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

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List of commits:

Subject	Hash	Author	Date (UTC)
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
typo	b7b8e2303ce99b2196402ec93334598598e71e5a	Thai Thien	2020-05-05 17:32:31
increase epoch	67f89509e4294c4310b42e790425c82279df16b3	Thai Thien	2020-05-05 17:25:17
H1 t8	1c692b37536bd72abaa0995001d3a396b82bc2f0	Thai Thien	2020-05-05 17:24:56
OMP_NUM_THREADS=5	ac76431f8ca1ada27ca7ffdaa289996baee064c1	Thai Thien	2020-05-05 17:14:41
train	da020f46703ca4fae867a09960593ef6818b4a91	Thai Thien	2020-05-05 17:05:06
batch_size 10	6b6478b9570f9133489c8a9427a857c14a14fb13	Thai Thien	2020-05-02 11:26:38
change dataset preprocess for t3	267d31931fd80178714812fced9f86a27479d54f	Thai Thien	2020-05-02 11:23:19
t3	e2a1c6f6e8a6d34b36aa8d6c86a5509bc8d41cdd	Thai Thien	2020-05-02 11:20:05
batch size 20	ea5737c694cb2967cb041db99ca391d06a66100d	Thai Thien	2020-05-02 11:19:18
ccn v7 shb fixed 15	4b28c4049c4b25a6afeb563864f76907a1e2360e	Thai Thien	2020-05-02 11:16:14
shb	7af5a7bb61d8858a2f6ef36d44844506cde917c3	Thai Thien	2020-05-02 11:14:03
batch đéo	62e1e9124b7e611c6749c1544c60687abd30895e	Thai Thien	2020-05-01 17:10:44

Commit ea6391257cd243098cbbb771e705f1f115b845df - train h1 bigtail
Author: Thai Thien
Author date (UTC): 2020-05-12 16:58
Committer name: Thai Thien
Committer date (UTC): 2020-05-12 16:58
Parent(s): e96c22a36e305681d7fed415a5a949fa0c1791c9
Signing key:
Tree: b35f42516fd34b989d386cafec1755f088c3709c

File	Lines added	Lines deleted
experiment_meow_main.py	5	1
models/meow_experiment/ccnn_head.py	0	1
models/meow_experiment/kitten_meow_1.py	66	1
train_script/combine/h1_bigtail_t1_sha.sh	3	3
train_script/combine/h1_bigtail_t1_shb_fixed.sh	4	5
train_script/combine/h1_bigtail_t2_sha.sh	4	4
train_script/combine/h1_bigtail_t2_shb_fixed.sh	5	6

File experiment_meow_main.py changed (mode: 100644) (index 9beb6c8..049432d)
...	...	from torch import nn
13	13	from models.meow_experiment.kitten_meow_1 import M1, M2, M3, M4	from models.meow_experiment.kitten_meow_1 import M1, M2, M3, M4
14	14	from models.meow_experiment.ccnn_tail import BigTailM1, BigTailM2, BigTail3	from models.meow_experiment.ccnn_tail import BigTailM1, BigTailM2, BigTail3
15	15	from models.meow_experiment.ccnn_head import H1, H2	from models.meow_experiment.ccnn_head import H1, H2
	16		from models.meow_experiment.kitten_meow_1 import H1_Bigtail3
16	17	from models import CustomCNNv2	from models import CustomCNNv2
17	18	import os	import os
18	19	from model_util import get_lr	from model_util import get_lr

...	...	if __name__ == "__main__":
82	83	elif model_name == "H1":	elif model_name == "H1":
83	84	model = H1()	model = H1()
84	85	elif model_name == "H2":	elif model_name == "H2":
85		model = H1()
	86		model = H2()
	87		elif model_name == "H1_Bigtail3":
	88		model = H1_Bigtail3()
	89
86	90	else:	else:
87	91	print("error: you didn't pick a model")	print("error: you didn't pick a model")
88	92	exit(-1)	exit(-1)

File models/meow_experiment/ccnn_head.py changed (mode: 100644) (index a7d6359..2b34f59)
...	...	class H1(nn.Module):
31	31
32	32	self.c1 = nn.Conv2d(40, 60, 3, padding=1)	self.c1 = nn.Conv2d(40, 60, 3, padding=1)
33	33
34		# ideal from CSRNet
35	34	self.c2 = nn.Conv2d(60, 40, 3, padding=1)	self.c2 = nn.Conv2d(60, 40, 3, padding=1)
36	35	self.c3 = nn.Conv2d(40, 20, 3, padding=1)	self.c3 = nn.Conv2d(40, 20, 3, padding=1)
37	36	self.c4 = nn.Conv2d(20, 10, 3, padding=1)	self.c4 = nn.Conv2d(20, 10, 3, padding=1)

