[Format] Convert all Python code w/o CI (#6448)

* Add black setup

* Tweak pyproject.toml

* Fix syntax issues

* Fix

* Tweak

* Black all Python code

Author: jroesch

apps/android_camera/models/prepare_model.py

@@ -28,52 +28,62 @@
 from tvm.contrib import util, ndk, graph_runtime as runtime
 from tvm.contrib.download import download_testdata, download
 
-target = 'llvm -mtriple=arm64-linux-android'
+target = "llvm -mtriple=arm64-linux-android"
 target_host = None
 
+
 def del_dir(target: Union[Path, str], only_if_empty: bool = False):
     target = Path(target).expanduser()
     assert target.is_dir()
-    for p in sorted(target.glob('**/*'), reverse=True):
+    for p in sorted(target.glob("**/*"), reverse=True):
         if not p.exists():
             continue
         p.chmod(0o666)
         if p.is_dir():
             p.rmdir()
         else:
             if only_if_empty:
-                raise RuntimeError(f'{p.parent} is not empty!')
+                raise RuntimeError(f"{p.parent} is not empty!")
             p.unlink()
     target.rmdir()
 
+
 def get_model(model_name, batch_size=1):
-    if model_name == 'resnet18_v1':
+    if model_name == "resnet18_v1":
         import mxnet as mx
         from mxnet import gluon
         from mxnet.gluon.model_zoo import vision
+
         gluon_model = vision.get_model(model_name, pretrained=True)
         img_size = 224
         data_shape = (batch_size, 3, img_size, img_size)
         net, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
         return (net, params)
-    elif model_name == 'mobilenet_v2':
+    elif model_name == "mobilenet_v2":
         import keras
         from keras.applications.mobilenet_v2 import MobileNetV2
+
         keras.backend.clear_session()  # Destroys the current TF graph and creates a new one.
-        weights_url = ''.join(['https://github.com/JonathanCMitchell/',
-                               'mobilenet_v2_keras/releases/download/v1.1/',
-                               'mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224.h5'])
-        weights_file = 'mobilenet_v2_weights.h5'
-        weights_path = download_testdata(weights_url, weights_file, module='keras')
-        keras_mobilenet_v2 = MobileNetV2(alpha=0.5, include_top=True, weights=None,
-                                        input_shape=(224, 224, 3), classes=1000)
+        weights_url = "".join(
+            [
+                "https://github.com/JonathanCMitchell/",
+                "mobilenet_v2_keras/releases/download/v1.1/",
+                "mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.5_224.h5",
+            ]
+        )
+        weights_file = "mobilenet_v2_weights.h5"
+        weights_path = download_testdata(weights_url, weights_file, module="keras")
+        keras_mobilenet_v2 = MobileNetV2(
+            alpha=0.5, include_top=True, weights=None, input_shape=(224, 224, 3), classes=1000
+        )
         keras_mobilenet_v2.load_weights(weights_path)
-        
+
         img_size = 224
         data_shape = (batch_size, 3, img_size, img_size)
-        mod, params = relay.frontend.from_keras(keras_mobilenet_v2,  {'input_1': data_shape})
+        mod, params = relay.frontend.from_keras(keras_mobilenet_v2, {"input_1": data_shape})
         return (mod, params)
 
