Fine-tuning a Pretrained Network for Style Recognition
In [1]:
caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
import numpy as np
from pylab import *
%matplotlib inline
import tempfile
# Helper function for deprocessing preprocessed images, e.g., for display.
def deprocess_net_image(image):
image = image.copy() # don't modify destructively
image = image[::-1] # BGR -> RGB
image = image.transpose(1, 2, 0) # CHW -> HWC
image += [123, 117, 104] # (approximately) undo mean subtraction
# clamp values in [0, 255]
image[image < 0], image[image > 255] = 0, 255
# round and cast from float32 to uint8
image = np.round(image)
image = np.require(image, dtype=np.uint8)
return image
In [2]:
# Download just a small subset of the data for this exercise.
# (2000 of 80K images, 5 of 20 labels.)
# To download the entire dataset, set `full_dataset = True`.
full_dataset = False
if full_dataset:
NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1
else:
NUM_STYLE_IMAGES = 2000
NUM_STYLE_LABELS = 5
# This downloads the ilsvrc auxiliary data (mean file, etc),
# and a subset of 2000 images for the style recognition task.
import os
os.chdir(caffe_root) # run scripts from caffe root
!data/ilsvrc12/get_ilsvrc_aux.sh
!scripts/download_model_binary.py models/bvlc_reference_caffenet
!python examples/finetune_flickr_style/assemble_data.py \
--workers=-1 --seed=1701 \
--images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS
# back to examples
os.chdir('examples')
In [3]:
import os
weights = os.path.join(caffe_root, 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')
assert os.path.exists(weights)
In [4]:
# Load ImageNet labels to imagenet_labels
imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'
imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\t'))
assert len(imagenet_labels) == 1000
print 'Loaded ImageNet labels:\n', '\n'.join(imagenet_labels[:10] + ['...'])
# Load style labels to style_labels
style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'
style_labels = list(np.loadtxt(style_label_file, str, delimiter='\n'))
if NUM_STYLE_LABELS > 0:
style_labels = style_labels[:NUM_STYLE_LABELS]
print '\nLoaded style labels:\n', ', '.join(style_labels)
In [5]:
from caffe import layers as L
from caffe import params as P
weight_param = dict(lr_mult=1, decay_mult=1)
bias_param = dict(lr_mult=2, decay_mult=0)
learned_param = [weight_param, bias_param]
frozen_param = [dict(lr_mult=0)] * 2
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,
param=learned_param,
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0.1)):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, group=group,
param=param, weight_filler=weight_filler,
bias_filler=bias_filler)
return conv, L.ReLU(conv, in_place=True)
def fc_relu(bottom, nout, param=learned_param,
weight_filler=dict(type='gaussian', std=0.005),
bias_filler=dict(type='constant', value=0.1)):
fc = L.InnerProduct(bottom, num_output=nout, param=param,
weight_filler=weight_filler,
bias_filler=bias_filler)
return fc, L.ReLU(fc, in_place=True)
def max_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def caffenet(data, label=None, train=True, num_classes=1000,
classifier_name='fc8', learn_all=False):
"""Returns a NetSpec specifying CaffeNet, following the original proto text
specification (./models/bvlc_reference_caffenet/train_val.prototxt)."""
