Remove FConv blocks

4 files changed, 26 insertions, 45 deletions

diff --git a/config.py b/config.py
index 424bf5b..88ad371 100644
--- a/config.py
+++ b/config.py
@@ -49,22 +49,14 @@ config: Configuration = {
             # Auto-encoder feature channels coefficient
             'ae_feature_channels': 64,
             # Appearance, canonical and pose feature dimensions
-            'f_a_c_p_dims': (128, 128, 64),
+            'f_a_c_p_dims': (192, 192, 96),
             # Use 1x1 convolution in dimensionality reduction
             'hpm_use_1x1conv': False,
             # HPM pyramid scales, of which sum is number of parts
             'hpm_scales': (1, 2, 4),
             # Global pooling method
             'hpm_use_avg_pool': True,
-            'hpm_use_max_pool': False,
-            # FConv feature channels coefficient
-            'fpfe_feature_channels': 32,
-            # FConv blocks kernel sizes
-            'fpfe_kernel_sizes': ((5, 3), (3, 3), (3, 3)),
-            # FConv blocks paddings
-            'fpfe_paddings': ((2, 1), (1, 1), (1, 1)),
-            # FConv blocks halving
-            'fpfe_halving': (0, 2, 3),
+            'hpm_use_max_pool': True,
             # Attention squeeze ratio
             'tfa_squeeze_ratio': 4,
             # Number of parts after Part Net
diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index 1ef7494..e6a3e60 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -106,14 +106,14 @@ class Decoder(nn.Module):
         self.trans_conv4 = DCGANConvTranspose2d(feature_channels, out_channels,
                                                 is_last_layer=True)
 
-    def forward(self, f_appearance, f_canonical, f_pose, cano_only=False):
+    def forward(self, f_appearance, f_canonical, f_pose, is_feature_map=False):
         x = torch.cat((f_appearance, f_canonical, f_pose), dim=1)
         x = self.fc(x)
         x = x.view(-1, self.feature_channels * 8, self.h_0, self.w_0)
         x = F.relu(x, inplace=True)
         x = self.trans_conv1(x)
         x = self.trans_conv2(x)
-        if cano_only:
+        if is_feature_map:
             return x
         x = self.trans_conv3(x)
         x = torch.sigmoid(self.trans_conv4(x))
diff --git a/models/part_net.py b/models/part_net.py
index 62a2bac..29cf9cd 100644
--- a/models/part_net.py
+++ b/models/part_net.py
@@ -110,32 +110,22 @@ class TemporalFeatureAggregator(nn.Module):
 class PartNet(nn.Module):
     def __init__(
             self,
-            in_channels: int = 3,
-            feature_channels: int = 32,
-            kernel_sizes: tuple[tuple, ...] = ((5, 3), (3, 3), (3, 3)),
-            paddings: tuple[tuple, ...] = ((2, 1), (1, 1), (1, 1)),
-            halving: tuple[int, ...] = (0, 2, 3),
+            in_channels: int = 128,
             squeeze_ratio: int = 4,
             num_part: int = 16
     ):
         super().__init__()
         self.num_part = num_part
-        self.fpfe = FrameLevelPartFeatureExtractor(
-            in_channels, feature_channels, kernel_sizes, paddings, halving
-        )
-
-        num_fconv_blocks = len(self.fpfe.fconv_blocks)
-        self.tfa_in_channels = feature_channels * 2 ** (num_fconv_blocks - 1)
         self.tfa = TemporalFeatureAggregator(
-            self.tfa_in_channels, squeeze_ratio, self.num_part
+            in_channels, squeeze_ratio, self.num_part
         )
 
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
         self.max_pool = nn.AdaptiveMaxPool2d(1)
 
     def forward(self, x):
-        n, t, _, _, _ = x.size()
-        x = self.fpfe(x)
+        n, t, c, h, w = x.size()
+        x = x.view(n * t, c, h, w)
         # n * t x c x h x w
 
