Merge branch 'master' into data_parallel_py3.8

6 files changed, 74 insertions, 54 deletions

diff --git a/config.py b/config.py
index 547d1a3..424bf5b 100644
--- a/config.py
+++ b/config.py
@@ -16,7 +16,7 @@ config: Configuration = {
         # Name of dataset (CASIA-B or FVG)
         'name': 'CASIA-B',
         # Path to dataset root (required)
-        'root_dir': 'data/CASIA-B-MRCNN/SEG',
+        'root_dir': 'data/CASIA-B-MRCNN-V2/SEG',
         # The number of subjects for training
         'train_size': 74,
         # Number of sampled frames per sequence (Training only)
@@ -27,8 +27,8 @@ config: Configuration = {
         'discard_threshold': 15,
         # Number of input channels of model
         'num_input_channels': 3,
-        # Resolution after resize, height : width should be 2 : 1
-        'frame_size': (64, 32),
+        # Resolution after resize, can be divided 16
+        'frame_size': (64, 48),
         # Cache dataset or not
         'cache_on': False,
     },
diff --git a/models/auto_encoder.py b/models/auto_encoder.py
index 7b9b29f..c6bc52f 100644
--- a/models/auto_encoder.py
+++ b/models/auto_encoder.py
@@ -13,39 +13,46 @@ class Encoder(nn.Module):
     def __init__(
             self,
             in_channels: int = 3,
+            frame_size: tuple[int, int] = (64, 48),
             feature_channels: int = 64,
             output_dims: Tuple[int, int, int] = (128, 128, 64)
     ):
         super().__init__()
         self.feature_channels = feature_channels
+        h_0, w_0 = frame_size
+        h_1, w_1 = h_0 // 2, w_0 // 2
+        h_2, w_2 = h_1 // 2, w_1 // 2
+        self.feature_size = self.h_3, self.w_3 = h_2 // 4, w_2 // 4
         # Appearance features, canonical features, pose features
         (self.f_a_dim, self.f_c_dim, self.f_p_dim) = output_dims
 
-        # Conv1      in_channels x 64 x 32
-        #    -> feature_map_size x 64 x 32
+        # Conv1      in_channels x H x W
+        #    -> feature_map_size x H x W
         self.conv1 = VGGConv2d(in_channels, feature_channels)
-        # MaxPool1 feature_map_size x 64 x 32
-        #       -> feature_map_size x 32 x 16
-        self.max_pool1 = nn.AdaptiveMaxPool2d((32, 16))
-        # Conv2 feature_map_size    x 32 x 16
-        #   -> (feature_map_size*4) x 32 x 16
+        # MaxPool1 feature_map_size x    H x    W
+        #       -> feature_map_size x H//2 x W//2
+        self.max_pool1 = nn.AdaptiveMaxPool2d((h_1, w_1))
+        # Conv2 feature_map_size   x H//2 x W//2
+        #   ->  feature_map_size*4 x H//2 x W//2
         self.conv2 = VGGConv2d(feature_channels, feature_channels * 4)
-        # MaxPool2 (feature_map_size*4) x 32 x 16
-        #       -> (feature_map_size*4) x 16 x 8
-        self.max_pool2 = nn.AdaptiveMaxPool2d((16, 8))
-        # Conv3 (feature_map_size*4) x 16 x 8
-        #    -> (feature_map_size*8) x 16 x 8
+        # MaxPool2 feature_map_size*4 x H//2 x W//2
+        #       -> feature_map_size*4 x H//4 x W//4
+        self.max_pool2 = nn.AdaptiveMaxPool2d((h_2, w_2))
+        # Conv3 feature_map_size*4 x H//4 x W//4
+        #    -> feature_map_size*8 x H//4 x W//4
         self.conv3 = VGGConv2d(feature_channels * 4, feature_channels * 8)
-        # Conv4 (feature_map_size*8) x 16 x 8
-        #    -> (feature_map_size*8) x 16 x 8 (for large dataset)
+        # Conv4 feature_map_size*8 x H//4 x W//4
+        #    -> feature_map_size*8 x H//4 x W//4 (for large dataset)
         self.conv4 = VGGConv2d(feature_channels * 8, feature_channels * 8)
-        # MaxPool3 (feature_map_size*8) x 16 x 8
-        # ->       (feature_map_size*8) x  4 x 2
-        self.max_pool3 = nn.AdaptiveMaxPool2d((4, 2))
+        # MaxPool3 feature_map_size*8 x  H//4 x  W//4
+        # ->       feature_map_size*8 x H//16 x W//16
+        self.max_pool3 = nn.AdaptiveMaxPool2d(self.feature_size)
 
