Merge branch 'master' into master

benchmarks/auto_benchmark.py

@@ -3,10 +3,10 @@
 import os
 import ruamel.yaml
 import shutil
+from subprocess import Popen
 from utils import gen_model_config_json
 from utils import gen_md_report
 from utils import gen_metrics_json
-from subprocess import Popen
 
 CWD = os.getcwd()
 MODEL_JSON_CONFIG_PATH = CWD + '/model_json_config'
@@ -231,7 +231,7 @@ def execute(command, wait=False, stdout=None, stderr=None, shell=True):
 
 def get_torchserve_version():
     # fetch the torchserve version from version.txt file
-    with open(CWD + '/ts/version.txt', 'r') as file:
+    with open(os.path.join(CWD, 'ts', 'version.txt'), 'r') as file:
         version = file.readline().rstrip()
     return version
 
@@ -258,4 +258,4 @@ def main():
     print("benchmark_serving.sh finished successfully.")
 
 if __name__ == "__main__":
-    main()
+    main()

benchmarks/benchmark-ab.py

@@ -385,20 +385,20 @@ def prepare_common_dependency():
         ignore_errors=True,
     )
     os.makedirs(
-        os.path.join(execution_params["tmp_dir"], "benchmark/conf"), exist_ok=True
+        os.path.join(execution_params["tmp_dir"], "benchmark", "conf"), exist_ok=True
     )
     os.makedirs(
-        os.path.join(execution_params["tmp_dir"], "benchmark/logs"), exist_ok=True
+        os.path.join(execution_params["tmp_dir"], "benchmark", "logs"), exist_ok=True
     )
     os.makedirs(
         os.path.join(execution_params["report_location"], "benchmark"), exist_ok=True
     )
 
     shutil.copy(
         execution_params["config_properties"],
-        os.path.join(execution_params["tmp_dir"], "benchmark/conf/"),
+        os.path.join(execution_params["tmp_dir"], "benchmark", "conf"),
     )
-    shutil.copyfile(input, os.path.join(execution_params["tmp_dir"], "benchmark/input"))
+    shutil.copyfile(input, os.path.join(execution_params["tmp_dir"], "benchmark", "input"))
 
 
 def getAPIS():
@@ -426,10 +426,10 @@ def update_exec_params(input_param):
         if default_ab_params[k] != input_param[k]:
             execution_params[k] = input_param[k]
     execution_params["result_file"] = os.path.join(
-        execution_params["tmp_dir"], "benchmark/result.txt"
+        execution_params["tmp_dir"], "benchmark", "result.txt"
     )
     execution_params["metric_log"] = os.path.join(
-        execution_params["tmp_dir"], "benchmark/logs/model_metrics.log"
+        execution_params["tmp_dir"], "benchmark", "logs", "model_metrics.log"
     )
 
     getAPIS()
@@ -509,7 +509,7 @@ def generate_csv_output():
         int(artifacts["TS failed requests"]) / execution_params["requests"] * 100
     )
 
-    with open(os.path.join(execution_params["tmp_dir"], "benchmark/predict.txt")) as f:
+    with open(os.path.join(execution_params["tmp_dir"], "benchmark", "predict.txt")) as f:
         lines = f.readlines()
         lines.sort(key=float)
         artifacts["Model_p50"] = lines[line50].strip()
@@ -528,7 +528,7 @@ def generate_csv_output():
             artifacts[m.split(".txt")[0] + "_mean"] = df["data"].values.mean().round(2)
 
     with open(
-        os.path.join(execution_params["report_location"], "benchmark/ab_report.csv"),
+        os.path.join(execution_params["report_location"], "benchmark", "ab_report.csv"),
         "w",
     ) as csv_file:
         csvwriter = csv.writer(csv_file)
@@ -549,7 +549,7 @@ def extract_entity(data, pattern, index, delim=" "):
 def generate_latency_graph():
     click.secho("*Preparing graphs...", fg="green")
     df = pd.read_csv(
-        os.path.join(execution_params["tmp_dir"], "benchmark/predict.txt"),
+        os.path.join(execution_params["tmp_dir"], "benchmark", "predict.txt"),
         header=None,
         names=["latency"],
     )

