Added Ptv Image Folder Dataset node.

tests/pytorch_wrapper/pt_evaluate_classification_conv_model_test2.py

@@ -0,0 +1,80 @@
+import os
+import sys
+import unittest
+import torch
+import torch.nn as nn
+
+PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
+MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
+sys.path.append(MODULE_ROOT)
+from pytorch_wrapper.ptn_conv_model import PtnConvModel
+from pytorch_wrapper.ptv_image_folder_dataset import PtvImageFolderDataset
+from pytorch_wrapper.ptv_transforms_to_tensor import PtvTransformsToTensor
+from pytorch_wrapper.pt_data_loader import PtDataLoader
+from pytorch_wrapper.pt_evaluate_classification_model import PtEvaluateClassificationModel
+from pytorch_wrapper.pt_load_model import PtLoadModel
+ 
+class TestPtEvaluate(unittest.TestCase):
+    def setUp(self):
+        """Set up test instance."""
+        # Transform
+        compose_node = PtvTransformsToTensor()
+        transform = compose_node.f()[0]
+        
+        # Dataset
+        self.node = PtvImageFolderDataset()
+        dataset = self.node.f("dog_and_cat/val",
+                    transform=transform)[0]
+
+        # Data loader
+        data_loader_node = PtDataLoader()
+        self.data_loader = data_loader_node.f(
+            dataset=dataset,
+            batch_size=32,
+            shuffle=True,
+            parameters='{"num_workers":1}'
+        )[0]
+
+        # Model
+        self.model_node = PtnConvModel()
+        self.model = self.model_node.f(
+                 input_dim="(3, 512, 512)",
+                 penultimate_dim=0,
+                 output_dim=2,
+                 channel_list="[32,64,128,256,512]", # To 256, 128, 64, 32, 16
+                 kernel_size_list="[3,3,3,3,3]",
+                 padding_list="[1,1,1,1,1]",
+                 downsample_list="[True,True,True,True,True]"
+        )[0]
+
+        # Load weights
+        self.load_model = PtLoadModel()
+        epochs = 7
+        self.loaded_model = self.load_model.f(self.model,f"dog_cat_{epochs}_epochs_conv.pt")
+
+        # Instantiates evaluation node
+        self.evaluate = PtEvaluateClassificationModel()
+
+    def test_evaluation(self):
+        """
+        Tests evaluation.  
+
+        Note that this test requires a trained model file on the file system.
+        Unit test will fail if you run this for the first time.
+        Run the unit test for training to generate the weight, and re-run this unit test.
+        """
+        accuracy, precision, recall, f1, num_samples = \
+            self.evaluate.f(self.model, self.data_loader, use_gpu=True)
+        print(f"""
+            accuracy: {accuracy}
+            precision: {precision}
+            recall: {recall}
+            f1: {f1}
+            Number of samples: {num_samples}
+        """)
+        self.assertTrue(accuracy > 0.7)
+        self.assertEqual(num_samples, 300)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/pytorch_wrapper/pt_train_classification_conv_model_test2.py

@@ -0,0 +1,60 @@
+import os
+import sys
+import unittest
+import torch
+import torch.nn as nn
+
+PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
+MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
+sys.path.append(MODULE_ROOT)
+
+from pytorch_wrapper.ptn_conv_model import PtnConvModel
+from pytorch_wrapper.pto_adam import PtoAdam
+from pytorch_wrapper.ptv_image_folder_dataset import PtvImageFolderDataset
+from pytorch_wrapper.ptv_transforms_to_tensor import PtvTransformsToTensor
+from pytorch_wrapper.pt_data_loader import PtDataLoader
+from pytorch_wrapper.pt_train_classification_model import PtTrainClassificationModel
+from pytorch_wrapper.pt_save_model import PtSaveModel
+
+ 
+class TestPtTrainConvModel2(unittest.TestCase):
+    def setUp(self):
+        """Set up test instance."""
+        compose_node = PtvTransformsToTensor()
+        transform = compose_node.f()[0]
+        self.node = PtvImageFolderDataset()
+        dataset = self.node.f("dog_and_cat/train",
+                    transform=transform)[0]
+        data_loader_node = PtDataLoader()
+        self.train_loader = data_loader_node.f(
+            dataset=dataset,
+            batch_size=32,
+            shuffle=True,
+            parameters='{"num_workers":1}'
+        )[0]
+
+        self.model_node = PtnConvModel()
+        self.model = self.model_node.f(
+                 input_dim="(3, 512, 512)",
+                 penultimate_dim=0,
+                 output_dim=2,
+                 channel_list="[32,64,128,256,512]", # To 256, 128, 64, 32, 16
+                 kernel_size_list="[3,3,3,3,3]",
+                 padding_list="[1,1,1,1,1]",
+                 downsample_list="[True,True,True,True,True]"
+        )[0]
+
+        self.optimizer = PtoAdam().f(self.model, 0.0001, 0.9, 0.999)[0]
+        self.trainer = PtTrainClassificationModel()
+        self.save_model = PtSaveModel()
+
+    def test_1(self):
+        epochs = 7
+        trained_model = self.trainer.f(self.model, self.train_loader, self.optimizer,
+                       epochs, # epochs
+                       use_gpu=True)[0]
+        self.save_model.f(trained_model, f"dog_cat_{epochs}_epochs_conv.pt")
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/pytorch_wrapper/ptv_image_folder_dataset_test.py

@@ -0,0 +1,47 @@
+"""
+This unit test requires the dog_and_cat dataset to be set up under
+project_root/datasets/.
+"""
+import os
+import sys
+import unittest
+
+PROJECT_ROOT = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
+MODULE_ROOT = os.path.join(PROJECT_ROOT, "modules")
+sys.path.append(MODULE_ROOT)
+from pytorch_wrapper.ptv_image_folder_dataset import PtvImageFolderDataset
+from pytorch_wrapper.ptv_transforms_to_tensor import PtvTransformsToTensor
+
+
+class TestPtvImageDataset(unittest.TestCase):
+    def setUp(self):
+        """Set up test data."""
+        self.node = PtvImageFolderDataset()
+        self.compose_node = PtvTransformsToTensor()
+
+    def test_with_tensor_transform(self):
+        """Tests downloading with transform"""
+        transform = self.compose_node.f()[0]
+        dc = self.node.f("dog_and_cat/train",
+                    transform=transform)[0]
+        expected_classes = ["cats", "dogs"]
+        expected_dict = [("cats", 0), ("dogs", 1)]
+        for i, c in enumerate(dc.classes):
+            self.assertEqual(c, expected_classes[i])
+
+        for i, (k, w) in enumerate(dc.class_to_idx.items()):
+            self.assertEqual(k, expected_dict[i][0])
+            self.assertEqual(w, expected_dict[i][1])
+
+        # Read data
+        data = next(iter(dc))
+
+        # Check data shape
+        self.assertEqual(data[0].size(), (3, 512, 512))
+
+        # Check class
+        self.assertTrue(data[1] == 0)  # cat
+
+
+if __name__ == "__main__":
+    unittest.main()