Fixed problem with eval, metrics now make sense. This is a run with 1 epoch of training

dvc.lock

@@ -50,8 +50,8 @@ eval:
     md5: c94ea029ed76ca94bb1ad4c1655e5e68
     size: 1916
   - path: src/code/eval.py
-    md5: fcc66ed80bb4466ab0438f556acd125c
-    size: 1775
+    md5: 9ea6a6624fa14f15b4d51f9139395663
+    size: 1893
   - path: src/code/eval_metric_calculation.py
     md5: 2fc866e1107042a996087d5716d44bf0
     size: 2999
@@ -67,5 +67,9 @@ eval:
     size: 494927196
   outs:
   - path: logs/test_metrics.csv
-    md5: 0add355c58eb4dfa1ae7e28e47750d33
-    size: 340
+    md5: 80d51ef1a70b1314947919a5c37f3220
+    size: 341
+  - path: src/eval/examples/
+    md5: cc137b33b5f5930e304b0b65f4b546ca.dir
+    size: 759235
+    nfiles: 10

dvc.yaml

@@ -31,6 +31,8 @@ stages:
     - src/code/eval.py
     - src/models/model.pth
     - src/data/processed/test
+    outs:
+    - src/eval/examples/
     metrics:
     - logs/test_metrics.csv:
         cache: false

logs/test_metrics.csv

@@ -1,10 +1,10 @@
 Name,Value,Timestamp,Step
-"a1",0.056999333,1613824849186,1
-"a2",0.118539445,1613824849186,1
-"a3",0.19929159,1613824849186,1
-"abs_rel",2.5860002,1613824849186,1
-"sq_rel",15.912783,1613824849186,1
-"rmse",5.257741,1613824849186,1
-"rmse_log",1.2291939,1613824849186,1
-"log10",0.49469143,1613824849186,1
-"silog",43.5198,1613824849186,1
+"a1",0.5386207,1615290337662,1
+"a2",0.8303751,1615290337662,1
+"a3",0.93854135,1615290337662,1
+"abs_rel",0.29692116,1615290337662,1
+"sq_rel",0.32333422,1615290337662,1
+"rmse",0.848144,1615290337662,1
+"rmse_log",0.32479692,1615290337662,1
+"log10",0.11296987,1615290337662,1
+"silog",27.511873,1615290337662,1

src/code/eval.py

@@ -2,7 +2,7 @@ import sys
 import yaml
 import torch
 from torchvision import transforms
-from fastai.vision.all import unet_learner, Path, resnet34, MSELossFlat, get_files, L, tuplify
+from fastai.vision.all import unet_learner, Path, resnet34, MSELossFlat, get_files, L, PILImageBW
 from custom_data_loading import create_data
 from eval_metric_calculation import compute_eval_metrics
 from dagshub import dagshub_logger
@@ -34,10 +34,14 @@ if __name__ == "__main__":
     filenames = get_files(Path(data_path), extensions='.jpg')
     test_files = L([Path(i) for i in filenames])
 
-    for sample in tqdm(test_files.items, desc="Predicting on test images", total=len(test_files.items)):
+    for i, sample in tqdm(enumerate(test_files.items),
+                          desc="Predicting on test images",
+                          total=len(test_files.items)):
         pred = learner.predict(sample)[0]
-        pred = transforms.ToPILImage()(pred[:, :, :].type(torch.FloatTensor)).convert('L')
+        pred = PILImageBW.create(pred).convert('L')
         pred.save("src/eval/" + str(sample.stem) + "_pred.png")
+        if i < 10:
+            pred.save("src/eval/examples/" + str(sample.stem) + "_pred.png")
 
     print("Calculating metrics...")
     metrics = compute_eval_metrics(test_files)