benchmarks/utils/gen_md_report.py

@@ -23,7 +23,7 @@ def iterate_subdir(input_dir, output, hw, ts_version):
 
     for model in models.keys():
         mdFile.new_header(level=2, title=model)
-        files = os.path.join('{}/{}_*/ab_report.csv'.format(input_dir, model))
+        files = os.path.join(input_dir, f'{model}_*', 'ab_report.csv')
         files = glob.glob(files)
         files.sort()
         df = pd.concat(map(pd.read_csv, files), ignore_index=True)

examples/dcgan_fashiongen/dcgan_fashiongen_handler.py

@@ -33,14 +33,14 @@ def initialize(self, context):
             ("cpu", torch.device("cpu"), False)
 
         # If not already extracted, Extract model source code
-        if not os.path.exists(model_dir + "/models"):
-            with zipfile.ZipFile(model_dir + "/" + MODELSZIP, "r") as zip_ref:
+        if not os.path.exists(os.path.join(model_dir, "models")):
+            with zipfile.ZipFile(os.path.join(model_dir, MODELSZIP), "r") as zip_ref:
                 zip_ref.extractall(model_dir)
 
         # Load Model
         from models.DCGAN import DCGAN
         self.dcgan_model = DCGAN(useGPU=self.use_gpu, storeAVG=self.store_avg)
-        state_dict = torch.load(model_dir + "/" + CHECKPOINT, map_location=self.map_location)
+        state_dict = torch.load(os.path.join(model_dir, CHECKPOINT), map_location=self.map_location)
         self.dcgan_model.load_state_dict(state_dict)
 
         self.initialized = True

examples/text_to_speech_synthesizer/create_mar.py

@@ -13,7 +13,7 @@
 download_save("https://github.com/nvidia/DeepLearningExamples/archive/torchhub.zip", TMP_DIR)
 shutil.unpack_archive(os.path.join(TMP_DIR, "torchhub.zip"), extract_dir=TMP_DIR)
 rm_file("tacotron.zip")
-shutil.copytree(os.path.join(TMP_DIR, "DeepLearningExamples-torchhub/PyTorch/SpeechSynthesis/"),
+shutil.copytree(os.path.join(TMP_DIR, "DeepLearningExamples-torchhub", "PyTorch", "SpeechSynthesis"),
                 "PyTorch/SpeechSynthesis/")
 shutil.make_archive("tacotron", "zip", base_dir="PyTorch")
 download_save("https://api.ngc.nvidia.com/v2/models/nvidia/tacotron2pyt_fp32/versions/1/files/"

test/pytest/test_gRPC_inference_api.py

@@ -7,7 +7,7 @@
 import test_utils
 
 
-inference_data_json = "/../postman/inference_data.json"
+inference_data_json = "../postman/inference_data.json"
 
 
 def setup_module(module):
@@ -42,7 +42,7 @@ def __infer(stub, model_name, model_input):
 
 
 def test_inference_apis():
-    with open(os.path.dirname(__file__) + inference_data_json, 'rb') as f:
+    with open(os.path.join(os.path.dirname(__file__), inference_data_json), 'rb') as f:
         test_data = json.loads(f.read())
 
     for item in test_data:
@@ -61,7 +61,7 @@ def test_inference_apis():
 
         print(response.msg)
 
-        model_input = os.path.dirname(__file__) + "/../" + item['file']
+        model_input = os.path.join(os.path.dirname(__file__), "..", item['file'])
         prediction = __infer(test_gRPC_utils.get_inference_stub(), item['model_name'], model_input)
 
         print("Prediction is : ", str(prediction))

test/pytest/test_gRPC_management_apis.py

@@ -6,7 +6,7 @@
 from urllib import parse
 
 
-management_data_json = "/../postman/management_data.json"
+management_data_json = "../postman/management_data.json"
 
 
 def setup_module(module):
@@ -92,7 +92,7 @@ def test_management_apis():
         "describe": "DescribeModel"
     }
 
