Add the hmr head and discriminator for SMPL parameters. Add test code…

…s and test data.

mmpose/models/__init__.py

@@ -3,6 +3,7 @@
 from .detectors import *  # noqa
 from .keypoint_heads import *  # noqa
 from .losses import *  # noqa
+from .mesh_heads import *  # noqa
 from .registry import BACKBONES, HEADS, LOSSES, POSENETS
 
 __all__ = [

mmpose/models/mesh_heads/__init__.py

@@ -0,0 +1,3 @@
+from .hmr_head import MeshHMRHead
+
+__all__ = ['MeshHMRHead']

mmpose/models/mesh_heads/discriminator.py

@@ -0,0 +1,286 @@
+# ------------------------------------------------------------------------------
+# Adapted from https://github.com/akanazawa/hmr
+# Original licence: Copyright (c) 2018 akanazawa, under the MIT License.
+# ------------------------------------------------------------------------------
+
+from abc import abstractmethod
+
+import torch
+import torch.nn as nn
+
+from .geometric_layers import batch_rodrigues
+
+
+class BaseDiscriminator(nn.Module):
+    """Base linear module for SMPL parameter discriminator.
+
+    Args:
+        fc_layers (Tuple): Tuple of neuron count,
+            such as (9, 32, 32, 1)
+        use_dropout (Tuple): Tuple of bool define use dropout or not
+            for each layer, such as (True, True, False)
+        drop_prob (Tuple): Tuple of float defined the drop prob,
+            such as (0.5, 0.5, 0)
+        use_activation(Tuple): Tuple of bool define use active function
+            or not, such as (True, True, False)
+    """
+
+    def __init__(self, fc_layers, use_dropout, drop_prob, use_activation):
+        super().__init__()
+        self.fc_layers = fc_layers
+        self.use_dropout = use_dropout
+        self.drop_prob = drop_prob
+        self.use_activation = use_activation
+        self._check()
+        self.create_layers()
+
+    def _check(self):
+        """Check input to avoid ValueError."""
+        if not isinstance(self.fc_layers, tuple):
+            raise TypeError(f'fc_layers require tuple, '
+                            f'get {type(self.fc_layers)}')
+
+        if not isinstance(self.use_dropout, tuple):
+            raise TypeError(f'use_dropout require tuple, '
+                            f'get {type(self.use_dropout)}')
+
+        if not isinstance(self.drop_prob, tuple):
+            raise TypeError(f'drop_prob require tuple, '
+                            f'get {type(self.drop_prob)}')
+
+        if not isinstance(self.use_activation, tuple):
+            raise TypeError(f'use_activation require tuple, '
+                            f'get {type(self.use_activation)}')
+
+        l_fc_layer = len(self.fc_layers)
+        l_use_drop = len(self.use_dropout)
+        l_drop_prob = len(self.drop_prob)
+        l_use_activation = len(self.use_activation)
+
+        pass_check = (
+            l_fc_layer >= 2 and l_use_drop < l_fc_layer
+            and l_drop_prob < l_fc_layer and l_use_activation < l_fc_layer
+            and l_drop_prob == l_use_drop)
+
+        if not pass_check:
+            msg = 'Wrong BaseDiscriminator parameters!'
+            raise ValueError(msg)
+
+    def create_layers(self):
+        """Create layers."""
+        l_fc_layer = len(self.fc_layers)
+        l_use_drop = len(self.use_dropout)
+        l_use_activation = len(self.use_activation)
+
+        self.fc_blocks = nn.Sequential()
+
+        for i in range(l_fc_layer - 1):
+            self.fc_blocks.add_module(
+                name=f'regressor_fc_{i}',
+                module=nn.Linear(
+                    in_features=self.fc_layers[i],
+                    out_features=self.fc_layers[i + 1]))
+
+            if i < l_use_activation and self.use_activation[i]:
+                self.fc_blocks.add_module(
+                    name=f'regressor_af_{i}', module=nn.ReLU())
+
+            if i < l_use_drop and self.use_dropout[i]:
+                self.fc_blocks.add_module(
+                    name=f'regressor_fc_dropout_{i}',
+                    module=nn.Dropout(p=self.drop_prob[i]))
+
+    @abstractmethod
+    def forward(self, inputs):
+        """Forward function."""
+        msg = 'the base class [BaseDiscriminator] is not callable!'
+        raise NotImplementedError(msg)
+
+
+class ShapeDiscriminator(BaseDiscriminator):
+    """Discriminator for SMPL shape parameters, the inputs is (batch size x 10)
+
+    Args:
+        fc_layers (Tuple): Tuple of neuron count,
+         such as (10, 5, 1)
+        use_dropout (Tuple): Tuple of bool define use dropout or
+            not for each layer, such as (True, True, False)
+        drop_prob (Tuple): Tuple of float defined the drop prob,
+            such as (0.5, 0)
+        use_activation(Tuple): Tuple of bool define use active
+            function or not, such as (True, False)
+    """
+
+    def __init__(self, fc_layers, use_dropout, drop_prob, use_activation):
+        if fc_layers[-1] != 1:
+            msg = f'the neuron count of the last layer ' \
+                  f'must be 1, but got {fc_layers[-1]}'
+            raise ValueError(msg)
+
+        super().__init__(fc_layers, use_dropout, drop_prob, use_activation)
+
+    def forward(self, inputs):
+        """Forward function."""
+        return self.fc_blocks(inputs)
+
+
+class PoseDiscriminator(nn.Module):
+    """Discriminator for SMPL pose parameters of each joint. It is composed of
+    discriminators for each joints. The inputs is (batch size x joint_count x
+    9)
+
+    Args:
+        channels (Tuple): Tuple of channel number,
+            such as (9, 32, 32, 1)
+        joint_count (int): Joint number, such as 23
+    """
+
+    def __init__(self, channels, joint_count):
+        super().__init__()
+        if channels[-1] != 1:
+            msg = f'the neuron count of the last layer ' \
+                  f'must be 1, but got {channels[-1]}'
+            raise ValueError(msg)
+        self.joint_count = joint_count
+
