mirror of https://github.com/alibaba/EasyCV.git
add mobilenet of designer (#292)
gulou
GitHub
parent
c49edc3490
commit
c3def063b4
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@ -1,4 +1,4 @@
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_base_ = '../common/classification_base.py'
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_base_ = '../common/dataset/imagenet_classification.py'
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num_classes = 1000
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# model settings
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@ -15,160 +15,6 @@ model = dict(
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),
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num_classes=num_classes))
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CLASSES = [
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'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',
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'14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25',
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'26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37',
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'38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49',
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'50', '51', '52', '53', '54', '55', '56', '57', '58', '59', '60', '61',
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'62', '63', '64', '65', '66', '67', '68', '69', '70', '71', '72', '73',
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'74', '75', '76', '77', '78', '79', '80', '81', '82', '83', '84', '85',
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'86', '87', '88', '89', '90', '91', '92', '93', '94', '95', '96', '97',
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'98', '99', '100', '101', '102', '103', '104', '105', '106', '107', '108',
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'109', '110', '111', '112', '113', '114', '115', '116', '117', '118',
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'119', '120', '121', '122', '123', '124', '125', '126', '127', '128',
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'129', '130', '131', '132', '133', '134', '135', '136', '137', '138',
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'139', '140', '141', '142', '143', '144', '145', '146', '147', '148',
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'149', '150', '151', '152', '153', '154', '155', '156', '157', '158',
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'159', '160', '161', '162', '163', '164', '165', '166', '167', '168',
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'169', '170', '171', '172', '173', '174', '175', '176', '177', '178',
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'179', '180', '181', '182', '183', '184', '185', '186', '187', '188',
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'189', '190', '191', '192', '193', '194', '195', '196', '197', '198',
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'199', '200', '201', '202', '203', '204', '205', '206', '207', '208',
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'209', '210', '211', '212', '213', '214', '215', '216', '217', '218',
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'219', '220', '221', '222', '223', '224', '225', '226', '227', '228',
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'229', '230', '231', '232', '233', '234', '235', '236', '237', '238',
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'239', '240', '241', '242', '243', '244', '245', '246', '247', '248',
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'249', '250', '251', '252', '253', '254', '255', '256', '257', '258',
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'259', '260', '261', '262', '263', '264', '265', '266', '267', '268',
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'269', '270', '271', '272', '273', '274', '275', '276', '277', '278',
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'279', '280', '281', '282', '283', '284', '285', '286', '287', '288',
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'289', '290', '291', '292', '293', '294', '295', '296', '297', '298',
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'299', '300', '301', '302', '303', '304', '305', '306', '307', '308',
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'309', '310', '311', '312', '313', '314', '315', '316', '317', '318',
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'319', '320', '321', '322', '323', '324', '325', '326', '327', '328',
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'329', '330', '331', '332', '333', '334', '335', '336', '337', '338',
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'339', '340', '341', '342', '343', '344', '345', '346', '347', '348',
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'349', '350', '351', '352', '353', '354', '355', '356', '357', '358',
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'359', '360', '361', '362', '363', '364', '365', '366', '367', '368',
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'369', '370', '371', '372', '373', '374', '375', '376', '377', '378',
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'379', '380', '381', '382', '383', '384', '385', '386', '387', '388',
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'389', '390', '391', '392', '393', '394', '395', '396', '397', '398',
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'399', '400', '401', '402', '403', '404', '405', '406', '407', '408',
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'409', '410', '411', '412', '413', '414', '415', '416', '417', '418',
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'419', '420', '421', '422', '423', '424', '425', '426', '427', '428',
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'429', '430', '431', '432', '433', '434', '435', '436', '437', '438',
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'439', '440', '441', '442', '443', '444', '445', '446', '447', '448',
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'449', '450', '451', '452', '453', '454', '455', '456', '457', '458',
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'459', '460', '461', '462', '463', '464', '465', '466', '467', '468',
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'469', '470', '471', '472', '473', '474', '475', '476', '477', '478',
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'479', '480', '481', '482', '483', '484', '485', '486', '487', '488',
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'489', '490', '491', '492', '493', '494', '495', '496', '497', '498',
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'499', '500', '501', '502', '503', '504', '505', '506', '507', '508',
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'509', '510', '511', '512', '513', '514', '515', '516', '517', '518',
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'519', '520', '521', '522', '523', '524', '525', '526', '527', '528',
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'529', '530', '531', '532', '533', '534', '535', '536', '537', '538',
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'539', '540', '541', '542', '543', '544', '545', '546', '547', '548',
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'549', '550', '551', '552', '553', '554', '555', '556', '557', '558',
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'559', '560', '561', '562', '563', '564', '565', '566', '567', '568',
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'569', '570', '571', '572', '573', '574', '575', '576', '577', '578',
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'579', '580', '581', '582', '583', '584', '585', '586', '587', '588',
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'589', '590', '591', '592', '593', '594', '595', '596', '597', '598',
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'599', '600', '601', '602', '603', '604', '605', '606', '607', '608',
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'609', '610', '611', '612', '613', '614', '615', '616', '617', '618',
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'619', '620', '621', '622', '623', '624', '625', '626', '627', '628',
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'629', '630', '631', '632', '633', '634', '635', '636', '637', '638',
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'639', '640', '641', '642', '643', '644', '645', '646', '647', '648',
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'649', '650', '651', '652', '653', '654', '655', '656', '657', '658',
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'659', '660', '661', '662', '663', '664', '665', '666', '667', '668',