-    with open(os.path.dirname(__file__) + management_data_json, 'rb') as f:
+    with open(os.path.join(os.path.dirname(__file__), management_data_json), 'rb') as f:
         test_data = json.loads(f.read())
 
     for item in test_data:

test/pytest/test_handler.py

@@ -7,12 +7,12 @@
 import ast 
 import pytest
 import torch
-REPO_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../")
-snapshot_file_kf = os.path.join(REPO_ROOT,"test/config_kf.properties")
-snapshot_file_tf = os.path.join(REPO_ROOT,"test/config_ts.properties")
-data_file_mnist = os.path.join(REPO_ROOT, 'examples/image_classifier/mnist/test_data/1.png')
-input_json_mnist = os.path.join(REPO_ROOT, "kubernetes/kserve/kf_request_json/v1/mnist.json")
-input_json_mmf = os.path.join(REPO_ROOT, "examples/MMF-activity-recognition/372CC.info.json")
+REPO_ROOT = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../"))
+snapshot_file_kf = os.path.join(REPO_ROOT, "test", "config_kf.properties")
+snapshot_file_tf = os.path.join(REPO_ROOT,"test", "config_ts.properties")
+data_file_mnist = os.path.join(REPO_ROOT, "examples", "image_classifier", "mnist", "test_data", "1.png")
+input_json_mnist = os.path.join(REPO_ROOT, "kubernetes", "kserve", "kf_request_json", "v1", "mnist.json")
+input_json_mmf = os.path.join(REPO_ROOT, "examples", "MMF-activity-recognition", "372CC.info.json")
 logger = logging.getLogger(__name__)
 
 
@@ -38,7 +38,8 @@ def getAPIS(snapshot_file):
 def setup_module(module):
     test_utils.torchserve_cleanup()
     response = requests.get("https://torchserve.pytorch.org/mar_files/mnist.mar", allow_redirects=True)
-    open(test_utils.MODEL_STORE + "/mnist.mar", 'wb').write(response.content)
+    with open(os.path.join(test_utils.MODEL_STORE, "mnist.mar"), 'wb') as f:
+        f.write(response.content)
 
 def teardown_module(module):
     test_utils.torchserve_cleanup()
@@ -243,7 +244,7 @@ def test_huggingface_bert_batch_inference():
     )
     test_utils.start_torchserve(no_config_snapshots=True)
     test_utils.register_model_with_params(params)
-    input_text = os.path.join(REPO_ROOT, 'examples/Huggingface_Transformers/Seq_classification_artifacts/sample_text.txt')
+    input_text = os.path.join(REPO_ROOT, 'examples', 'Huggingface_Transformers', 'Seq_classification_artifacts', 'sample_text.txt')
 
     # Make 2 curl requests in parallel with &
     # curl --header \"X-Forwarded-For: 1.2.3.4\" won't work since you can't access local host anymore
@@ -293,7 +294,7 @@ def test_huggingface_bert_model_parallel_inference():
         )
         test_utils.start_torchserve(no_config_snapshots=True)
         test_utils.register_model_with_params(params)
-        input_text = os.path.join(REPO_ROOT, 'examples/Huggingface_Transformers/Text_gen_artifacts/sample_text_captum_input.txt')
+        input_text = os.path.join(REPO_ROOT, 'examples', 'Huggingface_Transformers', 'Text_gen_artifacts', 'sample_text_captum_input.txt')
 
         response = os.popen(f"curl http://127.0.0.1:8080/predictions/Textgeneration -T {input_text}")
         response = response.read()

