def generate_layers(path, filename, out_path): for i, layer in enumerate(layers): print i sys.stdout.flush() ts = time.time() im = np.asarray(Image.open(os.path.join(path, filename)).convert('RGB').resize((256, 256), resample=Image.ANTIALIAS)) \ .transpose(2, 0, 1).reshape((1, 3, 256, 256))[:, :, 16:-16][:, :, :, 16:-16] im_name = filename[:filename.find('.')] dirname = os.path.join(out_path,im_name) if not os.path.exists(dirname): os.mkdir(dirname) if layer is not None: STATS = STAT_LIST[:layer] else: STATS = STAT_LIST stim_name = os.path.join(dirname, im_name+'_layer'+str(i)+'.npy') r, targets = thing4.main(pf, sf, stim_name, [(im, STATS)], use_bounds=True, data_layer='data', start_layer='conv1_1', start_normal=False, crop=(16, -16, 16, -16), save_dir = os.path.join(dirname, im_name+'_layer'+str(i)+'_history'), save_freq=100, seed=0) save_stim(stim_name) print time.time()-ts sys.stdout.flush()
def generate_layers(path, filename, out_path): seed = np.random.randint(2**32) print 'Seed used:', seed for i, layer in enumerate(layers): print i sys.stdout.flush() ts = time.time() im = get_image(os.path.join(path, filename)) im_name = filename[:filename.find('.')] dirname = os.path.join(out_path, im_name) if not os.path.exists(dirname): os.mkdir(dirname) orig = im.squeeze().transpose((1, 2, 0)).astype('uint8') img = Image.fromarray(orig, 'RGB') img.save(os.path.join(dirname, im_name + '.o.jpg')) if layer is not None: STATS = STAT_LIST[:layer] else: STATS = STAT_LIST stim_name = os.path.join(dirname, im_name + '_layer' + str(i) + '.npy') r, targets = thing4.main(pf, sf, stim_name, [(im, STATS)], use_bounds=True, data_layer='data', start_layer='conv1_1', start_normal=False, crop=(16, -16, 16, -16), save_dir=os.path.join( dirname, im_name + '_layer' + str(i) + '_history'), save_freq=100, seed=seed) save_stim(stim_name) print time.time() - ts sys.stdout.flush()
('pool4', 100, 'corr_rs')] pf = 'model.prototxt' sf = 'model_parameters.caffemodel' layers = [1, 3, 6, 11, None] for i, layer in enumerate(layers): print i sys.stdout.flush() ts = time.time() im = 'campbell256.o.jpg' im2 = np.asarray(Image.open('textures/texture_jpgs/'+im).convert('RGB').resize((256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape((1, 3, 256, 256))[:, :, 16:-16][:, :, :, 16:-16] im_name = im[:im.find('.')]+'_p2_layer'+str(i) dirname = 'textures/generated/'+im_name if layer is not None: STATS = STAT_LIST[:layer] else: STATS = STAT_LIST r, targets = thing4.main(pf, sf, os.path.join(dirname, 'genstim.npy'), [(im2, STATS)], use_bounds=True, data_layer='data', start_layer='conv1_1', start_normal=False, crop=(16, -16, 16, -16), save_dir = os.path.join(dirname, 'history'), save_freq=100, seed=0) print time.time()-ts sys.stdout.flush()
mean = np.load('/home/ubuntu/new/caffe/audio_batches_mean.npy') x = cPickle.load(open('/home/ubuntu/new/caffe/cgrams.cpy')) im = Image.fromarray((np.tile(x['all_cgrams'][:, :, -14], (3, 1, 1)) * 255).astype(np.uint8).transpose( (1, 2, 0))).resize((225, 225)) im = np.asarray(im).transpose((2, 0, 1)).reshape((1, 3, 225, 225)) print(im.shape) r, targets = thing4.main( pf, sf, '/home/ubuntu/test_audio_real2/test_real.npy', [( im, [ #('conv1', 1, 'corr_t'), #('pool1', 1, 'corr_t'), #('conv2', 1, 'corr_t'), #('pool2', 1, 'corr_t'), #('conv3', 1, 'corr_t'), #('conv4', 1, 'corr_t'), #('conv5', 1, 'corr_t'), ('pool5', 1, 'corr_t') ])], 'data', seed=0, use_bounds=True, mean=mean, save_dir='/home/ubuntu/test_audio_real2/things/', save_freq=100, start_normal='zeros')
