def execute_similarity_search(img_path, num_algs=32):
# this part can be refactored into a "setup model" func
# the func can have a flag for initing the hook or not.
path = "../saved_models/cifar_model_300_epochs.pth"
model = SimpleNet(10)
model.load_state_dict(torch.load(path))
# Init forward hook for exposing dense layer vectors
sf = SaveFeatureVectors(model.conv13[2])
model.eval()
is_correct = 0.0
path_list = []
for inputs, labels, paths in _get_single_image_batch(img_path):
outputs = model(inputs.float())
_, pred = outputs.max(1)
is_correct += (pred == labels.long()).sum().item()
print(f"Num correct: {is_correct}")
path_list.extend(paths)
# Generate Hash: todo-try stuff here.
m = MinHash(num_perm=num_algs)
m.update(sf.features.flatten())
def execute_inference(create_hashes=False,
num_algs=32,
hash_index="lsh-demo-3"):
path = "../saved_models/cifar_model_300_epochs.pth"
model = SimpleNet(10)
model.load_state_dict(torch.load(path))
# Init forward hook for exposing dense layer vectors
sf = SaveFeatureVectors(model.conv13[2])
model.eval()
is_correct = 0.0
path_list = []
for inputs, labels, paths in _get_test_batch():
outputs = model(inputs.float())
_, pred = outputs.max(1)
is_correct += (pred == labels.long()).sum().item()
print(f"Num correct: {is_correct}")
path_list.extend(paths)
print("Extracting Vectors")
if create_hashes:
generate_hash_tables(path_list, sf.features)
def _load_model(self, model_path: str) -> SimpleNet:
model = SimpleNet(1)
model.load_state_dict(torch.load(model_path))
return model
from simple_net import SimpleNet
from predictFromArchive import predictFromArchive
import torch
from glob import glob
import os
networkWeights = 'trained_simplenet.torch'
wantedShape = (41, 53, 38, 6)
crop = (slice(4, 28), slice(20, 44), slice(7, 31))
resizeFactor = 2
dimIn = 12 * 12 * 12 * 6
dimOut = 4
net = SimpleNet(dimIn=dimIn, dimOut=dimOut).cpu()
net.load_state_dict(torch.load(networkWeights, map_location='cpu'))
archivPath = os.path.dirname(os.path.abspath(__file__))
for archiv in glob("/flywheel/v0/input/dtiInitArchive/dti*.zip"):
predictFromArchive(archiv, "/flywheel/v0/output/neuro-detect_report.txt",
net)
