The complete SageMaker script for building an MXNet model that counts shapes in an image.

object-counting-sagemaker-script.py

import base64
import json
import logging
from pickle import load

import mxnet as mx
import numpy as np
from mxnet import autograd, nd, gluon
from mxnet.gluon import Trainer
from mxnet.gluon.loss import L2Loss
from mxnet.gluon.nn import Conv2D, MaxPool2D, Dropout, Flatten, Dense, Sequential
from mxnet.initializer import Xavier

logging.basicConfig(level=logging.INFO)


def train(hyperparameters, channel_input_dirs, num_gpus):
    batch_size = hyperparameters.get("batch_size", 64)
    epochs = hyperparameters.get("epochs", 3)

    mx.random.seed(42)

    training_dir = channel_input_dirs['training']

    logging.info("Loading data from {}".format(training_dir))

    with open("{}/train/data.p".format(training_dir), "rb") as pickle:
        train_nd = load(pickle)
    with open("{}/validation/data.p".format(training_dir), "rb") as pickle:
        validation_nd = load(pickle)

    train_data = DataLoader(train_nd, batch_size, shuffle=True)
    validation_data = DataLoader(validation_nd, batch_size, shuffle=True)

    net = Sequential()
    with net.name_scope():
        net.add(Conv2D(channels=32, kernel_size=(3, 3),
                       padding=0, activation="relu"))
        net.add(Conv2D(channels=32, kernel_size=(3, 3),
                       padding=0, activation="relu"))
        net.add(MaxPool2D(pool_size=(2, 2)))
        net.add(Dropout(.25))
        net.add(Flatten())
        net.add(Dense(1))

    ctx = mx.gpu() if num_gpus > 0 else mx.cpu()

    net.collect_params().initialize(Xavier(magnitude=2.24), ctx=ctx)
    loss = L2Loss()

    trainer = Trainer(net.collect_params(), optimizer="adam")

    smoothing_constant = .01

    for e in range(epochs):
        moving_loss = 0
        for i, (data, label) in enumerate(train_data):
            data = data.as_in_context(ctx)
            label = label.as_in_context(ctx)
            with autograd.record():
                output = net(data)
                loss_result = loss(output, label)
            loss_result.backward()
            trainer.step(batch_size)

            curr_loss = nd.mean(loss_result).asscalar()
            if (i == 0) and (e == 0):
                moving_loss = curr_loss
            else:
                moving_loss = (1 - smoothing_constant) * moving_loss + \
                              smoothing_constant * curr_loss

        trn_total, trn_detected = calc_perf(net, ctx, train_data)
        val_total, val_detected = calc_perf(net, ctx, validation_data)

        log = "Epoch: {} loss: {:0.4f} perf_test: {:0.2f} perf_val: {:0.2f}" \
            .format(e, moving_loss,
                    trn_detected / trn_total,
                    val_detected / val_total)
        logging.info(log)

    return net


def calc_perf(model, ctx, data_iter):
    raw_predictions = np.array([])
    rounded_predictions = np.array([])
    actual_labels = np.array([])
    for i, (data, label) in enumerate(data_iter):
        data = data.as_in_context(ctx)
        label = label.as_in_context(ctx)
        output = model(data)
        predictions = nd.round(output)
        raw_predictions = np.append(raw_predictions,
                                    output.asnumpy().squeeze())
        rounded_predictions = np.append(rounded_predictions,
                                        predictions.asnumpy().squeeze())
        actual_labels = np.append(actual_labels,
                                  label.asnumpy().squeeze())

    results = np.concatenate((raw_predictions.reshape((-1, 1)),
                              rounded_predictions.reshape((-1, 1)),
                              actual_labels.reshape((-1, 1))), axis=1)
    detected = 0
    i = -1
    for i in range(int(results.size / 3)):
        if results[i][1] == results[i][2]:
            detected += 1
    return i + 1, detected


def save(net, model_dir):
    y = net(mx.sym.var("data"))
    y.save("{}/model.json".format(model_dir))
    net.collect_params().save("{}/model.params".format(model_dir))
    
def model_fn(model_dir):
    with open("{}/model.json".format(model_dir), "r") as model_file:
        model_json = model_file.read()
    outputs = mx.sym.load_json(model_json)
    inputs = mx.sym.var("data")
    param_dict = gluon.ParameterDict("model_")
    net = gluon.SymbolBlock(outputs, inputs, param_dict)
    # We will serve the model on CPU
    net.load_params("{}/model.params".format(model_dir), ctx=mx.cpu())
    return net
    
def transform_fn(model, input_data, content_type, accept):
    if content_type == "application/png":
        img = img2arr(input_data)
        response = model(img).asnumpy().ravel().tolist()
        return json.dumps(response), accept
    else:
        raise ValueError("Cannot decode input to the prediction.")
    
def img2arr(base64img):
    img = base64.b64decode(base64img)
    img = np.asarray(bytearray(img), dtype=np.uint8)
    img = cv2.imdecode(img, cv2.IMREAD_COLOR)
    img = img.astype(np.float32)
    img = mx.nd.array(img)
    img = mx.nd.transpose(img, (2, 0, 1))
    img = img / 255
    img = img.reshape((1, 3, 128, 128))
    img = img.asnumpy()
    return img