import numpy as np import thing4 reload(thing4) import dldata.stimulus_sets.hvm as hvm from collections import OrderedDict pf = '/home/ubuntu/new/caffe/examples/cifar10/caffenet_rand.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/bvlc_reference_caffenet.caffemodel' #im0 = 255*imgs[0].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) #im1 = 255*imgs[-1].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) var = np.array([-1] + [0] * 4095).astype(np.float32) r = thing4.main(pf, sf, '/home/ubuntu/test_O3_bounds_s.npy', targets0=[(None, [('fc6', 1, ('max', var))])], crop=(14, -15, 14, -15), use_bounds=True, seed=1)
import thing4 import dldata.stimulus_sets.hvm as hvm from collections import OrderedDict pf = '/home/ubuntu/new/caffe/examples/cifar10/cifar10_quick_train_test_rand.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/cifar10_quick_iter_5000.caffemodel.h5' import numpy as np v = np.load('/home/ubuntu/new/caffe/data_target.npy') im = v[2] #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_xc.npy', [(im, [('conv1', 200, 'corr'), ('conv2', 200, 'corr'), ('c#onv3', 200, 'corr')])], 'data', mean=np.zeros_like(v[0])) r, targets = thing4.main(pf, sf, '/home/ubuntu/test_yc1.npy', [(im, [('pool3', 100, 'corr')])], 'data', mean=np.zeros_like(v[0]))
im = Image.fromarray((np.tile(x['all_cgrams'][:, :, 8], (3, 1, 1)) * 255).astype(np.uint8).transpose( (1, 2, 0))).resize((225, 225), resample=Image.ANTIALIAS) im = np.asarray(im).transpose((2, 0, 1)).reshape((1, 3, 225, 225)) print(im.shape) r, targets = thing4.main( pf, sf, '/home/ubuntu/test_audio_stim4a_diag/test_real.npy', [( im, [ ('conv1', 1, 'corr_diag'), ('pool1', 1, 'corr_diag'), ('conv2', 1, 'corr_diag'), ('pool2', 1, 'corr_diag'), ('conv3', 1000, 'corr_diag'), ('conv4', 100000, 'corr_diag'), ('conv5', 100000, 'corr_diag'), ('pool5', 100000, 'corr_diag'), #('fc6', 100000, 'ss') ])], 'data', seed=2, use_bounds=True, #start_normal=False, start_im = im, mean=mean, save_dir='/home/ubuntu/test_audio_stim4a_diag/things/', save_freq=100)
r, targets = thing4.main( pf, sf, os.path.join(dirname, 'genstim.npy'), [( im2, [ ('conv1', 100, 'corr'), ('norm1', 100, 'corr'), ('pool1', 100, 'corr'), ( 'conv2', 10000, 'corr', ), ('norm2', 100, 'corr'), ('pool2', 100, 'corr'), ('conv3', 10000, 'corr'), ('conv4', 100000, 'corr'), ('conv5', 1000000, 'corr'), ('pool5', 1000000, 'corr'), #('fc6', 10000, 'ss'), #('fc7', 10000, 'ss'), #('fc8', 10000, 'ss') ])], use_bounds=True, data_layer='data', start_normal=False, crop=(14, -15, 14, -15), save_dir=os.path.join(dirname, 'history'), save_freq=100, seed=0)
im = np.asarray( Image.open('/home/ubuntu/imgres.jpg').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] im1 = np.asarray( Image.open('/home/ubuntu/imgres-1.jpg').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] var = np.zeros((1000, )).astype(np.float32) #var = np.zeros((4096,)).astype(np.float32) var[0] = 1 #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_something4.npy', [([im], [('pool1', 100, 'corr')]), (None, [('fc8', 1, ('max', var))])], 'data', use_bounds=True, crop=(14, -15, 14, -15)) r, targets = thing4.main(pf, sf, '/home/ubuntu/genstim10/genstim.npy', [(None, [('data', .01, 'smooth'), ('fc8', (10000, var), 'softmax')]), ([im], [('pool1', 20, 'corr')])], data_layer='data', use_bounds=True, crop=(14, -15, 14, -15), start_normal=True, seed=1, start_im=im, save_dir='/home/ubuntu/genstim10/things', save_freq=100)
