Merge branch 'epoch_testing_fixed' of OperationSavta/SavtaDepth into master

dvc.lock

@@ -23,8 +23,8 @@ train:
     md5: c94ea029ed76ca94bb1ad4c1655e5e68
     size: 1916
   - path: src/code/params.yml
-    md5: 2263ca2167c1bb4b0f53a9aedb5f238e
-    size: 217
+    md5: 88b982495a09b6d9355903e31e0b3b3f
+    size: 218
   - path: src/code/training.py
     md5: e3dff7f4b59e4ebf818d7631d3e6803a
     size: 1683
@@ -34,14 +34,14 @@ train:
     nfiles: 1590
   outs:
   - path: logs/train_metrics.csv
-    md5: 437a06e6c6c5b4f6eec5e546c1ce6930
-    size: 103916
+    md5: 15b14c6b8a3c310a7149b3f1bac5d86f
+    size: 5920790
   - path: logs/train_params.yml
-    md5: e06e92ac0f3ac1d367c22a10c28cccf9
-    size: 886
+    md5: 4d148d75cab3dbaa91ec5fccb3382541
+    size: 887
   - path: src/models/
-    md5: fab42526c433987e0e6370db31a1869d.dir
-    size: 494927196
+    md5: 8586da76f372efa83d832a9d0e664817.dir
+    size: 494927324
     nfiles: 1
 eval:
   cmd: python3 src/code/eval.py src/data/processed/test
@@ -50,22 +50,26 @@ 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
   - path: src/code/params.yml
-    md5: 2263ca2167c1bb4b0f53a9aedb5f238e
-    size: 217
+    md5: 88b982495a09b6d9355903e31e0b3b3f
+    size: 218
   - path: src/data/processed/test
     md5: bcccd66f3f561b53ba97c89a558c08a0.dir
     size: 88596370
     nfiles: 1308
   - path: src/models/model.pth
-    md5: 2fd77305fd779eefd11e307ee3f201d7
-    size: 494927196
+    md5: f421fb113498c7186fb734928484e013
+    size: 494927324
   outs:
   - path: logs/test_metrics.csv
-    md5: 0add355c58eb4dfa1ae7e28e47750d33
+    md5: ee70c01208cdd018a567debd0abb1643
     size: 340
+  - path: src/eval/examples/
+    md5: 70fdd803300cbdc6dd76dec2148e0e0c.dir
+    size: 425678
+    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",0.6736493,1615292013951,1
+"a2",0.9112526,1615292013951,1
+"a3",0.975165,1615292013951,1
+"abs_rel",0.21377242,1615292013951,1
+"sq_rel",0.18530104,1615292013951,1
+"rmse",0.6518149,1615292013951,1
+"rmse_log",0.24310246,1615292013951,1
+"log10",0.08433308,1615292013951,1
+"silog",20.038778,1615292013951,1

logs/train_params.yml

@@ -1,5 +1,5 @@
 DAGsHubLogger: true
-Learner: <fastai.learner.Learner object at 0x7f051ecfcac8>
+Learner: <fastai.learner.Learner object at 0x7fcf56717fd0>
 ParamScheduler: true
 ProgressCallback: true
 Recorder: {add_time: true, train_metrics: false, valid_metrics: true}
@@ -14,8 +14,8 @@ dls.after_batch: "Pipeline: IntToFloatTensor -- {'div': 255.0, 'div_mask': 1} ->
   \     [[0.2250]]]], device='cuda:0'), 'axes': (0, 2, 3)}"
 dls.after_item: 'Pipeline: ToTensor'
 dls.before_batch: 'Pipeline: '
-frozen: true
-frozen idx: 2
+frozen: false
+frozen idx: 0
 input 1 dim 1: 4
 input 1 dim 2: 3
 input 1 dim 3: 480

requirements.txt

@@ -6,5 +6,5 @@ opencv-python==4.4.0.42
 tqdm==4.52.0
 numpy==1.19.4
 scikit-learn==0.23.2
-dagshub==0.1.5
+dagshub==0.1.6
 tables==3.6.1

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)

src/code/params.yml

@@ -4,7 +4,7 @@ batch_size: 4
 num_workers: 0
 weight_decay: 1e-2
 learning_rate: 1e-3
-epochs: 1
+epochs: 50
 num_outs: 3
 source_dir: src
 model_dir: models