import tvm
from tvm import relay
import datetime
import numpy as np
import tvm.contrib.graph_runtime as runtime
from tvm.contrib.download import download_testdata

# PyTorch imports
import torch
import torchvision

######################################################################
# Load a pretrained PyTorch model
# -------------------------------
model_name = 'resnet50'
model = getattr(torchvision.models, model_name)(pretrained=True)
model = model.eval()

# We grab the TorchScripted model via tracing
input_shape = [1, 3, 224, 224]
input_data = torch.randn(input_shape)
scripted_model = torch.jit.trace(model, input_data).eval()

######################################################################
# Load a test image
# -----------------
# Classic cat example!
from PIL import Image
img_url = 'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true'
img_path = download_testdata(img_url, 'cat.png', module='data')
img = Image.open(img_path).resize((224, 224))

# Preprocess the image and convert to tensor
from torchvision import transforms
my_preprocess = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])
img = my_preprocess(img)
img = np.expand_dims(img, 0)

######################################################################
# Import the graph to Relay
# -------------------------
# Convert PyTorch graph to Relay graph. The input name can be arbitrary.
input_name = 'input0'
shape_list = [(input_name, img.shape)]
mod, params = relay.frontend.from_pytorch(scripted_model,
                                          shape_list)

######################################################################
# Relay Build
# -----------
# Compile the graph to llvm target with given input specification.
target = 'opencl'
ctx = tvm.opencl()
with tvm.transform.PassContext(opt_level=3):
    graph, lib, params = relay.build(mod,
                                     target=target,
                                     params=params)

######################################################################
# Execute the portable graph on TVM
# ---------------------------------
# Now we can try deploying the compiled model on target.
from tvm.contrib import graph_runtime
dtype = 'float32'
m = graph_runtime.create(graph, lib, ctx)
# Set inputs

m.set_input(input_name, tvm.nd.array(img.astype(dtype)))
m.set_input(**params)
start=datetime.datetime.now()
# Execute
m.run()
end=datetime.datetime.now()
# Get outputs
tvm_output = m.get_output(0)

#####################################################################
# Look up synset name
# -------------------
# Look up prediction top 1 index in 1000 class synset.
synset_url = ''.join(['https://raw.githubusercontent.com/Cadene/',
                      'pretrained-models.pytorch/master/data/',
                      'imagenet_synsets.txt'])
synset_name = 'imagenet_synsets.txt'
synset_path = download_testdata(synset_url, synset_name, module='data')
with open(synset_path) as f:
    synsets = f.readlines()

synsets = [x.strip() for x in synsets]
splits = [line.split(' ') for line in synsets]
key_to_classname = {spl[0]:' '.join(spl[1:]) for spl in splits}

class_url = ''.join(['https://raw.githubusercontent.com/Cadene/',
                      'pretrained-models.pytorch/master/data/',
                      'imagenet_classes.txt'])
class_name = 'imagenet_classes.txt'
class_path = download_testdata(class_url, class_name, module='data')
with open(class_path) as f:
    class_id_to_key = f.readlines()

class_id_to_key = [x.strip() for x in class_id_to_key]

# Get top-1 result for TVM
top1_tvm = np.argmax(tvm_output.asnumpy()[0])
tvm_class_key = class_id_to_key[top1_tvm]

# Convert input to PyTorch variable and get PyTorch result for comparison
with torch.no_grad():

    torch_img = torch.from_numpy(img)
    start2=datetime.datetime.now() 
    output = model(torch_img)
    end2=datetime.datetime.now()
    # Get top-1 result for PyTorch
    top1_torch = np.argmax(output.numpy())
    torch_class_key = class_id_to_key[top1_torch]

print('Relay top-1 id: {}, class name: {}'.format(top1_tvm, key_to_classname[tvm_class_key]))
print('>>TVM cost %.2f ms' %
        (((end - start).seconds*1000.0 + (end - start).microseconds/1000.0)))
print('Torch top-1 id: {}, class name: {}'.format(top1_torch, key_to_classname[torch_class_key]))
print('>>Torch cost %.2f ms' %
        (((end2 - start2).seconds*1000.0 + (end2 - start2).microseconds/1000.0)))
# evaluate2
module = runtime.create(graph, lib, ctx)
print("Evaluate inference time cost...")
ftimer = module.module.time_evaluator("run", ctx, number=1,repeat=600)
prof_res = np.array(ftimer().results) * 1000  # convert tomillisecond
print("Mean inference time (std dev): %.2f ms (%.2f ms)" %
              (np.mean(prof_res), np.std(prof_res)))

import os

import numpy as np

import tvm
from tvm import te
from tvm import autotvm
from tvm import relay
import tvm.relay.testing
from tvm.autotvm.tuner import XGBTuner, GATuner, RandomTuner, GridSearchTuner
from tvm.contrib.util import tempdir
import tvm.contrib.graph_runtime as runtime

def get_network(name, batch_size):
    """Get the symbol definition and random weight of a network"""
    input_shape = (batch_size, 3, 224, 224)
    output_shape = (batch_size, 1000)