n = caffe.NetSpec()
n.data = data
param = learned_param if learn_all else frozen_param
n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)
n.pool1 = max_pool(n.relu1, 3, stride=2)
n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)
n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)
n.pool2 = max_pool(n.relu2, 3, stride=2)
n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)
n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)
n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)
n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)
n.pool5 = max_pool(n.relu5, 3, stride=2)
n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)
if train:
n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)
else:
fc7input = n.relu6
n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)
if train:
n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)
else:
fc8input = n.relu7
# always learn fc8 (param=learned_param)
fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)
# give fc8 the name specified by argument `classifier_name`
n.__setattr__(classifier_name, fc8)
if not train:
n.probs = L.Softmax(fc8)
if label is not None:
n.label = label
n.loss = L.SoftmaxWithLoss(fc8, n.label)
n.acc = L.Accuracy(fc8, n.label)
# write the net to a temporary file and return its filename
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(str(n.to_proto()))
return f.name
In [6]:
dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))
imagenet_net_filename = caffenet(data=dummy_data, train=False)
imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)
In [7]:
def style_net(train=True, learn_all=False, subset=None):
if subset is None:
subset = 'train' if train else 'test'
source = caffe_root + 'data/flickr_style/%s.txt' % subset
transform_param = dict(mirror=train, crop_size=227,
mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')
style_data, style_label = L.ImageData(
transform_param=transform_param, source=source,
batch_size=50, new_height=256, new_width=256, ntop=2)
return caffenet(data=style_data, label=style_label, train=train,
num_classes=NUM_STYLE_LABELS,
classifier_name='fc8_flickr',
learn_all=learn_all)
In [8]:
untrained_style_net = caffe.Net(style_net(train=False, subset='train'),
weights, caffe.TEST)
untrained_style_net.forward()
style_data_batch = untrained_style_net.blobs['data'].data.copy()
style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)
In [9]:
def disp_preds(net, image, labels, k=5, name='ImageNet'):
input_blob = net.blobs['data']
net.blobs['data'].data[0, ...] = image
probs = net.forward(start='conv1')['probs'][0]
top_k = (-probs).argsort()[:k]
print 'top %d predicted %s labels =' % (k, name)
print '\n'.join('\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])
for i, p in enumerate(top_k))
def disp_imagenet_preds(net, image):
disp_preds(net, image, imagenet_labels, name='ImageNet')
def disp_style_preds(net, image):
disp_preds(net, image, style_labels, name='style')
In [10]:
batch_index = 8
image = style_data_batch[batch_index]
plt.imshow(deprocess_net_image(image))
print 'actual label =', style_labels[style_label_batch[batch_index]]
In [11]:
disp_imagenet_preds(imagenet_net, image)
In [12]:
disp_style_preds(untrained_style_net, image)
In [13]:
diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]
error = (diff ** 2).sum()
assert error < 1e-8
In [14]:
del untrained_style_net
In [15]:
from caffe.proto import caffe_pb2
def solver(train_net_path, test_net_path=None, base_lr=0.001):
s = caffe_pb2.SolverParameter()
# Specify locations of the train and (maybe) test networks.
s.train_net = train_net_path
if test_net_path is not None:
s.test_net.append(test_net_path)
s.test_interval = 1000 # Test after every 1000 training iterations.
s.test_iter.append(100) # Test on 100 batches each time we test.
# The number of iterations over which to average the gradient.
# Effectively boosts the training batch size by the given factor, without
# affecting memory utilization.
s.iter_size = 1
s.max_iter = 100000 # # of times to update the net (training iterations)
# Solve using the stochastic gradient descent (SGD) algorithm.
# Other choices include 'Adam' and 'RMSProp'.
s.type = 'SGD'
# Set the initial learning rate for SGD.
s.base_lr = base_lr
# Set `lr_policy` to define how the learning rate changes during training.
# Here, we 'step' the learning rate by multiplying it by a factor `gamma`
# every `stepsize` iterations.
s.lr_policy = 'step'
s.gamma = 0.1
s.stepsize = 20000
# Set other SGD hyperparameters. Setting a non-zero `momentum` takes a
# weighted average of the current gradient and previous gradients to make
# learning more stable. L2 weight decay regularizes learning, to help prevent
# the model from overfitting.
s.momentum = 0.9
s.weight_decay = 5e-4
# Display the current training loss and accuracy every 1000 iterations.
s.display = 1000
# Snapshots are files used to store networks we've trained. Here, we'll
# snapshot every 10K iterations -- ten times during training.
s.snapshot = 10000
s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'
# Train on the GPU. Using the CPU to train large networks is very slow.
s.solver_mode = caffe_pb2.SolverParameter.GPU
# Write the solver to a temporary file and return its filename.
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(str(s))
return f.name
In [16]:
def run_solvers(niter, solvers, disp_interval=10):
"""Run solvers for niter iterations,
returning the loss and accuracy recorded each iteration.