         # Horizontal Pooling
diff --git a/models/rgb_part_net.py b/models/rgb_part_net.py
index 408bca0..936ec46 100644
--- a/models/rgb_part_net.py
+++ b/models/rgb_part_net.py
@@ -17,16 +17,13 @@ class RGBPartNet(nn.Module):
             hpm_scales: tuple[int, ...] = (1, 2, 4),
             hpm_use_avg_pool: bool = True,
             hpm_use_max_pool: bool = True,
-            fpfe_feature_channels: int = 32,
-            fpfe_kernel_sizes: tuple[tuple, ...] = ((5, 3), (3, 3), (3, 3)),
-            fpfe_paddings: tuple[tuple, ...] = ((2, 1), (1, 1), (1, 1)),
-            fpfe_halving: tuple[int, ...] = (0, 2, 3),
             tfa_squeeze_ratio: int = 4,
             tfa_num_parts: int = 16,
             embedding_dims: int = 256,
             image_log_on: bool = False
     ):
         super().__init__()
+        self.h, self.w = ae_in_size
         (self.f_a_dim, self.f_c_dim, self.f_p_dim) = f_a_c_p_dims
         self.hpm_num_parts = sum(hpm_scales)
         self.image_log_on = image_log_on
@@ -34,18 +31,17 @@ class RGBPartNet(nn.Module):
         self.ae = AutoEncoder(
             ae_in_channels, ae_in_size, ae_feature_channels, f_a_c_p_dims
         )
+        self.pn_in_channels = ae_feature_channels * 2
         self.pn = PartNet(
-            ae_in_channels, fpfe_feature_channels, fpfe_kernel_sizes,
-            fpfe_paddings, fpfe_halving, tfa_squeeze_ratio, tfa_num_parts
+            self.pn_in_channels, tfa_squeeze_ratio, tfa_num_parts
         )
-        out_channels = self.pn.tfa_in_channels
         self.hpm = HorizontalPyramidMatching(
-            ae_feature_channels * 2, out_channels, hpm_use_1x1conv,
+            ae_feature_channels * 2, self.pn_in_channels, hpm_use_1x1conv,
             hpm_scales, hpm_use_avg_pool, hpm_use_max_pool
         )
         self.num_total_parts = self.hpm_num_parts + tfa_num_parts
         empty_fc = torch.empty(self.num_total_parts,
-                               out_channels, embedding_dims)
+                               self.pn_in_channels, embedding_dims)
         self.fc_mat = nn.Parameter(empty_fc)
 
     def fc(self, x):
@@ -82,17 +78,20 @@ class RGBPartNet(nn.Module):
         if self.training:
             ((f_a_, f_c_, f_p_), losses) = self.ae(x_c1_t2, x_c1_t1, x_c2_t2)
             # Decode features
-            with torch.no_grad():
-                x_c = self._decode_cano_feature(f_c_, n, t, device)
-                x_p = self._decode_pose_feature(f_p_, n, t, c, h, w, device)
+            x_c = self._decode_cano_feature(f_c_, n, t, device)
+            x_p_ = self._decode_pose_feature(f_p_, n, t, c, h, w, device)
+            x_p = x_p_.view(n, t, self.pn_in_channels, self.h // 4, self.w // 4)
 
-                i_a, i_c, i_p = None, None, None
-                if self.image_log_on:
+            i_a, i_c, i_p = None, None, None
+            if self.image_log_on:
+                with torch.no_grad():
                     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))
-                    i_p = x_p
+                    i_p_ = self.ae.decoder.trans_conv3(x_p_)
+                    i_p_ = torch.sigmoid(self.ae.decoder.trans_conv4(i_p_))
+                    i_p = i_p_.view(n, t, c, h, w)
 
             return (x_c, x_p), losses, (i_a, i_c, i_p)
 
@@ -119,7 +118,7 @@ class RGBPartNet(nn.Module):
             torch.zeros((n, self.f_a_dim), device=device),
             f_c.mean(1),
             torch.zeros((n, self.f_p_dim), device=device),
-            cano_only=True
+            is_feature_map=True
         )
         return x_c
 
@@ -128,7 +127,7 @@ class RGBPartNet(nn.Module):
         x_p_ = self.ae.decoder(
             torch.zeros((n * t, self.f_a_dim), device=device),
             torch.zeros((n * t, self.f_c_dim), device=device),
-            f_p_
+            f_p_,
+            is_feature_map=True
         )
-        x_p = x_p_.view(n, t, c, h, w)
-        return x_p
+        return x_p_