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Source code for tllib.alignment.regda

"""
@author: Junguang Jiang
@contact: JiangJunguang1123@outlook.com
"""
from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np

from tllib.modules.gl import WarmStartGradientLayer
from tllib.utils.metric.keypoint_detection import get_max_preds


class FastPseudoLabelGenerator2d(nn.Module):
    def __init__(self, sigma=2):
        super().__init__()
        self.sigma = sigma
    
    def forward(self, heatmap: torch.Tensor):
        heatmap = heatmap.detach()
        height, width = heatmap.shape[-2:]
        idx = heatmap.flatten(-2).argmax(dim=-1) # B, K
        pred_h, pred_w = idx.div(width, rounding_mode='floor'), idx.remainder(width) # B, K
        delta_h = torch.arange(height, device=heatmap.device) - pred_h.unsqueeze(-1) # B, K, H
        delta_w = torch.arange(width, device=heatmap.device) - pred_w.unsqueeze(-1) # B, K, W
        gaussian = (delta_h.square().unsqueeze(-1) + delta_w.square().unsqueeze(-2)).div(-2 * self.sigma * self.sigma).exp() # B, K, H, W
        ground_truth = F.threshold(gaussian, threshold=1e-2, value=0.)

        ground_false = (ground_truth.sum(dim=1, keepdim=True) - ground_truth).clamp(0., 1.)
        return ground_truth, ground_false


