add Market1501Attr and DukeMTMCAttr dataloader

projects/FastAttr/configs/dukemtmc.yml

@@ -0,0 +1,11 @@
+_BASE_: Base-attribute.yml
+
+DATASETS:
+  NAMES: ("DukeMTMCAttr",)
+  TESTS: ("DukeMTMCAttr",)
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 23
+
+OUTPUT_DIR: projects/FastAttr/logs/dukemtmc/strong_baseline

projects/FastAttr/configs/market1501.yml

@@ -0,0 +1,11 @@
+_BASE_: Base-attribute.yml
+
+DATASETS:
+  NAMES: ("Market1501Attr",)
+  TESTS: ("Market1501Attr",)
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 27
+
+OUTPUT_DIR: projects/FastAttr/logs/market1501/strong_baseline

projects/FastAttr/fastattr/datasets/__init__.py

@@ -6,3 +6,5 @@
 
 # Attributed datasets
 from .pa100k import PA100K
+from .market1501attr import Market1501Attr
+from .dukemtmcattr import DukeMTMCAttr

projects/FastAttr/fastattr/datasets/dukemtmcattr.py

@@ -0,0 +1,74 @@
+# encoding: utf-8
+"""
+@author:  liaoxingyu
+@contact: liaoxingyu2@jd.com
+"""
+
+import glob
+import os.path as osp
+import re
+import mat4py
+import numpy as np
+
+from fastreid.data.datasets import DATASET_REGISTRY
+
+from .bases import Dataset
+
+
+@DATASET_REGISTRY.register()
+class DukeMTMCAttr(Dataset):
+    """DukeMTMCAttr.
+
+    Reference:
+        Lin, Yutian, et al. "Improving person re-identification by attribute and identity learning."
+        Pattern Recognition 95 (2019): 151-161.
+
+    URL: `<https://github.com/vana77/DukeMTMC-attribute>`_
+
+    The folder structure should be:
+        DukeMTMC-reID/
+            bounding_box_train/ # images
+            bounding_box_test/ # images
+            duke_attribute.mat
+    """
+    dataset_dir = 'DukeMTMC-reID'
+    dataset_url = 'http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-reID.zip'
+    dataset_name = "dukemtmc"
+
+    def __init__(self, root='datasets', **kwargs):
+        self.root = root
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+        self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')
+        self.test_dir = osp.join(self.dataset_dir, 'bounding_box_test')
+
+        required_files = [
+            self.dataset_dir,
+            self.train_dir,
+            self.test_dir,
+        ]
+        self.check_before_run(required_files)
+
+        duke_attr = mat4py.loadmat(osp.join(self.dataset_dir, 'duke_attribute.mat'))['duke_attribute']
+        sorted_attrs = sorted(duke_attr['train'].keys())
+        sorted_attrs.remove('image_index')
+        attr_dict = {i: str(attr) for i, attr in enumerate(sorted_attrs)}
+
+        train = self.process_dir(self.train_dir, duke_attr['train'], sorted_attrs)
+        test = val = self.process_dir(self.test_dir, duke_attr['test'], sorted_attrs)
+
+        super(DukeMTMCAttr, self).__init__(train, val, test, attr_dict=attr_dict, **kwargs)
+
+    def process_dir(self, dir_path, annotation, sorted_attrs):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        data = []
+        for img_path in img_paths:
+            pid, camid = map(int, pattern.search(img_path).groups())
+            assert 1 <= camid <= 8
+
+            img_index = annotation['image_index'].index(str(pid).zfill(4))
+            attrs = np.array([int(annotation[i][img_index]) - 1 for i in sorted_attrs], dtype=np.float32)
+            data.append((img_path, attrs))
+
+        return data

projects/FastAttr/fastattr/datasets/market1501attr.py

@@ -0,0 +1,90 @@
+# encoding: utf-8
+"""
+@author:  sherlock
+@contact: sherlockliao01@gmail.com
+"""
+
+import glob
+import os.path as osp
+import re
+import warnings
+import mat4py
+import numpy as np
+
+from fastreid.data.datasets import DATASET_REGISTRY
+
+from .bases import Dataset
+
+
+@DATASET_REGISTRY.register()
+class Market1501Attr(Dataset):
+    """Market1501Attr.
+
+    Reference:
+        Lin, Yutian, et al. "Improving person re-identification by attribute and identity learning."
+        Pattern Recognition 95 (2019): 151-161.
+
+    URL: `<https://github.com/vana77/Market-1501_Attribute>`_
+
+    The folder structure should be:
+        Market-1501-v15.09.15/
+            bounding_box_train/ # images
+            bounding_box_test/ # images
+            market_attribute.mat
+    """
+    _junk_pids = [0, -1]
+    dataset_dir = ''
+    dataset_url = 'http://188.138.127.15:81/Datasets/Market-1501-v15.09.15.zip'
+    dataset_name = "market1501"
+
+    def __init__(self, root='datasets', market1501_500k=False, **kwargs):
+        self.root = root
+        self.dataset_dir = osp.join(self.root, self.dataset_dir)
+
+        # allow alternative directory structure
+        self.data_dir = self.dataset_dir
+        data_dir = osp.join(self.data_dir, 'Market-1501-v15.09.15')
+        if osp.isdir(data_dir):
+            self.data_dir = data_dir
+        else:
+            warnings.warn('The current data structure is deprecated. Please '
+                          'put data folders such as "bounding_box_train" under '
+                          '"Market-1501-v15.09.15".')
+
+        self.train_dir = osp.join(self.data_dir, 'bounding_box_train')
+        self.test_dir = osp.join(self.data_dir, 'bounding_box_test')
+
+        required_files = [
+            self.data_dir,
+            self.train_dir,
+            self.test_dir,
+        ]
+        self.check_before_run(required_files)
+
+        market_attr = mat4py.loadmat(osp.join(self.data_dir, 'market_attribute.mat'))['market_attribute']
+        sorted_attrs = sorted(market_attr['train'].keys())
+        sorted_attrs.remove('image_index')
+        attr_dict = {i: str(attr) for i, attr in enumerate(sorted_attrs)}
+
+        train = self.process_dir(self.train_dir, market_attr['train'], sorted_attrs)
+        test = val = self.process_dir(self.test_dir, market_attr['test'], sorted_attrs)
+
+        super(Market1501Attr, self).__init__(train, val, test, attr_dict=attr_dict, **kwargs)
+
+    def process_dir(self, dir_path, annotation, sorted_attrs):
+        img_paths = glob.glob(osp.join(dir_path, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        data = []
+        for img_path in img_paths:
+            pid, camid = map(int, pattern.search(img_path).groups())
+            if pid == -1 or pid == 0:
+                continue  # junk images are just ignored
+            assert 0 <= pid <= 1501  # pid == 0 means background
+            assert 1 <= camid <= 6
+
+            img_index = annotation['image_index'].index(str(pid).zfill(4))
+            attrs = np.array([int(annotation[i][img_index])-1 for i in sorted_attrs], dtype=np.float32)
+            data.append((img_path, attrs))
+
+        return data