye_to_eye = eye_right - eye_left
+                eye_to_mouth = mouth_avg - eye_avg
+
+                # Get the oriented crop rectangle
+                # x: half width of the oriented crop rectangle
+                x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+                #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
+                # norm with the hypotenuse: get the direction
+                x /= np.hypot(*x)  # get the hypotenuse of a right triangle
+                rect_scale = 1.5
+                x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
+                # y: half height of the oriented crop rectangle
+                y = np.flipud(x) * [-1, 1]
+
+                # c: center
+                c = eye_avg + eye_to_mouth * 0.1
+                # quad: (left_top, left_bottom, right_bottom, right_top)
+                quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+                # qsize: side length of the square
+                qsize = np.hypot(*x) * 2
+                border = max(int(np.rint(qsize * 0.1)), 3)
+
+                # get pad
+                # pad: (width_left, height_top, width_right, height_bottom)
+                pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+                       int(np.ceil(max(quad[:, 1]))))
+                pad = [
+                    max(-pad[0] + border, 1),
+                    max(-pad[1] + border, 1),
+                    max(pad[2] - self.input_img.shape[0] + border, 1),
+                    max(pad[3] - self.input_img.shape[1] + border, 1)
+                ]
+
+                if max(pad) > 1:
+                    # pad image
+                    pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+                    # modify landmark coords
+                    landmarks[:, 0] += pad[0]
+                    landmarks[:, 1] += pad[1]
+                    # blur pad images
+                    h, w, _ = pad_img.shape
+                    y, x, _ = np.ogrid[:h, :w, :1]
+                    mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
+                                                       np.float32(w - 1 - x) / pad[2]),
+                                      1.0 - np.minimum(np.float32(y) / pad[1],
+                                                       np.float32(h - 1 - y) / pad[3]))
+                    blur = int(qsize * blur_ratio)
+                    if blur % 2 == 0:
+                        blur += 1
+                    blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur))
+                    # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0)
+
+                    pad_img = pad_img.astype('float32')
+                    pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+                    pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0)
+                    pad_img = np.clip(pad_img, 0, 255)  # float32, [0, 255]
+                    self.pad_input_imgs.append(pad_img)
+                else:
+                    self.pad_input_imgs.append(np.copy(self.input_img))
+
+        return len(self.all_landmarks_5)
+
+    def align_warp_face(self, save_cropped_path=None, border_mode='constant'):
+        """Align and warp faces with face template.
+        """
+        if self.pad_blur:
+            assert len(self.pad_input_imgs) == len(
+                self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}'
+        for idx, landmark in enumerate(self.all_landmarks_5):
+            # use 5 landmarks to get affine matrix
+            # use cv2.LMEDS method for the equivalence to skimage transform
+            # ref: https://blog.csdn.net/yichxi/article/details/115827338
+            affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0]
+            self.affine_matrices.append(affine_matrix)
+            # warp and crop faces
+            if border_mode == 'constant':
+                border_mode = cv2.BORDER_CONSTANT
+            elif border_mode == 'reflect101':
+                border_mode = cv2.BORDER_REFLECT101
+            elif border_mode == 'reflect':
+                border_mode = cv2.BORDER_REFLECT
+            if self.pad_blur:
+                input_img = self.pad_input_imgs[idx]
+            else:
+                input_img = self.input_img
+            cropped_face = cv2.warpAffine(
+                input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132))  # gray
+            self.cropped_faces.append(cropped_face)
+            # save the cropped face
+            if save_cropped_path is not None:
+                path = os.path.splitext(save_cropped_path)[0]
+                save_path = f'{path}_{idx:02d}.{self.save_ext}'
+                imwrite(cropped_face, save_path)
+
+    def get_inverse_affine(self, save_inverse_affine_path=None):
+        """Get inverse affine matrix."""
+        for idx, affine_matrix in enumerate(self.affine_matrices):
+            inverse_affine = cv2.invertAffineTransform(affine_matrix)
