Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x
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%matplotlib inline
%reload_ext autoreload
%autoreload 2
You can get the data via:
wget http://pjreddie.com/media/files/cifar.tgz
Important: Before proceeding, the student must reorganize the downloaded dataset files to match the expected directory structure, so that there is a dedicated folder for each class under 'test' and 'train', e.g.:
* test/airplane/airplane-1001.png
* test/bird/bird-1043.png
* train/bird/bird-10018.png
* train/automobile/automobile-10000.pngThe filename of the image doesn't have to include its class.
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from fastai.conv_learner import *
PATH = "data/cifar10/"
os.makedirs(PATH,exist_ok=True)
!ls {PATH}
if not os.path.exists(f"{PATH}/train/bird"):
raise Exception("expecting class subdirs under 'train/' and 'test/'")
!ls {PATH}/train
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classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
stats = (np.array([ 0.4914 , 0.48216, 0.44653]), np.array([ 0.24703, 0.24349, 0.26159]))
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def get_data(sz,bs):
tfms = tfms_from_stats(stats, sz, aug_tfms=[RandomFlip()], pad=sz//8)
return ImageClassifierData.from_paths(PATH, val_name='test', tfms=tfms, bs=bs)
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bs=256
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data = get_data(32,4)
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x,y=next(iter(data.trn_dl))
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plt.imshow(data.trn_ds.denorm(x)[0]);
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plt.imshow(data.trn_ds.denorm(x)[1]);
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data = get_data(32,bs)
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lr=1e-2
From this notebook by our student Kerem Turgutlu:
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class SimpleNet(nn.Module):
def __init__(self, layers):
super().__init__()
self.layers = nn.ModuleList([
nn.Linear(layers[i], layers[i + 1]) for i in range(len(layers) - 1)])
def forward(self, x):
x = x.view(x.size(0), -1)
for l in self.layers:
l_x = l(x)
x = F.relu(l_x)
return F.log_softmax(l_x, dim=-1)
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learn = ConvLearner.from_model_data(SimpleNet([32*32*3, 40,10]), data)
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learn, [o.numel() for o in learn.model.parameters()]
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learn.summary()
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learn.lr_find()
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learn.sched.plot()
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%time learn.fit(lr, 2)
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%time learn.fit(lr, 2, cycle_len=1)
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class ConvNet(nn.Module):
def __init__(self, layers, c):
super().__init__()
self.layers = nn.ModuleList([
nn.Conv2d(layers[i], layers[i + 1], kernel_size=3, stride=2)
for i in range(len(layers) - 1)])
self.pool = nn.AdaptiveMaxPool2d(1)
self.out = nn.Linear(layers[-1], c)
def forward(self, x):
for l in self.layers: x = F.relu(l(x))
x = self.pool(x)
x = x.view(x.size(0), -1)
return F.log_softmax(self.out(x), dim=-1)
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learn = ConvLearner.from_model_data(ConvNet([3, 20, 40, 80], 10), data)
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learn.summary()
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learn.lr_find(end_lr=100)
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learn.sched.plot()
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%time learn.fit(1e-1, 2)
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%time learn.fit(1e-1, 4, cycle_len=1)
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class ConvLayer(nn.Module):
def __init__(self, ni, nf):
super().__init__()
self.conv = nn.Conv2d(ni, nf, kernel_size=3, stride=2, padding=1)
def forward(self, x): return F.relu(self.conv(x))
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class ConvNet2(nn.Module):
def __init__(self, layers, c):
super().__init__()
self.layers = nn.ModuleList([ConvLayer(layers[i], layers[i + 1])
for i in range(len(layers) - 1)])
self.out = nn.Linear(layers[-1], c)
def forward(self, x):
for l in self.layers: x = l(x)
x = F.adaptive_max_pool2d(x, 1)
x = x.view(x.size(0), -1)
return F.log_softmax(self.out(x), dim=-1)
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learn = ConvLearner.from_model_data(ConvNet2([3, 20, 40, 80], 10), data)
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learn.summary()
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%time learn.fit(1e-1, 2)
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%time learn.fit(1e-1, 2, cycle_len=1)
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class BnLayer(nn.Module):
def __init__(self, ni, nf, stride=2, kernel_size=3):
super().__init__()
self.conv = nn.Conv2d(ni, nf, kernel_size=kernel_size, stride=stride,
bias=False, padding=1)