[docs]class PseudoLabelGenerator2d(nn.Module): """ Generate ground truth heatmap and ground false heatmap from a prediction. Args: num_keypoints (int): Number of keypoints height (int): height of the heatmap. Default: 64 width (int): width of the heatmap. Default: 64 sigma (int): sigma parameter when generate the heatmap. Default: 2 Inputs: - y: predicted heatmap Outputs: - ground_truth: heatmap conforming to Gaussian distribution - ground_false: ground false heatmap Shape: - y: :math:`(minibatch, K, H, W)` where K means the number of keypoints, H and W is the height and width of the heatmap respectively. - ground_truth: :math:`(minibatch, K, H, W)` - ground_false: :math:`(minibatch, K, H, W)` """ def __init__(self, num_keypoints, height=64, width=64, sigma=2): super(PseudoLabelGenerator2d, self).__init__() self.height = height self.width = width self.sigma = sigma heatmaps = np.zeros((width, height, height, width), dtype=np.float32) tmp_size = sigma * 3 for mu_x in range(width): for mu_y in range(height): # Check that any part of the gaussian is in-bounds ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)] br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)] # Generate gaussian size = 2 * tmp_size + 1 x = np.arange(0, size, 1, np.float32) y = x[:, np.newaxis] x0 = y0 = size // 2 # The gaussian is not normalized, we want the center value to equal 1 g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2)) # Usable gaussian range g_x = max(0, -ul[0]), min(br[0], width) - ul[0] g_y = max(0, -ul[1]), min(br[1], height) - ul[1] # Image range img_x = max(0, ul[0]), min(br[0], width) img_y = max(0, ul[1]), min(br[1], height) heatmaps[mu_x][mu_y][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \ g[g_y[0]:g_y[1], g_x[0]:g_x[1]] self.heatmaps = heatmaps self.false_matrix = 1. - np.eye(num_keypoints, dtype=np.float32) def forward(self, y): B, K, H, W = y.shape y = y.detach() preds, max_vals = get_max_preds(y.cpu().numpy()) # B x K x (x, y) preds = preds.reshape(-1, 2).astype(np.int) ground_truth = self.heatmaps[preds[:, 0], preds[:, 1], :, :].copy().reshape(B, K, H, W).copy() ground_false = ground_truth.reshape(B, K, -1).transpose((0, 2, 1)) ground_false = ground_false.dot(self.false_matrix).clip(max=1., min=0.).transpose((0, 2, 1)).reshape(B, K, H, W).copy() return torch.from_numpy(ground_truth).to(y.device), torch.from_numpy(ground_false).to(y.device)
[docs]class RegressionDisparity(nn.Module): """ Regression Disparity proposed by `Regressive Domain Adaptation for Unsupervised Keypoint Detection (CVPR 2021) <https://arxiv.org/abs/2103.06175>`_. Args: pseudo_label_generator (PseudoLabelGenerator2d): generate ground truth heatmap and ground false heatmap from a prediction. criterion (torch.nn.Module): the loss function to calculate distance between two predictions. Inputs: - y: output by the main head - y_adv: output by the adversarial head - weight (optional): instance weights - mode (str): whether minimize the disparity or maximize the disparity. Choices includes ``min``, ``max``. Default: ``min``. Shape: - y: :math:`(minibatch, K, H, W)` where K means the number of keypoints, H and W is the height and width of the heatmap respectively. - y_adv: :math:`(minibatch, K, H, W)` - weight: :math:`(minibatch, K)`. - Output: depends on the ``criterion``. Examples:: >>> num_keypoints = 5 >>> batch_size = 10 >>> H = W = 64 >>> pseudo_label_generator = PseudoLabelGenerator2d(num_keypoints) >>> from tllibvision.models.keypoint_detection.loss import JointsKLLoss >>> loss = RegressionDisparity(pseudo_label_generator, JointsKLLoss()) >>> # output from source domain and target domain >>> y_s, y_t = torch.randn(batch_size, num_keypoints, H, W), torch.randn(batch_size, num_keypoints, H, W) >>> # adversarial output from source domain and target domain >>> y_s_adv, y_t_adv = torch.randn(batch_size, num_keypoints, H, W), torch.randn(batch_size, num_keypoints, H, W) >>> # minimize regression disparity on source domain >>> output = loss(y_s, y_s_adv, mode='min') >>> # maximize regression disparity on target domain >>> output = loss(y_t, y_t_adv, mode='max') """ def __init__(self, pseudo_label_generator: PseudoLabelGenerator2d, criterion: nn.Module): super(RegressionDisparity, self).__init__() self.criterion = criterion self.pseudo_label_generator = pseudo_label_generator def forward(self, y, y_adv, weight=None, mode='min'): assert mode in ['min', 'max'] ground_truth, ground_false = self.pseudo_label_generator(y.detach()) self.ground_truth = ground_truth self.ground_false = ground_false if mode == 'min': return self.criterion(y_adv, ground_truth, weight) else: return self.criterion(y_adv, ground_false, weight)
[docs]class PoseResNet2d(nn.Module): """ Pose ResNet for RegDA has one backbone, one upsampling, while two regression heads. Args: backbone (torch.nn.Module): Backbone to extract 2-d features from data upsampling (torch.nn.Module): Layer to upsample image feature to heatmap size feature_dim (int): The dimension of the features from upsampling layer. num_keypoints (int): Number of keypoints gl (WarmStartGradientLayer): finetune (bool, optional): Whether use 10x smaller learning rate in the backbone. Default: True num_head_layers (int): Number of head layers. Default: 2 Inputs: - x (tensor): input data Outputs: - outputs: logits outputs by the main regressor - outputs_adv: logits outputs by the adversarial regressor Shape: - x: :math:`(minibatch, *)`, same shape as the input of the `backbone`. - outputs, outputs_adv: :math:`(minibatch, K, H, W)`, where K means the number of keypoints. .. note:: Remember to call function `step()` after function `forward()` **during training phase**! For instance, >>> # x is inputs, model is an PoseResNet >>> outputs, outputs_adv = model(x) >>> model.step() """ def __init__(self, backbone, upsampling, feature_dim, num_keypoints, gl: Optional[WarmStartGradientLayer] = None, finetune: Optional[bool] = True, num_head_layers=2): super(PoseResNet2d, self).__init__() self.backbone = backbone self.upsampling = upsampling self.head = self._make_head(num_head_layers, feature_dim, num_keypoints) self.head_adv = self._make_head(num_head_layers, feature_dim, num_keypoints) self.finetune = finetune self.gl_layer = WarmStartGradientLayer(alpha=1.0, lo=0.0, hi=0.1, max_iters=1000, auto_step=False) if gl is None else gl @staticmethod def _make_head(num_layers, channel_dim, num_keypoints): layers = [] for i in range(num_layers-1): layers.extend([ nn.Conv2d(channel_dim, channel_dim, 3, 1, 1), nn.BatchNorm2d(channel_dim), nn.ReLU(), ]) layers.append( nn.Conv2d( in_channels=channel_dim, out_channels=num_keypoints, kernel_size=1, stride=1, padding=0 ) ) layers = nn.Sequential(*layers) for m in layers.modules(): if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight, std=0.001) nn.init.constant_(m.bias, 0) return layers def forward(self, x): x = self.backbone(x) f = self.upsampling(x) f_adv = self.gl_layer(f) y = self.head(f) y_adv = self.head_adv(f_adv) if self.training: return y, y_adv else: return y def get_parameters(self, lr=1.): return [ {'params': self.backbone.parameters(), 'lr': 0.1 * lr if self.finetune else lr}, {'params': self.upsampling.parameters(), 'lr': lr}, {'params': self.head.parameters(), 'lr': lr}, {'params': self.head_adv.parameters(), 'lr': lr}, ] def step(self): """Call step() each iteration during training. Will increase :math:`\lambda` in GL layer. """ self.gl_layer.step()

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