def composite_dataset(dataset="objectome24", threshold=12000, mongo_reload=False):
if dataset == "objectome24":
collections = ["objectome64", "objectome_imglvl", "ko_obj24_basic_2ways", "monkobjectome"]
meta = obj.objectome24_meta()
elif dataset == "hvm10":
collections = [
"hvm10_basic_2ways",
"hvm10_allvar_basic_2ways",
] # , 'hvm10_basic_2ways_newobj', 'hvm10-finegrain']
meta = obj.hvm10_meta()
fns = ["sample_obj", "id", "dist_obj", "choice", "WorkerID"]
col_data = ()
for col in collections:
dset = obj.psychophysDatasetObject(col, {}, meta, mongo_reload=mongo_reload)
col_data = col_data + (dset.trials,)
trials = tb.rowstack(col_data)
# segregate into pool and individuals
workers = trials["WorkerID"]
col_data_seg = {"all": trials, "pool": ()}
for uw in np.unique(workers):
tw = np.nonzero([w == uw for w in workers])[0]
if len(tw) < threshold:
col_data_seg["pool"] = col_data_seg["pool"] + (trials[tw],)
else:
col_data_seg[uw] = trials[tw]
col_data_seg["pool"] = tb.rowstack(col_data_seg["pool"])
return col_data_seg
def get_model_data(dataset="objectome24"):
if dataset == "objectome24":
featurespath = "/mindhive/dicarlolab/u/rishir/stimuli/objectome24s100/features/"
meta = obj.objectome24_meta()
all_metas, all_features = {}, {}
# f_oi = ['ALEXNET_fc6', 'ALEXNET_fc8', 'RESNET101_conv5', 'VGG_fc6', 'VGG_fc8', 'ALEXNET_fc7', 'GOOGLENET_pool5', 'V1', 'VGG_fc7']
f_oi = ["RESNET101_conv5"]
for f in f_oi:
data = np.load(featurespath + f + ".npy")
all_features[f] = data
all_metas[f] = meta
return obj.testFeatures(all_features, all_metas, f_oi, obj.models_combined24)
