diff --git a/config.yml b/config.yml
index 783ce55dd53d9d48f5d4781e62a6feb61e4aa60e..dce0a3ccb616b311177376a3e720d802b43ddc2f 100644
--- a/config.yml
+++ b/config.yml
@@ -1,7 +1,7 @@
# 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
diff --git a/logs/main_unit_test.log b/logs/main_unit_test.log
index 38daef58f7cd0d49949d994431bc7b5dea593041..8f016a55f0006b877b3e43fb14e63892d1404617 100644
--- a/logs/main_unit_test.log
+++ b/logs/main_unit_test.log
@@ -2573,3 +2573,78 @@ INFO:root:Generating the index
INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2017-testing.nc.bin_index.idx
INFO:root:I loaded 112860 values in the test set
INFO:root:= Filling in the submission file
+INFO:root:= Dataloaders
+INFO:root: - Dataset creation
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Generating the index
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - Loaded a dataset with 677160 samples
+INFO:root: - Splitting the data in training and validation sets
+INFO:root:Generating the subset files from 677160 samples
+INFO:root: - Subset dataset
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - The train fold has 541958 samples
+INFO:root: - The valid fold has 135202 samples
+INFO:root:= Dataloaders
+INFO:root: - Dataset creation
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Generating the index
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - Loaded a dataset with 677160 samples
+INFO:root: - Splitting the data in training and validation sets
+INFO:root:Generating the subset files from 677160 samples
+INFO:root: - Subset dataset
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - The train fold has 542123 samples
+INFO:root: - The valid fold has 135037 samples
+INFO:root:= Dataloaders
+INFO:root: - Dataset creation
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Generating the index
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - Loaded a dataset with 677160 samples
+INFO:root: - Splitting the data in training and validation sets
+INFO:root:Generating the subset files from 677160 samples
+INFO:root: - Subset dataset
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - The train fold has 542201 samples
+INFO:root: - The valid fold has 134959 samples
+INFO:root:= Dataloaders
+INFO:root: - Dataset creation
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Generating the index
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - Loaded a dataset with 677160 samples
+INFO:root: - Splitting the data in training and validation sets
+INFO:root:Generating the subset files from 677160 samples
+INFO:root: - Subset dataset
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - The train fold has 541381 samples
+INFO:root: - The valid fold has 135779 samples
+INFO:root:= Dataloaders
+INFO:root: - Dataset creation
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Generating the index
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - Loaded a dataset with 677160 samples
+INFO:root: - Splitting the data in training and validation sets
+INFO:root:Generating the subset files from 677160 samples
+INFO:root: - Subset dataset
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root:The loaded dataset contains 25 latitudes, 37 longitudes, 28 depths and 2222 time points
+INFO:root:Loading the index from sub_2CMEMS-MEDSEA-2010-2016-training.nc.bin_index.idx
+INFO:root: - The train fold has 541940 samples
+INFO:root: - The valid fold has 135220 samples
diff --git a/main.py b/main.py
index d0d75d933c6822113fe926713583097fa8770a71..f9ca5f6baed6992bde691d512f6ebb3fa004f05a 100644
--- a/main.py
+++ b/main.py
@@ -1,9 +1,8 @@
#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)")
diff --git a/model.py b/model.py
index 2fd3b44e67a4f06f284337c37b15f3caace918c2..313f58032016c1ef2ca8f6c0d1e471329d1c2ec1 100644
--- a/model.py
+++ b/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])
diff --git a/test.py b/my_test.py
similarity index 100%
rename from test.py
rename to my_test.py
diff --git a/train_indices.subset b/train_indices.subset
index 50424bf1058e47db24ac20b9f5063a2f58757f59..7c4d7fd4d5a7a65aaa34f8a80ddc63f15a8ec829 100644
Binary files a/train_indices.subset and b/train_indices.subset differ
diff --git a/utils.py b/utils.py
index 38b9089d4b31f8be656ad2bf4f28d94e2fe13428..b4293545ee08b7692043b2342dbd9684eaac345a 100644
--- a/utils.py
+++ b/utils.py
@@ -1,5 +1,15 @@
-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()
+
+
diff --git a/valid_indices.subset b/valid_indices.subset
index e6a98afb07d8cac740590901c0f7627a17c95064..1bd7deacda9183635879207df14248df38fbffa1 100644
Binary files a/valid_indices.subset and b/valid_indices.subset differ