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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)
add 1 move layer to ccnn	10d5d3711dda204d7b059b79d775c0359d1a964d	Thai Thien	2020-03-14 10:04:17
continue train	3627b8cbf4192856d7453b72972a11659c34ae5f	Thai Thien	2020-03-14 01:58:25
simple v4 t2	4dee2eba24246cf21a84dd7f9ef74d6c434edf1d	Thai Thien	2020-03-13 18:36:33
nll to loss	958f0895b81d42e6d31a0bbd5787211538c081fe	Thai Thien	2020-03-13 16:27:27
ccnn_v4_t1	1f950b91f4fb89a0f08baf08dffb2a501546b64f	Thai Thien	2020-03-13 16:23:39
add proxy	9afb66a73e3ae24b2144faf311b128dbe5768f3c	Thai Thien	2020-03-13 16:09:30
add comet, add scheduler to simple	4ef5939124745dfc54ad8a87936954a2bde8a5a2	Thai Thien	2020-03-13 16:06:24
ccnn v1_t5 lr scheduler	31f7a693eff8e60a07bd7bc439575cc7712fe31b	Thai Thien	2020-03-13 15:37:37
ccnn_v1_t4_scheduler, also change lr milestones	c27beba80ebf7686dc7f046b95d5e2739b6dae66	Thai Thien	2020-03-12 16:23:02
add load_data_shanghaitech_keepfull_and_crop	5af55c5b483263683db80bcf4b870bfbb241d668	Thai Thien	2020-03-12 16:19:19
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

Commit 10d5d3711dda204d7b059b79d775c0359d1a964d - add 1 move layer to ccnn
Author: Thai Thien
Author date (UTC): 2020-03-14 10:04
Committer name: Thai Thien
Committer date (UTC): 2020-03-14 10:04
Parent(s): 3627b8cbf4192856d7453b72972a11659c34ae5f
Signing key:
Tree: a864f5fda4bfddd0823cb1095b1f20b9f79b13bc

File	Lines added	Lines deleted
explore_model_summary.py	11	7
models/__init__.py	1	1
models/compact_cnn.py	43	0

File explore_model_summary.py changed (mode: 100644) (index 9d5c32d..651839b)
1		from models import CompactCNN, AttnCanAdcrowdNetSimpleV3, CompactDilatedCNN, DefDilatedCCNN
	1		from models import CompactCNN, AttnCanAdcrowdNetSimpleV3, CompactDilatedCNN, DefDilatedCCNN, CompactCNNV2
2	2	from torchsummary import summary	from torchsummary import summary
3	3
4	4	def very_simple_param_count(model):	def very_simple_param_count(model):

...	...	if __name__ == "__main__":
15	15	# print(summary(ccnn, (3, 512, 512)))	# print(summary(ccnn, (3, 512, 512)))
16	16	# print("simple count", very_simple_param_count(ccnn))	# print("simple count", very_simple_param_count(ccnn))
17	17	# print("===========================================================================")	# print("===========================================================================")
18		# print("dilate ccnn")
19		# dcnn1 = CompactDilatedCNN()
20		# print(summary(dcnn1, (3, 512, 512)))
21		# print("=============================================================================")
22		print("dilate ccnn")
	18		print("ccnn")
	19		dcnn1 = CompactCNN()
	20		print(summary(dcnn1, (3, 512, 512), device="cpu"))
	21		print("=============================================================================")
	22		print("ccnn v2")
	23		dcnn2 = CompactCNNV2()
	24		print(summary(dcnn2, (3, 512, 512), device="cpu"))
	25		print("=============================================================================")
	26		print("DefDilatedCCNN")
23	27	dcnn2 = DefDilatedCCNN()	dcnn2 = DefDilatedCCNN()
24		print(summary(dcnn2, (3, 512, 512)))
	28		print(summary(dcnn2, (3, 512, 512), device="cpu"))
25	29	print("=============================================================================")	print("=============================================================================")
26	30	# print("simple_v3")	# print("simple_v3")
27	31	# simplev3 = AttnCanAdcrowdNetSimpleV3()	# simplev3 = AttnCanAdcrowdNetSimpleV3()

