RocketGit

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)
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
dilated ccnn	5c5d92bdc0a288dd5d4ec5f1367d8cb928175bbe	Thai Thien	2020-03-06 19:04:01
done	9f05e093ec7c10284a4aedf0738f9e61d5ac6fb6	Thai Thien	2020-03-06 18:02:34
with lr scheduler	466c364b60ed22c77319b14ccc9a201614b908bf	Thai Thien	2020-03-04 17:57:49
train with learning rate scheduler	fcd5a3c8da2dd6763e0d40742edf47b49c95fcfb	Thai Thien	2020-03-04 17:55:11
ccnn no padding at output layer	57563fc07f656c63f807de4d80712ff11345109d	Thai Thien	2020-03-04 17:36:43
fix dimension of ccnn	f4439d9a78273ab3ba450f31a528509816b4352f	Thai Thien	2020-03-04 17:32:48

Commit 75d2989232a8a68eba9b4920ab2374ac28438e0e - DilatedCCNNv2
Author: Thai Thien
Author date (UTC): 2020-03-10 05:11
Committer name: Thai Thien
Committer date (UTC): 2020-03-10 05:11
Parent(s): 57928056d13bc9b1f9b11e14dd305005a3a5aeea
Signing key:
Tree: ef4c7a1547265098f139e9a2ae60884c6aa64bea

File	Lines added	Lines deleted
models/__init__.py	1	1
models/compact_cnn.py	43	1
train_custom_compact_cnn.py	6	3
train_custom_compact_cnn_lrscheduler.py	1	1
train_script/CCNN_custom/dilated_ccnn_v2_t1.sh	3	3

File models/__init__.py changed (mode: 100644) (index a83ed52..3c1cb01)
...	...	from .deform_conv_v2 import DeformConv2d
5	5	from .attn_can_adcrowdnet import AttnCanAdcrowdNet	from .attn_can_adcrowdnet import AttnCanAdcrowdNet
6	6	from .attn_can_adcrowdnet_freeze_vgg import AttnCanAdcrowdNetFreezeVgg	from .attn_can_adcrowdnet_freeze_vgg import AttnCanAdcrowdNetFreezeVgg
7	7	from .attn_can_adcrowdnet_simple import AttnCanAdcrowdNetSimpleV1, AttnCanAdcrowdNetSimpleV2, AttnCanAdcrowdNetSimpleV3, AttnCanAdcrowdNetSimpleV4	from .attn_can_adcrowdnet_simple import AttnCanAdcrowdNetSimpleV1, AttnCanAdcrowdNetSimpleV2, AttnCanAdcrowdNetSimpleV3, AttnCanAdcrowdNetSimpleV4
8		from .compact_cnn import CompactCNN, CompactDilatedCNN, DefDilatedCCNN
	8		from .compact_cnn import CompactCNN, CompactDilatedCNN, DefDilatedCCNN, DilatedCCNNv2

File models/compact_cnn.py changed (mode: 100644) (index 25cef10..584995a)
...	...	class CompactDilatedCNN(nn.Module):
83	83
84	84	class DefDilatedCCNN(nn.Module):	class DefDilatedCCNN(nn.Module):
85	85	"""	"""
86
	86		fail reason: out of cuda memory at red_cnn
	87		possible fix: try torchvision deform conv
87	88	"""	"""
88	89	def __init__(self, load_weights=False):	def __init__(self, load_weights=False):
89	90	super(DefDilatedCCNN, self).__init__()	super(DefDilatedCCNN, self).__init__()

...	...	class DefDilatedCCNN(nn.Module):
123	124	x = self.output(x)	x = self.output(x)
124	125	return x	return x
125	126
	127		class DilatedCCNNv2(nn.Module):
	128		"""
	129
	130		"""
	131		def __init__(self, load_weights=False):
	132		super(DilatedCCNNv2, self).__init__()
	133
	134		self.red_cnn = nn.Conv2d(3, 10, 9, padding=4)
	135		self.green_cnn = nn.Conv2d(3, 14, 7, padding=3)
	136		self.blue_cnn = nn.Conv2d(3, 16, 5, padding=2)
	137
	138		self.max_pooling = nn.MaxPool2d(kernel_size=2, stride=2)
	139
	140		self.c1 = nn.Conv2d(40, 60, 3, dilation=2, padding=2, bias=False)
	141		self.bn1 = nn.BatchNorm2d(60)
	142		self.c2 = nn.Conv2d(60, 40, 3, dilation=2, padding=2, bias=False)
	143		self.bn2 = nn.BatchNorm2d(40)
	144		self.c3 = nn.Conv2d(40, 20, 3, dilation=2, padding=2, bias=False)
	145		self.bn3 = nn.BatchNorm2d(20)
	146		self.c4 = nn.Conv2d(20, 10, 3, dilation=2, padding=2, bias=False)
	147		self.bn4 = nn.BatchNorm2d(10)
	148		self.output = nn.Conv2d(10, 1, 1)
	149
	150		def forward(self,x):
	151		x_red = self.max_pooling(F.relu(self.red_cnn(x), inplace=True))
	152		x_green = self.max_pooling(F.relu(self.green_cnn(x), inplace=True))
	153		x_blue = self.max_pooling(F.relu(self.blue_cnn(x), inplace=True))
	154
	155		x = torch.cat((x_red, x_green, x_blue), 1)
	156		x = F.relu(self.bn1(self.c1(x)), inplace=True)
	157
	158		x = F.relu(self.bn2(self.c2(x)), inplace=True)
	159		x = self.max_pooling(x)
	160
	161		x = F.relu(self.bn3(self.c3(x)), inplace=True)
	162		x = self.max_pooling(x)
	163
	164		x = F.relu(self.bn4(self.c4(x)), inplace=True)
	165
	166		x = self.output(x)
	167		return x

