Adapt designer (#235)

1. Use original config as startup script. (For details, see refactor config parsing method #225) 2. Refactor the splicing rules of the check_base_cfg_path function in the EasyCV/easycv/utils/config_tools.py 3. Support three ways to pass class_list parameter. 4. Fix the bug that clsevalutor may make mistakes when evaluating top5. 5. Fix the bug that the distributed export cannot export the model. 6. Fix the bug that the load pretrained model key does not match. 7. support cls data source itag.
alibaba · Dec 1, 2022 · 23f2b0e · 23f2b0e
1 parent 4c88875
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diff --git a/configs/classification/imagenet/common/classification_base.py b/configs/classification/imagenet/common/classification_base.py
@@ -0,0 +1,20 @@
+_base_ = 'configs/base.py'
+
+log_config = dict(
+    interval=10,
+    hooks=[dict(type='TextLoggerHook'),
+           dict(type='TensorboardLoggerHook')])
+
+image_size2 = 224
+image_size1 = int((256 / 224) * image_size2)
+img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+predict = dict(
+    type='ClassificationPredictor',
+    pipelines=[
+        dict(type='Resize', size=image_size1),
+        dict(type='CenterCrop', size=image_size2),
+        dict(type='ToTensor'),
+        dict(type='Normalize', **img_norm_cfg),
+        dict(type='Collect', keys=['img'])
+    ])
diff --git a/configs/classification/imagenet/common/dataset/imagenet_classification.py b/configs/classification/imagenet/common/dataset/imagenet_classification.py
@@ -0,0 +1,157 @@
+_base_ = '../classification_base.py'
+
+class_list = [
+    '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',
+    '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25',
+    '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37',
+    '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49',
+    '50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61',
+    '62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73',
+    '74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85',
+    '86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97',
+    '98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108',
+    '109', '110', '111', '112', '113', '114', '115', '116', '117', '118',
+    '119', '120', '121', '122', '123', '124', '125', '126', '127', '128',
+    '129', '130', '131', '132', '133', '134', '135', '136', '137', '138',
+    '139', '140', '141', '142', '143', '144', '145', '146', '147', '148',
+    '149', '150', '151', '152', '153', '154', '155', '156', '157', '158',
+    '159', '160', '161', '162', '163', '164', '165', '166', '167', '168',
+    '169', '170', '171', '172', '173', '174', '175', '176', '177', '178',
+    '179', '180', '181', '182', '183', '184', '185', '186', '187', '188',
+    '189', '190', '191', '192', '193', '194', '195', '196', '197', '198',
+    '199', '200', '201', '202', '203', '204', '205', '206', '207', '208',
+    '209', '210', '211', '212', '213', '214', '215', '216', '217', '218',
+    '219', '220', '221', '222', '223', '224', '225', '226', '227', '228',
+    '229', '230', '231', '232', '233', '234', '235', '236', '237', '238',
+    '239', '240', '241', '242', '243', '244', '245', '246', '247', '248',
+    '249', '250', '251', '252', '253', '254', '255', '256', '257', '258',
+    '259', '260', '261', '262', '263', '264', '265', '266', '267', '268',
+    '269', '270', '271', '272', '273', '274', '275', '276', '277', '278',
+    '279', '280', '281', '282', '283', '284', '285', '286', '287', '288',
+    '289', '290', '291', '292', '293', '294', '295', '296', '297', '298',
+    '299', '300', '301', '302', '303', '304', '305', '306', '307', '308',
+    '309', '310', '311', '312', '313', '314', '315', '316', '317', '318',
+    '319', '320', '321', '322', '323', '324', '325', '326', '327', '328',
+    '329', '330', '331', '332', '333', '334', '335', '336', '337', '338',
+    '339', '340', '341', '342', '343', '344', '345', '346', '347', '348',
+    '349', '350', '351', '352', '353', '354', '355', '356', '357', '358',
+    '359', '360', '361', '362', '363', '364', '365', '366', '367', '368',
+    '369', '370', '371', '372', '373', '374', '375', '376', '377', '378',