+            inverse_affine *= self.upscale_factor
+            self.inverse_affine_matrices.append(inverse_affine)
+            # save inverse affine matrices
+            if save_inverse_affine_path is not None:
+                path, _ = os.path.splitext(save_inverse_affine_path)
+                save_path = f'{path}_{idx:02d}.pth'
+                torch.save(inverse_affine, save_path)
+
+    def add_restored_face(self, face):
+        self.restored_faces.append(face)
+
+    def paste_faces_to_input_image(self, save_path=None, upsample_img=None):
+        h, w, _ = self.input_img.shape
+        h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor)
+
+        if upsample_img is None:
+            # simply resize the background
+            upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
+        else:
+            upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4)
+
+        assert len(self.restored_faces) == len(
+            self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')
+        for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):
+            # Add an offset to inverse affine matrix, for more precise back alignment
+            if self.upscale_factor > 1:
+                extra_offset = 0.5 * self.upscale_factor
+            else:
+                extra_offset = 0
+            inverse_affine[:, 2] += extra_offset
+            inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
+
+            if self.use_parse:
+                # inference
+                face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR)
+                face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True)
+                normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+                face_input = torch.unsqueeze(face_input, 0).to(self.device)
+                with torch.no_grad():
+                    out = self.face_parse(face_input)[0]
+                out = out.argmax(dim=1).squeeze().cpu().numpy()
+
+                mask = np.zeros(out.shape)
+                MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0]
+                for idx, color in enumerate(MASK_COLORMAP):
+                    mask[out == idx] = color
+                #  blur the mask
+                mask = cv2.GaussianBlur(mask, (101, 101), 11)
+                mask = cv2.GaussianBlur(mask, (101, 101), 11)
+                # remove the black borders
+                thres = 10
+                mask[:thres, :] = 0
+                mask[-thres:, :] = 0
+                mask[:, :thres] = 0
+                mask[:, -thres:] = 0
+                mask = mask / 255.
+
+                mask = cv2.resize(mask, restored_face.shape[:2])
+                mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3)
+                inv_soft_mask = mask[:, :, None]
+                pasted_face = inv_restored
+
+            else:  # use square parse maps
+                mask = np.ones(self.face_size, dtype=np.float32)
+                inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
+                # remove the black borders
+                inv_mask_erosion = cv2.erode(
+                    inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8))
+                pasted_face = inv_mask_erosion[:, :, None] * inv_restored
+                total_face_area = np.sum(inv_mask_erosion)  # // 3
+                # compute the fusion edge based on the area of face
+                w_edge = int(total_face_area**0.5) // 20
+                erosion_radius = w_edge * 2
+                inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+                blur_size = w_edge * 2
+                inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+                if len(upsample_img.shape) == 2:  # upsample_img is gray image
+                    upsample_img = upsample_img[:, :, None]
+                inv_soft_mask = inv_soft_mask[:, :, None]
+
+            if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4:  # alpha channel
+                alpha = upsample_img[:, :, 3:]
+                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3]
+                upsample_img = np.concatenate((upsample_img, alpha), axis=2)
+            else:
+                upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img
+
+        if np.max(upsample_img) > 256:  # 16-bit image
+            upsample_img = upsample_img.astype(np.uint16)
+        else:
+            upsample_img = upsample_img.astype(np.uint8)
+        if save_path is not None:
+            path = os.path.splitext(save_path)[0]
+            save_path = f'{path}.{self.save_ext}'
+            imwrite(upsample_img, save_path)
+        return upsample_img
+
+    def clean_all(self):
+        self.all_landmarks_5 = []
+        self.restored_faces = []
+        self.affine_matrices = []
+        self.cropped_faces = []
+        self.inverse_affine_matrices = []
+        self.det_faces = []
+        self.pad_input_imgs = []

