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import numpy as np
import cv2
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im = cv2.imread('/home/lwp/workspace/sintel2/albedo/alley_1/frame_0001.png')
ret = im[100:100+256,400:400+256,:]
h,w,c = ret.shape
s = 2
x = ret
for i in range(5):
# x = cv2.resize(x, (h//2**(i+1),w//2**(i+1)), cv2.INTER_NEAREST)
x = cv2.resize(x, (h//2**(i+1),w//2**(i+1)))
# x = cv2.resize(x, (h//32,w//32), cv2.INTER_NEAREST)
y = x
# for i in range(5): y = cv2.resize(y, (h//2**(4-i),w//2**(4-i)), cv2.INTER_NEAREST)
for i in range(5): y = cv2.resize(y, (h//2**(4-i),w//2**(4-i)))
cv2.imwrite('y1.png',y)
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im = cv2.imread('/home/lwp/workspace/sintel2/albedo/alley_1/frame_0001.png')
ret = im[100:100+256,400:400+256,:]
h,w,c = ret.shape
s = 2
x = ret
x = cv2.resize(x, (h//32,w//32), cv2.INTER_NEAREST)
y = cv2.resize(x, (h,w), cv2.INTER_NEAREST)
cv2.imwrite('y.png',y)
In [12]:
def calOutputChannel(input_channel, blocks, bn_size=4, growth_rate=32, transition_scale=2):
output_channel = input_channel
sum_ = 0
print(blocks)
for b in blocks:
output_channel += b * growth_rate
sum_ += b * output_channel
sum_ += b * growth_rate
output_channel //= transition_scale
sum_ += output_channel
print (output_channel)
print('sum = ', sum_)
return output_channel
# a = [64,64,128,256,1024]
# a = [x//2 for x in a]
# print (a)
# print (calOutputChannel(512, (24,24,24), bn_size=4, growth_rate=32, transition_scale=2))
# print (calOutputChannel(1024, (24,24,24), bn_size=4, growth_rate=32, transition_scale=8))
calOutputChannel(3, (5,5), bn_size=4, growth_rate=32, transition_scale=2)
print ()
calOutputChannel(6, [7], bn_size=4, growth_rate=32, transition_scale=2)
# print (calOutputChannel(1024, (24,24,24), bn_size=4, growth_rate=32, transition_scale=2))
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from tensorboardX import SummaryWriter
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writer = SummaryWriter(comment='hahaha')
writer.add_text('aa','bb')
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import torch
import torch.nn as nn
from torch.autograd import Variable
# x = Variable(torch.ones(2, 2), requires_grad=True)
# print(x)
class A(nn.Module):
def __init__(self):
super(A, self).__init__()
self.layerA = nn.Sigmoid()
def forward(self, x):
return self.layerA(x)
class B(nn.Module):
def __init__(self):
super(B, self).__init__()
self.layerB = nn.Sigmoid()
def forward(self, x):
return self.layerB(x)
a = A().cuda(2)
b = B().cuda(3)
x = Variable(torch.ones(2, 2), requires_grad=True).cuda(2)
print(x)
y1 = a(x)
print(y1)
y2 = y1.cuda(3)
print(y2)
z = b(y2)
print(z)
print(z.grad_fn)
print(z.grad_fn.next_functions[0][0])
print(z.grad_fn.next_functions[0][0].next_functions[0][0])
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# Create tensors.
x = Variable(torch.Tensor([1]), requires_grad=True)
w = Variable(torch.Tensor([2]), requires_grad=True)
b = Variable(torch.Tensor([3]), requires_grad=True)
# Build a computational graph.
y = w * x + b # y = 2 * x + 3
# Compute gradients.
y.backward()
# Print out the gradients.
print(x.grad) # x.grad = 2
print(w.grad) # w.grad = 1
print(b.grad) # b.grad = 1
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class Foo(object):
class Bar(object):
pass
def __init__(self):
self.bar = self.Bar()
foo = Foo()
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def func():
return 2, []
a,b = func()
print(a,b)
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import torch
import torch.nn as nn
from torch.autograd import Variable
x = Variable(torch.Tensor([[2,3,4],[3,4,5]]))
h,w=x.size()
print(h,w)
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from tensorboardX import SummaryWriter
from myargs import Args
ss = 6
s0 = ss*2
args = Args()
args.display_curindex = 0
args.base_lr = 0.05
args.display_interval = 20
args.momentum = 0.9
args.epoches = 120
args.training_thresholds = [0,0,0,0,0,s0]
args.training_merge_thresholds = [s0+ss*9,s0+ss*6, s0+ss*3, s0, -1, s0+ss*12]
args.power = 0.5
writer = SummaryWriter(comment='-{}'.format('test lr'))
optimizer=None
lr = args.base_lr
def adjust_learning_rate(optimizer, epoch, beg, end, reset_lr=None):
"""Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
global lr
print('adjust', epoch, beg, end, lr)
lr = args.base_lr * (float(end-epoch)/(end-beg)) ** (args.power)
if lr < 1.0e-8: lr = 1.0e-8
writer.add_scalar('lr', lr, global_step=epoch)
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32 * 5
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