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# This sample uses a UFF MNIST model to create a TensorRT Inference Engine
from random import randint
from PIL import Image
import numpy as np

import pycuda.driver as cuda
# This import causes pycuda to automatically manage CUDA context creation and cleanup.
import pycuda.autoinit

import tensorrt as trt

import sys, os

# You can set the logger severity higher to suppress messages (or lower to display more messages).
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

# Loads a test case into the provided pagelocked_buffer.
def load_normalized_test_case(image_file, pagelocked_buffer):
    # Flatten the image into a 1D array, normalize, and copy to pagelocked memory.
    img = np.array(Image.open(image_file)).ravel()
    for idx, pixel in enumerate(img):
        print('@' if pixel >= 128 else ' ', end='')
        if idx % 28 == 27:
            print('')
    np.copyto(pagelocked_buffer, 1.0 - img / 255.0)


class HostDeviceMem(object):
    def __init__(self, host_mem, device_mem):
        self.host = host_mem
        self.device = device_mem

    def __str__(self):
        return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)

    def __repr__(self):
        return self.__str__()


def allocate_buffers(engine):
    inputs = []
    outputs = []
    bindings = []
    stream = cuda.Stream()
    for binding in engine:
        size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
        dtype = trt.nptype(engine.get_binding_dtype(binding))
        # Allocate host and device buffers
        host_mem = cuda.pagelocked_empty(size, dtype)
        device_mem = cuda.mem_alloc(host_mem.nbytes)
        # Append the device buffer to device bindings.
        bindings.append(int(device_mem))
        # Append to the appropriate list.
        if engine.binding_is_input(binding):
            inputs.append(HostDeviceMem(host_mem, device_mem))
        else:
            outputs.append(HostDeviceMem(host_mem, device_mem))
    return inputs, outputs, bindings, stream


# This function is generalized for multiple inputs/outputs.
# inputs and outputs are expected to be lists of HostDeviceMem objects.
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
    # Transfer input data to the GPU.
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
    # Run inference.
    context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
    # Transfer predictions back from the GPU.
    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
    # Synchronize the stream
    stream.synchronize()
    # Return only the host outputs.
    return [out.host for out in outputs]


def main():
    if len(sys.argv) != 3:
        print(f'{sys.argv[0]} file.engine image.pgm')
        return
    engine_file = sys.argv[1]
    image_file = sys.argv[2]

    with open(engine_file, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime:
      with runtime.deserialize_cuda_engine(f.read()) as engine:
	  	  # Build an engine, allocate buffers and create a stream.
        # For more information on buffer allocation, refer to the introductory samples.
        inputs, outputs, bindings, stream = allocate_buffers(engine)
        with engine.create_execution_context() as context:
            load_normalized_test_case(image_file, pagelocked_buffer=inputs[0].host)
            # For more information on performing inference, refer to the introductory samples.
            # The common.do_inference function will return a list of outputs - we only have one in this case.
            [output] = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
            for digit, prob in enumerate(output):
                print(f'{digit}: {prob:.6f}')
            pred = np.argmax(output)
            print(f'Prediction: {pred}')


if __name__ == '__main__':
    main()