         embedding_dim = sum(output_dims)
-        # FC (feature_map_size*8) * 4 * 2 -> 320
-        self.fc = BasicLinear(feature_channels * 8 * 2 * 4, embedding_dim)
+        # FC feature_map_size*8 * H//16 * W//16 -> embedding_dim
+        self.fc = BasicLinear(
+            (feature_channels * 8) * self.h_3 * self.w_3, embedding_dim
+        )
 
     def forward(self, x):
         x = self.conv1(x)
@@ -55,7 +62,7 @@ class Encoder(nn.Module):
         x = self.conv3(x)
         x = self.conv4(x)
         x = self.max_pool3(x)
-        x = x.view(-1, (self.feature_channels * 8) * 2 * 4)
+        x = x.view(-1, (self.feature_channels * 8) * self.h_3 * self.w_3)
         embedding = self.fc(x)
 
         f_appearance, f_canonical, f_pose = embedding.split(
@@ -71,36 +78,41 @@ class Decoder(nn.Module):
             self,
             input_dims: Tuple[int, int, int] = (128, 128, 64),
             feature_channels: int = 64,
+            feature_size: tuple[int, int] = (4, 3),
             out_channels: int = 3,
     ):
         super().__init__()
         self.feature_channels = feature_channels
+        self.h_0, self.w_0 = feature_size
 
         embedding_dim = sum(input_dims)
-        # FC 320 -> (feature_map_size*8) * 4 * 2
-        self.fc = BasicLinear(embedding_dim, feature_channels * 8 * 2 * 4)
+        # FC 320 -> feature_map_size*8 * H * W
+        self.fc = BasicLinear(
+            embedding_dim, (feature_channels * 8) * self.h_0 * self.w_0
+        )
 
-        # TransConv1 (feature_map_size*8) x 4 x 2
-        #         -> (feature_map_size*4) x 8 x 4
+        # TransConv1 feature_map_size*8 x   H x   W
+        #         -> feature_map_size*4 x H*2 x W*2
         self.trans_conv1 = DCGANConvTranspose2d(feature_channels * 8,
                                                 feature_channels * 4)
-        # TransConv2 (feature_map_size*4) x  8 x 4
-        #         -> (feature_map_size*2) x 16 x 8
+        # TransConv2 feature_map_size*4 x H*2 x W*2
+        #         -> feature_map_size*2 x H*4 x W*4
         self.trans_conv2 = DCGANConvTranspose2d(feature_channels * 4,
                                                 feature_channels * 2)
-        # TransConv3 (feature_map_size*2) x 16 x  8
-        #         ->  feature_map_size    x 32 x 16
+        # TransConv3 feature_map_size*2 x H*4 x W*4
+        #         -> feature_map_size   x H*8 x W*8
         self.trans_conv3 = DCGANConvTranspose2d(feature_channels * 2,
                                                 feature_channels)
-        # TransConv4 feature_map_size x 32 x 16
-        #         ->      in_channels x 64 x 32
+        # TransConv4 feature_map_size x  H*8 x  W*8
+        #         ->      in_channels x H*16 x W*16
         self.trans_conv4 = DCGANConvTranspose2d(feature_channels, out_channels,
                                                 is_last_layer=True)
 
     def forward(self, f_appearance, f_canonical, f_pose, cano_only=False):
         x = torch.cat((f_appearance, f_canonical, f_pose), dim=1)
         x = self.fc(x)
-        x = F.relu(x.view(-1, self.feature_channels * 8, 4, 2), inplace=True)
+        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:
@@ -115,12 +127,15 @@ class AutoEncoder(nn.Module):
     def __init__(
             self,
             channels: int = 3,
+            frame_size: tuple[int, int] = (64, 48),
             feature_channels: int = 64,
             embedding_dims: Tuple[int, int, int] = (128, 128, 64)
     ):
         super().__init__()
-        self.encoder = Encoder(channels, feature_channels, embedding_dims)
-        self.decoder = Decoder(embedding_dims, feature_channels, channels)
+        self.encoder = Encoder(channels, frame_size,
+                               feature_channels, embedding_dims)
+        self.decoder = Decoder(embedding_dims, feature_channels,
+                               self.encoder.feature_size, channels)
 
     def forward(self, x_c1_t2, x_c1_t1=None, x_c2_t2=None):
         n, t, c, h, w = x_c1_t2.size()
diff --git a/models/layers.py b/models/layers.py
index 98e4c10..ae61583 100644
--- a/models/layers.py
+++ b/models/layers.py
@@ -162,7 +162,7 @@ class BasicConv1d(nn.Module):
         return self.conv(x)
 