+
 def main(model_str, output_path):
     if output_path.exists():
         del_dir(output_path)
@@ -90,34 +100,40 @@ def main(model_str, output_path):
     with tvm.transform.PassContext(opt_level=3):
         graph, lib, params = relay.build(net, target, target_host=target_host, params=params)
     print("dumping lib...")
-    lib.export_library(output_path_str + '/' + 'deploy_lib_cpu.so', ndk.create_shared)
+    lib.export_library(output_path_str + "/" + "deploy_lib_cpu.so", ndk.create_shared)
     print("dumping graph...")
-    with open(output_path_str + '/' + 'deploy_graph.json', 'w') as f:
+    with open(output_path_str + "/" + "deploy_graph.json", "w") as f:
         f.write(graph)
     print("dumping params...")
-    with open(output_path_str + '/' + 'deploy_param.params', 'wb') as f:
+    with open(output_path_str + "/" + "deploy_param.params", "wb") as f:
         f.write(relay.save_param_dict(params))
     print("dumping labels...")
-    synset_url = ''.join(['https://gist.githubusercontent.com/zhreshold/',
-        '4d0b62f3d01426887599d4f7ede23ee5/raw/',
-        '596b27d23537e5a1b5751d2b0481ef172f58b539/',
-        'imagenet1000_clsid_to_human.txt'])
-    synset_path = output_path_str + '/image_net_labels'
-    download(synset_url, output_path_str + '/image_net_labels')
+    synset_url = "".join(
+        [
+            "https://gist.githubusercontent.com/zhreshold/",
+            "4d0b62f3d01426887599d4f7ede23ee5/raw/",
+            "596b27d23537e5a1b5751d2b0481ef172f58b539/",
+            "imagenet1000_clsid_to_human.txt",
+        ]
+    )
+    synset_path = output_path_str + "/image_net_labels"
+    download(synset_url, output_path_str + "/image_net_labels")
     with open(synset_path) as fi:
         synset = eval(fi.read())
-        with open(output_path_str + '/image_net_labels.json', "w") as fo:
+        with open(output_path_str + "/image_net_labels.json", "w") as fo:
             json.dump(synset, fo, indent=4)
     os.remove(synset_path)
 
-if __name__ == '__main__':
-    if environ.get('TVM_NDK_CC') is None:
+
+if __name__ == "__main__":
+    if environ.get("TVM_NDK_CC") is None:
         raise RuntimeError("Require environment variable TVM_NDK_CC")
-    models_path = Path().absolute().parent.joinpath('app/src/main/assets/models/')
+    models_path = Path().absolute().parent.joinpath("app/src/main/assets/models/")
     if not models_path.exists():
         models_path.mkdir()
-    models = {'mobilenet_v2': models_path.joinpath('mobilenet_v2'),
-              'resnet18_v1': models_path.joinpath('resnet18_v1')
-            }
+    models = {
+        "mobilenet_v2": models_path.joinpath("mobilenet_v2"),
+        "resnet18_v1": models_path.joinpath("resnet18_v1"),
+    }
     for model, output_path in models.items():
         main(model, output_path)

apps/android_rpc/tests/android_rpc_test.py

@@ -43,18 +43,18 @@
 # whether enable to execute test on Vulkan target
 test_vulkan = False
 
+
 def test_rpc_module():
     # graph
     n = tvm.runtime.convert(1024)
-    A = te.placeholder((n,), name='A')
-    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
+    A = te.placeholder((n,), name="A")
+    B = te.compute(A.shape, lambda *i: A(*i) + 1.0, name="B")
     a_np = np.random.uniform(size=1024).astype(A.dtype)
     temp = util.tempdir()
 
     # Establish remote connection with target hardware
     tracker = rpc.connect_tracker(tracker_host, tracker_port)
-    remote = tracker.request(key, priority=0,
-                             session_timeout=60)
+    remote = tracker.request(key, priority=0, session_timeout=60)
 
     # Compile the Graph for CPU target
     s = te.create_schedule(B.op)
@@ -67,15 +67,15 @@ def test_rpc_module():
     f.export_library(path_dso_cpu, ndk.create_shared)
 
     # Execute the portable graph on cpu target
-    print('Run CPU test ...')
+    print("Run CPU test ...")
     ctx = remote.cpu(0)
     remote.upload(path_dso_cpu)
     f2 = remote.load_module("cpu_lib.so")
     a = tvm.nd.array(a_np, ctx)
     b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
     time_f = f2.time_evaluator(f2.entry_name, ctx, number=10)
     cost = time_f(a, b).mean
-    print('%g secs/op\n' % cost)
+    print("%g secs/op\n" % cost)
     np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
 
     # Compile the Graph for OpenCL target
@@ -90,15 +90,15 @@ def test_rpc_module():
         path_dso_cl = temp.relpath("dev_lib_cl.so")
         f.export_library(path_dso_cl, ndk.create_shared)
 
-        print('Run GPU(OpenCL Flavor) test ...')
+        print("Run GPU(OpenCL Flavor) test ...")
         ctx = remote.cl(0)
         remote.upload(path_dso_cl)
         f1 = remote.load_module("dev_lib_cl.so")
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
         time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
         cost = time_f(a, b).mean
-        print('%g secs/op\n' % cost)
+        print("%g secs/op\n" % cost)
         np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
 