imaginairy/vendored/facexlib/utils/face_utils.py

@@ -0,0 +1,250 @@
+import cv2
+import numpy as np
+import torch
+
+
+def compute_increased_bbox(bbox, increase_area, preserve_aspect=True):
+    left, top, right, bot = bbox
+    width = right - left
+    height = bot - top
+
+    if preserve_aspect:
+        width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
+        height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
+    else:
+        width_increase = height_increase = increase_area
+    left = int(left - width_increase * width)
+    top = int(top - height_increase * height)
+    right = int(right + width_increase * width)
+    bot = int(bot + height_increase * height)
+    return (left, top, right, bot)
+
+
+def get_valid_bboxes(bboxes, h, w):
+    left = max(bboxes[0], 0)
+    top = max(bboxes[1], 0)
+    right = min(bboxes[2], w)
+    bottom = min(bboxes[3], h)
+    return (left, top, right, bottom)
+
+
+def align_crop_face_landmarks(img,
+                              landmarks,
+                              output_size,
+                              transform_size=None,
+                              enable_padding=True,
+                              return_inverse_affine=False,
+                              shrink_ratio=(1, 1)):
+    """Align and crop face with landmarks.
+
+    The output_size and transform_size are based on width. The height is
+    adjusted based on shrink_ratio_h/shring_ration_w.
+
+    Modified from:
+    https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py
+
+    Args:
+        img (Numpy array): Input image.
+        landmarks (Numpy array): 5 or 68 or 98 landmarks.
+        output_size (int): Output face size.
+        transform_size (ing): Transform size. Usually the four time of
+            output_size.
+        enable_padding (float): Default: True.
+        shrink_ratio (float | tuple[float] | list[float]): Shring the whole
+            face for height and width (crop larger area). Default: (1, 1).
+
+    Returns:
+        (Numpy array): Cropped face.
+    """
+    lm_type = 'retinaface_5'  # Options: dlib_5, retinaface_5
+
+    if isinstance(shrink_ratio, (float, int)):
+        shrink_ratio = (shrink_ratio, shrink_ratio)
+    if transform_size is None:
+        transform_size = output_size * 4
+
+    # Parse landmarks
+    lm = np.array(landmarks)
+    if lm.shape[0] == 5 and lm_type == 'retinaface_5':
+        eye_left = lm[0]
+        eye_right = lm[1]
+        mouth_avg = (lm[3] + lm[4]) * 0.5
+    elif lm.shape[0] == 5 and lm_type == 'dlib_5':
+        lm_eye_left = lm[2:4]
+        lm_eye_right = lm[0:2]
+        eye_left = np.mean(lm_eye_left, axis=0)
+        eye_right = np.mean(lm_eye_right, axis=0)
+        mouth_avg = lm[4]
+    elif lm.shape[0] == 68:
+        lm_eye_left = lm[36:42]
+        lm_eye_right = lm[42:48]
+        eye_left = np.mean(lm_eye_left, axis=0)
+        eye_right = np.mean(lm_eye_right, axis=0)
+        mouth_avg = (lm[48] + lm[54]) * 0.5
+    elif lm.shape[0] == 98:
+        lm_eye_left = lm[60:68]
+        lm_eye_right = lm[68:76]
+        eye_left = np.mean(lm_eye_left, axis=0)
+        eye_right = np.mean(lm_eye_right, axis=0)
+        mouth_avg = (lm[76] + lm[82]) * 0.5
+
+    eye_avg = (eye_left + eye_right) * 0.5
+    eye_to_eye = eye_right - eye_left
+    eye_to_mouth = mouth_avg - eye_avg
+
+    # Get the oriented crop rectangle
+    # x: half width of the oriented crop rectangle
+    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
+    #  - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise
+    # norm with the hypotenuse: get the direction
+    x /= np.hypot(*x)  # get the hypotenuse of a right triangle
+    rect_scale = 1  # TODO: you can edit it to get larger rect
+    x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale)
+    # y: half height of the oriented crop rectangle
+    y = np.flipud(x) * [-1, 1]
+
+    x *= shrink_ratio[1]  # width
+    y *= shrink_ratio[0]  # height
+
+    # c: center
+    c = eye_avg + eye_to_mouth * 0.1
+    # quad: (left_top, left_bottom, right_bottom, right_top)
+    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
+    # qsize: side length of the square
+    qsize = np.hypot(*x) * 2
+
+    quad_ori = np.copy(quad)
+    # Shrink, for large face
+    # TODO: do we really need shrink
+    shrink = int(np.floor(qsize / output_size * 0.5))
+    if shrink > 1:
+        h, w = img.shape[0:2]
+        rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink)))
+        img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA)
+        quad /= shrink
+        qsize /= shrink
+
+    # Crop
+    h, w = img.shape[0:2]
+    border = max(int(np.rint(qsize * 0.1)), 3)
+    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+            int(np.ceil(max(quad[:, 1]))))
+    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h))
+    if crop[2] - crop[0] < w or crop[3] - crop[1] < h:
+        img = img[crop[1]:crop[3], crop[0]:crop[2], :]