import thing4 import dldata.stimulus_sets.hvm as hvm from collections import OrderedDict preproc = OrderedDict([(u'normalize', False), (u'dtype', u'float32'), (u'resize_to', [256, 256, 3]), (u'mode', u'RGB'), (u'crop', None), (u'mask', None)]) dataset = hvm.HvMWithDiscfade() imgs = dataset.get_images(preproc=preproc) pf = '/home/ubuntu/new/caffe/examples/cifar10/caffenet_rand.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/bvlc_reference_caffenet.caffemodel' #im0 = 255*imgs[0].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) #im1 = 255*imgs[-1].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) meta = dataset.meta inds = (meta['category'] == 'Cars').nonzero()[0] ims = [ 255 * imgs[i][14:-15][:, 14:-15].transpose((2, 0, 1)).reshape( (1, 3, 227, 227)) for i in inds[::20][:10] ] r, targets = thing4.main(pf, sf, '/home/ubuntu/test_fc6_cn_cars3.npy', [(ims[0], [('conv1', .5, 'ss')]), (ims[1], [('conv5', 10, 'corr')])], 'data', crop=(14, -15, 14, -15))
im_name = im[:im.find('.')] + '_p2' dirname = 'textures/generated/' + im_name r, targets = thing4.main(pf, sf, os.path.join(dirname, 'genstim.npy'), [(im2, [('conv1_1', 1000, 'corr_rs'), ('conv1_2', 100, 'corr_rs'), ('pool1', 100, 'corr_rs'), ('conv2_1', 100, 'corr_rs'), ('conv2_2', 100, 'corr_rs'), ('pool2', 100, 'corr_rs'), ('conv3_1', 100, 'corr_rs'), ('conv3_2', 100, 'corr_rs'), ('conv3_3', 100, 'corr_rs'), ('conv3_4', 100, 'corr_rs'), ('pool3', 10, 'corr_rs'), ('conv4_1', 100, 'corr_rs'), ('conv4_2', 100, 'corr_rs'), ('conv4_3', 100, 'corr_rs'), ('conv4_4', 1000, 'corr_rs'), ('pool4', 100, 'corr_rs')])], use_bounds=True, data_layer='data', start_layer='conv1_1', start_normal=False, crop=(16, -16, 16, -16), save_dir=os.path.join(dirname, 'history'), save_freq=100, seed=0) print time.time() - ts sys.stdout.flush()
meta = dataset.meta inds = (meta['category'] == 'Cars').nonzero()[0] ims = [ 255 * imgs[i][14:-15][:, 14:-15].transpose((2, 0, 1)).reshape( (1, 3, 227, 227)) for i in inds ] import numpy as np from PIL import Image im = np.asarray( Image.open('/home/ubuntu/imgres.jpg').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] var = np.zeros((1000, )).astype(np.float32) var[55] = -1 r, targets = thing4.main(pf, sf, '/home/ubuntu/genstim2.npy', [(None, [('data', .1, 'smooth'), ('fc8', (100, var), 'max2')])], 'data', use_bounds=True, crop=(14, -15, 14, -15), start_normal=True, seed=2) #r, targets = thing4.main(pf, sf, '/home/ubuntu/genstim2.npy', [(None, [('fc8', (100, var), 'max')])], 'data', use_bounds=True, crop=(14, -15, 14, -15), start_normal=True, seed=2) #r, targets = thing4.main(pf, sf, '/home/ubuntu/genstim2.npy', [(None, [('data', 1, 'smooth')])], 'data', use_bounds=False, crop=(14, -15, 14, -15), start_normal=True, seed=0)
im1 = np.asarray( Image.open('/home/ubuntu/test.png').convert('RGB').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] im2 = np.asarray( Image.open('/home/ubuntu/imgres-2.png').convert('RGB').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_y4a.npy', [([ims[i] for i in range(640)], [('fc7', 100, 'ss'), ('fc6', 100, 'ss'), ('fc8', 100, 'ss')])], 'data', crop=(14, -15, 14, -15)) #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_rocks_cn_pool.npy', [(im1, [('conv1', 100, 'corr'), ('pool1', 100, 'corr'), ('conv2', 100, 'corr', ), ('pool2', 100, 'corr'), ('conv3', 100, 'corr'), ('conv4', 100, 'corr'), ('conv5', 100, 'corr'), ('pool5', 100, 'corr')])], 'data', crop=(14, -15, 14, -15)) r, targets = thing4.main(pf, sf, '/home/ubuntu/test_fruits_cn_all.npy', [(im2, [('conv1', 1000, 'corr'), ('pool1', 1000, 'corr'), ('conv2', 1000, 'corr'), ('pool2', 1000, 'corr'), ('conv5', 1000, 'corr'), ('pool5', 1000, 'corr'), ('fc6', 1000, 'ss'), ('fc7', 1000, 'ss')])], use_bounds=True, data_layer='data', crop=(14, -15, 14, -15)) #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_y3d.npy', [(ims[0], [('conv1', 100, 'corr'), ('conv2', 100, 'corr', ), ('conv3', 100, 'corr'), ('conv4', 100, 'corr'), ('conv5', 100, 'corr')])], 'data', crop=(14, -15, 14, -15), use_bounds=True, start_normal=False)