File train_custom_compact_cnn.py changed (mode: 100644) (index f28d251..e6bf570)
...	...	from ignite.engine import Engine
6	6	from ignite.handlers import Checkpoint, DiskSaver	from ignite.handlers import Checkpoint, DiskSaver
7	7	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError	from crowd_counting_error_metrics import CrowdCountingMeanAbsoluteError, CrowdCountingMeanSquaredError
8	8	from visualize_util import get_readable_time	from visualize_util import get_readable_time
9
	9		from torchsummary import summary
10	10	import torch	import torch
11	11	from torch import nn	from torch import nn
12		from models import DefDilatedCCNN
	12		from models import DilatedCCNNv2
13	13	import os	import os
14	14
15	15	if __name__ == "__main__":	if __name__ == "__main__":

...	...	if __name__ == "__main__":
39	39	print("len train_loader ", len(train_loader))	print("len train_loader ", len(train_loader))
40	40
41	41	# model	# model
42		model = DefDilatedCCNN()
	42		model = DilatedCCNNv2()
43	43	model = model.to(device)	model = model.to(device)
44	44
45	45	# loss function	# loss function

...	...	if __name__ == "__main__":
58	58	}, device=device)	}, device=device)
59	59	print(model)	print(model)
60	60
	61		print (summary(model, (3, 512, 512)))
	62
61	63	print(args)	print(args)
62	64
	65
63	66	if len(args.load_model) > 0:	if len(args.load_model) > 0:
64	67	load_model_path = args.load_model	load_model_path = args.load_model
65	68	print("load mode " + load_model_path)	print("load mode " + load_model_path)

File train_custom_compact_cnn_lrscheduler.py changed (mode: 100644) (index 4a5d6ff..943fa68)
...	...	if __name__ == "__main__":
75	75	else:	else:
76	76	print("do not load, keep training")	print("do not load, keep training")
77	77
78		trainer.add_event_handler(Events.ITERATION_COMPLETED, lr_scheduler)
	78		trainer.add_event_handler(Events.EPOCH_STARTED, lr_scheduler)
79	79
80	80
81	81	@trainer.on(Events.ITERATION_COMPLETED(every=50))	@trainer.on(Events.ITERATION_COMPLETED(every=50))

File train_script/CCNN_custom/dilated_ccnn_v2_t1.sh copied from file train_script/CCNN_custom/dilated_ccnn_v1_t1_scheduler.sh (similarity 65%) (mode: 100644) (index f48d434..5e75eed)
1	1	CUDA_VISIBLE_DEVICES=5 nohup python train_custom_compact_cnn.py \	CUDA_VISIBLE_DEVICES=5 nohup python train_custom_compact_cnn.py \
2		--task_id dilated_ccnn_v1_t3 \
	2		--task_id dilated_ccnn_v2_t1 \
3	3	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \	--input /data/rnd/thient/thient_data/ShanghaiTech/part_A \
4		--lr 1e-6 \
	4		--lr 1e-5 \
5	5	--decay 5e-5 \	--decay 5e-5 \
6	6	--datasetname shanghaitech \	--datasetname shanghaitech \
7		--epochs 400 > logs/dilated_ccnn_v1_t3.log &
	7		--epochs 400 > logs/dilated_ccnn_v2_t1.log &

Hints:
Before first commit, do not forget to setup your git environment:

git config --global user.name "your_name_here"
git config --global user.email "your@email_here"

Clone this repository using HTTP(S):

git clone https://rocketgit.com/user/hahattpro/crowd_counting_framework

Clone this repository using ssh (do not forget to upload a key first):

git clone ssh://rocketgit@ssh.rocketgit.com/user/hahattpro/crowd_counting_framework

Clone this repository using git:

git clone git://git.rocketgit.com/user/hahattpro/crowd_counting_framework

You are allowed to anonymously push to this repository.
This means that your pushed commits will automatically be transformed into a merge request:

... clone the repository ...
... make some changes and some commits ...
git push origin main