Week 2 - AlexNet

Week 2 - AlexNet/Alexnet/README.md

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+AlexNet
+
+Download weights to the same folder(Alexnet) from the link given below.
+
+Weights: [here](http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/bvlc_alexnet.npy)

Week 2 - AlexNet/Alexnet/alex.py

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+
+
+from numpy import *
+import os
+import numpy as np
+
+import time
+from scipy.misc import imread
+from scipy.misc import imresize
+import matplotlib.image as mpimg
+from scipy.ndimage import filters
+import urllib
+from numpy import random
+
+
+import tensorflow as tf
+
+from caffe_classes import class_names
+
+train_x = zeros((1, 227,227,3)).astype(float32)
+train_y = zeros((1, 1000))
+xdim = train_x.shape[1:]
+ydim = train_y.shape[1]
+
+#Read Image, and change to BGR
+
+
+im1 = (imread("laska.png")[:,:,:3]).astype(float32)
+im1 = im1 - mean(im1)
+im1[:, :, 0], im1[:, :, 2] = im1[:, :, 2], im1[:, :, 0]
+
+im2 = (imread("poodle.png")[:,:,:3]).astype(float32)
+im2[:, :, 0], im2[:, :, 2] = im2[:, :, 2], im2[:, :, 0]
+
+
+net_data = load(open("bvlc_alexnet.npy", "rb"), encoding="latin1").item()
+
+def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w,  padding="VALID", group=1):
+
+    c_i = input.get_shape()[-1]
+    assert c_i%group==0
+    assert c_o%group==0
+    convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
+
+
+    if group==1:
+        conv = convolve(input, kernel)
+    else:
+        input_groups =  tf.split(input, group, 3)   #tf.split(3, group, input)
+        kernel_groups = tf.split(kernel, group, 3)  #tf.split(3, group, kernel) 
+        output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)]
+        conv = tf.concat(output_groups, 3)          #tf.concat(3, output_groups)
+    return  tf.reshape(tf.nn.bias_add(conv, biases), [-1]+conv.get_shape().as_list()[1:])
+
+
+
+x = tf.placeholder(tf.float32, (None,) + xdim)
+
+
+#conv1
+k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
+conv1W = tf.Variable(net_data["conv1"][0])
+conv1b = tf.Variable(net_data["conv1"][1])
+conv1_in = conv(x, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1)
+conv1 = tf.nn.relu(conv1_in)
+
+#lrn1
+radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
+lrn1 = tf.nn.local_response_normalization(conv1,
+                                                  depth_radius=radius,
+                                                  alpha=alpha,
+                                                  beta=beta,
+                                                  bias=bias)
+
+#maxpool1
+k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
+maxpool1 = tf.nn.max_pool(lrn1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
+
+
+#conv2
+k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2
+conv2W = tf.Variable(net_data["conv2"][0])
+conv2b = tf.Variable(net_data["conv2"][1])
+conv2_in = conv(maxpool1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
+conv2 = tf.nn.relu(conv2_in)
+
+
+#lrn2
+radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
+lrn2 = tf.nn.local_response_normalization(conv2,
+                                                  depth_radius=radius,
+                                                  alpha=alpha,
+                                                  beta=beta,
+                                                  bias=bias)
+
+#maxpool2
+k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
+maxpool2 = tf.nn.max_pool(lrn2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
+
+#conv3
+k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1
+conv3W = tf.Variable(net_data["conv3"][0])
+conv3b = tf.Variable(net_data["conv3"][1])
+conv3_in = conv(maxpool2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
+conv3 = tf.nn.relu(conv3_in)
+
+#conv4
+#conv(3, 3, 384, 1, 1, group=2, name='conv4')
+k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2
+conv4W = tf.Variable(net_data["conv4"][0])
+conv4b = tf.Variable(net_data["conv4"][1])
+conv4_in = conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
+conv4 = tf.nn.relu(conv4_in)
+
+
+#conv5
+k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2
+conv5W = tf.Variable(net_data["conv5"][0])
+conv5b = tf.Variable(net_data["conv5"][1])
+conv5_in = conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
+conv5 = tf.nn.relu(conv5_in)
+
+#maxpool5
+k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
+maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
+
+#fc6
+fc6W = tf.Variable(net_data["fc6"][0])
+fc6b = tf.Variable(net_data["fc6"][1])
+fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b)
+
+#fc7
+fc7W = tf.Variable(net_data["fc7"][0])
+fc7b = tf.Variable(net_data["fc7"][1])
+fc7 = tf.nn.relu_layer(fc6, fc7W, fc7b)
+
+#fc8
+fc8W = tf.Variable(net_data["fc8"][0])
+fc8b = tf.Variable(net_data["fc8"][1])
+fc8 = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
+
+
+#prob
+#softmax(name='prob'))
+prob = tf.nn.softmax(fc8)
+
+init = tf.initialize_all_variables()
+sess = tf.Session()
+sess.run(init)
+
+output = sess.run(prob, feed_dict = {x:[im1,im2]})
+
+#Output:
+
+
+for input_im_ind in range(output.shape[0]):
+    inds = argsort(output)[input_im_ind,:]
+    print('\n')
+    print("#@#@#@#@#@#@#@#@#")
+    print('\n')
+    print("Image", input_im_ind)
+    for i in range(5):
+        print(class_names[inds[-1-i]], output[input_im_ind, inds[-1-i]])
+