`solvers` is a list of (name, solver) tuples."""
blobs = ('loss', 'acc')
loss, acc = ({name: np.zeros(niter) for name, _ in solvers}
for _ in blobs)
for it in range(niter):
for name, s in solvers:
s.step(1) # run a single SGD step in Caffe
loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()
for b in blobs)
if it % disp_interval == 0 or it + 1 == niter:
loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %
(n, loss[n][it], np.round(100*acc[n][it]))
for n, _ in solvers)
print '%3d) %s' % (it, loss_disp)
# Save the learned weights from both nets.
weight_dir = tempfile.mkdtemp()
weights = {}
for name, s in solvers:
filename = 'weights.%s.caffemodel' % name
weights[name] = os.path.join(weight_dir, filename)
s.net.save(weights[name])
return loss, acc, weights
In [17]:
niter = 200 # number of iterations to train
# Reset style_solver as before.
style_solver_filename = solver(style_net(train=True))
style_solver = caffe.get_solver(style_solver_filename)
style_solver.net.copy_from(weights)
# For reference, we also create a solver that isn't initialized from
# the pretrained ImageNet weights.
scratch_style_solver_filename = solver(style_net(train=True))
scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)
print 'Running solvers for %d iterations...' % niter
solvers = [('pretrained', style_solver),
('scratch', scratch_style_solver)]
loss, acc, weights = run_solvers(niter, solvers)
print 'Done.'
train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']
train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']
style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']
# Delete solvers to save memory.
del style_solver, scratch_style_solver, solvers
In [18]:
plot(np.vstack([train_loss, scratch_train_loss]).T)
xlabel('Iteration #')
ylabel('Loss')
Out[18]:
In [19]:
plot(np.vstack([train_acc, scratch_train_acc]).T)
xlabel('Iteration #')
ylabel('Accuracy')
Out[19]:
In [20]:
def eval_style_net(weights, test_iters=10):
test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)
accuracy = 0
for it in xrange(test_iters):
accuracy += test_net.forward()['acc']
accuracy /= test_iters
return test_net, accuracy
In [21]:
test_net, accuracy = eval_style_net(style_weights)
print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )
scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)
print 'Accuracy, trained from random initialization: %3.1f%%' % (100*scratch_accuracy, )
In [22]:
end_to_end_net = style_net(train=True, learn_all=True)
# Set base_lr to 1e-3, the same as last time when learning only the classifier.
# You may want to play around with different values of this or other
# optimization parameters when fine-tuning. For example, if learning diverges
# (e.g., the loss gets very large or goes to infinity/NaN), you should try
# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value
# for which learning does not diverge).
base_lr = 0.001
style_solver_filename = solver(end_to_end_net, base_lr=base_lr)
style_solver = caffe.get_solver(style_solver_filename)
style_solver.net.copy_from(style_weights)
scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)
scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)
scratch_style_solver.net.copy_from(scratch_style_weights)
print 'Running solvers for %d iterations...' % niter
solvers = [('pretrained, end-to-end', style_solver),
('scratch, end-to-end', scratch_style_solver)]
_, _, finetuned_weights = run_solvers(niter, solvers)
print 'Done.'
style_weights_ft = finetuned_weights['pretrained, end-to-end']
scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']
# Delete solvers to save memory.
del style_solver, scratch_style_solver, solvers
In [23]:
test_net, accuracy = eval_style_net(style_weights_ft)
print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )
scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)
print 'Accuracy, finetuned from random initialization: %3.1f%%' % (100*scratch_accuracy, )
In [24]:
plt.imshow(deprocess_net_image(image))
disp_style_preds(test_net, image)
In [25]:
batch_index = 1
image = test_net.blobs['data'].data[batch_index]
plt.imshow(deprocess_net_image(image))
print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]
In [26]:
disp_style_preds(test_net, image)
In [27]:
disp_style_preds(scratch_test_net, image)
In [28]:
disp_imagenet_preds(imagenet_net, image)