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model.py.back
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model.py.back
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function
from collections import OrderedDict
from torch.legacy.nn import SpatialCrossMapLRN as SpatialCrossMapLRNOld
"""
OrderedDict([('conv1', Conv2d (3, 96, kernel_size=(11, 11), stride=(4, 4))), ('relu1', ReLU(inplace)), ('pool1', MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))), ('norm1', LRN(size=5, alpha=0.000100, beta=0.750000, k=1)), ('conv2', Conv2d (96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=2)), ('relu2', ReLU(inplace)), ('pool2', MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))), ('norm2', LRN(size=5, alpha=0.000100, beta=0.750000, k=1)), ('conv3', Conv2d (256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))), ('relu3', ReLU(inplace)), ('conv4', Conv2d (384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)), ('relu4', ReLU(inplace)), ('conv5', Conv2d (384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)), ('relu5', ReLU(inplace)), ('conv6', Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)), ('relu6', ReLU(inplace)), ('pool6', MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))), ('fc7_new', Sequential(
(0): view(nB, -1)
(1): Linear(in_features=9216, out_features=4096)
)), ('relu7', ReLU(inplace)), ('drop7', Dropout(p=0.5, inplace)), ('fc8_new', Sequential(
(0): view(nB, -1)
(1): Linear(in_features=4096, out_features=2543)
)), ('prob', Softmax(axis=1))])
"""
class LRN(nn.Module):
def __init__(self, local_size=1, alpha=1.0, beta=0.75, ACROSS_CHANNELS=True):
super(LRN, self).__init__()
self.ACROSS_CHANNELS = ACROSS_CHANNELS
if ACROSS_CHANNELS:
self.average=nn.AvgPool3d(kernel_size=(local_size, 1, 1),
stride=1,
padding=(int((local_size-1.0)/2), 0, 0))
else:
self.average=nn.AvgPool2d(kernel_size=local_size,
stride=1,
padding=int((local_size-1.0)/2))
self.alpha = alpha
self.beta = beta
def forward(self, x):
if self.ACROSS_CHANNELS:
div = x.pow(2).unsqueeze(1)
div = self.average(div).squeeze(1)
div = div.mul(self.alpha).add(1.0).pow(self.beta)
else:
div = x.pow(2)
div = self.average(div)
div = div.mul(self.alpha).add(1.0).pow(self.beta)
x = x.div(div)
return x
"""
class LRNFunc(Function):
def __init__(self, local_size, alpha=1e-4, beta=0.75, k=1):
super(LRNFunc, self).__init__()
self.size = local_size
self.alpha = alpha
self.beta = beta
self.k = k
def forward(self, input):
self.save_for_backward(input)
self.lrn = SpatialCrossMapLRNOld(self.size, self.alpha, self.beta, self.k)
self.lrn.type(input.type())
return self.lrn.forward(input)
def backward(self, grad_output):
input, = self.saved_tensors
return self.lrn.backward(input, grad_output)
# use this one instead
class LRN(nn.Module):
def __init__(self, local_size, alpha=1e-4, beta=0.75, k=1):
super(LRN, self).__init__()
self.size = local_size
self.alpha = alpha
self.beta = beta
self.k = k
def __repr__(self):
return 'LRN(size=%d, alpha=%f, beta=%f, k=%d)' % (self.size, self.alpha, self.beta, self.k)
def forward(self, input):
return LRNFunc(self.size, self.alpha, self.beta, self.k)(input)
class Reshape(nn.Module):
def __init__(self, dims):
super(Reshape, self).__init__()
self.dims = dims
def __repr__(self):
return 'Reshape(dims=%s)' % (self.dims)
def forward(self, x):
orig_dims = x.size()
#assert(len(orig_dims) == len(self.dims))
new_dims = [orig_dims[i] if self.dims[i] == 0 else self.dims[i] for i in range(len(self.dims))]
return x.view(*new_dims).contiguous()
"""
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=0)
self.relu1 = nn.ReLU(inplace=True)
self.pool1 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))
self.norm1 = LRN(local_size=5, alpha=0.000100, beta=0.750000)
self.conv2 = nn.Conv2d (96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=2)
self.relu2 = nn.ReLU(inplace=True)
self.pool2 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))
self.norm2 = LRN(local_size=5, alpha=0.000100, beta=0.750000)