+    '379', '380', '381', '382', '383', '384', '385', '386', '387', '388',
+    '389', '390', '391', '392', '393', '394', '395', '396', '397', '398',
+    '399', '400', '401', '402', '403', '404', '405', '406', '407', '408',
+    '409', '410', '411', '412', '413', '414', '415', '416', '417', '418',
+    '419', '420', '421', '422', '423', '424', '425', '426', '427', '428',
+    '429', '430', '431', '432', '433', '434', '435', '436', '437', '438',
+    '439', '440', '441', '442', '443', '444', '445', '446', '447', '448',
+    '449', '450', '451', '452', '453', '454', '455', '456', '457', '458',
+    '459', '460', '461', '462', '463', '464', '465', '466', '467', '468',
+    '469', '470', '471', '472', '473', '474', '475', '476', '477', '478',
+    '479', '480', '481', '482', '483', '484', '485', '486', '487', '488',
+    '489', '490', '491', '492', '493', '494', '495', '496', '497', '498',
+    '499', '500', '501', '502', '503', '504', '505', '506', '507', '508',
+    '509', '510', '511', '512', '513', '514', '515', '516', '517', '518',
+    '519', '520', '521', '522', '523', '524', '525', '526', '527', '528',
+    '529', '530', '531', '532', '533', '534', '535', '536', '537', '538',
+    '539', '540', '541', '542', '543', '544', '545', '546', '547', '548',
+    '549', '550', '551', '552', '553', '554', '555', '556', '557', '558',
+    '559', '560', '561', '562', '563', '564', '565', '566', '567', '568',
+    '569', '570', '571', '572', '573', '574', '575', '576', '577', '578',
+    '579', '580', '581', '582', '583', '584', '585', '586', '587', '588',
+    '589', '590', '591', '592', '593', '594', '595', '596', '597', '598',
+    '599', '600', '601', '602', '603', '604', '605', '606', '607', '608',
+    '609', '610', '611', '612', '613', '614', '615', '616', '617', '618',
+    '619', '620', '621', '622', '623', '624', '625', '626', '627', '628',
+    '629', '630', '631', '632', '633', '634', '635', '636', '637', '638',
+    '639', '640', '641', '642', '643', '644', '645', '646', '647', '648',
+    '649', '650', '651', '652', '653', '654', '655', '656', '657', '658',
+    '659', '660', '661', '662', '663', '664', '665', '666', '667', '668',
+    '669', '670', '671', '672', '673', '674', '675', '676', '677', '678',
+    '679', '680', '681', '682', '683', '684', '685', '686', '687', '688',
+    '689', '690', '691', '692', '693', '694', '695', '696', '697', '698',
+    '699', '700', '701', '702', '703', '704', '705', '706', '707', '708',
+    '709', '710', '711', '712', '713', '714', '715', '716', '717', '718',
+    '719', '720', '721', '722', '723', '724', '725', '726', '727', '728',
+    '729', '730', '731', '732', '733', '734', '735', '736', '737', '738',
+    '739', '740', '741', '742', '743', '744', '745', '746', '747', '748',
+    '749', '750', '751', '752', '753', '754', '755', '756', '757', '758',
+    '759', '760', '761', '762', '763', '764', '765', '766', '767', '768',
+    '769', '770', '771', '772', '773', '774', '775', '776', '777', '778',
+    '779', '780', '781', '782', '783', '784', '785', '786', '787', '788',
+    '789', '790', '791', '792', '793', '794', '795', '796', '797', '798',
+    '799', '800', '801', '802', '803', '804', '805', '806', '807', '808',
+    '809', '810', '811', '812', '813', '814', '815', '816', '817', '818',
+    '819', '820', '821', '822', '823', '824', '825', '826', '827', '828',
+    '829', '830', '831', '832', '833', '834', '835', '836', '837', '838',
+    '839', '840', '841', '842', '843', '844', '845', '846', '847', '848',
+    '849', '850', '851', '852', '853', '854', '855', '856', '857', '858',
+    '859', '860', '861', '862', '863', '864', '865', '866', '867', '868',
+    '869', '870', '871', '872', '873', '874', '875', '876', '877', '878',
+    '879', '880', '881', '882', '883', '884', '885', '886', '887', '888',
+    '889', '890', '891', '892', '893', '894', '895', '896', '897', '898',
+    '899', '900', '901', '902', '903', '904', '905', '906', '907', '908',
+    '909', '910', '911', '912', '913', '914', '915', '916', '917', '918',
+    '919', '920', '921', '922', '923', '924', '925', '926', '927', '928',
+    '929', '930', '931', '932', '933', '934', '935', '936', '937', '938',
+    '939', '940', '941', '942', '943', '944', '945', '946', '947', '948',
