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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

Clone URLs: https://rocketgit.com/user/hahattpro/crowd_counting_framework ssh://rocketgit@ssh.rocketgit.com/user/hahattpro/crowd_counting_framework git://git.rocketgit.com/user/hahattpro/crowd_counting_framework

List of commits:

Subject	Hash	Author	Date (UTC)
nll to loss	958f0895b81d42e6d31a0bbd5787211538c081fe	Thai Thien	2020-03-13 16:27:27
ccnn_v4_t1	1f950b91f4fb89a0f08baf08dffb2a501546b64f	Thai Thien	2020-03-13 16:23:39
add proxy	9afb66a73e3ae24b2144faf311b128dbe5768f3c	Thai Thien	2020-03-13 16:09:30
add comet, add scheduler to simple	4ef5939124745dfc54ad8a87936954a2bde8a5a2	Thai Thien	2020-03-13 16:06:24
ccnn v1_t5 lr scheduler	31f7a693eff8e60a07bd7bc439575cc7712fe31b	Thai Thien	2020-03-13 15:37:37
ccnn_v1_t4_scheduler, also change lr milestones	c27beba80ebf7686dc7f046b95d5e2739b6dae66	Thai Thien	2020-03-12 16:23:02
add load_data_shanghaitech_keepfull_and_crop	5af55c5b483263683db80bcf4b870bfbb241d668	Thai Thien	2020-03-12 16:19:19
change on how it log epoch	adcb2aa917f02d4b5d567a9b70f08ec519f896d1	Thai Thien	2020-03-11 17:32:35
fix the milestone	4e8c048ff8db59cbedc819e4df2a48094568fc2c	Thai Thien	2020-03-11 17:10:03
add COMET_ML_API	7fbec715d751544bcdafb34a686602839a2696de	Thai Thien	2020-03-11 17:04:19
fix script name	631c37c068377706ae2cad4513baeea7e62ae0b5	Thai Thien	2020-03-11 16:59:24
intergrate comet ml into compact cnn	058e90a617acb76a7788e6a5d44f52563342490b	Thai Thien	2020-03-11 16:57:17
something ?!	fff52ff87af2a90452384a01bd6d6e6c4b91654e	Thai Thien	2020-03-11 15:55:04
DilatedCCNNv2	75d2989232a8a68eba9b4920ab2374ac28438e0e	Thai Thien	2020-03-10 05:11:12
fix script for ccnn_v2_t1_c2	57928056d13bc9b1f9b11e14dd305005a3a5aeea	Thai Thien	2020-03-10 04:56:33
fix trash code	33c406b13b5d45527b05dfb7f4281c3966c6471e	Thai Thien	2020-03-10 04:49:52
repair dir in config	baf522825f906a3d1fc5524f42a80da33d059640	Thai Thien	2020-03-10 04:45:11
v3 t1 c2	2d4727f47f4262833dca2087fb9e48f0d117e334	Thai Thien	2020-03-10 04:29:23
dilated ccnn v1 t1	7807d7a979353fa84d0b7319820386e93dbe5cc4	Thai Thien	2020-03-09 17:20:58
new ccnn	44a669c1f918be9d74313f29a5dbbc876c29f2fc	Thai Thien	2020-03-09 17:16:49

Commit 958f0895b81d42e6d31a0bbd5787211538c081fe - nll to loss
Author: Thai Thien
Author date (UTC): 2020-03-13 16:27
Committer name: Thai Thien
Committer date (UTC): 2020-03-13 16:27
Parent(s): 1f950b91f4fb89a0f08baf08dffb2a501546b64f
Signing key:
Tree: 75050eb10209402f493d5e382f9eaa8a6bd654f8

File	Lines added	Lines deleted
train_compact_cnn.py	3	3

File train_compact_cnn.py changed (mode: 100644) (index dda90bb..a79ba4a)
...	...	if __name__ == "__main__":
64	64	metrics={	metrics={
65	65	'mae': CrowdCountingMeanAbsoluteError(),	'mae': CrowdCountingMeanAbsoluteError(),
66	66	'mse': CrowdCountingMeanSquaredError(),	'mse': CrowdCountingMeanSquaredError(),
67		'nll': Loss(loss_fn)
	67		'loss': Loss(loss_fn)
68	68	}, device=device)	}, device=device)
69	69	print(model)	print(model)
70	70

...	...	if __name__ == "__main__":
96	96	metrics = evaluator.state.metrics	metrics = evaluator.state.metrics
97	97	timestamp = get_readable_time()	timestamp = get_readable_time()
98	98	print(timestamp + " Training set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"	print(timestamp + " Training set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
99		.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))
	99		.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['loss']))
100	100	experiment.log_metric("epoch", trainer.state.epoch)	experiment.log_metric("epoch", trainer.state.epoch)
101	101	experiment.log_metric("train_mae", metrics['mae'])	experiment.log_metric("train_mae", metrics['mae'])
102	102	experiment.log_metric("train_mse", metrics['mse'])	experiment.log_metric("train_mse", metrics['mse'])

...	...	if __name__ == "__main__":
109	109	metrics = evaluator.state.metrics	metrics = evaluator.state.metrics
110	110	timestamp = get_readable_time()	timestamp = get_readable_time()
111	111	print(timestamp + " Validation set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"	print(timestamp + " Validation set Results - Epoch: {} Avg mae: {:.2f} Avg mse: {:.2f} Avg loss: {:.2f}"
112		.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['nll']))
	112		.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['loss']))
113	113	experiment.log_metric("valid_mae", metrics['mae'])	experiment.log_metric("valid_mae", metrics['mae'])
114	114	experiment.log_metric("valid_mse", metrics['mse'])	experiment.log_metric("valid_mse", metrics['mse'])
115	115	experiment.log_metric("valid_loss", metrics['loss'])	experiment.log_metric("valid_loss", metrics['loss'])

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