inds = (meta['category'] == 'Cars').nonzero()[0] ims = [255*imgs[i][14:-15][:, 14:-15].transpose((2, 0, 1)).reshape((1, 3, 227, 227)) for i in inds] import numpy as np from PIL import Image im = np.asarray(Image.open('/home/ubuntu/imgres.jpg').resize((256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape((1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_y4a.npy', [([ims[i] for i in range(640)], [('fc7', 100, 'ss'), ('fc6', 100, 'ss'), ('fc8', 100, 'ss')])], 'data', crop=(14, -15, 14, -15)) #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_y3b.npy', [(ims[-1], [('conv1', 100, 'corr'), ('conv2', 100, 'corr', ), ('conv5', 100, 'corr'), ('pool5', 100, 'ss')])], 'data', crop=(14, -15, 14, -15)) #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_X.npy', [([ims[i] for i in range(1)], [('conv5', 100, 'corr'), ('pool5', 100, 'ss'), ('fc7', 100, 'ss'), ('fc6', 100, 'ss'), ('fc8', 100, 'ss')])], 'data', crop=(14, -15, 14, -15)) #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_X.npy', [([ims[i] for i in range(640)], [('fc8', 10000, 'ss')])], 'data', crop=(14, -15, 14, -15), use_bounds=True) #inds = (meta['category'] == 'Faces').nonzero()[0] #ims = [255*imgs[i][14:-15][:, 14:-15].transpose((2, 0, 1)).reshape((1, 3, 227, 227)) for i in inds] im = np.asarray(Image.open('/home/ubuntu/imgres.jpg').resize((256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape((1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] im1 = np.asarray(Image.open('/home/ubuntu/imgres-1.jpg').resize((256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape((1, 3, 256, 256))[:, :, 14:-15][:, :,:, 14:-15] var = np.zeros((1000,)).astype(np.float32) var[23] = -1 #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_something4.npy', [([im], [('pool1', 100, 'corr')]), (None, [('fc8', 1, ('max', var))])], 'data', use_bounds=True, crop=(14, -15, 14, -15)) r, targets = thing4.main(pf, sf, '/home/ubuntu/genstim7/genstim.npy', [(None, [('data', .001, 'smooth')]), ([im], [('fc8', 1, 'ss')])], data_layer = 'data', use_bounds=True, crop=(14, -15, 14, -15), start_normal=True, seed=0, save_dir='/home/ubuntu/genstim7/things', save_freq=100)
var = .001 * np.ones((1000, )).astype(np.float32) #var = np.zeros((4096,)).astype(np.float32) var[801] = -1 var2 = np.zeros((1000, )).astype(np.float32) #var = np.zeros((4096,)).astype(np.float32) var2[2] = 1 #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_something4.npy', [([im], [('pool1', 100, 'corr')]), (None, [('fc8', 1, ('max', var))])], 'data', use_bounds=True, crop=(14, -15, 14, -15)) dirname = '/home/ubuntu/genstim11d' r, targets = thing4.main( pf, sf, os.path.join(dirname, 'genstim.npy'), [( None, [('data', 0.5, 'smoothsep2'), ('data', .01, 'smoothsep'), ('fc8', (25000, var), 'max') #('fc8', (25000, var2), 'logsoftmax') ])], data_layer='data', use_bounds=True, crop=(14, -15, 14, -15), start_normal=False, seed=2, #start_im = im, save_dir=os.path.join(dirname, 'things'), save_freq=100)
