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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)
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
fix script	aa331331b12e5b454d372a550524b30a4bebe706	Thai Thien	2020-03-07 18:32:06
try reproduct ccnn with keepfull and lr 1e-5	814c520cbd1bb2d7fd50d2a8d3579d43da79fe60	Thai Thien	2020-03-07 18:30:42
my simple v4 with addition deform cnn at the end	5392aaf6c14fdd910f52096dbb921bed7470c4f7	Thai Thien	2020-03-07 18:15:22
fix the scheduler	77e6737a040f5aa5745b8a8830f5bec12322b10f	Thai Thien	2020-03-07 17:46:02
t4 lr 1e-5	acd41ed30c95f63e01a05a6d9929410637852d9e	Thai Thien	2020-03-06 19:41:49
no more lr scheduler	7289adb41de7807258eb8c29e6108fa65f59525a	Thai Thien	2020-03-06 19:35:49
reduce learning rate	bc8241e5b88b91c18bb7999a8d5d12fc79a5e3f7	Thai Thien	2020-03-06 19:28:27

Commit adcb2aa917f02d4b5d567a9b70f08ec519f896d1 - change on how it log epoch
Author: Thai Thien
Author date (UTC): 2020-03-11 17:32
Committer name: Thai Thien
Committer date (UTC): 2020-03-11 17:32
Parent(s): 4e8c048ff8db59cbedc819e4df2a48094568fc2c
Signing key:
Tree: f987c034f82f77c01f8853a65d41f3b0f0b5b3b3

File	Lines added	Lines deleted
train_compact_cnn_lrscheduler.py	1	2
train_script/CCNN/ccnn_v1_t3_scheduler.sh	7	0

File train_script/CCNN/ccnn_v1_t3_scheduler.sh added (mode: 100644) (index 0000000..25230f7)
	1	CUDA_VISIBLE_DEVICES=2 HTTPS_PROXY="http://10.30.58.36:81" nohup python train_compact_cnn_lrscheduler.py \
	2	--task_id ccnn_v1_t3_scheduler \
	3	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
	4	--lr 1e-4 \
	5	--decay 0 \
	6	--datasetname shanghaitech \
	7	--epochs 300 > logs/ccnn_v1_t3_scheduler.log &

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File train_compact_cnn_lrscheduler.py changed (mode: 100644) (index dcfe26e..0f5d332)
...	...	if __name__ == "__main__":
102	102	timestamp = get_readable_time()	timestamp = get_readable_time()
103	103	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}"
104	104	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['loss']))	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['loss']))
105		experiment.set_epoch(epoch=trainer.state.epoch)
	105		experiment.log_metric("epoch", trainer.state.epoch)
106	106	experiment.log_metric("train_mae", metrics['mae'])	experiment.log_metric("train_mae", metrics['mae'])
107	107	experiment.log_metric("train_mse", metrics['mse'])	experiment.log_metric("train_mse", metrics['mse'])
108	108	experiment.log_metric("train_loss", metrics['loss'])	experiment.log_metric("train_loss", metrics['loss'])

...	...	if __name__ == "__main__":
115	115	timestamp = get_readable_time()	timestamp = get_readable_time()
116	116	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}"
117	117	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['loss']))	.format(trainer.state.epoch, metrics['mae'], metrics['mse'], metrics['loss']))
118		experiment.set_epoch(epoch=trainer.state.epoch)
119	118	experiment.log_metric("valid_mae", metrics['mae'])	experiment.log_metric("valid_mae", metrics['mae'])
120	119	experiment.log_metric("valid_mse", metrics['mse'])	experiment.log_metric("valid_mse", metrics['mse'])
121	120	experiment.log_metric("valid_loss", metrics['loss'])	experiment.log_metric("valid_loss", metrics['loss'])