    if "resnet" in name:
        n_layer = int(name.split('-')[1])
        mod, params = relay.testing.resnet.get_workload(num_layers=n_layer, batch_size=batch_size, dtype=dtype)
    elif "vgg" in name:
        n_layer = int(name.split('-')[1])
        mod, params = relay.testing.vgg.get_workload(num_layers=n_layer, batch_size=batch_size, dtype=dtype)
    elif name == 'mobilenet':
        mod, params = relay.testing.mobilenet.get_workload(batch_size=batch_size, dtype=dtype)
    elif name == 'squeezenet_v1.1':
        mod, params = relay.testing.squeezenet.get_workload(batch_size=batch_size, version='1.1', dtype=dtype)
    elif name == 'inception_v3':
        input_shape = (1, 3, 299, 299)
        mod, params = relay.testing.inception_v3.get_workload(batch_size=batch_size, dtype=dtype)
    elif name == 'mxnet':
        # an example for mxnet model
        from mxnet.gluon.model_zoo.vision import get_model
        block = get_model('resnet18_v1', pretrained=True)
        mod, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)
        net = mod["main"]
        net = relay.Function(net.params, relay.nn.softmax(net.body), None, net.type_params, net.attrs)
        mod = tvm.IRModule.from_expr(net)
    else:
        raise ValueError("Unsupported network: " + name)

    return mod, params, input_shape, output_shape

#### DEVICE CONFIG ####
target = 'opencl'

#### TUNING OPTION ####
network = 'resnet-18'
log_file = "%s.log" % network
dtype = 'float32'

tuning_option = {
    'log_filename': log_file,

    'tuner': 'xgb',
    'n_trial': 2000,
    'early_stopping': 600,

    'measure_option': autotvm.measure_option(
        builder=autotvm.LocalBuilder(timeout=10),
        runner=autotvm.LocalRunner(number=20, repeat=3, timeout=4, min_repeat_ms=150),
        #runner=autotvm.RPCRunner(
            #'1080ti',  # change the device key to your key
            #'0.0.0.0', 9190,
            #number=20, repeat=3, timeout=4, min_repeat_ms=150)
    ),
}

def tune_tasks(tasks,
               measure_option,
               tuner='xgb',
               n_trial=1000,
               early_stopping=None,
               log_filename='tuning.log',
               use_transfer_learning=True):
    # create tmp log file
    tmp_log_file = log_filename + ".tmp"
    if os.path.exists(tmp_log_file):
        os.remove(tmp_log_file)

    for i, tsk in enumerate(reversed(tasks)):
        prefix = "[Task %2d/%2d] " %(i+1, len(tasks))

        # create tuner
        if tuner == 'xgb' or tuner == 'xgb-rank':
            tuner_obj = XGBTuner(tsk, loss_type='rank')
        elif tuner == 'ga':
            tuner_obj = GATuner(tsk, pop_size=100)
        elif tuner == 'random':
            tuner_obj = RandomTuner(tsk)
        elif tuner == 'gridsearch':
            tuner_obj = GridSearchTuner(tsk)
        else:
            raise ValueError("Invalid tuner: " + tuner)

        if use_transfer_learning:
            if os.path.isfile(tmp_log_file):
                tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))

        # do tuning
        tsk_trial = min(n_trial, len(tsk.config_space))
        tuner_obj.tune(n_trial=tsk_trial,
                       early_stopping=early_stopping,
                       measure_option=measure_option,
                       callbacks=[
                           autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
                           autotvm.callback.log_to_file(tmp_log_file)
                       ])

    # pick best records to a cache file
    autotvm.record.pick_best(tmp_log_file, log_filename)
    os.remove(tmp_log_file)

def tune_and_evaluate(tuning_opt):
    # extract workloads from relay program
    print("Extract tasks...")
    mod, params, input_shape, out_shape = get_network(network, batch_size=1)
    tasks = autotvm.task.extract_from_program(mod["main"], target=target,
                                              params=params,
                                              ops=(relay.op.get("nn.conv2d"),))

    # run tuning tasks
    print("Tuning...")
    tune_tasks(tasks, **tuning_opt)

    # compile kernels with history best records
    with autotvm.apply_history_best(log_file):
        print("Compile...")
        with tvm.transform.PassContext(opt_level=3):
            graph, lib, params = relay.build_module.build(
                mod, target=target, params=params)

        # export library
        tmp = tempdir()
        filename = "net.tar"
        lib.export_library(tmp.relpath(filename))

        # load parameters
        ctx = tvm.context(str(target), 0)
        module = runtime.create(graph, lib, ctx)
        data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
        module.set_input('data', data_tvm)
        module.set_input(**params)

        # evaluate
        print("Evaluate inference time cost...")
        ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=600)
        prof_res = np.array(ftimer().results) * 1000  # convert to millisecond
        print("Mean inference time (std dev): %.2f ms (%.2f ms)" %
              (np.mean(prof_res), np.std(prof_res)))

# We do not run the tuning in our webpage server since it takes too long.
# Uncomment the following line to run it by yourself.

tune_and_evaluate(tuning_option)