Train with multi channels

bin/train.sh

@@ -1,10 +1,10 @@
 #!/usr/bin/env bash
 
-export CUDA_VISIBLE_DEVICES=2
+export CUDA_VISIBLE_DEVICES=2,3
 RUN_CONFIG=config.yml
 
 
-LOGDIR=/raid/bac/kaggle/logs/recursion_cell/test/c1234_s1_smooth_nadam_rndsite/se_resnext50_32x4d/
+LOGDIR=/raid/bac/kaggle/logs/recursion_cell/test/c1234_s1_smooth_nadam_rndsite_64/se_resnext50_32x4d/
 catalyst-dl run \
     --config=./configs/${RUN_CONFIG} \
     --logdir=$LOGDIR \

bin/train_multi_channels.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+export CUDA_VISIBLE_DEVICES=2,3
+RUN_CONFIG=config.yml
+
+
+for channels in [1,2,3,5] [1,2,3,6] [1,2,4,5] [1,2,4,6] [1,2,5,6] [1,3,4,5] [1,3,4,6] [1,3,5,6] [1,4,5,6] [2,3,4,5] [2,3,4,6] [2,3,5,6] [2,4,5,6] [3,4,5,6]; do
+    LOGDIR=/raid/bac/kaggle/logs/recursion_cell/search_channels/$channels/se_resnext50_32x4d/
+    catalyst-dl run \
+        --config=./configs/${RUN_CONFIG} \
+        --logdir=$LOGDIR \
+        --out_dir=$LOGDIR:str \
+        --stages/data_params/channels=$channels:list \
+        --verbose
+done

configs/config.yml

@@ -35,7 +35,7 @@ stages:
     momentum_range: [0.85, 0.95]
 
   data_params:
-    batch_size: 32
+    batch_size: 64
     num_workers: 8
     drop_last: False
 
@@ -57,7 +57,7 @@ stages:
         callback: LabelSmoothCriterionCallback
       optimizer:
         callback: OptimizerCallback
-        accumulation_steps: 4
+        accumulation_steps: 2
       accuracy:
         callback: AccuracyCallback
         accuracy_args: [1]

src/inference.py

@@ -0,0 +1,94 @@
+import pandas as pd
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as Ftorch
+from torch.utils.data import DataLoader
+import os
+import glob
+import click
+from tqdm import *
+
+from models import *
+from augmentation import *
+from dataset import RecursionCellularSite
+
+
+device = torch.device('cuda')
+
+
+def predict(model, loader):
+    model.eval()
+    preds = []
+    with torch.no_grad():
+        for dct in tqdm(loader, total=len(loader)):
+            images = dct['images'].to(device)
+            pred = model(images)
+            pred = Ftorch.softmax(pred)
+            pred = pred.detach().cpu().numpy()
+            preds.append(pred)
+
+    preds = np.concatenate(preds, axis=0)
+    return preds
+
+
+def predict_all():
+    test_csv = '/raid/data/kaggle/recursion-cellular-image-classification/test.csv'
+    # test_csv = './csv/valid_0.csv'
+    model_name = 'se_resnext50_32x4d'
+
+    for channel_str in ["[1,2,4,5]", "[1,2,3,5]", "[1,2,5,6]", "[1,3,4,5]"]:
+        experiment = 'c1234_s1_smooth_nadam_rndsite_64'
+
+        log_dir = f"/raid/bac/kaggle/logs/recursion_cell/search_channels/{channel_str}/{model_name}/"
+        root = "/raid/data/kaggle/recursion-cellular-image-classification/"
+        sites = [1]
+        channels = [int(i) for i in channel_str[1:-1].split(',')]
+
+        preds = []
+        model = cell_senet(
+            model_name="se_resnext50_32x4d",
+            num_classes=1108,
+            n_channels=len(channels) * len(sites)
+        )
+
+        checkpoint = f"{log_dir}/checkpoints/best.pth"
+        checkpoint = torch.load(checkpoint)
+        model.load_state_dict(checkpoint['model_state_dict'])
+        model = model.to(device)
+        model = nn.DataParallel(model)
+
+        for site in [1, 2]:
+            # Dataset
+            dataset = RecursionCellularSite(
+                csv_file=test_csv,
+                root=root,
+                transform=valid_aug(512),
+                mode='test',
+                sites=[site],
+                channels=channels
+            )
+
+            loader = DataLoader(
+                dataset=dataset,
+                batch_size=8,
+                shuffle=False,
+                num_workers=4,
+            )
+
+            pred = predict(model, loader)
+            preds.append(pred)
+
+        preds = np.asarray(preds).mean(axis=0)
+        all_preds = np.argmax(preds, axis=1)
+        df = pd.read_csv(test_csv)
+        submission = df.copy()
+        submission['sirna'] = all_preds.astype(int)
+        os.makedirs("prediction", exist_ok=True)
+        submission.to_csv(f'./prediction/{model_name}_{channel_str}.csv', index=False, columns=['id_code', 'sirna'])
+        np.save(f"./prediction/{model_name}_{channel_str}.npy", preds)
+
+
+if __name__ == '__main__':
+    predict_all()

src/make_submission.py

@@ -2,6 +2,7 @@
 import numpy as np
 
 import torch
+import torch.nn as nn
 import torch.nn.functional as Ftorch
 from torch.utils.data import DataLoader
 import os
@@ -36,14 +37,15 @@ def predict_all():
     test_csv = '/raid/data/kaggle/recursion-cellular-image-classification/test.csv'
     # test_csv = './csv/valid_0.csv'
 
-    experiment = 'c1234_s1_smooth_nadam_rndsite'
+    experiment = 'c1234_s1_smooth_nadam_rndsite_64'
     model_name = 'se_resnext50_32x4d'
 
     log_dir = f"/raid/bac/kaggle/logs/recursion_cell/test/{experiment}/{model_name}/"
     root = "/raid/data/kaggle/recursion-cellular-image-classification/"
     sites = [1]
     channels = [1,2,3,4]
 
+    preds = []
     model = cell_senet(
         model_name="se_resnext50_32x4d",
         num_classes=1108,
@@ -54,8 +56,7 @@ def predict_all():
     checkpoint = torch.load(checkpoint)
     model.load_state_dict(checkpoint['model_state_dict'])
     model = model.to(device)
-
-    preds = []
+    model = nn.DataParallel(model)
 
     for site in [1, 2]:
         # Dataset
@@ -84,8 +85,8 @@ def predict_all():
     submission = df.copy()
     submission['sirna'] = all_preds.astype(int)
     os.makedirs("submission", exist_ok=True)
-    submission.to_csv(f'./submission/{model_name}_{experiment}.csv', index=False, columns=['id_code', 'sirna'])
-    np.save(f"./submission/{model_name}_{experiment}.npy", pred)
+    submission.to_csv(f'./submission/{model_name}_{experiment}_3ckps.csv', index=False, columns=['id_code', 'sirna'])
+    np.save(f"./submission/{model_name}_{experiment}_3ckps.npy", preds)
 
 
 if __name__ == '__main__':