inference code copied here ... this accually was the validation step with plot output.

train.py

@@ -1,15 +1,18 @@
-from confic import (DEVICE, NUM_CLASSES, NUM_EPOCHS, OUTDIR, NUM_WORKERS, DATA_DIR)
+from confic import (DEVICE, NUM_CLASSES, NUM_EPOCHS, OUTDIR, NUM_WORKERS, DATA_DIR, IMG_SIZE, IMG_DPI, INFERENCE_OUTDIR)
 from model import create_model
-
-from tqdm.auto import tqdm
-
 from datasets import create_train_loader, create_valid_loader, create_train_or_test_dataset
 from custom_utils import Averager, SaveBestModel, save_model, save_loss_plot
 
+from tqdm.auto import tqdm
+
+import os
 import torch
 import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from matplotlib.patches import Rectangle
 import time
 
+from pathlib import Path
 from IPython import embed
 
 def train(train_loader, model, optimizer, train_loss):
@@ -18,8 +21,7 @@ def train(train_loader, model, optimizer, train_loss):
     prog_bar = tqdm(train_loader, total=len(train_loader))
     for samples, targets in prog_bar:
         images = list(image.to(DEVICE) for image in samples)
-
+        # img_names = [t['image_name'] for t in targets]
-        # targets = [{k: v.to(DEVICE) for k, v in t.items()} for t in targets]
         targets = [{k: v.to(DEVICE) for k, v in t.items() if k != 'image_name'} for t in targets]
 
         loss_dict = model(images, targets)
@@ -43,11 +45,8 @@ def validate(test_loader, model, val_loss):
     prog_bar = tqdm(test_loader, total=len(test_loader))
     for samples, targets in prog_bar:
         images = list(image.to(DEVICE) for image in samples)
-
-        # targets = [{k: v.to(DEVICE) for k, v in t.items()} for t in targets]
         targets = [{k: v.to(DEVICE) for k, v in t.items() if k != 'image_name'} for t in targets]
 
-
         with torch.inference_mode():
             loss_dict = model(images, targets)
 
@@ -55,15 +54,67 @@ def validate(test_loader, model, val_loss):
         loss_value = losses.item()
         val_loss_hist.send(loss_value) # this is a global instance !!!
         val_loss.append(loss_value)
-
-        # optimizer.zero_grad()
-        # losses.backward()
-        # optimizer.step()
-
         prog_bar.set_description(desc=f"Loss: {loss_value:.4f}")
 
     return val_loss
 
+def best_model_validation_with_plots(test_loader):
+    model = create_model(num_classes=NUM_CLASSES)
+    checkpoint = torch.load(f'{OUTDIR}/best_model.pth', map_location=DEVICE)
+    model.load_state_dict(checkpoint["model_state_dict"])
+    model.to(DEVICE).eval()
+
+    validate_with_plots(test_loader, model)
+
+
+def validate_with_plots(test_loader, model, detection_th=0.8):
+    print('Final validation with image putput')
+
+    prog_bar = tqdm(test_loader, total=len(test_loader))
+    for samples, targets in prog_bar:
+        images = list(image.to(DEVICE) for image in samples)
+
+        img_names = [t['image_name'] for t in targets]
+        targets = [{k: v for k, v in t.items() if k != 'image_name'} for t in targets]
+
+        with torch.inference_mode():
+            outputs = model(images)
+
+        for image, img_name, output, target in zip(images, img_names, outputs, targets):
+            plot_validation(image, img_name, output, target, detection_th)
+
+
+def plot_validation(img_tensor, img_name, output, target, detection_threshold):
+
+    fig = plt.figure(figsize=IMG_SIZE, num=img_name)
+    gs = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1)  #
+    ax = fig.add_subplot(gs[0, 0])
+
+    ax.imshow(img_tensor.cpu().squeeze().permute(1, 2, 0),  aspect='auto')
+    for (x0, y0, x1, y1), l, score in zip(output['boxes'].cpu(), output['labels'].cpu(), output['scores'].cpu()):
+        if score < detection_threshold:
+            continue
+    #     print(x0, y0, x1, y1, l)
+        ax.text(x0, y0, f'{score:.2f}', ha='left', va='bottom', fontsize=12, color='white')
+        ax.add_patch(
+            Rectangle((x0, y0),
+                      (x1 - x0),
+                      (y1 - y0),
+                      fill=False, color="tab:green", linestyle='--', linewidth=2, zorder=10)
+        )
+    for (x0, y0, x1, y1), l in zip(target['boxes'], target['labels']):
+        ax.add_patch(
+            Rectangle((x0, y0),
+                      (x1 - x0),
+                      (y1 - y0),
+                      fill=False, color="white", linewidth=2, zorder=9)
+        )
+
+    ax.set_axis_off()
+    plt.savefig(Path(INFERENCE_OUTDIR)/(os.path.splitext(img_name)[0] +'_inferred.png'), dpi=IMG_DPI)
+    plt.close()
+    # plt.show()
+
 if __name__ == '__main__':
     train_data = create_train_or_test_dataset(DATA_DIR)
     test_data = create_train_or_test_dataset(DATA_DIR, train=False)
@@ -91,6 +142,7 @@ if __name__ == '__main__':
         val_loss_hist.reset()
 
         train_loss = train(train_loader, model, optimizer, train_loss)
+
         val_loss = validate(test_loader, model, val_loss)
 
 
@@ -102,3 +154,5 @@ if __name__ == '__main__':
 
         save_loss_plot(OUTDIR, train_loss, val_loss)
 
+    # load best model and perform inference with plot output
+    best_model_validation_with_plots(test_loader)