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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)
bigtal4	1a434051955cfaeda47780ab801924cfc9c22f94	Thai Thien	2020-06-02 18:00:43
l1 1e-4	2ea7293b60423704648df4a610a012f1da822ec9	Thai Thien	2020-05-29 16:38:44
fix log epoch	84e8beff8e305f81de7a05add2e58dcfe389ede5	Thai Thien	2020-05-29 16:38:31
skip evaluate on training set	a38d5731a9d6209bc98a9cca0ad023b4798c68d5	Thai Thien	2020-05-28 16:15:29
t7	ac1f0d71c2d3e0a01241622c639a9090a5b3c427	Thai Thien	2020-05-28 15:55:56
rename	4e9754c3dff98577616d3f043f7f260aeef40ac6	Thai Thien	2020-05-27 15:54:54
not use gpu2	8324400a3d65c58cfdd5a63b0a940217f3277769	Thai Thien	2020-05-27 15:54:11
remove call loss in eval	06f13484f24b85b12cab15e5af16db4d086db497	Thai Thien	2020-05-27 15:52:44
expermient, bug fix	e83e7e058b6f1d7ec1237677a2d4770f1f788ea0	Thai Thien	2020-05-27 15:43:58
surpress python warning	82806340334b3beb9916b34bb00a66381ce57aae	Thai Thien	2020-05-26 12:00:49
sha total crop no keepfull	d0783ddab88ed042607738fb665b34028d485d0d	Thai Thien	2020-05-26 11:49:10
sha 60p	d5146c9f28b62fac137a15c862d99e1af30ce5fa	Thai Thien	2020-05-25 17:28:12
t12	35b7a40082505da5f79caf4cc16023144e18a7d8	Thai Thien	2020-05-24 12:00:45
add sha 20p random	597c821a0103344b5e3b7b80eb2f61c335275ed2	Thai Thien	2020-05-24 11:52:18
h2 t3 shb	babec8c08d4aebd8af14558eddfa2898918ff152	Thai Thien	2020-05-23 16:49:58
y_pred	e1c1e8096230344dabfc1c85ccae832ba08aaad6	Thai Thien	2020-05-23 16:37:24
when not train, return img and true count	93d3b48fc1cd3a5b43ac0e8782c57f2ea00a48bc	Thai Thien	2020-05-23 16:33:00
map->mat	c64a1548b1e9745c109c211f76e8dc2554cf6747	Thai Thien	2020-05-23 16:27:48
fix wrong file name	950ad302d9b0ec6ed04fb4d4a870087dda57281c	Thai Thien	2020-05-23 16:17:30
fix scipy.io	766d0f3ba48e0c3e0d2562136c5549e20070b805	Thai Thien	2020-05-23 15:44:44

Commit 1a434051955cfaeda47780ab801924cfc9c22f94 - bigtal4
Author: Thai Thien
Author date (UTC): 2020-06-02 18:00
Committer name: Thai Thien
Committer date (UTC): 2020-06-02 18:00
Parent(s): 2ea7293b60423704648df4a610a012f1da822ec9
Signer:
Signing key:
Signing status: N
Tree: f571e845b39b2b38a1568b05a6fbd8697f78dbde

File	Lines added	Lines deleted
experiment_main.py	3	1
models/meow_experiment/ccnn_tail.py	42	0
train_script/meow_one/big_tail/bigtail4_t1_sha.sh	5	5
train_script/meow_one/big_tail/bigtail4_t2_sha.sh	5	5

File experiment_main.py changed (mode: 100644) (index 2d2b3cf..99f4a01)
...	...	from visualize_util import get_readable_time
11	11	import torch	import torch
12	12	from torch import nn	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		from models.meow_experiment.ccnn_tail import BigTailM1, BigTailM2, BigTail3
	14		from models.meow_experiment.ccnn_tail import BigTailM1, BigTailM2, BigTail3, BigTail4
15	15	from models.meow_experiment.ccnn_head import H1, H2	from models.meow_experiment.ccnn_head import H1, H2
16	16	from models.meow_experiment.kitten_meow_1 import H1_Bigtail3	from models.meow_experiment.kitten_meow_1 import H1_Bigtail3
17	17	from models import CustomCNNv2, CompactCNNV7	from models import CustomCNNv2, CompactCNNV7

...	...	if __name__ == "__main__":
83	83	model = BigTailM2()	model = BigTailM2()
84	84	elif model_name == "BigTail3":	elif model_name == "BigTail3":
85	85	model = BigTail3()	model = BigTail3()
	86		elif model_name == "BigTail4":
	87		model = BigTail4()
86	88	elif model_name == "H1":	elif model_name == "H1":
87	89	model = H1()	model = H1()
88	90	elif model_name == "H2":	elif model_name == "H2":