+    '949', '950', '951', '952', '953', '954', '955', '956', '957', '958',
+    '959', '960', '961', '962', '963', '964', '965', '966', '967', '968',
+    '969', '970', '971', '972', '973', '974', '975', '976', '977', '978',
+    '979', '980', '981', '982', '983', '984', '985', '986', '987', '988',
+    '989', '990', '991', '992', '993', '994', '995', '996', '997', '998', '999'
+]
+
+data_source_type = 'ClsSourceImageList'
+data_train_list = 'data/imagenet_raw/meta/train_labeled.txt'
+data_train_root = 'data/imagenet_raw/train/'
+data_test_list = 'data/imagenet_raw/meta/val_labeled.txt'
+data_test_root = 'data/imagenet_raw/validation/'
+image_size2 = 224
+image_size1 = int((256 / 224) * image_size2)
+
+dataset_type = 'ClsDataset'
+img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+train_pipeline = [
+    dict(type='RandomResizedCrop', size=image_size2),
+    dict(type='RandomHorizontalFlip'),
+    dict(type='ToTensor'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Collect', keys=['img', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='Resize', size=image_size1),
+    dict(type='CenterCrop', size=image_size2),
+    dict(type='ToTensor'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Collect', keys=['img', 'gt_labels'])
+]
+
+data = dict(
+    imgs_per_gpu=32,  # total 256
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        data_source=dict(
+            list_file=data_train_list,
+            root=data_train_root,
+            type=data_source_type),
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        data_source=dict(
+            list_file=data_test_list,
+            root=data_test_root,
+            type=data_source_type),
+        pipeline=test_pipeline))
+
+eval_config = dict(initial=False, interval=1, gpu_collect=True)
+eval_pipelines = [
+    dict(
+        mode='test',
+        data=data['val'],
+        dist_eval=True,
+        evaluators=[
+            dict(type='ClsEvaluator', topk=(1, 5), class_list=class_list)
+        ],
+    )
+]
diff --git a/configs/classification/imagenet/hrnet/hrnetw18_b32x8_100e_jpg.py b/configs/classification/imagenet/hrnet/hrnetw18_b32x8_100e_jpg.py
@@ -1,8 +1,4 @@
-_base_ = 'configs/base.py'
-log_config = dict(
-    interval=10,
-    hooks=[dict(type='TextLoggerHook'),
-           dict(type='TensorboardLoggerHook')])
+_base_ = '../common/dataset/imagenet_classification.py'
 
 # model settings
 model = dict(
@@ -19,61 +15,6 @@
         ),
         num_classes=1000))
 
-data_train_list = 'data/imagenet_raw/meta/train_labeled.txt'
-data_train_root = 'data/imagenet_raw/train/'
-data_test_list = 'data/imagenet_raw/meta/val_labeled.txt'
-data_test_root = 'data/imagenet_raw/validation/'
-data_all_list = 'data/imagenet_raw/meta/all_labeled.txt'
-data_root = 'data/imagenet_raw/'
-
-dataset_type = 'ClsDataset'
-img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-train_pipeline = [
-    dict(type='RandomResizedCrop', size=224),
-    dict(type='RandomHorizontalFlip'),
-    dict(type='ToTensor'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Collect', keys=['img', 'gt_labels'])
-]
-test_pipeline = [
-    dict(type='Resize', size=256),
-    dict(type='CenterCrop', size=224),
-    dict(type='ToTensor'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Collect', keys=['img', 'gt_labels'])
-]
-
-data = dict(
-    imgs_per_gpu=32,  # total 256
-    workers_per_gpu=4,
-    train=dict(
-        type=dataset_type,
-        data_source=dict(
-            list_file=data_train_list,
-            root=data_train_root,
-            type='ClsSourceImageList'),
-        pipeline=train_pipeline),
-    val=dict(
-        type=dataset_type,
-        data_source=dict(
-            list_file=data_test_list,
-            root=data_test_root,
-            type='ClsSourceImageList'),
-        pipeline=test_pipeline))
-
-eval_config = dict(initial=True, interval=100, gpu_collect=True)
-eval_pipelines = [
-    dict(
-        mode='test',
-        data=data['val'],
-        dist_eval=True,
-        evaluators=[dict(type='ClsEvaluator', topk=(1, 5))],
-    )
-]
-
-# additional hooks
-custom_hooks = []
-
 # optimizer
 optimizer = dict(type='SGD', lr=0.05, momentum=0.9, weight_decay=0.0001)