import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# Make sure that caffe is on the python path:
caffe_root = '../'  # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')

import caffe

plt.rcParams['figure.figsize'] = (10, 10)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

import os
if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):
    print("Downloading pre-trained CaffeNet model...")
    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet

"""
Explanation: Instant Recognition with Caffe
In this example we'll classify an image with the bundled CaffeNet model based on the network architecture of Krizhevsky et al. for ImageNet. We'll compare CPU and GPU operation then reach into the model to inspect features and the output.
(These feature visualizations follow the DeCAF visualizations originally by Yangqing Jia.)
First, import required modules, set plotting parameters, and run ./scripts/download_model_binary.py models/bvlc_reference_caffenet to get the pretrained CaffeNet model if it hasn't already been fetched.
End of explanation
"""


caffe.set_mode_cpu()
net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',
                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',
                caffe.TEST)

# input preprocessing: 'data' is the name of the input blob == net.inputs[0]
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel
transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]
transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB

"""
Explanation: Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing.
End of explanation
"""


# set net to batch size of 50
net.blobs['data'].reshape(50,3,227,227)

"""
Explanation: Let's start with a simple classification. We'll set a batch of 50 to demonstrate batch processing, even though we'll only be classifying one image. (Note that the batch size can also be changed on-the-fly.)
End of explanation
"""


net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/elephants.jpg'))
out = net.forward()
print("Predicted class is #{}.".format(out['prob'][0].argmax()))

"""
Explanation: Feed in the image (with some preprocessing) and classify with a forward pass.
End of explanation
"""


plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))

"""
Explanation: What did the input look like?
End of explanation
"""


# load labels
imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'
try:
    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')
except:
    !../data/ilsvrc12/get_ilsvrc_aux.sh
    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')

# sort top k predictions from softmax output
top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]
print labels[top_k]

"""
Explanation: Adorable, but was our classification correct?
End of explanation
"""


# CPU mode
net.forward()  # call once for allocation
%timeit net.forward()

"""
Explanation: Indeed! But how long did it take?
End of explanation
"""


# GPU mode
caffe.set_device(0)
caffe.set_mode_gpu()
net.forward()  # call once for allocation
%timeit net.forward()

"""
Explanation: That's a while, even for a batch size of 50 images. Let's switch to GPU mode.
End of explanation
"""


[(k, v.data.shape) for k, v in net.blobs.items()]

"""
Explanation: Much better. Now let's look at the net in more detail.
First, the layer features and their shapes (1 is the batch size, corresponding to the single input image in this example).
End of explanation
"""


[(k, v[0].data.shape) for k, v in net.params.items()]

"""
Explanation: The parameters and their shapes. The parameters are net.params['name'][0] while biases are net.params['name'][1].
End of explanation
"""


# take an array of shape (n, height, width) or (n, height, width, channels)
# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)
def vis_square(data, padsize=1, padval=0):
    data -= data.min()
    data /= data.max()
    
    # force the number of filters to be square
    n = int(np.ceil(np.sqrt(data.shape[0])))
    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)
    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))
    
    # tile the filters into an image
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))
    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
    
    plt.imshow(data)

"""
Explanation: Helper functions for visualization
End of explanation
"""


# the parameters are a list of [weights, biases]
filters = net.params['conv1'][0].data
vis_square(filters.transpose(0, 2, 3, 1))

"""
Explanation: The input image
The first layer filters, conv1
End of explanation
"""


feat = net.blobs['conv1'].data[0, :36]
vis_square(feat, padval=1)

"""
Explanation: The first layer output, conv1 (rectified responses of the filters above, first 36 only)
End of explanation
"""


filters = net.params['conv2'][0].data
vis_square(filters[:48].reshape(48**2, 5, 5))

"""
Explanation: The second layer filters, conv2
There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row.
End of explanation
"""


feat = net.blobs['conv2'].data[0, :36]
vis_square(feat, padval=1)

"""
Explanation: The second layer output, conv2 (rectified, only the first 36 of 256 channels)
End of explanation
"""


feat = net.blobs['conv3'].data[0]
vis_square(feat, padval=0.5)

"""
Explanation: The third layer output, conv3 (rectified, all 384 channels)
End of explanation
"""


feat = net.blobs['conv4'].data[0]
vis_square(feat, padval=0.5)

"""
Explanation: The fourth layer output, conv4 (rectified, all 384 channels)
End of explanation
"""


feat = net.blobs['conv5'].data[0]
vis_square(feat, padval=0.5)

"""
Explanation: The fifth layer output, conv5 (rectified, all 256 channels)
End of explanation
"""


feat = net.blobs['pool5'].data[0]
vis_square(feat, padval=1)

"""
Explanation: The fifth layer after pooling, pool5
End of explanation
"""


feat = net.blobs['fc6'].data[0]
plt.subplot(2, 1, 1)
plt.plot(feat.flat)
plt.subplot(2, 1, 2)
_ = plt.hist(feat.flat[feat.flat > 0], bins=100)

"""
Explanation: The first fully connected layer, fc6 (rectified)
We show the output values and the histogram of the positive values
End of explanation
"""


feat = net.blobs['fc7'].data[0]
plt.subplot(2, 1, 1)
plt.plot(feat.flat)
plt.subplot(2, 1, 2)
_ = plt.hist(feat.flat[feat.flat > 0], bins=100)

"""
Explanation: The second fully connected layer, fc7 (rectified)
End of explanation
"""


feat = net.blobs['prob'].data[0]
plt.plot(feat.flat)

"""
Explanation: The final probability output, prob
End of explanation
"""


# load labels
imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'
try:
    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')
except:
    !../data/ilsvrc12/get_ilsvrc_aux.sh
    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')

# sort top k predictions from softmax output
top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]
print labels[top_k]

"""
Explanation: Let's see the top 5 predicted labels.
End of explanation
"""


