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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

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)
wip: pacnn	37620e5a9bc0f9516ea964ec58d9bdaa1c40ff36	Thai Thien	2019-09-22 03:14:42
fix training flow	2b87b1b26c7296b64493fdc49fedb421b249dfa3	Thai Thien	2019-09-17 18:00:35
dataset script	bc5c052f5f956510ab95ef9a45434fd486c57fae	Thai Thien	2019-09-16 17:21:13
evaluator	ffc5bf8290ae0c469a9a18a2d061cfd1bfeee822	Thai Thien	2019-09-14 04:56:35
some more test for data loader	25173578cde7d4e9fe6c6140d1ee01caa4fcfc32	Thai Thien	2019-09-14 02:51:58
some visualize to debug data loader	e4f52007616acf307bddbde79c0fb4f8c649c785	Thai Thien	2019-09-13 17:35:45
wip	d7d44cad6774355bdfa45414258763f6c6a0c299	Thai Thien	2019-08-31 16:58:16
commit all	6dad7a58f7dbf9fc288ce9dd3e92be538851c2a7	Thai Thien	2019-08-29 19:10:44
input d1,d2,d3 match	fc2a809241f8b6356d964c63d40cbebd55ca5f6c	Thai Thien	2019-08-28 17:57:05
WIP	39eab26d061e61dfffbf164dbd5fd878299b7250	thient	2019-08-28 11:09:12
output of de is ok	dd770386674df3e0fbebafdfc48a9352bc28967d	thient	2019-08-28 10:54:09
code pacnn	c49537b5cc91e96e4e35c9338d2c95b9bb41c672	Thai Thien	2019-08-27 16:35:27
crowd counting stuff	da9f27a39cba9bdd021b6b5c562f5f7c2be50190	Thai Thien	2019-08-24 18:27:44
seem ok	53fa176c31669a0e89b04adf290cb398f0316c45	Thai Thien	2019-08-24 18:26:31
flow ok	ad849681000818dfbcd0c1715c2858aed7236041	Thai Thien	2019-08-24 17:00:02
wip	23c3ec48497782bbc91d829e1c8a682502360ab9	Thai Thien	2019-08-24 14:19:22
work in progress, try to use https://pytorch.org/ignite/quickstart.html	39c824fe8fc2501628ee42c236a844df45521007	Thai Thien	2019-08-24 07:41:46
Work in progress	984be31d85e5cbdb2af296ccdb128381fe9bf09e	Thai Thien	2019-08-24 05:30:51
README	045706df1fa3452d150a190675c60e80ebd18e08	Thai Thien	2019-08-24 04:11:49
init	18f7c296fb05c4340c1c20ca84c60fef4f93bb1f	Thai Thien	2019-08-24 04:08:16

Commit 37620e5a9bc0f9516ea964ec58d9bdaa1c40ff36 - wip: pacnn
Author: Thai Thien
Author date (UTC): 2019-09-22 03:14
Committer name: Thai Thien
Committer date (UTC): 2019-09-22 03:14
Parent(s): 2b87b1b26c7296b64493fdc49fedb421b249dfa3
Signing key:
Tree: 765218689dc1699f01c70648209d4682666674a2

File	Lines added	Lines deleted
models/pacnn.py	48	0
models/playground.py	15	8

File models/pacnn.py changed (mode: 100644) (index ed60f80..a16141c)
1	1	import torch.nn as nn	import torch.nn as nn
2	2	import torch	import torch
	3
3	4	from torchvision import models	from torchvision import models
4	5	import numpy as np	import numpy as np
	6		import copy
5	7
6	8	# ssim lost function	# ssim lost function
7	9

...	...	class PACNN(nn.Module):
27	29	de3 = self.de3_11((self.de3net(x)))	de3 = self.de3_11((self.de3net(x)))
28	30	return de1.squeeze(0), de2.squeeze(0), de3.squeeze(0)	return de1.squeeze(0), de2.squeeze(0), de3.squeeze(0)
29	31
	32
	33		class PACNNWithPerspectiveMap(nn.Module):
	34		def __init__(self):
	35		super(PACNNWithPerspectiveMap, self).__init__()
	36		self.backbone = models.vgg16(pretrained=True).features
	37		self.de1net = self.backbone[0:23]
	38
	39		self.de2net = self.backbone[0:30]
	40
	41
	42		list_vgg16 = list(self.backbone)
	43		self.conv6_1_1 = nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
	44		list_vgg16.append(self.conv6_1_1)
	45		self.de3net = nn.Sequential(*list_vgg16)
	46
	47
	48		self.conv5_2_3_stack = copy.deepcopy(self.backbone[23:30])
	49		self.perspective_net = nn.Sequential(self.backbone[0:23], self.conv5_2_3_stack)
	50
	51
	52		# 1 1 convolution
	53		self.de1_11 = nn.Conv2d(512, 1, kernel_size=1)
	54		self.de2_11 = nn.Conv2d(512, 1, kernel_size=1)
	55		self.de3_11 = nn.Conv2d(512, 1, kernel_size=1)
	56		self.perspective_11 = nn.Conv2d(512, 1, kernel_size=1)
	57
	58		# deconvolution upsampling
	59		self.up12 = nn.ConvTranspose2d(512, 1, 2, 2)
	60		self.up23 = nn.ConvTranspose2d(512, 1, 2, 2)
	61
	62		# if true, use perspective aware
	63		# if false, use average
	64		self.perspective_aware_mode = False
	65
	66		def forward(self, x):
	67		de1 = self.de1_11((self.de1net(x)))
	68		de2 = self.de2_11((self.de2net(x)))
	69		de3 = self.de3_11((self.de3net(x)))
	70		if self.perspective_aware_mode:
	71		respective = self.perspective_11(self.perspective_net)
	72		# TODO: code more here
	73		else:
	74		de23 = (de2 + self.up23(de3))/2
	75		de = (de1 + self.up12(de23))/2
	76		return de
	77
30	78	def count_param(net):	def count_param(net):
31	79	pytorch_total_params = sum(p.numel() for p in net.parameters())	pytorch_total_params = sum(p.numel() for p in net.parameters())
32	80	return pytorch_total_params	return pytorch_total_params

File models/playground.py changed (mode: 100644) (index ba0106f..4aa73cd)
...	...	class M0(nn.Module):
24	24	d = self.backbone(x)	d = self.backbone(x)
25	25	return d	return d
26	26
	27		def deconvolutuion():
	28		transpose = nn.ConvTranspose2d(3, 1, 2, 2)
	29		in_tensor = torch.rand((1, 3, 224, 224))
	30		out = transpose(in_tensor)
	31		print(out.size())
27	32
28		if __name__ == "__main__":
29		m0 = M0()
30		m1 = M1()
31
32		m0_param = list(m0.parameters())
33		m1_param = list(m1.parameters())
34	33
35		print(len(m0_param))
36		print(len(m1_param))
	34		if __name__ == "__main__":
	35		deconvolutuion()
	36		# m0 = M0()
	37		# m1 = M1()
	38		#
	39		# m0_param = list(m0.parameters())
	40		# m1_param = list(m1.parameters())
	41		#
	42		# print(len(m0_param))
	43		# print(len(m1_param))

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