Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

implement onnx export for inception3/4, resnext, mobilenetv2 #346

Merged
merged 12 commits into from
Jul 18, 2024
Merged

implement onnx export for inception3/4, resnext, mobilenetv2 #346

Show file tree
Hide file tree
Changes from 8 commits
Commits
Show all changes
12 commits
Select commit Hold shift + click to select a range
5d17168
add inceptionv4 backbone; the performance is not checked
lostkevin Jul 4, 2024
03c825d
fix for inception DDP training
lostkevin Jul 5, 2024
1b8ff54
Merge branch 'dev_fix_inception' into dev_inceptionv4
lostkevin Jul 5, 2024
6c4a183
add inceptionv4 backbone/training settings
lostkevin Jul 5, 2024
80105f2
fix config
lostkevin Jul 5, 2024
1cf7cfe
add converted backbone, top-1 acc 80.08
lostkevin Jul 8, 2024
83ea6b7
fix bug caused by the comment duplicated BasicConv2d
lostkevin Jul 9, 2024
1388f0c
fix onnx export for inception3/4, resnext, mobilenetv2
lostkevin Jul 9, 2024
4e2f393
fix format
lostkevin Jul 10, 2024
d05b7e7
fix upon comments
lostkevin Jul 10, 2024
bf3cf8b
fix bug when work_dir in test_export does not exist
lostkevin Jul 12, 2024
a392bb0
fit jit test
lostkevin Jul 18, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
35 changes: 26 additions & 9 deletions configs/classification/imagenet/inception/inceptionv3_b32x8_100e.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,16 +4,31 @@
# model settings
model = dict(
type='Classification',
backbone=dict(type='Inception3'),
head=dict(
type='ClsHead',
with_avg_pool=True,
in_channels=2048,
loss_config=dict(
type='CrossEntropyLossWithLabelSmooth',
label_smooth=0,
backbone=dict(type='Inception3', num_classes=1000),
head=[
dict(
type='ClsHead',
with_fc=False,
in_channels=2048,
loss_config=dict(
type='CrossEntropyLossWithLabelSmooth',
label_smooth=0,
),
num_classes=num_classes,
input_feature_index=[1],
),
num_classes=num_classes))
dict(
type='ClsHead',
with_fc=False,
in_channels=768,
loss_config=dict(
type='CrossEntropyLossWithLabelSmooth',
label_smooth=0,
),
num_classes=num_classes,
input_feature_index=[0],
)
])

class_list = [
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',
Expand Down Expand Up @@ -196,3 +211,5 @@
interval=10,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])

export = dict(export_type='raw', export_neck=True)
215 changes: 215 additions & 0 deletions configs/classification/imagenet/inception/inceptionv4_b32x8_100e.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,215 @@
_base_ = 'configs/base.py'

num_classes = 1000
# model settings
model = dict(
type='Classification',
backbone=dict(type='Inception4', num_classes=num_classes),
head=[
dict(
type='ClsHead',
with_fc=False,
in_channels=1536,
loss_config=dict(
type='CrossEntropyLossWithLabelSmooth',
label_smooth=0,
),
num_classes=num_classes,
input_feature_index=[1],
),
dict(
type='ClsHead',
with_fc=False,
in_channels=768,
loss_config=dict(
type='CrossEntropyLossWithLabelSmooth',
label_smooth=0,
),
num_classes=num_classes,
input_feature_index=[0],
)
])

class_list = [
Copy link
Collaborator

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

inception这几个配置文件可以提取一个base 配置吗，看起来重复度很高

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

OK

'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'
base_root = 'data/imagenet_raw/'
data_train_list = base_root + 'meta/train_labeled.txt'
data_train_root = base_root + 'train/'
data_test_list = base_root + 'meta/val_labeled.txt'
data_test_root = base_root + 'validation/'
image_size2 = 299
image_size1 = int((256 / 224) * image_size2)

dataset_type = 'ClsDataset'
img_norm_cfg = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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)
],
)
]

# optimizer
optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)

# learning policy
lr_config = dict(policy='step', step=[30, 60, 90])
checkpoint_config = dict(interval=10)

# runtime settings
total_epochs = 100

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'])
])

log_config = dict(
interval=10,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])

export = dict(export_type='raw', export_neck=True)
Loading
Loading