     # Compile the Graph for Vulkan target
@@ -113,15 +113,15 @@ def test_rpc_module():
         path_dso_vulkan = temp.relpath("dev_lib_vulkan.so")
         f.export_library(path_dso_vulkan, ndk.create_shared)
 
-        print('Run GPU(Vulkan Flavor) test ...')
+        print("Run GPU(Vulkan Flavor) test ...")
         ctx = remote.vulkan(0)
         remote.upload(path_dso_vulkan)
         f1 = remote.load_module("dev_lib_vulkan.so")
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
         time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
         cost = time_f(a, b).mean
-        print('%g secs/op\n' % cost)
+        print("%g secs/op\n" % cost)
         np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
 
 

apps/benchmark/arm_cpu_imagenet_bench.py

@@ -40,12 +40,12 @@ def evaluate_network(network, target, target_host, repeat):
 
     print_progress("%-20s building..." % network)
     with tvm.transform.PassContext(opt_level=3):
-        graph, lib, params = relay.build(
-            net, target=target, target_host=target_host, params=params)
+        graph, lib, params = relay.build(net, target=target, target_host=target_host, params=params)
 
     tmp = tempdir()
-    if 'android' in str(target):
+    if "android" in str(target):
         from tvm.contrib import ndk
+
         filename = "%s.so" % network
         lib.export_library(tmp.relpath(filename), ndk.create_shared)
     else:
@@ -60,38 +60,55 @@ def evaluate_network(network, target, target_host, repeat):
     rlib = remote.load_module(filename)
     module = runtime.create(graph, rlib, ctx)
     data_tvm = tvm.nd.array((np.random.uniform(size=input_shape)).astype(dtype))
-    module.set_input('data', data_tvm)
+    module.set_input("data", data_tvm)
     module.set_input(**params)
 
     # evaluate
     print_progress("%-20s evaluating..." % network)
     ftimer = module.module.time_evaluator("run", ctx, number=1, repeat=repeat)
     prof_res = np.array(ftimer().results) * 1000  # multiply 1000 for converting to millisecond
-    print("%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res)))
+    print(
+        "%-20s %-19s (%s)" % (network, "%.2f ms" % np.mean(prof_res), "%.2f ms" % np.std(prof_res))
+    )
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--network", type=str, choices=
-                        ['resnet-18', 'resnet-34', 'resnet-50',
-                         'vgg-16', 'vgg-19', 'densenet-121', 'inception_v3',
-                         'mobilenet', 'squeezenet_v1.0', 'squeezenet_v1.1'],
-                        help='The name of neural network')
-    parser.add_argument("--model", type=str, choices=
-                        ['rk3399', 'mate10', 'mate10pro', 'p20', 'p20pro',
-                         'pixel2', 'rasp3b', 'pynq'], default='rk3399',
-                        help="The model of the test device. If your device is not listed in "
-                             "the choices list, pick the most similar one as argument.")
-    parser.add_argument("--host", type=str, default='localhost')
+    parser.add_argument(
+        "--network",
+        type=str,
+        choices=[
+            "resnet-18",
+            "resnet-34",
+            "resnet-50",
+            "vgg-16",
+            "vgg-19",
+            "densenet-121",
+            "inception_v3",
+            "mobilenet",
+            "squeezenet_v1.0",
+            "squeezenet_v1.1",
+        ],
+        help="The name of neural network",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        choices=["rk3399", "mate10", "mate10pro", "p20", "p20pro", "pixel2", "rasp3b", "pynq"],
+        default="rk3399",
+        help="The model of the test device. If your device is not listed in "
+        "the choices list, pick the most similar one as argument.",
+    )
+    parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--port", type=int, default=9190)
     parser.add_argument("--rpc-key", type=str, required=True)
     parser.add_argument("--repeat", type=int, default=10)
     args = parser.parse_args()
 
-    dtype = 'float32'
+    dtype = "float32"
 
     if args.network is None:
-        networks = ['squeezenet_v1.1', 'mobilenet', 'resnet-18', 'vgg-16']
+        networks = ["squeezenet_v1.1", "mobilenet", "resnet-18", "vgg-16"]
     else:
         networks = [args.network]
 
@@ -103,4 +120,3 @@ def evaluate_network(network, target, target_host, repeat):
     print("--------------------------------------------------")
     for network in networks:
         evaluate_network(network, target, target_host, args.repeat)
-