update template code

tests/__init__.py

@@ -0,0 +1,52 @@
+import ast
+from pathlib import Path
+
+MMP_PARENTS = ["mmp"] + [f"a{i}" for i in range(1, 9)]
+MMP_LOCAL_MODULES = [
+    "anchor_grid",
+    "annotation",
+    "bbr",
+    "dataset",
+    "evallib",
+    "label_grid",
+    "main",
+    "model",
+    "nms",
+]
+
+
+def check_bad_imports(file_path: Path | list[Path]):
+    if not isinstance(file_path, list):
+        file_path = [file_path]
+    bad_imports = []
+    
+    for path in file_path:
+        with open(path, "r") as file:
+            tree = ast.parse(file.read(), filename=path)
+
+        absolute_imports = set()
+        for node in ast.walk(tree):
+            if isinstance(node, ast.Import):
+                for n in node.names:
+                    for req in MMP_PARENTS:
+                        if req in n.name:
+                            absolute_imports.add(n.lineno)
+                    for req in MMP_LOCAL_MODULES:
+                        if req == n.name:
+                            absolute_imports.add(n.lineno)
+            if isinstance(node, ast.ImportFrom):
+                if node.level == 0:
+                    for req in MMP_PARENTS:
+                        if req in node.module:
+                            absolute_imports.add(node.lineno)
+                    for req in MMP_LOCAL_MODULES:
+                        if req == node.module:
+                            absolute_imports.add(node.lineno)
+        if len(absolute_imports) > 0:
+            bad_imports.append((path, sorted(absolute_imports)))
+    if len(bad_imports) != 0:
+        message = "\n There are absolute imports in the following files:\n"
+        for path, line_numbers in bad_imports:
+            message += f"{path}: {', '.join(str(num) for num in line_numbers)}\n"
+        assert len(bad_imports) == 0, message
+    

tests/test_a1.py

@@ -0,0 +1,62 @@
+from datetime import datetime
+import pytest
+from pathlib import Path
+from tempfile import TemporaryDirectory
+import numpy as np
+from PIL import Image
+import torch
+import torchvision
+
+from . import check_bad_imports
+from mmp.a1 import main, tensors
+
+current_assignment = pytest.mark.skipif(
+    not (datetime.now() <= datetime(2025, 10, 22, 23, 59, 59)),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a1/*.py"))
+    check_bad_imports(paths)    
+
+
+@current_assignment
+def test_main():
+    # for testing: generate random image
+    img1 = Image.fromarray((np.random.rand(128, 128, 3) * 255).astype(np.uint8))
+    img2 = Image.fromarray((np.random.rand(128, 128, 3) * 255).astype(np.uint8))
+    tfm = torchvision.transforms.Resize(320)
+    with TemporaryDirectory() as img_dir:
+        img_dir = Path(img_dir)
+        p1 = str(img_dir / "img1.jpg")
+        p2 = str(img_dir / "img2.jpg")
+        img1.save(p1)
+        img2.save(p2)
+        batch = main.build_batch([p1, p2])
+        batch_transformed = main.build_batch([p1, p2], transform=tfm)
+    assert isinstance(batch, torch.Tensor)
+    assert len(batch) == 2
+
+    model = main.get_model()
+    assert isinstance(model, torch.nn.Module)
+
+    main.main
+
+
+@current_assignment
+def test_tensors():
+    avg = tensors.avg_color(torch.rand(3, 128, 128))
+    assert isinstance(avg, torch.Tensor)
+    assert len(avg) == 3
+
+    masked = tensors.mask(
+        torch.rand(3, 128, 128), torch.rand(3, 128, 128), torch.rand(128, 128), 0.3
+    )
+    assert isinstance(masked, torch.Tensor)
+    assert masked.shape == (3, 128, 128)
+
+    result = tensors.add_matrix_vector(torch.rand(3, 4), torch.rand(3))
+    assert isinstance(result, torch.Tensor)
+    assert result.shape == (3, 4)

tests/test_a2.py

@@ -0,0 +1,42 @@
+from datetime import datetime
+import pytest
+from pathlib import Path
+import torch
+import os
+
+from . import check_bad_imports
+from mmp.a2 import main
+
+current_assignment = pytest.mark.skipif(
+    not (datetime(2025, 10, 23) <= datetime.now() <= datetime(2025, 10, 29, 23, 59, 59)),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a2/*.py"))
+    check_bad_imports(paths)    
+
+
+@current_assignment
+def test_main():
+    assert issubclass(main.MmpNet, torch.nn.Module)
+    net = main.MmpNet(12)
+    assert isinstance(net(torch.rand(2, 3, 128, 128)), torch.Tensor)
+
+    loader = main.get_dataloader(
+        is_train=False,
+        data_root=os.path.join(os.environ["TORCH_HOME"], "datasets"),
+        batch_size=2,
+        num_workers=0,
+    )
+    x = next(iter(loader))
+    crit, opt = main.get_criterion_optimizer(net)
+    assert isinstance(
+        crit(torch.rand(10, 4), (torch.rand(10) * 2).long()), torch.Tensor
+    )
+    assert isinstance(opt, torch.optim.Optimizer)
+    main.train_epoch
+    main.eval_epoch
+    main.main

