To generate images using trained model

generate.py

@@ -0,0 +1,41 @@
+import caffe
+import numpy as np
+import sys
+import cv2
+import scipy.io
+import scipy.misc
+
+nz = 100
+img_size = 64
+batch_size = 64
+
+caffe.set_mode_gpu()
+gen_net = caffe.Net(sys.argv[1], sys.argv[2], caffe.TEST)
+
+# Fix the seed to debug
+np.random.seed(0) 
+gen_net.blobs['feat'].data[...] = np.random.normal(0, 1, (batch_size, nz)).astype(np.float32)
+
+gen_net.forward_simple()
+
+generated_img = gen_net.blobs['generated'].data
+
+print generated_img.shape
+
+print generated_img[0].transpose(1,2,0)
+max_val, min_val = np.max(generated_img[0]), np.min(generated_img[0])
+
+#matfile = scipy.io.loadmat('/data/Repo/release_deepsim_v0.5_train/trained_models/caffenet/ilsvrc_2012_mean.mat')
+#image_mean = matfile['image_mean']
+#print image_mean.shape, image_mean, image_mean[14:241, 14:241, :] + generated_img[0].transpose(1,2,0)
+
+# Concat all images into a big 8*8 image
+flatten_img = ((generated_img.transpose((0,2,3,1)))[:] - min_val) / (max_val-min_val)
+print flatten_img.shape
+#print flatten_img.reshape(2, 2, 64, 64, 3).shape
+#scipy.misc.imsave('test1.png', flatten_img.reshape(8,8,img_size,img_size,3).swapaxes(1,2).reshape(8*img_size,8*img_size, 3))
+cv2.imshow('test1', flatten_img.reshape(8,8,img_size,img_size,3).swapaxes(1,2).reshape(8*img_size,8*img_size, 3))
+cv2.waitKey()
+
+#cv2.imshow('test', ((generated_img.transpose((0,2,3,1)))[2] - min_val) / (max_val-min_val))
+#cv2.waitKey()