 
-class HorizontalPyramidPooling(BasicConv2d):
+class HorizontalPyramidPooling(nn.Module):
     def __init__(
             self,
             in_channels: int,
@@ -172,8 +172,10 @@ class HorizontalPyramidPooling(BasicConv2d):
             use_max_pool: bool = False,
             **kwargs
     ):
-        super().__init__(in_channels, out_channels, kernel_size=1, **kwargs)
+        super().__init__()
         self.use_1x1conv = use_1x1conv
+        if use_1x1conv:
+            self.conv = BasicConv2d(in_channels, out_channels, 1, **kwargs)
         self.use_avg_pool = use_avg_pool
         self.use_max_pool = use_max_pool
         assert use_avg_pool or use_max_pool, 'Pooling layer(s) required.'
@@ -188,5 +190,5 @@ class HorizontalPyramidPooling(BasicConv2d):
         elif not self.use_avg_pool and self.use_max_pool:
             x = self.max_pool(x)
         if self.use_1x1conv:
-            x = super().forward(x)
+            x = self.conv(x)
         return x
diff --git a/models/model.py b/models/model.py
index 2715b26..3d619fe 100644
--- a/models/model.py
+++ b/models/model.py
@@ -55,6 +55,7 @@ class Model:
 
         self.is_train: bool = True
         self.in_channels: int = 3
+        self.in_size: tuple[int, int] = (64, 48)
         self.pr: Optional[int] = None
         self.k: Optional[int] = None
 
@@ -147,7 +148,7 @@ class Model:
         hpm_optim_hp = optim_hp.pop('hpm', {})
         fc_optim_hp = optim_hp.pop('fc', {})
         sched_hp = self.hp.get('scheduler', {})
-        self.rgb_pn = RGBPartNet(self.in_channels, **model_hp,
+        self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp,
                                  image_log_on=self.image_log_on)
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = nn.DataParallel(self.rgb_pn)
@@ -230,16 +231,16 @@ class Model:
                 if self.image_log_on:
                     i_a, i_c, i_p = images
                     self.writer.add_images(
+                        'Appearance image', i_a, self.curr_iter
+                    )
+                    self.writer.add_images(
                         'Canonical image', i_c, self.curr_iter
                     )
-                    for (i, (o, a, p)) in enumerate(zip(x_c1, i_a, i_p)):
+                    for i, (o, p) in enumerate(zip(x_c1, i_p)):
                         self.writer.add_images(
                             f'Original image/batch {i}', o, self.curr_iter
                         )
                         self.writer.add_images(
-                            f'Appearance image/batch {i}', a, self.curr_iter
-                        )
-                        self.writer.add_images(
                             f'Pose image/batch {i}', p, self.curr_iter
                         )
                 time_used = datetime.now() - start_time
@@ -306,7 +307,7 @@ class Model:
 
         # Init models
         model_hp = self.hp.get('model', {})
-        self.rgb_pn = RGBPartNet(ae_in_channels=self.in_channels, **model_hp)
+        self.rgb_pn = RGBPartNet(self.in_channels, self.in_size, **model_hp)
         # Try to accelerate computation using CUDA or others
         self.rgb_pn = nn.DataParallel(self.rgb_pn)
         self.rgb_pn = self.rgb_pn.to(self.device)
@@ -467,6 +468,7 @@ class Model:
             dataset_config: DatasetConfiguration
     ) -> Union[CASIAB]:
         self.in_channels = dataset_config.get('num_input_channels', 3)
+        self.in_size = dataset_config.get('frame_size', (64, 48))
         self._dataset_sig = self._make_signature(
             dataset_config,
             popped_keys=['root_dir', 'cache_on']
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):
diff --git a/preprocess.py b/preprocess.py
index 894c7c1..eef59ba 100644
--- a/preprocess.py
+++ b/preprocess.py
@@ -52,7 +52,7 @@ def result_handler(frame_: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
             mask_x1 = (mask_xc + mask_half_width).ceil().int()
 
             # Skip incomplete frames
-            if (height < 64 or width < 64 / MASK_BOX_RATIO) \
+            if (height < 64 or width < 64 / BOX_RATIO_THRESHOLD[1]) \
                     or (mask_x0 < 0 or mask_x1 > 320) \
                     or (mask_y0 < 0 or mask_y1 > 240):
                 continue