+        quad -= crop[0:2]
+
+    # Pad
+    # pad: (width_left, height_top, width_right, height_bottom)
+    h, w = img.shape[0:2]
+    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
+           int(np.ceil(max(quad[:, 1]))))
+    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0))
+    if enable_padding and max(pad) > border - 4:
+        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
+        img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
+        h, w = img.shape[0:2]
+        y, x, _ = np.ogrid[:h, :w, :1]
+        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0],
+                                           np.float32(w - 1 - x) / pad[2]),
+                          1.0 - np.minimum(np.float32(y) / pad[1],
+                                           np.float32(h - 1 - y) / pad[3]))
+        blur = int(qsize * 0.02)
+        if blur % 2 == 0:
+            blur += 1
+        blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur))
+
+        img = img.astype('float32')
+        img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
+        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
+        img = np.clip(img, 0, 255)  # float32, [0, 255]
+        quad += pad[:2]
+
+    # Transform use cv2
+    h_ratio = shrink_ratio[0] / shrink_ratio[1]
+    dst_h, dst_w = int(transform_size * h_ratio), transform_size
+    template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
+    # use cv2.LMEDS method for the equivalence to skimage transform
+    # ref: https://blog.csdn.net/yichxi/article/details/115827338
+    affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0]
+    cropped_face = cv2.warpAffine(
+        img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132))  # gray
+
+    if output_size < transform_size:
+        cropped_face = cv2.resize(
+            cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR)
+
+    if return_inverse_affine:
+        dst_h, dst_w = int(output_size * h_ratio), output_size
+        template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]])
+        # use cv2.LMEDS method for the equivalence to skimage transform
+        # ref: https://blog.csdn.net/yichxi/article/details/115827338
+        affine_matrix = cv2.estimateAffinePartial2D(
+            quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0]
+        inverse_affine = cv2.invertAffineTransform(affine_matrix)
+    else:
+        inverse_affine = None
+    return cropped_face, inverse_affine
+
+
+def paste_face_back(img, face, inverse_affine):
+    h, w = img.shape[0:2]
+    face_h, face_w = face.shape[0:2]
+    inv_restored = cv2.warpAffine(face, inverse_affine, (w, h))
+    mask = np.ones((face_h, face_w, 3), dtype=np.float32)
+    inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h))
+    # remove the black borders
+    inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8))
+    inv_restored_remove_border = inv_mask_erosion * inv_restored
+    total_face_area = np.sum(inv_mask_erosion) // 3
+    # compute the fusion edge based on the area of face
+    w_edge = int(total_face_area**0.5) // 20
+    erosion_radius = w_edge * 2
+    inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
+    blur_size = w_edge * 2
+    inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
+    img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img
+    # float32, [0, 255]
+    return img
+
+
+if __name__ == '__main__':
+    import os
+
+    from imaginairy.vendored.facexlib.detection import init_detection_model
+    from imaginairy.vendored.facexlib.utils.face_restoration_helper import get_largest_face
+    from imaginairy.vendored.facexlib.visualization import visualize_detection
+
+    img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png'
+    img_name = os.splitext(os.path.basename(img_path))[0]
+
+    # initialize model
+    det_net = init_detection_model('retinaface_resnet50', half=False)
+    img_ori = cv2.imread(img_path)
+    h, w = img_ori.shape[0:2]
+    # if larger than 800, scale it
+    scale = max(h / 800, w / 800)
+    if scale > 1:
+        img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR)
+
+    with torch.no_grad():
+        bboxes = det_net.detect_faces(img, 0.97)
+    if scale > 1:
+        bboxes *= scale  # the score is incorrect
+    bboxes = get_largest_face(bboxes, h, w)[0]
+    visualize_detection(img_ori, [bboxes], f'tmp/{img_name}_det.png')
+
+    landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)])
+
+    cropped_face, inverse_affine = align_crop_face_landmarks(
+        img_ori,
+        landmarks,
+        output_size=512,
+        transform_size=None,
+        enable_padding=True,
+        return_inverse_affine=True,
+        shrink_ratio=(1, 1))
+
+    cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face)
+    img = paste_face_back(img_ori, cropped_face, inverse_affine)
+    cv2.imwrite(f'tmp/{img_name}_back.png', img)

