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hahattpro / crowd_counting_framework (public) (License: Unspecified) (since 2020-12-23) (hash sha1)

backupfrom https://github.com/ttpro1995/crowd_counting_framework

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List of commits:

Subject	Hash	Author	Date (UTC)
update experiment_main	e87e1f9e3f0ebe12201c86f61b14a95b9552c199	Thai Thien	2020-05-23 14:58:28
fix checkAndRecord, add gt count to test data loader	4290b7bb534ef0f785757b508c8e839ec6b3f9dc	Thai Thien	2020-05-23 14:38:51
log some more	be343dcc3b65f15f30e395eac3ca8271c09d1634	Thai Thien	2020-05-21 17:43:04
cometml project name	57ca8f68cbb0b650da74f7117ee60bd25bf0d8d4	Thai Thien	2020-05-21 17:39:54
train	1acb89895774528dcec61fad2f2872f2613bff75	Thai Thien	2020-05-21 17:34:11
train	e67e3fcb8b77db53f990f8582e43c053f69d6c09	Thai Thien	2020-05-21 17:31:12
fix script	e864e72a9b1fd0e6dfe200b3a9b569016f9894c0	Thai Thien	2020-05-21 17:24:38
rename	4f5e84a74bfd4f2a4f60554b9037eb097bafb0df	Thai Thien	2020-05-21 17:23:08
implement the main flow	f31d93a1f3f78575c9ee06bc47b541007789643a	Thai Thien	2020-05-21 17:16:52
script	4d177e367c6609a0592525ee44b32bcbe43536a6	Thai Thien	2020-05-19 16:30:08
python code to split train data to train and validate	0dfb94063b0bdc7aa660b78ab61b9ee5e61a4199	Thai Thien	2020-05-18 16:19:21
fix dim mismatch	93ea7669d891301e9c00aadccdea27bb5e138656	Thai Thien	2020-05-12 17:19:33
train h1 bigtail	ea6391257cd243098cbbb771e705f1f115b845df	Thai Thien	2020-05-12 16:58:26
mse mean	e96c22a36e305681d7fed415a5a949fa0c1791c9	Thai Thien	2020-05-10 18:32:02
no fix	7bd97e91de5d7c2d307407287c82e60e893c0c92	Thai Thien	2020-05-10 18:22:45
no fix	fc20ae6922c2e53f7d37f4228fb921894cd78eab	Thai Thien	2020-05-10 18:19:59
t9	d8ef865ea602670548e897d8b7ac4c925cc9b393	Thai Thien	2020-05-10 18:19:30
test with L1 loss	6492b65da4bdf6351b661f39b6bce6f08d37f17c	Thai Thien	2020-05-10 18:10:49
H2	1d6d11b2eeecb67dd7d329e38de61b872870a9aa	Thai Thien	2020-05-06 17:42:52
do something with l1 loss	5268c4fc163bb512f293fbac381a64a75c4fe462	Thai Thien	2020-05-06 17:32:45

Commit e87e1f9e3f0ebe12201c86f61b14a95b9552c199 - update experiment_main
Author: Thai Thien
Author date (UTC): 2020-05-23 14:58
Committer name: Thai Thien
Committer date (UTC): 2020-05-23 14:58
Parent(s): 4290b7bb534ef0f785757b508c8e839ec6b3f9dc
Signing key:
Tree: 2b4e606be137fe1d12a6084cde98c2fc801740ce

File	Lines added	Lines deleted
experiment_main.py	7	7

File experiment_main.py changed (mode: 100644) (index 974dcfc..08c5dd3)
...	...	from data_flow import get_dataloader, create_image_list
5	5	from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator	from ignite.engine import Events, create_supervised_trainer, create_supervised_evaluator
6	6	from ignite.metrics import Loss	from ignite.metrics import Loss
7	7	from ignite.handlers import Checkpoint, DiskSaver, Timer	from ignite.handlers import Checkpoint, DiskSaver, Timer
8		from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError
	8		from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError, CrowdCountingMeanAbsoluteErrorWithCount, CrowdCountingMeanSquaredErrorWithCount
9	9	from visualize_util import get_readable_time	from visualize_util import get_readable_time
10	10
11	11	import torch	import torch

...	...	if __name__ == "__main__":
114	114	trainer = create_supervised_trainer(model, optimizer, loss_fn, device=device)	trainer = create_supervised_trainer(model, optimizer, loss_fn, device=device)
115	115	evaluator_train = create_supervised_evaluator(model,	evaluator_train = create_supervised_evaluator(model,
116	116	metrics={	metrics={
117		'mae': CrowdCountingMeanAbsoluteError(),
118		'mse': CrowdCountingMeanSquaredError(),
	117		'mae': CrowdCountingMeanAbsoluteErrorWithCount(),
	118		'mse': CrowdCountingMeanSquaredErrorWithCount(),
119	119	'loss': Loss(loss_fn)	'loss': Loss(loss_fn)
120	120	}, device=device)	}, device=device)
121	121
122	122	evaluator_validate = create_supervised_evaluator(model,	evaluator_validate = create_supervised_evaluator(model,
123	123	metrics={	metrics={
124		'mae': CrowdCountingMeanAbsoluteError(),
125		'mse': CrowdCountingMeanSquaredError(),
	124		'mae': CrowdCountingMeanAbsoluteErrorWithCount(),
	125		'mse': CrowdCountingMeanSquaredErrorWithCount(),
126	126	'loss': Loss(loss_fn)	'loss': Loss(loss_fn)
127	127	}, device=device)	}, device=device)
128	128
129	129	evaluator_test = create_supervised_evaluator(model,	evaluator_test = create_supervised_evaluator(model,
130	130	metrics={	metrics={
131		'mae': CrowdCountingMeanAbsoluteError(),
132		'mse': CrowdCountingMeanSquaredError(),
	131		'mae': CrowdCountingMeanAbsoluteErrorWithCount(),
	132		'mse': CrowdCountingMeanSquaredErrorWithCount(),
133	133	'loss': Loss(loss_fn)	'loss': Loss(loss_fn)
134	134	}, device=device)	}, device=device)
135	135

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