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utils_svhn.py
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utils_svhn.py
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import torch.nn.functional as F
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
import scipy.io as sio
import torch.nn as nn
import math
def prepareData():
for split in ["train", "test"]:
load_ori = sio.loadmat("svhn/data/raw/{0}_32x32.mat".format(split))
load_data = load_ori['X']
load_labels = load_ori['y'].astype(np.int64).squeeze()
np.place(load_labels, load_labels == 10, 0)
load_data = np.transpose(load_data, (3, 2, 0, 1))
if split == "train":
num = 50000
elif split == "test":
num = 10000
load_cut = {'X': load_data[:num], 'y': load_labels[:num]}
sio.savemat("../sharedRes/svhn/data/processed/{0}_cut.mat".format(split), load_cut)
class svhnDataset(Dataset):
def __init__(self, split="train", transform=None):
self.transform = transform
self.split = split # training set or test set
ori_mat = sio.loadmat("svhn/data/processed/{0}_cut.mat".format(split))
self.data = ori_mat['X']
labels = ori_mat['y']
self.labels = labels.flatten()
def __getitem__(self, index):
img, target = self.data[index], self.labels[index]
img = Image.fromarray(np.transpose(img, (1, 2, 0)))
if self.transform is not None:
img = self.transform(img)
return img, target
def __len__(self):
return self.labels.shape[0]
class VGG(nn.Module):
def __init__(self, cfg, batch_norm=False, num_classes=10):
super(VGG, self).__init__()
self.block0 = self._make_layers(cfg[0], batch_norm, 3)
self.block1 = self._make_layers(cfg[1], batch_norm, cfg[0][-1])
self.block2 = self._make_layers(cfg[2], batch_norm, cfg[1][-1])
self.block3 = self._make_layers(cfg[3], batch_norm, cfg[2][-1])
self.block4 = self._make_layers(cfg[4], batch_norm, cfg[3][-1])
self.pool0 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
self.pool4 = nn.AdaptiveAvgPool2d((1, 1))
# self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.classifier = nn.Linear(512, num_classes)
self._initialize_weights()
def get_feat_modules(self):
feat_m = nn.ModuleList([])
feat_m.append(self.block0)
feat_m.append(self.pool0)
feat_m.append(self.block1)
feat_m.append(self.pool1)
feat_m.append(self.block2)
feat_m.append(self.pool2)
feat_m.append(self.block3)
feat_m.append(self.pool3)
feat_m.append(self.block4)
feat_m.append(self.pool4)
return feat_m
def get_bn_before_relu(self):
bn1 = self.block1[-1]
bn2 = self.block2[-1]
bn3 = self.block3[-1]
bn4 = self.block4[-1]
return [bn1, bn2, bn3, bn4]
def forward(self, x, is_feat=False, preact=False):
h = x.shape[2]
x = F.relu(self.block0(x))
f0 = x
x = self.pool0(x)
x = self.block1(x)
f1_pre = x
x = F.relu(x)
f1 = x
x = self.pool1(x)
x = self.block2(x)
f2_pre = x
x = F.relu(x)
f2 = x
x = self.pool2(x)
x = self.block3(x)
f3_pre = x
x = F.relu(x)
f3 = x
if h == 64:
x = self.pool3(x)
x = self.block4(x)
f4_pre = x
x = F.relu(x)
f4 = x
x = self.pool4(x)
x = x.view(x.size(0), -1)
f5 = x
x = self.classifier(x)
if is_feat:
if preact:
return [f0, f1_pre, f2_pre, f3_pre, f4_pre, f5], x
else:
return [f0, f1, f2, f3, f4, f5], x
else:
return x
@staticmethod
def _make_layers(cfg, batch_norm=False, in_channels=3):
layers = []
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
layers = layers[:-1]
return nn.Sequential(*layers)
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
n = m.weight.size(1)
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def vgg8(**kwargs):
model = VGG([[64], [128], [256], [512], [512]], batch_norm=True, **kwargs)
return model