imaginairy/vendored/facexlib/utils/misc.py

@@ -0,0 +1,118 @@
+import cv2
+import os
+import os.path as osp
+import torch
+from torch.hub import download_url_to_file, get_dir
+from urllib.parse import urlparse
+
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+def imwrite(img, file_path, params=None, auto_mkdir=True):
+    """Write image to file.
+
+    Args:
+        img (ndarray): Image array to be written.
+        file_path (str): Image file path.
+        params (None or list): Same as opencv's :func:`imwrite` interface.
+        auto_mkdir (bool): If the parent folder of `file_path` does not exist,
+            whether to create it automatically.
+
+    Returns:
+        bool: Successful or not.
+    """
+    if auto_mkdir:
+        dir_name = os.path.abspath(os.path.dirname(file_path))
+        os.makedirs(dir_name, exist_ok=True)
+    return cv2.imwrite(file_path, img, params)
+
+
+def img2tensor(imgs, bgr2rgb=True, float32=True):
+    """Numpy array to tensor.
+
+    Args:
+        imgs (list[ndarray] | ndarray): Input images.
+        bgr2rgb (bool): Whether to change bgr to rgb.
+        float32 (bool): Whether to change to float32.
+
+    Returns:
+        list[tensor] | tensor: Tensor images. If returned results only have
+            one element, just return tensor.
+    """
+
+    def _totensor(img, bgr2rgb, float32):
+        if img.shape[2] == 3 and bgr2rgb:
+            if img.dtype == 'float64':
+                img = img.astype('float32')
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        img = torch.from_numpy(img.transpose(2, 0, 1))
+        if float32:
+            img = img.float()
+        return img
+
+    if isinstance(imgs, list):
+        return [_totensor(img, bgr2rgb, float32) for img in imgs]
+    else:
+        return _totensor(imgs, bgr2rgb, float32)
+
+
+def load_file_from_url(url, model_dir=None, progress=True, file_name=None, save_dir=None):
+    """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
+    """
+    if model_dir is None:
+        hub_dir = get_dir()
+        model_dir = os.path.join(hub_dir, 'checkpoints')
+
+    if save_dir is None:
+        save_dir = os.path.join(ROOT_DIR, model_dir)
+    os.makedirs(save_dir, exist_ok=True)
+
+    parts = urlparse(url)
+    filename = os.path.basename(parts.path)
+    if file_name is not None:
+        filename = file_name
+    cached_file = os.path.abspath(os.path.join(save_dir, filename))
+    if not os.path.exists(cached_file):
+        print(f'Downloading: "{url}" to {cached_file}\n')
+        download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
+    return cached_file
+
+
+def scandir(dir_path, suffix=None, recursive=False, full_path=False):
+    """Scan a directory to find the interested files.
+    Args:
+        dir_path (str): Path of the directory.
+        suffix (str | tuple(str), optional): File suffix that we are
+            interested in. Default: None.
+        recursive (bool, optional): If set to True, recursively scan the
+            directory. Default: False.
+        full_path (bool, optional): If set to True, include the dir_path.
+            Default: False.
+    Returns:
+        A generator for all the interested files with relative paths.
+    """
+
+    if (suffix is not None) and not isinstance(suffix, (str, tuple)):
+        raise TypeError('"suffix" must be a string or tuple of strings')
+
+    root = dir_path
+
+    def _scandir(dir_path, suffix, recursive):
+        for entry in os.scandir(dir_path):
+            if not entry.name.startswith('.') and entry.is_file():
+                if full_path:
+                    return_path = entry.path
+                else:
+                    return_path = osp.relpath(entry.path, root)
+
+                if suffix is None:
+                    yield return_path
+                elif return_path.endswith(suffix):
+                    yield return_path
+            else:
+                if recursive:
+                    yield from _scandir(entry.path, suffix=suffix, recursive=recursive)
+                else:
+                    continue
+
+    return _scandir(dir_path, suffix=suffix, recursive=recursive)

imaginairy/vendored/facexlib/visualization/__init__.py

@@ -0,0 +1,5 @@
+from .vis_alignment import visualize_alignment
+from .vis_detection import visualize_detection
+from .vis_headpose import visualize_headpose
+
+__all__ = ['visualize_detection', 'visualize_alignment', 'visualize_headpose']

imaginairy/vendored/facexlib/visualization/vis_alignment.py

@@ -0,0 +1,18 @@
+import cv2
+import numpy as np
+
+
+def visualize_alignment(img, landmarks, save_path=None, to_bgr=False):
+    img = np.copy(img)
+    h, w = img.shape[0:2]
+    circle_size = int(max(h, w) / 150)
+    if to_bgr:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    for landmarks_face in landmarks:
+        for lm in landmarks_face:
+            cv2.circle(img, (int(lm[0]), int(lm[1])), 1, (0, 150, 0), circle_size)
+
+    # save img
+    if save_path is not None:
+        cv2.imwrite(save_path, img)