self.a = nn.Parameter(torch.zeros(nf,1,1))
self.m = nn.Parameter(torch.ones(nf,1,1))
def forward(self, x):
x = F.relu(self.conv(x))
x_chan = x.transpose(0,1).contiguous().view(x.size(1), -1)
if self.training:
self.means = x_chan.mean(1)[:,None,None]
self.stds = x_chan.std (1)[:,None,None]
return (x-self.means) / self.stds *self.m + self.a
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class ConvBnNet(nn.Module):
def __init__(self, layers, c):
super().__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5, stride=1, padding=2)
self.layers = nn.ModuleList([BnLayer(layers[i], layers[i + 1])
for i in range(len(layers) - 1)])
self.out = nn.Linear(layers[-1], c)
def forward(self, x):
x = self.conv1(x)
for l in self.layers: x = l(x)
x = F.adaptive_max_pool2d(x, 1)
x = x.view(x.size(0), -1)
return F.log_softmax(self.out(x), dim=-1)
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learn = ConvLearner.from_model_data(ConvBnNet([10, 20, 40, 80, 160], 10), data)
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learn.summary()
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%time learn.fit(3e-2, 2)
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%time learn.fit(1e-1, 4, cycle_len=1)
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class ConvBnNet2(nn.Module):
def __init__(self, layers, c):
super().__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5, stride=1, padding=2)
self.layers = nn.ModuleList([BnLayer(layers[i], layers[i+1])
for i in range(len(layers) - 1)])
self.layers2 = nn.ModuleList([BnLayer(layers[i+1], layers[i + 1], 1)
for i in range(len(layers) - 1)])
self.out = nn.Linear(layers[-1], c)
def forward(self, x):
x = self.conv1(x)
for l,l2 in zip(self.layers, self.layers2):
x = l(x)
x = l2(x)
x = F.adaptive_max_pool2d(x, 1)
x = x.view(x.size(0), -1)
return F.log_softmax(self.out(x), dim=-1)
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learn = ConvLearner.from_model_data(ConvBnNet2([10, 20, 40, 80, 160], 10), data)
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%time learn.fit(1e-2, 2)
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%time learn.fit(1e-2, 2, cycle_len=1)
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class ResnetLayer(BnLayer):
def forward(self, x): return x + super().forward(x)
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class Resnet(nn.Module):
def __init__(self, layers, c):
super().__init__()
self.conv1 = nn.Conv2d(3, 10, kernel_size=5, stride=1, padding=2)
self.layers = nn.ModuleList([BnLayer(layers[i], layers[i+1])
for i in range(len(layers) - 1)])
self.layers2 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i + 1], 1)
for i in range(len(layers) - 1)])
self.layers3 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i + 1], 1)
for i in range(len(layers) - 1)])
self.out = nn.Linear(layers[-1], c)
def forward(self, x):
x = self.conv1(x)
for l,l2,l3 in zip(self.layers, self.layers2, self.layers3):
x = l3(l2(l(x)))
x = F.adaptive_max_pool2d(x, 1)
x = x.view(x.size(0), -1)
return F.log_softmax(self.out(x), dim=-1)
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learn = ConvLearner.from_model_data(Resnet([10, 20, 40, 80, 160], 10), data)
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wd=1e-5
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%time learn.fit(1e-2, 2, wds=wd)
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%time learn.fit(1e-2, 3, cycle_len=1, cycle_mult=2, wds=wd)
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%time learn.fit(1e-2, 8, cycle_len=4, wds=wd)
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class Resnet2(nn.Module):
def __init__(self, layers, c, p=0.5):
super().__init__()
self.conv1 = BnLayer(3, 16, stride=1, kernel_size=7)
self.layers = nn.ModuleList([BnLayer(layers[i], layers[i+1])
for i in range(len(layers) - 1)])
self.layers2 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i + 1], 1)
for i in range(len(layers) - 1)])
self.layers3 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i + 1], 1)
for i in range(len(layers) - 1)])
self.out = nn.Linear(layers[-1], c)
self.drop = nn.Dropout(p)
def forward(self, x):
x = self.conv1(x)
for l,l2,l3 in zip(self.layers, self.layers2, self.layers3):
x = l3(l2(l(x)))
x = F.adaptive_max_pool2d(x, 1)
x = x.view(x.size(0), -1)
x = self.drop(x)
return F.log_softmax(self.out(x), dim=-1)
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learn = ConvLearner.from_model_data(Resnet2([16, 32, 64, 128, 256], 10, 0.2), data)
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wd=1e-6
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%time learn.fit(1e-2, 2, wds=wd)
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%time learn.fit(1e-2, 3, cycle_len=1, cycle_mult=2, wds=wd)
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%time learn.fit(1e-2, 8, cycle_len=4, wds=wd)
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learn.save('tmp3')
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log_preds,y = learn.TTA()
preds = np.mean(np.exp(log_preds),0)
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metrics.log_loss(y,preds), accuracy_np(preds,y)
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