def inference(opts):
infer_graph, uid_embedding, mid_embedding, cat_embedding = generic_init_graph(
opts, False)
path = opts['model_path']
if path is not None and os.path.exists(path + ".meta"):
infer_graph.saver.restore(infer_graph.session, path)
tf_log.info(f"model {path} restored")
else:
tf_log.info(f"Do not restore since no model under path {path}")
iterations = VALIDATION_DATA_SIZE * opts['epochs'] / (opts['batch_size'] *
opts['replicas'])
total_time = 0
i = 0
stored_arr = []
tf_log.info(f"iterations: {iterations}")
accs = []
# Register the host embeddings with the session.
with uid_embedding.register(infer_graph.session), mid_embedding.register(
infer_graph.session), cat_embedding.register(infer_graph.session):
while i < iterations:
start = time.time()
infer_graph.session.run(infer_graph.ops_val)
prob, target, acc = infer_graph.session.run(infer_graph.outfeed)
total_time = time.time() - start
i += opts['batches_per_step']
accuracy = np.mean(acc)
accs.append(accuracy)
prob_1 = prob.reshape(
[opts['batches_per_step'] * opts['batch_size'], 2])
prob_1 = prob_1[:, 0].tolist()
target_1 = target.reshape(
[opts['batches_per_step'] * opts['batch_size'], 2])
target_1 = target_1[:, 0].tolist()
for p, t in zip(prob_1, target_1):
stored_arr.append([p, t])
throughput = opts["batch_size"] * opts[
"batches_per_step"] / total_time
tf_log.info(
f"i={i // opts['batches_per_step']}, validation accuracy: {accuracy:.4f}, throughput:{throughput}, latency:{total_time * 1000 / opts['batches_per_step']}"
)
total_time = time.time() - start
test_auc = calc_auc(stored_arr)
test_acc = np.mean(accs)
tf_log.info(f"test_auc={test_auc:.4f} test_acc={test_acc:.4f}")
infer_graph.session.close()
def inference(opts):
infer, uid_embedding, mid_embedding, cat_embedding = generic_graph(
opts, False)
infer.session.run(infer.init)
infer.session.run(infer.iterator.initializer)
path = opts['model_path']
if path is not None and os.path.exists(path + ".meta"):
infer.saver.restore(infer.session, path)
tf_log.debug(f"model {path} restored")
else:
tf_log.debug(f"Do not restore since no model under path {path}")
steps = VALIDATION_DATA_SIZE * opts['epochs'] / opts['batch_size'] / opts[
"batches_per_step"]
i = 0
stored_arr = []
tf_log.debug(f"steps: {steps}")
accs = []
total_time = 0
with uid_embedding.register(infer.session), mid_embedding.register(
infer.session), cat_embedding.register(infer.session):
while i < steps:
start = time.time()
infer.session.run(infer.ops)
prob, target, acc = infer.session.run(infer.outfeed)
time_one_iteration = time.time() - start
if i > 0:
total_time = total_time + time_one_iteration
i += 1
accuracy = np.mean(acc)
accs.append(accuracy)
prob_1 = prob.reshape([
opts['batches_per_step'] * opts['batch_size'],
2 * opts['replicas']
])
prob_1 = prob_1[:, 0].tolist()
target_1 = target.reshape([
opts['batches_per_step'] * opts['batch_size'],
2 * opts['replicas']
])
target_1 = target_1[:, 0].tolist()
for p, t in zip(prob_1, target_1):
stored_arr.append([p, t])
throughput = opts["batch_size"] * opts[
"batches_per_step"] / time_one_iteration
tf_log.info(
f"i={i // opts['batches_per_step']},validation accuracy: {accuracy}, throughput:{throughput}, latency:{time_one_iteration * 1000 / opts['batches_per_step']}"
)
test_auc = calc_auc(stored_arr)
test_acc = np.mean(accs)
tf_log.info(f"test_auc={test_auc:.4f} test_acc={test_acc:.4f}")
infer.session.close()
if steps > 1:
total_recomm_num = opts["batch_size"] * (i -
1) * opts["batches_per_step"]
throughput = float(total_recomm_num) / float(total_time)
latency = float(total_time) * 1000 / float(
(i - 1) * opts["batches_per_step"])
tf_log.info(f"Total recommendations: {total_recomm_num:d}")
tf_log.info(f"Process time in seconds is {total_time:.3f}")
tf_log.info(f"recommendations/second is {throughput:.3f}")
tf_log.info(f"latency in miliseconds is {latency:.3f}")