feat: Added loading method for PyTorch artefact detection models from…

… HF Hub (#836)

* refactor: Refactored FasterRCNN

* feat: Added factory method from_hub

* chore: Updated requirements

* test: Added unittest

* chore: Updated mypy config

* test: Updated unittests

* test: Fixed unittest

* test: Fixed unittest

* feat: Added cfg to model

* test: Fixed unittest

doctr/models/obj_detection/factory/__init__.py

@@ -0,0 +1,4 @@
+from doctr.file_utils import is_torch_available
+
+if is_torch_available():
+    from .pytorch import *

doctr/models/obj_detection/factory/pytorch.py

@@ -0,0 +1,50 @@
+# Copyright (C) 2022, Mindee.
+
+# This program is licensed under the Apache License version 2.
+# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.
+
+import json
+from typing import Any
+
+import torch
+from huggingface_hub import hf_hub_download
+
+from doctr.models import obj_detection
+
+__all__ = ['from_hub']
+
+
+def from_hub(repo_id: str, **kwargs: Any) -> torch.nn.Module:
+    """Instantiate & load a pretrained model from HF hub.
+
+    Example::
+        >>> from doctr.models.obj_detection import from_hub
+        >>> model = from_hub("mindee/fasterrcnn_mobilenet_v3_large_fpn").eval()
+        >>> input_tensor = torch.rand((1, 3, 1024, 1024), dtype=torch.float32)
+        >>> with torch.no_grad(): out = model(input_tensor)
+
+    Args:
+        repo_id: HuggingFace model hub repo
+        kwargs: kwargs of `hf_hub_download`
+    Returns:
+        Model loaded with the checkpoint
+    """
+
+    # Get the config
+    with open(hf_hub_download(repo_id, filename='config.json', **kwargs), 'rb') as f:
+        cfg = json.load(f)
+
+    model = obj_detection.__dict__[cfg['arch']](
+        pretrained=False,
+        image_mean=cfg['mean'],
+        image_std=cfg['std'],
+        max_size=cfg['input_shape'][-1],
+        num_classes=len(cfg['classes']),
+    )
+
+    # Load the checkpoint
+    state_dict = torch.load(hf_hub_download(repo_id, filename='pytorch_model.bin', **kwargs), map_location='cpu')
+    model.load_state_dict(state_dict)
+    model.cfg = cfg
+
+    return model

doctr/models/obj_detection/faster_rcnn/pytorch.py

@@ -17,9 +17,7 @@
         'input_shape': (3, 1024, 1024),
         'mean': (0.485, 0.456, 0.406),
         'std': (0.229, 0.224, 0.225),
-        'anchor_sizes': [32, 64, 128, 256, 512],
-        'anchor_aspect_ratios': (0.5, 1., 2.),
-        'num_classes': 5,
+        'classes': ["background", "qr_code", "bar_code", "logo", "photo"],
         'url': 'https://github.com/mindee/doctr/releases/download/v0.4.1/fasterrcnn_mobilenet_v3_large_fpn-d5b2490d.pt',
     },
 }
@@ -31,11 +29,11 @@ def _fasterrcnn(arch: str, pretrained: bool, **kwargs: Any) -> FasterRCNN:
         "image_mean": default_cfgs[arch]['mean'],
         "image_std": default_cfgs[arch]['std'],
         "box_detections_per_img": 150,
-        "box_score_thresh": 0.15,
+        "box_score_thresh": 0.5,
         "box_positive_fraction": 0.35,
         "box_nms_thresh": 0.2,
         "rpn_nms_thresh": 0.2,
-        "num_classes": default_cfgs[arch]['num_classes'],
+        "num_classes": len(default_cfgs[arch]['classes']),
     }
 
     # Build the model

mypy.ini

@@ -75,3 +75,7 @@ ignore_missing_imports = True
 [mypy-h5py.*]
 
 ignore_missing_imports = True
+
+[mypy-huggingface_hub.*]
+
+ignore_missing_imports = True

requirements-pt.txt

@@ -14,3 +14,4 @@ torchvision>=0.9.0
 Pillow>=8.3.2
 tqdm>=4.30.0
 rapidfuzz>=1.6.0
+huggingface-hub>=0.4.0

requirements.txt

@@ -15,3 +15,4 @@ tqdm>=4.30.0
 tensorflow-addons>=0.13.0
 rapidfuzz>=1.6.0
 keras<2.7.0
+huggingface-hub>=0.4.0

setup.py

@@ -60,6 +60,7 @@
     "tensorflow-addons>=0.13.0",
     "rapidfuzz>=1.6.0",
     "keras<2.7.0",
+    "huggingface-hub>=0.4.0",
     # Testing
     "pytest>=5.3.2",
     "coverage>=4.5.4",
@@ -104,6 +105,7 @@ def deps_list(*pkgs):
     deps["Pillow"],
     deps["tqdm"],
     deps["rapidfuzz"],
+    deps["huggingface-hub"],
 ]
 
 extras = {}

tests/common/test_headers.py

@@ -7,18 +7,24 @@ def test_headers():
     shebang = ["#!usr/bin/python\n"]
     blank_line = "\n"
 
-    _copyright_str = f"-{datetime.now().year}" if datetime.now().year > 2021 else ""
-    copyright_notice = [f"# Copyright (C) 2021{_copyright_str}, Mindee.\n"]
+    starting_year = 2021
+    current_year = datetime.now().year
+    year_str = [current_year] + [f"{starting_year}-{current_year}" for year in range(starting_year, current_year)]
+    if starting_year == current_year:
+        year_str = year_str[:1]
+
+    copyright_notices = [[f"# Copyright (C) {_str}, Mindee.\n"] for _str in year_str]
     license_notice = [
         "# This program is licensed under the Apache License version 2.\n",
         "# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0.txt> for full license details.\n"
     ]
 
     # Define all header options
-    headers = [
+    headers = [[
         shebang + [blank_line] + copyright_notice + [blank_line] + license_notice,
         copyright_notice + [blank_line] + license_notice
-    ]
+    ] for copyright_notice in copyright_notices]
+    headers = [_header for year_header in headers for _header in year_header]
 
     excluded_files = ["version.py", "__init__.py"]
     invalid_files = []

tests/pytorch/test_models_obj_detection_pt.py

@@ -1,7 +1,9 @@
 import pytest
 import torch
+from torchvision.models.detection import FasterRCNN
 
 from doctr.models import obj_detection
+from doctr.models.obj_detection.factory import from_hub
 
 
 @pytest.mark.parametrize(
@@ -32,3 +34,8 @@ def test_detection_models(arch_name, input_shape, pretrained):
         target = [{k: v.cuda() for k, v in t.items()} for t in target]
     out = model(input_tensor, target)
     assert isinstance(out, dict) and all(isinstance(v, torch.Tensor) for v in out.values())
+
+
+def test_obj_det_from_hub():
+    model = from_hub("mindee/fasterrcnn_mobilenet_v3_large_fpn").eval()
+    assert isinstance(model, FasterRCNN)

tests/tensorflow/test_transforms_tf.py

@@ -451,7 +451,7 @@ def test_random_shadow(input_dtype, input_shape):
     assert transformed.shape == input_shape
     assert transformed.dtype == input_dtype
     # The shadow will darken the picture
-    assert tf.math.reduce_mean(input_t) > tf.math.reduce_mean(transformed)
+    assert tf.math.reduce_mean(input_t) >= tf.math.reduce_mean(transformed)
     assert tf.math.reduce_all(transformed >= 0)
     if input_dtype == tf.uint8:
         assert tf.reduce_all(transformed <= 255)