+        self.conv_blocks = nn.Sequential()
+        len_channels = len(channels)
+        for idx in range(len_channels - 2):
+            self.conv_blocks.add_module(
+                name=f'conv_{idx}',
+                module=nn.Conv2d(
+                    in_channels=channels[idx],
+                    out_channels=channels[idx + 1],
+                    kernel_size=1,
+                    stride=1))
+
+        self.fc_layer = nn.ModuleList()
+        for idx in range(joint_count):
+            self.fc_layer.append(
+                nn.Linear(
+                    in_features=channels[len_channels - 2], out_features=1))
+
+    def forward(self, inputs):
+        """Forward function.
+
+        The input is (batch size x joint_count x 9)
+        """
+        inputs = inputs.transpose(1, 2).\
+            unsqueeze(2).contiguous()  # to N x 9 x 1 x joint_count
+        internal_outputs = self.conv_blocks(
+            inputs)  # to N x c x 1 x joint_count
+        outputs = []
+        for idx in range(self.joint_count):
+            outputs.append(self.fc_layer[idx](internal_outputs[:, :, 0, idx]))
+
+        return torch.cat(outputs, 1), internal_outputs
+
+
+class FullPoseDiscriminator(BaseDiscriminator):
+    """Discriminator for SMPL pose parameters of all joints.
+
+    Args:
+        fc_layers (Tuple): Tuple of neuron count,
+         such as (736, 1024, 1024, 1)
+        use_dropout (Tuple): Tuple of bool define use dropout or not
+         for each layer, such as (True, True, False)
+        drop_prob (Tuple): Tuple of float defined the drop prob,
+         such as (0.5, 0.5, 0)
+        use_activation(Tuple): Tuple of bool define use active
+         function or not, such as (True, True, False)
+    """
+
+    def __init__(self, fc_layers, use_dropout, drop_prob, use_activation):
+        if fc_layers[-1] != 1:
+            msg = f'the neuron count of the last layer must be 1,' \
+                  f' but got {fc_layers[-1]}'
+            raise ValueError(msg)
+
+        super().__init__(fc_layers, use_dropout, drop_prob, use_activation)
+
+    def forward(self, inputs):
+        """Forward function."""
+        return self.fc_blocks(inputs)
+
+
+class SMPLDiscriminator(nn.Module):
+    """Discriminator for SMPL pose and shape parameters. It is composed of a
+    discriminator for SMPL shape parameters, a discriminator for SMPL pose
+    parameters of all joints  and a discriminator for SMPL pose parameters of
+    each joint.
+
+    Args:
+        beta_channel (tuple of int): Tuple of neuron count of the
+            discriminator of shape parameters. Defaults to (10, 5, 1)
+        per_joint_channel (tuple of int): Tuple of neuron count of the
+            discriminator of each joint. Defaults to (9, 32, 32, 1)
+        full_pose_channel (tuple of int): Tuple of neuron count of the
+            discriminator of full pose. Defaults to (23*32, 1024, 1024, 1)
+    """
+
+    def __init__(self,
+                 beta_channel=(10, 5, 1),
+                 per_joint_channel=(9, 32, 32, 1),
+                 full_pose_channel=(23 * 32, 1024, 1024, 1)):
+        super().__init__()
+        self.joint_count = 23
+        # The count of SMPL shape parameter is 10.
+        assert beta_channel[0] == 10
+        # Use 3 x 3 rotation matrix as the pose parameters
+        # of each joint, so the input channel is 9.
+        assert per_joint_channel[0] == 9
+        assert self.joint_count * per_joint_channel[-2] \
+            == full_pose_channel[0]
+
+        self.beta_channel = beta_channel
+        self.per_joint_channel = per_joint_channel
+        self.full_pose_channel = full_pose_channel
+        self._create_sub_modules()
+
+    def _create_sub_modules(self):
+        """Create sub discriminators."""
+
+        # create theta discriminator for each joint
+        self.pose_discriminator = PoseDiscriminator(self.per_joint_channel,
+                                                    self.joint_count)
+
+        # create full pose discriminator for total joints
+        fc_layers = self.full_pose_channel
+        use_dropout = tuple([False] * (len(fc_layers) - 1))
+        drop_prob = tuple([0.5] * (len(fc_layers) - 1))
+        use_activation = tuple([True] * (len(fc_layers) - 2) + [False])
+
+        self.full_pose_discriminator = FullPoseDiscriminator(
+            fc_layers, use_dropout, drop_prob, use_activation)
+
+        # create shape discriminator for betas
+        fc_layers = self.beta_channel
+        use_dropout = tuple([False] * (len(fc_layers) - 1))
+        drop_prob = tuple([0.5] * (len(fc_layers) - 1))
+        use_activation = tuple([True] * (len(fc_layers) - 2) + [False])
+        self.shape_discriminator = ShapeDiscriminator(fc_layers, use_dropout,
+                                                      drop_prob,
+                                                      use_activation)
+
+    def forward(self, thetas):
+        """Forward function."""
+        cams, poses, shapes = thetas
+
+        batch_size = poses.shape[0]
+        shape_disc_value = self.shape_discriminator(shapes)
+
+        # The first rotation matrix is global rotation
+        # and is NOT used in discriminator.
+        if poses.dim() == 2:
+            rotate_matrixs = \
+                batch_rodrigues(poses.contiguous().view(-1, 3)
+                                ).view(batch_size, 24, 9)[:, 1:, :]
+        else:
+            rotate_matrixs = poses.contiguous().view(batch_size, 24,
+                                                     9)[:, 1:, :].contiguous()
+        pose_disc_value, pose_inter_disc_value \
+            = self.pose_discriminator(rotate_matrixs)
+        full_pose_disc_value = self.full_pose_discriminator(
+            pose_inter_disc_value.contiguous().view(batch_size, -1))
+        return torch.cat(
+            (pose_disc_value, full_pose_disc_value, shape_disc_value), 1)