self.conv3 = nn.Conv2d (256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d (384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)
self.relu4 = nn.ReLU(inplace=True)
self.conv5 = nn.Conv2d (384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)
self.relu5 = nn.ReLU(inplace=True)
self.conv6 = nn.Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)
self.relu6 = nn.ReLU(inplace=True)
self.pool6 = nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))
self.fc7_new = nn.Linear(in_features=9216, out_features=4096)
self.relu7 = nn.ReLU(inplace=True)
self.drop7 = nn.Dropout(p=0.5, inplace=True)
self.fc8_new = nn.Linear(in_features=4096, out_features=2543)
self.prob = nn.Softmax()
"""
self.features = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=0),
nn.ReLU(inplace=True),
LRN(local_size=5, alpha=0.0001, beta=0.75),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(96, 256, kernel_size=5, padding=2, groups=2),
nn.ReLU(inplace=True),
LRN(local_size=5, alpha=0.0001, beta=0.75),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(256, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 384, kernel_size=3, padding=1, groups=2),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 2543),
)
"""
"""
self.features = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d (3, 96, kernel_size=(11, 11), stride=(4, 4))),
('relu1', nn.ReLU(inplace=True)),
('pool1', nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))),
('norm1', LRN(local_size=5, alpha=0.000100, beta=0.750000)),
('conv2', nn.Conv2d (96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=2)),
('relu2', nn.ReLU(inplace=True)),
('pool2', nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))),
('norm2', LRN(local_size=5, alpha=0.000100, beta=0.750000)),
('conv3', nn.Conv2d (256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))),
('relu3', nn.ReLU(inplace=True)),
('conv4', nn.Conv2d (384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)),
('relu4', nn.ReLU(inplace=True)),
('conv5', nn.Conv2d (384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)),
('relu5', nn.ReLU(inplace=True)),
('conv6', nn.Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2)),
('relu6', nn.ReLU(inplace=True)),
('pool6', nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2), dilation=(1, 1))),
]))
self.classifier = nn.Sequential(OrderedDict([
('fc7_new', nn.Linear(in_features=9216, out_features=4096)),
('relu7', nn.ReLU(inplace=True)),
('drop7', nn.Dropout(p=0.5, inplace=True)),
('fc8_new', nn.Linear(in_features=4096, out_features=2543))
# ('prob', nn.Softmax(axis=1))
]))
"""
def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
x = self.norm1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
x = self.norm2(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.relu4(x)
x = self.conv5(x)
x = self.relu5(x)
x = self.conv6(x)
x = self.relu6(x)
x = self.pool6(x)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.fc7_new(x)
x = self.relu7(x)
if (self.training == True):
x = self.drop7(x)
x = self.fc8_new(x)
# x = F.relu(F.max_pool2d(self.conv1(x), 2))
# x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
# x = x.view(-1, 80)
# x = F.relu(self.fc1(x))
# x = F.dropout(x, training=self.training)
# x = self.fc2(x)
# return F.log_softmax(x, dim=1)
# x = F.relu(F.max_pool2d(self.conv1(x), 2))
# x = self.features(x)
# x = x.view(x.size(0), 256 * 6 * 6)
# x = self.classifier(x)
return self.prob(x)
def features_extraction(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
x = self.norm1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
x = self.norm2(x)
x = self.conv3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.relu4(x)
x = self.conv5(x)
# return x.view(x.size(0), 256 * 13 * 13)
x = self.relu5(x)
x = self.conv6(x)
# return x.view(x.size(0), 256 * 13 * 13)
x = self.relu6(x)
x = self.pool6(x)
return x.view(x.size(0), 256 * 6 * 6)
x = x.view(x.size(0), 256 * 6 * 6)
x = self.fc7_new(x)
x = self.relu7(x)
x = self.fc8_new(x)