pf = '/home/ubuntu/new/caffe/examples/cifar10/caffenet_rand.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/bvlc_reference_caffenet.caffemodel' #im0 = 255*imgs[0].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) #im1 = 255*imgs[-1].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) meta = dataset.meta inds = (meta['category'] == 'Cars').nonzero()[0] ims = [ 255 * imgs[i][14:-15][:, 14:-15].transpose((2, 0, 1)).reshape( (1, 3, 227, 227)) for i in inds ] import numpy as np from PIL import Image im = np.asarray( Image.open('/home/ubuntu/imgres.jpg').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] var = np.zeros((1000, )).astype(np.float32) var[23] = -1 r, targets = thing4.main(pf, sf, '/home/ubuntu/genstim1.npy', [(None, [('fc8', (100, var), 'max')])], 'data', use_bounds=False, crop=(14, -15, 14, -15), start_normal=False, start_im=im)
import thing4 import dldata.stimulus_sets.hvm as hvm from collections import OrderedDict preproc = OrderedDict([(u'normalize', False), (u'dtype', u'float32'), (u'resize_to', [256, 256, 3]), (u'mode', u'RGB'), (u'crop', None), (u'mask', None)]) pf = '/home/ubuntu/new/caffe/examples/cifar10/audio2.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/audio.caffemodel.h5' import numpy as np from PIL import Image im = np.asarray(Image.open('/home/ubuntu/imgres.jpg').resize((256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape((1, 3, 256, 256)) im1 = np.asarray(Image.open('/home/ubuntu/imgres-2.png').convert('RGB').resize((225, 225), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape((1, 3, 225, 225)) mean = np.load('/home/ubuntu/new/caffe/audio_batches_mean.npy') r, targets = thing4.main(pf, sf, '/home/ubuntu/test_audio_corr/test_bork.npy', [(im1, [('conv1', 1, 'corr_rs'), ('pool1', 1, 'corr_rs'), ('conv2', 1, 'corr_rs'), ('pool2', 1, 'corr_rs'), ('conv3', 1, 'corr_rs'), ('conv4', 1, 'corr_rs'), ('conv5', 1, 'corr_rs'), ('pool5', 1, 'corr_rs')])], 'data', seed=2, use_bounds=True, mean=mean, save_dir = '/home/ubuntu/test_audio_corr/things/', save_freq=100)
import thing4 import dldata.stimulus_sets.hvm as hvm from collections import OrderedDict preproc = OrderedDict([(u'normalize', False), (u'dtype', u'float32'), (u'resize_to', [256, 256, 3]), (u'mode', u'RGB'), (u'crop', None), (u'mask', None)]) dataset = hvm.HvMWithDiscfade() imgs = dataset.get_images(preproc=preproc) pf = '/home/ubuntu/new/caffe/examples/cifar10/roschinet_larger_rand_test.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/roschinet_larger.caffemodel.h5' im0 = imgs[0].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) im1 = imgs[-1].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) meta = dataset.meta inds = meta['category'] == 'Cars' ims = [ imgs[i].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) for i in inds[::20][:10] ] r, targets = thing4.main(pf, sf, '/home/ubuntu/test_ig.npy', [(ims[i], [('conv4', 100, 'ss')]) for i in range(10)], 'data')
(1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] var = .001 * np.ones((1000, )).astype(np.float32) #var = np.zeros((4096,)).astype(np.float32) var[350] = -1 var2 = np.zeros((1000, )).astype(np.float32) #var = np.zeros((4096,)).astype(np.float32) var2[2] = 1 #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_something4.npy', [([im], [('pool1', 100, 'corr')]), (None, [('fc8', 1, ('max', var))])], 'data', use_bounds=True, crop=(14, -15, 14, -15)) r, targets = thing4.main( pf, sf, '/home/ubuntu/genstim15/genstim.npy', [( None, [('data', 0.5, 'smoothsep2'), ('data', .001, 'smoothsep'), ('fc8', (25000, var), 'max') #('fc8', (25000, var2), 'logsoftmax') ])], data_layer='data', use_bounds=True, crop=(14, -15, 14, -15), start_normal=False, seed=10, #start_im = im, save_dir='/home/ubuntu/genstim15/things', save_freq=100)