imaginairy/vendored/facexlib/visualization/vis_detection.py

@@ -0,0 +1,29 @@
+import cv2
+import numpy as np
+
+
+def visualize_detection(img, bboxes_and_landmarks, save_path=None, to_bgr=False):
+    """Visualize detection results.
+
+    Args:
+        img (Numpy array): Input image. CHW, BGR, [0, 255], uint8.
+    """
+    img = np.copy(img)
+    if to_bgr:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+
+    for b in bboxes_and_landmarks:
+        # confidence
+        cv2.putText(img, f'{b[4]:.4f}', (int(b[0]), int(b[1] + 12)), cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))
+        # bounding boxes
+        b = list(map(int, b))
+        cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+        # landmarks (for retinaface)
+        cv2.circle(img, (b[5], b[6]), 1, (0, 0, 255), 4)
+        cv2.circle(img, (b[7], b[8]), 1, (0, 255, 255), 4)
+        cv2.circle(img, (b[9], b[10]), 1, (255, 0, 255), 4)
+        cv2.circle(img, (b[11], b[12]), 1, (0, 255, 0), 4)
+        cv2.circle(img, (b[13], b[14]), 1, (255, 0, 0), 4)
+    # save img
+    if save_path is not None:
+        cv2.imwrite(save_path, img)

imaginairy/vendored/facexlib/visualization/vis_headpose.py

@@ -0,0 +1,91 @@
+import cv2
+import numpy as np
+from math import cos, sin
+
+
+def draw_axis(img, yaw, pitch, roll, tdx=None, tdy=None, size=100):
+    """draw head pose axis."""
+
+    pitch = pitch * np.pi / 180
+    yaw = -yaw * np.pi / 180
+    roll = roll * np.pi / 180
+
+    if tdx is None or tdy is None:
+        height, width = img.shape[:2]
+        tdx = width / 2
+        tdy = height / 2
+
+    # X axis pointing to right, drawn in red
+    x1 = size * (cos(yaw) * cos(roll)) + tdx
+    y1 = size * (cos(pitch) * sin(roll) + cos(roll) * sin(pitch) * sin(yaw)) + tdy
+    # Y axis pointing downside, drawn in green
+    x2 = size * (-cos(yaw) * sin(roll)) + tdx
+    y2 = size * (cos(pitch) * cos(roll) - sin(pitch) * sin(yaw) * sin(roll)) + tdy
+    # Z axis, out of the screen, drawn in blue
+    x3 = size * (sin(yaw)) + tdx
+    y3 = size * (-cos(yaw) * sin(pitch)) + tdy
+
+    cv2.line(img, (int(tdx), int(tdy)), (int(x1), int(y1)), (0, 0, 255), 3)
+    cv2.line(img, (int(tdx), int(tdy)), (int(x2), int(y2)), (0, 255, 0), 3)
+    cv2.line(img, (int(tdx), int(tdy)), (int(x3), int(y3)), (255, 0, 0), 2)
+
+    return img
+
+
+def draw_pose_cube(img, yaw, pitch, roll, tdx=None, tdy=None, size=150.):
+    """draw head pose cube.
+    Where (tdx, tdy) is the translation of the face.
+    For pose we have [pitch yaw roll tdx tdy tdz scale_factor]
+    """
+
+    p = pitch * np.pi / 180
+    y = -yaw * np.pi / 180
