# Load the data. Make sure you have the right path to the imagenet model file.
%matplotlib inline
from decaf.scripts import imagenet
from decaf.util import smalldata
from decaf.util import visualize
from matplotlib import pyplot
import numpy as np
import pylab

# We will use a larger figure size since many figures are fairly big.
pylab.rcParams['figure.figsize'] = (10.0, 10.0)
data_root='/Users/jiayq/Research/models/'
net = imagenet.DecafNet(data_root+'imagenet.decafnet.epoch90', data_root+'imagenet.decafnet.meta')
lena = smalldata.lena()

# Run a classification pass to create all the intermediate features
scores = net.classify(lena, center_only=True)

print 'blobs:', net._net.blobs.keys()

# Show the input image
image = net.feature('data')[0,::-1]
image -= image.min()
image /= image.max()
_ = visualize.show_single(image)

# Show the first layer filters
filters = net._net.layers['conv1'].param()[0].data()
_ = visualize.show_multiple(filters.T)
pyplot.title('First layer Filters')

# show the first layer output. There are 96 channels,
# but we will just show 32 channels and their corresponding filters.
feat = net.feature('conv1_cudanet_out')[0,::-1, :, ::3]
filters = net._net.layers['conv1'].param()[0].data()
_ = visualize.show_multiple(filters.T[::3])
pyplot.title('Filters')
pyplot.figure()
_ = visualize.show_channels(feat)
pyplot.title('Output')

# show what the second layer filters look like.
# It is a bit complex, since there are 128 filters,
# and each filter is 5*5*48. We show the channels separately,
# and show only the first 48 filters, one row per filter.
filters = net._net.layers['conv2'].param()[0].data()
# make the right filter shape
filters = filters.T.reshape(128, 5, 5, 48)
filters = filters.swapaxes(2,3).swapaxes(1,2).reshape(128*48, 5, 5)
_ = visualize.show_multiple(filters[:48*48], ncols=48)
pyplot.title('Second layer filters, each filter is shown as a row of channels.')

# show what the second layer output look like.
# There are 256 channels, but we are going to only show the first 36 filters.
feat = net.feature('conv2_cudanet_out')[0, ::-1, :]
_ = visualize.show_channels(feat[:, :, :36])

# show what the third layer output look like.
# There are 384 channels
feat = net.feature('conv3_cudanet_out')[0, ::-1, :]
print feat.shape
_ = visualize.show_channels(feat)

# show what the fourth layer output look like.
# There are 384 channels
feat = net.feature('conv4_cudanet_out')[0, ::-1, :]
print feat.shape
_ = visualize.show_channels(feat)

# show what the fifth layer output look like.
# There are 256 channels.
feat = net.feature('conv5_cudanet_out')[0, ::-1, :]
print feat.shape
_ = visualize.show_channels(feat)

# show what the fifth layer output look like after ReLU and pooling.
# There are 256 channels. This is the last layer of
# convolutional neural networks. After this layer, all computations
# are fully connected.
feat = net.feature('pool5_cudanet_out')[0, ::-1, :]
print feat.shape
_ = visualize.show_channels(feat, bg_func=np.max)

# show the feature of the first fully connected layers.
feat = net.feature('fc6_cudanet_out')[0]
print feat.shape
pyplot.plot(feat)
pyplot.title('feature')
pyplot.figure()
_ = pyplot.hist(feat, bins=100)
pyplot.title('histogram')

# show the feature of the second fully connected layers.
feat = net.feature('fc7_cudanet_out')[0]
print feat.shape
pyplot.plot(feat)
pyplot.figure()
_ = pyplot.hist(feat, bins=100)

# show the final prediction probability
feat = net.feature('probs_cudanet_out')[0]
print feat.shape
pyplot.plot(feat)

# Now, print the top 5 predictions.
print net.top_k_prediction(scores, 5)[1]