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'669', '670', '671', '672', '673', '674', '675', '676', '677', '678',
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'679', '680', '681', '682', '683', '684', '685', '686', '687', '688',
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'689', '690', '691', '692', '693', '694', '695', '696', '697', '698',
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'699', '700', '701', '702', '703', '704', '705', '706', '707', '708',
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'709', '710', '711', '712', '713', '714', '715', '716', '717', '718',
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'719', '720', '721', '722', '723', '724', '725', '726', '727', '728',
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'729', '730', '731', '732', '733', '734', '735', '736', '737', '738',
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'739', '740', '741', '742', '743', '744', '745', '746', '747', '748',
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'749', '750', '751', '752', '753', '754', '755', '756', '757', '758',
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'759', '760', '761', '762', '763', '764', '765', '766', '767', '768',
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'769', '770', '771', '772', '773', '774', '775', '776', '777', '778',
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'779', '780', '781', '782', '783', '784', '785', '786', '787', '788',
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'789', '790', '791', '792', '793', '794', '795', '796', '797', '798',
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'799', '800', '801', '802', '803', '804', '805', '806', '807', '808',
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'809', '810', '811', '812', '813', '814', '815', '816', '817', '818',
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'819', '820', '821', '822', '823', '824', '825', '826', '827', '828',
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'829', '830', '831', '832', '833', '834', '835', '836', '837', '838',
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'839', '840', '841', '842', '843', '844', '845', '846', '847', '848',
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'849', '850', '851', '852', '853', '854', '855', '856', '857', '858',
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'859', '860', '861', '862', '863', '864', '865', '866', '867', '868',
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'869', '870', '871', '872', '873', '874', '875', '876', '877', '878',
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'879', '880', '881', '882', '883', '884', '885', '886', '887', '888',
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'889', '890', '891', '892', '893', '894', '895', '896', '897', '898',
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'899', '900', '901', '902', '903', '904', '905', '906', '907', '908',
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'909', '910', '911', '912', '913', '914', '915', '916', '917', '918',
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'919', '920', '921', '922', '923', '924', '925', '926', '927', '928',
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'929', '930', '931', '932', '933', '934', '935', '936', '937', '938',
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'939', '940', '941', '942', '943', '944', '945', '946', '947', '948',
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'949', '950', '951', '952', '953', '954', '955', '956', '957', '958',
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'959', '960', '961', '962', '963', '964', '965', '966', '967', '968',
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'969', '970', '971', '972', '973', '974', '975', '976', '977', '978',
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'979', '980', '981', '982', '983', '984', '985', '986', '987', '988',
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'989', '990', '991', '992', '993', '994', '995', '996', '997', '998', '999'
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]
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data_source_type = 'ClsSourceImageList'
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data_train_list = 'data/imagenet_raw/meta/train_labeled.txt'
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data_train_root = 'data/imagenet_raw/train/'
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data_test_list = 'data/imagenet_raw/meta/val_labeled.txt'
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data_test_root = 'data/imagenet_raw/validation/'
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image_size2 = 224
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image_size1 = int((256 / 224) * image_size2)
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dataset_type = 'ClsDataset'
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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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train_pipeline = [
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dict(type='RandomResizedCrop', size=image_size2),
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dict(type='RandomHorizontalFlip'),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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test_pipeline = [
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dict(type='Resize', size=image_size1),
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dict(type='CenterCrop', size=image_size2),
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dict(type='ToTensor'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Collect', keys=['img', 'gt_labels'])
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]
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data = dict(
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imgs_per_gpu=32, # total 256
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workers_per_gpu=4,
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train=dict(
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type=dataset_type,
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data_source=dict(
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list_file=data_train_list,
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root=data_train_root,
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type=data_source_type),
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pipeline=train_pipeline),
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val=dict(
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type=dataset_type,
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data_source=dict(
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list_file=data_test_list,
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root=data_test_root,
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type=data_source_type),
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pipeline=test_pipeline))
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eval_config = dict(initial=False, interval=1, gpu_collect=True)
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eval_pipelines = [
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dict(
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mode='test',
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data=data['val'],
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dist_eval=True,
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evaluators=[dict(type='ClsEvaluator', topk=(1, ), class_list=CLASSES)],
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)
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]
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# optimizer
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optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
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@ -513,6 +513,8 @@ CONFIG_TEMPLATE_ZOO = {
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'configs/classification/imagenet/vit/imagenet_vit_base_patch16_224_jpg.py',
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'CLASSIFICATION_SWINT':
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'configs/classification/imagenet/swint/imagenet_swin_tiny_patch4_window7_224_jpg.py',
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'CLASSIFICATION_M0BILENET':
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'configs/classification/imagenet/mobilenet/mobilenetv2.py',
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# metric learning
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'METRICLEARNING':
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