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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)
jhu float32	c0b004d6a733f83e469bb3d48f7e20a9c7113957	Thai Thien	2020-09-10 16:14:09
fix float	8867f5d5e160c38a1a7d67b2fac728cf60e6649e	Thai Thien	2020-09-10 16:03:15
try catch empty file	e7d392cd6ec807f3ae242ef5090f5725ced9f897	Thai Thien	2020-09-10 15:40:14
case no label at all	51b423b2ee9271e3639a332ecab092944636d8f2	Thai Thien	2020-09-09 13:49:29
remove experiment	06478164cb8e00ed5512a0d4db0dbc6edc1b5ad1	Thai Thien	2020-09-08 15:10:33
val train test	5a43d57ba16667cb96f9514dcf4b81167ff8bd5a	Thai Thien	2020-09-08 15:01:51
parallel, try catch excemption	9ac6a36880a113c1820b1e6a702ed5c08ebcb03f	Thai Thien	2020-09-08 14:55:08
now show the number of point in log	54a1a16adf32c866ed65808d16da5f997d27b54f	Thai Thien	2020-09-07 17:19:18
gt to ground-truth	3996d1c19d68f3669b5f54e5b621635f0b87a9fc	Thai Thien	2020-09-07 17:06:15
change data path	5e6605625ba130310fbeaade259e4a5b98987dad	Thai Thien	2020-09-07 17:04:43
test train val	e817f1d6f4743286735811fff60f64080a8e76ed	Thai Thien	2020-09-07 17:02:57
add profiler	fac9aa609d6de1e8cd9cb8a8a97f00063aaae310	Thai Thien	2020-09-07 16:59:36
generate dataset again	7475799e0ff4bdc51ce86ca98751535c841d49c9	Thai Thien	2020-09-07 16:52:00
try to fix cannot convert float infinity to integer	b29a994c2f3e73e78ec2ca7d1ad808049f1008e9	Thai Thien	2020-09-07 16:49:44
jhu adamw1_bigtail13i_t1_jhu	77bb789fa2ffadfc07ec6f99e1bcf44bde24bfc3	Thai Thien	2020-09-07 16:38:13
train_dev_test	cbfc478aaf7db3accb41a763b0818910999d25a5	Thai Thien	2020-09-06 17:29:37
seem working	c44af597b7adf98ecaf3a71e565de8688f7ff6ba	Thai Thien	2020-09-06 15:55:28
done single sample	cee46d309e9bb91ac4185b0e1b74deddefbc8553	Thai Thien	2020-09-06 15:24:39
a	ec2103c6617ad84375e5a7a0f3cde3b32ab012a0	Thai Thien	2020-08-23 15:39:51
pred_count	d9168903486d7a88c0763a8e93b18d1ed89d2d40	Thai Thien	2020-08-23 15:37:02

Commit c0b004d6a733f83e469bb3d48f7e20a9c7113957 - jhu float32
Author: Thai Thien
Author date (UTC): 2020-09-10 16:14
Committer name: Thai Thien
Committer date (UTC): 2020-09-10 16:14
Parent(s): 8867f5d5e160c38a1a7d67b2fac728cf60e6649e
Signing key:
Tree: d9b7a4ac186453f55c561d7659f57e3a5b047f50

File	Lines added	Lines deleted
data_flow.py	2	2
train_script/learnstuff/l3/adamw1_bigtail13i_t2_jhu.sh	4	4

File data_flow.py changed (mode: 100644) (index c9960d9..b250689)
...	...	def load_data_jhucrowd_256(img_path, train=True, debug=False):
1025	1025	gt_path = img_path.replace('.jpg', '.h5').replace('images', 'ground-truth-h5')	gt_path = img_path.replace('.jpg', '.h5').replace('images', 'ground-truth-h5')
1026	1026	img_origin = Image.open(img_path).convert('RGB')	img_origin = Image.open(img_path).convert('RGB')
1027	1027	gt_file = h5py.File(gt_path, 'r')	gt_file = h5py.File(gt_path, 'r')
1028		target = np.asarray(gt_file['density'])
	1028		target = np.asarray(gt_file['density']).astype('float32')
1029	1029	target_factor = 8	target_factor = 8
1030	1030	crop_sq_size = 256	crop_sq_size = 256
1031	1031	if train:	if train:

...	...	def load_data_jhucrowd_256(img_path, train=True, debug=False):
1055	1055	interpolation=cv2.INTER_CUBIC) * target_factor * target_factor	interpolation=cv2.INTER_CUBIC) * target_factor * target_factor
1056	1056	# target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output	# target1 = target1.unsqueeze(0) # make dim (batch size, channel size, x, y) to make model output
1057	1057	target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output	target1 = np.expand_dims(target1, axis=0) # make dim (batch size, channel size, x, y) to make model output
1058		return img, target1.astype('float32')
	1058		return img, target1
1059	1059
1060	1060
1061	1061	def data_augmentation(img, target):	def data_augmentation(img, target):

File train_script/learnstuff/l3/adamw1_bigtail13i_t2_jhu.sh copied from file train_script/learnstuff/l3/adamw1_bigtail13i_t1_jhu.sh (similarity 71%) (mode: 100644) (index b6dc13c..b5a6e93)
1		task="adamw1_bigtail13i_t1_jhu"
	1		task="adamw1_bigtail13i_t2_jhu"
2	2
3		CUDA_VISIBLE_DEVICES=3 OMP_NUM_THREADS=2 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \
	3		CUDA_VISIBLE_DEVICES=3 OMP_NUM_THREADS=6 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \
4	4	--task_id $task \	--task_id $task \
5	5	--note "adamW with extrem high lr and decay, msel1mean on jhu" \	--note "adamW with extrem high lr and decay, msel1mean on jhu" \
6	6	--model "BigTail13i" \	--model "BigTail13i" \

...	...	CUDA_VISIBLE_DEVICES=3 OMP_NUM_THREADS=2 PYTHONWARNINGS="ignore" HTTPS_PROXY="ht
8	8	--lr 1e-3 \	--lr 1e-3 \
9	9	--decay 0.1 \	--decay 0.1 \
10	10	--loss_fn "MSEL1Mean" \	--loss_fn "MSEL1Mean" \
11		--batch_size 40 \
	11		--batch_size 70 \
12	12	--datasetname jhucrowd_256 \	--datasetname jhucrowd_256 \
13	13	--optim adamw \	--optim adamw \
14		--epochs 1201 > logs/$task.log &
	14		--epochs 401 > logs/$task.log &
15	15
16	16	echo logs/$task.log # for convenience	echo logs/$task.log # for convenience

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