File models/__init__.py changed (mode: 100644) (index 15d156f..8f69b2b)
...	...	from .can_adcrowdnet import CanAdcrowdNet
6	6	from .attn_can_adcrowdnet import AttnCanAdcrowdNet	from .attn_can_adcrowdnet import AttnCanAdcrowdNet
7	7	from .attn_can_adcrowdnet_freeze_vgg import AttnCanAdcrowdNetFreezeVgg	from .attn_can_adcrowdnet_freeze_vgg import AttnCanAdcrowdNetFreezeVgg
8	8	from .attn_can_adcrowdnet_simple import AttnCanAdcrowdNetSimpleV1, AttnCanAdcrowdNetSimpleV2, AttnCanAdcrowdNetSimpleV3, AttnCanAdcrowdNetSimpleV4	from .attn_can_adcrowdnet_simple import AttnCanAdcrowdNetSimpleV1, AttnCanAdcrowdNetSimpleV2, AttnCanAdcrowdNetSimpleV3, AttnCanAdcrowdNetSimpleV4
9		from .compact_cnn import CompactCNN, CompactDilatedCNN, DefDilatedCCNN, DilatedCCNNv2
	9		from .compact_cnn import CompactCNN, CompactCNNV2, CompactDilatedCNN, DefDilatedCCNN, DilatedCCNNv2

File models/compact_cnn.py changed (mode: 100644) (index 584995a..946ef2f)
...	...	class CompactCNN(nn.Module):
10	10	"""	"""
11	11	A REAL-TIME DEEP NETWORK FOR CROWD COUNTING	A REAL-TIME DEEP NETWORK FOR CROWD COUNTING
12	12	https://arxiv.org/pdf/2002.06515.pdf	https://arxiv.org/pdf/2002.06515.pdf
	13		:deprecated: I think implement incorrectly, please use CompactCNNV2
13	14	"""	"""
14	15	def __init__(self, load_weights=False):	def __init__(self, load_weights=False):
15	16	super(CompactCNN, self).__init__()	super(CompactCNN, self).__init__()

...	...	class CompactCNN(nn.Module):
44	45	x = self.output(x)	x = self.output(x)
45	46	return x	return x
46	47
	48
	49		class CompactCNNV2(nn.Module):
	50		"""
	51		A REAL-TIME DEEP NETWORK FOR CROWD COUNTING
	52		https://arxiv.org/pdf/2002.06515.pdf
	53		"""
	54		def __init__(self, load_weights=False):
	55		super(CompactCNNV2, self).__init__()
	56		self.red_cnn = nn.Conv2d(3, 10, 9, padding=4)
	57		self.green_cnn = nn.Conv2d(3, 14, 7, padding=3)
	58		self.blue_cnn = nn.Conv2d(3, 16, 5, padding=2)
	59		self.c0 = nn.Conv2d(40, 40, 3, padding=1)
	60
	61		self.max_pooling = nn.MaxPool2d(kernel_size=2, stride=2)
	62
	63		self.c1 = nn.Conv2d(40, 60, 3, padding=1)
	64		self.c2 = nn.Conv2d(60, 40, 3, padding=1)
	65		self.c3 = nn.Conv2d(40, 20, 3, padding=1)
	66		self.c4 = nn.Conv2d(20, 10, 3, padding=1)
	67		self.output = nn.Conv2d(10, 1, 1)
	68
	69		def forward(self,x):
	70		x_red = self.max_pooling(F.relu(self.red_cnn(x), inplace=True))
	71		x_green = self.max_pooling(F.relu(self.green_cnn(x), inplace=True))
	72		x_blue = self.max_pooling(F.relu(self.blue_cnn(x), inplace=True))
	73
	74		x = torch.cat((x_red, x_green, x_blue), 1)
	75		x = F.relu(self.c0(x), inplace=True)
	76
	77		x = F.relu(self.c1(x), inplace=True)
	78
	79		x = F.relu(self.c2(x), inplace=True)
	80		x = self.max_pooling(x)
	81
	82		x = F.relu(self.c3(x), inplace=True)
	83		x = self.max_pooling(x)
	84
	85		x = F.relu(self.c4(x), inplace=True)
	86
	87		x = self.output(x)
	88		return x
	89
47	90	class CompactDilatedCNN(nn.Module):	class CompactDilatedCNN(nn.Module):
48	91	"""	"""
49	92

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