def get_conv1_filter(param):
param_images = []
for i in range(param.shape[0]):
img = param[i,:,:,:]
img = np.transpose(img,(1,2,0))
img = img - img.min()
img = img / img.max()
img = img*255
img = img.astype(np.uint8)
param_images.append(img)
#print img
show_filters = show_tile(param_images,100,1)[:,:,::-1]
return show_filters
save_iter = 200
while True:
print "train stage 01 iter: %d to %d" % (train_iter01 * iter01,
(train_iter01 + 1) * iter01)
net = train_stage01.Train(np.int((train_iter01 + 1) * iter01), net)
train_iter01 = train_iter01 + 1
#train_stage02.ShareTrainedLayersWith(net)
#train_stage02.net.share_with(train_stage01.net)
print "train stage 02 iter: %d to %d" % (train_iter02 * iter02,
(train_iter02 + 1) * iter02)
net = train_stage02.Train(np.int((train_iter02 + 1) * iter02), net)
train_iter02 = train_iter02 + 1
#train_stage01.ShareTrainedLayersWith(net)
#train_stage02.net.share_with(train_stage01.net)
if count % save_iter == 0:
net.forward()
imgs = net.blobs['conv8_g'].data
imgs = np.transpose(imgs, [0, 2, 3, 1])
all_imgs = []
for i in range(imgs.shape[0]):
img_tmp = hard_tanh(imgs[i])
all_imgs.append(img_tmp)
show_imgs = show_tile(all_imgs, 144, 4)[:, :, ::-1]
plt.imshow(show_imgs)
plt.draw()
#plt.show(block=False)
plt.pause(0.001)
plt.imsave('fig/exp2_epoch_%d.png' % (count / save_iter), show_imgs)
count = count + 1
all_imgs.append(img_tmp)
imgs = net.blobs['data'].data.copy()
imgs = np.transpose(imgs, [0, 2, 3, 1])
for i in range(4): #imgs.shape[0]):
img_tmp = hard_tanh(imgs[i])
all_imgs.append(img_tmp)
#train_stage02.ShareTrainedLayersWith(net)
#train_stage02.net.share_with(train_stage01.net)
print "train stage 02 iter: %d to %d" % (train_iter02 * iter02,
(train_iter02 + 1) * iter02)
net = train_stage02.Train(np.int((train_iter02 + 1) * iter02), net)
train_iter02 = train_iter02 + 1
#train_stage01.ShareTrainedLayersWith(net)
#train_stage02.net.share_with(train_stage01.net)
if count % save_iter == 0:
net.forward()
imgs = net.blobs['re_conv'].data.copy()
imgs = np.transpose(imgs, [0, 2, 3, 1])
#all_imgs = []
for i in range(4): #imgs.shape[0]):
img_tmp = hard_tanh(imgs[i])
all_imgs.append(img_tmp)
show_imgs = show_tile(all_imgs, 144, 4)
plt.imshow(show_imgs)
plt.draw()
#plt.show(block=False)
plt.pause(0.001)
#plt.imsave('fig/exp2_epoch_%d.png'%(count / save_iter),show_imgs)
count = count + 1
input_diff = net.blobs['data'].diff.copy()
#input_diff = np.sqrt((input_diff[0]**2).sum(axis=0))
input_diff = np.transpose(input_diff[0], [1, 2, 0])
input_diff = input_diff - input_diff.min()
input_diff = input_diff / input_diff.max()
input_diff = input_diff**2
input_diff = input_diff / input_diff.max()
input_diff = (input_diff[h_st:h_end, w_st:w_end, :] *
255).astype(np.uint8)
if not input_diff.shape[0] * input_diff.shape[1] == 0:
top9_diff.append(input_diff)
else:
top9_diff.append(np.zeros((100, 100, 3)))
tmp_img = show_tile(top9_patch, 100, 1)[:, :, ::-1]
all_top9_patch.append(tmp_img)
if is_vis_diff:
tmp_diff = show_tile((top9_diff), 100, 1)[:, :, ::-1]
all_top9_diff.append(tmp_diff)
tmp_img = show_tile(all_top9_patch, all_top9_patch[0].shape[0], 5)
plt.imsave(save_name, tmp_img)
if is_vis_diff:
diff_img = show_tile(all_top9_diff, all_top9_diff[0].shape[0], 5)
plt.imsave(save_name.replace(".png", "_diff.png"), diff_img)
with open(save_name.replace(".png", "_top9.pickle"), 'wb') as fid:
pickle.dump(top9_info, fid)
#input_diff = np.sqrt((input_diff[0]**2).sum(axis=0))
input_diff = np.transpose(input_diff[0],[1,2,0])
input_diff = input_diff - input_diff.min()
input_diff = input_diff / input_diff.max()
input_diff = input_diff ** 2
input_diff = input_diff / input_diff.max()
input_diff = (input_diff[h_st:h_end,w_st:w_end,:]*255).astype(np.uint8)
if not input_diff.shape[0]*input_diff.shape[1]==0:
top9_diff.append(input_diff)
else:
top9_diff.append(np.zeros((100,100,3)))
tmp_img = show_tile(top9_patch,100,1)[:,:,::-1]
all_top9_patch.append(tmp_img)
if is_vis_diff:
tmp_diff = show_tile((top9_diff),100,1)[:,:,::-1]
all_top9_diff.append(tmp_diff)
total_top9_patch.append(all_top9_patch)
if is_vis_diff:
total_top9_diff.append(all_top9_diff)
#tmp_img = show_tile(all_top9_patch,all_top9_patch[0].shape[0],5)
#plt.imsave(save_name.replace(".png","_sub%02d.png"%(mask_idx)),tmp_img)
#if is_vis_diff:
#diff_img = show_tile(all_top9_diff,all_top9_diff[0].shape[0],5)
#plt.imsave(save_name.replace(".png","_sub%02d_diff.png"%(mask_idx)),diff_img)
all_vis_img = []
all_vis_diff = []