tests/test_a3.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+from datetime import datetime
+from pathlib import Path
+import torch
+import pytest
+
+from . import check_bad_imports
+
+
+current_assignment = pytest.mark.skipif(
+    not (datetime(2025, 10, 30) <= datetime.now() <= datetime(2025, 11, 5, 23, 59, 59)),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a3/*.py"))
+    check_bad_imports(paths)
+
+
+@current_assignment
+def test_dataset():
+    from mmp.a3 import dataset
+
+    assert issubclass(dataset.MMP_Dataset, torch.utils.data.Dataset)
+
+
+@current_assignment
+def test_annotation():
+    from mmp.a3 import annotation
+
+    rect = annotation.AnnotationRect(x1=3, y1=2, x2=40, y2=50)
+    assert rect.x1 == 3
+    assert rect.y1 == 2
+    assert rect.x2 == 40
+    assert rect.y2 == 50
+    rect = annotation.AnnotationRect(x1=3, y1=2, x2=40, y2=50)
+    assert rect.x1 == 3
+    assert rect.y1 == 2
+    assert rect.x2 == 40
+    assert rect.y2 == 50
+
+
+@current_assignment
+def test_main():
+    from mmp.a3 import main
+
+    main.main

tests/test_a4.py

@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+
+from datetime import datetime
+from pathlib import Path
+import pytest
+
+from . import check_bad_imports
+from mmp.a4 import anchor_grid, label_grid, dataset
+
+current_assignment = pytest.mark.skipif(
+    not (datetime(2025, 11, 6) <= datetime.now() <= datetime(2025, 11, 12, 23, 59, 59)),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a4/*.py"))
+    check_bad_imports(paths)
+
+
+@current_assignment
+def test_anchor_grid():
+    grid = anchor_grid.get_anchor_grid(
+        num_rows=4,
+        num_cols=5,
+        scale_factor=12.0,
+        anchor_widths=[30.0, 80.0],
+        aspect_ratios=[0.5, 1.0],
+    )
+    assert grid.ndim == 5
+    assert grid.shape[-1] == 4
+
+
+@current_assignment
+def test_iou():
+    label_grid.iou
+
+
+@current_assignment
+def test_label_grid():
+    lg, *_ = label_grid.get_label_grid(
+        anchor_grid=anchor_grid.get_anchor_grid(2, 2, 10, [10.0], [1.0]),
+        gts=[],
+        min_iou=0.5,
+    )
+    assert lg.dtype == bool
+
+
+@current_assignment
+def test_dataset():
+    dataset.MMP_Dataset
+    dataset.calculate_max_coverage

tests/test_a5.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+
+from datetime import datetime
+from pathlib import Path
+import pytest
+import torch
+
+from . import check_bad_imports
+from mmp.a5 import model, main
+
+current_assignment = pytest.mark.skipif(
+    not (datetime(2025, 11, 13) <= datetime.now() <= datetime(2025, 11, 26, 23, 59, 59)),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a5/*.py"))
+    check_bad_imports(paths)
+
+
+@current_assignment
+def test_model():
+    net = model.MmpNet(num_widths=4, num_aspect_ratios=2)
+    assert isinstance(net, torch.nn.Module)
+    output = net(torch.rand(2, 3, 224, 224))
+
+
+@current_assignment
+def test_main():
+    main.get_random_sampling_mask
+    main.main
+    main.step

tests/test_a6.py

@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+
+from datetime import datetime
+from pathlib import Path
+import pytest
+
+from . import check_bad_imports
+from mmp.a6 import nms, main
+
+
+current_assignment = pytest.mark.skipif(
+    not (datetime(2025, 11, 27) <= datetime.now() <= datetime(2025, 12, 10, 23, 59, 59)),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a6/*.py"))
+    check_bad_imports(paths)
+
+
+@current_assignment
+def test_nms():
+    nms.non_maximum_suppression
+
+
+@current_assignment
+def test_main():
+    main.evaluate_test
+    main.evaluate
+    main.batch_inference

tests/test_a7.py

@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+
+from datetime import datetime
+from pathlib import Path
+import pytest
+
+from . import check_bad_imports
+from mmp.a7 import main, dataset
+
+
+current_assignment = pytest.mark.skipif(
+    not (datetime(2025, 12, 11) <= datetime.now() <= datetime(2025, 12, 17, 23, 59, 59)),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a7/*.py"))
+    check_bad_imports(paths)
+
+
+@current_assignment
+def test_main():
+    main.main
+
+
+@current_assignment
+def test_dataset():
+    dataset.MMP_Dataset
+    dataset.get_dataloader

tests/test_a8.py

@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+
+from datetime import datetime
+from pathlib import Path
+import pytest
+import torch
+
+from . import check_bad_imports
+from mmp.a8 import bbr
+
+
+current_assignment = pytest.mark.skipif(
+    not (datetime(2025, 12, 18) <= datetime.now()),
+    reason="This is not the current assignment.",
+)
+
+
+@current_assignment
+def test_no_abs_import():
+    paths = list(Path().glob("mmp/a8/*.py"))
+    check_bad_imports(paths)
+
+
+@current_assignment
+def test_bbr():
+    anchor_boxes = torch.tensor([10, 100, 10, 100]).unsqueeze(0)
+    adjustments = torch.tensor([0.2, 0.2, 0.2, 0.2]).unsqueeze(0)
+    groundtruths = torch.tensor([20, 100, 20, 90]).unsqueeze(0)
+    loss = bbr.get_bbr_loss(anchor_boxes, adjustments, groundtruths)
+    assert isinstance(loss, torch.Tensor)
+    assert loss.ndim == 0
+
+    anchor_pos = torch.tensor([10, 100, 10, 100])
+    adj = torch.tensor([0.2, 0.2, 0.2, 0.2])
+    rect = bbr.apply_bbr(anchor_pos, adj)
+    assert isinstance(rect.x1, float)