# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

load("@kelvin_sw//benchmarks:benchmarks.bzl", "kelvin_benchmark_simulator", "kelvin_benchmark_devices")

kelvin_benchmark_simulator(
    name = "person_detection_benchmark_simulator",
    model = "//quant_models:person_detection.tflite",
    iterations = 2,
    hw_test_size = "medium",
    iss_test_size = "medium",
)

kelvin_benchmark_simulator(
    name = "mobilenet_v1_075_224_benchmark_simulator",
    model = "//quant_models:mobilenet_v1_0.75_224_int8_dummy.tflite",
    iterations = 3,
    iss_test_size = "large",
)

kelvin_benchmark_simulator(
    name = "mobilenet_v1_050_224_benchmark_simulator",
    model = "//quant_models:mobilenet_v1_0.5_224_int8_dummy.tflite",
    iterations = 3,
    iss_test_size = "enormous",
)

kelvin_benchmark_simulator(
    name = "mobilenet_v1_025_224_benchmark_simulator",
    model = "//quant_models:mobilenet_v1_0.25_224_int8_dummy.tflite",
    iterations = 3,
    iss_test_size = "large",
)

kelvin_benchmark_simulator(
    name = "mobilenet_v2_025_224_benchmark_simulator",
    model = "//quant_models:mobilenet_v2_0.25_224_int8_dummy.tflite",
    iterations = 3,
    iss_test_size = "large",
)

kelvin_benchmark_simulator(
    name = "mobilenet_v2_050_224_benchmark_simulator",
    model = "//quant_models:mobilenet_v2_0.50_224_int8_dummy.tflite",
    iterations = 3,
    iss_test_size = "large",
)

# Builds both ASIC and FPGA.
kelvin_benchmark_devices(
    name = "person_detection_benchmark_device",
    model = "//quant_models:person_detection.tflite",
    iterations = 2,
)

kelvin_benchmark_devices(
    name = "mobilenet_v1_075_224_benchmark_device",
    model = "//quant_models:mobilenet_v1_0.75_224_int8_dummy.tflite",
    iterations = 3,
)

kelvin_benchmark_devices(
    name = "mobilenet_v1_050_224_benchmark_device",
    model = "//quant_models:mobilenet_v1_0.5_224_int8_dummy.tflite",
    iterations = 3,
)

kelvin_benchmark_devices(
    name = "mobilenet_v1_025_224_benchmark_device",
    model = "//quant_models:mobilenet_v1_0.25_224_int8_dummy.tflite",
    iterations = 3,
)

kelvin_benchmark_devices(
    name = "mobilenet_v2_025_224_benchmark_device",
    model = "//quant_models:mobilenet_v2_0.25_224_int8_dummy.tflite",
    iterations = 3,
)

kelvin_benchmark_devices(
    name = "mobilenet_v2_050_224_benchmark_device",
    model = "//quant_models:mobilenet_v2_0.50_224_int8_dummy.tflite",
    iterations = 3,
)

kelvin_benchmark_simulator(
    name = "mlperf_tiny_image_classification_benchmark_simulator",
    model = "@mlperf_tiny//:benchmark/training/image_classification/trained_models/pretrainedResnet_quant.tflite",
    iterations = 1,
)

kelvin_benchmark_devices(
    name = "mlperf_tiny_image_classification_benchmark_device",
    model = "@mlperf_tiny//:benchmark/training/image_classification/trained_models/pretrainedResnet_quant.tflite",
    iterations = 1,
)

kelvin_benchmark_simulator(
    name = "mlperf_tiny_anomaly_detection_benchmark_simulator",
    model = "@mlperf_tiny//:benchmark/training/anomaly_detection/trained_models/ad01_int8.tflite",
    iterations = 1,
)

kelvin_benchmark_devices(
    name = "mlperf_tiny_anomaly_detection_benchmark_device",
    model = "@mlperf_tiny//:benchmark/training/anomaly_detection/trained_models/ad01_int8.tflite",
    iterations = 1,
)

kelvin_benchmark_simulator(
    name = "mlperf_tiny_visual_wake_words_benchmark_simulator",
    model = "@mlperf_tiny//:benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite",
    iterations = 1,
)

kelvin_benchmark_devices(
    name = "mlperf_tiny_visual_wake_words_benchmark_device",
    model = "@mlperf_tiny//:benchmark/training/visual_wake_words/trained_models/vww_96_int8.tflite",
    iterations = 1,
)

kelvin_benchmark_simulator(
    name = "mlperf_tiny_keyword_spotting_benchmark_simulator",
    model = "@mlperf_tiny//:benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite",
    iterations = 1,
)

kelvin_benchmark_devices(
    name = "mlperf_tiny_keyword_spotting_benchmark_device",
    model = "@mlperf_tiny//:benchmark/training/keyword_spotting/trained_models/kws_ref_model.tflite",
    iterations = 1,
)