File models/meow_experiment/ccnn_tail.py changed (mode: 100644) (index 88dd56a..c40cd57)
...	...	class BigTail3(nn.Module):
147	147	return x	return x
148	148
149	149
	150		class BigTail4(nn.Module):
	151		"""
	152		we set max tail at 60 only
	153		remove c5 comparing to bigtal3
	154		"""
	155		def __init__(self, load_weights=False):
	156		super(BigTail4, self).__init__()
	157		self.model_note = "small taill 100"
	158		self.red_cnn = nn.Conv2d(3, 10, 9, padding=4)
	159		self.green_cnn = nn.Conv2d(3, 14, 7, padding=3)
	160		self.blue_cnn = nn.Conv2d(3, 16, 5, padding=2)
	161
	162		self.max_pooling = nn.MaxPool2d(kernel_size=2, stride=2)
	163
	164		self.c0 = nn.Conv2d(40, 60, 3, padding=2, dilation=2)
	165		self.c1 = nn.Conv2d(60, 60, 3, padding=2, dilation=2)
	166		self.c2 = nn.Conv2d(60, 60, 3, padding=2, dilation=2)
	167		self.c3 = nn.Conv2d(60, 30, 3, padding=2, dilation=2)
	168		self.c4 = nn.Conv2d(30, 10, 3, padding=2, dilation=2)
	169		self.output = nn.Conv2d(10, 1, 1)
	170
	171		def forward(self,x):
	172		x_red = self.max_pooling(F.relu(self.red_cnn(x), inplace=True))
	173		x_green = self.max_pooling(F.relu(self.green_cnn(x), inplace=True))
	174		x_blue = self.max_pooling(F.relu(self.blue_cnn(x), inplace=True))
	175
	176		x = torch.cat((x_red, x_green, x_blue), 1)
	177		x = F.relu(self.c0(x), inplace=True)
	178
	179		x = F.relu(self.c1(x), inplace=True)
	180
	181		x = F.relu(self.c2(x), inplace=True)
	182		x = self.max_pooling(x)
	183
	184		x = F.relu(self.c3(x), inplace=True)
	185		x = self.max_pooling(x)
	186
	187		x = F.relu(self.c4(x), inplace=True)
	188
	189		x = self.output(x)
	190		return x
	191

File train_script/meow_one/big_tail/bigtail4_t1_sha.sh copied from file train_script/meow_one/head/H2_t8_sha.sh (similarity 72%) (mode: 100644) (index 0c77d99..494338d)
1		task="H2_t8_sha"
	1		task="bigtail4_t1_sha"
2	2
3	3	CUDA_VISIBLE_DEVICES=5 OMP_NUM_THREADS=2 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \	CUDA_VISIBLE_DEVICES=5 OMP_NUM_THREADS=2 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \
4	4	--task_id $task \	--task_id $task \
5		--note "a H2 with L1, hope better than baseline" \
6		--model "H2" \
	5		--note "bigtail4" \
	6		--model "BigTail4" \
7	7	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
8	8	--lr 1e-4 \	--lr 1e-4 \
9		--decay 1e-4 \
	9		--decay 1e-4 \
10	10	--loss_fn "L1" \	--loss_fn "L1" \
11	11	--skip_train_eval \	--skip_train_eval \
12	12	--datasetname shanghaitech_crop_random \	--datasetname shanghaitech_crop_random \
13		--epochs 1201 > logs/$task.log &
	13		--epochs 800 > logs/$task.log &
14	14
15	15	echo logs/$task.log # for convenience	echo logs/$task.log # for convenience

File train_script/meow_one/big_tail/bigtail4_t2_sha.sh copied from file train_script/meow_one/big_tail/bigtail3_t7_sha.sh (similarity 64%) (mode: 100644) (index 9503373..8874e0f)
1		task="bigtail3_t7_sha"
	1		task="bigtail4_t2_sha"
2	2
3		CUDA_VISIBLE_DEVICES=4 OMP_NUM_THREADS=2 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \
	3		CUDA_VISIBLE_DEVICES=6 OMP_NUM_THREADS=2 PYTHONWARNINGS="ignore" HTTPS_PROXY="http://10.60.28.99:86" nohup python experiment_main.py \
4	4	--task_id $task \	--task_id $task \
5		--note "bigtail3 L1" \
6		--model "BigTail3" \
	5		--note "bigtail4" \
	6		--model "BigTail4" \
7	7	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
8	8	--lr 1e-4 \	--lr 1e-4 \
9	9	--decay 1e-4 \	--decay 1e-4 \
10	10	--loss_fn "MSE" \	--loss_fn "MSE" \
11	11	--skip_train_eval \	--skip_train_eval \
12	12	--datasetname shanghaitech_crop_random \	--datasetname shanghaitech_crop_random \
13		--epochs 1001 > logs/$task.log &
	13		--epochs 800 > logs/$task.log &
14	14
15	15	echo logs/$task.log # for convenience	echo logs/$task.log # for convenience

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