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| author | Jordan Gong <jordan.gong@protonmail.com> | 2021-02-18 18:38:31 +0800 |
|---|---|---|
| committer | Jordan Gong <jordan.gong@protonmail.com> | 2021-02-18 18:38:31 +0800 |
| commit | 8012fce3e595aad31f4a52dc316b46e558234dff (patch) | |
| tree | 4a5e19a21ad8a4470931f5884777c127197153c0 /models/rgb_part_net.py | |
| parent | 2988c1b9afd4e869b629a8629abedbf63d2452aa (diff) | |
| parent | 84a3d5991f2f7272d1be54ad6cfe6ce695f915a0 (diff) | |
Merge branch 'master' into data_parallel_py3.8
Diffstat (limited to 'models/rgb_part_net.py')
| -rw-r--r-- | models/rgb_part_net.py | 17 |
1 files changed, 9 insertions, 8 deletions
diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py index 845a477..80b3e17 100644 --- a/models/rgb_part_net.py +++ b/models/rgb_part_net.py @@ -13,6 +13,7 @@ class RGBPartNet(nn.Module): def __init__( self, ae_in_channels: int = 3, + ae_in_size: tuple[int, int] = (64, 48), ae_feature_channels: int = 64, f_a_c_p_dims: Tuple[int, int, int] = (128, 128, 64), hpm_use_1x1conv: bool = False, @@ -35,7 +36,7 @@ class RGBPartNet(nn.Module): self.image_log_on = image_log_on self.ae = AutoEncoder( - ae_in_channels, ae_feature_channels, f_a_c_p_dims + ae_in_channels, ae_in_size, ae_feature_channels, f_a_c_p_dims ) self.pn = PartNet( ae_in_channels, fpfe_feature_channels, fpfe_kernel_sizes, @@ -103,7 +104,7 @@ class RGBPartNet(nn.Module): i_a, i_c, i_p = None, None, None if self.image_log_on: - i_a = self._decode_appr_feature(f_a_, n, t, c, h, w, device) + i_a = self._decode_appr_feature(f_a_, n, t, device) # Continue decoding canonical features i_c = self.ae.decoder.trans_conv3(x_c) i_c = torch.sigmoid(self.ae.decoder.trans_conv4(i_c)) @@ -117,14 +118,14 @@ class RGBPartNet(nn.Module): x_p = self._decode_pose_feature(f_p_, n, t, c, h, w, device) return (x_c, x_p), None, None - def _decode_appr_feature(self, f_a_, n, t, c, h, w, device): + def _decode_appr_feature(self, f_a_, n, t, device): # Decode appearance features - x_a_ = self.ae.decoder( - f_a_, - torch.zeros((n * t, self.f_c_dim), device=device), - torch.zeros((n * t, self.f_p_dim), device=device) + f_a = f_a_.view(n, t, -1) + x_a = self.ae.decoder( + f_a.mean(1), + torch.zeros((n, self.f_c_dim), device=device), + torch.zeros((n, self.f_p_dim), device=device) ) - x_a = x_a_.view(n, t, c, h, w) return x_a def _decode_cano_feature(self, f_c_, n, t, device): |