def get_stim(fname, stat_n, stat_n2, seed, out_fname, save_freq, maxfun): impath = os.path.join( '/Users/babylab/Desktop/sandbox/textures/texture_jpgs/', fname) im_arr = np.asarray( Image.open(impath).convert('RGB').resize( (224, 224), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 224, 224)) if stat_n is not None: stat_list = STAT_LIST[:stat_n] else: stat_list = STAT_LIST[:] if stat_n2 is not None: stat_list += STAT_LIST_2[:stat_n2] dirname = os.path.join('/Users/babylab/Desktop/sandbox/textures/generated', out_fname) final_path = os.path.join(dirname, 'genstim.npy') if os.path.exists(final_path): print('%s already finished, exiting' % dirname) return else: hdir = os.path.join(dirname, 'history') if os.path.isdir(hdir): hist = filter(lambda x: x.startswith('im_'), os.listdir(hdir)) else: hist = [] if len(hist) > 0: histints = [int(x.split('_')[-1].split('.')[0]) for x in hist] mhist = max(histints) start_path = os.path.join(hdir, 'im_%d.npy' % mhist) print('Starting with %s' % start_path) x0 = np.load(start_path) r, targets = thing4.main(pf, sf, final_path, [(im_arr, stat_list)], use_bounds=True, data_layer='data', start_layer='conv1_1', start_normal=False, start_im=x0, count_start=mhist, crop=(16, -16, 16, -16), save_dir=hdir, save_freq=save_freq, seed=seed, opt_kwargs={'maxfun': maxfun}) else: print('Starting %s from scratch' % dirname) r, targets = thing4.main(pf, sf, final_path, [(im_arr, stat_list)], use_bounds=True, data_layer='data', start_layer='conv1_1', start_normal=False, crop=(16, -16, 16, -16), save_dir=hdir, save_freq=save_freq, seed=seed, opt_kwargs={'maxfun': maxfun}) return r, targets
x = cPickle.load(open('/home/ubuntu/new/caffe/cgrams.cpy')) im = Image.fromarray((np.tile(x['all_cgrams'][:, :, -14], (3, 1, 1)) * 255).astype(np.uint8).transpose( (1, 2, 0))).resize((225, 225), resample=Image.ANTIALIAS) im = np.asarray(im).transpose((2, 0, 1)).reshape((1, 3, 225, 225)) print(im.shape) r, targets = thing4.main( pf, sf, '/home/ubuntu/test_audio_real_stim1a_max/test_real.npy', [( im, [ #('conv1', .1, 'corr_rs'), #('pool1', .1, 'corr_rs'), #('conv2', 1, 'corr_rs'), ('pool2', 1, 'ss'), ('conv3', 1, 'ss'), ('conv4', 1, 'ss'), ('conv5', 1, 'ss'), ('pool5', 1, 'ss'), ('fc6', 1, 'ss') ])], 'data', seed=0, use_bounds=True, mean=mean, save_dir='/home/ubuntu/test_audio_real_stim1a_max/things/', save_freq=100)
from collections import OrderedDict preproc = OrderedDict([(u'normalize', False), (u'dtype', u'float32'), (u'resize_to', [256, 256, 3]), (u'mode', u'RGB'), (u'crop', None), (u'mask', None)]) pf = '/home/ubuntu/new/caffe/examples/cifar10/audio2.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/audio.caffemodel.h5' import numpy as np from PIL import Image im = np.asarray( Image.open('/home/ubuntu/imgres.jpg').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256)) im1 = np.asarray( Image.open('/home/ubuntu/imgres-2.png').convert('RGB').resize( (225, 225), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 225, 225)) mean = np.load('/home/ubuntu/new/caffe/audio_batches_mean.npy') r, targets = thing4.main(pf, sf, '/home/ubuntu/test_audio2.npy', [(im1, [('pool1', 1, 'ss')])], 'data', seed=2, use_bounds=True, mean=mean)
