def save_results(self,
params,
results,
prefix_template,
brave=False,
skipbig=False):
if prefix_template is None:
return
results_and_params = combine_dicts(
(('p.', params.__dict__), ('r.', results.__dict__)))
prefix = prefix_template % results_and_params
if os.path.isdir(prefix):
if prefix[-1] != '/':
prefix += '/' # append slash for dir-only template
else:
dirname = os.path.dirname(prefix)
if dirname:
mkdir_p(dirname)
# Don't overwrite previous results
if os.path.exists('%sinfo.txt' % prefix) and not brave:
raise Exception('Cowardly refusing to overwrite ' +
'%sinfo.txt' % prefix)
output_majority = False
if output_majority:
if results.majority_xx is not None:
asimg = results.majority_xx[
self.channel_swap_to_rgb].transpose((1, 2, 0))
saveimagescc('%smajority_X.jpg' % prefix, asimg, 0)
saveimagesc('%smajority_Xpm.jpg' % prefix,
asimg + self._data_mean_rgb_img) # PlusMean
if results.best_xx is not None:
asimg = results.best_xx[self.channel_swap_to_rgb].transpose(
(1, 2, 0))
saveimagescc('%sbest_X.jpg' % prefix, asimg, 0)
saveimagesc('%sbest_Xpm.jpg' % prefix,
asimg + self._data_mean_rgb_img) # PlusMean
with open('%sinfo.txt' % prefix, 'w') as ff:
print >> ff, params
print >> ff
print >> ff, results
if not skipbig:
with open('%sinfo_big.pkl' % prefix, 'w') as ff:
pickle.dump((params, results), ff, protocol=-1)
results.trim_arrays()
with open('%sinfo.pkl' % prefix, 'w') as ff:
pickle.dump((params, results), ff, protocol=-1)
def save_results(self, params, results, prefix_template, brave = False, skipbig = False):
if prefix_template is None:
return
results_and_params = combine_dicts((('p.', params.__dict__),
('r.', results.__dict__)))
prefix = prefix_template % results_and_params
if os.path.isdir(prefix):
if prefix[-1] != '/':
prefix += '/' # append slash for dir-only template
else:
dirname = os.path.dirname(prefix)
if dirname:
mkdir_p(dirname)
# Don't overwrite previous results
if os.path.exists('%sinfo.txt' % prefix) and not brave:
raise Exception('Cowardly refusing to overwrite ' + '%sinfo.txt' % prefix)
output_majority = False
if output_majority:
if results.majority_xx is not None:
asimg = results.majority_xx[self.channel_swap_to_rgb].transpose((1,2,0))
saveimagescc('%smajority_X.jpg' % prefix, asimg, 0)
saveimagesc('%smajority_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img) # PlusMean
if results.best_xx is not None:
asimg = results.best_xx[self.channel_swap_to_rgb].transpose((1,2,0))
saveimagescc('%sbest_X.jpg' % prefix, asimg, 0)
saveimagesc('%sbest_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img) # PlusMean
with open('%sinfo.txt' % prefix, 'w') as ff:
print >>ff, params
print >>ff
print >>ff, results
if not skipbig:
with open('%sinfo_big.pkl' % prefix, 'w') as ff:
pickle.dump((params, results), ff, protocol=-1)
results.trim_arrays()
with open('%sinfo.pkl' % prefix, 'w') as ff:
pickle.dump((params, results), ff, protocol=-1)
def save_results(self,
params,
results,
prefix_template,
brave=False,
skipbig=False,
skipsmall=False):
if prefix_template is None:
return
for batch_index in range(params.batch_size):
[best_X_name, best_Xpm_name, majority_X_name, majority_Xpm_name, info_name, info_pkl_name, info_big_pkl_name] = \
self.generate_output_names(batch_index, params, results, prefix_template, self.settings.caffevis_outputs_dir)
# Don't overwrite previous results
if os.path.exists(info_name) and not brave:
raise Exception('Cowardly refusing to overwrite ' + info_name)
output_majority = False
if output_majority:
# NOTE: this section wasn't tested after changes to code, so some minor index tweaking are in order
if results[batch_index].majority_xx is not None:
asimg = results[batch_index].majority_xx[
self.channel_swap_to_rgb].transpose((1, 2, 0))
saveimagescc(majority_X_name, asimg, 0)
saveimagesc(majority_Xpm_name,
asimg + self._data_mean_rgb_img) # PlusMean
if results[batch_index].best_xx is not None:
# results[batch_index].best_xx.shape is (6,224,224)
def save_output(data, channel_swap_to_rgb, best_X_image_name):
# , best_Xpm_image_name, data_mean_rgb_img):
asimg = data[channel_swap_to_rgb].transpose((1, 2, 0))
saveimagescc(best_X_image_name, asimg, 0)
# get center position, relative to layer, of best maximum
[temp_ii, temp_jj] = results[batch_index].idxmax[
results[batch_index].best_ii][1:3]
is_spatial = params.is_spatial
layer_name = params.push_layer
size_ii, size_jj = get_max_data_extent(self.net, self.settings,
layer_name, is_spatial)
data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[
2:4]
[out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
compute_data_layer_focus_area(is_spatial, temp_ii, temp_jj, self.settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
selected_input_index = self.find_selected_input_index(
layer_name)
out_arr = extract_patch_from_image(
results[batch_index].best_xx, self.net,
selected_input_index, self.settings, data_ii_end,
data_ii_start, data_jj_end, data_jj_start, out_ii_end,
out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
if self.settings.is_siamese:
save_output(
out_arr,
channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1,
2]],
best_X_image_name=best_X_name)
else:
save_output(out_arr,
channel_swap_to_rgb=self.channel_swap_to_rgb,
best_X_image_name=best_X_name)
if self.settings.optimize_image_generate_plus_mean:
out_arr_pm = extract_patch_from_image(
results[batch_index].best_xx + self.batched_data_mean,
self.net, selected_input_index, self.settings,
data_ii_end, data_ii_start, data_jj_end, data_jj_start,
out_ii_end, out_ii_start, out_jj_end, out_jj_start,
size_ii, size_jj)
if self.settings.is_siamese:
save_output(
out_arr_pm,
channel_swap_to_rgb=self.channel_swap_to_rgb[[
0, 1, 2
]],
best_X_image_name=best_Xpm_name)
else:
save_output(
out_arr_pm,
channel_swap_to_rgb=self.channel_swap_to_rgb,
best_X_image_name=best_Xpm_name)
with open(info_name, 'w') as ff:
print >> ff, params
print >> ff
print >> ff, results[batch_index]
if not skipbig:
with open(info_big_pkl_name, 'w') as ff:
pickle.dump((params, results[batch_index]),
ff,
protocol=-1)
if not skipsmall:
results[batch_index].trim_arrays()
with open(info_pkl_name, 'w') as ff:
pickle.dump((params, results[batch_index]),
ff,
protocol=-1)