+    r = roll * np.pi / 180
+    if tdx is not None and tdy is not None:
+        face_x = tdx - 0.50 * size
+        face_y = tdy - 0.50 * size
+    else:
+        height, width = img.shape[:2]
+        face_x = width / 2 - 0.5 * size
+        face_y = height / 2 - 0.5 * size
+
+    x1 = size * (cos(y) * cos(r)) + face_x
+    y1 = size * (cos(p) * sin(r) + cos(r) * sin(p) * sin(y)) + face_y
+    x2 = size * (-cos(y) * sin(r)) + face_x
+    y2 = size * (cos(p) * cos(r) - sin(p) * sin(y) * sin(r)) + face_y
+    x3 = size * (sin(y)) + face_x
+    y3 = size * (-cos(y) * sin(p)) + face_y
+
+    # Draw base in red
+    cv2.line(img, (int(face_x), int(face_y)), (int(x1), int(y1)), (0, 0, 255), 3)
+    cv2.line(img, (int(face_x), int(face_y)), (int(x2), int(y2)), (0, 0, 255), 3)
+    cv2.line(img, (int(x2), int(y2)), (int(x2 + x1 - face_x), int(y2 + y1 - face_y)), (0, 0, 255), 3)
+    cv2.line(img, (int(x1), int(y1)), (int(x1 + x2 - face_x), int(y1 + y2 - face_y)), (0, 0, 255), 3)
+    # Draw pillars in blue
+    cv2.line(img, (int(face_x), int(face_y)), (int(x3), int(y3)), (255, 0, 0), 2)
+    cv2.line(img, (int(x1), int(y1)), (int(x1 + x3 - face_x), int(y1 + y3 - face_y)), (255, 0, 0), 2)
+    cv2.line(img, (int(x2), int(y2)), (int(x2 + x3 - face_x), int(y2 + y3 - face_y)), (255, 0, 0), 2)
+    cv2.line(img, (int(x2 + x1 - face_x), int(y2 + y1 - face_y)),
+             (int(x3 + x1 + x2 - 2 * face_x), int(y3 + y2 + y1 - 2 * face_y)), (255, 0, 0), 2)
+    # Draw top in green
+    cv2.line(img, (int(x3 + x1 - face_x), int(y3 + y1 - face_y)),
+             (int(x3 + x1 + x2 - 2 * face_x), int(y3 + y2 + y1 - 2 * face_y)), (0, 255, 0), 2)
+    cv2.line(img, (int(x2 + x3 - face_x), int(y2 + y3 - face_y)),
+             (int(x3 + x1 + x2 - 2 * face_x), int(y3 + y2 + y1 - 2 * face_y)), (0, 255, 0), 2)
+    cv2.line(img, (int(x3), int(y3)), (int(x3 + x1 - face_x), int(y3 + y1 - face_y)), (0, 255, 0), 2)
+    cv2.line(img, (int(x3), int(y3)), (int(x3 + x2 - face_x), int(y3 + y2 - face_y)), (0, 255, 0), 2)
+
+    return img
+
+
+def visualize_headpose(img, yaw, pitch, roll, save_path=None, to_bgr=False):
+    img = np.copy(img)
+    if to_bgr:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+    show_string = (f'y {yaw[0].item():.2f}, p {pitch[0].item():.2f}, ' + f'r {roll[0].item():.2f}')
+    cv2.putText(img, show_string, (30, img.shape[0] - 30), fontFace=1, fontScale=1, color=(0, 0, 255), thickness=2)
+    draw_pose_cube(img, yaw[0], pitch[0], roll[0], size=100)
+    draw_axis(img, yaw[0], pitch[0], roll[0], tdx=50, tdy=50, size=100)
+    # save img
+    if save_path is not None:
+        cv2.imwrite(save_path, img)