test/pytest/test_metrics.py

@@ -16,7 +16,8 @@
 def setup_module(module):
     test_utils.torchserve_cleanup()
     response = requests.get("https://torchserve.pytorch.org/mar_files/densenet161.mar", allow_redirects=True)
-    open(test_utils.MODEL_STORE + "/densenet161.mar", 'wb').write(response.content)
+    with open(path.join(test_utils.MODEL_STORE, "densenet161.mar"), 'wb') as f:
+        f.write(response.content)
 
 def teardown_module(module):
     test_utils.torchserve_cleanup()

test/pytest/test_metrics_kf.py

@@ -11,13 +11,16 @@
 import test_utils
 
 NUM_STARTUP_CFG = 0
-REPO_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../")
-snapshot_file = os.path.join(REPO_ROOT,"test/config_kf.properties")
+REPO_ROOT = os.path.normpath(
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../")
+)
+snapshot_file = os.path.join(REPO_ROOT, "test", "config_kf.properties")
 
 def setup_module(module):
     test_utils.torchserve_cleanup()
     response = requests.get("https://torchserve.pytorch.org/mar_files/mnist.mar", allow_redirects=True)
-    open(test_utils.MODEL_STORE + "/mnist.mar", 'wb').write(response.content)
+    with open(os.path.join(test_utils.MODEL_STORE, "mnist.mar"), 'wb') as f:
+        f.write(response.content)
 
 
 def teardown_module(module):

test/pytest/test_model_archiver.py

@@ -12,7 +12,8 @@
 def setup_module(module):
     test_utils.torchserve_cleanup()
     response = requests.get('https://download.pytorch.org/models/' + MODEL_SFILE_NAME, allow_redirects=True)
-    open(test_utils.MODEL_STORE + "/" + MODEL_SFILE_NAME, 'wb').write(response.content)
+    with open(os.path.join(test_utils.MODEL_STORE, MODEL_SFILE_NAME), 'wb') as f:
+        f.write(response.content)
 
 
 def teardown_module(module):
@@ -23,10 +24,10 @@ def create_resnet_archive(model_name="resnset-18", version="1.0", force=False):
     cmd = test_utils.model_archiver_command_builder(
         model_name,
         version,
-        "{}/examples/image_classifier/resnet_18/model.py".format(test_utils.CODEBUILD_WD),
-        "{}resnet18-f37072fd.pth".format(test_utils.MODEL_STORE),
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "resnet_18", "model.py"),
+        os.path.join(test_utils.MODEL_STORE, "resnet18-f37072fd.pth"),
         "image_classifier",
-        "{}/examples/image_classifier/index_to_name.json".format(test_utils.CODEBUILD_WD),
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "index_to_name.json"),
         force
     )
     print(cmd)
@@ -36,7 +37,7 @@ def create_resnet_archive(model_name="resnset-18", version="1.0", force=False):
 
 
 def clean_mar_file(mar_name):
-    path = "{}{}".format(test_utils.MODEL_STORE, mar_name)
+    path = os.path.join(test_utils.MODEL_STORE, mar_name)
     if os.path.exists(path):
         os.remove(path)
 
@@ -120,10 +121,10 @@ def test_model_archiver_without_handler_flag():
     cmd = test_utils.model_archiver_command_builder(
         "resnet-18",
         "1.0",
-        "{}/examples/image_classifier/resnet_18/model.py".format(test_utils.CODEBUILD_WD),
-        "{}/resnet18-f37072fd.pth".format(test_utils.MODEL_STORE),
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "resnet_18", "model.py"),
+        os.path.join(test_utils.MODEL_STORE, "resnet18-f37072fd.pth"),
         None,
-        "{}/examples/image_classifier/index_to_name.json".format(test_utils.CODEBUILD_WD)
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "index_to_name.json")
     )
     cmd = cmd.split(" ")
     try:
@@ -138,10 +139,10 @@ def test_model_archiver_without_model_name_flag():
     cmd = test_utils.model_archiver_command_builder(
         None,
         "1.0",
-        "{}/examples/image_classifier/resnet_18/model.py".format(test_utils.CODEBUILD_WD),
-        "{}/resnet18-f37072fd.pth".format(test_utils.MODEL_STORE),
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "resnet_18", "model.py"),
+        os.path.join(test_utils.MODEL_STORE, "resnet18-f37072fd.pth"),
         "image_classifier",
-        "{}/examples/image_classifier/index_to_name.json".format(test_utils.CODEBUILD_WD)
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "index_to_name.json")
     )
     cmd = cmd.split(" ")
     assert (0 != subprocess.run(cmd).returncode), "Mar file couldn't be created." \
@@ -153,9 +154,9 @@ def test_model_archiver_without_model_file_flag():
         "resnet-18",
         "1.0",
         None,
-        "{}/resnet18-f37072fd.pth".format(test_utils.MODEL_STORE),
+        os.path.join(test_utils.MODEL_STORE, "resnet18-f37072fd.pth"),
         "image_classifier",
-        "{}/examples/image_classifier/index_to_name.json".format(test_utils.CODEBUILD_WD),
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "index_to_name.json"),
         True
     )
 