File models/meow_experiment/kitten_meow_1.py changed (mode: 100644) (index 2daba3f..11453a6)
...	...	class M4(nn.Module):
256	256	x = F.relu(self.c5(x), inplace=True)	x = F.relu(self.c5(x), inplace=True)
257	257
258	258	x = self.output(x)	x = self.output(x)
259		return x
	259		return x
	260
	261
	262		class H1_Bigtail3(nn.Module):
	263		"""
	264		A REAL-TIME DEEP NETWORK FOR CROWD COUNTING
	265		https://arxiv.org/pdf/2002.06515.pdf
	266		the improve version
	267
	268		we change 5x5 7x7 9x9 with 3x3
	269		Keep the tail
	270		"""
	271		def __init__(self, load_weights=False):
	272		super(H1_Bigtail3, self).__init__()
	273		self.model_note = "headh1, all 20, with bigtail3 dilated"
	274
	275		# ideal from crowd counting using DMCNN
	276		self.front_cnn_1 = nn.Conv2d(3, 10, 3, padding=1)
	277		self.front_cnn_2 = nn.Conv2d(10, 20, 3, padding=1)
	278		self.front_cnn_3 = nn.Conv2d(20, 20, 3, padding=1)
	279		self.front_cnn_4 = nn.Conv2d(20, 20, 3, padding=1)
	280
	281		self.max_pooling = nn.MaxPool2d(kernel_size=2, stride=2)
	282
	283		self.c0 = nn.Conv2d(40, 60, 3, padding=2, dilation=2)
	284		self.c1 = nn.Conv2d(60, 60, 3, padding=2, dilation=2)
	285		self.c2 = nn.Conv2d(60, 60, 3, padding=2, dilation=2)
	286		self.c3 = nn.Conv2d(60, 30, 3, padding=2, dilation=2)
	287		self.c4 = nn.Conv2d(30, 15, 3, padding=2, dilation=2)
	288		self.c5 = nn.Conv2d(15, 10, 3, padding=2, dilation=2)
	289
	290		self.output = nn.Conv2d(10, 1, 1)
	291
	292		def forward(self, x):
	293		x_red = F.relu(self.front_cnn_1(x), inplace=True)
	294		x_red = F.relu(self.front_cnn_2(x_red), inplace=True)
	295		x_red = F.relu(self.front_cnn_3(x_red), inplace=True)
	296		x_red = F.relu(self.front_cnn_4(x_red), inplace=True)
	297		x_red = self.max_pooling(x_red)
	298
	299		x_green = F.relu(self.front_cnn_1(x), inplace=True)
	300		x_green = F.relu(self.front_cnn_2(x_green), inplace=True)
	301		x_green = F.relu(self.front_cnn_3(x_green), inplace=True)
	302		x_green = self.max_pooling(x_green)
	303
	304		x_blue = F.relu(self.front_cnn_1(x), inplace=True)
	305		x_blue = F.relu(self.front_cnn_2(x_blue), inplace=True)
	306		x_blue = self.max_pooling(x_blue)
	307
	308		# x = self.max_pooling(x)
	309		x = torch.cat((x_red, x_green, x_blue), 1)
	310		x = F.relu(self.c0(x), inplace=True)
	311
	312		x = F.relu(self.c1(x), inplace=True)
	313
	314		x = F.relu(self.c2(x), inplace=True)
	315		x = self.max_pooling(x)
	316
	317		x = F.relu(self.c3(x), inplace=True)
	318		x = self.max_pooling(x)
	319
	320		x = F.relu(self.c4(x), inplace=True)
	321		x = F.relu(self.c5(x), inplace=True)
	322
	323		x = self.output(x)
	324		return x

File train_script/combine/h1_bigtail_t1_sha.sh copied from file train_script/meow_one/big_tail/bigtail3_t4_sha.sh (similarity 73%) (mode: 100644) (index 8777e85..fae9b31)
1		task="bigtail3_t4_sha"
	1		task="h1_bigtail_t1_sha"
2	2
3		CUDA_VISIBLE_DEVICES=4 OMP_NUM_THREADS=5 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
	3		CUDA_VISIBLE_DEVICES=1 OMP_NUM_THREADS=4 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
4	4	--task_id $task \	--task_id $task \
5	5	--note "bigtail3 L1" \	--note "bigtail3 L1" \
6		--model "BigTail3" \
	6		--model "H1_Bigtail3" \
7	7	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
8	8	--lr 1e-4 \	--lr 1e-4 \
9	9	--decay 1e-4 \	--decay 1e-4 \