import thing4 import dldata.stimulus_sets.hvm as hvm from collections import OrderedDict preproc = OrderedDict([(u'normalize', False), (u'dtype', u'float32'), (u'resize_to', [256, 256, 3]), (u'mode', u'RGB'), (u'crop', None), (u'mask', None)]) dataset = hvm.HvMWithDiscfade() imgs = dataset.get_images(preproc=preproc) pf = '/home/ubuntu/new/caffe/examples/cifar10/roschinet_larger_rand_test.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/roschinet_larger.caffemodel.h5' im0 = 255*imgs[0].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) im1 = 255*imgs[-1].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) ims = [255*imgs[i].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) for i in range(10)] r, targets = thing4.main(pf, sf, '/home/ubuntu/test_x2_1.npy', [(ims[i], [('conv3', 10, 'ss')]) for i in range(10)] + \ [(ims[i], [('pool3', 10, 'ss')]) for i in range(10)], 'data')
import thing4 import dldata.stimulus_sets.hvm as hvm from collections import OrderedDict preproc = OrderedDict([(u'normalize', False), (u'dtype', u'float32'), (u'resize_to', [256, 256, 3]), (u'mode', u'RGB'), (u'crop', None), (u'mask', None)]) dataset = hvm.HvMWithDiscfade() imgs = dataset.get_images(preproc=preproc) pf = '/home/ubuntu/new/caffe/examples/cifar10/roschinet_larger_rand_test.prototxt' sf = '/home/ubuntu/new/caffe/examples/cifar10/roschinet_larger.caffemodel.h5' im0 = imgs[0].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) im1 = imgs[-1].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) meta = dataset.meta inds = (meta['category'] == 'Cars').nonzero()[0] ims = [ imgs[i].transpose((2, 0, 1)).reshape((1, 3, 256, 256)) for i in inds[::20][:10] ] r, targets = thing4.main(pf, sf, '/home/ubuntu/test_ih1.npy', [([ims[i] for i in range(10)], [('fc6', 100, 'ss')])], 'data')
#r, targets = thing4.main(pf, sf, '/home/ubuntu/test_something4.npy', [([im], [('pool1', 100, 'corr')]), (None, [('fc8', 1, ('max', var))])], 'data', use_bounds=True, crop=(14, -15, 14, -15)) dirname = '/home/ubuntu/genstim14a' r, targets = thing4.main( pf, sf, os.path.join(dirname, 'genstim.npy'), #[(None, [('data', .5, 'smoothsep2'), # ('data', .005, 'smoothsep')])] + \ [([im1], [ ('fc8', 10000000, 'ss'), ('fc7', 100000, 'ss'), ('fc6', 1000000, 'ss'), ('pool5', 1000, 'corr_diag'), ('conv5', 100, 'corr_diag'), ('conv4', 100, 'corr_diag'), ('conv3', 100, 'corr_diag'), ('pool2', 100, 'corr_diag'), ('pool1', 100, 'corr_diag'), ('conv2', 100, 'corr_diag'), ('conv1', 100, 'corr_diag'), ]) for i in range(1)], data_layer='data', use_bounds=True, crop=(14, -15, 14, -15), start_normal=False, seed=1, #start_im = im, save_dir=os.path.join(dirname, 'things'), save_freq=100)
ims = [ 255 * imgs[i][14:-15][:, 14:-15].transpose((2, 0, 1)).reshape( (1, 3, 227, 227)) for i in inds ] import numpy as np from PIL import Image im = np.asarray( Image.open('/home/ubuntu/imgres.jpg').resize( (256, 256), resample=Image.ANTIALIAS)).transpose(2, 0, 1).reshape( (1, 3, 256, 256))[:, :, 14:-15][:, :, :, 14:-15] #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_y4a.npy', [([ims[i] for i in range(640)], [('fc7', 100, 'ss'), ('fc6', 100, 'ss'), ('fc8', 100, 'ss')])], 'data', crop=(14, -15, 14, -15)) #r, targets = thing4.main(pf, sf, '/home/ubuntu/test_y3c.npy', [(im, [('conv1', 100, 'corr'), ('conv2', 100, 'corr', ), ('conv3', 100, 'corr'), ('conv4', 100, 'corr'), ('conv5', 100, 'corr')])], 'data', crop=(14, -15, 14, -15), use_bounds=True start_normal=False) r, targets = thing4.main(pf, sf, '/home/ubuntu/test_y3d.npy', [(ims[0], [('conv1', 100, 'corr'), ( 'conv2', 100, 'corr', ), ('conv3', 100, 'corr'), ('conv4', 100, 'corr'), ('conv5', 100, 'corr')])], 'data', crop=(14, -15, 14, -15), use_bounds=True, start_normal=False)