mmpose/models/mesh_heads/geometric_layers.py

@@ -0,0 +1,67 @@
+import torch
+from torch.nn import functional as F
+
+
+def rot6d_to_rotmat(x):
+    """Convert 6D rotation representation to 3x3 rotation matrix.
+
+    Based on Zhou et al., "On the Continuity of Rotation
+    Representations in Neural Networks", CVPR 2019
+    Input:
+        (B,6) Batch of 6-D rotation representations
+    Output:
+        (B,3,3) Batch of corresponding rotation matrices
+    """
+    x = x.view(-1, 3, 2)
+    a1 = x[:, :, 0]
+    a2 = x[:, :, 1]
+    b1 = F.normalize(a1)
+    b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
+    b3 = torch.cross(b1, b2)
+    return torch.stack((b1, b2, b3), dim=-1)
+
+
+def batch_rodrigues(theta):
+    """Convert axis-angle representation to rotation matrix.
+    Args:
+        theta: size = [B, 3]
+    Returns:
+        Rotation matrix corresponding to the quaternion
+            -- size = [B, 3, 3]
+    """
+    l2norm = torch.norm(theta + 1e-8, p=2, dim=1)
+    angle = torch.unsqueeze(l2norm, -1)
+    normalized = torch.div(theta, angle)
+    angle = angle * 0.5
+    v_cos = torch.cos(angle)
+    v_sin = torch.sin(angle)
+    quat = torch.cat([v_cos, v_sin * normalized], dim=1)
+    return quat_to_rotmat(quat)
+
+
+def quat_to_rotmat(quat):
+    """Convert quaternion coefficients to rotation matrix.
+    Args:
+        quat: size = [B, 4] 4 <===>(w, x, y, z)
+    Returns:
+        Rotation matrix corresponding to the quaternion
+            -- size = [B, 3, 3]
+    """
+    norm_quat = quat
+    norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)
+    w, x, y, z = norm_quat[:, 0], norm_quat[:, 1],\
+        norm_quat[:, 2], norm_quat[:, 3]
+
+    B = quat.size(0)
+
+    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
+    wx, wy, wz = w * x, w * y, w * z
+    xy, xz, yz = x * y, x * z, y * z
+
+    rotMat = torch.stack([
+        w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy,
+        w2 - x2 + y2 - z2, 2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz,
+        w2 - x2 - y2 + z2
+    ],
+                         dim=1).view(B, 3, 3)
+    return rotMat