classes_number

timm/data/bag_sampler.py

@@ -2,6 +2,7 @@ import torch
 import random
 from torch.utils.data.sampler import Sampler
 
+##TODO: Ensure that all videos are covered. Make a copy of indices and remove the indices that were already picked.
 class BagSampler(Sampler):
     def __init__(self, dataset):
         halfway_point = int(len(dataset)/2)

timm/loss/mil_ranking.py

@@ -9,15 +9,15 @@ class MilRankingLoss(nn.Module):
     #def forward(self, x: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
     #    pass
     #def mil_ranking(y_true, y_pred):
-    def forward(self, y_true, y_pred):
+    def forward(self, y_pred, y_true):
         'Custom Objective function'
 
         y_true = torch.flatten(y_true)
         y_pred = torch.flatten(y_pred)
         print("MIL_Ranking")
-        print(y_true)
+        #print(y_true)
         #print(y_true.type)
-        print(y_pred)
+        #print(y_pred)
         #print(y_pred.type)
 
         n_seg = 32  # Because we have 32 segments per video.

timm/models/mlp_mixer.py

@@ -85,7 +85,7 @@ default_cfgs = dict(
     # Mixer ImageNet-21K-P pretraining
     mixer_b16_224_miil_in21k=_cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil_in21k.pth',
-        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=2,
+        mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=1, #11221
     ),
     mixer_b16_224_miil=_cfg(
         url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil.pth',
@@ -287,8 +287,23 @@ class MlpMixer(nn.Module):
         """
         self.norm = norm_layer(embed_dim)
         self.head = nn.Linear(embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
-        
-        self.init_weights(nlhb=nlhb)
+        #self.head = nn.Sequential(
+        #     nn.Linear(embed_dim, self.num_classes),
+        #     nn.ReLU(),
+        #     nn.Dropout(p=0.3),
+        #     nn.Linear(self.num_classes, 1024),
+        #     nn.ReLU(),
+        #     nn.Dropout(p=0.3),
+        #     nn.Linear(1024, 512),
+        #     nn.ReLU(),
+        #     nn.Dropout(p=0.3),
+        #     nn.Linear(512, 256),
+        #     nn.ReLU(),
+        #     nn.Dropout(p=0.3),
+        #     nn.Linear(256, 1)
+        # )
+        self.sigmoid = nn.Sigmoid()
+        #self.init_weights(nlhb=nlhb)
 
     def init_weights(self, nlhb=False):
         head_bias = -math.log(self.num_classes) if nlhb else 0.
@@ -304,17 +319,21 @@ class MlpMixer(nn.Module):
     def forward_features(self, x):
         #x = self.stem(x)
         #print(x.shape)
+        print("In_Model")
         x = self.blocks(x)
+        print(x)
         x = self.norm(x)
+        print(x)
         x = x.mean(dim=1)
+        print(x)
         return x
 
     def forward(self, x):
         x = self.forward_features(x)
         x = self.head(x)
-        #print("In_model")
-        #print(x.shape)
-        #print(x)
+        print(x)
+        x = self.sigmoid(x)
+        print(x)
         return x