File train_script/combine/h1_bigtail_t1_shb_fixed.sh copied from file train_script/meow_one/big_tail/bigtail3_t4_shb_fixed.sh (similarity 66%) (mode: 100644) (index 1de7d38..7d5aba9)
1		task="bigtail3_t4_shb_fixed"
	1		task="h1_bigtail_t1_shb_fixed"
2	2
3		CUDA_VISIBLE_DEVICES=2 OMP_NUM_THREADS=5 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
	3		CUDA_VISIBLE_DEVICES=3 OMP_NUM_THREADS=4 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
4	4	--task_id $task \	--task_id $task \
5	5	--note "bigtail3 L1" \	--note "bigtail3 L1" \
6		--model "BigTail3" \
	6		--model "H1_Bigtail3" \
7	7	--input /data/rnd/thient/thient_data/shanghaitech_with_people_density_map/ShanghaiTech_fixed_sigma/part_B \	--input /data/rnd/thient/thient_data/shanghaitech_with_people_density_map/ShanghaiTech_fixed_sigma/part_B \
8	8	--lr 1e-4 \	--lr 1e-4 \
9	9	--decay 1e-4 \	--decay 1e-4 \
10		--batch_size 8 \
11	10	--loss_fn "L1" \	--loss_fn "L1" \
12	11	--datasetname shanghaitech_rnd \	--datasetname shanghaitech_rnd \
13		--epochs 601 > logs/$task.log &
	12		--epochs 1201 > logs/$task.log &
14	13
15	14	echo logs/$task.log # for convenience	echo logs/$task.log # for convenience

File train_script/combine/h1_bigtail_t2_sha.sh copied from file train_script/meow_one/big_tail/bigtail3_t4_sha.sh (similarity 68%) (mode: 100644) (index 8777e85..08e7382)
1		task="bigtail3_t4_sha"
	1		task="h1_bigtail_t2_sha"
2	2
3		CUDA_VISIBLE_DEVICES=4 OMP_NUM_THREADS=5 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
	3		CUDA_VISIBLE_DEVICES=2 OMP_NUM_THREADS=4 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
4	4	--task_id $task \	--task_id $task \
5	5	--note "bigtail3 L1" \	--note "bigtail3 L1" \
6		--model "BigTail3" \
	6		--model "H1_Bigtail3" \
7	7	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
8	8	--lr 1e-4 \	--lr 1e-4 \
9	9	--decay 1e-4 \	--decay 1e-4 \
10		--loss_fn "L1" \
	10		--loss_fn "MSE" \
11	11	--datasetname shanghaitech_20p \	--datasetname shanghaitech_20p \
12	12	--epochs 1201 > logs/$task.log &	--epochs 1201 > logs/$task.log &
13	13

File train_script/combine/h1_bigtail_t2_shb_fixed.sh copied from file train_script/meow_one/big_tail/bigtail3_t4_shb_fixed.sh (similarity 62%) (mode: 100644) (index 1de7d38..7586edf)
1		task="bigtail3_t4_shb_fixed"
	1		task="h1_bigtail_t2_shb_fixed"
2	2
3		CUDA_VISIBLE_DEVICES=2 OMP_NUM_THREADS=5 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
	3		CUDA_VISIBLE_DEVICES=4 OMP_NUM_THREADS=4 HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_meow_main.py \
4	4	--task_id $task \	--task_id $task \
5	5	--note "bigtail3 L1" \	--note "bigtail3 L1" \
6		--model "BigTail3" \
	6		--model "H1_Bigtail3" \
7	7	--input /data/rnd/thient/thient_data/shanghaitech_with_people_density_map/ShanghaiTech_fixed_sigma/part_B \	--input /data/rnd/thient/thient_data/shanghaitech_with_people_density_map/ShanghaiTech_fixed_sigma/part_B \
8	8	--lr 1e-4 \	--lr 1e-4 \
9	9	--decay 1e-4 \	--decay 1e-4 \
10		--batch_size 8 \
11		--loss_fn "L1" \
	10		--loss_fn "MSE" \
12	11	--datasetname shanghaitech_rnd \	--datasetname shanghaitech_rnd \
13		--epochs 601 > logs/$task.log &
	12		--epochs 1201 > logs/$task.log &
14	13
15	14	echo logs/$task.log # for convenience	echo logs/$task.log # for convenience

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