def plot_dreams(model_file, test_imagepath, itr_per_octave, step_size,
max_octaves, octave_scale, layer_idx_of_interest, **kwargs):
assert(model_file)
runid = model_runid(model_file)
nn_image_size = get_nn_image_size(model_file)
# precalc octaves and tiles, as well as max batch size
dsb = DreamStudyBuffer(test_imagepath, nn_image_size, max_octaves, octave_scale)
max_nn_pass = len(dsb.octave_sizes) * itr_per_octave
column = load_column(model_file, batch_size=dsb.max_batch_size, layer_idx_of_interest=layer_idx_of_interest, **kwargs)
try:
nn_pass = 0
for octave, octave_size in reversed(list(enumerate(dsb.octave_sizes))):
for itr in range(itr_per_octave):
next_batch = dsb.tile_source_into_batch( octave, column.ds.mean, column.ds.std )
column.x_buffer.set_value(lasagne.utils.floatX(next_batch), borrow=True)
batch_updates = column.dream_batch()
dsb.update_source( batch_updates, octave, step_size )
nn_pass += 1
if (nn_pass in set([0] + [int(i) for i in numpy.logspace(0,numpy.log10(max_nn_pass),10)])):
name = 'data/dreams/%i_nnpass_%i_itr_%i_octave.png' % (nn_pass, itr, octave)
print("saving %s" % name)
scipy.misc.toimage(numpy.uint8(dsb.source)).save(name, "PNG")
except KeyboardInterrupt:
print "[ERROR] User terminated Dream Study"
print "Done"
def plot_occluded_activations(model_file, test_path, patch_size, **kwargs):
assert(model_file)
runid = model_runid(model_file)
column = load_column(model_file, **kwargs)
os = OcclusionStudy(column.ds, patch_size)
try:
for path in file_iter(test_path):
os.test_imagepath = path
all_test_predictions, all_test_output = column.test(override_buffer=os.buffer_occluded_dataset, override_num_examples=os.num_patch_centers)
os.accumulate_patches_into_heatmaps(all_test_output, runid)
except KeyboardInterrupt:
print "[ERROR] User terminated Occlusion Study"
print "Done"
def plot_occluded_activations(model_file, test_path, patch_size, **kwargs):
assert (model_file)
runid = model_runid(model_file)
column = load_column(model_file, **kwargs)
os = OcclusionStudy(column.ds, patch_size)
try:
for path in file_iter(test_path):
os.test_imagepath = path
all_test_predictions, all_test_output = column.test(
override_buffer=os.buffer_occluded_dataset,
override_num_examples=os.num_patch_centers)
os.accumulate_patches_into_heatmaps(all_test_output, runid)
except KeyboardInterrupt:
print "[ERROR] User terminated Occlusion Study"
print "Done"
import sys
from os import listdir, path
import re
import cPickle
import numpy
import natsort
from my_code.predict import model_runid, save_prediction
import pdb
TEST_IMAGE_DIR = 'data/test/centered_crop/'
# usage:
if __name__ == '__main__':
raw_output_files = sys.argv[1].split(',')
raw_outputs = [cPickle.load(open(f)) for f in raw_output_files]
avg = sum(raw_outputs) / float(len(raw_output_files))
runids = [model_runid(n) for n in raw_output_files]
name = '+'.join(runids)
labels = (avg > 0.5).sum(axis=1)
img_names = numpy.array([
path.splitext(f)[0] for f in listdir(TEST_IMAGE_DIR)
if re.search('\.(jpeg|png)', f)
])
save_prediction(name, natsort.natsorted(img_names), labels)
img = scipy.misc.toimage(filt[m][:,:,n])
else:
img = scipy.misc.toimage(filt[n][m,:,:])
nd = int(filter_rows*filter_enlargement)
new_size = nd, nd
img_3x = img.resize(new_size, Image.NEAREST)
out_img[i_px_start:i_px_end, j_px_start:j_px_end] = img_3x
return out_img
if __name__ == '__main__':
model_file = sys.argv[1]
if len(sys.argv) == 3:
filter_shape = sys.argv[2]
else:
filter_shape = 'c01b'
runid = model_runid(model_file)
f = open(model_file)
# discard first slot
all_saves = []
while True:
try:
all_saves.append(cPickle.load(f))
except EOFError:
break
all_layers_flat = all_saves[-1]
all_layers = zip(all_layers_flat[0::2], all_layers_flat[1::2])
conv_layers = [(W,b) for W,b in all_layers if len(W.shape) == 4]
outpaths = []
for i,Wb in enumerate(conv_layers):
W,b = Wb
import sys
from os import listdir, path
import re
import cPickle
import numpy
import natsort
from my_code.predict import model_runid, save_prediction
import pdb
TEST_IMAGE_DIR = 'data/test/centered_crop/'
# usage:
if __name__ == '__main__':
raw_output_files = sys.argv[1].split(',')
raw_outputs = [cPickle.load(open(f)) for f in raw_output_files]
avg = sum(raw_outputs) / float(len(raw_output_files))
runids = [model_runid(n) for n in raw_output_files]
name = '+'.join(runids)
labels = (avg > 0.5).sum(axis=1)
img_names = numpy.array([path.splitext(f)[0] for f in listdir(TEST_IMAGE_DIR) if re.search('\.(jpeg|png)', f)])
save_prediction(name, natsort.natsorted(img_names), labels)
