Instantly share code, notes, and snippets.

Embed
What would you like to do?
The complete SageMaker script for building an MXNet model that counts shapes in an image.
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
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment