cleaning codebase by moving create_submission to utils

config.yml

 # Dataset Configuration
 Dataset:
   num_days: 73 # Number of days in each sequence - should be the same as in Test -
-  batch_size: 64
+  batch_size: 128
   num_workers: 7
   valid_ratio: 0.2
   max_num_samples: None #1000
@@ -61,7 +61,7 @@ BidirectionalLSTM:
   HiddenSize: 16
   NumLayers: 2
   LSTMDropout: 0
-  FFNDropout: 0.2
+  FFNDropout: 0
   NumFFN: 3
   Initialization: init_he
 

main.py

 #Internal imports
 import dataloader
 import model
-import test
+import my_test
 import my_train
-import create_submission
 import utils
 
 #External imports
@@ -16,7 +15,7 @@ import torch.nn as nn
 import os
 import argparse
 
-def optimizer(cfg, network):
+def choose_optimizer(cfg, network):
     result = {"Adam" : torch.optim.Adam(network.parameters())}
     return result[cfg["Optimizer"]]
 
@@ -91,7 +90,7 @@ def train(args, cfg):
 
     f_loss = model.RMSLELoss()
 
-    optimizer = optimizer(cfg, network)
+    optimizer = choose_optimizer(cfg, network)
 
     scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
         optimizer, 
@@ -113,10 +112,10 @@ def train(args, cfg):
 
     best_val_loss = None
     for t in range(cfg["Training"]["Epochs"]):
-        print("Epoch {}".format(t))
+        print(f"Epoch {t+1}")
         my_train.train(args, network, train_loader, f_loss, optimizer, device, log_interval)
 
-        val_loss = test.test(network, valid_loader, f_loss, device)
+        val_loss = my_test.test(network, valid_loader, f_loss, device)
 
         if best_val_loss != None:
             if val_loss < best_val_loss :
@@ -126,7 +125,7 @@ def train(args, cfg):
         scheduler.step(val_loss)
 
 
-        print(" Validation : Loss : {:.4f}".format(val_loss))
+        print("Validation : Loss : {:.4f}".format(val_loss))
         if not args.no_wandb:
             wandb.log({"val_loss": val_loss})
 
@@ -155,7 +154,7 @@ def test(args):
 
     network.load_state_dict(torch.load(model_path))
 
-    create_submission.create_submission(args, network, eval(dataset_transform), device, rootDir, logdir)
+    utils.create_submission(args, network, eval(dataset_transform), device, rootDir, logdir)
 
     logging.info(f"The submission csv file has been created in the folder : {logdir}")
 
@@ -209,5 +208,5 @@ if __name__ == "__main__":
     config_file = open("config.yml")
     cfg = yaml.load(config_file, Loader=yaml.FullLoader)
 
-    eval(f"{args.command}(args)")
+    eval(f"{args.command}(args, cfg)")
 

model.py

@@ -80,7 +80,14 @@ class BidirectionalLSTM(nn.Module):
         self.FFN_dropout    = cfg["BidirectionalLSTM"]["FFNDropout"]
         self.num_ffn    = cfg["BidirectionalLSTM"]["NumFFN"]
 
-        self.lstm = nn.LSTM(input_size, self.hidden_size, self.num_layers, batch_first = True, bidirectional =True, dropout = self.LSTM_dropout)
+        self.lstm = nn.LSTM(
+            input_size, 
+            self.hidden_size, 
+            self.num_layers, 
+            batch_first = True, 
+            bidirectional =True, 
+            dropout = self.LSTM_dropout)
+
         self.fc = nn.Sequential()
         
         for layer in range(self.num_ffn):
@@ -105,7 +112,8 @@ class BidirectionalLSTM(nn.Module):
         if use_cuda :
             device = torch.device('cuda')
         else :
-            device = toch.device('cpu')
+            device = torch.device('cpu')
+
         h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
         c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
 
@@ -114,6 +122,7 @@ class BidirectionalLSTM(nn.Module):
         result = self.fc(out)
         
         result = nn.ReLU()(result)
+        
         return result
 
 
@@ -122,7 +131,7 @@ class CNN1D(torch.nn.Module):
 
     def __init__(self, cfg, num_inputs):
         super(CNN1D, self).__init__()
-        self.model = torch.nn.Sequential(
+        self.block = torch.nn.Sequential(
             *conv_block(num_inputs, 32),
             *conv_block(32, 128)
         )
@@ -137,9 +146,8 @@ class CNN1D(torch.nn.Module):
     def forward(self, x):
         x = torch.transpose(x, 1, 2)
 
-        out = self.model(x)
+        out = self.block(x)
 
-        print(f"This is after CNN : {out}")
         out = self.avg_pool(out)
 
         out = out.view([out.shape[0], -1])

utils.py

-import os
+# Standard imports
 import sys
+import logging
+import os
+import datetime
+
+# External imports
+import tqdm
+import torch
+import torch.nn as nn
+import argparse
+import yaml
 
 def generate_unique_logpath(logdir, raw_run_name):
     i = 0
@@ -46,4 +56,6 @@ def write_summary(logdir, model, optimizer, val_loss):
 
     """.format(val_loss," ".join(sys.argv), model, sum(p.numel() for p in model.parameters() if p.requires_grad), optimizer)
     summary_file.write(summary_text)
-    summary_file.close()
\ No newline at end of file
+    summary_file.close()
+
+