# Normalize all images
print("Normalizing query images")
imgs_query = normalize_img(imgs_query)
print("Normalizing gallery images")
imgs_gallery = normalize_img(imgs_gallery)
# Convert images to numpy array of right dimensions
print("\nConverting validation images to numpy array of right dimensions")
X_query = np.array(imgs_query).reshape((-1, ) + input_shape_model)
X_gallery = np.array(imgs_gallery).reshape((-1, ) + input_shape_model)
print(">>> X_query.shape = " + str(X_query.shape))
print(">>> X_gallery.shape = " + str(X_gallery.shape))
if args.mode != "training model":
print("\nLoading model...")
model.load_models(loss=args.loss, optimizer="adam")
# Create embeddings using model
print("\nCreating embeddings...")
E_query = model.predict(X_query)
E_query_flatten = E_query.reshape((-1, np.prod(output_shape_model)))
E_gallery = model.predict(X_gallery)
E_gallery_flatten = E_gallery.reshape((-1, np.prod(output_shape_model)))
# Compute top-k
def topk_accuracy(gt_label, matched_label, k=1):
matched_label = matched_label[:, :k]
total = matched_label.shape[0]
correct = 0
for q_idx, q_lbl in enumerate(gt_label):
if args.model == 'convAE':
# Build models
autoencoderFile = os.path.join(OutputDir, "ConvAE_autoecoder.h5")
print("autoencoder file", autoencoderFile)
encoderFile = os.path.join(OutputDir, "ConvAE_encoder.h5")
model = AutoEncoder(shape_img, autoencoderFile, encoderFile)
model.set_arch()
input_shape_model = tuple([int(x) for x in model.encoder.input.shape[1:]])
output_shape_model = tuple(
[int(x) for x in model.encoder.output.shape[1:]])
# Loading model
model.load_models(loss='mse', optimizer="adam")
# Convert images to numpy array of right dimensions
print("\nConverting to numpy array of right dimensions")
X_query = np.array(QueryImgs).reshape((-1, ) + input_shape_model)
X_gallery = np.array(GalleryImgs).reshape((-1, ) + input_shape_model)
print(">>> X_query.shape = " + str(X_query.shape))
print(">>> X_gallery.shape = " + str(X_gallery.shape))
# Create embeddings using model
print("\nCreating embeddings")
E_query = model.predict(X_query)
E_query_flatten = E_query.reshape((-1, np.prod(output_shape_model)))
E_gallery = model.predict(X_gallery)
E_gallery_flatten = E_gallery.reshape((-1, np.prod(output_shape_model)))
