bug fixed: for loss and accuracy calculation in train.py

.gitignore

@@ -1,3 +1,6 @@
 .DS_store
 .idea
-*.pyc
+*.pyc
+eenet/
+datasets/cifar-100-python
+datasets/cifar-100-python.tar.gz

main.py

@@ -17,9 +17,9 @@
 from config import Config
 from data_tools.dataloader import get_dataloaders
 from predict import validate
-from predict_utils import dynamic_evaluate
+from utils.predict_utils import dynamic_evaluate
 from train import train
-from utils import save_checkpoint, load_checkpoint, measure_flops, load_state_dict
+from utils.utils import save_checkpoint, load_checkpoint, measure_flops, load_state_dict
 
 # import sys
 # if all(['models' not in sys.path]):

predict.py

@@ -10,7 +10,7 @@
 import torch.nn.parallel
 import torch.optim
 
-from utils import accuracy, AverageMeter
+from utils.utils import accuracy, AverageMeter
 
 
 def validate(model, val_loader, criterion, args):
@@ -40,17 +40,19 @@ def validate(model, val_loader, criterion, args):
             if not isinstance(output, list):
                 output = [output]
 
-            loss = torch.zeros(0)
+            #loss = torch.zeros(0)
+            loss = torch.tensor(0.0, dtype=torch.float)
             for j in range(len(output)):
                 if 'bert' in model.__class__.__name__:
-                    loss += (j + 1) * criterion(output[j], target_var) / (args.num_exits * (args.num_exits + 1))
+                    loss += (j + 1) * criterion(output[j][0][0], target_var) / (args.num_exits * (args.num_exits + 1))
                 else:
-                    loss += criterion(output[j], target_var) / args.num_exits
-
+                    loss += criterion(output[j][0][0], target_var) / args.num_exits
+            
             losses.update(loss.item(), input.size(0))
 
             for j in range(len(output)):
-                prec1, prec5 = accuracy(output[j].data, target, topk=(1, 5))
+                relevant_output = output[j][0][0].detach()
+                prec1, prec5 = accuracy(relevant_output, target, topk=(1, 5))
                 top1[j].update(prec1.item(), input.size(0))
                 top5[j].update(prec5.item(), input.size(0))
 

train.py

@@ -10,7 +10,7 @@
 import torch.nn.parallel
 import torch.optim
 
-from utils import adjust_learning_rate, accuracy, AverageMeter
+from utils.utils import adjust_learning_rate, accuracy, AverageMeter
 
 
 def train(model, train_loader, criterion, optimizer, epoch, args, train_params):
@@ -52,18 +52,23 @@ def train(model, train_loader, criterion, optimizer, epoch, args, train_params):
         if not isinstance(output, list):
             output = [output]
 
-        loss = torch.tensor(0)
+        #the loss variable must be float type as it handles floating point values from the dataset
+        loss = torch.tensor(0.0, dtype=torch.float)
+        #print(f"Data type of loss: {loss.dtype}")
         for j in range(len(output)):
-            loss += (j + 1) * criterion(output[j], target_var) / (args.num_exits * (args.num_exits + 1))
-            if epoch > train_params['num_epoch'] * 0.75 and j < len(output) - 1:
-                T = 3
-                alpha_kl = 0.01
-                loss += torch.nn.KLDivLoss()(torch.log_softmax(output[j] / T, dim=-1),  torch.softmax(output[-1] / T, dim=-1)) * alpha_kl * T * T
+          #torch._C._nn.cross_entropy_loss function accepts a tensor.
+          loss += (j + 1) * criterion(output[j][0][0], target_var) / (args.num_exits * (args.num_exits + 1))
+          if epoch > train_params['num_epoch'] * 0.75 and j < len(output) - 1:
+            T = 3
+            alpha_kl = 0.01
+            loss += torch.nn.KLDivLoss()(torch.log_softmax(output[j] / T, dim=-1),  torch.softmax(output[-1] / T, dim=-1)) * alpha_kl * T * T
 
         losses.update(loss.item(), input.size(0))
 
         for j in range(len(output)):
-            prec1, prec5 = accuracy(output[j].data, target, topk=(1, 5))
+            #tensor has data attribute
+            relevant_output = output[j][0][0].detach()
+            prec1, prec5 = accuracy(relevant_output, target, topk=(1, 5))
             top1[j].update(prec1.item(), input.size(0))
             top5[j].update(prec5.item(), input.size(0))
 

utils/predict_utils.py

@@ -10,7 +10,7 @@
 
 import pandas as pd
 
-from predict_helpers import *
+from utils.predict_helpers import *
 
 
 def dynamic_evaluate(model, test_loader, val_loader, args):

utils/utils.py

@@ -6,7 +6,7 @@
 import models
 import torch
 
-from op_counter import measure_model
+from utils.op_counter import measure_model
 
 
 def save_checkpoint(state, args, is_best, filename, result, prec1_per_exit, prec5_per_exit):