Best Python code snippet using localstack_python
inference.py
Source: inference.py
1import imp, sys2import numpy as np3import os4import pickle5import torch6import io7import boto38import json9from torchvision import transforms10from PIL import Image11from six import BytesIO12str_labels = ["FP", "GOOD", "VG", "VG-EX", "EX", "EX-MT", "NM", "NM-MT", "MINT", "GEM-MT"]13 14def train(hyperparameters, input_data_config, channel_input_dirs, output_data_dir,15 num_gpus, num_cpus, hosts, current_host, **kwargs):16 pass17def test(ctx, net, val_data):18 pass19def model_fn(model_dir):20 """Creates pytorch model specified via model_dir.21 22 Args:23 model_dir (str): path to model root dir. 24 """25 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")26 model = torch.load(open(os.path.join(model_dir, 'model/model.pth'), "rb")).to(device)27 print('[INFO] model loaded:')28 print(model.eval())29 return model 30def predict_fn(input_object, model):31 """ Uses Image library to load image and transfrom into float numbers for model prediction.32 33 Args:34 input_object (Image): image loaded using PIL.Image35 model (Object): unpickled model provided using model_fn36 """37 transformer = transforms.Compose([38 transforms.Resize((255, 255)),39 transforms.ToTensor(),40 transforms.Normalize(mean=[0.485, 0.456, 0.406],41 std=[0.229, 0.224, 0.225]),42 ])43 print("[INFO] Running model inference on image: {}".format(input_object))44 X = transformer(input_object)45 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')46 model.to(device)47 model.eval()48 with torch.no_grad():49 logits = model(X[None, ...].to(device))50 print("[INFO]: The prediction logits are: {}".format(logits))51 pred = logits.argmax(dim=1)52 return str_labels[pred]53def input_fn(request_body, request_content_type):54 """ Loads input from request content and returns image specified in request_body as PIL.Image.55 56 Args:57 request_body (bytes): serialized dictionary with two required key: "bucket" and "key" which specify an image to predict.58 request_content_type (str): type of content59 60 """61 print('[INFO] request_body: {}'.format(request_body))62 data = json.loads(request_body.decode())63 s3 = boto3.resource('s3')64 bucket = s3.Bucket(data['bucket'])65 object = bucket.Object(data['key'])66 response = object.get()67 file_stream = response['Body']68 image = Image.open(file_stream)69 70 #print('[INFO] Got Image of shape {} with scale {}'.format(image.shape, image.max()))71 return image72def output_fn(prediction, content_type):73 """Outputs result of prediction.74 75 Args:76 prediction (str): Label predicted by predict_fn77 content_type (str): type of content78 """79 print('[INFO] prediction: {}'.format(prediction))80 return {'statusCode': 200, 'body': '{}'.format(prediction) }81if __name__ == "__main__":82 """83 Test script - please run this at the root directory of model/, i.e., ./model/..84 """85 86 test_model_dir = "./"87 test_request_body = json.dumps({'bucket': 'ccbd-mint-cv-model', 'key': 'test_data.jpeg'}).encode('utf-8')88 test_request_content_type = "test_type" 89 test_response_content_type = "test_type"90 91 # Deserialize the Invoke request body into an object we can perform prediction on92 input_object = input_fn(test_request_body, test_request_content_type)93 test_model = model_fn(test_model_dir)94 95 # Perform prediction on the deserialized object, with the loaded model96 prediction = predict_fn(input_object, test_model)97 # Serialize the prediction result into the desired response content type98 output = output_fn(prediction, test_response_content_type)99 ...
test_GeniosSite.py
Source: test_GeniosSite.py
...14 if actual_response:15 assert actual_response == BASE_URL16 else:17 assert False18def test_response_content_type():19 headers = site.get_response_headers()20 content_type = headers["Content-Type"]21 assert content_type == "text/html; charset=UTF-8"22def test_site_connection_type():23 headers = site.get_response_headers()24 connection_type = headers["Connection"]25 assert connection_type == "Upgrade, Keep-Alive"26def test_site_link_lists():27 actual_links = site.get_site_link_lists()28 expected_links = csv_manipulation(SITE_LIST).read_csv_lines()29 if len(actual_links) == len(expected_links):30 check_link_list = zip(sorted(actual_links), sorted(expected_links))31 for actual, expected in check_link_list:32 assert actual == expected33 else:34 assert False35def test_all_cubes_types():36 cube_name = csv_manipulation(CUBE_TYPES).read_csv_specific_column(0)37 cube_link = csv_manipulation(CUBE_TYPES).read_csv_specific_column(1)38 expected_cube_types = dict(zip(cube_name, cube_link))39 actual_cube_types = site.get_all_cubes_types()40 if len(actual_cube_types) == len(expected_cube_types):41 for key in actual_cube_types.keys():42 assert actual_cube_types[key] == expected_cube_types[key]43 else:44 assert False45if __name__ == '__main__':46 # test_response_content_type()47 # test_site_connection_type()48 # test_site_link_lists()...
renderers_test.py
Source: renderers_test.py
1# -*- coding: utf-8 -*-2from h import renderers3def test_response_content_type(pyramid_request):4 renderer = renderers.CSV({})5 renderer({}, {'request': pyramid_request})6 assert pyramid_request.response.content_type == 'text/csv'7def test_render_simple_csv(pyramid_request):8 renderer = renderers.CSV({})9 sys = {'request': pyramid_request}10 value = {'header': ['One', 'Two'],11 'rows': [[1, 2], [3, 4]]}12 assert renderer(value, sys) == "One,Two\r\n1,2\r\n3,4\r\n"13def test_render_unicode_csv(pyramid_request):14 renderer = renderers.CSV({})15 sys = {'request': pyramid_request}16 value = {'header': [u'Ó', u'Ä'],17 'rows': [[u'ñ', u'ã'], [u'ïº', u'Óª']]}...
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