#
# Copyright (c) 2021-2023, NVIDIA CORPORATION. All rights reserved.
#
# 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.
#

import os

import calibrator
import numpy as np
import tensorrt as trt
from cuda import cudart

shape = [1, 1, 28, 28]
nCalibration = 10
cacheFile = "model.INT8Cache"

os.system("rm -rf %s" % cacheFile)
np.random.seed(31193)
np.set_printoptions(precision=3, linewidth=200, suppress=True)
cudart.cudaDeviceSynchronize()

logger = trt.Logger(trt.Logger.ERROR)
builder = trt.Builder(logger)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
profile = builder.create_optimization_profile()
config = builder.create_builder_config()

tensorDictionary = {}  # build a dictionary to pass information to calibrator
tensorDictionary["inputT0"] = {"shape": shape[1:], "dataType": trt.float32}  # remove Batch dimension of input tensors to fit the old Calibrator API
myCalibrator = calibrator.MyInt8EntropyCalibrator2(nCalibration, 1, tensorDictionary, cacheFile)  # use calibrator inherited from trt.IInt8EntropyCalibrator2
print("myCalibrator.get_algorithm() =", myCalibrator.get_algorithm())  # print the algorithm the calibrator uses

config.set_flag(trt.BuilderFlag.INT8)  # set INT8 mdoe and corresponding Calibrator
config.int8_calibrator = myCalibrator

inputTensor = network.add_input("inputT0", trt.float32, [-1] + shape[1:])
profile.set_shape(inputTensor.name, [1] + shape[1:], [2] + shape[1:], [4] + shape[1:])
config.add_optimization_profile(profile)

w = np.ascontiguousarray(np.random.rand(32, 1, 5, 5).astype(np.float32))
b = np.ascontiguousarray(np.random.rand(32).astype(np.float32))
_0 = network.add_convolution_nd(inputTensor, 32, [5, 5], w, b)
_0.padding_nd = [2, 2]
_1 = network.add_activation(_0.get_output(0), trt.ActivationType.RELU)
_2 = network.add_pooling_nd(_1.get_output(0), trt.PoolingType.MAX, [2, 2])
_2.stride_nd = [2, 2]

w = np.ascontiguousarray(np.random.rand(64, 32, 5, 5).astype(np.float32))
b = np.ascontiguousarray(np.random.rand(64).astype(np.float32))
_3 = network.add_convolution_nd(_2.get_output(0), 64, [5, 5], w, b)
_3.padding_nd = [2, 2]
_4 = network.add_activation(_3.get_output(0), trt.ActivationType.RELU)
_5 = network.add_pooling_nd(_4.get_output(0), trt.PoolingType.MAX, [2, 2])
_5.stride_nd = [2, 2]

_6 = network.add_shuffle(_5.get_output(0))
_6.first_transpose = (0, 2, 3, 1)
_6.reshape_dims = (-1, 64 * 7 * 7)

w = np.ascontiguousarray(np.random.rand(64 * 7 * 7, 1024).astype(np.float32))
b = np.ascontiguousarray(np.random.rand(1, 1024).astype(np.float32))
_7 = network.add_constant(w.shape, trt.Weights(w))
_8 = network.add_matrix_multiply(_6.get_output(0), trt.MatrixOperation.NONE, _7.get_output(0), trt.MatrixOperation.NONE)
_9 = network.add_constant(b.shape, trt.Weights(b))
_10 = network.add_elementwise(_8.get_output(0), _9.get_output(0), trt.ElementWiseOperation.SUM)
_11 = network.add_activation(_10.get_output(0), trt.ActivationType.RELU)

w = np.ascontiguousarray(np.random.rand(1024, 10).astype(np.float32))
b = np.ascontiguousarray(np.random.rand(1, 10).astype(np.float32))
_12 = network.add_constant(w.shape, trt.Weights(w))
_13 = network.add_matrix_multiply(_11.get_output(0), trt.MatrixOperation.NONE, _12.get_output(0), trt.MatrixOperation.NONE)
_14 = network.add_constant(b.shape, trt.Weights(b))
_15 = network.add_elementwise(_13.get_output(0), _14.get_output(0), trt.ElementWiseOperation.SUM)

_16 = network.add_softmax(_15.get_output(0))
_16.axes = 1 << 1

_17 = network.add_topk(_16.get_output(0), trt.TopKOperation.MAX, 1, 1 << 1)

network.mark_output(_17.get_output(1))

engineString = builder.build_serialized_network(network, config)

print("Finish!")