@@ -171,10 +172,10 @@ def test_model_archiver_without_serialized_flag():
     cmd = test_utils.model_archiver_command_builder(
         "resnet-18",
         "1.0",
-        "{}/examples/image_classifier/resnet_18/model.py".format(test_utils.CODEBUILD_WD),
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "resnet_18", "model.py"),
         None,
         "image_classifier",
-        "{}/examples/image_classifier/index_to_name.json".format(test_utils.CODEBUILD_WD)
+        os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "index_to_name.json")
     )
 
     cmd = cmd.split(" ")

test/pytest/test_pytorch_profiler.py

@@ -15,15 +15,17 @@
 import test_utils
 from concurrent import futures
 
-REPO_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../")
-data_file_mnist = os.path.join(REPO_ROOT, "examples/image_classifier/mnist/test_data/1.png")
+REPO_ROOT = os.path.normpath(
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "../../")
+)
+data_file_mnist = os.path.join(REPO_ROOT, "examples", "image_classifier", "mnist", "test_data", "1.png")
 data_file_resnet = os.path.join(
-    REPO_ROOT, "examples/image_classifier/resnet_152_batch/images/kitten.jpg"
+    REPO_ROOT, "examples", "image_classifier", "resnet_152_batch", "images", "kitten.jpg"
 )
 data_file_resnet_dog = os.path.join(
-    REPO_ROOT, "examples/image_classifier/resnet_152_batch/images/dog.jpg"
+    REPO_ROOT, "examples", "image_classifier", "resnet_152_batch", "images", "dog.jpg"
 )
-profiler_utils = os.path.join(REPO_ROOT, "test/pytest/profiler_utils")
+profiler_utils = os.path.join(REPO_ROOT, "test", "pytest", "profiler_utils")
 
 TF_INFERENCE_API = "http://127.0.0.1:8080"
 TF_MANAGEMENT_API = "http://127.0.0.1:8081"
@@ -49,20 +51,17 @@ def set_custom_handler(handler_name):
             "https://download.pytorch.org/models/resnet152-394f9c45.pth", allow_redirects=True
         )
         assert response.status_code == 200
-        open(serialized_file, "wb").write(response.content)
+        with open(serialized_file, "wb") as f:
+            f.write(response.content)
 
     ## Generate mar file
     cmd = test_utils.model_archiver_command_builder(
         model_name="resnet-152-batch",
         version="1.0",
-        model_file="{}/examples/image_classifier/resnet_152_batch/model.py".format(
-            test_utils.CODEBUILD_WD
-        ),
+        model_file=os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "resnet_152_batch", "model.py"),
         serialized_file=serialized_file,
         handler=handler_name,
-        extra_files="{}/examples/image_classifier/index_to_name.json".format(
-            test_utils.CODEBUILD_WD
-        ),
+        extra_files=os.path.join(test_utils.CODEBUILD_WD, "examples", "image_classifier", "index_to_name.json"),
         force=True,
     )
     print(cmd)