requirements-dev.txt

@@ -38,12 +38,8 @@ colorama==0.4.6
     # via
     #   griffe
     #   mkdocs-material
-contourpy==1.2.0
-    # via matplotlib
 coverage==7.4.0
     # via -r requirements-dev.in
-cycler==0.12.1
-    # via matplotlib
 diffusers==0.25.0
     # via imaginAIry (setup.py)
 einops==0.7.0
@@ -52,8 +48,6 @@ exceptiongroup==1.2.0
     # via
     #   anyio
     #   pytest
-facexlib==0.3.0
-    # via imaginAIry (setup.py)
 fastapi==0.108.0
     # via imaginAIry (setup.py)
 filelock==3.13.1
@@ -62,10 +56,6 @@ filelock==3.13.1
     #   huggingface-hub
     #   torch
     #   transformers
-filterpy==1.4.5
-    # via facexlib
-fonttools==4.47.0
-    # via matplotlib
 fsspec==2023.12.2
     # via
     #   huggingface-hub
@@ -86,7 +76,7 @@ httpcore==1.0.2
     # via httpx
 httpx==0.26.0
     # via -r requirements-dev.in
-huggingface-hub==0.20.1
+huggingface-hub==0.20.2
     # via
     #   diffusers
     #   open-clip-torch
@@ -112,12 +102,8 @@ jinja2==3.1.2
     #   mkdocs-material
     #   mkdocstrings
     #   torch
-kiwisolver==1.4.5
-    # via matplotlib
 kornia==0.7.1
     # via imaginAIry (setup.py)
-llvmlite==0.41.1
-    # via numba
 markdown==3.5.1
     # via
     #   mkdocs
@@ -131,10 +117,6 @@ markupsafe==2.1.3
     #   jinja2
     #   mkdocs
     #   mkdocstrings
-matplotlib==3.7.4
-    # via
-    #   -c tests/constraints.txt
-    #   filterpy
 mergedeep==1.3.4
     # via mkdocs
 mkdocs==1.5.3
@@ -164,20 +146,13 @@ mypy-extensions==1.0.0
     # via mypy
 networkx==3.2.1
     # via torch
-numba==0.58.1
-    # via facexlib
 numpy==1.24.4
     # via
     #   -c tests/constraints.txt
-    #   contourpy
     #   diffusers
-    #   facexlib
-    #   filterpy
     #   imageio
     #   imaginAIry (setup.py)
     #   jaxtyping
-    #   matplotlib
-    #   numba
     #   opencv-python
     #   scipy
     #   torchvision
@@ -187,14 +162,11 @@ omegaconf==2.3.0
 open-clip-torch==2.23.0
     # via imaginAIry (setup.py)
 opencv-python==4.9.0.80
-    # via
-    #   facexlib
-    #   imaginAIry (setup.py)
+    # via imaginAIry (setup.py)
 packaging==23.2
     # via
     #   huggingface-hub
     #   kornia
-    #   matplotlib
     #   mkdocs
     #   pytest
     #   pytest-sugar
@@ -206,10 +178,8 @@ pathspec==0.12.1
 pillow==10.2.0
     # via
     #   diffusers
-    #   facexlib
     #   imageio
     #   imaginAIry (setup.py)
-    #   matplotlib
     #   torchvision
 platformdirs==4.1.0
     # via
@@ -236,7 +206,7 @@ pymdown-extensions==10.7
     #   mkdocs-material
     #   mkdocstrings
 pyparsing==3.1.1
-    # via matplotlib
+    # via imaginAIry (setup.py)
 pytest==7.4.4
     # via
     #   -r requirements-dev.in
@@ -250,9 +220,7 @@ pytest-randomly==3.15.0
 pytest-sugar==0.9.7
     # via -r requirements-dev.in
 python-dateutil==2.8.2
-    # via
-    #   ghp-import
-    #   matplotlib
+    # via ghp-import
 pyyaml==6.0.1
     # via
     #   huggingface-hub
@@ -292,8 +260,6 @@ safetensors==0.4.1
     #   transformers
 scipy==1.10.1
     # via
-    #   facexlib
-    #   filterpy
     #   imaginAIry (setup.py)
     #   torchdiffeq
 sentencepiece==0.1.99
@@ -324,7 +290,6 @@ tomli==2.0.1
     #   pytest
 torch==2.1.2
     # via
-    #   facexlib
     #   imaginAIry (setup.py)
     #   kornia
     #   open-clip-torch
@@ -335,13 +300,11 @@ torchdiffeq==0.2.3
     # via imaginAIry (setup.py)
 torchvision==0.16.2
     # via
-    #   facexlib
     #   imaginAIry (setup.py)
     #   open-clip-torch
     #   timm
 tqdm==4.66.1
     # via
-    #   facexlib
     #   huggingface-hub
     #   imaginAIry (setup.py)
     #   open-clip-torch
@@ -350,13 +313,13 @@ transformers==4.36.2
     # via imaginAIry (setup.py)
 typeguard==2.13.3
     # via jaxtyping
-types-pillow==10.1.0.2
+types-pillow==10.1.0.20240106
     # via -r requirements-dev.in
-types-psutil==5.9.5.17
+types-psutil==5.9.5.20240106
     # via -r requirements-dev.in
-types-requests==2.31.0.20231231
+types-requests==2.31.0.20240106
     # via -r requirements-dev.in
-types-tqdm==4.66.0.5
+types-tqdm==4.66.0.20240106
     # via -r requirements-dev.in
 typing-extensions==4.9.0
     # via
@@ -378,7 +341,7 @@ uvicorn==0.25.0
     # via imaginAIry (setup.py)
 watchdog==3.0.0
     # via mkdocs
-wcwidth==0.2.12
+wcwidth==0.2.13
     # via ftfy
 wheel==0.42.0
     # via -r requirements-dev.in

setup.py

@@ -78,7 +78,6 @@ setup(
         "click-help-colors>=0.9.1",
         "click-shell>=2.0",
         "protobuf != 3.20.2, != 3.19.5",
-        "facexlib>=0.2.1.1",
         "fastapi>=0.70.0",
         "ftfy>=6.0.1",  # for vendored clip
         "torch>=2.1.0",
@@ -94,6 +93,8 @@ setup(
         "opencv-python>=4.4.0.46",
         # need to migration to 2.0
         "pydantic>=2.3.0",
+        # pyparsing used for masking logic and creating text images
+        "pyparsing>=3.0.0",
         "requests>=2.28.1",
         # "refiners>=0.2.0",
         "jaxtyping>=0.2.23",  # refiners dependency